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WORLD DEVELOPMENT INDICATORS
Low income Afghanistan Bangladesh Benin Burkina Faso Burundi Cambodia Central African Republic Chad Comoros Congo, Dem. Rep. Eritrea Ethiopia Gambia, The Guinea Guinea-Bissau Haiti Kenya Korea, Dem. Rep. Kyrgyz Republic Liberia Madagascar Malawi Mali Mozambique Myanmar Nepal Niger Rwanda Sierra Leone Somalia Tajikistan Tanzania Togo Uganda Zimbabwe Lower middle income Angola Armenia Belize Bhutan Bolivia Cameroon Cape Verde Congo, Rep. Côte d'Ivoire Djibouti Egypt, Arab Rep. El Salvador Fiji Georgia Ghana Guatemala Guyana Honduras India Indonesia Iraq Kiribati
Kosovo Lao PDR Lesotho Marshall Islands Mauritania Micronesia, Fed. Sts. Moldova Mongolia Morocco Nicaragua Nigeria Pakistan Papua New Guinea Paraguay Philippines Samoa São Tomé and Príncipe Senegal Solomon Islands Sri Lanka South Sudan Sudan Swaziland Syrian Arab Republic Timor-Leste Tonga Turkmenistan Tuvalu Ukraine Uzbekistan Vanuatu Vietnam West Bank and Gaza Yemen, Rep. Zambia Upper middle income Albania Algeria American Samoa Antigua and Barbuda Argentina Azerbaijan Belarus Bosnia and Herzegovina Botswana Brazil Bulgaria Chile China Colombia Costa Rica Cuba Dominica Dominican Republic Ecuador Gabon Grenada Iran, Islamic Rep. Jamaica
Jordan Kazakhstan Latvia Lebanon Libya Lithuania Macedonia, FYR Malaysia Maldives Mauritius Mayotte Mexico Montenegro Namibia Palau Panama Peru Romania Russian Federation Serbia Seychelles South Africa St. Kitts and Nevis St. Lucia St. Vincent & Grenadines Suriname Thailand Tunisia Turkey Uruguay Venezuela, RB High income Andorra Aruba Australia Austria Bahamas, The Bahrain Barbados Belgium Bermuda Brunei Darussalam Canada Cayman Islands Channel Islands Croatia Curaçao Cyprus Czech Republic Denmark Equatorial Guinea Estonia Faeroe Islands Finland France French Polynesia Germany Gibraltar Greece
Greenland Guam Hong Kong SAR, China Hungary Iceland Ireland Isle of Man Israel Italy Japan Korea, Rep. Kuwait Liechtenstein Luxembourg Macao SAR, China Malta Monaco Netherlands New Caledonia New Zealand Northern Mariana Islands Norway Oman Poland Portugal Puerto Rico Qatar San Marino Saudi Arabia Singapore Sint Maarten Slovak Republic Slovenia Spain St. Martin Sweden Switzerland Trinidad and Tobago Turks and Caicos Islands United Arab Emirates United Kingdom United States Virgin Islands (U.S.)
INCOME MAP
The world by income
The world by income Low ($1,005 or less)
Classified according to World Bank estimates of 2010 GNI per capita
Lower middle ($1,006–$3,975) Upper middle ($3,976–$12,275) High ($12,276 or more) No data
Greenland (Den)
Iceland
Norway
Faeroe Islands (Den)
Sweden
Finland Russian Federation
The Netherlands
Estonia Denmark Russian Latvia Fed. Lithuania United Belarus Germany Poland Kingdom Belgium Ukraine Moldova Romania France Italy
Isle of Man (UK)
Canada
Ireland Channel Islands (UK)
Luxembourg Liechtenstein Switzerland Andorra United States
Kazakhstan
Bulgaria
Portugal
Spain Monaco
Turkey
Greece
Gibraltar (UK) Bermuda (UK)
Tunisia
Cyprus Lebanon Israel
Syrian Arab Rep.
West Bank and Gaza
Jordan
Malta
Morocco
Georgia Armenia
Azerbaijan
Cayman Is.(UK)
Libya
Former Spanish Sahara
Saudi Arabia
Cuba Belize Jamaica Guatemala Honduras El Salvador Nicaragua Costa Rica
Mauritania
Haiti
Cape Verde
Mali
Senegal The Gambia Guinea-Bissau Guinea
Panama
R.B. de Venezuela
Guyana Suriname
Sierra Leone Liberia
French Guiana (Fr)
Colombia
Niger
Benin Côte Ghana d’Ivoire
Cameroon
Congo
Malawi
Zimbabwe
Tonga
Namibia Paraguay Germany
Antigua and Barbuda
U.S. Virgin Islands (US)
St. Kitts and Nevis
Curaçao (Neth)
Argentina
Dominica St. Lucia
St. Vincent and the Grenadines
Barbados Grenada Trinidad and Tobago
Guam (US)
Philippines
Federated States of Micronesia
Brunei Darussalam Malaysia
Marshall Islands
Palau Nauru
Singapore
Botswana
South Africa
Poland
Kiribati
Comoros
Solomon Islands
Papua New Guinea
Indonesia
Tuvalu
Mayotte (Fr)
Madagascar
Vanuatu
Fiji
Mauritius Réunion (Fr)
Australia
New Caledonia (Fr)
Lesotho
Czech Republic Ukraine Slovak Republic Austria
Guadeloupe (Fr)
Martinique (Fr) Aruba (Neth)
Chile
Uruguay
N. Mariana Islands (US)
Seychelles
Mozambique
Swaziland St. Maarten (Neth)
Lao P.D.R.
Maldives
Kenya
Rwanda Dem.Rep.of Burundi Congo Tanzania
Zambia
St. Martin (Fr)
Myanmar
Sri Lanka Somalia
Uganda Gabon
Bolivia
Puerto Rico (US)
India
Vietnam Cambodia
South Sudan
Angola
American Samoa (US)
Bangladesh
Thailand
Brazil
Peru
Bhutan
Nepal
Rep. of Yemen
Ethiopia
Central African Republic
Japan
Timor-Leste
French Polynesia (Fr)
Dominican Republic
Pakistan
Djibouti Nigeria
Kiribati
Samoa
Eritrea
Sudan
Burkina Faso
Togo Equatorial Guinea São Tomé and Príncipe
Ecuador
Chad
Rep.of Korea
China
Afghanistan
United Arab Emirates Oman
Turks and Caicos Is. (UK)
Mexico
Fiji
Arab Rep. of Egypt
Dem.People’s Rep.of Korea
Tajikistan
Bahrain Qatar
Algeria The Bahamas
Turkmenistan
Islamic Rep. of Iran Kuwait
Iraq
Mongolia Kyrgyz Rep.
Uzbekistan
Romania
Bosnia and Herzegovina San Marino Italy Montenegro Vatican City
New Zealand
Hungary
Slovenia Croatia
Serbia
Kosovo Bulgaria FYR Macedonia Albania Greece
R.B. de Venezuela
Antarctica
IBRD 39125 MARCH 2012
Designed, edited, and produced by Communications Development Incorporated, Washington, D.C., with Peter Grundy Art & Design, London
2012
WORLD DEVELOPMENT INDICATORS
Copyright 2012 by the International Bank for Reconstruction and Development/THE WORLD BANK 1818 H Street NW, Washington, D.C. 20433 USA All rights reserved Manufactured in the United States of America First printing April 2012 This volume is a product of the staff of the Development Data Group of the World Bank’s Development Economics Vice Presidency, and the judgments herein do not necessarily reflect the views of the World Bank’s Board of Executive Directors or the countries they represent. The World Bank does not guarantee the accuracy of the data included in this publication and accepts no responsibility whatsoever for any consequence of their use. The boundaries, colors, denominations, and other information shown on any map in this volume do not imply on the part of the World Bank any judgment on the legal status of any territory or the endorsement or acceptance of such boundaries. This publication uses the Robinson projection for maps, which represents both area and shape reasonably well for most of the earth’s surface. Nevertheless, some distortions of area, shape, distance, and direction remain. The material in this publication is copyrighted. Requests for permission to reproduce portions of it should be sent to the Office of the Publisher at the address in the copyright notice above. The World Bank encourages dissemination of its work and will normally give permission promptly and, when reproduction is for noncommercial purposes, without asking a fee. Permission to photocopy portions for classroom use is granted through the Copyright Center, Inc., Suite 910, 222 Rosewood Drive, Danvers, MA 01923 USA. Photo credits: World Bank photo library, except page 282, David Cieslikowski/World Bank. If you have questions or comments about this product, please contact: Development Data Group The World Bank 1818 H Street NW, Room MC2-812, Washington, D.C. 20433 USA Hotline: 800 590 1906 or 202 473 7824; fax 202 522 1498 Email: data@worldbank.org Web site: www.worldbank.org or data.worldbank.org ISBN 978-0-8213-8985-0 ECO -AUDIT Environmental Benefits Statement The World Bank is committed to preserving endangered forests and natural resources. The Office of the Publisher has chosen to print World Development Indicators 2012 on recycled paper with 50 percent postconsumer fiber in accordance with the recommended standards for paper usage set by the Green Press Initiative, a nonprofit program supporting publishers in using fiber that is not sourced from endangered forests. For more information, visit www. greenpressinitiative.org. Saved: 64 trees 26 million Btu of total energy 6,503 pounds of net greenhouse gases 29,321 gallons of waste water 1,859 pounds of solid waste
2012
WORLD DEVELOPMENT INDICATORS
PREFACE World Development Indicators 2012 is a compilation of relevant, high-quality, and internationally comparable statistics about development and the quality of people’s lives. Organized around six themes—world view, people, the environment, the economy, states and markets, and global links—it aims to put data into the hands of policy makers, development specialists, students, and the public. We encourage and applaud the use of the data presented here to help reduce poverty and to solve the world’s most pressing development challenges. The full dataset used to produce World Development Indicators contains more than 1,000 indicators for 216 economies, with many time series extending back to 1960. Highly visual, interactive, and multilingual presentations of the data are available at the popular website http://data.worldbank.org and through the DataFinder application for mobile devices. And, as a major part of the World Bank’s Open Data Initiative, the data are freely available for use and reuse under an open license. A companion printed volume, The Little Data Book 2012, presents a selection of indicators for each economy, and the biennial Statistics for Small States presents data for less-populated developing countries. This 16th edition of World Development Indicators relies heavily on statistics produced by national authorities and agencies. Since the first edition in 1997, there has been a substantial increase in the availability and quality of the data, thanks to improvements in statistical capacity in many countries. More remains to be done: the capacity to use statistical data remains weak; demand is growing for greater disaggregation of indicators (for instance by sex, age, or geography); and data in some key areas, such as agriculture, are often missing or outdated. A new global statistical action plan (www.paris21.org/busan-action-plan), endorsed in November 2011 at the highest political levels at the Fourth High Level Forum on Aid Effectiveness in Busan, Republic of Korea, provides an important framework to address remaining challenges, to integrate statistics into decision making, to promote open access to data and improve their use, and to increase resources for statistical systems. World Development Indicators is possible only through the excellent collaboration of many partners who provide the data for this collection, and I would like to thank them all: the United Nations family, the International Monetary Fund, the International Telecommunication Union, the Organisation for Economic Co-operation and Development, the statistical offices of more than 200 economies, and countless others whose support and advice have made this unique product possible. As always, we welcome your ideas for making the data in World Development Indicators useful and relevant for improving the lives of people around the world. Shaida Badiee Director Development Economics Data Group
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ACKNOWLEDGMENTS This book was prepared by a team led by Soong Sup Lee under the management of Neil Fantom and comprising Awatif Abuzeid, Azita Amjadi, Maja Bresslauer, David Cieslikowski, Liu Cui, Mahyar Eshragh-Tabary, Shota Hatakeyama, Masako Hiraga, Wendy Ven-dee Huang, Bala Bhaskar Naidu Kalimili, Buyant Khaltarkhuu, Elysee Kiti, Alison Kwong, Ibrahim Levent, Hiroko Maeda, Johan Mistiaen, Maurice Nsabimana, Sulekha Patel, Beatriz Prieto-Oramas, William Prince, Premi Rathan Raj, Evis Rucaj, Emi Suzuki, Eric Swanson, Jomo Tariku, and Estela Zamora, working closely with other teams in the Development Economics Vice Presidency’s Development Data Group. World Development Indicators electronic products were prepared by a team led by Reza Farivari and comprising Ramvel Chandrasekaran, Ying Chi, Jean-Pierre Djomalieu, Ramgopal Erabelly, Federico Escaler, Shelley Fu, Gytis Kanchas, Ugendran Makhachkala, Vilas Mandlekar, Nacer Megherbi, Shanmugam Natarajan, Parastoo Oloumi, Atsushi Shimo, Maryna Taran, Malarvizhi Veerappan, and Vera Wen. The work was carried out under the direction of Shaida Badiee. Valuable advice was provided by Zia M. Qureshi and David Rosenblatt. The choice of indicators and text content was shaped through close consultation with and substantial contributions from staff in the World Bank’s four thematic networks—Financial and Private Sector Development, Human Development, Poverty Reduction and Economic Management, and Sustainable Development—and staff of the International Finance Corporation and the Multilateral Investment Guarantee Agency. Most important, the team received substantial help, guidance, and data from external partners. For individual acknowledgments of contributions to the book’s content, please see Credits. For a listing of our key partners, see Partners. Communications Development Incorporated provided overall design direction, editing, and layout, led by Meta de Coquereaumont, Bruce Ross-Larson, and Christopher Trott and assisted by Rob Elson. Elaine Wilson created the cover and graphics and typeset the book. Joseph Caponio provided production assistance. Peter Grundy, of Peter Grundy Art & Design, designed the report. Staff from External Affairs oversaw printing and dissemination of the book.
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TABLE OF CONTENTS FRONT Preface Acknowledgments Partners Users guide
v vii xii xxii
1. WORLD VIEW Introduction
1.4 1.5 1.6
Tables Size of the economy Millennium Development Goals: eradicating poverty and saving lives Millennium Development Goals: protecting our common environment Millennium Development Goals: overcoming obstacles Women in development Key indicators for other economies
1a 1b 1c 1d 1e 1f 1g 1h 1i 1j 1k 1l 1m 1n 1o 1p 1q 1r 1s
Text figures, tables, and boxes Poverty rates fell sharply in the new millennium Fewer people living in extreme poverty Progress toward poverty reduction Progress toward reducing undernourishment More and less income equality Many children remain malnourished The last step toward education for all 64 million children out of school Progress toward education for all The missing enrollments How much schooling Increasing participation by girls at all levels of education Progress toward gender equality in education Women have become a larger part of the workforce More women decisionmakers A slim lead for girls Still far to go in reducing under-five mortality Most deaths occur in the first year of life Progress toward reducing child mortality
1.1 1.2 1.3
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20 24 28 32 34 38
2 2 3 3 3 3 4 4 5 5 5 6 7 7 7 7 8 8 9
1t 1u 1v 1w 1x 1y 1z 1aa 1bb 1cc 1dd 1ee 1ff 1gg 1hh 1ii 1jj 1kk 1ll 1mm 1nn 1oo 1pp 1qq 1rr 1.2a 1.3a 1.4a
Preventing childhood diseases For some, better than expected improvements Maternal mortality rates have been falling but large regional differences persist The 12 countries with highest lifetime risk of maternal death Progress in reducing maternal mortality Planning for motherhood Fewer young women giving birth Help for mothers Bringing HIV/AIDS under control Millions of people still afflicted with HIV/AIDS Progress toward reversing the HIV epidemic Turning the tide of tuberculosis Protecting children from malaria Carbon dioxide emissions continue to rise Forest losses and gains Progress toward improved sanitation Progress toward improved water sources Many still lack access to sanitation Water demand strains supplies Most donors have maintained their aid levels But their domestic subsidies to agricultural are greater Developing countries have easier access to Organisation for Economic Co-operation and Development markets Cellular phones are connecting developing countries Debt service burdens have been falling A more connected world Location of indicators for Millennium Development Goals 1–4 Location of indicators for Millennium Development Goals 5–7 Location of indicators for Millennium Development Goal 8
9 9 10 10 11 11 11 11 12 12 13 13 13 14 14 15 15 15 15 16 16 17 17 17 17 27 31 33
2. PEOPLE Introduction
2.1 2.2 2.3 2.4 2.5 2.6 2.7 2.8 2.9 2.10 2.11 2.12 2.13 2.14 2.15 2.16 2.17 2.18 2.19 2.20 2.21 2.22 2.23 2.24 2.8a 2.8b 2.8c
Tables Population dynamics Labor force structure Employment by economic activity Decent work and productive employment Unemployment Children at work Poverty rates at national poverty lines Poverty rates at international poverty lines Distribution of income or consumption Assessing vulnerability and security Education inputs Participation in education Education efficiency Education completion and outcomes Education gaps by income and gender Health systems Health information Disease prevention coverage and quality Reproductive health Nutrition and growth Nutrition intake and supplements Health risk factors and future challenges Mortality Health gaps by income Text figures, tables, and boxes While the number of people living on less than $1.25 a day has fallen, the number living on $1.25–$2 a day has increased Poverty rates are falling in all developing regions Regional poverty estimates
3. ENVIRONMENT 41
42 46 50 54 58 62 66 72 74 78 82 86 90 94 98 100 104 108 112 116 120 124 128 132
Introduction
137
3.10 3.11 3.12 3.13 3.14 3.15 3.16 3.17 3.18
Tables Rural population and land use Agricultural inputs Agricultural output and productivity Deforestation and biodiversity Freshwater Water pollution Energy production and use Electricity production, sources, and access Energy dependency and efficiency and carbon dioxide emissions Trends in greenhouse gas emissions Carbon dioxide emissions by sector Climate variability, exposure to impact, and resilience Urbanization Urban housing conditions Traffic and congestion Air pollution Government commitment Contribution of natural resources to gross domestic product
170 174 178 182 186 190 194 198 200 204
3.1a
Text figures, tables, and boxes What is rural? Urban?
141
3.1 3.2 3.3 3.4 3.5 3.6 3.7 3.8 3.9
138 142 146 150 154 158 162 166
71 71 72
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TABLE OF CONTENTS 4. ECONOMY 4.a 4.1 4.2 4.3 4.4 4.5 4.6 4.7 4.8 4.9 4.10 4.11 4.12 4.13 4.14 4.15 4.16 4.17
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5. STATES AND MARKETS
Introduction
209
Tables Recent economic performance Growth of output Structure of output Structure of manufacturing Structure of merchandise exports Structure of merchandise imports Structure of service exports Structure of service imports Structure of demand Growth of consumption and investment Toward a broader measure of national income Toward a broader measure of savings Central government finances Central government expenses Central government revenues Monetary indicators Exchange rates and prices Balance of payments current account
210 214 218 222 226 230 234 238 242 246 250 254 258 262 266 270 274 278
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5.1 5.2 5.3 5.4 5.5 5.6 5.7 5.8 5.9 5.10 5.11 5.12 5.13
Introduction
283
Tables Private sector in the economy Business environment: enterprise surveys Business environment: Doing Business indicators Stock markets Financial access, stability, and efficiency Tax policies Military expenditures and arms transfers Fragile situations Public policies and institutions Transport services Power and communications The information society Science and technology
284 288 292 296 300 304 308 312 316 320 324 328 332
6. GLOBAL LINKS Introduction
6.1 6.2 6.3 6.4 6.5 6.6 6.7 6.8 6.9 6.10 6.11 6.12 6.13 6.14 6.15 6.6a 6.13a
Tables Growth of merchandise trade Direction and growth of merchandise trade High-income economy trade with low- and middle-income economies Direction of trade of developing economies Primary commodity prices Regional trade blocs Tariff barriers Trade facilitation External debt Global private financial flows Net official financial flows Aid dependency Distribution of net aid by Development Assistance Committee members Movement of people across borders Travel and tourism Text ďŹ gures, tables, and boxes Global Preferential Trade Agreement Database Official development assistance from non-DAC donors, 2006–10
BACK 337
338 342
Primary data documentation Statistical methods Credits Bibliography Index of indicators
391 402 404 406 414
344 346 349 352 354 358 362 366 370 374 378 382 386
353 381
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PARTNERS Defining, gathering, and disseminating international statistics is a collective effort of many people and organizations. The indicators presented in World Development Indicators are the fruit of decades of work at many levels, from the field workers who administer censuses and household surveys to the committees and working parties of the national and international statistical agencies that develop the nomenclature, classifications, and standards fundamental to an international statistical system. Nongovernmental organizations and the private sector have also made important contributions, both in gathering primary data and in organizing and publishing their results. And academic researchers have played a crucial role in developing statistical methods and carrying on a continuing dialogue about the quality and interpretation of statistical indicators. All these contributors have a strong belief that available, accurate data will improve the quality of public and private decisionmaking. The organizations listed here have made World Development Indicators possible by sharing their data and their expertise with us. More important, their collaboration contributes to the World Bank’s efforts, and to those of many others, to improve the quality of life of the world’s people. We acknowledge our debt and gratitude to all who have helped to build a base of comprehensive, quantitative information about the world and its people. For easy reference, Web addresses are included for each listed organization. The addresses shown were active on March 1, 2012. Information about the World Bank is also provided.
International and government agencies Carbon Dioxide Information Analysis Center The Carbon Dioxide Information Analysis Center (CDIAC) is the primary global climate change data and information analysis center of the U.S. Department of Energy. The CDIAC’s scope includes anything that would potentially be of value to those concerned with the greenhouse effect and global climate change, including concentrations of carbon dioxide and other radiatively active gases in the atmosphere, the role of the terrestrial biosphere and the oceans in the biogeochemical cycles of greenhouse gases, emissions of carbon dioxide to the atmosphere, long-term climate trends, the effects of elevated carbon dioxide on vegetation, and the vulnerability of coastal areas to rising sea levels. For more information, see http://cdiac.ornl.gov. Centre for Research on the Epidemiology of Disasters Since 1988 the World Health Organization Collaborating Centre for Research on the Epidemiology of Disasters has maintained the Emergency Events Database, which was created with support from the Belgian government. The main objective of the database is to serve the purposes of humanitarian action at the national and international levels. It aims to rationalize decisionmaking for disaster preparedness and provide an objective base for vulnerability assessment and priority setting. The database contains essential core data—compiled from various sources, including UN agencies, nongovernmental organizations, insurance companies, research institutes, and press agencies—on the occurrence and effects of more than 18,000 mass disasters since 1900. For more information, see www.emdat.be.
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Deutsche Gesellschaft für Internationale Zusammenarbeit The Deutsche Gesellschaft für Internationale Zusammenarbeit (GIZ) GmbH is a German government-owned corporation for international cooperation with worldwide operations. GIZ’s aim is to positively shape political, economic, ecological, and social development in partner countries, thereby improving people’s living conditions and prospects. For more information, see www.giz.de.
Food and Agriculture Organization The Food and Agriculture Organization, a specialized agency of the United Nations, was founded in October 1945 with a mandate to raise nutrition levels and living standards, to increase agricultural productivity, and to better the condition of rural populations. The organization provides direct development assistance; collects, analyzes, and disseminates information; offers policy and planning advice to governments; and serves as an international forum for debate on food and agricultural issues. For more information, see www.fao.org.
Internal Displacement Monitoring Centre The Internal Displacement Monitoring Centre was established in 1998 by the Norwegian Refugee Council and is the leading international body monitoring conflict-induced internal displacement worldwide. The center contributes to improving national and international capacities to protect and assist the millions of people around the globe who have been displaced within their own country as a result of conflicts or human rights violations. For more information, see www.internal-displacement.org.
International Civil Aviation Organization The International Civil Aviation Organization (ICAO), a specialized agency of the United Nations, is responsible for establishing international standards and recommended practices and procedures for the technical, economic, and legal aspects of international civil aviation operations. ICAO’s strategic objectives include enhancing global aviation safety and security and the efficiency of aviation operations, minimizing the adverse effect of global civil aviation on the environment, maintaining the continuity of aviation operations, and strengthening laws governing international civil aviation. For more information, see www.icao.int.
International Energy Agency Founded in 1974, the International Energy Agency’s (IEA) mandate is to facilitate cooperation among member countries in order to increase energy efficiency, promote use of clean energy and technology, and diversify energy sources while protecting the environment. The IEA publishes annual and quarterly statistical publications covering both Organisation for Economic Co-operation and Development (OECD) and non-OECD countries’ data on oil, gas, coal, electricity, and renewable sources of energy; energy supply and consumption; and energy prices and taxes. The IEA also analyzes all aspects of sustainable development globally and provides policy recommendations. For more information, see www.iea.org.
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PARTNERS International Labour Organization The International Labour Organization (ILO), a specialized agency of the United Nations, seeks the promotion of social justice and internationally recognized human and labor rights. ILO helps advance the creation of decent jobs and the kinds of economic and working conditions that give working people and business people a stake in lasting peace, prosperity, and progress. As part of its mandate, the ILO maintains an extensive statistical publication program. For more information, see www.ilo.org.
International Monetary Fund The International Monetary Fund (IMF) is an international organization of 187 member countries established to promote international monetary cooperation, a stable system of exchange rates, and the balanced expansion of international trade and to foster economic growth and high levels of employment. The IMF reviews national, regional, and global economic and financial developments; provides policy advice to member countries; and serves as a forum where they can discuss the national, regional, and global consequences of their policies. The IMF also makes financing temporarily available to member countries to help them address balance of payments problems. Among the IMF’s core missions are the collection and dissemination of high-quality macroeconomic and financial statistics as an essential prerequisite for formulating appropriate policies. The IMF provides technical assistance and training to member countries in areas of its core expertise, including the development of economic and financial data in accordance with international standards. For more information, see www.imf.org.
International Telecommunication Union The International Telecommunication Union (ITU) is the leading UN agency for information and communication technologies. ITU’s mission is to enable the growth and sustained development of telecommunications and information networks and to facilitate universal access so that people everywhere can participate in, and benefit from, the emerging information society and global economy. A key priority lies in bridging the so-called Digital Divide by building information and communication infrastructure, promoting adequate capacity building, and developing confidence in the use of cyberspace through enhanced online security. ITU also concentrates on strengthening emergency communications for disaster prevention and mitigation. For more information, see www.itu.int.
National Science Foundation The National Science Foundation (NSF) is an independent U.S. government agency whose mission is to promote the progress of science; to advance the national health, prosperity, and welfare; and to secure the national defense. NSF’s goals—discovery, learning, research infrastructure, and stewardship—provide an integrated strategy to advance the frontiers of knowledge, cultivate a world-class, broadly inclusive science and engineering workforce, expand the scientific literacy of all citizens, build the nation’s research capability through investments in advanced instrumentation and facilities, and support excellence in science and engineering research and education through a capable and responsive organization. For more information, see www.nsf.gov.
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The Office of U.S. Foreign Disaster Assistance On November 3, 1961, U.S. President John F. Kennedy established the U.S. Agency for International Development (USAID), the first U.S. foreign assistance organization whose primary emphasis was long-range economic and social development assistance to foreign countries. The Office of U.S. Foreign Disaster Assistance is the office within USAID responsible for providing nonfood humanitarian assistance in response to international crises and disasters. The USAID administrator is designated as the president’s special coordinator for international disaster assistance, which the Office of U.S. Foreign Disaster Assistance assists in coordinating. For more information see www.globalcorps.com/ofda.html and www.usaid.gov/our_work/humanitarian_ assistance/disaster_assistance.
Organisation for Economic Co-operation and Development The Organisation for Economic Co-operation and Development (OECD) includes 34 member countries sharing a commitment to democratic government and the market economy to support sustainable economic growth, boost employment, raise living standards, maintain financial stability, assist other countries’ economic development, and contribute to growth in world trade. With active relationships with some 100 other countries, it has a global reach. It is best known for its publications and statistics, which cover economic and social issues from macroeconomics to trade, education, development, and science and innovation. The Development Assistance Committee (DAC, www.oecd.org/dac) is one of the principal bodies through which the OECD deals with issues related to cooperation with developing countries. The DAC is a key forum of major bilateral donors, who work together to increase the effectiveness of their common efforts to support sustainable development. The DAC concentrates on two key areas: the contribution of international development to the capacity of developing countries to participate in the global economy and the capacity of people to overcome poverty and participate fully in their societies. For more information, see www.oecd.org.
Stockholm International Peace Research Institute The Stockholm International Peace Research Institute (SIPRI) conducts research on questions of conflict and cooperation of importance for international peace and security, with the aim of contributing to an understanding of the conditions for peaceful solutions to international conflicts and for a stable peace. SIPRI’s main publication, SIPRI Yearbook, is an authoritative and independent source on armaments and arms control and other conflict and security issues. For more information, see www.sipri.org.
Understanding Children’s Work As part of broader efforts to develop effective and long-term solutions to child labor, the International Labour Organization, the United Nations Children’s Fund (UNICEF), and the World Bank initiated the joint interagency research program “Understanding Children’s Work and Its Impact” in December 2000. The Understanding Children’s Work (UCW) project was located at UNICEF’s Innocenti Research Centre in Florence, Italy, until June 2004, when it moved to the Centre for International Studies on Economic Growth in Rome. The UCW project addresses the crucial need for more and better data on child labor. UCW’s online database contains data by country on child labor and the status of children. For more information, see www.ucw-project.org. 2012 World Development Indicators
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PARTNERS United Nations The United Nations currently has 193 member states. The purposes of the United Nations, as set forth in its charter, are to maintain international peace and security; to develop friendly relations among nations; to cooperate in solving international economic, social, cultural, and humanitarian problems and in promoting respect for human rights and fundamental freedoms; and to be a center for harmonizing the actions of nations in attaining these ends. For more information, see www.un.org.
United Nations Centre for Human Settlements, Global Urban Observatory The Urban Indicators Programme of the United Nations Human Settlements Programme was established to address the urgent global need to improve the urban knowledge base by helping countries and cities design, collect, and apply policy-oriented indicators related to development at the city level. With the Urban Indicators and Best Practices programs, the Global Urban Observatory is establishing a worldwide information, assessment, and capacity-building network to help governments, local authorities, the private sector, and nongovernmental and other civil society organizations. For more information, see www.unhabitat.org.
United Nations Children’s Fund The United Nations Children’s Fund (UNICEF) works with other UN bodies and with governments and nongovernmental organizations to improve children’s lives in more than 190 countries through various programs in education and health. UNICEF focuses primarily on five areas: child survival and development, basic education and gender equality (including girls’ education), child protection, HIV/AIDS, and policy advocacy and partnerships. For more information, see www.unicef.org.
United Nations Conference on Trade and Development The United Nations Conference on Trade and Development (UNCTAD) is the principal organ of the United Nations General Assembly in the field of trade and development. Its mandate is to accelerate economic growth and development, particularly in developing countries. UNCTAD discharges its mandate through policy analysis; intergovernmental deliberations, consensus building, and negotiation; monitoring, implementation, and follow-up; and technical cooperation. For more information, see www.unctad.org.
United Nations Department of Peacekeeping Operations The United Nations Department of Peacekeeping Operations contributes to the most important function of the United Nations—maintaining international peace and security. The department helps countries torn by conflict to create the conditions for lasting peace. The first peacekeeping mission was established in 1948 and has evolved to meet the demands of different conflicts and a changing political landscape. Today’s peacekeepers undertake a wide variety of complex tasks, from helping build sustainable institutions of governance, to monitoring human rights, to assisting in security sector reform, to disarming, demobilizing, and reintegrating former combatants. For more information, see www.un.org/en/peacekeeping.
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United Nations Educational, Scientific, and Cultural Organization, Institute for Statistics The United Nations Educational, Scientific, and Cultural Organization (UNESCO) is a specialized agency of the United Nations that promotes international cooperation among member states and associate members in education, science, culture, and communications. The UNESCO Institute for Statistics is the organization’s statistical branch, established in July 1999 to meet the growing needs of UNESCO member states and the international community for a wider range of policy-relevant, timely, and reliable statistics on these topics. For more information, see www.uis.unesco.org.
United Nations Environment Programme The mandate of the United Nations Environment Programme is to provide leadership and encourage partnership in caring for the environment by inspiring, informing, and enabling nations and people to improve their quality of life without compromising that of future generations. For more information, see www.unep.org.
United Nations Industrial Development Organization The United Nations Industrial Development Organization was established to act as the central coordinating body for industrial activities and to promote industrial development and cooperation at the global, regional, national, and sectoral levels. Its mandate is to help develop scientific and technological plans and programs for industrialization in the public, cooperative, and private sectors. For more information, see www.unido.org.
United Nations International Strategy for Disaster Reduction Created in December 1999 as the successor to the International Decade for Natural Disaster Reduction, the mandate of the secretariat of the United Nations International Strategy for Disaster Reduction is to serve as the focal point in the UN system for coordination of disaster reduction and to ensure synergies among disaster relief activities. For more information, see www.unisdr.org.
United Nations Office on Drugs and Crime The United Nations Office on Drugs and Crime was established in 1977 and is a global leader in the fight against illicit drugs and international crime. The office assists member states in their struggle against illicit drugs, crime, and terrorism by helping build capacity, conducting research and analytical work, and assisting in the ratification and implementation of relevant international treaties and domestic legislation related to drugs, crime, and terrorism. For more information, see www.unodc.org.
United Nations Office of the High Commissioner for Refugees The United Nations Office of the High Commissioner for Refugees (UNHCR) is mandated to lead and coordinate international action to protect refugees and resolve refugee problems worldwide. Its primary purpose is to safeguard the rights and well-being of refugees. UNHCR also collects and disseminates statistics on refugees. For more information, see www.unhcr.org.
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PARTNERS Upsalla Conflict Data Program The Upsalla Conflict Data Program has collected information on armed violence since 1946 and is one of the most accurate and well used data sources on global armed conflicts. Its definition of armed conflict is becoming a standard in how conflicts are systematically defined and studied. In addition to data collection on armed violence, its researchers conduct theoretically and empirically based analyses of the causes, escalation, spread, prevention, and resolution of armed conflict. For more information, see www.pcr.uu.se/research/UCDP.
World Bank The World Bank is a vital source of financial and technical assistance for developing countries. The World Bank is made up of two unique development institutions owned by 187 member countries—the International Bank for Reconstruction and Development (IBRD) and the International Development Association (IDA). These institutions play different but collaborative roles to advance the vision of an inclusive and sustainable globalization. The IBRD focuses on middle-income and creditworthy poor countries, while IDA focuses on the poorest countries. Together they provide low-interest loans, interest-free credits, and grants to developing countries for a wide array of purposes, including investments in education, health, public administration, infrastructure, financial and private sector development, agriculture, and environmental and natural resource management. The World Bank’s work focuses on achieving the Millennium Development Goals by working with partners to alleviate poverty. For more information, see http://data.worldbank.org.
World Health Organization The objective of the World Health Organization (WHO), a specialized agency of the United Nations, is the attainment by all people of the highest possible level of health. It is responsible for providing leadership on global health matters, shaping the health research agenda, setting norms and standards, articulating evidencebased policy options, providing technical support to countries, and monitoring and assessing health trends. For more information, see www.who.int.
World Intellectual Property Organization The World Intellectual Property Organization (WIPO) is a specialized agency of the United Nations dedicated to developing a balanced and accessible international intellectual property (IP) system, which rewards creativity, stimulates innovation, and contributes to economic development while safeguarding the public interest. WIPO carries out a wide variety of tasks related to the protection of IP rights. These include developing international IP laws and standards, delivering global IP protection services, encouraging the use of IP for economic development, promoting better understanding of IP, and providing a forum for debate. For more information, see www.wipo.int.
World Tourism Organization The World Tourism Organization is an intergovernmental body entrusted by the United Nations with promoting and developing tourism. It serves as a global forum for tourism policy issues and a source of tourism know-how. For more information, see www.unwto.org.
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2012 World Development Indicators
World Trade Organization The World Trade Organization (WTO) is the only international organization dealing with the global rules of trade between nations. Its main function is to ensure that trade flows as smoothly, predictably, and freely as possible. It does this by administering trade agreements, acting as a forum for trade negotiations, settling trade disputes, reviewing national trade policies, assisting developing countries in trade policy issues—through technical assistance and training programs—and cooperating with other international organizations. At the heart of the system—known as the multilateral trading system—are the WTO’s agreements, negotiated and signed by a large majority of the world’s trading nations and ratified by their parliaments. For more information, see www.wto.org.
Private and nongovernmental organizations Center for International Earth Science Information Network The Center for International Earth Science Information Network, a center within the Earth Institute at Columbia University, works at the intersection of the social, natural, and information sciences and specializes in online data and information management, spatial data integration and training, and interdisciplinary research related to human interactions in the environment. For more information, see www.ciesin.org. Containerisation International Containerisation International Yearbook is one of the most authoritative reference books on the container industry. The information can be accessed on the Containerisation International Web site, which also provides a comprehensive online daily business news and information service for the container industry. For more information, see www.ci-online.co.uk.
DHL DHL provides shipping and customized transportation solutions for customers in more than 220 countries and territories. It offers expertise in express, air, and ocean freight; overland transport; contract logistics solutions; and international mail services. For more information, see www.dhl.com.
International Institute for Strategic Studies The International Institute for Strategic Studies (IISS) provides information and analysis on strategic trends and facilitates contacts between government leaders, business people, and analysts that could lead to better public policy in international security and international relations. The IISS is a primary source of accurate, objective information on international strategic issues. For more information, see www.iiss.org.
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PARTNERS International Road Federation The International Road Federation (IRF) is a nongovernmental, not-for-profit organization whose mission is to encourage and promote development and maintenance of better, safer, and more sustainable roads and road networks. Working together with its members and associates, the IRF promotes social and economic benefits that flow from well planned and environmentally sound road transport networks. It helps put in place technological solutions and management practices that provide maximum economic and social returns from national road investments. The IRF works in all aspects of road policy and development worldwide with governments and financial institutions, members, and the community of road professionals. For more information, see www.irfnet.org.
Netcraft Netcraft provides Internet security services such as antifraud and antiphishing services, application testing, code reviews, and automated penetration testing. Netcraft also provides research data and analysis on many aspects of the Internet and is a respected authority on the market share of web servers, operating systems, hosting providers, Internet service providers, encrypted transactions, electronic commerce, scripting languages, and content technologies on the Internet. For more information, see http://news.netcraft.com.
PwC PwC provides industry-focused services in the fields of assurance, tax, human resources, transactions, performance improvement, and crisis management services to help address client and stakeholder issues. For more information, see www.pwc.com.
Standard & Poor’s Standard & Poor’s is the world’s foremost provider of independent credit ratings, indexes, risk evaluation, investment research, and data. S&P’s Global Stock Markets Factbook draws on data from S&P’s Emerging Markets Database (EMDB) and other sources covering data on more than 100 markets with comprehensive market profiles for 82 countries. Drawing a sample of stocks in each EMDB market, Standard & Poor’s calculates indexes to serve as benchmarks that are consistent across national boundaries. For more information, see www.standardandpoors.com.
World Conservation Monitoring Centre The World Conservation Monitoring Centre provides information on the conservation and sustainable use of the world’s living resources and helps others to develop information systems of their own. It works in close collaboration with a wide range of people and organizations to increase access to the information needed for wise management of the world’s living resources. For more information, see www.unep-wcmc.org.
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World Economic Forum The World Economic Forum (WEF) is an independent international organization committed to improving the state of the world by engaging leaders in partnerships to shape global, regional, and industry agendas. Economic research at the WEF—led by the Global Competitiveness Programme—focuses on identifying the impediments to growth so that strategies to achieve sustainable economic progress, reduce poverty, and increase prosperity can be developed. The WEF’s competitiveness reports range from global coverage, such as Global Competitiveness Report, to regional and topical coverage, such as Africa Competitiveness Report, The Lisbon Review, and Global Information Technology Report. For more information, see: www.weforum.org.
World Resources Institute The World Resources Institute is an independent center for policy research and technical assistance on global environmental and development issues. The institute provides—and helps other institutions provide— objective information and practical proposals for policy and institutional change that will foster environmentally sound, socially equitable development. The institute’s current areas of work include trade, forests, energy, economics, technology, biodiversity, human health, climate change, sustainable agriculture, resource and environmental information, and national strategies for environmental and resource management. For more information, see www.wri.org.
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USERS GUIDE Tables
are totals (designated by a t if the aggregates include
cross-country and intertemporal comparisons involve
The tables are numbered by section and display
gap-filled estimates for missing data and by an s,
complex technical and conceptual problems that can-
the identifying icon of the section. Countries and
for simple totals, where they do not), median values
not be resolved unequivocally. Data coverage may
economies are listed alphabetically (except for Hong
(m), weighted averages (w), or simple averages (u).
not be complete because of special circumstances
Kong SAR, China, which appears after China). Data
Gap filling of amounts not allocated to countries may
affecting the collection and reporting of data, such
are shown for 158 economies with a population of
result in discrepancies between subgroup aggregates
as problems stemming from conflicts.
more than 1 million, as well as for Taiwan, China, in
and overall totals. For further discussion of aggrega-
selected tables. Table 1.6 presents selected indi-
tion methods, see Statistical methods.
cators for 58 other economies—small economies
For these reasons, although data are drawn from the sources thought to be most authoritative, they should be construed only as indicating trends and
with a population between 30,000 and 1 million and
Aggregate measures for regions
characterizing major differences among economies
smaller economies if they are members of the Inter-
The aggregate measures for regions cover only low-
rather than as offering precise quantitative mea-
national Bank for Reconstruction and Development
and middle-income economies, including econo-
sures of those differences. Discrepancies in data
or, as it is commonly known, the World Bank. Data for
mies with populations of less than 1 million listed
presented in different editions of World Development
these economies are included on the World Develop-
in table 1.6.
Indicators reflect updates by countries as well as revi-
ment Indicators CD-ROM and the World Bank’s Open
The country composition of regions is based on
sions to historical series and changes in methodol-
Data website (http://data.worldbank.org). The term
the World Bank’s analytical regions and may differ
ogy. Thus readers are advised not to compare data
country, used interchangeably with economy, does
from common geographic usage. For regional clas-
series across editions of World Development Indica-
not imply political independence but refers to any
sifications, see the map on the inside back cover and
tors or across World Bank publications. Consistent
territory for which authorities report separate social
the list on the back cover flap. For further discussion
time series data for 1960–2010 are available on
or economic statistics. When available, aggregate
of aggregation methods, see Statistical methods.
the World Development Indicators CD-ROM and the
measures for income and regional groups appear at the end of each table.
World Bank’s Open Data website (http://data.world-
Statistics
bank.org).
Indicators are shown for the most recent year
Data are shown for economies as they were con-
Except where otherwise noted, growth rates are
or period for which data are available and, in most
stituted in 2010, and historical data are revised to
in real terms. (See Statistical methods for information
tables, for an earlier year or period (usually 1990 or
reflect current political arrangements. Exceptions are
on the methods used to calculate growth rates.) Data
2000 in this edition). Time series data for all 216
noted throughout the tables.
for some economic indicators for some economies
economies are available on the World Development
Additional information about the data is provided
are presented in fiscal years rather than calendar
Indicators CD-ROM and the World Bank’s Open Data
in Primary data documentation, which summarizes
years; see Primary data documentation. The methods
website (http://data.worldbank.org).
national and international efforts to improve basic
used for converting national currencies are described
Known deviations from standard definitions or
data collection and gives country-level information
in Statistical methods.
breaks in comparability over time or across countries
on primary sources, census years, fiscal years,
are either footnoted in the tables or noted in About
statistical methods and concepts used, and other
Country notes
the data. When available data are deemed to be
background information. Statistical methods provides
•
too weak to provide reliable measures of levels and
technical information on some of the general calcula-
trends or do not adequately adhere to international
tions and formulas used throughout the book.
standards, the data are not shown.
Unless otherwise noted, data for China do not include data for Hong Kong SAR, China; Macao SAR, China; or Taiwan, China.
•
Data for Indonesia include Timor-Leste through
•
Montenegro declared independence from Ser-
Data consistency, reliability, and comparability
1999 unless otherwise noted.
Aggregate measures for income groups
Considerable effort has been made to standardize
The aggregate measures for income groups include
the data, but full comparability cannot be assured,
bia and Montenegro on June 3, 2006. When
216 economies (the economies listed in the main
and care must be taken in interpreting the indicators.
available, data for each country are shown
tables plus those in table 1.6) whenever data are
Many factors affect data availability, comparability,
separately. However, some indicators for Serbia
available. To maintain consistency in the aggregate
and reliability: statistical systems in many develop-
continue to include data for Montenegro through
measures over time and between tables, missing
ing economies are still weak; statistical methods,
2005; these data are footnoted in the tables.
data are imputed where possible. The aggregates
coverage, practices, and definitions differ widely; and
Moreover, data for most indicators from 1999
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2012 World Development Indicators
•
onward for Serbia exclude data for Kosovo, which
Low-income economies are those with a GNI per
in 1999 became a territory under international
capita of $1,005 or less in 2010. Middle-income
administration pursuant to UN Security Council
economies are those with a GNI per capita of $1,006–
Resolution 1244 (1999); any exceptions are
$12,275. Lower middle-income and upper middle-
noted. Kosovo became a World Bank member
income economies are separated at a GNI per capita
on June 29, 2009, and its data are shown in the
of $3,976. High-income economies are those with a
tables when available.
GNI per capita of $12,276 or more. The 17 participat-
Netherlands Antilles, for which data were listed in
ing member countries of the euro area are presented
previous editions, ceased to exist on October 10,
as a subgroup under high-income economies.
•
Data for years that are more than three years from the range shown are footnoted.
The cutoff date for data is February 1, 2012.
2010. Data for Curaçao and Sint Maarten, which
•
became countries within the Kingdom of the
Symbols
Netherlands, are now listed separately. Data for
..
Bonaire, Saba, and St. Eustatius, which became
means that data are not available or that aggregates
special municipalities of the Netherlands, are
cannot be calculated because of missing data in the
included in data for the Netherlands.
years shown.
South Sudan declared its independence on July 9, 2011. When available, data are shown
0 or 0.0
separately for South Sudan; data for Sudan
means zero or small enough that the number would
include South Sudan unless otherwise noted.
round to zero at the displayed number of decimal places.
Classification of economies For operational and analytical purposes the World
/
Bank’s main criterion for classifying economies is
in dates, as in 2009/10, means that the period of
gross national income (GNI) per capita (calculated
time, usually 12 months, straddles two calendar
by the World Bank Atlas method). Every economy
years and refers to a crop year, a survey year, or a
is classified as low income, middle income (subdi-
fiscal year.
vided into lower middle and upper middle), or high income. For income classifications see the map on
$
the inside front cover and the list on the front cover
means current U.S. dollars unless otherwise noted.
flap. Low- and middle-income economies are sometimes referred to as developing economies. The term
>
is used for convenience; it is not intended to imply
means more than.
that all economies in the group are experiencing similar development or that other economies have
<
reached a preferred or final stage of development.
means less than.
Note that classification by income does not necessarily reflect development status. Because GNI per
Data presentation conventions
capita changes over time, the country composition
•
of income groups may change from one edition of
A blank means not applicable or, for an aggregate, not analytically meaningful.
World Development Indicators to the next. Once the
•
A billion is 1,000 million.
classification is fixed for an edition, based on GNI
•
A trillion is 1,000 billion.
per capita in the most recent year for which data are
•
Figures in italics refer to years or periods other
available (2010 in this edition), all historical data
than those specified or to growth rates calculated
presented are based on the same country grouping.
for less than the full period specified.
2012 World Development Indicators
xxiii
WORLD VIEW
W
e now have data to monitor the first 10 years of the Millennium Development Goals. Results are starting to appear, and we have a better view of where we will be in 2015. We will not achieve all the targets we set for ourselves, but progress measured against 1990 benchmarks accelerated in the last decade, lifting millions of people out of poverty, enrolling millions of children in school, and sharply reducing loss of life from preventable causes. We know this because we have access to greatly improved statistics. The need for reliable and timely statistics was recognized long before the Millennium Development Goals were proposed in 2000, but the widespread attention given to their quantitative targets has increased demand for regular and uniform reporting of key indicators. The International Development Goals proposed by the Organisation for Economic Co-operation and Development in 1996 (OECD DAC 1996) included 21 indicators under seven headings that anticipated the Millennium Development Goals. The World Bank’s 1992 Poverty Reduction Handbook (World Bank 1992) noted the need for an overall strategy for country statistical capacity and institution building. Faced with large gaps in the international database, the Partnership in Statistics for Development in the 21st Century (PARIS21) was established in 1999 to coordinate efforts to increase developing countries’ statistical capacity. In 2004 the Second Roundtable on Managing for Development Results endorsed the Marrakech Action Plan for Statistics, creating an international agenda for support to statistics in developing countries. Subsequently the Accra Agenda for Action made broad commitments on behalf of donors and developing countries to strengthen national statistical systems; provide more data disaggregated by sex, region, and economic status; and “invest in strengthening developing countries’ national statistical
capacity and information systems, including those for managing aid” (OECD 2008a). More recently, the 2009 Dakar Declaration on the Development of Statistics reaffirmed that “concerted and co-ordinated actions are required to make more effective use of statistical data to support poverty reduction policies and programs and to strengthen and sustain the capacity of statistical systems especially in developing countries” (PARIS21 2009a). Much progress has been made. When the current round of censuses concludes in 2014, 98 percent of the world’s population will have been counted. Since donors began reporting support for statistical capacity development in 2008, financial commitments to statistics have increased 60 percent to $1.6 billion over 2008– 10. More than 55 developing countries have improved the data collection, management, and dissemination of household surveys. The United Nations Inter-Agency and Expert Group on the Millennium Development Goal Indicators has conducted a series of regional workshops aimed at improving the MDG monitoring and has reported annually on progress. The quality of statistics as measured by the World Bank’s statistical capacity indicator has improved from its benchmark level of 54 in 1999 to 67 in 2011. The availability of data for monitoring the Millennium Development Goals has improved commensurately: in 2003 only 4 countries had two data points for at least 16 of 22 principal Millennium Development Goals indicators; by 2009 this had risen to 118 countries (PARIS21 2009b). Any assessment of the Millennium Development Goals must acknowledge that amid all the signs of progress, there are gaps. Shortcomings. Disappointments. Some targets will not be reached in this decade or the next. Likewise the statistical record is still incomplete, Continuing progress will require renewed commitment and careful monitoring.
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2012 World Development Indicators
1
Eradicate extreme poverty and hunger
Goal 1
P
overty and hunger remain, but fewer people live in extreme poverty. The proportion of people living on less than $1.25 a day fell from 43.1 percent in 1990 to 22.2Â percent in 2008. While the food, fuel, and financial crises over the past four years have worsened the situation of vulnerable populations and slowed poverty reduction in some countries, global poverty rates have continued to fall. Between 2005 and 2008 both the poverty rate and the number of people living in extreme poverty fell in all six developing country regions, the first time that has happened. Preliminary estimates for 2010 show that the extreme poverty rate fell further, reaching the global target of the Millennium Development Goals of halving world poverty five years early. Further progress is possible and likely before the 2015 target date of the Millennium Development Goals, if developing countries maintain the robust growth rates achieved over much of the past decade. But even then, hundreds of millions of people will remain mired in poverty, especially in Sub-Saharan Africa and South Asia and wherever poor health and lack of education deprive people of productive employment; environmental resources have been depleted or spoiled; and corruption, conflict, and misgovernance waste public resources and discourage private investment.
1a
Poverty rates fell sharply in the new millennium Poverty rate at $1.25 a day (percent)
1b
Fewer people living in extreme poverty People living on $1.25 a day or less (billions) 2.0
75
Europe & Central Asia Middle East & North Africa Sub-Saharan Africa
1.5
Latin America & Caribbean
50 South Asia
East Asia & Pacific
1.0 25
Sub-Saharan Africa
East Asia & Pacific
0.5 Latin America & Caribbean Middle East & North Africa
0 1993
1996
1999
2002
2005
2008
Source: World Bank staff estimates.
The most rapid decline in poverty occurred in East Asia and the Pacific, where extreme poverty in China fell from 60 percent in 1990 to 13 percent. In developing countries outside China, the poverty rate fell from 37 percent to 25 percent. Poverty remains widespread in Sub-Saharan Africa and South Asia, but progress has been substantial. In South Asia the poverty rate fell from 54 percent in 1990 to 36 percent in 2008. And over 2005â&#x20AC;&#x201C; 2008 the poverty rate in Sub-Saharan Africa fell 4.8 percentage points to less than 50 percent, the largest drop in Sub-Saharan Africa since international poverty rates have been computed.
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2012 World Development Indicators
South Asia
0
Europe & Central Asia
1990
1990
1993
1996
1999
2002
2005
2008
Source: World Bank staff estimates.
In 2008, 1.28 billion people lived on less than $1.25 a day. Since 1990 the number of people living in extreme poverty has fallen in all regions except Sub-Saharan Africa, where the rate of population growth exceeded the rate of poverty reduction, increasing the number of extremely poor people from 290Â million in 1990 to 356 million in 2008. The largest number of poor people remain in South Asia, where 571 million people live on less than $1.25 a day, down from a peak of 641 million in 2002.
WORLD VIEW
1c
Progress toward poverty reduction Share of countries making progress toward poverty reduction (percent)
Reached target Seriously off track
On track Off track Insufficient data
100
1e
More and less income equality Low income Upper middle income
Gini coefficient (most recent value, 2000–09)
Lower middle income No change
70 Less equal 60
50 50 0 40 50 30 More equal 100 East Asia & Pacific
Europe & Latin America Middle East Central Asia & Carib. & N. Africa
South Asia
Sub-Saharan Africa
Source: World Bank staff calculations.
30
40 50 60 Gini coefficient (most recent value, 1990–99)
70
Source: World Development Indicators database.
Individual country progress is assessed by comparing the rate of poverty reduction with the average rate required to achieve a 50 percent reduction in 25 years. Countries that have already reached the target are listed as “achieved.” Those matching the required rate are listed as “on track.” Countries that will take longer but could reach the target by 2040, based on past performance, are listed as “off track.” And those that would need still longer or where poverty rates have increased are listed as “seriously off track.” 1d
Progress toward reducing undernourishment Share of countries making progress toward reducing undernourishment (percent)
20 20
Reached target Seriously off track
On track Off track Insufficient data
Is income inequality improving or getting worse? At any level of income per person, the less equal the distribution of income the greater the poverty rate. The Gini coefficient is a common measure of inequality. A higher value indicates greater inequality. Poor countries often have less equal distributions of income than do rich countries, but there are significant regional differences as well. Data for 81 countries with values measured before and after 2000 show Gini coefficients fell in 44, including many low-income economies. 1f
Many children remain malnourished Malnutrition prevalence, weight for age (percent of children under age 5)
Low income Lower middle income Upper middle income
50
100 40 50 30 0 20 50
10
0
100 East Asia & Pacific
Europe & Latin America Middle East Central Asia & Carib. & N. Africa
South Asia
Sub-Saharan Africa
Source: World Bank staff calculations.
Undernourishment measures the availability of food to meet people’s basic energy needs. The Millennium Development Goals call for halving the proportion of undernourished people, but few countries will reach that target by 2015. Rising agricultural production has kept ahead of population growth, but increasing food prices and the diversion of food crops to fuel production have reversed the declining rate of undernourishment since 2004–06. The Food and Agriculture Organization estimated that there were 739 million people without adequate daily food intake in 2008.
1990
2010
Source: World Health Organization and World Development Indicators database.
Malnutrition rates have dropped substantially since 1990, but more than 100 million children under age 5 remain malnourished. Only 40 of 90 countries with adequate data to monitor trends are on track to reach the Millennium Development Goal target of halving the number of people who suffer from hunger. Malnutrition in children often begins at birth, when poorly nourished mothers give birth to underweight babies. Malnourished children develop more slowly, enter school later, and perform less well. Programs to encourage breastfeeding and improve the diets of mothers and children can help. 2012 World Development Indicators
3
Achieve universal primary education
Goal 2
T
he commitment to provide primary education to every child is the oldest of the Millennium Development Goals, having been set at the first Education for All conference in Jomtien, Thailand, more than 20 years ago. Achieving this goal has often seemed tantalizingly near, but only Latin America and the Caribbean has reached the goal, although East Asia and Pacific and Europe and Central Asia are close. Progress among the poorest countries, slow in the 1990s, has accelerated since 2000, particularly in South Asia and Sub-Saharan Africa, but full enrollment remains elusive. Many children start school but drop out before completing the primary stage, discouraged by cost, distance, physical danger, and failure to progress. Even as countries approach the target of Millennium Development Goal 2, the education demands of modern economies expand. In the 21st century primary education will be of value only as a stepping stone toward secondary and higher education.
1g
The last step toward education for all Primary school completion rate (percent)
1h
64 million children out of school Number of children not attending primary school (millions)
1999
2009
50
110 East Asia & Pacific, actual
East Asia & Pacific, target
Latin America & Caribbean, actual
100
40
Europe & Central Asia, target Europe & Central Asia, actual
90 80
Latin America & Caribbean, target
70
South Asia, target
Middle East & North Africa, target
Middle East & North Africa, actual
30
20
South Asia, actual
10 60 Sub-Saharan Africa, target
50 1991
1995
0
Sub-Saharan Africa, actual
2000
2005
2010
2015
Source: United Nations Educational, Scientific and Cultural Organization Institute of Statistics and World Development Indicators database.
In 2009, 87 percent of children in developing countries completed primary school. In most regions school enrollment picked up after the Millennium Development Goals were promulgated in 2000, when the completion rate was 80 percent. Sub-Saharan Africa and South Asia, which started out farthest behind, have made substantial progress but will still fall short of the goal. The Middle East and North Africa has stalled at completion rates of around 90 percent, while Europe and Central Asia and East Asia and Pacific are within striking distance but have made little progress in the last five years.
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2012 World Development Indicators
East Asia & Pacific
Europe & Latin America Middle East Central Asia & Carib. & N. Africa
South Asia
Sub-Saharan Africa
Source: United Nations Educational, Scientific and Cultural Organization Institute of Statistics.
Many children enroll in primary school but attend intermittently or drop out entirely. This is particularly the case for girls whose work is needed at home. In rural areas the work of children of both sexes may be needed during planting and harvest. Other obstacles, including the lack of suitable facilities, absence of teachers, and school fees, discourage parents from sending their children to school. The problem is worst in South Asia and Sub-Saharan Africa, where more than 48 million children of primary school age are not in school.
WORLD VIEW
1i
Progress toward education for all Share of countries making progress toward universal primary education(percent)
Reached target Seriously off track
On track Off track Insufficient data
1k
How much schooling Madagascar, 2008 Share of people ages 15–19 completing each year of schooling, by year and wealth quintile (percent)
100
100
50
75 Second quintile
0
50
50
25
Richest quintile
Third quintile
Poorest quintile
100 East Asia & Pacific
Europe & Latin America Middle East Central Asia & Carib. & N. Africa
South Asia
Sub-Saharan Africa
Fourth quintile
0 Year 1
Year 2
Year 3
Year 4
Year 5
Year 6
Year 7
Year 8
Year 9
Source: World Bank staff calculations.
Sixty developing countries, half the countries for which adequate data are available, have achieved or are on track to achieve the Millennium Development Goal target of a full course of primary schooling for all children. Twelve more will miss the 2015 deadline. At their current rate of progress they will achieve full enrollment sometime after 2015. That leaves at least 48 countries seriously off track, making little or no progress, 30 of them in Sub-Saharan Africa.
Bangladesh, 2007 Share of people ages 15–19 completing each year of schooling, by sex and location (percent) 100
75 Urban, girls Urban, boys
Rural, girls
50 Rural, boys
25
0 Year 1
1j
The missing enrollments
Year 2
Year 3
Year 4
Year 5
Year 6
Year 7
Year 8
Year 9
Cambodia, 2005 Share of people ages 15–19 completing each year of schooling, by parents’ education level (percent)
Ratio of girls’ to boys’ primary school enrollment (percent)
100 Boys underenrolled
100 Girls underenrolled
75
Secondary Some higher
90 50
Incomplete secondary
80
Primary
25 70 30
Incomplete primary No education
50
70
90
100
Net primary enrollment rate (percent) Source: United Nations Educational, Scientific and Cultural Organization Institute of Statistics and World Development Indicators database.
A major obstacle to achieving universal primary education is the shortfall in girls’ enrollments. Almost all school systems with low enrollment rates show underenrollment of girls in primary school. In only a few places are boys’ enrollment rates lower than girls’. Starting at such a disadvantage, most girls will never catch up. Achieving the Millennium Development Goal target to enroll and keep girls in school is essential.
0 Year 1
Year 2
Year 3
Year 4
Year 5
Year 6
Year 7
Year 8
Year 9
Source: Demographic and Health Surveys.
Many factors affect how long students stay in school. Children from poor families are less likely to attend or stay in school. In most countries girls and children from rural areas are also less likely to attend, but Bangladesh has used targeted incentives to raise girls’ attendance rates. In Cambodia and everywhere else parents with lower levels of education are less likely to keep their children in school. Achieving the Millennium Development Goal target will require breaking the cycle of lack of education– poverty– low enrollment.
2012 World Development Indicators
5
Promote gender equity and empower women
Goal 3
W
omen are making progress along the three dimensions of gender equality and women’s empowerment that the Millennium Development Goals monitor: education, employment, and participation in public decisionmaking. These are important, but there are others. Efforts are under way to improve monitoring of women’s access to financial services, entrepreneurship, and migration and remittances as well as of violence against women. Time-use surveys, for example, can illuminate differences in the roles of women and men within the household and the workplace. Disaggregating other statistical indicators by sex can also reveal patterns of disadvantage or, occasionally, advantage for women. Whatever the case, women make important contributions to economic and social development. Expanding opportunities for them in the public and private sectors is a core development strategy. And good statistics are essential for developing policies that effectively promote gender equity and increase the welfare and productivity of women.
1l
Increasing participation by girls at all levels of education Ratio of girls’ to boys’ enrollment rate, 2009 (percent) 150 125 100 75 50 25 0 Primary Secondary Tertiary
Primary Secondary Tertiary
Primary Secondary Tertiary
Primary Secondary Tertiary
Primarya Secondary Tertiarya
Primary Secondary Tertiary
East Asia & Pacific
Europe & Central Asia
Latin America & Caribbean
Middle East & North Africa
South Asia
Sub-Saharan Africa
a. Data are for 2008. Source: United Nations Educational, Scientific and Cultural Organization Institute of Statistics and World Development Indicators database.
Girls have made substantial gains in primary and secondary school enrollment. In many countries girls’ enrollment rates outnumber boys’, particularly in secondary school. And more girls are staying in school. In 1991 only 73 percent of girls in developing countries finished primary school; by 2010 the completion rate stood at 86 percent. But this comparison obscures the underlying problem of underenrollment. Girls are still less likely to enroll in primary school or to stay through the end of primary
6
2012 World Development Indicators
school. In some countries the situation changes at the secondary level. Girls who complete primary school may be more likely to stay in school, while boys drop out. In Europe and Central Asia and Latin American and the Caribbean the differences in higher education enrollment are substantial. This is an unsatisfactory path to equity. Rapid growth and poverty reduction truly require education for all.
WORLD VIEW
1m
Progress toward gender equality in education Share of countries making progress toward gender equality in primary and secondary education (percent)
Reached target Seriously off track
On track Off track Insufficient data
1o
More women decisionmakers Proportion of seats held by women in national parliaments (percent) 25
Latin America & Caribbean
100
High income
20
South Asia
East Asia & Pacific
50 15 Europe & Central Asia
Sub-Saharan Africa
0
10 Middle East & North Africa
50
5
0
100 East Asia & Pacific
Europe & Latin America Middle East Central Asia & Carib. & N. Africa
South Asia
Sub-Saharan Africa
Source: World Bank staff calculations.
1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 Source: Inter-Parliamentary Union and World Development Indicators database.
Substantial progress has been made toward increasing the proportion of girls enrolled in primary and secondary education. By the end of the 2009/10 school year, 96 countries had achieved equality in enrollment rates and 7 more were on track to do so by 2015. That leaves only 27 countries off track or seriously off track, mostly low- and lower middle-income economies in the Middle East and North Africa, South Asia, and Sub-Saharan Africa. Fourteen countries lacked adequate data to assess progress. 1n
Women have become a larger part of the workforce Share of women employed in the nonagricultural sector (percent of total nonagricultural employment)
1990
Most recent year
The proportion of parliamentary seats held by women has increased everywhere. In Latin America and the Caribbean women now hold 24 percent of all parliamentary seats. The most impressive gains have been made in South Asia, where the number of seats held by women tripled between 1999 and 2000. In Sub-Saharan Africa Rwanda leads the way, making history in 2008 when it elected a parliament composed 56 percent of women. The Middle East and North Africa lags far behind.
Underweight children under age 5, most recent year available, 2005–11 (percent)
50
50
40
40
30
30
20
20
10
10
Female
Male
Note: Insufficient data are available for Sub-Saharan Africa. Source: International Labour Organization and World Development Indicators database.
Women’s share in paid employment in the nonagricultural sector has risen marginally but remains less than 20 percent in the Middle East and North Africa and South Asia. In many countries the majority of women who work hold insecure jobs outside the formal sector. Overall labor force participation rates of women follow a similar pattern, but they are highest in Sub-Saharan Africa, where 60 percent of women ages 15 and older are in the labor force, although many are employed as unpaid family workers.
Dj ib ou ti
PD R La o
Su da n
So m al ia
South Asia
Et hi op ia
Middle East & N. Africa
Ne pa l
Latin America & Carib.
Ni ge r
Europe & Central Asia
In di a Ba ng la de sh
0 East Asia & Pacific
Ti m or -Le st e
0
1p
A slim lead for girls
Source: World Health Organization and World Bank staff calculations.
Girls are less likely to attend school, have secure jobs, or hold public office. But by most measures, they have an advantage in one area: malnutrition. Out of 99 countries with data for 2005–11, 19 had a larger proportion of underweight girls than of underweight boys; 74 had a larger proportion of underweight boys than of underweight girls, and 6 had no difference. The chart shows the 10 countries with the highest proportion of underweight children during the period.
2012 World Development Indicators
7
Reduce child mortality
Goal 4
I
n 1990, 12 million children died before their fifth birthday. By 1999 there were fewer than 10 million child deaths, and the number has continued to fall to just over 7.5 million in 2010. That is good news, but the ambitious Millennium Development Goal target of a two-thirds reduction in the under-five mortality rate will be met by no more than 40 countries. Only Latin America and the Caribbean and upper middle-income economies as a whole will, on average, reach the target. Most children die from causes that are readily preventable or curable with existing interventions, such as acute respiratory infections, diarrhea, measles, and malaria. And most die in the first year of life. Rapid improvements prior to 1990 in a few countries gave hope that mortality rates for infants and children could be cut further in the following 25 years, but progress slowed almost everywhere after 1990, leaving most countries far behind the target before the goals were announced. Was the target too challenging? Perhaps. But the more important question is whether it encouraged countries to use their resources wisely to achieve the fastest possible progress.
1q
Still far to go in reducing under-ďŹ ve mortality Under-five mortality rate (per 1,000 live births) 200
1r
Most deaths occur in the ďŹ rst year of life Deaths, 2010 (millions) 4
Children ages 1â&#x20AC;&#x201C;5
Infants
Sub-Saharan Africa
3
150 South Asia
2
100 Middle East & North Africa East Asia & Pacific
50
Europe & Central Asia
1
Latin America & Caribbean High income
0
0 1990
1992
1994
1996
1998
2000
2002
2004
2006
2008
2010
Source: World Health Organization and World Development Indicators database.
Mortality rates have been falling everywhere. In developing countries the mortality rate fell from an average of 98 per 1,000 live births in 1990 to 63 in 2010. But rates remain much higher in many countries, especially in Sub-Saharan Africa and parts of South Asia. In Sub-Saharan Africa one child in eight dies before his or her fifth birthday. The odds are somewhat better in South Asia, where 1 child in 15 dies. But even in these regions there are countries exhibiting rapid progress.
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2012 World Development Indicators
East Asia & Pacific
Europe & Latin America Middle East Central Asia & Carib. & N. Africa
South Asia
Sub-Saharan Africa
Source: World Health Organization and World Development Indicators database.
Almost 70 percent of deaths of children under age 5 occur in the first year of life, and half in the first month. Therefore, reducing child mortality requires addressing the causes of neonatal and infant deaths: inadequate care at birth and afterward, malnutrition, poor sanitation, and exposure to acute and chronic disease. Improvements in infant and child mortality are, in turn, the largest contributors to increased life expectancy in most countries.
WORLD VIEW
1s
Progress toward reducing child mortality Share of countries making progress toward reducing child mortality (percent)
Reached target Seriously off track
On track Off track Insufficient data
100
50
0
50
100 East Asia & Pacific
Europe & Latin America Middle East Central Asia & Carib. & N. Africa
South Asia
Sub-Saharan Africa
Source: World Bank staff calculations.
A concerted effort by academic researchers and international statistical agencies has greatly improved measurement of infant and child mortality. Therefore, few countries lack estimates of child mortality rates, although many are derived from statistical models. Ten countries have already achieved a two-thirds reduction in under-five mortality rates since 1990, and 26 are on track to do so by 2015. But that leaves 105 countries, with half of developing countries’ population, off track or seriously off track. 1t
Preventing childhood diseases Children ages 12–23 months immunized against measles (percent)
1u
For some, better than expected improvements Reduction since 1990
2010, actual
2010, target
2010, predicted
Seychelles Mauritius Botswana Cape Verde South Africa Namibia Zimbabwe Lesotho Gabon São Tomé and Príncipe Swaziland Kenya Congo, Rep. Ghana Mauritania Sudan Comoros Cameroon Senegal Eritrea Togo Côte d’Ivoire Tanzania Madagascar Rwanda Central African Republic Gambia, The Uganda Benin Somalia Congo, Dem. Rep. Zambia Burundi Ethiopia Equatorial Guinea Burkina Faso Chad Guinea-Bissau Nigeria Mozambique Malawi Liberia Guinea Angola Mali Sierra Leone Niger
100 0
High income
100
200
300
400
Under-five mortality rate (deaths per 1,000 live births) Upper middle income
Source: World Bank staff calculations.
75 Lower middle income Low income
50
25
0 1990
1992
1994
1996
1998
2000
2002
2004
2006
2008
2010
Source: World Health Organization and World Development Indicators database.
Illnesses that could be prevented by early childhood vaccinations still account for many child deaths. Despite years of vaccination campaigns, many children in low- and lower middle-income economies remain unprotected. Measles is one example. Other recommended immunizations include diphtheria, pertussis, tetanus, and the BCG immunization for tuberculosis. To be successful, vaccination campaigns must reach all children and be sustained over time.
In 1990 the under-five mortality rate in Niger stood at 311 per 1,000 live births, the worst in the world. In the same year, Seychelles, with an under-five mortality rate of 16, was the best in Sub-Saharan Africa. How have they fared since? In the 20 years from the Millennium Development Goals baseline, Niger’s mortality rate fell by 168, the greatest in the region, while Seychelles’s fell by 3. In proportional terms Niger experienced a 54 percent reduction—second greatest in the region—and Seychelles a 16 percent reduction. Both are short of the Millennium Development Goal target, but Niger, having started in last place, has progressed faster. Has this been the general rule? On average, countries in Sub-Saharan Africa that started in worse positions have done better. But experience has been mixed: conflict- affected countries such as the Democratic Republic of the Congo and Somalia have made almost no progress, while similarly situated countries such as Uganda and Zambia have done much better. Two countries, Madagascar and Malawi, are on track to achieve the Millennium Development Goal target. Several others, including Eritrea, Niger, and Tanzania, are close. Only one country, Zimbabwe, moved backward from 1990 to 2010. 2012 World Development Indicators
9
Reduce maternal mortality
Goal 5
A
n estimated 358,000 maternal deaths occurred worldwide in 2008, a 34 percent decrease since 1990. The Millennium Development Goals call for reducing the maternal mortality ratio by 75Â percent between 1990 and 2015, but few countries and no developing country region on average will achieve this target. What makes maternal mortality such a compelling problem is that it strikes young women experiencing a natural life event. They die because they are poor. Malnourished. Weakened by disease. They die because they lack access to trained health care workers and modern medical facilities. And because women in poor countries have more children, their lifetime risk of maternal death may be more than 200 times greater than for women in Western Europe and North America. Reducing maternal mortality requires a comprehensive approach to womenâ&#x20AC;&#x2122;s reproductive health, starting with family planning and access to contraception. Many health problems among pregnant women are preventable or treatable through visits with trained health workers before childbirth. Good nutrition, vaccinations, and treatment of infections can improve outcomes for mother and child. Skilled attendants at time of delivery and access to hospital treatments are essential for dealing with life-threatening emergencies such as severe bleeding and hypertensive disorders.
Maternal mortality rates have been falling, but large regional differences persist Maternal mortality ratio, modeled estimate (per 100,000 live births) 1,000
The 12 countries with highest lifetime risk of maternal death
1v 1990 2005
1995 2008
2000
1w
Lifetime risk of maternal death, 2008 (percent) 10
8
750
6
4
500
2 250
South Asia
Sub-Saharan Africa
Source: World Health Organization and World Development Indicators database.
About half of all maternal deaths occur in Sub-Saharan Africa and a third in South Asia, but mothers in other regions face substantial risks as well. The maternal mortality ratio may be many times higher in fragile and conflict-afflicted states than in countries with strong institutions and well organized health systems.
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2012 World Development Indicators
So m al ia
Ni ge r
M al i
Ch ad Af gh an is ta n
Europe & Latin America Middle East Central Asia & Carib. & N. Africa
Le on e Li be Gu ria in ea -B is sa u
East Asia & Pacific
Si er ra
0
Co Bu ng ru nd o, i De m .R ep . Ni ge ria Ta nz an ia
0
Source: World Health Organization and World Development Indicators database.
In high-fertility countries women are repeatedly exposed to the risk of maternal mortality. In Afghanistan in 2008, where the lifetime risk of maternal death was over 9 percent, one woman in 11 is expected to die in childbirth; in Burundi one woman in 25 is at risk. In high-income economies the lifetime risk is less than 0.03 percent, or less than 1 woman in 3,500.
WORLD VIEW
1x
Progress in reducing maternal mortality Share of countries making progress toward reducing maternal mortality (percent)
1z
Fewer young women giving birth Adolescent fertility rate (births per 1,000 women ages 15â&#x20AC;&#x201C;19)
Reached target Seriously off track
On track Off track Insufficient data
150 Sub-Saharan Africa
100
100
50
South Asia
Latin America & Caribbean
0 50 50
Middle East & North Africa
Europe & Central Asia East Asia & Pacific
0
100 East Asia & Pacific
Europe & Latin America Middle East Central Asia & Carib. & N. Africa
South Asia
Sub-Saharan Africa
Source: World Bank staff calculations.
2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 Source: World Health Organization and World Development Indicators database.
Progress in reducing maternal mortality ratios has been slow, far slower than imagined by the Millennium Development Goal target of a 75 percent reduction from 1990 levels. Only four countries have achieved this target, and five more are on track. Accurately measuring maternal mortality is difficult and requires specialized surveys and good reporting of vital events. Recent efforts by statisticians have improved estimates, but for many countries the need for improved monitoring of maternal health will continue long past 2015. 1y
Planning for motherhood Contraceptive prevalence, 2010 (percent of women ages 15â&#x20AC;&#x201C;49)
The adolescent fertility rate is highest in Sub- Saharan Africa and is declining slowly. Women who give birth at an early age are likely to bear more children and are at greater risk of death or serious complications from pregnancy. In many developing countries the number of women ages 15â&#x20AC;&#x201C;19 is still increasing. Preventing unintended pregnancies and delaying childbirth among young women increase the chances of their attending school and eventually obtaining paid employment.
1aa
Help for mothers Births attended by skilled health staff, 2010 (percent)
80
100
60
75
40
50
20
25
0
0 East Asia & Pacific
Europe & Latin America Middle East Central Asia & Carib. & N. Africa
South Asia
Sub-Saharan Africa
Source: World Health Organization and World Development Indicators database.
Contraceptive use has increased in most developing countries for which data are available. In almost all regions more than half of women who are married or in union use some method of birth control. More than 200 million women want to delay or cease childbearing, and a substantial proportion say that their last birth was unwanted or mistimed. Worldwide an estimated 120 million women have an unmet need for family planning.
East Asia & Pacific
Europe & Latin America Middle East Central Asia & Carib. & N. Africa
South Asia
Sub-Saharan Africa
Source: World Health Organization and World Development Indicators database.
In South Asia and Sub- Saharan Africa less than half of all births are attended by doctors, nurses, or trained midwives. Having skilled health workers present for deliveries is key to reducing maternal mortality. In many places women have only untrained caregivers or family members to attend them during childbirth. Skilled health workers are trained to give necessary care before, during, and after delivery; they can conduct deliveries on their own, summon additional help in emergencies, and provide care for newborns.
2012 World Development Indicators
11
Combat HIV/AIDS, malaria, and other diseases
Goal 6
E
pidemic diseases exact a huge toll in human suffering and lost opportunities for development. Poverty, armed conflict, and natural disasters contribute to the spread of disease and are made worse by it. In Africa the spread of HIV/AIDS has reversed decades of improvement in life expectancy and left millions of children orphaned. It is draining the supply of teachers and eroding the quality of education. There are 300–500 million cases of malaria each year, leading to more than 1 million deaths. Malaria is a disease of poverty. Nearly all the cases occur in Sub-Saharan Africa, where the most lethal form of the malaria parasite is abundant. Most deaths from malaria are among children under age 5, but the disease can be debilitating in adults as well. Tuberculosis kills some 2 million people a year, most of them ages 15–45. The disease, once controlled by antibiotics, is spreading again because of the emergence of drug-resistant strains. People living with HIV/AIDS, which reduces resistance to tuberculosis, are particularly vulnerable, as are refugees, displaced persons, and prisoners living in close quarters and unsanitary conditions. Well managed medical intervention using appropriate drug therapy is the key to stopping the spread of tuberculosis.
1bb
Bringing HIV/AIDS under control Prevalence of HIV (percent of population ages 15–49)
1cc
Millions of people still afflicted with HIV/AIDS Population living with HIV, 2009 (millions) 6
7.5
5 Sub-Saharan Africa
4 5.0 3 2 2.5 1
1992
1994
1996
1998
2000
2002
2004
2006
2009
Source: World Health Organization, Joint United Nations Programme on HIV/AIDS, and World Development Indicators database.
Sub-Saharan Africa remains the center of the HIV/AIDS epidemic, but the proportion of adults living with AIDS has begun to fall even as the survival rate of those with access to antiretroviral drugs has increased. In Africa 58 percent of adults with HIV/AIDS are women. Among young people ages 15–24, the prevalence rate among women is more than twice that among men. Latin America and the Caribbean, where 0.5 percent of adults are infected, has the next highest prevalence rate.
12
2012 World Development Indicators
Ke ny a
In di a Ni ge So ria ut h Af ric a
1990
Ch in a M al aw Fe i de ra tio n Za m bi a Ug an da Zi m ba bw M oz e am bi qu e Ta nz an ia
0.0
0
Ru ss ia n
Latin America & Caribbean
Other developing regions
Ca m er oo n
High income
Source: World Health Organization, Joint United Nations Programme on HIV/AIDS, and World Development Indicators database.
In 2009, 31–33 million people were living with HIV/AIDS, and approximately 1.5 million of them were under age 15. Another 16.9 million children, 14.8 million of them in Sub-Saharan Africa, have lost one or both parents to AIDS. By the end of 2009, 5.25 million people were receiving antiretroviral drugs, or 36 percent of the population for which the World Health Organization recommends treatment.
WORLD VIEW
1dd
Progress toward reversing the HIV epidemic Share of countries making progress against HIV/AIDS (percent)
Halted and reversed Stable low prevalence
Halted or reversed Not improving No data
100
Protecting children from malaria
1ff
First observation (2000 or earlier)
Most recent observation (2006 or later)
Swaziland Mauritania Côte d’Ivoire Guinea
50
Congo, Rep. Comoros 0
Burkina Faso Chad Somalia
50
Cameroon Central African Republic
100 East Asia & Pacific
Europe & Latin America Middle East Central Asia & Carib. & N. Africa
South Asia
Sub-Saharan Africa
Source: World Bank staff calculations.
Zimbabwe Angola Benin Mozambique
The Millennium Development Goals call for halting and then reversing the spread of HIV/AIDS by 2015. The progress assessment shown here is based on prevalence rates for adults ages 15–49. Countries that have a declining prevalence rate since 2005 are assessed to have halted the epidemic; those that have a prevalence rate less than their earliest measured rate have reversed the epidemic. Countries that have a prevalence rate of less than 0.2 percent are considered stable. Countries that have a prevalence rate greater than 0.2 percent and that have neither halted nor reversed the epidemic are shown as not improving.
Sudan Sierra Leone Liberia Ghana Nigeria Senegal Uganda Ethiopia Namibia Guinea-Bissau Congo, Dem. Rep. Burundi Madagascar
1ee
Turning the tide of tuberculosis
Kenya Eritrea
Tuberculosis incidence and prevalence rates (per 100,000 people)
Gambia, The
500
Low income, prevalence
Zambia Lower middle income, prevalence
Gabon
400
São Tomé and Príncipe Malawi
300
Low income, incidence
Togo Tanzania
Lower middle income, incidence
200
Niger
Upper middle income, prevalence
Rwanda
100 Upper middle income, incidence
0
0 1990
Mali
1992
1994
1996
1998
2000
2002
2004
2006
2008
2010
25
50
75
Use of insecticide-treated nets (percent of population under age 5) Source: World Health Organization and World Development Indicators database.
Source: World Health Organization and World Development Indicators database.
Tuberculosis is one of the main causes of adult deaths from a single infectious agent in developing countries. The data shown here illustrate the association of tuberculosis with poverty. The incidence rate is three times higher in low-income economies than in upper middle-income economies. The number of new tuberculosis cases peaked in 2004, and prevalence rates are also declining, but the targets of halving the 1990 prevalence and death rates by 2015 are unlikely to be met.
Malaria is endemic in most tropical and subtropical regions, but 90 percent of malaria deaths occur in Sub-Saharan Africa. Those most severely affected are children under age 5. Even those who survive malaria do not escape unharmed. Repeated episodes of fever and anemia take a toll on mental and physical development. Insecticide-treated nets have proved to be an effective preventative, and their use has grown rapidly. Between 2008 and 2010, 290 million nets were distributed in Sub-Saharan Africa. But coverage remains uneven. In some countries with large numbers of reported cases, use of nets for children remains at less than 20 percent. 2012 World Development Indicators
13
Ensure environmental sustainability
Goal 7
S
ustainable development can be ensured only by protecting the environment and using its resources wisely. Poor people, often dependent on natural resources for their livelihood, are the most affected by environmental degradation and natural disasters (fires, storms, earthquakes), whose effects are worsened by environmental mismanagement. Poor people also suffer from shortcomings in the built environment: whether in urban or rural areas, they are more likely to live in substandard housing, lack basic services, and be exposed to unhealthy living conditions. The seventh goal of the Millennium Development Goals is the most wide ranging but perhaps the least well specified. Among its 10 indicators, only 3 are associated with quantified and timebound targets. For others we can only monitor trends. Most countries have adopted principles of sustainable development and agreed to international accords on protecting the environment. But the failure to reach a comprehensive agreement on limiting greenhouse gas emissions leaves billions of people and future generations vulnerable to the impacts of climate change. Growing populations put more pressure on marginal lands and expose more people to hazardous conditions that will be exacerbated by global warming.
1gg
Carbon dioxide emissions continue to rise
1hh
Forest losses and gains
Average annual change in forest area, 2000â&#x20AC;&#x201C;10 (thousands of square kilometers)
Carbon dioxide emissions (millions of metric tons)
25
30 High income
0
20
Upper middle income
â&#x20AC;&#x201C;25
10
Lower middle income Low income
0 1990
1992
1994
1996
1998
2000
2002
2004
2006
2008
â&#x20AC;&#x201C;50 East Asia Europe & Latin & Pacific Central Asia America & Carib.
Middle East & N. Africa
South Asia
Sub-Saharan High Africa income
Source: Carbon Dioxide Information Analysis Center and World Development Indicators database.
Source: Food and Agriculture Organization and World Development Indicators database.
Annual emissions of carbon dioxide reached 32 million metric tons in 2008 and are still rising. High-income economies remain the largest emitters, but the rapidly growing upper middleincome economies are not far behind. Measured by emissions per capita, however, emissions by high-income economies are more than three times higher than average emissions by lowand middle-income economies.
Loss of forests threatens the livelihood of poor people, destroys habitat that harbors biodiversity, and eliminates an important carbon sink that helps moderate climate change. Net losses since 1990 have been substantial, especially in Latin American and the Caribbean and Sub-Saharan Africa, and only partially compensated for by net gains in Asia and high-income economies.
14
2012 World Development Indicators
WORLD VIEW
1ii
Progress toward improved sanitation Share of countries making progress toward universal access to improved sanitation (percent)
1kk
Many still lack access to sanitation Share of population with access to improved sanitation, 2008 (percent)
Reached target Seriously off track
On track Off track Insufficient data
Rural
Urban
100
100 75 50 50 0 25 50 0 100 East Asia & Pacific
Europe & Latin America Middle East Central Asia & Carib. & N. Africa
South Asia
Source: World Bank staff calculations.
Europe & Latin America Middle East Central Asia & Carib. & N. Africa
South Asia
Sub-Saharan Africa
Source: World Health Organization and World Development Indicators database.
The Millennium Development Goals call for cutting the proportion of the population without access to improved sanitation and water sources in half by 2015. By 2010, 2.7 billion people still lacked access to improved sanitation, and more than 1 billion people practiced open defecation, posing enormous health risks. At the present pace only 37 countries are likely to reach the targetâ&#x20AC;&#x201D;an increase of two since 2008. East Asia and Pacific and Middle East and North Africa are the only developing regions on track to reach the target by 2015. 1jj
Progress toward improved water sources Share of countries making progress toward universal access to an improved water source (percent)
East Asia & Pacific
Sub-Saharan Africa
In 1990, 63 percent of the people living in low- and middleincome economies lacked access to a flush toilet or other form of improved sanitation. By 2010 the access rate had improved 19 percentage points to 44 percent. The situation is worse in rural areas, where 57 percent of the population lack access to improved sanitation. The large urbanâ&#x20AC;&#x201C;rural disparity, especially in Sub-Saharan Africa and South Asia, is the principal reason the sanitation target of the Millennium Development Goals will not be reached. 1ll
Water demand strains supplies Annual freshwater withdrawals, 2009 (percent of internal resources)
Reached target Seriously off track
On track Off track Insufficient data
75
100
50
50
0 25 50 0 100 East Asia & Pacific
Europe & Latin America Middle East Central Asia & Carib. & N. Africa
South Asia
Sub-Saharan Africa
Source: World Bank staff calculations.
In 1990 more than 1 billion people lacked access to drinking water from a convenient, protected source, but the situation is improving. The proportion of people in developing countries with access to an improved water source increased from 71 percent in 1990 to 86 percent in 2008, reaching the Millennium Development Goal target of halving the proportion of people without access to an improved water source. Seventy-three countries have reached or are on track to reach the target. At this rate, only Middle East and North Africa and Sub-Saharan Africa will fall short.
East Asia Europe & Latin & Pacific Central Asia America & Carib.
Middle East & N. Africa
South Asia
Sub-Saharan High Africa income
Source: Food and Agriculture Organization and World Development Indicators database.
Worldwide more than 70 percent of freshwater withdrawals go to agricultural and 20 percent to industrial uses. Only 10 percent go to households, many of which are underserved. The potential impacts of global warming and increased demand for water will require more efficient use of available resources. As pressure grows on internal water resources, conflicts over shared, external resources may also increase.
2012 World Development Indicators
15
Develop a global partnership for development
Goal 8
T
he eighth and final goal distinguishes the Millennium Development Goals from previous sets of resolutions and targeted programs. It recognizes the multidimensional nature of development and the need for wealthy countries and developing countries to work together to create an environment in which rapid, sustainable development is possible. Following the Millennium Summit, world leaders meeting at Monterrey, Mexico, in 2002 agreed to provide financing for development through a coherent process that recognized the need for domestic as well as international resources. Subsequent highlevel meetings expanded on these commitments. Along with increased aid flows and debt relief for the poorest, highly indebted countries, goal 8 recognizes the need to reduce barriers to trade and to share the benefits of new medical and communication technologies. Goal 8 also reminds us that development challenges differ for large and small countries and for those that are landlocked or isolated by large expanses of ocean. Building and sustaining a partnership is an ongoing process that does not stop at a specific date or when a target is reached. However it is measured, a strong commitment to partnership should be the continuing legacy of the Millennium Development Goals.
1mm
Most donors have maintained their aid levels
1nn
But their domestic subsidies to agricultural are greater Agricultural support (percent of GDP) 9
Official development assistance (percent of GNI) 1.25 Norway
Korea, Rep.
1.00 6 0.75 United Kingdom
0.50
Norway Germany
3 Switzerland Japan
0.25
Canada Japan
United States All DAC donors
0.00 1990
1992
1994
1996
1998
2000
2002
0 2004
2006
2008
2010
Source: Organisation for Economic Co-operation and Development.
The financial crisis that began in 2008 and fiscal austerity in many high-income economies have threatened to undermine commitments to increase official development assistance. So far leading donors have maintained their level of support. Total disbursements by members of the Organisation for Economic Co-operation and Developmentâ&#x20AC;&#x2122;s Development Assistance Committee reached $130 billion in 2010, a real increase of 4.3 percent over 2008.
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2012 World Development Indicators
United States All DAC donors (ODA as share of GNI)
1990
1992
1994
1996
1998
2000
2002
2004
2006
2008
2010
Source: Organisation for Economic Co-operation and Development.
Organisation for Economic Co-operation and Development (OECD) members (which include some upper middle-income economies such as Chile and Mexico) spend more on support to domestic agricultural producers than on official development assistance. In 2010 the OECD producer support estimate was $227Â billion, down about 10 percent from the previous three years.
WORLD VIEW
Developing countries have easier access to OECD markets
1oo
1qq
Debt service burdens have been falling Total debt service (percent of exports of goods, services, and income)
Share of total imports from least developed countries admitted free of duty, excluding arms (percent) 100
50
European Union
Latin America & Caribbean United States
40
75
South Asia
30
Europe & Central Asia
Japan
50
Middle East & North Africa
20
25
East Asia & Pacific
10
Sub-Saharan Africa
0 0
1990 1996
1998
2000
2002
2004
2006
1992
1994
1996
1998
2000
2002
2004
2006
2008
2010
2009
Source: World Trade Organization, International Trade Center, and United Nations Conference on Trade and Development.
Source: World Development Indicators database.
Many rich countries have pledged to admit the exports of the least developed countries duty free. However, arcane rules of origin and phytosanitary standards keep many countries from qualifying for duty-free access. And uncertainty over market access may inhibit development of export industries. Compared with the European Union, the large U.S. market retains many barriers to the exports of the poorest countries.
1pp
Cellular phones are connecting developing countries
Growing economies, better debt management, and debt relief for the poorest countries have allowed developing countries to substantially reduce their debt burdens. Despite the financial crisis and a 2.3 percent contraction in the global economy in 2009, debt service ratios continued to fall in most developing country regions. Only in Europe and Central Asia has the ratio of debt service to exports risen since 2008, although rising export earnings have helped moderate the trend.
1rr
A more connected world Internet users (per 100 people) 100
Fixed telephones and mobile cellular subscriptions (per 100 people) 125 High income, mobile subscriptions
100 Upper middle income, mobile subscriptions
High income
75
75 Lower middle income, mobile subscriptions
High income, fixed telephones
50
50 Europe & Central Asia
Upper middle income, fixed telephones
25
Low income, mobile subscriptions
Latin America & Caribbean
25
East Asia & Pacific Middle East & North Africa
Lower middle income, fixed telephones
Sub-Saharan Africa
0 1990
Low income, fixed telephones
1992
1994
1996
1998
2000
2002
2004
2006
2008
2010
Source: International Telecommunication Union and World Development Indicators database.
Telecommunications is an essential tool for development, and new technologies are creating new opportunities everywhere. The growth of fixed-telephone systems has peaked in highincome economies and will never reach the same level of use in developing countries, where mobile cellular subscriptions continue to grow rapidly. In high-income economies, with more than one subscription per person, the pace of growth appears to be slowing. Gradually the world is becoming more connected.
South Asia
0 1990
1992
1994
1996
1998
2000
2002
2004
2006
2008
2010
Source: International Telecommunication Union and World Development Indicators database.
In 2000 Internet use was spreading rapidly in high-income economies but was barely under way in developing country regions. Now developing countries are beginning to catch up. By 2010 there were an average of 34 internet users per 100 people in upper middle-income economies. Like telephones, Internet use is strongly correlated with income. In low-income economies there were only 5.4 users per 100 people in 2010. But growth is picking up.
2012 World Development Indicators
17
Millennium Development Goals Goals and targets from the Millennium Declaration Indicators for monitoring progress Goal 1 Eradicate extreme poverty and hunger Target 1.A Halve, between 1990 and 2015, the proportion of people whose income is less than $1 a day
1.1
Target 1.B Achieve full and productive employment and decent work for all, including women and young people
1.2 1.3 1.4 1.5 1.6 1.7
Target 1.C Halve, between 1990 and 2015, the proportion of people who suffer from hunger
1.8 1.9
Goal 2 Achieve universal primary education Target 2.A Ensure that by 2015 children everywhere, boys and girls alike, will be able to complete a full course of primary schooling
2.1 2.2 2.3
Goal 3 Promote gender equality and empower women Target 3.A Eliminate gender disparity in primary and secondary education, preferably by 2005, and in all levels of education no later than 2015
3.1 3.2 3.3
Goal 4 Reduce child mortality Target 4.A Reduce by two-thirds, between 1990 and 2015, the under-five mortality rate
Goal 5 Improve maternal health Target 5.A Reduce by three-quarters, between 1990 and 2015, the maternal mortality ratio Target 5.B Achieve by 2015 universal access to reproductive health
Target 6.B Achieve by 2010 universal access to treatment for HIV/AIDS for all those who need it Target 6.C Have halted by 2015 and begun to reverse the incidence of malaria and other major diseases
Net enrollment ratio in primary education Proportion of pupils starting grade 1 who reach last grade of primary education Literacy rate of 15- to 24-year-olds, women and men Ratios of girls to boys in primary, secondary, and tertiary education Share of women in wage employment in the nonagricultural sector Proportion of seats held by women in national parliament
4.1 4.2 4.3
Under-five mortality rate Infant mortality rate Proportion of one-year-old children immunized against measles
5.1 5.2 5.3 5.4 5.5
Maternal mortality ratio Proportion of births attended by skilled health personnel Contraceptive prevalence rate Adolescent birth rate Antenatal care coverage (at least one visit and at least four visits) Unmet need for family planning
5.6 Goal 6 Combat HIV/AIDS, malaria, and other diseases Target 6.A Have halted by 2015 and begun to reverse the spread of HIV/AIDS
Proportion of population below $1 purchasing power parity (PPP) a daya Poverty gap ratio [incidence × depth of poverty] Share of poorest quintile in national consumption Growth rate of GDP per person employed Employment to population ratio Proportion of employed people living below $1 (PPP) a day Proportion of own-account and contributing family workers in total employment Prevalence of underweight children under five years of age Proportion of population below minimum level of dietary energy consumption
6.1 6.2 6.3
HIV prevalence among population ages 15–24 years Condom use at last high-risk sex Proportion of population ages 15–24 years with comprehensive, correct knowledge of HIV/AIDS 6.4 Ratio of school attendance of orphans to school attendance of nonorphans ages 10–14 years 6.5 Proportion of population with advanced HIV infection with access to antiretroviral drugs 6.6 Incidence and death rates associated with malaria 6.7 Proportion of children under age five sleeping under insecticide-treated bednets 6.8 Proportion of children under age five with fever who are treated with appropriate antimalarial drugs 6.9 Incidence, prevalence, and death rates associated with tuberculosis 6.10 Proportion of tuberculosis cases detected and cured under directly observed treatment short course
The Millennium Development Goals and targets come from the Millennium Declaration, signed by 189 countries, including 147 heads of state and government, in September 2000 (www. un.org/millennium/declaration/ares552e.htm) as updated by the 60th UN General Assembly in September 2005. The revised Millennium Development Goal (MDG) monitoring framework shown here, including new targets and indicators, was presented to the 62nd General Assembly, with new numbering as recommended by the Inter-agency and Expert Group on MDG Indicators at its 12th meeting on November 14, 2007. The goals and targets are interrelated and should be seen as a whole. They represent a partnership between the developed countries and the developing countries “to create an environment—at the national and global levels alike—which is conducive to development and the elimination of poverty.” All indicators should be disaggregated by sex and urban-rural location as far as possible.
18
2012 World Development Indicators
Goals and targets from the Millennium Declaration Indicators for monitoring progress Goal 7 Ensure environmental sustainability Target 7.A Integrate the principles of sustainable development into country policies and programs and reverse the loss of environmental resources Target 7.B Reduce biodiversity loss, achieving, by 2010, a significant reduction in the rate of loss
Target 7.C Halve by 2015 the proportion of people without sustainable access to safe drinking water and basic sanitation Target 7.D Achieve by 2020 a significant improvement in the lives of at least 100 million slum dwellers Goal 8 Develop a global partnership for development Target 8.A Develop further an open, rule-based, predictable, nondiscriminatory trading and financial system
7.1 7.2
Proportion of land area covered by forest Carbon dioxide emissions, total, per capita and per $1 GDP (PPP) 7.3 Consumption of ozone-depleting substances 7.4 Proportion of fish stocks within safe biological limits 7.5 Proportion of total water resources used 7.6 Proportion of terrestrial and marine areas protected 7.7 Proportion of species threatened with extinction 7.8 Proportion of population using an improved drinking water source 7.9 Proportion of population using an improved sanitation facility 7.10 Proportion of urban population living in slumsb
Some of the indicators listed below are monitored separately for the least developed countries (LDCs), Africa, landlocked developing countries, and small island developing states.
(Includes a commitment to good governance, development, and poverty reductionâ&#x20AC;&#x201D;both nationally and internationally.)
Target 8.B
Target 8.C
Target 8.D
Target 8.E
Target 8.F
Official development assistance (ODA) 8.1 Net ODA, total and to the least developed countries, as percentage of OECD/DAC donorsâ&#x20AC;&#x2122; gross national income 8.2 Proportion of total bilateral, sector-allocable ODA of OECD/DAC donors to basic social services (basic education, primary health care, nutrition, safe water, and Address the special needs of the least developed sanitation) countries 8.3 Proportion of bilateral official development assistance of OECD/DAC donors that is untied (Includes tariff and quota-free access for the least 8.4 ODA received in landlocked developing countries as a developed countriesâ&#x20AC;&#x2122; exports; enhanced program of proportion of their gross national incomes debt relief for heavily indebted poor countries (HIPC) and cancellation of official bilateral debt; and more 8.5 ODA received in small island developing states as a proportion of their gross national incomes generous ODA for countries committed to poverty reduction.) Market access Address the special needs of landlocked 8.6 Proportion of total developed country imports (by value developing countries and small island developing and excluding arms) from developing countries and least states (through the Programme of Action for developed countries, admitted free of duty the Sustainable Development of Small Island 8.7 Average tariffs imposed by developed countries on Developing States and the outcome of the 22nd agricultural products and textiles and clothing from special session of the General Assembly) developing countries 8.8 Agricultural support estimate for OECD countries as a percentage of their GDP 8.9 Proportion of ODA provided to help build trade capacity Deal comprehensively with the debt problems of developing countries through national and Debt sustainability international measures in order to make debt 8.10 Total number of countries that have reached their HIPC sustainable in the long term decision points and number that have reached their HIPC completion points (cumulative) 8.11 Debt relief committed under HIPC Initiative and Multilateral Debt Relief Initiative (MDRI) 8.12 Debt service as a percentage of exports of goods and services In cooperation with pharmaceutical companies, 8.13 Proportion of population with access to affordable provide access to affordable essential drugs in essential drugs on a sustainable basis developing countries In cooperation with the private sector, make 8.14 Telephone lines per 100 population available the benefits of new technologies, 8.15 Cellular subscribers per 100 population especially information and communications 8.16 Internet users per 100 population
a. Where available, indicators based on national poverty lines should be used for monitoring country poverty trends. b. The proportion of people living in slums is measured by a proxy, represented by the urban population living in households with at least one of these characteristics: lack of access to improved water supply, lack of access to improved sanitation, overcrowding (3 or more persons per room), and dwellings made of nondurable material. 2012 World Development Indicators
19
1.1
Size of the economy Population Surface Population area density
Afghanistan Albania Algeria Angola Argentina Armenia Australia Austria Azerbaijan Bahrain Bangladesh Belarus Belgium Benin Bolivia Bosnia and Herzegovina Botswana Brazil Bulgaria Burkina Faso Burundi Cambodia Cameroon Canada Central African Republic Chad Chile China Hong Kong SAR, China Colombia Congo, Dem. Rep. Congo, Rep. Costa Rica Côte d’Ivoire Croatia Cuba Cyprus Czech Republic Denmark Dominican Republic Ecuador Egypt, Arab Rep. El Salvador Eritrea Estonia Ethiopia Finland France Gabon Gambia, The Georgia Germany Ghana Greece Guatemala Guinea Guinea-Bissau Haiti
20
millions 2010
thousand sq. km 2010
34 3 35 19 40 3 22 8 9 1 149 9 11 9 10 4 2 195 8 16 8 14 20 34 4 11 17 1,338 7 46 66 4 5 20 4 11 1 11 6 10 14 81 6 5 1 83 5 65 2 2 4d 82 24 11 14 10 2 10
652 29 2,382 1,247 2,780 30 7,741 84 87 1 144 208 31 113 1,099 51 582 8,515 111 274 28 181 475 9,985 623 1,284 756 9,600 1 1,142 2,345 342 51 322 57 110 9 79 43 49 256 1,001 21 118 45 1,104 338 549 268 11 70 357 239 132 109 246 36 28
2012 World Development Indicators
people per sq. km 2010
53 117 15 15 15 109 3 102 110 1,661 1,142 47 360 80 9 74 4 23 69 60 326 80 41 4 7 9 23 143 6,783 42 29 12 91 62 79 106 119 136 131 205 58 81 299 52 32 83 18 118 6 173 78d 235 107 88 134 41 54 363
Gross national income, Atlas method
Gross national income per capita, Atlas method
Purchasing power parity gross national income
$ billions 2010
Rank 2010
$ 2010
Rank 2010
$ billions 2010
14.3 12.7 155.7 75.2 348.4 9.9 1,030.3 394.6 48.3 19.7 104.7 56.5 499.5 7.0 17.9 17.9 13.6 1,830.4 47.3 9.0 1.4 10.6 23.2 1,475.9 2.1 6.9 173.2 5,720.8 231.7 255.3 12.0 8.7 31.7 23.0 61.4 62.2 23.7c 188.3 329.5 49.9 55.7 196.2 21.0 1.8 19.4 32.4 255.2 2,749.8 11.7 0.8 12.0 d 3,522.0 30.1 305.0 39.4 4.0 0.9 6.6
109 117 49 62 26 127 13 25 74 99 57 67 20 139 104 105 113 8 75 131 184 121 95 10 177 140 47 2 37 34 119 135 87 96 66 64 93 43 29 72 69 41 97 180 101 85 35 5 120 198 118 4 88 31 79 160 194 141
410 3,960 b 4,390 3,940 8,620 3,200 46,200 47,030 5,330 18,730 700 5,950 45,840 780 1,810 4,770 6,790 9,390 6,280 550 170 750 1,180 43,250 470 620 10,120 4,270 32,780 5,510 180 2,150 6,810 1,160 13,890 5,520 29,430 c 17,890 59,400 5,030 3,850 b 2,420 3,380 340 14,460 390 47,570 42,370 7,740 450 2,690 d 43,070 1,230 26,950 2,740 400 590 670
204 124 119 125 85 135 20 18 109 62 187 104 21 184 158 112 95 82 101 193 215 185 168 23 199 189 78 121 36 108 214 152 94 171 67 107 40 59 10 110 126 148 131 209 66 207 16 26 89 201 145 25 166 44 142 206 191 188
36.5a 27.3 287.2a 103.1 629.3 17.5 823.0 333.9 83.9 26.0 269.7 129.0 417.3 14.0 46.0 33.5 27.5 2,144.9 101.2 20.7 3.4 29.4 44.4 1,309.5 3.5 13.7 250.5 10,221.7 335.6 419.6 21.4 13.0 52.5a 35.8 83.4 .. 24.4 c 241.0 228.0 89.6a 114.0 491.3 40.6a 2.8a 26.5 86.1 198.9 2,254.9 19.8 2.2 22.2d 3,115.4 40.5 312.7 66.8a 10.2 1.8 11.6a
Per capita $ 2010
1,060a 8,520 8,100a 5,410 15,570 5,660 36,910 39,790 9,270 24,710 1,810 13,590 38,290 1,590 4,640 8,910 13,700 11,000 13,440 1,250 400 2,080 2,270 38,370 790 1,220 14,640 7,640 47,480 9,060 320 3,220 11,270a 1,810 18,890 .. 30,300 c 22,910 41,100 9,030a 7,880 6,060 6,550a 540a 19,810 1,040 37,070 34,750 13,180 1,300 4,990 d 38,100 1,660 27,630 4,650a 1,020 1,180 1,180a
Gross domestic product
Rank 2010
% growth 2009–10
Per capita % growth 2009–10
199 114 117 135 78 133 32 24 108 55 182 88 28 186 142 113 86 98 89 190 213 174 170 27 209 193 80 120 15 109 215 160 96 182 73 42 63 23 111 118 129 126 212 68 200 31 34 90 189 139 29 185 51 141 202 196 195
8.2 3.5 3.3 2.3 9.2 2.1 2.3 2.3 5.0 6.3 6.1 7.6 2.3 3.0 4.1 0.8 7.2 7.5 0.2 9.2 3.9 6.0 2.6 3.2 3.3 4.3 5.2 10.4 7.0 4.3 7.2 8.8 4.2 3.0 –1.2 4.3 1.0 c 2.3 1.3 7.8 3.6 5.1 1.4 2.2 3.1 10.1 3.7 1.5 5.7 5.0 6.4 d 3.7 6.6 –3.5 2.8 1.9 3.5 –5.1
5.2 3.1 1.8 3.0 8.2 1.9 0.7 2.0 3.8 –6.5 4.9 7.8 1.3 0.1 2.5 1.0 5.9 6.6 0.9 6.0 1.3 4.8 1.0 2.0 1.4 1.6 4.2 9.8 6.0 2.9 4.3 6.0 2.7 1.0 –0.9 4.3 0.6c 2.0 0.9 6.3 2.1 3.3 0.9 –0.8 3.1 7.8 3.2 0.9 3.8 2.1 5.4 d 3.8 5.2 –3.8 0.2 –0.3 1.4 –6.3
Population Surface Population area density
Honduras Hungary India Indonesia Iran, Islamic Rep. Iraq Ireland Israel Italy Jamaica Japan Jordan Kazakhstan Kenya Korea, Dem. Rep. Korea, Rep. Kosovo Kuwait Kyrgyz Republic Lao PDR Latvia Lebanon Lesotho Liberia Libya Lithuania Macedonia, FYR Madagascar Malawi Malaysia Mali Mauritania Mauritius Mexico Moldova Mongolia Morocco Mozambique Myanmar Namibia Nepal Netherlands New Zealand Nicaragua Niger Nigeria Norway Oman Pakistan Panama Papua New Guinea Paraguay Peru Philippines Poland Portugal Puerto Rico Qatar
Gross national income, Atlas method
Gross national income per capita, Atlas method
Purchasing power parity gross national income
1.1
WORLD VIEW
Size of the economy
Gross domestic product
millions 2010
thousand sq. km 2010
people per sq. km 2010
$ billions 2010
Rank 2010
$ 2010
Rank 2010
$ billions 2010
Per capita $ 2010
Rank 2010
% growth 2009–10
Per capita % growth 2009–10
8 10 1,225 240 74 32 4 8 60 3 127 6 16 41 24 49 2 3 5 6 2 4 2 4 6 3 2 21 15 28 15 3 1 113 4g 3 32 23 48 2 30 17 4 6 16 158 5 3 174 4 7 6 29 93 38 11 4 2
112 93 3,287 1,905 1,745 435 70 22 301 11 378 89 2,725 580 121 100 11 18 200 237 65 10 30 111 1,760 65 26 587 118 331 1,240 1,031 2 1,964 34 1,564 447 799 677 824 147 42 268 130 1,267 924 324 310 796 75 463 407 1,285 300 313 92 9 12
68 110 412 132 45 74 65 352 206 249 350 68 6 71 202 503 167 154 28 27 36 413 72 41 4 52 82 36 158 86 13 3 631 58 124g 2 72 30 73 3 209 493 17 48 12 174 16 9 225 47 15 16 23 313 126 116 448 152
14.2 128.6 1,553.9 599.2 330.4 74.9 187.1 207.2 2,159.3 13.0 5,334.4 26.3 123.8 31.8 .. 972.3 6.0 .. 4.5 6.5 26.1 37.5 2.3 0.8 77.1 37.8 9.4 8.8 4.9 220.4 9.2 3.6 9.9 1,008.0 6.5g 5.2 92.6h 10.3 .. 10.3 14.5 814.8 124.2 6.4 5.7 186.4 427.1 49.5 182.8 24.5 8.9 17.6 136.7 192.2 474.9 232.7 61.7 ..
110 53 9 18 27 63 44 39 7 115 3 90 56 86 15 147 157 144 91 82 174 197 61 81 128 134 156 38 130 162 126 14 145 153 59 123 124 108 16 54 146 149 45 24 71 46 92 133 106 52 42 21 36 65
1,870 12,860 1,270 2,500 4,520 2,340 41,820 27,180 35,700 4,800 41,850 4,340 7,580 790 ..e 19,890 3,290 ..f 830 1,050 11,640 8,880 1,040 200 12,320 11,510 4,570 430 330 7,760 600 1,030 7,750 8,890 1,810 g 1,870 2,850h 440 ..e 4,510 490 49,030 28,770 1,110 370 1,180 87,350 18,260 1,050 6,970 1,300 2,720 4,700 2,060 12,440 21,870 15,500 ..f
155 69 164 147 116 149 29 43 33 111 28 120 90 183 55 132 181 176 74 84 178 213 72 76 115 203 211 87 190 179 88 83 158 155 140 202 118 197 14 42 173 208 168 4 58 176 92 161 144 114 153 71 48 63
28.6a 195.5 4,159.7 1,008.2 840.0 108.1 150.1 210.8 1,923.7 19.7a 4,411.7 35.1 175.7 68.1 .. 1,422.7 .. .. 11.3 15.3 36.7 59.5 4.3 1.4 105.7a 59.4 22.5 19.9 12.7 403.9 15.8 6.6 17.9 1,627.0 12.0 g 10.1 149.3h 21.7 93.5a 14.7 36.2 694.7 121.3 16.1a 11.2 344.2 286.3 68.3 484.4 44.9a 16.6a 32.8 259.6 370.7 731.5 261.6 .. ..
3,770a 19,550 3,400 4,200 11,490 3,370 33,540 27,660 31,810 7,310a 34,610 5,800 10,770 1,680 .. 29,110 .. .. 2,070 2,460 16,380 14,090 1,960 340 16,880a 18,060 10,920 960 850 14,220 1,030 1,910 13,960 14,340 3,360 g 3,670 4,600h 930 1,950a 6,420 1,210 41,810 28,100 2,790a 720 2,170 58,570 25,190 2,790 12,770a 2,420a 5,080 8,930 3,980 19,160 24,590 .. ..
151 69 157 148 95 158 37 50 39 122 35 130 103 184 43 175 168 76 84 177 214 75 74 100 204 207 83 201 180 85 81 159 152 143 205 178 127 194 22 46 164 210 172 6 56 164 91 169 137 112 149 71 59
2.8 1.3 8.8 6.1 1.8 0.8 –0.4 4.7 1.5 –0.6 4.0 3.1 7.3 5.3 .. 6.2 4.0 .. –1.4 9.4 –0.3 7.0 3.3 5.5 2.1 1.3 1.8 1.6 7.1 7.2 4.5 5.0 4.0 5.4 6.9g 6.4 3.7h 7.2 10.4 4.8 4.6 1.7 –0.5 7.6 8.8 7.9 0.7 1.1 4.1 4.8 8.0 15.0 8.8 7.6 3.9 1.4 –2.1 8.6
0.7 1.5 7.3 5.0 0.6 –2.1 –0.8 2.8 1.1 –0.8 4.1 0.9 5.8 2.8 .. 5.9 3.4 .. –2.5 7.9 0.4 6.2 2.6 1.3 0.3 2.9 1.6 –1.3 3.8 5.5 1.4 2.7 3.7 4.1 7.1g 4.7 2.6h 4.8 9.6 2.9 2.7 1.2 –1.6 6.1 5.0 5.2 –0.6 –1.7 2.3 3.2 5.6 13.0 7.6 5.8 3.9 1.3 –2.3 –5.1
2012 World Development Indicators
21
1.1
Size of the economy Population Surface Population area density
millions 2010
Romania Russian Federation Rwanda Saudi Arabia Senegal Serbia Sierra Leone Singapore Slovak Republic Slovenia Somalia South Africa South Sudan Spain Sri Lanka Sudan Swaziland Sweden Switzerland Syrian Arab Republic Tajikistan Tanzania Thailand Timor-Leste Togo Trinidad and Tobago Tunisia Turkey Turkmenistan Uganda Ukraine United Arab Emirates United Kingdom United States Uruguay Uzbekistan Venezuela, RB Vietnam West Bank and Gaza Yemen, Rep. Zambia Zimbabwe World Low income Middle income Lower middle income Upper middle income Low & middle income East Asia & Pacific Europe & Central Asia Latin America & Carib. Middle East & N. Africa South Asia Sub-Saharan Africa High income Euro area
thousand sq. km 2010
people per sq. km 2010
Gross national income, Atlas method
$ billions 2010
21 238 93 168.2 142 17,098 9 1,403.9 11 26 431 5.5 13 434.1 27 2,150i 12 197 65 13.5 7 88 83 41.0 6 72 82 2.0 5 1 7,253 203.4 5 49 113 91.5 2 20 102 49.0 9 638 15 .. 50 1,219 41 304.6 .. .. .. .. 46 505 92 1,462.9 21 66 333 46.7 44 2,506 18 55.3 1 17 61 3.1 9 450 23 469.8 8 41 196 559.7 20 185 111 56.3 7 143 49 5.5 45 947 51 23.4k 69 513 135 286.6 1 15 76 2.5 6 57 111 3.0 1 5 261 20.6 11 164 68 43.9 73 784 95 719.9 5 488 11 19.1 33 242 167 16.6 46 604 79 137.8 8 84 90 290.9 62 244 257 2,377.2 309 9,832 34 14,645.6 3 176 19 34.3 28 447 66 36.1 29 912 33 334.1 87 331 280 101.1 4 6 690 .. 24 528 46 28.1 13 753 17 13.8 13 391 32 5.8 6,895 s 134,222 s 53 w 62,525.2 t 796 15,551 53 420.2 4,971 82,896 62 18,508.7 2,519 23,568 111 4,077.7 2,452 59,328 42 14,429.0 5,767 98,447 60 18,948.9 1,962 16,302 124 7,249.4 405 23,603 18 2,946.7 583 20,394 29 4,505.0 331 8,775 38 1,283.5 1,633 5,131 342 1,920.1 853 24,243 36 1,003.6 1,127 35,774 33 43,682.7 332 2,628 130 12,794.4
Gross national income per capita, Atlas method
Purchasing power parity gross national income
Gross domestic product
Rank 2010
$ 2010
Rank 2010
$ billions 2010
Per capita $ 2010
Rank 2010
% growth 2009–10
Per capita % growth 2009–10
48 12 151 23 114 78 178 40 60 73 32 11 76 70 168 22 19 68 152 94 33 171 169 98 77 17 103 107 51 30 6 1 84 83 28 58
7,850 9,900 520 16,190 1,090 5,630 340 40,070 16,840 23,900 ..e 6,090 ..j 31,750 2,240 1,270 2,950 50,100 71,520 2,750 800 530k 4,150 2,220 490 15,380 4,160 9,890 3,790 500 3,000 41,930 38,200 47,340 10,230 1,280 11,590 1,160 ..j 1,170 1,070 460 9,069 w 528 3,723 1,619 5,884 3,286 3,696 7,272 7,733 3,874 1,176 1,176 38,745 38,565
86 79 195 61 174 106 209 30 60 47 102 39 150 164 138 13 7 141 182 194 123 151 197 64 122 80 128 196 136 27 31 17 77 162 75 171 170 175 200
306.4 2,726.8 12.3 609.8 23.8 80.8 4.9 283.3 124.8 54.4 .. 517.9 .. 1,465.2 104.6 88.6 5.7 372.6 391.0 104.6 14.7 62.6k 565.8 4.0a 5.4 32.3a 95.6 1,129.9 37.8a 41.8 303.8 351.0 2,230.6 14,635.6 45.7 87.7a 350.2 267.0 .. 60.1 17.8 .. 76,295.6 t 1,040.5 33,538.4 9,147.8 24,447.4 34,577.9 13,058.0 5,428.2 6,365.1 2,627.3 5,101.4 1,833.4 42,072.5 11,399.6
14,290 19,240 1,150 22,750 1,910 11,090 830 55,790 22,980 26,530 .. 10,360 .. 31,800 5,010 2,030 5,430 39,730 49,960 5,120 2,140 1,430k 8,190 3,600a 890 24,050a 9,060 15,530 7,490a 1,250 6,620 50,580 35,840 47,310 13,620 3,110a 12,150 3,070 .. 2,500 1,380 .. 11,066 w 1,307 6,747 3,632 9,970 5,996 6,657 13,396 10,926 8,068 3,124 2,148 37,317 34,360
82 70 197 64 180 97 208 7 62 54 105 40 138 176 134 25 14 136 173 187 115 153 206 60 109 79 121 190 125 11 33 16 87 161 93 162 166 188
0.9 4.0 7.5 3.8 4.2 1.0 4.9 14.5 4.2 1.4 .. 2.8 .. –0.1 8.0 4.5 1.1 5.6 2.7 3.2 3.8 7.0k 7.8 7.4 3.4 0.1 3.7 9.0 9.2 5.2 4.2 1.4 2.1 3.0 8.5 8.5 –1.5 6.8 .. 8.0 7.6 9.0 4.2 w 5.9 7.6 6.9 7.8 7.6 9.7 5.7 6.2 4.3 8.1 4.8 3.1 2.0
1.1 4.1 4.3 1.3 1.4 1.4 2.7 12.5 4.0 0.9 .. 1.5 .. –0.5 7.0 1.9 0.8 4.7 1.6 1.1 2.4 3.9k 7.2 5.1 1.2 –0.2 2.6 7.6 7.9 1.9 4.6 –6.3 1.4 2.1 8.1 6.7 –3.0 5.7 .. 4.8 5.9 8.2 3.0 w 3.7 6.4 5.3 7.1 6.2 8.9 5.3 5.0 2.5 6.6 2.3 2.5 1.6
89 111 148
a. Based on regression; others are extrapolated from the 2005 International Comparison Program benchmark estimates. b. Included in the aggregates for upper middle-income economies based on earlier data. c. Refers to the area controlled by the government of the Republic of Cyprus. d. Excludes Abkhazia and South Ossetia. e. Estimated to be low income ($1,005 or less). f. Estimated to be high income ($12,276 or more). g. Excludes Transnistria. h. Includes Former Spanish Sahara. i. Provisional estimate. j. Estimated to be lower middle income ($1,006–$3,975). k. Covers mainland Tanzania only.
22
2012 World Development Indicators
About the data
1.1
WORLD VIEW
Size of the economy Definitions
Population, land area, income, and output are basic
conventional price indexes allow comparison of real
• Population is based on the de facto definition of
measures of the size of an economy. They also
values over time.
population, which counts all residents regardless of
provide a broad indication of actual and potential
PPP rates are calculated by simultaneously compar-
legal status or citizenship—except for refugees not
resources. Population, land area, income (as mea-
ing the prices of similar goods and services among a
permanently settled in the country of asylum, who
sured by gross national income, GNI), and output
large number of countries. In the most recent round
are generally considered part of the population of
(as measured by gross domestic product, GDP) are
of price surveys conducted by the International Com-
their country of origin. The values shown are midyear
therefore used throughout World Development Indica-
parison Program (ICP), 146 countries and territories
estimates. See also table 2.1. • Surface area is
tors to normalize other indicators.
participated in the data collection, including China
a country’s total area, including areas under inland
Population estimates are generally based on
for the first time, India for the first time since 1985,
bodies of water and some coastal waterways. • Pop-
extrapolations from the most recent national cen-
and almost all African countries. The PPP conver-
ulation density is midyear population divided by land
sus. For further discussion of the measurement of
sion factors presented in the table come from three
area in square kilometers. • Gross national income
population and population growth, see About the data
sources. For 47 high- and upper middle-income
(GNI) is the sum of value added by all resident pro-
for table 2.1.
countries conversion factors are provided by Euro-
ducers plus any product taxes (less subsidies) not
The surface area of an economy includes inland
stat and the Organisation for Economic Co-operation
included in the valuation of output plus net receipts
bodies of water and some coastal waterways. Sur-
and Development (OECD), with PPP estimates for
of primary income (compensation of employees and
face area thus differs from land area, which excludes
35 European countries incorporating new price data
property income) from abroad. Data are in current
bodies of water, and from gross area, which may
collected since 2005. For the remaining 2005 ICP
U.S. dollars converted using the World Bank Atlas
include offshore territorial waters. Land area is par-
countries the PPP estimates are extrapolated from
method (see Statistical methods). • Gross national
ticularly important for understanding an economy’s
the 2005 ICP benchmark results, which account for
income per capita is GNI divided by midyear popula-
agricultural capacity and the environmental effects
relative price changes between each economy and
tion. GNI per capita in U.S. dollars is converted using
of human activity. (For measures of land area and
the United States. For countries that did not partici-
the World Bank Atlas method. • Purchasing power
data on rural population density, land use, and agri-
pate in the 2005 ICP round, the PPP estimates are
parity (PPP) gross national income is GNI converted
cultural productivity, see tables 3.1–3.3.) Innova-
imputed using a statistical model. More information
to international dollars using PPP rates. An interna-
tions in satellite mapping and computer databases
on the results of the 2005 ICP is available at www.
tional dollar has the same purchasing power over GNI
have resulted in more precise measurements of land
worldbank.org/data/icp.
that a U.S. dollar has in the United States. • Gross
and water areas.
All 216 economies shown in World Development
domestic product (GDP) is the sum of value added
GNI measures total domestic and foreign value
Indicators are ranked by size, including those that
by all resident producers plus any product taxes (less
added claimed by residents. GNI comprises GDP
appear in table 1.6. The ranks are shown only in
subsidies) not included in the valuation of output.
plus net receipts of primary income (compensation
table 1.1. No rank is shown for economies for which
Growth is calculated from constant price GDP data in
of employees and property income) from nonresident
numerical estimates of GNI per capita are not pub-
local currency. • Gross domestic product per capita
sources. The World Bank uses GNI per capita in U.S.
lished. Economies with missing data are included in
is GDP divided by midyear population.
dollars to classify countries for analytical purposes
the ranking at their approximate level, so that the rel-
and to determine borrowing eligibility. For definitions
ative order of other economies remains consistent.
of the income groups in World Development Indicators, see Users guide. For discussion of the usefulness of national income and output as measures of productivity or welfare, see About the data for tables 4.1 and 4.2.
Data sources
When calculating GNI in U.S. dollars from GNI
Population estimates are prepared by World Bank
reported in national currencies, the World Bank fol-
staff from a variety of sources (see Data sources
lows the World Bank Atlas conversion method, using
for table 2.1). Data on surface and land area are
a three-year average of exchange rates to smooth
from the Food and Agriculture Organization (see
the effects of transitory fluctuations in exchange
Data sources for table 3.1). GNI, GNI per capita,
rates. (For further discussion of the World Bank Atlas
GDP growth, and GDP per capita growth are esti-
method, see Statistical methods.)
mated by World Bank staff based on national
Because exchange rates do not always refl ect
accounts data collected by World Bank staff during
differences in price levels between countries,
economic missions or reported by national statis-
the table also converts GNI and GNI per capita
tical offices to other international organizations
estimates into international dollars using purchas-
such as the OECD. PPP conversion factors are
ing power parity (PPP) rates. PPP rates provide
estimates by Eurostat/OECD and by World Bank
a standard measure allowing comparison of real
staff based on data collected by the ICP.
levels of expenditure between countries, just as
2012 World Development Indicators
23
1.2
Millennium Development Goals: eradicating poverty and saving lives Eradicate extreme poverty and hunger
Share of Vulnerable poorest quintile employment in national consumption Unpaid family workers and own-account workers or income % of total employment % a,b 2000–11 1990 2007–10a
Afghanistan Albania Algeria Angola Argentina Armenia Australia Austria Azerbaijan Bahrain Bangladesh Belarus Belgium Benin Bolivia Bosnia and Herzegovina Botswana Brazil Bulgaria Burkina Faso Burundi Cambodia Cameroon Canada Central African Republic Chad Chile China Hong Kong SAR, China Colombia Congo, Dem. Rep. Congo, Rep. Costa Rica Côte d’Ivoire Croatia Cuba Cyprus Czech Republic Denmark Dominican Republic Ecuador Egypt, Arab Rep. El Salvador Eritrea Estonia Ethiopia Finland France Gabon Gambia, The Georgia Germany Ghana Greece Guatemala Guinea Guinea-Bissau Haiti
24
9.4 8.1 .. 2.0 d 4.4 d 8.8 .. 8.6 8.0 .. 8.9 9.2 8.5 7.0 2.1 6.7 .. 2.9 8.5 6.7 9.0 7.5 6.7 7.2 3.4 6.3 4.3 5.0 .. 3.0 5.5 5.0 3.9 5.6 8.1 .. .. .. .. 4.7 4.3 9.2 3.7 .. 6.8 9.3 9.6 .. 6.2 4.8 5.3 8.5 5.2 6.7 3.1 6.4 7.3 2.4
2012 World Development Indicators
.. .. .. .. 26e .. 10 .. .. .. .. .. 17 .. 40e .. .. 29e .. .. .. .. .. .. .. 94 .. .. 6 28 .. .. 25 .. .. .. .. 7 7 39 36e 28 35 .. 2 92 .. 11 48 .. .. 5 .. 42 .. .. .. ..
.. .. .. .. 20e 38 9 9 55 2 .. .. 10 .. 57 .. .. 25 9 .. .. 83 .. .. .. .. 26 .. 7 49 .. .. 20 .. 18 .. 14 14 5 42 43 27 38 .. 5 .. 9 7 .. .. 63 7 .. 28 .. .. .. ..
Prevalence of malnutrition Underweight % of children under age 5 1990 2005–10a
.. .. 9.2 .. .. .. .. .. .. 6.3 61.5 .. .. .. 9.7 .. .. .. .. 29.6 30.2 .. 18 .. .. .. .. 12.6 .. 8.8 .. 21.1 2.5 .. .. .. .. 0.9 .. 8.4 .. 10.5 11.1 36.9 .. .. .. .. .. .. .. .. 24.1 .. 27.8 .. .. 23.7
.. 6.3 3.7 15.6 2.3 4.2 .. .. 8.4 .. 41.3 1.3 .. 20.2 4.5 1.6 11.2 2.2 .. 26.0 35.2 28.8 16.6 .. .. .. 0.5 3.4 .. 3.4 28.2 11.8 1.1 29.4 1.0 .. .. .. .. 3.4 .. 6.8 6.6 .. .. 34.6 .. .. .. 15.8 1.1 1.1 14.3 .. 13.0 20.8 17.2 18.9
Achieve universal primary education
Promote gender equality
Reduce child mortality
Primary completion rate % 1991 2010
Ratio of girls to boys enrollments in primary and secondary education % 1991 2010c
Under-fi ve mortality rate per 1,000 live births 1990 2010
28 .. 80 33 100 105 .. .. 95 97 41 94 79 22 71 .. 90 93 90 20 46 45 53 .. 28 18 .. 107 102 73 48 54 79 42 85 99 90 92 98 61 91 .. 65 18 .. 23 97 106 62 45 .. 100 64 99 .. 17 5 27
.. 86 96 47 106 101 .. 99 90 .. 65 103 90 63 99 70 94 .. 95 45 56 87 79 .. 41 33 96 .. 96 114 59 71 96 59 f 95 98 103 101 97 92 106 98 96 40 98 72 98 .. .. 71 116 100 94f 101 84 64 68 ..
54 96 83 .. 107 .. 100 95 100 101 75 .. 101 .. .. .. 109 .. 99 .. 82 .. 83 99 61 41 100 86 102 108 70 89 101 .. 103 106 100 98 101 .. 100 81 101 82 103 68 109 102 96 65 98 99 78 99 87 45 55 ..
64 98 98 79 104 102 98 97 99 .. 107 101 98 .. 99 102 100 102 97 89 f 94 94 85 99 69 65 99 103 102 104 79 .. 102 .. 102 98 100 101 101 97 103 .. 98 80 101 89 102 100 .. 99 97 96 96f 97 95 77 .. ..
209 41 68 243 27 55 9 9 93 17 143 17 10 178 121 19 59 59 22 205 183 121 137 8 165 207 19 48 .. 37 181 116 17 151 13 13 11 14 9 62 52 94 62 141 21 184 7 9 93 165 47 9 122 13 78 229 210 151
149 18 36 161 14 20 5 4 46 10 48 6 4 115 54 8 48 19 13 176 142 51 136 6 159 173 9 18 .. 22 170 93 10 123 6 6 4 4 4 27 20 22 16 61 5 106 3 4 74 98 22 4 74 4 32 130 150 165
Eradicate extreme poverty and hunger
Share of Vulnerable poorest quintile employment in national consumption Unpaid family workers and own-account workers or income % of total employment % a,b 2000–11 1990 2007–10a
Honduras Hungary India Indonesia Iran, Islamic Rep. Iraq Ireland Israel Italy Jamaica Japan Jordan Kazakhstan Kenya Korea, Dem. Rep. Korea, Rep. Kosovo Kuwait Kyrgyz Republic Lao PDR Latvia Lebanon Lesotho Liberia Libya Lithuania Macedonia, FYR Madagascar Malawi Malaysia Mali Mauritania Mauritius Mexico Moldova Mongolia Morocco Mozambique Myanmar Namibia Nepal Netherlands New Zealand Nicaragua Niger Nigeria Norway Oman Pakistan Panama Papua New Guinea Paraguay Peru Philippines Poland Portugal Puerto Rico Qatar
2.0 8.4 8.6 8.3 6.4 8.7 7.4 5.7 6.5 5.4 .. 7.7 9.1 4.8 .. .. .. .. 6.8 7.6 6.6 .. 3.0 6.4 .. 6.6 5.1 5.4 7.0 4.5 8.0 6.0 .. 4.4 7.8 7.1 6.5 5.2 .. 3.2 8.3 .. .. 6.2 8.1 4.4 9.6 .. 9.6 3.3 .. 3.3 3.9 6.0 7.7 .. .. 3.9
49e 7 .. .. .. .. 20 .. 27 42 19 .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. 84 84 29 .. .. 12 26 .. .. .. .. .. .. .. 9 13 43e .. .. .. .. .. 34 .. 23e 36e .. 28 24 .. ..
50 7 .. 64 42 .. 12 7 19 37 10 10 32 .. .. 24 .. .. .. .. 8 28 .. 79 .. 9 23 .. .. 22 83 .. 16 30 29 58 51 .. .. .. .. 11 11 45 .. .. 6 .. 63 31 .. 45 40e 44 19 18 .. 0
Prevalence of malnutrition Underweight % of children under age 5 1990 2005–10a
15.8 2.3 59.5 31.0 .. 10.4 .. .. .. 4.0 .. 4.8 .. 20.1 .. .. .. .. .. 39.8 .. .. 13.8 .. .. .. .. 35.5 24.4 22.1 29.0 43.3 .. 13.9 .. 10.8 8.1 .. 28.8 21.5 .. .. .. 9.6 41.0 35.1 .. 21.4 39.0 .. .. 2.8 8.8 29.8 .. .. .. ..
8.6 .. 43.5 17.5 .. 7.1 .. .. .. 1.9 .. 1.9 4.9 16.4 18.8 .. .. 1.7 2.7 31.6 .. .. 13.5 20.4 5.6 .. 1.8 .. 13.8 12.9 27.9 15.9 .. 3.4 3.2 5.3 .. 18.3 .. 17.5 38.8 .. .. 5.7 39.9 26.7 .. 8.6 .. .. 18.1 3.4 4.5 20.7 .. .. .. ..
1.2
WORLD VIEW
Millennium Development Goals: eradicating poverty and saving lives Achieve universal primary education
Promote gender equality
Reduce child mortality
Primary completion rate % 1991 2010
Ratio of girls to boys enrollments in primary and secondary education % 1991 2010c
Under-fi ve mortality rate per 1,000 live births 1990 2010
64 82 64 93 88 58 103 .. 98 94 102 101 103 .. .. 99 .. 57 .. 41 .. .. 59 .. .. .. 98 36 31 91 9 33 115 88 .. .. 48 26 .. 74 51 .. .. 42 17 .. 100 74 .. 86 46 68 .. 88 96 .. .. 71
99 98 96 105 104 65 .. 103 103 73 102 101 116f .. .. 101 .. 112 97 79 92 87 70 62 .. 96 92 72 67 .. 55f 75 96 104 93 108 85 61 104 84 .. .. .. 81 46f 74 100 101 67 97 .. 94 102 92 95 .. .. 100
104 100 73 93 85 79 104 105 100 103 101 101 .. 92 .. 99 .. 100 102 77 101 101 124 .. .. 96 99 96 82 101 58 71 102 97 105 109 70 71 95 106 59 97 100 119 53 77 102 89 48 99 80 98 96 99 101 103 .. 98
107 99 92 101 96 81 103 101 99 99 100 102 98f 95 .. 99 .. 105 99 87 98 104 106 .. .. 99 99 97 101 .. 83f 101 100 102 101 103 89 89 102 104 .. 99 103 102 78 90 99 98 80 101 .. 100 99 101 99 100 104 109
58 19 115 85 65 46 9 12 10 38 6 38 57 99 45 8 .. 15 72 145 21 38 89 227 45 17 39 159 222 18 255 124 24 49 37 107 86 219 112 73 141 8 11 68 311 213 9 47 124 33 90 50 78 59 17 15 .. 21
2012 World Development Indicators
24 6 63 35 26 39 4 5 4 24 3 22 33 85 33 5 .. 11 38 54 10 22 85 103 17 7 12 62 92 6 178 111 15 17 19 32 36 135 66 40 50 4 6 27 143 143 3 9 87 20 61 25 19 29 6 4 .. 8
25
1.2
Millennium Development Goals: eradicating poverty and saving lives Eradicate extreme poverty and hunger
Share of Vulnerable poorest quintile employment in national consumption Unpaid family workers and own-account workers or income % of total employment % a,b 2000–11 1990 2007–10a
Romania Russian Federation Rwanda Saudi Arabia Senegal Serbia Sierra Leone Singapore Slovak Republic Slovenia Somalia South Africa South Sudan Spain Sri Lanka Sudan Swaziland Sweden Switzerland Syrian Arab Republic Tajikistan Tanzania Thailand Timor-Leste Togo Trinidad and Tobago Tunisia Turkey Turkmenistan Uganda Ukraine United Arab Emirates United Kingdom United States Uruguay Uzbekistan Venezuela, RB Vietnam West Bank and Gaza Yemen, Rep. Zambia Zimbabwe World Low income Middle income Lower middle income Upper middle income Low & middle income East Asia & Pacific Europe & Central Asia Latin America & Carib. Middle East & N. Africa South Asia Sub-Saharan Africa High income Euro area
8.3 6.5 5.2 .. 6.2 8.9 6.1 .. 10.1 8.2 .. 2.7 .. 7.0 6.9 6.8 4.1 9.1 7.6 7.7 8.3 6.8 6.7 9.0 7.6 .. 5.9 5.7 .. 5.8 9.7 .. .. 5.4 4.9 7.1 4.3 7.4 7.4 7.2 3.6 .. .. .. .. .. .. .. .. .. .. .. .. .. .. ..
27 1 .. .. 83 .. .. 8 .. 12 .. .. .. 23 43e .. .. .. 9 .. .. 91e 70 .. .. 22 .. .. .. .. .. .. 10 .. .. .. .. .. .. .. 65 .. .. w .. .. .. .. .. .. .. 29 .. .. .. .. 15
33 6 .. .. .. 28 .. 10 12 14 .. 10 .. 11 40e .. .. 7 9 33 .. .. 53 .. .. .. .. 34 .. .. .. 1 11 .. 23e .. 31 .. 28 .. .. .. .. w .. .. 73 .. .. .. 19 31 36 81 .. .. 11
Prevalence of malnutrition Underweight % of children under age 5 1990 2005–10a
5.0 .. 24.3 .. 19.0 .. 25.4 .. .. .. .. .. .. .. 29.3 31.8 .. .. .. 11.5 .. 25.1 16.3 .. 21.2 4.7 8.5 8.7 .. 19.7 .. .. .. .. 6.5 .. 6.7 40.7 .. 29.6 21.2 8.0 .. w 40.2 .. 38.1 12.5 28.7 20.4 8.5 7.5 11.8 52.2 29.0 .. ..
.. .. 18.0 5.3 14.5 1.8 21.3 .. .. .. 32.8 8.7 .. .. 21.6 31.7 7.3 .. .. 10.1 15.0 16.2 7.0 45.3 20.5 .. 3.3 .. .. 16.4 .. .. .. .. .. 4.4 3.7 20.2 2.2 .. 14.9 14.0 .. w 23.0 .. 24.6 3.0 17.9 5.8 1.9 3.0 7.9 32.9 22.0 .. ..
Achieve universal primary education
Promote gender equality
Reduce child mortality
Primary completion rate % 1991 2010
Ratio of girls to boys enrollments in primary and secondary education % 1991 2010c
Under-fi ve mortality rate per 1,000 live births 1990 2010
96 92 50 .. 39 .. .. .. 95 95 .. 76 .. 104 101 .. 61 96 53 89 .. 55 .. .. 35 102 74 90 .. .. 92 103 .. .. 94 80 81 .. .. .. .. 97 79 w 44 83 68 97 78 101 92 84 .. 62 51 .. 101
91 98 70 93 59 96 74f .. 98 95 .. .. .. 102 101 58 77 94 95 104 104 90 .. 65 74 91 91 99 .. 57 98 .. .. 104 106 93f 94 .. 95 63 103 .. 88 w 65 92 88 98 87 97 95 102 88 86 67 97 101
99 105 95 .. 69 .. 64 .. 102 103 .. 104 .. 104 102 78 .. 102 97 85 .. 97 99 .. 59 101 86 81 .. 77 102 104 102 100 107 .. 105 .. .. .. 87 92 87 w 80 85 81 98 84 89 98 99 80 69 82 100 ..
99 98 102 97 100 101 .. .. 101 99 53 99 .. 102 .. 90 94 99 98 99 90 96 103 98 75 101 101 95 .. 99 99 .. 101 100 104 98f 102 102 104 75 96 .. 96 w 91 97 93 103 96 103 97 102 93 92 89 100 99
37 27 163 45 139 29 276 8 18 10 180 60 .. 11 32 125 96 7 8 38 116 155 32 169 147 37 49 80 98 175 21 22 9 11 23 77 33 51 45 128 183 78 90 w 165 85 113 49 98 56 51 54 74 120 175 12 10
14 12 91 18 75 7 174 3 8 3 180 57 .. 5 17 103 78 3 5 16 63 92 13 81 103 27 16 18 56 99 13 7 5 8 11 52 18 23 22 77 111 80 58 w 108 51 69 20 63 24 23 23 34 67 121 6 4
a. Data are for the most recent year available. b. See table 2.9 for survey year and whether share is based on income or consumption expenditure. c. Provisional data. d. Covers urban areas only. e. Limited coverage. f. Data are for 2011.
26
2012 World Development Indicators
About the data
1.2
WORLD VIEW
Millennium Development Goals: eradicating poverty and saving lives Definitions
Tables 1.2–1.4 present indicators for 17 of the 21
nutrients, and undernourished mothers who give
• Share of poorest quintile in national consump-
targets specified by the Millennium Development
birth to underweight children.
tion or income is the share of the poorest 20 per-
Goals. Each of the eight goals includes one or more
Progress toward universal primary education is
cent of the population in consumption or, in some
targets, and each target has several associated
measured by the primary completion rate. Because
cases, income. • Vulnerable employment is the sum
indicators for monitoring progress toward the target.
many school systems do not record school comple-
of unpaid family workers and own-account workers
Most of the targets are set as a value of a specific
tion on a consistent basis, it is estimated from the
as a percentage of total employment. • Prevalence
indicator to be attained by a certain date. In some
gross enrollment rate in the final grade of primary
of malnutrition is the percentage of children under
cases the target value is set relative to a level in
education, adjusted for repetition. Offi cial enroll-
age 5 whose weight for age is more than two stan-
1990. In others it is set at an absolute level. Some
ments sometimes differ signifi cantly from atten-
dard deviations below the median for the interna-
of the targets for goals 7 and 8 have not yet been
dance, and even school systems with high average
tional reference population ages 0–59 months. The
quantified.
enrollment ratios may have poor completion rates.
data are based on the new international child growth
The indicators in this table relate to goals 1–4.
Eliminating gender disparities in education would
standards for infants and young children, called the
Goal 1 has three targets between 1990 and 2015:
help increase the status and capabilities of women.
Child Growth Standards, released in 2006 by the
to halve the proportion of people whose income is
The ratio of female to male enrollments in primary
World Health Organization. • Primary completion
less than $1.25 a day, to achieve full and productive
and secondary education provides an imperfect mea-
rate is the percentage of students completing the
employment and decent work for all, and to halve the
sure of the relative accessibility of schooling for girls.
last year of primary education. It is calculated as
proportion of people who suffer from hunger. Esti-
The targets for reducing under-five mortality rates
the total number of students in the last grade of
mates of poverty rates are in tables 2.7 and 2.8.
are among the most challenging. Under-five mortal-
primary education, minus the number of repeaters
The indicator shown here, the share of the poorest
ity rates are harmonized estimates produced by a
in that grade, divided by the total number of children
quintile in national consumption or income, is a dis-
weighted least squares regression model and are
of official graduation age. • Ratio of girls to boys
tributional measure. Countries with more unequal
available at regular intervals for most countries.
enrollments in primary and secondary education is
distributions of consumption (or income) have a
Most of the 60 indicators relating to the Millennium
the ratio of the female to male gross enrollment rate
higher rate of poverty for a given average income.
Development Goals can be found in World Develop-
in primary and secondary education. • Under-five
Vulnerable employment measures the portion of the
ment Indicators. Table 1.2a shows where to find the
mortality rate is the probability of a child born in
labor force that receives the lowest wages and least
indicators for the first four goals. For more informa-
a specific year dying before reaching age 5, if sub-
security in employment. No single indicator captures
tion about data collection methods and limitations,
ject to the age-specific mortality rate of that year.
the concept of suffering from hunger. Child malnutri-
see About the data for the tables listed there. For
The probability is derived from life tables and is
tion is a symptom of inadequate food supply, lack
information about the indicators for goals 5–8, see
expressed as a rate per 1,000 live births.
of essential nutrients, illnesses that deplete these
About the data for tables 1.3 and 1.4.
1.2a
Location of indicators for Millennium Development Goals 1–4 Goal 1. Eradicate extreme poverty and hunger 1.1 Proportion of population below $1.25 a day 1.2 Poverty gap ratio 1.3 Share of poorest quintile in national consumption 1.4 Growth rate of GDP per person employed 1.5 Employment to population ratio 1.6 Proportion of employed people living below $1 per day 1.7 Proportion of own-account and unpaid family workers in total employment 1.8 Prevalence of underweight in children under age 5 1.9 Proportion of population below minimum level of dietary energy consumption Goal 2. Achieve universal primary education 2.1 Net enrollment ratio in primary education 2.2 Proportion of pupils starting grade 1 who reach last grade of primary 2.3 Literacy rate of 15- to 24-year-olds Goal 3. Promote gender equality and empower women 3.1 Ratio of girls to boys in primary, secondary, and tertiary education 3.2 Share of women in wage employment in the nonagricultural sector 3.3 Proportion of seats held by women in national parliament Goal 4. Reduce child mortality 4.1 Under-five mortality rate 4.2 Infant mortality rate 4.3 Proportion of one-year-old children immunized against measles
Table 2.8 2.8 1.2, 2.9 2.4 2.4 — 1.2, 2.4 1.2, 2.20 2.20 Data sources 2.12 2.13 2.14
The indicators here and throughout this book have been compiled by World Bank staff from primary and secondary sources. Efforts have been made
1.2, 2.12* 1.5, 2.3* 1.5
to harmonize the data series used to compile this
1.2, 2.23, 5.8 2.23 2.18
un.org/millenniumgoals), but some differences in
— No data are available in the World Development Indicators database. * Table shows information on related indicators.
table with those published on the United Nations Millennium Development Goals Web site (www. timing, sources, and definitions remain. For more information see the data sources for the indicators listed in table 1.2a.
2012 World Development Indicators
27
1.3
Millennium Development Goals: protecting our common environment Improve maternal health Maternal mortality ratio Modeled estimate per 100,000 live births 2008
Afghanistan Albania Algeria Angola Argentina Armenia Australia Austria Azerbaijan Bahrain Bangladesh Belarus Belgium Benin Bolivia Bosnia and Herzegovina Botswana Brazil Bulgaria Burkina Faso Burundi Cambodia Cameroon Canada Central African Republic Chad Chile China Hong Kong SAR, China Colombia Congo, Dem. Rep. Congo, Rep. Costa Rica Côte d’Ivoire Croatia Cuba Cyprus Czech Republic Denmark Dominican Republic Ecuador Egypt, Arab Rep. El Salvador Eritrea Estonia Ethiopia Finland France Gabon Gambia, The Georgia Germany Ghana Greece Guatemala Guinea Guinea-Bissau Haiti
28
1,400 31 120 610 70 29 8 5 38 19 340 15 5 410 180 9 190 58 13 560 970 290 600 12 850 1,200 26 38 .. 85 670 580 44 470 14 53 10 8 5 100 140 82 110 280 12 470 8 8 260 400 48 7 350 2 110 680 1,000 300
Combat HIV/AIDS and other diseases
Contraceptive prevalence rate % of married women ages 15–49 1990 2005–10b
.. .. 51 .. .. 56 .. .. .. 54 40 .. 78 .. 30 .. 33 59 .. .. .. .. 16 .. .. .. 56 85 86 66 8 .. .. .. .. .. .. 78 78 56 53 48 47 .. .. 5 77 81 .. 12 .. 70 17 .. .. .. .. 10
2012 World Development Indicators
23 69 61 .. 78 55 .. .. 51 .. 53 73 .. 17 61 36 53 81 .. 17 22 51 29 .. 19 5 58 85 80 79 17 44 80 13 .. 78 .. .. .. 73 .. 60 73 .. .. 15 .. 71 .. .. 53 .. 24 .. 54 9 14 32
HIV Incidence prevalence of tuberculosis % of population per 100,000 people ages 15–49 2009 2010
.. .. 0.1 2.0 0.5 0.1 0.1 0.3 0.1 .. <0.1 0.3 0.2 1.2 0.2 .. 24.8 .. 0.1 1.2 3.3 0.5 5.3 0.2 4.7 3.4 0.4 0.1c .. 0.5 .. 3.4 0.3 3.4 <0.1 0.1 .. <0.1 0.2 0.9 0.4 <0.1 0.8 0.8 1.2 .. 0.1 0.4 5.2 2.0 0.1 0.1 1.8 0.1 0.8 1.3 2.5 1.9
189 14 90 304 27 73 6 5 110 23 225 70 9 94 135 50 503 43 40 55 129 437 177 5 319 276 19 78 80 34 327 372 13 139 21 9 4 7 6 67 65 18 28 100 25 261 7 9 553 273 107 5 86 5 62 334 233 230
Ensure environmental sustainability
Carbon dioxide emissions per capita metric tons 1990 2008
0.1 2.3 3.1 0.4 3.4 .. 16.8 7.9 .. 24.1 0.1 .. 10.9 0.1 0.8 .. 1.6 1.4 8.9 0.1 0.1 0.0 0.1 16.2 0.1 0.0 2.6 2.2 4.8 1.7 0.1 0.5 1.0 0.5 .. 3.2 6.1 .. 9.8 1.3 1.6 1.3 0.5 .. .. 0.1 10.2 6.9 5.2 0.2 .. .. 0.3 7.2 0.6 0.2 0.2 0.1
0.0 1.3 3.2 1.4 4.8 1.8 18.6 8.1 5.4 21.4 0.3 6.5 9.8 0.5 1.3 8.3 2.5 2.1 6.6 0.1 0.0 0.3 0.3 16.3 0.1 0.0 4.4 5.3 5.5 1.5 0.0 0.5 1.8 0.4 5.3 2.8 7.9 11.2 8.4 2.2 1.9 2.7 1.0 0.1 13.6 0.1 10.6 5.9 1.7 0.3 1.2 9.6 0.4 8.7 0.9 0.1 0.2 0.3
Nationally protected terrestrial and marine areas % of total land area 2010
0.4 8.4 6.2 12.1 5.3 8.0 12.5 22.9 7.1 0.7 1.6 7.2 13.2 23.3 18.5 0.6 30.9 26.0 8.9 14.2 4.8 23.4 9.0 6.2 17.7 9.4 13.3 16.0 41.8 20.5 10.0 9.7 17.6 21.8 9.5 5.3 4.5 15.1 4.1 24.1 38.0 6.1 1.4 3.8 22.6 18.4 8.5 17.1 14.6 1.3 3.4 42.3 14.0 9.9 29.5 6.4 26.9 0.1
Develop a global partnership for development
Access to improved sanitation facilities % of population 1990 2010
.. 76 88 29 90 .. 100 100 .. .. 39 93 100 5 18 .. 38 68 99 8 44 9 48 100 11 8 84 24 .. 67 9 .. 93 20 99 80 100 100 100 73 69 72 75 9 95 3 100 100 .. .. 96 100 7 97 62 10 .. 26
37 94 95 58 .. 90 100 100 82 .. 56 93 100 13 27 95 62 79 100 17 46 31 49 100 34 13 96 64 .. 77 24 18 95 24 99 91 100 98 100 83 92 95 87 .. 95 21 100 100 33 68 95 100 14 98 78 18 20 17
Internet usersa per 100 people 2010
3.7 45.0 12.5 10.0 36.0 44.0 75.9 72.7 46.7 55.0 3.7 32.1 73.7 3.1 20.0 52.0 6.0 40.7 46.0 1.4 2.1 1.3 4.0 81.3 2.3 1.7 45.0 34.4 71.8 36.5 0.7 5.0 36.5 2.6 60.1 15.9 53.0 68.6 88.8 39.5 29.0 26.7 15.9 5.4 74.2 0.7 86.9 77.5 7.2 9.2 26.3 82.5 9.5 44.6 10.5 1.0 2.5 8.4
Improve maternal health Maternal mortality ratio Modeled estimate per 100,000 live births 2008
Honduras Hungary India Indonesia Iran, Islamic Rep. Iraq Ireland Israel Italy Jamaica Japan Jordan Kazakhstan Kenya Korea, Dem. Rep. Korea, Rep. Kosovo Kuwait Kyrgyz Republic Lao PDR Latvia Lebanon Lesotho Liberia Libya Lithuania Macedonia, FYR Madagascar Malawi Malaysia Mali Mauritania Mauritius Mexico Moldova Mongolia Morocco Mozambique Myanmar Namibia Nepal Netherlands New Zealand Nicaragua Niger Nigeria Norway Oman Pakistan Panama Papua New Guinea Paraguay Peru Philippines Poland Portugal Puerto Rico Qatar
110 13 230 240 30 75 3 7 5 89 6 59 45 530 250 18 .. 9 81 580 20 26 530 990 64 13 9 440 510 31 830 550 36 85 32 65 110 550 240 180 380 9 14 100 820 840 7 20 260 71 250 95 98 94 6 7 18 8
Combat HIV/AIDS and other diseases
Contraceptive prevalence rate % of married women ages 15–49 1990 2005–10b
47 .. 45 50 49 14 .. 68 .. 55 58 40 .. 27 62 79 .. .. .. .. .. .. 23 .. .. .. .. 17 13 50 .. 4 75 63 .. .. 42 .. 17 41 24 76 .. .. 4 6 74 9 15 .. .. 48 59 36 73 .. .. ..
65 .. 54 56 79 50 65 .. .. 72 54 59 51 46 .. 80 .. .. 48 38 .. .. 47 11 .. .. 14 40 41 .. 8 9 .. 73 68 55 .. 16 41 55 48 69 .. 72 18 15 88 24 27 52 32 79 74 51 .. 67 .. ..
HIV Incidence prevalence of tuberculosis % of population per 100,000 people ages 15–49 2009 2010
0.8 <0.1 0.3 0.2 0.2 .. 0.2 0.2 0.3 1.7 <0.1 .. 0.1 6.3 .. <0.1 .. .. 0.3 0.2 0.7 0.1 23.6 1.5 .. 0.1 .. 0.2 11.0 0.5 1.0 0.7 1.0 0.3 0.4 <0.1 0.1 11.5 0.6 13.1 0.4 0.2 0.1 0.2 0.8 3.6 0.1 0.1 0.1 0.9 0.9 0.3 0.4 <0.1 0.1 0.6 .. <0.1
51 15 185 189 17 64 8 5 5 7 21 5 151 298 345 97 .. 41 159 90 39 17 633 293 40 69 21 266 219 82 68 337 22 16 182 224 91 544 384 603 163 7 8 42 185 133 6 13 231 48 303 46 106 275 23 29 2 38
1.3
Ensure environmental sustainability
Carbon dioxide emissions per capita metric tons 1990 2008
0.5 6.1 0.8 0.8 4.1 2.9 8.7 7.2 7.5 3.3 8.9 3.3 .. 0.2 .. 5.7 .. 21.8 .. 0.1 .. 3.1 .. 0.2 9.3 .. .. 0.1 0.1 3.1 0.0 1.3 1.4 3.9 .. 4.6 1.0 0.1 0.1 0.0 0.0 11.0 7.0 0.6 0.1 0.5 7.4 5.5 0.6 1.3 0.5 0.5 1.0 0.7 9.6 4.4 .. 24.9
1.2 5.4 1.5 1.7 7.4 3.4 9.9 5.2 7.4 4.5 9.5 3.7 15.1 0.3 3.2 10.5 .. 30.1 1.2 0.3 3.3 4.1 .. 0.2 9.5 4.5 5.8 0.1 0.1 7.6 0.0 0.6 3.1 4.3 1.3 4.1 1.5 0.1 0.3 1.8 0.1 10.6 7.8 0.8 0.1 0.6 10.5 17.3 1.0 2.0 0.3 0.7 1.4 0.9 8.3 5.3 .. 49.1
Nationally protected terrestrial and marine areas % of total land area 2010
13.9 5.1 4.8 6.4 6.9 0.1 1.2 15.1 15.9 7.3 10.9 1.9 2.5 11.7 3.9 3.0 .. 1.1 6.9 16.6 16.4 0.4 0.5 1.6 0.1 14.4 4.9 2.5 15.0 13.7 2.4 1.1 0.7 11.9 1.4 13.4 1.5 14.8 5.2 14.7 17.0 15.2 20.0 36.8 7.1 12.6 10.9 9.3 9.8 11.5 1.4 5.4 13.1 5.0 21.8 6.1 4.4 1.4
Develop a global partnership for development
Access to improved sanitation facilities % of population 1990 2010
50 100 18 32 79 .. 99 100 .. 80 100 97 96 25 .. 100 .. 100 .. .. .. .. .. .. 97 .. .. 9 39 84 15 16 89 64 .. .. 53 11 .. 24 10 100 .. 43 5 37 100 82 27 58 47 37 54 57 .. 92 .. 100
WORLD VIEW
Millennium Development Goals: protecting our common environment
Internet usersa per 100 people 2010
77 100 34 54 100 73 99 100 .. 80 100 98 97 32 80 100 .. 100 93 63 .. .. 26 18 97 .. 88 15 51 96 22 26 89 85 85 51 70 18 76 32 31 100 .. 52 9 31 100 99 48 69 45 71 71 74 90 100 .. 100
2012 World Development Indicators
11.1 65.2 7.5 9.9 13.0 2.5 69.8 65.4 53.7 26.5 77.6 38.9 33.4 25.9 0.0 82.5 .. 38.3 19.6 7.0 71.5 31.0 3.9 7.0 14.0 62.8 51.9 1.7 2.3 56.3 2.7 3.0 28.7 31.1 40.1 12.9 49.0 4.2 0.2 6.5 7.9 90.7 83.0 10.0 0.8 28.4 93.3 62.0 16.8 42.7 1.3 19.8 34.3 25.0 62.5 51.3 42.7 81.6
29
1.3
Millennium Development Goals: protecting our common environment Improve maternal health Maternal mortality ratio Modeled estimate per 100,000 live births 2008
Romania Russian Federation Rwanda Saudi Arabia Senegal Serbia Sierra Leone Singapore Slovak Republic Slovenia Somalia South Africa South Sudan Spain Sri Lanka Sudan Swaziland Sweden Switzerland Syrian Arab Republic Tajikistan Tanzania Thailand Timor-Leste Togo Trinidad and Tobago Tunisia Turkey Turkmenistan Uganda Ukraine United Arab Emirates United Kingdom United States Uruguay Uzbekistan Venezuela, RB Vietnam West Bank and Gaza Yemen, Rep. Zambia Zimbabwe World Low income Middle income Lower middle income Upper middle income Low & middle income East Asia & Pacific Europe & Central Asia Latin America & Carib. Middle East & N. Africa South Asia Sub-Saharan Africa High income Euro area
27 39 540 24 410 8 970 9 6 18 1,200 410 .. 6 39 750 420 5 10 46 64 790 48 370 350 55 60 23 77 430 26 10 12 24 27 30 68 56 .. 210 470 790 260 w 590 210 300 60 290 89 34 86 88 290 650 15 7
Combat HIV/AIDS and other diseases
Contraceptive prevalence rate % of married women ages 15–49 1990 2005–10b
.. .. 21 .. .. .. 3 65 74 .. .. 57 .. .. .. 9 20 .. .. .. .. 10 66 25 34 .. 50 63 .. 5 .. .. 70 71 .. .. .. 53 .. 10 15 43 58 w 22 59 40 75 56 75 .. 58 42 42 15 70 ..
.. 80 52 24 12 41 8 .. .. .. 15 .. .. 66 68 8 49 .. .. 54 37 34 80 22 15 43 60 73 48 24 67 .. 84 79 78 65 .. 80 50 28 41 59d 62 w 34 65 50 81 61 78 69 75 62 51 22 .. ..
HIV Incidence prevalence of tuberculosis % of population per 100,000 people ages 15–49 2009 2010
0.1 1.0 2.9 .. 0.9 0.1 1.6 0.1 <0.1 <0.1 0.7 17.8 .. 0.4 <0.1 1.1 25.9 0.1 0.4 .. 0.2 5.6 1.3 .. 3.2 1.5 <0.1 <0.1 .. 6.5 1.1 .. 0.2 0.6 0.5 0.1 .. 0.4 .. .. 13.5 14.3 0.8 w 2.6 0.7 0.7 0.7 0.9 0.2 0.6 0.5 0.1 0.3 5.5 0.3 0.3
116 106 106 18 288 18 682 35 8 11 286 981 .. 16 66 119 1,287 7 8 20 206 177 137 498 455 19 25 28 66 209 101 3 13 4 21 128 33 199 5 49 462 633 128 w 264 132 174 89 150 123 90 43 42 192 271 14 ..
Ensure environmental sustainability
Carbon dioxide emissions per capita metric tons 1990 2008
6.8 .. 0.1 13.3 0.4 .. 0.1 15.4 .. .. 0.0 9.5 .. 5.9 0.2 0.2 0.5 6.0 6.4 3.0 .. 0.1 1.7 .. 0.2 14.0 1.6 2.8 .. 0.0 .. 28.8 10.0 19.5 1.3 .. 6.2 0.3 .. 0.8 0.3 1.5 4.2e w .. 2.4 1.1 3.6 2.2 1.8 9.8 2.3 2.6 0.7 0.9 11.8 8.9
4.4 12.0 0.1 16.6 0.4 6.8 0.2 6.7 6.9 8.5 0.1 8.9 .. 7.2 0.6 0.3 1.1 5.3 5.3 3.6 0.5 0.2 4.2 0.2 0.2 37.4 2.4 4.0 9.7 0.1 7.0 25.0 8.5 18.0 2.5 4.6 6.1 1.5 0.5 1.0 0.2 0.7 4.8e w 0.3 3.4 1.5 5.3 3.0 4.3 7.8 2.8 3.8 1.2 0.8 11.9 8.0
Nationally protected terrestrial and marine areas % of total land area 2010
7.8 9.2 10.0 29.9 23.5 6.0 4.3 3.4 23.2 13.1 0.5 6.9 .. 7.6 15.0 4.2 3.0 10.0 24.9 0.6 4.1 26.9 17.3 6.4 11.0 9.6 1.3 1.9 3.0 10.3 3.6 4.7 18.1 13.7 0.3 2.3 50.2 4.6 0.6 0.7 36.0 28.0 11.9 w 10.6 11.9 8.5 13.2 11.6 13.3 7.7 19.8 4.0 5.6 11.6 12.7 16.5
Develop a global partnership for development
Access to improved sanitation facilities % of population 1990 2010
71 74 36 .. 38 .. 11 99 100 100 .. 71 .. 100 70 27 48 100 100 85 .. 7 84 .. 13 93 74 84 98 27 .. 97 100 100 94 84 82 37 .. 24 46 41 47 w 21 39 29 46 37 30 80 68 73 22 26 100 100
.. 70 55 .. 52 92 13 100 100 100 23 79 .. 100 92 26 57 100 100 95 94 10 96 47 13 92 .. 90 98 34 94 98 100 100 100 100 .. 76 92 53 48 40 62 w 37 59 47 73 56 66 84 79 88 38 31 100 100
Internet usersa per 100 people 2010
40.0 43.4 13.0 41.0 16.0 43.1 0.3 71.1 79.9 69.3 1.2 12.3 .. 65.8 12.0 10.2 9.0 90.0 82.2 20.7 11.5 11.0 21.2 0.2 5.4 48.5 36.6 39.8 2.2 12.5 44.6 78.0 84.7 74.2 47.9 19.4 35.9 27.9 36.4 12.3 10.1 11.5 30.2 w 5.6 23.8 13.5 34.1 21.5 29.8 39.3 34.0 20.9 8.1 11.3 73.4 71.2
a. Data are from the International Telecommunication Union’s (ITU) World Telecommunication/ICT Indicators database. Please cite ITU for third-party use of these data. b. Data are for the most recent year available. c. Includes Hong Kong SAR, China. d. Data are for 2011. e. Includes emissions not allocated to specific countries.
30
2012 World Development Indicators
About the data
The Millennium Development Goals address concerns common to all economies. Diseases and environmental degradation do not respect national boundaries. Epidemic diseases, wherever they occur, pose a threat to people everywhere. And environmental damage in one location may affect the well-being of plants, animals, and humans far away. The indicators in the table relate to goals 5, 6, and 7 and the targets of goal 8 that address access to new technologies. For the other targets of goal 8, see table 1.4. The target of achieving universal access to reproductive health has been added to goal 5 to address the importance of family planning and health services in improving maternal health and preventing maternal death. Women with multiple pregnancies are more likely to die in childbirth. Access to contraception is an important way to limit and space births. Measuring disease prevalence or incidence can be difficult. Most developing economies lack reporting systems for monitoring diseases. Estimates are often derived from survey data and report data from sentinel sites, extrapolated to the general population. Tracking diseases such as HIV/AIDS, which has a long latency between contraction of the virus and the appearance
1.3
WORLD VIEW
Millennium Development Goals: protecting our common environment Definitions
of symptoms, or malaria, which has periods of dormancy, can be particularly difficult. The table shows the estimated prevalence of HIV among adults ages 15–49. Prevalence among older populations can be affected by life-prolonging treatment. The incidence of tuberculosis is based on case notifications and estimates of cases detected in the population. Carbon dioxide emissions are the primary source of greenhouse gases, which contribute to global warming, threatening human and natural habitats. In recognition of the vulnerability of animal and plant species, a new target of reducing biodiversity loss has been added to goal 7. Increasing the proportion of terrestrial and marine areas protected helps defend vulnerable plant and animal species and safeguard biodiversity. Access to reliable supplies of safe drinking water and sanitary disposal of excreta are two of the most important means of improving human health and protecting the environment. Improved sanitation facilities prevent human, animal, and insect contact with excreta. Internet use includes narrowband and broadband Internet. Narrowband is often limited to basic applications; broadband is essential to promote e-business, e-learning, e-government, and e-health.
1.3a
Location of indicators for Millennium Development Goals 5–7 Goal 5. Improve maternal health 5.1 Maternal mortality ratio 5.2 Proportion of births attended by skilled health personnel 5.3 Contraceptive prevalence rate 5.4 Adolescent fertility rate 5.5 Antenatal care coverage 5.6 Unmet need for family planning Goal 6. Combat HIV/AIDS, malaria, and other diseases 6.1 HIV prevalence among pregnant women ages 15–24 6.2 Condom use at last high-risk sex 6.3 Proportion of population ages 15–24 with comprehensive, correct knowledge of HIV/AIDS 6.4 Ratio of school attendance of orphans to school attendance of nonorphans ages 10–14 6.5 Proportion of population with advanced HIV infection with access to antiretroviral drugs 6.6 Incidence and death rates associated with malaria 6.7 Proportion of children under age 5 sleeping under insecticide-treated bednets 6.8 Proportion of children under age 5 with fever who are treated with appropriate antimalarial drugs 6.9 Incidence, prevalence, and death rates associated with tuberculosis 6.10 Proportion of tuberculosis cases detected and cured under directly observed treatment, short course Goal 7. Ensure environmental sustainability 7.1 Proportion of land area covered by forest 7.2 Carbon dioxide emissions, total, per capita, and per $1 purchasing power parity GDP 7.3 Consumption of ozone-depleting substances 7.4 Proportion of fish stocks within safe biological limits 7.5 Proportion of total water resources used 7.6 Proportion of terrestrial and marine areas protected 7.7 Proportion of species threatened with extinction 7.8 Proportion of population using an improved drinking water source 7.9 Proportion of population using an improved sanitation facility 7.10 Proportion of urban population living in slums
Table 1.3, 2.19 2.19 1.3, 2.19 2.19 1.5, 2.19 2.19 1.3*, 2.22* — —
• Maternal mortality ratio is the number of women who die from pregnancy-related causes during pregnancy and childbirth, per 100,000 live births. Data are from various years and adjusted to a common 2008 base year. The values are modeled estimates (see About the data for table 2.19). • Contraceptive prevalence rate is the percentage of women ages 15–49 married or in union who are practicing, or whose sexual partners are practicing, any form of contraception. • HIV prevalence is the percentage of people ages 15–49 who are infected with HIV. • Incidence of tuberculosis is the estimated number of new tuberculosis cases (pulmonary, smear positive, and extrapulmonary). • Carbon dioxide emissions are those stemming from the burning of fossil fuels and the manufacture of cement. They include emissions produced during consumption of solid, liquid, and gas fuels and gas flaring (see table 3.9). • Nationally protected terrestrial and marine areas are terrestrial and marine protected areas as a percentage of total territorial area, where all nationally designated protected areas with known location and extent are included. All overlaps between different designations and categories, buffered points, and polygons are removed, and all the undated protected areas are dated. • Access to improved sanitation facilities is the percentage of the population with at least adequate access to excreta disposal facilities (private or shared, but not public) that can effectively prevent human, animal, and insect contact with excreta (facilities do not have to include treatment to render sewage outfl ows innocuous). Improved facilities range from simple but protected pit latrines to flush toilets with a sewerage connection. To be effective, facilities must be correctly constructed and properly maintained. • Internet users are people with access to the worldwide network.
— 2.22 — 2.18 2.18 1.3, 2.22 2.18 3.1
Data sources
3.9 3.10* — 3.5 1.3 — 2.18, 3.5 1.3, 2.18, 3.13 —
The indicators here and throughout this book have been compiled by World Bank staff from primary and secondary sources. Efforts have been made to harmonize the data series used to compile this table with those published on the United Nations Millennium Development Goals Web site (www. un.org/millenniumgoals), but some differences in timing, sources, and definitions remain. For more information see the data sources for the indicators listed in tables 1.3a and 1.4a.
— No data are available in the World Development Indicators database. * Table shows information on related indicators.
2012 World Development Indicators
31
1.4
Millennium Development Goals: overcoming obstacles
Development Assistance Committee members Net official development assistance (ODA) by donor
Australia Canada European Union Austria Belgium Denmark Finland France Germany Greece Ireland Italy Luxembourg Netherlands Portugal Spain Sweden United Kingdom Japan Korea, Rep.b New Zealandb Norway Switzerland United States
% of donor GNI 2010
For basic social servicesa % of total sector-allocable ODA 2010
0.32 0.33 0.32 0.64 0.90 0.55 0.50 0.38 0.17 0.53 0.15 1.09 0.81 0.29 0.43 0.97 0.56 0.20 0.12 0.26 1.10 0.41 0.21
14.6 18.1 3.1 11.7 10.4 8.4 8.6 5.9 6.6 22.9 12.5 34.5 7.6 6.6 13.6 12.4 12.7 2.5 4.3 16.6 11.1 11.0 31.0
Least developed countries’ access to high-income markets Goods (excluding arms) admitted free of tariffs % of exports from least developed countries 2003 2009
Support to agriculture
Average tariff on exports of least developed countries % Agricultural products 2003 2009
99.9 97.5 96.6
100.0 100.0 98.4
0.2 0.2 1.8
44.8 25.1c 97.7 98.6 96.3 67.2
99.4 48.2 98.8 100.0 100.0 75.8
4.7 27.6c 4.0 4.7 5.4 6.3
Textiles
% of GDP 2010
2009
2003
2009
0.1 0.2 0.1
0.0 0.2 0.1
1.2 1.7 1.2
0.0 1.7 1.2
0.12 0.67 0.72
2.7 10.9c 0.3 0.0 0.0 6.4
2.6 6.4 0.0 0.0 0.0 5.7
0.1 11.3c 0.4 1.2 0.0 12.3
0.1 6.2 0.0 1.0 0.0 11.3
1.09 2.01 0.23 0.99 1.11 0.91
0.0 0.1 0.5
1.2 50.2 0.0 0.2 0.0 5.7
Clothing
2003
Heavily indebted poor countries (HIPCs) HIPC HIPC HIPC decision completion Initiative pointd assistance pointd
MDRI assistance
HIPC HIPC HIPC decision completion Initiative pointd pointd assistance
end-2010 net present value
end-2010 net present value
$ millions
Afghanistan Benin Boliviae Burkina Fasoe,f Burundi Cameroon Central African Republic Chad Comoros Congo, Dem. Rep. Congo, Rep. Côte d’Ivoire Ethiopiaf Gambia, The Ghana Guinea Guinea-Bissau Guyanae
Jul. 2007 Jul. 2000 Feb. 2000 Jul. 2000 Aug. 2005 Oct. 2000 Sep. 2007 May 2001 Jun. 2010 Jul. 2003 Mar. 2006 Mar. 2009 Nov. 2001 Dec. 2000 Feb. 2002 Dec. 2000 Dec. 2000 Nov. 2000
Jan. 2010 Mar. 2003 Jun. 2001 Apr. 2002 Jan. 2009 Apr. 2006 Jun. 2009 Floating Floating Jul. 2010 Jan. 2010 Floating Apr. 2004 Dec. 2007 Jul. 2004 Floating Dec. 2010 Dec. 2003
653 384 1,948 810 1,008 1,856 674 240 150 9,474 1,903 3,243 2,728 98 3,083 799 744 894
MDRI assistance
$ millions
20 756 1,956 765 91 885 231 669 45 528 130 1,095 1,865 244 2,549 862 79 488
Haiti Honduras Liberia Madagascar Malawif Malie Mauritania Mozambiquee Nicaragua Niger f Rwandaf São Tomé & Príncipef Senegal Sierra Leone Tanzania Togo Ugandae Zambia
Nov. 2006 Jul. 2000 Mar. 2008 Dec. 2000 Dec. 2000 Sep. 2000 Feb. 2000 Apr. 2000 Dec. 2000 Dec. 2000 Dec. 2000 Dec. 2000 Jun. 2000 Mar. 2002 Apr. 2000 Nov. 2008 Feb. 2000 Dec. 2000
Jun. 2009 Apr. 2005 Jun. 2010 Oct. 2004 Aug. 2006 Mar. 2003 Jun. 2002 Sep. 2001 Jan. 2004 Apr. 2004 Apr. 2005 Mar. 2007 Apr. 2004 Dec. 2006 Nov. 2001 Dec. 2010 May 2000 Apr. 2005
163 814 2,957 1,224 1,375 789 911 3,140 4,847 944 953 171 715 917 2,969 305 1,505 3,662
674 1,884 241 1,584 914 1,313 563 1,318 1,178 646 286 38 1,696 423 2,503 466 2,263 1,871
a. Includes primary education, basic life skills for youth, adult and early childhood education, basic health care, basic health infrastructure, basic nutrition, infectious disease control, health education, health personnel development, population policy and administrative management, reproductive health care, family planning, sexually transmitted disease control including HIV/AIDS, personnel development for population and reproductive health, basic drinking water supply and basic sanitation, and multisector aid for basic social services. b. Calculated by World Bank staff using the World Integrated Trade Solution based on the United Nations Conference on Trade and Development’s Trade Analysis and Information Systems database. c. Data are for 2004. d. Refers to the Enhanced HIPC Initiative. e. Also reached completion point under the original HIPC Initiative. The assistance includes original debt relief. f. Assistance includes topping up at completion point.
32
2012 World Development Indicators
About the data
1.4
WORLD VIEW
Millennium Development Goals: overcoming obstacles Definitions
Achieving the Millennium Development Goals
lines with “international peaks”). The averages in
• Net official development assistance (ODA) is grants
requires an open, rule-based global economy in
the table include ad valorem duties and equivalents.
and loans (net of repayments of principal) that meet
which all countries, rich and poor, participate. Many
Subsidies to agricultural producers and exporters
the DAC definition of ODA and are made to countries
poor countries, lacking the resources to finance
in OECD countries are another barrier to developing
on the DAC list of recipients. • ODA for basic social
development, burdened by unsustainable debt, and
economies’ exports. Agricultural subsidies in OECD
services is aid commitments by DAC donors for basic
unable to compete globally, need assistance from
economies are estimated at $366 billion in 2010.
education, primary health care, nutrition, population
rich countries. For goal 8—develop a global partner-
The Debt Initiative for Heavily Indebted Poor Coun-
policies and programs, reproductive health, and water
ship for development—many indicators therefore
tries (HIPCs), an important step in placing debt relief
and sanitation services. • Goods admitted free of
monitor the actions of members of the Organisa-
within the framework of poverty reduction, is the first
tariffs are exports of goods (excluding arms) from
tion for Economic Co-operation and Development’s
comprehensive approach to reducing the external
least developed countries admitted without tariff.
(OECD) Development Assistance Committee (DAC).
debt of the world’s poorest, most heavily indebted
• Average tariff is the unweighted average of the
Official development assistance (ODA) has risen
countries. A 1999 review led to an enhancement of
effectively applied rates for all products subject to
in recent years as a share of donor countries’ gross
the framework. In 2005, to further reduce the debt
tariffs. • Agricultural products are plant and animal
national income (GNI), but the poorest economies
of HIPCs and provide resources for meeting the Mil-
products, including tree crops but excluding timber
need additional assistance to achieve the Millennium
lennium Development Goals, the Multilateral Debt
and fish products. • Textiles and clothing are natu-
Development Goals. In 2010 net ODA from OECD
Relief Initiative (MDRI), proposed by the Group of
ral and synthetic fibers and fabrics and articles of
DAC members rose 3.2 percent in real terms, to
Eight countries, was launched.
clothing made from them. • Support to agriculture is
$131.1 billion or 0.33 percent of OECD DAC mem-
Under the MDRI four multilateral institutions—the
gross transfers from taxpayers and consumers arising
International Development Association (IDA), Inter-
from policy measures, net of associated budgetary
One important action that high-income economies
national Monetary Fund (IMF), African Development
receipts, regardless of their objectives and impacts on
can take is to reduce barriers to exports from low-
Fund (AfDF), and Inter-American Development Bank
farm production and income or consumption of farm
and middle- income economies. The European Union
(IDB)—provide 100 percent debt relief on eligible
products. • HIPC decision point is when a heavily
has begun to eliminate tariffs on imports of “every-
debts due to them from countries having completed
indebted poor country with an established track record
thing but arms” from least developed countries,
the HIPC Initiative process. Data in the table refer
of good performance under adjustment programs sup-
and the United States offers special concessions to
to status as of September 2011 and might not show
ported by the IMF and the World Bank commits to addi-
imports from Sub-Saharan Africa. However, these
countries that have since reached the decision or
tional reforms and a poverty reduction strategy and
programs still have many restrictions.
completion point. Debt relief under the HIPC Initiative
starts receiving debt relief. • HIPC completion point is
Average tariffs in the table refl ect high-income
has reduced future debt payments by $59 billion (in
when a country successfully completes the key struc-
OECD member tariff schedules for exports of coun-
end-2010 net present value terms) for 36 countries
tural reforms agreed on at the decision point, includ-
tries designated least developed countries by the
that have reached the decision point. And 32 coun-
ing implementing a poverty reduction strategy. The
United Nations. Although average tariffs have been
tries that have reached the completion point have
country then receives full debt relief under the HIPC
falling, averages may disguise high tariffs on specific
received additional assistance of $33 billion (in end-
Initiative without further policy conditions. • HIPC Ini-
goods (see table 6.7 for each country’s share of tariff
2010 net present value terms) under the MDRI.
bers’ combined GNI.
tiative assistance is the debt relief committed as of the decision point (assuming full participation of credi-
1.4a
Location of indicators for Millennium Development Goal 8
tors). Topping-up assistance and assistance provided under the original HIPC Initiative were committed in
Goal 8.1 8.2 8.3 8.4 8.5 8.6
8. Develop a global partnership for development Net ODA as a percentage of DAC donors’ gross national income Proportion of ODA for basic social services Proportion of ODA that is untied Proportion of ODA received in landlocked countries as a percentage of GNI Proportion of ODA received in small island developing states as a percentage of GNI Proportion of total developed country imports (by value, excluding arms) from least developed countries admitted free of duty 8.7 Average tariffs imposed by developed countries on agricultural products and textiles and clothing from least developed countries 8.8 Agricultural support estimate for OECD countries as a percentage of GDP 8.9 Proportion of ODA provided to help build trade capacity 8.10 Number of countries reaching HIPC decision and completion points 8.11 Debt relief committed under new HIPC initiative 8.12 Debt services as a percentage of exports of goods and services 8.13 Proportion of population with access to affordable, essential drugs on a sustainable basis 8.14 Fixed telephone lines per 100 people 8.15 Mobile cellular subscribers per 100 people 8.16 Internet users per 100 people
Table 1.4 1.4 — — — 1.4 1.4, 6.7* 1.4 — 1.4 1.4 6.9*
net present value terms as of the decision point and are converted to end-2010 terms. • MDRI assistance is 100 percent debt relief on eligible debt from IDA, IMF, AfDF, and IDB, delivered in full to countries having reached the HIPC completion point. Data sources Data on ODA are from the OECD. Data on goods admitted free of tariffs and average tariffs are from the World Trade Organization in collaboration with the United Nations Conference on Trade and Development and the International Trade Centre (www. mdg-trade.org). Data on support to agriculture are
— 5.11 5.11 1.3, 5.12
— No data are available in the World Development Indicators database. * Table shows information on related indicators.
from the OECD’s Producer and Consumer Support Estimates, OECD Database 1986–2010. Data on the HIPC Initiative and MDRI are from the World Bank’s Economic Policy and Debt Department.
2012 World Development Indicators
33
1.5
Women in development Female population
% of total 2010
Afghanistan Albania Algeria Angola Argentina Armenia Australia Austria Azerbaijan Bahrain Bangladesh Belarus Belgium Benin Bolivia Bosnia and Herzegovina Botswana Brazil Bulgaria Burkina Faso Burundi Cambodia Cameroon Canada Central African Republic Chad Chile China Hong Kong SAR, China Colombia Congo, Dem. Rep. Congo, Rep. Costa Rica Côte d’Ivoire Croatia Cuba Cyprus Czech Republic Denmark Dominican Republic Ecuador Egypt, Arab Rep. El Salvador Eritrea Estonia Ethiopia Finland France Gabon Gambia, The Georgia Germany Ghana Greece Guatemala Guinea Guinea-Bissau Haiti
34
48.3 49.9 49.5 50.5 51.1 53.5 50.2 51.2 50.5 37.6 49.4 53.5 51.0 50.7 50.1 51.9 49.6 50.8 51.7 50.4 50.9 51.1 50.1 50.4 50.7 50.3 50.6 48.1c 52.6 50.8 50.3 49.9 49.2 49.0 51.9 49.7 49.0 51.0 50.4 49.8 49.9 49.8 52.5 50.7 53.9 50.2 50.9 51.3 49.8 50.6 52.9 51.0 49.1 50.5 51.3 49.5 50.4 50.4
Life expectancy at birth
years Male Female 2010 2010
48 74 71 49 72 71 80 78 68 74 68 65 77 54 64 73 54 70 70 54 49 61 50 79 46 48 76 72c 80 70 47 56 77 54 74 77 77 74 77 70 73 71 67 59 71 57 77 78 61 57 70 78 63 78 67 52 46 61
2012 World Development Indicators
48 80 74 52 79 77 84 83 74 76 69 77 83 57 69 78 52 77 77 56 51 64 52 83 49 51 82 75c 86 77 50 58 82 56 80 81 82 81 81 76 78 75 77 63 81 60 83 85 63 59 77 83 65 83 74 55 49 63
Pregnant women receiving prenatal care
Teenage Women in wage mothers employment in nonagricultural sector
% 2005–10a
% of women ages 15–19 2005–10a
36 97 89 80 99 99 98 .. 77 100 53 99 .. 84 86 99 94 98 .. 85 99 89 82 100 69 53 .. 92 .. 97 86 86 90 85 100 100 99 .. .. 99 .. 74 94 .. .. 28 .. .. .. 98 98 .. 90 .. 93 88 93 85
.. 3 .. 29 .. 5 .. .. 6 .. 33 .. .. 21 18 .. .. .. .. .. .. 8 .. .. .. .. .. .. .. 20 24 27 .. .. 4 .. .. .. .. 21 .. 10 23 .. .. 17 .. .. .. .. 10 .. 13 .. 22 32 .. 14
Unpaid family workers
% of nonagricultural Male Female wage % of male % of female employment employment employment 2009 2010 2010
18 .. 15 .. .. 40 47 48 43 10 .. 56 47 .. 38 36 45 42 51 27 .. .. .. 51 .. .. 37 .. 50 48 .. .. 41 .. 46 43 48 46 50 39 .. 18 48 .. 54 .. 52 49 .. .. 48 48 .. 43 .. 29 .. ..
.. .. .. .. 0.4b 7.6 0.2 1.9 .. 0.5 .. .. 0.3 .. .. 2.6 .. 3.4 0.7 .. .. 25.2 .. 0.1 .. .. 1.1 .. 0.1 3.7 .. .. 0.9 .. 1.1 .. 1.3 0.3 0.1 .. 7.4 .. 7.1 .. 0.2 .. 0.6 0.3 .. .. 19.6 0.3 .. 3.3 .. .. .. ..
.. .. .. .. 1.2b 17.1 0.3 2.4 .. 0.8 .. .. 1.8 .. .. 10.4 .. 6.3 1.5 .. .. 60.8 .. 0.2 .. .. 2.5 .. 1.0 7.1 .. .. 2.6 .. 4.6 .. 2.9 1.0 0.5 .. 19.9 .. 7.2 .. 0.2 .. 0.5 0.9 .. .. 37.9 0.9 .. 9.2 .. .. .. ..
Female Female part-time legislators, employment senior officials, and managers
% of total 2005–10a
% of total 2005–10a
.. .. .. .. 65b 56 70 b 80 .. .. .. .. 80 .. 56 .. 55 68 52 .. .. .. .. 67b .. .. 58 .. .. 60 .. .. 64 .. 60 .. 64 69 63 50 54 .. 55 .. 67 .. 62 78 .. .. .. 80 .. 66 .. .. .. ..
.. .. 5 .. 23b 22 37 29 7 12 10 .. 34 .. 29 .. 30 36 34 31 .. 21 .. 36b .. .. 24 17 29 38 .. .. 30 .. 27 29 13 28 22 31 28b 11 25 .. 37 16b 30 39 .. .. 34 30 .. 30 .. .. .. ..
Women in parliaments
% of total seats 1990 2011
4 29 2 15 6 36 6 12 .. .. 10 .. 9 3 9 .. 5 5 21 .. .. .. 14 13 4 .. .. 21 .. 5 5 14 11 6 .. 34 2 .. 31 8 5 4 12 .. .. .. 32 7 13 8 .. .. .. 7 7 .. 20 ..
28 16 8 39 39 9 25 28 16 3 19 32 39 8 25 17 8 9 21 15 32 21 14 25 13 13 14 21 .. 13 10 7 39 9 24 43 11 22 38 21 32 2 19 22 20 28 43 19 15 8 7 33 8 17 12 .. 10 11
Female population
% of total 2010
Honduras Hungary India Indonesia Iran, Islamic Rep. Iraq Ireland Israel Italy Jamaica Japan Jordan Kazakhstan Kenya Korea, Dem. Rep. Korea, Rep. Kosovo Kuwait Kyrgyz Republic Lao PDR Latvia Lebanon Lesotho Liberia Libya Lithuania Macedonia, FYR Madagascar Malawi Malaysia Mali Mauritania Mauritius Mexico Moldova Mongolia Morocco Mozambique Myanmar Namibia Nepal Netherlands New Zealand Nicaragua Niger Nigeria Norway Oman Pakistan Panama Papua New Guinea Paraguay Peru Philippines Poland Portugal Puerto Rico Qatar
50.0 52.5 48.3 50.1 49.3 49.8 50.0 50.7 51.1 50.8 51.3 48.6 52.0 50.1 50.9 50.1 .. 40.3 50.7 50.1 54.0 51.2 50.9 49.8 49.3 53.5 49.9 50.2 50.0 49.3 50.0 49.7 50.6 50.7 52.6 50.6 51.0 51.3 50.7 50.3 50.4 50.4 50.9 50.5 49.7 49.4 50.0 41.3 49.2 49.6 49.0 49.5 49.9 49.8 51.8 51.6 51.9 24.3
Life expectancy at birth
years Male Female 2010 2010
71 71 64 67 71 65 78 80 79 70 80 72 64 55 65 77 68 74 65 66 69 70 48 55 72 68 73 65 53 72 50 57 69 74 65 64 70 49 63 62 68 79 79 71 54 51 79 71 64 73 60 70 71 65 72 76 75 78
75 78 67 71 75 72 83 83 84 76 86 75 73 58 72 84 72 76 73 68 78 75 47 57 77 79 77 68 54 76 52 60 77 79 73 72 74 51 66 63 69 83 83 77 55 52 83 76 66 79 65 74 76 72 81 82 83 78
Pregnant women receiving prenatal care
Teenage Women in wage mothers employment in nonagricultural sector
% 2005–10a
% of women ages 15–19 2005–10a
92 .. 75 95 98 84 .. .. .. 99 .. 99 100 92 100 .. .. 100 97 71 .. .. 92 79 93 .. 94 86 92 79 70 75 .. 96 98 100 .. 92 80 95 44 .. .. 90 46 58 .. 99 61 96 79 96 95 91 .. .. .. 100
22 .. 16 9 .. .. .. .. .. 14 .. 5 7 18 .. .. .. .. .. 17 .. .. .. 38 .. .. .. 32 .. .. 36 .. .. .. 6 .. .. .. .. 15 19 .. .. 25 39 23 .. .. 9 .. .. 12 26 10 .. .. .. ..
Unpaid family workers
Female Female part-time legislators, employment senior officials, and managers
% of nonagricultural Male Female wage % of male % of female employment employment employment 2009 2010 2010
42 49 .. 32 .. 12 52 50 44 48 42 16 50 .. .. 42 .. .. 51 .. 55 .. .. .. .. 53 42 .. .. 39 .. .. 37 39 54 51 21 .. .. .. .. 48 51 38 36 .. 50 22 13 42 .. 40 38 42 48 49 42 10
.. 0.2 .. 8.1 4.8 .. 0.7 0.1 1.2 0.4 1.1 0.5 0.6 .. .. 1.2 .. .. .. .. 1.3 .. .. 12.5 .. 1.5 6.3 .. .. 2.6 .. .. 1.0 4.8 1.3 10.3 15.0 .. .. .. .. 0.3 0.7 .. .. .. 0.2 .. 19.7 3.0 .. 10.4 4.5b 9.0 2.5 0.7 0.0 ..
.. 0.4 .. 32.4 29.7 .. 0.7 0.3 2.3 2.1 6.9 0.5 0.9 .. .. 12.5 .. .. .. .. 1.4 .. .. 19.7 .. 1.9 16.6 .. .. 8.1 .. .. 4.9 9.7 3.5 35.1 48.6 .. .. .. .. 1.1 1.3 .. .. .. 0.3 .. 65.0 6.8 .. 11.0 8.7b 17.4 5.5 1.2 0.0 ..
1.5
% of total 2005–10a
% of total 2005–10a
.. 66 .. .. .. .. 76 74 77 .. 70 .. .. .. .. 60 .. .. .. .. 61 .. .. .. .. 61 49 .. .. .. .. .. .. 54 .. .. .. .. .. .. .. 75 72b 51 .. .. 70 .. .. 48 .. 55 61b .. 68 66 .. ..
41 36 .. 22 13 .. 33 32 33 .. 10 .. 38 .. .. 10 .. 14 35 .. 41 8 52 .. .. 41 28 22 .. 24 .. .. 23 31 38 47 13 .. .. 36 14 29 40 b 41 .. .. 34 9 3 48 .. 34 19b 55 36 32 43 7
WORLD VIEW
Women in development
Women in parliaments
% of total seats 1990 2011
2012 World Development Indicators
10 21 5 12 2 11 8 7 13 5 1 0 .. 1 21 2 .. .. .. 6 .. 0 .. .. .. .. .. 7 10 5 .. .. 7 12 .. 25 0 16 .. 7 6 21 14 15 5 .. 36 .. 10 8 0 6 6 9 14 8 .. ..
18 9 11 18 3 25 15 19 21 13 11 11 18 10 15 16 .. 8 23 25 20 3 24 13 8 19 31 13 21 10 10 22 19 26 19 4 11 39 4 24 33 39 34 21 13 4 40 0 22 9 1 13 22 22 20 27 .. 0
35
1.5
Women in development Female population
% of total 2010
Romania Russian Federation Rwanda Saudi Arabia Senegal Serbia Sierra Leone Singapore Slovak Republic Slovenia Somalia South Africa South Sudan Spain Sri Lanka Sudan Swaziland Sweden Switzerland Syrian Arab Republic Tajikistan Tanzania Thailand Timor-Leste Togo Trinidad and Tobago Tunisia Turkey Turkmenistan Uganda Ukraine United Arab Emirates United Kingdom United States Uruguay Uzbekistan Venezuela, RB Vietnam West Bank and Gaza Yemen, Rep. Zambia Zimbabwe World Low income Middle income Lower middle income Upper middle income Low & middle income East Asia & Pacific Europe & Central Asia Latin America & Carib. Middle East & N. Africa South Asia Sub-Saharan Africa High income Euro area
51.5 53.7 50.9 44.6 50.4 50.5 51.2 49.6 51.4 51.1 50.4 50.5 .. 50.6 50.6 49.6 50.8 50.2 50.9 49.4 50.8 50.1 50.9 49.0 50.5 51.5 50.0 50.1 50.8 50.0 54.0 30.5 50.8 50.7 51.7 50.3 49.8 50.6 49.2 49.7 49.9 50.7 49.6 w 50.1 49.3 49.2 49.4 49.4 48.8 52.3 50.6 49.7 48.6 50.0 50.5 51.0
Life expectancy at birth
years Male Female 2010 2010
70 63 54 73 58 71 47 79 72 76 49 51 62 79 72 59 49 80 80 74 64 57 71 61 55 66 73 71 61 53 65 76 79 76 73 65 71 73 71 64 48 51 68 w 58 67 64 71 66 70 66 71 70 64 53 77 78
77 75 56 75 60 77 48 84 79 83 53 53 61 85 78 63 48 84 85 77 71 58 77 63 58 73 77 76 69 54 76 78 82 81 80 71 77 77 74 67 49 49 72 w 60 71 67 75 70 74 75 77 74 67 55 83 84
Pregnant women receiving prenatal care
% 2005–10a
.. .. 98 97 94 98 87 .. .. .. 26 97 .. .. 99 64 97 .. .. 88 80 88 99 84 87 96 96 95 99 94 99 100 .. .. 96 99 .. 91 99 47 94 90d 84 w 69 86 78 94 83 92 .. 97 85 71 74 .. ..
Teenage Women in wage mothers employment in nonagricultural sector % of women ages 15–19 2005–10a
.. .. 6 .. 18 .. 34 .. .. .. .. .. .. .. .. .. 23 .. .. .. .. 23 .. 7 .. .. .. .. .. 25 4 .. .. .. .. .. .. .. .. .. 28 21 .. w .. .. .. .. .. .. .. .. .. 16 .. .. ..
Unpaid family workers
% of nonagricultural Male Female wage % of male % of female employment employment employment 2009 2010 2010
46 53 .. 15 .. 44 .. 45 48 48 .. 45 .. 47 31 .. .. 50 48 15 .. .. 46 .. .. .. .. 24 .. .. 55 20 47 48 46 39 42 .. 18 6 .. .. .. w .. .. .. .. .. .. 48 41 .. .. .. 47 47
6.9 0.1 .. .. .. 4.1 .. 0.3 0.1 3.8 .. 0.4 .. 0.7 4.5b .. .. 0.3 1.6 3.3 .. .. 13.6 .. .. .. .. 5.1 .. .. 0.4 0.0 0.2 0.1 2.1b .. 0.5 .. 5.5 .. .. .. .. w .. .. .. .. .. .. 1.9 3.5 .. .. .. 0.6 0.7
a. Data are for the most recent year available. b. Limited coverage. c. Includes Taiwan, China. d. Data are for 2011.
36
2012 World Development Indicators
20.0 0.1 .. .. .. 15.6 .. 1.0 0.2 6.2 .. 1.4 .. 1.0 22.3b .. .. 0.2 2.4 12.4 .. .. 28.5 .. .. .. .. 35.2 .. .. 0.3 0.0 0.4 0.1 0.8b .. 1.2 .. 19.8 .. .. .. .. w .. .. .. .. .. .. 5.3 6.8 .. .. .. 2.2 1.5
Female Female part-time legislators, employment senior officials, and managers
% of total 2005–10a
47 62 .. .. .. .. .. .. 59 59 .. .. .. 78 .. .. .. 63 80 27 .. .. .. .. .. .. .. 58 .. .. .. .. 75 67 64b .. 64 .. .. .. .. .. .. w .. .. .. .. .. .. .. 62 .. .. .. 71 77
% of total 2005–10a
32 37 0 8 .. 36 .. 31 35 35 .. 30 .. 34 24b .. .. 31 33 10 .. 16 24 .. .. 43 .. 10 .. 33 39 10 36 43 40 .. 27 22 10 4 19 ..
Women in parliaments
% of total seats 1990 2011
34 .. 17 .. 13 .. .. 5 .. .. 4 3 .. 15 5 .. 4 38 14 9 .. .. 3 .. 5 17 4 1 26 12 .. 0 6 7 6 .. 10 18 .. 4 7 11 13 w .. 13 11 14 13 17 .. 12 4 6 .. 12 12
11 14 56 0 23 22 13 22 16 14 7 45 .. 37 6 26 14 45 29 12 19 36 13 29 11 29 28 14 17 35 8 23 22 17 15 22 17 24 .. 0 14 15 19 w 20 18 15 19 18 18 16 23 9 20 20 23 26
1.5
WORLD VIEW
Women in development About the data
Despite much progress in recent decades, gender
terms of earnings, work conditions, or legal and social
personnel for reasons related to pregnancy. • Teen-
inequalities remain pervasive in many dimensions of
protection. The indicator also does not reflect whether
age mothers are women ages 15–19 who already
life—worldwide. But while disparities exist through-
women reap the economic benefits of such employ-
have children or are currently pregnant. • Women
out the world, they are most prevalent in developing
ment. Finally, female employment and the employ-
in wage employment in nonagricultural sector are
countries. Gender inequalities in the allocation of
ment share of the agricultural sector for both men
female wage employees in the nonagricultural sec-
such resources as education, health care, nutrition,
and women tend to be underreported.
tor as a percentage of total nonagricultural wage
and political voice matter because of the strong asso-
Women’s wage work is important for economic
employment. • Unpaid family workers are those
ciation with well-being, productivity, and economic
growth and the well-being of families. But women
who work without pay in a market-oriented estab-
growth. These patterns of inequality begin at an early
often face such obstacles as restricted access to
lishment or activity operated by a related person
age, with boys routinely receiving a larger share of edu-
credit markets, capital, land, and training and edu-
living in the same household. There is no official
cation and health spending than do girls, for example.
cation; time constraints due to traditional family
International Labour Organization definition of full-
Because of biological differences girls are expected
responsibilities; and labor market bias and discrimi-
time work, so the definition of part-time workers dif-
to experience lower infant and child mortality rates
nation. These obstacles force women to limit their
fers across countries and thus comparisons should
and to have a longer life expectancy than boys. This
full participation in paid economic activities, to be
be made with caution. • Female part-time employ-
biological advantage may be overshadowed, however,
less productive, and to receive lower wages. More
ment is the percentage of part-time workers who
by gender inequalities in nutrition and medical inter-
women than men are in unpaid family employment
are female. Part-time workers are employed people
ventions and by inadequate care during pregnancy
and part-time employment. And men and women
whose normal hours of work are less than those of
and delivery, so that female rates of illness and
have different occupational distributions. There is no
comparable full-time workers. The definition of part-
death sometimes exceed male rates. Gender bias
official International Labour Organization definition
time varies across countries. • Female legislators,
can be seen in child mortality rates (table 2.23) and
of full-time work, so the definition of part-time work-
senior officials, and managers are the percentage
life expectancy at birth. Female child mortality rates
ers differs across countries, and thus comparisons
of legilsators, senior officials, and managers (Inter-
that are as high as or higher than male child mortality
should be made with caution.
rates may indicate discrimination against girls.
national Standard Classification of Occupations–88
The female share of high-skilled occupations such
category 1) who are female. • Women in parliaments
Having a child during the teenage years limits girls’
as legislators, senior officials, and managers indi-
are parliamentary seats in a single or lower chamber
opportunities for better education, jobs, and income.
cates gender segregation of employment. Women
held by women.
Pregnancy is more likely to be unintended during the
are vastly underrepresented in decisionmaking
teenage years, and births are more likely to be pre-
positions in government, although there is some
mature and are associated with greater risks of com-
evidence of recent improvement. Gender parity in
Data on female population are from the United
plications during delivery and of death. In many coun-
parliamentary representation is still far from being
Nations Population Division’s World Population Pros-
tries maternal mortality (tables 1.3 and 2.19) is a
realized. Without representation at this level, it is
pects: The 2010 Revision, and data on life expec-
leading cause of death among women of reproductive
difficult for women to influence policy.
tancy for more than half the countries in the table
Data sources
age, although most of those deaths are preventable.
For information on other aspects of gender, see
(most of them developing countries) are from its
Data on women in wage employment in the non-
tables 1.2 (Millennium Development Goals: eradicat-
World Population Prospects: The 2010 Revision, with
agricultural sector show the extent to which women
ing poverty and saving lives), 1.3 (Millennium Devel-
additional data from census reports, other statisti-
have access to paid employment—which affects their
opment Goals: protecting our common environment),
cal publications from national statistical offices,
integration into the monetary economy —and indicate
2.3 (Employment by economic activity), 2.4 (Decent
Eurostat’s Demographic Statistics, the Secretariat
the degree to which labor markets are open to women
work and productive employment), 2.5 (Unemploy-
of the Pacific Community’s Statistics and Demog-
in industry and services—which affects not only equal
ment), 2.6 (Children at work), 2.10 (Assessing vulner-
raphy Programme, and the U.S. Bureau of the Cen-
employment opportunity for women, but also eco-
ability and security), 2.13 (Education efficiency), 2.14
sus International Data Base. Data on pregnant
nomic efficiency through flexibility of the labor market
(Education completion and outcomes), 2.15 (Educa-
women receiving prenatal care are from the United
and the economy’s capacity to adapt to changes over
tion gaps by income and gender), 2.19 (Reproductive
Nations Children’s Fund’s (UNICEF) The State of the
time. In many developing countries nonagricultural
health), 2.22 (Health risk factors and future chal-
World’s Children 2012 based on household surveys,
wage employment accounts for only a small portion
lenges), and 2.23 (Mortality).
including MEASURE DHS Demographic and Health Surveys by ICF International and Multiple Indica-
of total employment. As a result, the contribution of women to the national economy is underestimated
Definitions
tor Cluster Surveys by UNICEF. Data on teenage
and therefore misrepresented. The indicator is dif-
• Female population is the percentage of the popu-
mothers are from MEASURE DHS Demographic and
ficult to interpret without additional information on the
lation that is female. • Life expectancy at birth is
Health Surveys by ICF International. Data on labor
share of women in total employment, which allows
the number of years a newborn infant would live if
force, employment, and occupation are from the
an assessment to be made of whether women are
prevailing patterns of mortality at the time of its birth
International Labour Organization’s Key Indicators
under- or overrepresented in nonagricultural wage
were to stay the same throughout its life. • Pregnant
of the Labour Market, 7th edition. Data on women in
employment. The indicator does not reveal differences
women receiving prenatal care are women attended
parliaments are from the Inter-Parliamentary Union.
in the quality of nonagricultural wage employment in
at least once during pregnancy by skilled health
2012 World Development Indicators
37
1.6
Key indicators for other economies Population Surface Population area density
Gross national income
Atlas method
American Samoa Andorra Antigua and Barbuda Aruba Bahamas, The Barbados Belize Bermuda Bhutan Brunei Darussalam Cape Verde Cayman Islands Channel Islands Comoros Curaçao Djibouti Dominica Equatorial Guinea Faeroe Islands Fiji French Polynesia Gibraltar Greenland Grenada Guam Guyana Iceland Isle of Man Kiribati
Gross domestic product
Carbon dioxide emissions
Purchasing power parity
thousands 2010
thousand sq. km 2010
people per sq. km 2010
$ millions 2010
Per capita $ 2010
68 85 88 108 343 274 345 65 726 399 496 56 153 735 143 889 68 700 49 860 271 29 57 104 179 755 318 83 100
0.2 0.5 0.4 0.2 13.9 0.4 23.0 0.1 38.4 5.8 4.0 0.3 0.2 1.9 0.4 23.2 0.8 28.1 1.4 18.3 4.0 0.0 410.5 0.3 0.5 215.0 103.0 0.6 0.8
342 181 200 600 34 637 15 1,292 19 76 123 234 807 395 321 38 91 25 35 47 74 2,924 0f 306 331 4 3 145 123
.. 3,447 1,169 .. 6,973 3,454 1,313 .. 1,361 12,461 1,620 .. .. 550 .. 1,105 458 10,182 .. 3,123 .. .. 1,466 724 .. 2,164 10,381 .. 200
..b .. .. 41,750 .. .. 1,795d 20,400 d 13,280 c .. .. ..e 20,610 8,392d 24,800d 12,660 5,183 19,000 6,200 d 3,810 2,139d .. .. ..e 1,870 3,622 4,990 31,800 19,661 50,180 3,270 1,893 3,820 .. .. ..e .. .. ..e 750 802 1,090 .. .. ..e 1,270 2,149 2,460 6,740 812d 11,940 d 14,550 16,635 23,760 .. .. ..e 3,630 3,880 4,510 .. .. ..e .. .. ..e 26,020 .. .. 9,930 d 6,960 1,033d .. .. ..e 3,450 d 2,870 2,606d 32,640 8,991 28,270 .. .. ..e 3,520 d 2,000 352d
About the data
Life Adult expectancy literacy at birth rate
$ millions 2010
Per capita $ 2010
% growth 2009–10
Per capita % growth 2009–10
years 2010
.. 3.6 –5.2 .. 0.9 –5.3 2.9 –8.1 7.4 –1.8 5.4 .. .. 2.1 .. 5.0 0.1 0.9 .. 0.3 .. .. –5.4 –0.8 .. 3.6 –4.0 .. 1.8
.. 2.1 –5.4 .. –0.5 –5.5 –0.6 –8.4 5.6 –3.6 4.5 .. .. –0.6 .. 3.0 –0.1 –1.8 .. –0.6 .. .. –5.4 –0.7 .. 3.3 –3.8 .. –0.2
.. .. .. 75 75 77 76 79 67 78 74 .. 80 61 .. 58 .. 51 80 69 75 .. 68 76 76 70 81 .. ..
% ages 15 thousand and older metric tons 2005–10a 2008
.. .. 99 98 .. .. .. .. 53 95 85 99 .. 74 .. .. .. 93 .. .. .. .. .. .. .. .. .. .. ..
.. 539 447 2,288 2,156 1,353 425 389 733 10,594 308 557 .. 125 .. 524 128 4,815 708 1,254 891 422 576 246 .. 1,525 2,230 .. 29
Definitions
The table shows data for economies with a popula-
• Population is based on the de facto definition of
included in the valuation of output plus net receipts
tion between 30,000 and 1 million and for smaller
population, which counts all residents regardless of
of primary income (compensation of employees and
economies if they are members of the World Bank.
legal status or citizenship—except for refugees not
property income) from abroad. Data are in current
Where data on gross national income (GNI) per capita
permanently settled in the country of asylum, who
U.S. dollars converted using the World Bank Atlas
are not available, the estimated range is given. For
are generally considered part of the population of
method (see Statistical methods). • Purchasing
more information on the calculation of GNI and pur-
their country of origin. The values shown are midyear
power parity (PPP) GNI is GNI converted to interna-
chasing power parity (PPP) conversion factors, see
estimates. For more information, see About the data
tional dollars using PPP rates. An international dollar
About the data for table 1.1. Additional data for the
for table 2.1. • Surface area is a country’s total
has the same purchasing power over GNI that a U.S.
economies in the table are available on the World
area, including areas under inland bodies of water
dollar has in the United States. • GNI per capita is
Development Indicators CD-ROM or at http://data.
and some coastal waterways. • Population density
GNI divided by midyear population. • Gross domes-
worldbank.org.
is midyear population divided by land area in square
tic product (GDP) is the sum of value added by all
kilometers. • Gross national income (GNI), Atlas
resident producers plus any product taxes (less sub-
method, is the sum of value added by all resident
sidies) not included in the valuation of output. Growth
producers plus any product taxes (less subsidies) not
is calculated from constant price GDP data in local
38
2012 World Development Indicators
Population Surface Population area density
Gross national income
Atlas method
Liechtenstein Luxembourg Macao SAR, China Maldives Malta Marshall Islands Mayotte Micronesia, Fed. Sts. Monaco Montenegro New Caledonia Northern Mariana Islands Palau Samoa San Marino São Tomé and Príncipe Seychelles Sint Maarten Solomon Islands St. Kitts and Nevis St. Lucia St. Martin St. Vincent & Grenadines Suriname Tonga Turks and Caicos Islands Tuvalu Vanuatu Virgin Islands (U.S.)
Per capita $ 2010
Gross domestic product
1.6
Life Adult expectancy literacy at birth rate
WORLD VIEW
Key indicators for other economies
Carbon dioxide emissions
Purchasing power parity
thousands 2010
thousand sq. km 2010
people per sq. km 2010
$ millions 2010
$ millions 2010
Per capita $ 2010
% growth 2009–10
Per capita % growth 2009–10
years 2010
36 507 544 316 416 54 204 111 35 632 247 61 20 184 32 165 87 38 538 52 174 30 109 525 104 38 10 240 110
0.2 2.6 0.0 0.3 0.3 0.2 0.4 0.7 0.0 13.8 18.6 0.5 0.5 2.8 0.1 1.0 0.5 0.0 28.9 0.3 0.6 0.1 0.4 163.8 0.8 1.0 0.0 12.2 0.4
225 196 19,429 1,053 1,300 300 551 159 17,704 47 14 132 45 65 526 172 189 1,113 19 200 285 556 279 3 144 40 328 20 314
4,903 137,070 .. 39,030 76,980 31,050 18,527 34,880 24,020 1,818 5,750 2,563 7,958 19,130 10,258 197 3,640 .. .. .. ..b 304 2,740 388d 6,479 183,150 .. 4,260 6,740 8,073 .. .. ..e .. .. ..e 134 6,560 225d 549 2,980 782d 1,572 50,400 .. 199 1,200 318 845 9,710 1,835d .. .. ..e 552 1,030 1,192d 615 11,830 831d 1,142 6,560 1,830d .. .. ..e 688 6,320 1,184 d 3,077 5,920 3,991d 342 3,290 477d .. .. ..e .. 47 4,760 g 633 2,640 1,035d .. .. ..e
.. 61,240 45,220 8,110 24,660 .. .. 3,490 d .. 12,770 .. .. 11,000 d 4,250 d .. 1,930 21,090 d .. 2,220 d 15,970 d 10,520 d .. 10,870 d 7,680d 4,580 d .. .. 4,310 d ..
–1.2 2.7 26.4 9.9 3.1 5.2 .. 3.1 –2.6 2.5 .. .. 2.0 1.7 1.9 4.5 6.2 .. 7.0 –5.0 3.1 .. –1.3 3.1 –0.5 .. –1.9 3.0 ..
–1.9 0.8 23.4 8.4 2.6 4.0 .. 2.8 –2.7 2.2 .. .. 1.4 0.8 1.3 2.9 6.6 .. 4.2 –5.5 2.1 .. –1.0 2.1 –0.9 .. –2.1 0.4 ..
.. 80 81 77 81 .. 78 69 .. 74 76 .. .. 72 83 64 73 .. 67 .. 74 .. 72 70 72 .. .. 71 79
% ages 15 thousand and older metric tons 2005–10a 2008
.. .. 93 98 92 .. .. .. .. .. 96 .. .. 99 .. 89 92 .. .. .. .. .. .. 95 99 .. .. 82 ..
.. 10,502 1,335 920 2,560 99 .. 62 .. 1,951 3,150 .. 213 161 .. 128 682 .. 198 249 396 .. 202 2,439 176 158 .. 92 ..
a. Data are for the most recent year available. b. Estimated to be upper middle income ($3,976–$12,275). c. Included in the aggregates for upper middle-income economies based on earlier data. d. Based on regression; others are extrapolated from the 2005 International Comparison Program benchmark estimates. e. Estimated to be high income ($12,276 or more). f. Less than 0.5. g. Included in the aggregates for lower middle-income economies based on earlier data.
currency. • GDP per capita is GDP divided by midyear population. • Life expectancy at birth is the number of years a newborn infant would live if prevailing patterns of mortality at the time of its birth were to stay the same throughout its life. • Adult literacy rate is the percentage of adults ages 15 and older who can,
Data sources
with understanding, read and write a short, simple
The indicators here and throughout the book
statement about their everyday life. • Carbon dioxide
are compiled by World Bank staff from primary
emissions are those stemming from the burning of
and secondary sources. More information about
fossil fuels and the manufacture of cement. They
the indicators and their sources can be found in
include carbon dioxide produced during consumption
the About the data, Definitions, and Data sources
of solid, liquid, and gas fuels and gas flaring.
entries that accompany each table in subsequent sections.
2012 World Development Indicators
39
PEOPLE
T
he People section documents demographic trends, labor force structure, poverty incidence, income distribution, education inputs and outcomes, and health services and status. Together these indicators provide a multidimensional portrait of human development and social welfare. For 2012 a new table has been added, and the contents of others have changed. Table 2.20 now shows data by sex for three measures of the prevalence of child malnutrition — underweight, stunting, and wasting— and for the proportion of overweight children. Data on antiretroviral therapy coverage and cause of death have been added to table 2.22. And table 2.23 now includes neonatal mortality rates computed with the same method ology used to estimate infant and under-five mortality rates. Also new is table 2.24, which presents selected health indicators by income quintile. These subnational data highlight substantial within-country disparities between the poor and rich in mortality and fertility rates and in child and reproductive health outcomes. Table 2.8 provides more recent data on poverty at international poverty lines for more countries, including global and regional poverty estimates released by the World Bank in February 2012. In 2008, 1.29 billion people, or 22 percent of developing countries’ population, lived below the extreme poverty line of $1.25 a day, down from 1.94 billion people, or 52 percent of developing countries’ population, in 1981. In 1990, the benchmark year for the Millennium Development Goals, the extreme poverty rate was 43.1 percent. Progress has been slower at the $2 a day poverty line, around which many people in lower middle-income economies live. The number of people living below $2 a day fell from 2.59 billion in 1981 to 2.47 billion in 2008 — a decrease of only 120 million — and the number of people living on $1.25–$2 a day almost doubled
to 1.18 billion in 2008. This bunching up just above the extreme poverty line indicates the vulnerabilities faced by a great many people in the world. Looking at the trend from 2005 to 2008, both the absolute number and the proportion of people living in extreme poverty declined in every developing country region. This acrossthe-board reduction over a three-year monitoring cycle is the first since the World Bank began monitoring extreme poverty. Since 2008 food, fuel, and financial crises have had sharp negative impacts on vulnerable populations and slowed poverty reduction in some countries, but global poverty kept falling. In fact, a preliminary survey-based estimate for 2010 — based on a much smaller sample than the global update — indicates that the global poverty rate at $1.25 a day fell to less than half its 1990 value. If these results are confirmed by follow-up studies, the first target of the Millennium Development Goals — cutting the extreme poverty rate to half its 1990 level — may have been achieved on a global level before the 2015 target date. The World Bank’s database of international poverty measures now includes income or consumption data collected by national statistical offices drawn from 850 household surveys and interviews with 1.23 million randomly sampled households in nearly 130 countries. The most recent year for which a reliable global estimate can be calculated is 2008, because for many low-income countries more recent data are either not available or not comparable with previous estimates. The availability, frequency, and quality of poverty monitoring data remain low, especially in small states and in countries and territories with fragile situations. The need to improve household survey programs for poverty monitoring in these countries is urgent. But institutional, political, and financial obstacles continue to hamper data collection, analysis, and public access.
2
2012 World Development Indicators
41
Tables
2.1
Population dynamics Population
Average annual population growth
Population age composition
Dependency ratio
%
Afghanistan Albania Algeria Angola Argentina Armenia Australia Austria Azerbaijan Bahrain Bangladesh Belarus Belgium Benin Bolivia Bosnia and Herzegovina Botswana Brazil Bulgaria Burkina Faso Burundi Cambodia Cameroon Canada Central African Republic Chad Chile China Hong Kong SAR, China Colombia Congo, Dem. Rep. Congo, Rep. Costa Rica Côte d’Ivoire Croatia Cuba Cyprus Czech Republic Denmark Dominican Republic Ecuador Egypt, Arab Rep. El Salvador Eritrea Estonia Ethiopia Finland France Gabon Gambia, The Georgia Germany Ghana Greece Guatemala Guinea Guinea-Bissau Haiti
42
2000
millions 2010
2020
2000–10
2010–20
Ages 0–14 2010
26.0 3.1 30.5 13.9 36.9 3.1 19.2 8.0 8.0 0.6 129.6 10.0 10.3 6.5 8.3 3.7 1.8 174.4 8.2 12.3 6.4 12.4 15.7 30.8 3.7 8.2 15.4 1,262.6 6.7 39.8 49.6 3.1 3.9 16.6 4.4 11.1 0.9 10.3 5.3 8.6 12.3 67.6 5.9 3.7 1.4 65.6 5.2 60.8 1.2 1.3 4.4 a 82.2 19.2 10.9 11.2 8.3 1.2 8.6
34.4 3.2 35.5 19.1 40.4 3.1 22.3 8.4 9.1 1.3 148.7 9.5 10.9 8.9 9.9 3.8 2.0 194.9 7.5 16.5 8.4 14.1 19.6 34.1 4.4 11.2 17.1 1,338.3 7.1 46.3 66.0 4.0 4.7 19.7 4.4 11.3 1.1 10.5 5.5 9.9 14.5 81.1 6.2 5.3 1.3 83.0 5.4 64.9 1.5 1.7 4.5a 81.8 24.4 11.3 14.4 10.0 1.5 10.0
44.8 3.3 40.1 24.8 43.7 3.1 25.2 8.5 10.1 1.5 167.1 9.2 11.1 11.5 11.6 3.6 2.2 209.6 7.0 22.1 10.1 15.9 24.1 37.1 5.3 14.4 18.4 1,381.6 7.8 52.1 85.0 5.0 5.3 24.5 4.3 11.1 1.2 10.7 5.7 11.1 16.3 94.8 6.6 6.8 1.3 100.9 5.5 67.6 1.8 2.2 4.2a 79.8 30.3 11.5 18.3 12.8 1.9 11.3
2.8 0.4 1.5 3.1 0.9 0.1 1.5 0.5 1.2 6.8 1.4 –0.5 0.6 3.1 1.8 0.2 1.3 1.1 –0.8 2.9 2.7 1.3 2.2 1.0 1.7 3.1 1.0 0.6 0.6 1.5 2.8 2.5 1.7 1.7 0.0 0.1 1.6 0.2 0.4 1.4 1.6 1.8 0.4 3.6 –0.2 2.3 0.4 0.7 2.0 2.9 0.1a –0.1 2.4 0.4 2.5 1.8 2.0 1.4
2.6 0.2 1.2 2.6 0.8 0.1 1.2 0.1 1.1 1.9 1.2 –0.4 0.2 2.6 1.5 –0.4 0.9 0.7 –0.7 2.9 1.8 1.2 2.1 0.8 1.9 2.5 0.8 0.3 0.9 1.2 2.5 2.1 1.2 2.2 –0.3 –0.1 1.0 0.2 0.3 1.1 1.2 1.6 0.6 2.6 –0.1 2.0 0.2 0.4 1.9 2.6 –0.7a –0.2 2.2 0.1 2.4 2.4 2.1 1.2
46 23 27 47 25 20 19 15 21 20 31 15 17 44 36 15 33 25 14 45 38 32 41 16 40 45 22 19 12 29 46 41 25 41 15 17 18 14 18 31 30 32 32 42 15 41 17 18 35 44 17 13 39 15 41 43 41 36
2012 World Development Indicators
%
Ages 15–64 2010
Ages 65+ 2010
51 68 68 51 65 69 68 68 73 78 64 71 66 53 59 71 63 68 69 52 59 64 56 69 56 52 69 72 76 66 51 56 69 55 68 70 71 71 66 63 63 63 61 56 67 55 66 65 60 54 69 66 58 67 54 54 55 60
2 10 5 2 11 11 13 18 7 2 5 14 17 3 5 14 4 7 18 2 3 4 4 14 4 3 9 8 13 6 3 4 7 4 17 12 12 15 16 6 6 5 7 2 17 3 17 17 4 2 14 20 4 19 4 3 3 4
% of working-age population Young Old 2010 2010
91 34 40 91 39 29 28 22 29 26 49 21 26 82 61 21 51 38 20 86 64 50 73 24 73 88 32 27 15 44 91 73 36 74 22 25 25 20 27 49 48 50 52 74 23 75 25 28 59 82 24 20 67 22 77 80 75 60
4 14 7 5 16 16 20 26 9 3 7 19 27 6 8 20 6 10 25 4 5 6 6 20 7 6 13 11 17 9 5 7 10 7 25 18 16 21 25 10 10 8 11 4 25 6 26 26 7 4 21 31 7 28 8 6 6 7
Crude death rate
Crude birth rate
per 1,000 people 2010
per 1,000 people 2010
16 6 5 14 8 9 6 9 6 3 6 15 10 12 7 10 13 6 15 12 14 8 14 8 16 16 6 7 6 5 16 11 4 12 12 7 7 10 10 6 5 5 7 8 12 10 10 8 9 9 11 11 8 9 5 13 17 9
44 13 20 42 17 15 13 9 19 20 20 11 12 40 26 9 24 15 10 43 34 22 36 11 35 45 14 12 13 20 43 35 16 34 10 10 12 11 11 22 21 23 20 36 12 31 11 13 27 38 12 8 32 10 32 39 38 27
Population
Average annual population growth
Population age composition
Dependency ratio
%
Honduras Hungary India Indonesia Iran, Islamic Rep. Iraq Ireland Israel Italy Jamaica Japan Jordan Kazakhstan Kenya Korea, Dem. Rep. Korea, Rep. Kosovo Kuwait Kyrgyz Republic Lao PDR Latvia Lebanon Lesotho Liberia Libya Lithuania Macedonia, FYR Madagascar Malawi Malaysia Mali Mauritania Mauritius Mexico Moldova Mongolia Morocco Mozambique Myanmar Namibia Nepal Netherlands New Zealand Nicaragua Niger Nigeria Norway Oman Pakistan Panama Papua New Guinea Paraguay Peru Philippines Poland Portugal Puerto Rico Qatar
2000
millions 2010
2020
2000–10
2010–20
Ages 0–14 2010
6.2 10.2 1,053.9 213.4 65.3 24.3 3.8 6.3 56.9 2.6 126.9 4.8 14.9 31.3 22.9 47.0 1.7 1.9 4.9 5.3 2.4 3.7 2.0 2.8 5.2 3.5 2.0 15.4 11.2 23.4 11.3 2.6 1.2 100.0 3.6b 2.4 28.8 18.2 45.0 1.9 24.4 15.9 3.9 5.1 10.9 123.7 4.5 2.3 144.5 3.0 5.4 5.3 25.9 77.3 38.5 10.2 3.8 0.6
7.6 10.0 1,224.6 239.9 74.0 32.0 4.5 7.6 60.5 2.7 127.5 6.0 16.3 40.5 24.3 48.9 1.8 2.7 5.4 6.2 2.2 4.2 2.2 4.0 6.4 3.3 2.1 20.7 14.9 28.4 15.4 3.5 1.3 113.4 3.6b 2.8 32.0 23.4 48.0 2.3 30.0 16.6 4.4 5.8 15.5 158.4 4.9 2.8 173.6 3.5 6.9 6.5 29.1 93.3 38.2 10.6 4.0 1.8
9.2 9.8 1,385.2 262.1 80.9 42.3 5.0 8.9 60.8 2.8 123.6 7.2 18.0 52.5 25.3 50.2 1.9 3.4 6.1 7.0 2.1 4.5 2.4 5.2 7.0 3.1 2.1 27.3 20.7 33.0 20.5 4.3 1.3 125.7 3.3b 3.2 35.0 29.1 51.7 2.7 35.1 17.0 4.8 6.6 22.0 203.7 5.2 3.3 205.2 4.0 8.5 7.6 32.3 109.6 38.1 10.5 4.0 2.3
2.0 –0.2 1.5 1.2 1.2 2.8 1.6 1.9 0.6 0.4 0.0 2.3 0.9 2.6 0.6 0.4 0.7 3.4 1.1 1.5 –0.6 1.2 1.0 3.4 1.9 –0.6 0.3 3.0 2.8 1.9 3.1 2.7 0.8 1.3 –0.2b 1.3 1.0 2.5 0.6 1.9 2.1 0.4 1.2 1.3 3.5 2.5 0.8 2.1 1.8 1.7 2.4 1.9 1.2 1.9 –0.1 0.4 0.4 10.9c
1.9 –0.2 1.2 0.9 0.9 2.8 1.0 1.5 0.1 0.3 –0.3 1.8 1.0 2.6 0.4 0.3 0.7 2.2 1.2 1.3 –0.4 0.6 1.0 2.6 1.0 –0.5 0.0 2.8 3.3 1.5 2.9 2.2 0.4 1.0 –0.6b 1.4 0.9 2.2 0.7 1.6 1.6 0.2 1.0 1.3 3.5 2.5 0.7 1.8 1.7 1.4 2.1 1.6 1.1 1.6 0.0 –0.1 –0.1 2.6
37 15 31 27 23 43 21 27 14 29 13 38 24 42 23 16 .. 27 30 35 14 25 37 43 30 15 18 43 46 30 47 40 22 29 17 28 28 44 26 36 36 18 20 34 49 43 19 27 35 29 39 34 30 35 15 15 21 13
%
Ages 15–64 2010
Ages 65+ 2010
59 69 64 67 72 54 67 62 66 63 64 59 69 55 68 72 .. 71 66 62 68 68 58 54 65 69 71 54 51 65 51 57 71 65 72 68 66 53 69 60 60 67 67 61 49 54 67 70 60 64 58 61 64 61 72 67 66 85
4 17 5 6 5 3 12 10 20 8 23 4 7 3 10 11 .. 3 4 4 18 7 4 3 4 16 12 3 3 5 2 3 7 6 11 4 5 3 5 4 4 15 13 5 2 3 15 3 4 7 3 5 6 4 14 18 13 1
% of working-age population Young Old 2010 2010
62 21 47 40 32 81 32 44 21 46 21 64 36 77 34 23 .. 38 46 56 20 36 64 81 47 22 25 80 90 47 93 69 31 45 23 40 42 84 37 61 61 26 31 57 100 80 28 39 59 45 67 55 47 58 21 23 32 16
7 24 8 8 7 6 17 17 31 12 35 7 10 5 14 15 .. 4 7 6 26 11 7 5 7 23 17 6 6 7 4 5 10 10 15 6 8 6 7 6 7 23 20 8 4 6 22 4 7 10 5 8 10 6 19 27 19 1
2.1
PEOPLE
Population dynamics
Crude death rate
Crude birth rate
per 1,000 people 2010
per 1,000 people 2010
5 13 8 7 5 6 6 5 10 7 10 4 9 11 10 5 7 3 7 6 13 7 16 11 4 13 9 6 13 5 15 10 7 5 13 6 6 15 9 8 6 8 7 5 13 14 9 4 7 5 8 5 5 6 10 10 8 2
27 9 22 18 17 35 17 22 9 16 9 25 22 38 14 9 19 18 27 23 9 15 28 39 23 11 11 35 44 20 46 34 12 20 12 23 20 38 17 26 24 11 15 24 49 40 13 18 27 20 30 24 20 25 11 10 13 13
2012 World Development Indicators
43
2.1
Population dynamics Population
Average annual population growth
Population age composition
Dependency ratio
%
2000
Romania Russian Federation Rwanda Saudi Arabia Senegal Serbia Sierra Leone Singapore Slovak Republic Slovenia Somalia South Africa South Sudan Spain Sri Lanka Sudan Swaziland Sweden Switzerland Syrian Arab Republic Tajikistan Tanzania Thailand Timor-Leste Togo Trinidad and Tobago Tunisia Turkey Turkmenistan Uganda Ukraine United Arab Emirates United Kingdom United States Uruguay Uzbekistan Venezuela, RB Vietnam West Bank and Gaza Yemen, Rep. Zambia Zimbabwe World Low income Middle income Lower middle income Upper middle income Low & middle income East Asia & Pacific Europe & Central Asia Latin America & Carib. Middle East & N. Africa South Asia Sub-Saharan Africa High income Euro area
millions 2010
22.4 21.4 146.3 141.8 8.1 10.6 20.0 27.4 9.5 12.4 7.5 7.3 4.1 5.9 4.0 5.1 5.4 5.4 2.0 2.0 7.4 9.3 44.0 50.0 .. .. 40.3 46.1 18.7 20.9 43.6e 34.2e 1.0 1.1 8.9 9.4 7.2 7.8 16.0 20.4 6.2 6.9 34.0 44.8 63.2 69.1 0.8 1.1 4.8 6.0 1.3 1.3 9.6 10.5 63.6 72.8 4.5 5.0 24.2 33.4 49.2 45.9 3.0 7.5 58.9 62.2 282.2 309.3 3.3 3.4 24.7 28.2 24.3 28.8 77.6 86.9 3.0 4.2 17.7 24.1 10.2 12.9 12.5 12.6 6,117.8 s 6,894.6 s 643.7 796.3 4,424.5 4,970.8 2,146.7 2,518.7 2,277.9 2,452.1 5,068.2 5,767.2 1,813.8 1,961.6 398.5 405.2 514.3 582.6 277.4 331.3 1,398.0 1,633.1 666.3 853.4 1,049.6 1,127.4 315.0 331.8
% 2020
20.9 139.3 14.0 33.1 16.0 7.2 7.2 5.6 5.5 2.1 12.2 52.4 .. 48.4 22.3 54.9e 1.2 9.9 8.0 24.3 7.9 61.0 71.9 1.5 7.3 1.4 11.6 80.7 5.7 45.3 43.3 9.2 65.7 334.9 3.5 31.6 33.1 95.2 5.5 32.2 17.7 15.5 7,635.2 s 979.1 5,478.1 2,901.8 2,576.2 6,457.2 2,068.9 414.4 642.4 386.5 1,860.8 1,084.2 1,178.1 336.7
2000–10
2010–20
–0.5 –0.3 2.7 3.1 2.7 –0.3 3.5 2.3 0.1 0.3 2.3 1.3 .. 1.3 1.1 2.4 e 0.4 0.6 0.9 2.5 1.1 2.8 0.9 3.0 2.3 0.4 1.0 1.3 1.1 3.2 –0.7 9.1 0.6 0.9 0.2 1.4 1.7 1.1 3.2 3.1 2.4 0.0 1.2 w 2.1 1.2 1.6 0.7 1.3 0.8 0.2 1.2 1.8 1.6 2.5 0.7 0.5
–0.3 –0.2 2.8 1.9 2.5 –0.2 2.0 0.9 0.1 0.1 2.7 0.5 .. 0.5 0.7 2.3e 1.2 0.5 0.3 1.7 1.4 3.1 0.4 2.8 2.0 0.2 0.9 1.0 1.2 3.0 –0.6 2.0 0.5 0.8 0.3 1.1 1.4 0.9 2.7 2.9 3.1 2.1 1.0 w 2.1 1.0 1.4 0.5 1.1 0.5 0.2 1.0 1.6 1.3 2.4 0.4 0.1
Ages 0–14 2010
15 15 43 30 44 18d 43 17 15 14 45 30 .. 15 25 40e 38 17 15 37 37 45 21 46 40 21 23 26 29 48 14 17 17 20 23 29 29 24 42 44 46 39 27 w 39 27 32 22 29 22 19 28 31 32 42 17 15
Ages 15–64 2010
70 72 55 67 54 68d 55 74 73 70 52 65 .. 68 67 56e 58 65 68 59 60 52 71 51 57 72 70 68 67 49 70 83 66 67 64 66 65 70 55 53 51 57 66 w 57 67 63 70 65 71 70 65 64 64 54 67 66
Ages 65+ 2010
15 13 3 3 2 14 d 2 9 12 16 3 5 .. 17 8 4e 3 18 17 4 3 3 9 3 3 7 7 6 4 3 15 0 17 13 14 4 6 6 3 3 3 4 8w 4 6 5 8 6 7 11 7 5 5 3 16 18
% of working-age population Young Old 2010 2010
22 21 78 46 81 26d 78 24 21 20 86 46 .. 22 37 71e 66 25 22 62 62 86 29 91 70 28 34 39 44 99 20 21 26 30 35 44 45 34 78 83 92 68 41 w 69 40 51 31 44 31 28 43 48 50 78 26 23
21 18 5 4 4 21d 3 12 17 24 5 7 .. 25 12 6e 6 28 25 7 6 6 13 6 6 10 10 9 6 5 22 1 25 20 22 7 9 9 5 5 6 7 12 w 6 10 8 11 9 10 15 10 7 8 6 23 28
Crude death rate
Crude birth rate
per 1,000 people 2010
per 1,000 people 2010
12 14 12 4 9 14 16 4 10 9 15 15 21 8 7 9e 14 10 8 4 6 10 7 8 11 8 6 5 8 12 15 1 9 8 9 5 5 5 4 6 16 13 8w 11 8 8 7 8 7 11 6 5 8 13 8 9
10 13 41 22 37 9 39 9 11 11 44 21 30 11 18 33e 29 12 10 23 28 41 12 38 32 15 18 18 22 45 11 13 13 14 14 23 21 17 33 38 46 29 20 w 33 19 24 14 21 14 15 19 23 23 37 12 10
a. Excludes Abkhazia and South Ossetia. b. Excludes Transnistria. c. Increase is due to a surge in the number of migrants since 2004. d. Includes Kosovo. e. Includes South Sudan.
44
2012 World Development Indicators
About the data
2.1
PEOPLE
Population dynamics Definitions
Population estimates are usually based on national
and declining mortality rates are now reflected in the
• Population is based on the de facto definition of
population censuses. Estimates for the years before
larger share of the working-age population.
population, which counts all residents regardless of
and after the census are interpolations or extrapo-
Dependency ratios capture variations in the pro-
lations based on demographic models. Errors and
portions of children, elderly people, and working-age
permanently settled in the country of asylum, who
undercounting occur even in high-income countries;
people in the population that imply the dependency
are generally considered part of the population of
in developing countries errors may be substantial
burden that the working-age population bears in
their country of origin. The values shown are mid-
because of limits in the transport, communications,
relation to children and the elderly. But dependency
year estimates for 2000 and 2010 and projections
and other resources required to conduct and analyze
ratios show only the age composition of a popula-
for 2020. • Average annual population growth is
a full census.
tion, not economic dependency. Some children and
the exponential change for the period indicated. See
elderly people are part of the labor force, and many
Statistical methods for more information. • Popula-
working-age people are not.
tion age composition is the percentage of the total
The quality and reliability of official demographic data are also affected by public trust in the govern-
legal status or citizenship—except for refugees not
ment, government commitment to full and accurate
Vital rates are based on data from birth and death
population that is in specific age groups. • Depen-
enumeration, confidentiality and protection against
registration systems, censuses, and sample surveys
dency ratio is the ratio of dependents—people
misuse of census data, and census agencies’ inde-
by national statistical offices and other organiza-
younger than 15 or older than 64—to the working-
pendence from political influence. Moreover, compara-
tions, or on demographic analysis. Data for 2010
age population —those ages 15–64. • Crude death
bility of population indicators is limited by differences
for some high-income countries are provisional esti-
rate and crude birth rate are the number of deaths
in the concepts, definitions, collection procedures,
mates based on vital registers. The estimates for
and the number of live births occurring during the
and estimation methods used by national statistical
many countries are projections based on extrapola-
year, per 1,000 people, estimated at midyear. Sub-
agencies and other organizations that collect the data.
tions of levels and trends from earlier years or inter-
tracting the crude death rate from the crude birth
Of the 158 economies in the table and the 58
polations of population estimates and projections
rate provides the rate of natural increase, which is
from the United Nations Population Division.
equal to the population growth rate in the absence
economies in table 1.6, 180 (about 86 percent) conducted a census during the 2000 census round
Vital registers are the preferred source for these
(1995–2004). As of January 2012, 141 countries
data, but in many developing countries systems for
have completed a census for the 2010 census round
registering births and deaths are absent or incomplete
(2005–14). The currentness of a census and the avail-
because of deficiencies in the coverage of events or
ability of complementary data from surveys or registra-
geographic areas. Many developing countries carry out
tion systems are objective ways to judge demographic
special household surveys that ask respondents about
data quality. Some European countries’ registration
recent births and deaths. Estimates derived in this
systems offer complete information on population in
way are subject to sampling errors and recall errors.
the absence of a census. See table 2.17 and Primary
The United Nations Statistics Division monitors the
data documentation for the most recent census or
completeness of vital registration systems. Some
survey year and for the completeness of registration.
countries have made progress over the last 60 years,
of migration.
Data sources
Current population estimates for developing coun-
but others still have deficiencies in civil registration
The World Bank’s population estimates are com-
tries that lack recent census data and pre- and
systems. For example, only 57 percent of countries
piled and produced by its Development Data
post-census estimates for countries with census
and areas register at least 90 percent of births, and
Group in consultation with its Human Develop-
data are provided by the United Nations Population
only 53 percent register at least 90 percent of deaths.
ment Network, operational staff, and country
Division and other agencies. The cohort component
Some of the most populous developing countries—
offices. The United Nations Population Division’s
method—a standard method for estimating and
Bangladesh, Brazil, China, India, Indonesia, Nigeria,
World Population Prospects: The 2010 Revision is
projecting population —requires fertility, mortality,
Pakistan —lack complete vital registration systems.
a source of the demographic data for more than
and net migration data, often collected from sample
International migration is the only other factor
half the countries, most of them developing coun-
surveys, which can be small or limited in coverage.
besides birth and death rates that directly deter-
tries, and the source of data on age composition
Population estimates are from demographic model-
mines a country’s population growth. From 1990 to
and dependency ratios for all countries. Other
ing and so are susceptible to biases and errors from
2005 the number of migrants in high-income coun-
important sources are census reports and other
shortcomings in the model and in the data. Because
tries rose 40 million. About 195 million people (3
statistical publications from national statistical
the five-year age group is the cohort unit and five-year
percent of the world population) live outside their
offices; household surveys by national agencies,
period data are used, interpolations to obtain annual
home country. Estimating migration is difficult. At
ICF International (for MEASURE DHS), and the
data or single age structure may not reflect actual
any time many people are located outside their home
U.S. Centers for Disease Control and Prevention;
events or age composition.
country as tourists, workers, or refugees or for other
Eurostat’s Demographic Statistics; Secretariat of
The growth rate of the total population conceals
reasons. Standards for the duration and purpose of
the Pacific Community, Statistics and Demogra-
age-group differences in growth rates. In many devel-
international moves that qualify as migration vary,
phy Programme; and U.S. Bureau of the Census,
oping countries the once rapidly growing under-15
and estimates require information on flows into and
International Data Base.
population is shrinking. Previously high fertility rates
out of countries that is difficult to collect.
2012 World Development Indicators
45
2.2
Labor force structure Labor force participation rate
Labor force
% ages 15 and older Male
Afghanistan Albania Algeria Angola Argentina Armenia Australia Austria Azerbaijan Bahrain Bangladesh Belarus Belgium Benin Bolivia Bosnia and Herzegovina Botswana Brazil Bulgaria Burkina Faso Burundi Cambodia Cameroon Canada Central African Republic Chad Chile China Hong Kong SAR, China Colombia Congo, Dem. Rep. Congo, Rep. Costa Rica Côte d’Ivoire Croatia Cuba Cyprus Czech Republic Denmark Dominican Republic Ecuador Egypt, Arab Rep. El Salvador Eritrea Estonia Ethiopia Finland France Gabon Gambia, The Georgia Germany Ghana Greece Guatemala Guinea Guinea-Bissau Haiti
46
Ages 15 and older average annual % growth
Total millions
Female
Female % of labor force
2000
2010
2000
2010
2000
2010
2000–10
2000
2010
81 73 76 75 74 73 72 69 71 86 86 65 61 81 82 58 80 82 57 91 84 83 77 72 86 80 75 83 73 82 73 71 81 82 63 70 72 70 72 80 84 73 79 90 67 91 67 63 67 83 74 68 77 65 86 78 79 69
80 71 72 77 75 70 73 68 68 87 84 62 61 78 81 59 82 81 60 91 82 87 77 72 85 80 74 80 68 80 72 73 79 81 60 70 71 68 69 79 83 74 79 90 68 90 65 62 65 83 74 67 72 65 88 78 78 71
13 51 12 67 43 58 55 48 57 35 54 53 44 64 60 33 70 55 48 77 86 77 62 59 71 65 35 71 49 49 71 65 37 49 45 38 50 52 60 46 50 20 45 75 52 73 57 48 55 71 55 49 73 40 42 63 63 57
16 50 15 63 47 49 59 54 61 39 57 50 48 67 64 35 72 59 49 78 84 79 64 62 73 65 47 68 51 55 70 68 46 52 46 43 57 49 60 51 54 24 47 80 57 78 56 51 56 72 56 53 67 45 49 65 68 60
6.5 1.3 8.8 5.2 15.4 1.5 9.6 3.9 3.5 0.3 57.3 4.7 4.4 2.6 3.5 1.3 0.8 83.7 3.6 5.5 2.9 5.8 6.2 16.3 1.7 3.2 6.1 724.5 3.3 17.3 18.5 1.3 1.6 6.4 2.0 4.7 0.4 5.2 2.9 3.5 5.4 20.1 2.2 1.7 0.7 29.0 2.6 27.2 0.4 0.5 2.2a 40.3 8.4 4.9 4.0 3.3 0.5 3.2
9.1 1.5 11.2 7.1 18.4 1.4 11.8 4.3 4.6 0.7 72.3 4.5 4.9 3.6 4.6 1.5 1.0 101.6 3.5 7.5 4.3 8.0 8.2 19.0 2.1 4.4 8.0 799.8 3.7 22.1 25.3 1.7 2.2 7.8 2.0 5.3 0.6 5.3 2.9 4.4 6.9 27.1 2.6 2.6 0.7 40.8 2.7 29.9 0.6 0.8 2.4 a 42.2 10.4 5.3 5.7 4.1 0.6 4.2
3.3 1.2 2.4 3.1 1.8 –0.2 2.1 1.1 2.6 8.6 2.3 –0.6 1.0 3.5 2.6 1.1 2.4 1.9 –0.3 3.2 3.9 3.2 2.8 1.6 2.1 3.2 2.8 1.0 1.0 2.5 3.1 3.0 3.2 2.0 0.0 1.1 2.7 0.2 0.3 2.2 2.4 3.0 1.6 4.5 0.6 3.4 0.3 0.9 2.8 3.2 0.8a 0.4 2.1 0.8 3.6 2.2 2.6 2.5
13.3 41.7 13.7 48.4 38.1 48.8 43.8 43.5 47.0 21.4 37.5 48.9 42.9 46.5 43.2 39.1 46.9 41.2 47.1 48.2 53.2 51.2 45.1 45.8 46.4 45.5 33.1 45.0 41.9 38.7 50.1 48.1 30.8 35.0 44.1 35.0 40.8 44.4 46.6 36.6 37.4 21.5 39.6 47.3 48.7 45.2 47.5 45.5 45.5 47.0 46.2 43.7 48.1 39.1 34.6 44.6 45.5 46.5
15.2 41.6 16.9 45.9 40.2 46.5 45.3 45.9 49.1 19.3 39.9 49.0 45.3 47.5 44.8 40.0 46.3 43.7 46.8 47.6 52.1 49.9 45.7 47.1 47.1 45.3 39.6 44.6 46.0 42.5 49.9 48.6 36.2 37.4 45.9 38.0 43.5 43.3 47.2 39.4 39.7 24.2 41.4 48.6 50.4 47.2 47.8 47.1 46.3 47.9 47.0 45.6 47.6 41.5 38.1 45.2 47.3 47.0
2012 World Development Indicators
Labor force participation rate
Labor force
% ages 15 and older Male
Honduras Hungary India Indonesia Iran, Islamic Rep. Iraq Ireland Israel Italy Jamaica Japan Jordan Kazakhstan Kenya Korea, Dem. Rep. Korea, Rep. Kosovo Kuwait Kyrgyz Republic Lao PDR Latvia Lebanon Lesotho Liberia Libya Lithuania Macedonia, FYR Madagascar Malawi Malaysia Mali Mauritania Mauritius Mexico Moldova Mongolia Morocco Mozambique Myanmar Namibia Nepal Netherlands New Zealand Nicaragua Niger Nigeria Norway Oman Pakistan Panama Papua New Guinea Paraguay Peru Philippines Poland Portugal Puerto Rico Qatar
2.2
PEOPLE
Labor force structure
Ages 15 and older average annual % growth
Total millions
Female
Female % of labor force
2000
2010
2000
2010
2000
2010
2000–10
2000
2010
88 58 83 85 73 69 71 61 61 78 76 68 76 73 88 73 .. 82 74 81 65 71 80 62 73 67 66 90 81 82 66 78 81 83 64 66 79 83 81 65 90 73 73 83 88 67 72 78 84 82 74 87 83 82 64 70 60 92
83 58 81 84 72 69 68 62 60 72 72 65 77 72 84 72 .. 82 78 79 66 71 73 64 77 63 69 89 81 77 70 79 76 81 45 65 75 83 82 70 88 72 74 80 90 63 70 80 83 83 74 86 85 79 64 68 54 95
44 41 34 50 14 13 47 48 35 59 49 13 65 63 74 49 .. 44 56 79 49 19 68 58 27 55 41 84 77 45 37 23 41 39 55 56 29 88 74 49 82 53 57 38 38 45 60 23 16 45 71 51 58 49 49 53 35 39
42 44 29 51 16 14 52 53 38 56 50 15 66 61 72 49 .. 43 55 77 55 23 59 58 30 54 43 84 85 44 37 28 44 44 38 54 26 86 75 58 80 58 62 46 40 48 62 28 22 49 71 57 67 50 48 56 35 52
2.4 4.2 409.4 99.6 18.5 5.5 1.8 2.5 23.3 1.2 67.6 1.2 7.5 11.9 13.6 22.7 .. 1.0 2.1 2.5 1.1 1.1 0.8 1.0 1.8 1.7 0.8 7.3 4.8 9.9 3.1 0.8 0.5 40.3 1.6b 0.9 10.2 8.7 24.2 0.6 12.4 8.2 1.9 1.8 3.5 39.2 2.4 0.8 43.0 1.3 2.3 2.3 12.0 30.9 17.4 5.2 1.4 0.3
3.0 4.3 472.6 118.0 25.3 7.5 2.1 3.2 25.1 1.2 66.7 1.6 8.8 15.5 14.6 24.6 .. 1.4 2.5 3.2 1.2 1.5 0.9 1.4 2.4 1.6 0.9 10.1 6.7 12.0 4.3 1.1 0.6 49.6 1.2b 1.2 11.4 11.1 28.0 0.9 16.0 8.9 2.4 2.4 5.1 50.3 2.6 1.2 59.7 1.6 3.0 3.1 15.5 38.7 18.2 5.6 1.4 1.3
2.4 0.3 1.4 1.7 3.1 3.1 1.9 2.6 0.8 0.3 –0.1 2.5 1.5 2.7 0.7 0.8 .. 3.5 2.0 2.5 0.6 2.4 0.5 3.6 2.8 –0.3 1.3 3.3 3.3 1.9 3.4 3.8 1.2 2.1 –3.0 b 2.2 1.1 2.4 1.5 3.8 2.6 0.8 2.0 2.8 3.7 2.5 1.0 4.4 3.3 2.5 2.7 3.0 2.6 2.2 0.5 0.7 0.3 13.7
34.1 44.7 27.8 37.7 16.0 16.0 40.6 45.7 38.7 44.3 40.7 14.3 49.2 46.8 47.5 40.5 .. 25.1 44.6 49.9 48.1 22.9 49.1 49.0 26.1 49.5 38.8 48.7 49.7 34.7 37.5 23.3 34.5 32.9 49.7 46.8 27.9 55.0 48.3 44.4 48.9 43.1 45.2 32.5 30.9 40.1 46.5 17.1 15.3 35.4 48.5 36.6 41.2 37.5 45.8 45.3 39.5 15.6
34.1 46.0 25.3 38.2 17.9 17.5 43.7 47.1 40.3 45.1 42.3 18.0 49.4 46.5 47.7 41.3 .. 23.9 42.7 49.8 50.1 25.5 46.0 47.7 28.0 50.3 38.6 48.9 51.5 35.8 35.5 26.5 37.7 36.5 49.2 46.4 27.1 53.6 48.9 46.3 49.2 45.6 46.7 37.9 31.2 42.8 46.9 17.9 20.7 37.3 48.3 39.7 44.6 38.8 45.1 47.4 42.3 12.4
2012 World Development Indicators
47
2.2
Labor force structure Labor force participation rate
Labor force
% ages 15 and older Male 2000
Romania Russian Federation Rwanda Saudi Arabia Senegal Serbia Sierra Leone Singapore Slovak Republic Slovenia Somalia South Africa South Sudan Spain Sri Lanka Sudan Swaziland Sweden Switzerland Syrian Arab Republic Tajikistan Tanzania Thailand Timor-Leste Togo Trinidad and Tobago Tunisia Turkey Turkmenistan Uganda Ukraine United Arab Emirates United Kingdom United States Uruguay Uzbekistan Venezuela, RB Vietnam West Bank and Gaza Yemen, Rep. Zambia Zimbabwe World Low income Middle income Lower middle income Upper middle income Low & middle income East Asia & Pacific Europe & Central Asia Latin America & Carib. Middle East & N. Africa South Asia Sub-Saharan Africa High income Euro area
71 69 85 74 89 .. 63 78 68 64 78 61 .. 66 77 76 72 68 78 80 75 91 81 75 82 77 72 74 74 83 65 92 70 74 76 72 82 83 67 71 85 82 79 w 83 80 81 80 81 83 69 81 74 83 77 71 65
Female 2010
65 71 85 74 88 67 69 77 68 65 77 60 .. 67 76 77 71 68 75 72 75 90 80 74 81 78 70 71 76 80 66 92 69 70 77 74 80 81 66 72 86 90 77 w 83 79 79 78 79 81 70 80 72 81 76 69 65
2000
58 55 86 16 64 .. 67 53 53 51 37 44 .. 41 37 29 43 58 58 20 58 87 65 38 76 47 24 27 48 81 52 34 54 59 52 48 48 74 11 22 75 69 52 w 66 51 39 60 52 68 50 48 18 35 61 51 45
a. Excludes Abkhazia and South Ossetia. b. Excludes Transnistria. c. Includes Kosovo.
48
2012 World Development Indicators
Ages 15 and older average annual % growth
Total millions 2010
48 56 86 17 66 51 66 57 51 53 38 44 .. 52 35 31 44 59 61 13 57 88 64 38 80 55 25 28 46 76 53 44 56 58 55 48 52 73 15 25 73 83 51 w 68 49 37 59 51 65 50 53 20 32 63 52 50
2000
11.8 73.3 3.8 6.0 3.9 .. 1.6 2.1 2.6 1.0 2.3 15.2 .. 18.2 7.8 10.3 0.3 4.6 4.0 4.8 2.4 16.7 34.8 0.2 2.2 0.6 3.2 21.9 1.7 10.1 23.4 1.7 29.5 147.1 1.6 9.2 10.5 41.3 0.6 4.2 4.5 5.5 2,770.2 t 279.3 1,987.5 828.5 1,159.1 2,266.8 991.2 176.8 224.6 80.3 536.8 257.2 503.4 144.9
2010
10.2 75.5 5.2 9.6 5.4 3.5c 2.3 2.8 2.7 1.0 2.9 18.2 .. 23.2 8.6 14.0 0.4 5.0 4.5 5.5 2.8 22.1 39.4 0.3 2.9 0.7 3.8 26.5 2.2 13.4 23.2 4.9 31.8 157.5 1.7 12.1 13.4 51.1 1.0 6.5 5.5 6.6 3,223.0 t 363.5 2,308.6 994.1 1,314.4 2,672.1 1,118.0 191.8 278.2 104.9 638.8 340.4 550.9 159.6
2000–10
–1.5 0.3 3.2 4.7 3.1 .. 3.7 3.0 0.5 0.8 2.2 1.8 .. 2.4 0.9 3.0 2.2 0.9 1.2 1.2 1.8 2.8 1.2 3.6 3.1 1.8 1.8 1.9 2.2 2.8 –0.1 10.5 0.8 0.7 0.8 2.8 2.4 2.1 4.6 4.3 2.1 1.9 1.5 w 2.6 1.5 1.8 1.3 1.6 1.2 0.8 2.1 2.7 1.7 2.8 0.9 1.0
Female % of labor force 2000
2010
46.6 48.6 52.3 14.7 42.9 .. 53.2 40.6 45.6 46.3 32.8 43.1 .. 39.5 33.1 27.9 40.3 47.1 44.3 20.2 44.1 49.7 46.1 32.6 49.0 39.9 24.9 26.9 40.6 50.0 49.1 12.0 45.2 45.8 43.2 40.6 37.2 49.1 13.4 24.0 47.2 46.4 39.9 w 44.9 38.4 32.1 42.8 39.2 44.3 45.0 38.3 20.0 28.2 44.9 43.2 42.6
44.7 48.9 51.8 14.8 43.9 43.9 50.7 42.3 44.6 46.3 33.6 42.8 .. 44.3 32.2 28.7 39.7 47.0 45.8 15.2 45.2 49.8 45.7 33.3 50.5 43.2 26.9 28.7 39.3 49.3 49.3 14.8 45.9 46.1 44.5 39.8 39.3 48.5 17.8 25.8 46.1 49.3 39.9 w 45.6 38.0 31.5 43.0 39.1 44.1 44.8 41.2 21.5 27.1 45.6 43.9 44.8
About the data
2.2
PEOPLE
Labor force structure Definitions
The labor force is the supply of labor available for pro-
The labor force participation rates in the table are
• Labor force participation rate is the proportion
ducing goods and services in an economy. It includes
from the ILO’s Key Indicators of the Labour Market,
of the population ages 15 and older that engages
people who are currently employed and people who
7th edition, database. These harmonized estimates
actively in the labor market, by either working or
are unemployed but seeking work as well as first-time
use strict data selection criteria and enhanced meth-
looking for work during a reference period. • Total
job-seekers. Not everyone who works is included,
ods to ensure comparability across countries and
labor force is people ages 15 and older who engage
however. Unpaid workers, family workers, and stu-
over time to avoid the inconsistencies mentioned
actively in the labor market, either by working or look-
dents are often omitted, and some countries do not
above. Estimates are based mainly on labor force
ing for work during a reference period. It includes
count members of the armed forces. Labor force size
surveys, with other sources (population censuses
both the employed and the unemployed. • Average
tends to vary during the year as seasonal workers
and nationally reported estimates) used only when
annual percentage growth of the labor force is cal-
enter and leave.
no survey data are available.
culated using the exponential endpoint method (see
Data on the labor force are compiled by the Inter-
The labor force estimates in the table were calcu-
Statistical methods for more information). • Female
national Labour Organization (ILO) from labor force
lated by applying labor force participation rates from
labor force as a percentage of the labor force shows
surveys, censuses, establishment censuses and
the ILO database to World Bank population estimates
the share of women active in the total labor force.
surveys, and administrative records such as employ-
to create a series consistent with these population
ment exchange registers and unemployment insur-
estimates. This procedure sometimes results in
ance schemes. For some countries a combination
labor force estimates that differ slightly from those
of these sources is used. Labor force surveys are
in the ILO’s Yearbook of Labour Statistics and its data-
the most comprehensive source for internationally
base Key Indicators of the Labour Market.
comparable labor force data. They can cover all non-
Estimates of women in the labor force and employ-
institutionalized civilians, all branches and sectors of
ment are generally lower than those of men and are
the economy, and all categories of workers, including
not comparable internationally, reflecting that demo-
people holding multiple jobs. By contrast, labor force
graphic, social, legal, and cultural trends and norms
data from population censuses are often based on a
determine whether women’s activities are regarded
limited number of questions on the economic char-
as economic. In many countries many women work
acteristics of individuals, with little scope to probe.
on farms or in other family enterprises without pay,
The resulting data often differ from labor force survey
and others work in or near their homes, mixing work
data and vary considerably by country, depending on
and family activities during the day.
the census scope and coverage. Establishment censuses and surveys provide data only on the employed population, not unemployed workers, workers in small establishments, or workers in the informal sector (ILO, Key Indicators of the Labour Market 2001–2002). The reference period of a census or survey is another important source of differences: in some countries data refer to people’s status on the day of the census or survey or during a specific period before the inquiry date, while in others data are recorded without reference to any period. In developing countries, where the household is often the basic unit of production and all members contribute to output, but some at low intensity or irregularly, the estimated labor force may be much smaller than the numbers actually working. Differing definitions of employment age also affect comparability. For most countries the working age is
Data sources
15 and older, but in some countries children younger
Data on labor force participation rates are from
than 15 work full- or part-time and are included in the
the ILO’s Key Indicators of the Labour Market, 7th
estimates. Similarly, some countries have an upper
edition, database. Labor force numbers were cal-
age limit. As a result, calculations may systemati-
culated by World Bank staff, applying labor force
cally over- or underestimate actual rates. For further
participation rates from the ILO database to popu-
information on source, reference period, or defini-
lation estimates.
tion, consult the original source.
2012 World Development Indicators
49
2.3
Employment by economic activity Agriculture
Male % of male employment 1990–92a 2007–10a
Afghanistan Albania Algeria Angola Argentina Armenia Australia Austria Azerbaijan Bahrain Bangladesh Belarus Belgium Benin Bolivia Bosnia and Herzegovina Botswana Brazil Bulgaria Burkina Faso Burundi Cambodia Cameroon Canada Central African Republic Chad Chile China Hong Kong SAR, China Colombia Congo, Dem. Rep. Congo, Rep. Costa Rica Côte d’Ivoire Croatia Cuba Cyprus Czech Republic Denmark Dominican Republic Ecuador Egypt, Arab Rep. El Salvador Eritrea Estonia Ethiopia Finland France Gabon Gambia, The Georgia Germany Ghana Greece Guatemala Guinea Guinea-Bissau Haiti
50
.. .. .. .. 0b .. 6 6 .. 3 54 .. 3 .. 3b .. .. 31b .. .. .. .. .. 6b .. .. 24 .. 1 2b .. .. 32 .. .. .. 11 .. 7 26 10 b 35 48b .. 23 .. 11 7 .. .. .. 4 66 20 19b .. .. 76
.. .. .. .. 2b 39 4 5 37 .. .. 15 2 .. 34 .. .. 21 8 .. .. 69 .. 3b .. .. 15 .. 0 26 .. .. 17 .. 14 25 5 4 4 21 33 28 33 .. 6 9 6 4 .. .. 51 2 .. 12 .. .. .. ..
2012 World Development Indicators
Industry
Female % of female employment 1990–92a
.. .. .. .. 0b .. 4 8 .. 0 85 .. 3 .. 1b .. .. 25b .. .. .. .. .. 2b .. .. 6 .. 0 1b .. .. 5 .. .. .. 13 .. 3 3 2b 52 15b .. 13 .. 6 5 .. .. .. 4 59 26 3b .. .. 50
2007–10a
.. .. .. .. 0b 49 2 5 40 .. .. 9 1 .. 38 .. .. 12 5 .. .. 75 .. 1b .. .. 6 .. 0 5 .. .. 4 .. 16 9 3 2 1 2 22 46 5 .. 3 10 3 2 .. .. 57 1 .. 13 .. .. .. ..
Male % of male employment 1990–92a
.. .. .. .. 40 b .. 32 47 .. 33 16 .. 41 .. 42b .. .. 27b .. .. .. .. .. 31b .. .. 32 .. 37 35b .. .. 27 .. .. .. 31 .. 37 23 29b 25 23b .. 42 .. 38 39 .. .. .. 50 10 29 36b .. .. 9
2007–10a
.. .. .. .. 33b 25 32 37 19 .. .. 33 34 .. 28 .. .. 29 41 .. .. 8 .. 32b .. .. 31 .. 19 23 .. .. 27 .. 38 22 30 49 29 26 24 27 22 .. 43 25 36 33 .. .. 17 40 .. 28 .. .. .. ..
Services
Female % of female employment 1990–92a
.. .. .. .. 18b .. 12 20 .. 7 9 .. 16 .. 17b .. .. 10 b .. .. .. .. .. 11b .. .. 15 .. 27 25b .. .. 25 .. .. .. 23 .. 16 21 17b 10 23b .. 30 .. 15 17 .. .. .. 24 10 17 27b .. .. 9
2007–10a
.. .. .. .. 10 b 8 9 12 7 .. .. 24 10 .. 9 .. .. 13 25 .. .. 9 .. 10 b .. .. 11 .. 4 16 .. .. 13 .. 15 12 9 23 9 14 11 6 18 .. 18 20 10 10 .. .. 4 14 .. 8 .. .. .. ..
Male % of male employment 1990–92a
.. .. .. .. 59b .. 61 46 .. 64 25 .. 56 .. 55b .. .. 43b .. .. .. .. .. 64b .. .. 45 .. 63 63b .. .. 41 .. .. .. 56 .. 56 52 62b 41 29b .. 36 .. 51 54 .. .. .. 46 23 51 45b .. .. 13
2007–10a
.. .. .. .. 65b 35 64 58 44 .. .. 37 64 .. 38 .. .. 50 51 .. .. 23 .. 65b .. .. 54 .. 80 51 .. .. 51 .. 48 53 65 47 67 48 43 44 45 .. 50 76 58 63 .. .. 33 58 .. 60 .. .. .. ..
Female % of female employment 1990–92a
.. .. .. .. 81b .. 84 72 .. 92 2 .. 82 .. 83b .. .. 65b .. .. .. .. .. 87b .. .. 79 .. 73 74b .. .. 69 .. .. .. 63 .. 81 76 81b 37 63b .. 57 .. 78 78 .. .. .. 73 32 57 70 b .. .. 38
2007–10a
.. .. .. .. 89b 43 88 84 53 .. .. 64 89 .. 53 .. .. 75 70 .. .. 16 .. 89b .. .. 83 .. 96 79 .. .. 82 .. 69 80 88 75 90 83 67 49 77 .. 78 64 87 88 .. .. 40 84 .. 79 .. .. .. ..
Agriculture
Male % of male employment 1990–92a 2007–10a
Honduras Hungary India Indonesia Iran, Islamic Rep. Iraq Ireland Israel Italy Jamaica Japan Jordan Kazakhstan Kenya Korea, Dem. Rep. Korea, Rep. Kosovo Kuwait Kyrgyz Republic Lao PDR Latvia Lebanon Lesotho Liberia Libya Lithuania Macedonia, FYR Madagascar Malawi Malaysia Mali Mauritania Mauritius Mexico Moldova Mongolia Morocco Mozambique Myanmar Namibia Nepal Netherlands New Zealand Nicaragua Niger Nigeria Norway Oman Pakistan Panama Papua New Guinea Paraguay Peru Philippines Poland Portugal Puerto Rico Qatar
53b 19 .. 54 .. .. 17 5 8 36 6 .. .. .. .. 14 .. .. .. .. .. .. .. .. .. .. .. .. .. 23 .. .. 15 34 .. .. 4b .. .. 45 75 5 13b .. .. .. 7 .. 45 36 .. 3b 1b 53 .. 10 5 ..
48 6 46b 39 19 17 8 3 5 28 4 2 31 .. .. 6 .. .. .. .. 12 .. .. 50 .. 12 20 .. .. 17 .. .. 10 19 34 41b 34 .. .. 23 .. 4 9 42 .. .. 4 .. 37 24 .. 31 1b 42 13 11 2 3
Industry
Female % of female employment 1990–92a
6b 13 .. 57 .. .. 3 2 9 16 7 .. .. .. .. 18 .. .. .. .. .. .. .. .. .. .. .. .. .. 20 .. .. 13 11 .. .. 3b .. .. 52 91 2 8b .. .. .. 3 .. 69 3 .. 0b 0b 32 .. 13 0 ..
2007–10a
10 2 65b 38 31 51 1 1 3 10 4 1 29 .. .. 7 .. .. .. .. 6 .. .. 48 .. 7 20 .. .. 9 .. .. 8 4 28 39b 59 .. .. 8 .. 2 4 8 .. .. 1 .. 75 7 .. 19 1b 24 13 11 1 0
Male % of male employment 1990–92a
18b 43 .. 15 .. .. 35 38 41 25 40 .. .. .. .. 40 .. .. .. .. .. .. .. .. .. .. .. .. .. 31 .. .. 36 25 .. .. 33b .. .. 21 4 33 31b .. .. .. 34 .. 20 19 .. 33b 30 b 17 .. 39 27 ..
2007–10a
22 40 24b 22 33 22 29 30 39 24 33 21 26 .. .. 20 .. .. .. .. 34 .. .. 14 .. 33 33 .. .. 32 .. .. 32 30 26 19b 24 .. .. 24 .. 24 31 21 .. .. 31 .. 22 24 .. 25 32b 18 42 38 25 58
PEOPLE
2.3
Employment by economic activity
Services
Female % of female employment 1990–92a
25b 29 .. 13 .. .. 18 15 23 12 27 .. .. .. .. 28 .. .. .. .. .. .. .. .. .. .. .. .. .. 32 .. .. 48 19 .. .. 46b .. .. 8 1 10 13b .. .. .. 10 .. 15 11 .. 19b 13b 14 .. 24 19 ..
2007–10a
22 20 18b 15 27 4 9 10 14 7 15 9 12 .. .. 13 .. .. .. .. 14 .. .. 5 .. 16 28 .. .. 23 .. .. 22 18 14 11b 15 .. .. 9 .. 6 10 19 .. .. 7 .. 12 10 .. 10 14b 10 16 16 10 5
Male % of male employment 1990–92a
29b 38 .. 31 .. .. 49 57 52 39 54 .. .. .. .. 46 .. .. .. .. .. .. .. .. .. .. .. .. .. 46 .. .. 48 41 .. .. 63b .. .. 34 20 60 56b .. .. .. 58 .. 35 45 .. 64b 69b 29 .. 51 67 ..
2007–10a
30 53 30 b 40 47 61 63 67 57 48 62 77 43 .. .. 73 .. .. .. .. 53 .. .. 37 .. 55 47 .. .. 51 .. .. 58 51 41 40 b 42 .. .. 53 .. 61 61 37 .. .. 65 .. 41 52 .. 44 67b 40 45 51 73 39
Female % of female employment 1990–92a
69b 58 .. 31 .. .. 79 83 68 72 65 .. .. .. .. 54 .. .. .. .. .. .. .. .. .. .. .. .. .. 48 .. .. 39 70 .. .. 51b .. .. 40 8 81 79b .. .. .. 86 .. 16 86 .. 80 b 87b 55 .. 63 80 ..
2012 World Development Indicators
2007–10a
68 78 17b 47 42 46 90 89 83 83 80 90 59 .. .. 81 .. .. .. .. 80 .. .. 47 .. 77 52 .. .. 68 .. .. 70 78 58 50 b 25 .. .. 83 .. 84 86 72 .. .. 92 .. 13 82 .. 71 86b 66 71 73 90 95
51
2.3
Employment by economic activity Agriculture
Male % of male employment 1990–92a 2007–10a
Romania Russian Federation Rwanda Saudi Arabia Senegal Serbia Sierra Leone Singapore Slovak Republic Slovenia Somalia South Africa South Sudan Spain Sri Lanka Sudan Swaziland Sweden Switzerland Syrian Arab Republic Tajikistan Tanzania Thailand Timor-Leste Togo Trinidad and Tobago Tunisia Turkey Turkmenistan Uganda Ukraine United Arab Emirates United Kingdom United States Uruguay Uzbekistan Venezuela, RB Vietnam West Bank and Gaza Yemen, Rep. Zambia Zimbabwe World Low income Middle income Lower middle income Upper middle income Low & middle income East Asia & Pacific Europe & Central Asia Latin America & Carib. Middle East & N. Africa South Asia Sub-Saharan Africa High income Euro area
29 .. .. .. .. .. .. 1 .. .. .. .. .. 11 .. .. .. 5 5 23 .. 78b 60 .. .. 15 .. 33 .. .. .. .. 3 4 7b .. 17 .. .. 44 47 .. .. w .. .. .. .. .. .. .. 21 .. .. .. 6 7
29 11 .. 5 .. 25 .. 2 4 9 .. 6 .. 6 30 b .. .. 3 4 14 .. .. 44 .. .. 5 .. 18 .. .. .. 5 2 2 16 .. 13 .. 10 .. .. .. .. w .. .. .. .. .. .. 16 19 23 46 .. 4 4
Industry
Female % of female employment 1990–92a
38 .. .. .. .. .. .. 0 .. .. .. .. .. 8 .. .. .. 2 4 54 .. 90 b 62 .. .. 6 .. 72 .. .. .. .. 1 1 1b .. 2 .. .. 83 56 .. .. w .. .. .. .. .. .. .. 15 .. .. .. 5 6
2007–10a
31 7 .. 0 .. 23 .. 1 2 9 .. 4 .. 3 37b .. .. 1 2 24 .. .. 39 .. .. 2 .. 39 .. .. .. 0 1 1 5 .. 2 .. 28 .. .. .. .. w .. .. .. .. .. .. 15 8 43 65 .. 3 3
Male % of male employment 1990–92a
2007–10a
44 .. .. .. .. .. .. 36 .. .. .. .. .. 41 .. .. .. 40 39 28 .. 7b 18 .. .. 34 .. 26 .. .. .. .. 41 34 36b .. 32 .. .. 14 15 .. .. w .. .. .. .. .. .. .. 30 .. .. .. 38 42
Note: Data across sectors may not sum to 100 percent because of workers not classified by sector. a. Data are for the most recent year available. b. Limited coverage.
52
2012 World Development Indicators
36 38 .. 23 .. 32 .. 26 50 43 .. 35 .. 34 25b .. .. 31 30 36 .. .. 21 .. .. 44 .. 30 .. .. .. 28 29 25 29 .. 31 .. 29 .. .. .. .. w .. .. .. .. .. .. 35 29 29 24 .. 31 36
Services
Female % of female employment 1990–92a
30 .. .. .. .. .. .. 32 .. .. .. .. .. 17 .. .. .. 12 15 8 .. 1b 13 .. .. 14 .. 11 .. .. .. .. 16 14 21b .. 16 .. .. 2 3 .. .. w .. .. .. .. .. .. .. 14 .. .. .. 19 20
2007–10a
20 19 .. 2 .. 16 .. 17 21 21 .. 13 .. 10 25b .. .. 8 10 9 .. .. 18 .. .. 15 .. 16 .. .. .. 7 7 7 13 .. 11 .. 11 .. .. .. .. w .. .. .. .. .. .. 18 14 14 18 .. 11 12
Male % of male employment 1990–92a
28 .. .. .. .. .. .. 63 .. .. .. .. .. 49 .. .. .. 55 57 49 .. 15b 22 .. .. 51 .. 41 .. .. .. .. 55 62 57b .. 52 .. .. 38 22 .. .. w .. .. .. .. .. .. .. 49 .. .. .. 56 50
2007–10a
35 51 .. 72 .. 43 .. 73 46 48 .. 59 .. 60 27b .. .. 66 61 51 .. .. 35 .. .. 51 .. 52 .. .. .. 66 68 72 56 .. 57 .. 61 .. .. .. .. w .. .. .. .. .. .. 49 52 48 30 .. 64 59
Female % of female employment 1990–92a
33 .. .. .. .. .. .. 68 .. .. .. .. .. 75 .. .. .. 86 81 38 .. 8b 25 .. .. 80 .. 17 .. .. .. .. 82 85 78b .. 82 .. .. 13 18 .. .. w .. .. .. .. .. .. .. 71 .. .. .. 76 73
2007–10a
49 74 .. 98 .. 61 .. 83 77 71 .. 83 .. 88 27b .. .. 91 82 67 .. .. 43 .. .. 82 .. 45 .. .. .. 93 91 92 83 .. 87 .. 61 .. .. .. .. w .. .. .. .. .. .. 66 78 43 17 .. 86 85
About the data
2.3
PEOPLE
Employment by economic activity Definitions
The International Labour Organization (ILO) classifies
services. Such broad classification may obscure fun-
• Agriculture corresponds to division 1 (ISIC revi-
economic activity using the International Standard
damental shifts within countries’ industrial patterns.
sion 2), tabulation categories A and B (ISIC revi-
Industrial Classification (ISIC) of All Economic Activi-
A slight majority of countries report economic activity
sion 3), or tabulation category A (ISIC revision 4) and
ties, revision 2 (1968), revision 3 (1990), and revi-
according to the ISIC revision 2 instead of revision 3
includes hunting, forestry, and fishing. • Industry cor-
sion 4 (2008). Because this classification is based
or revision 4. The use of one classification or the
responds to divisions 2–5 (ISIC revision 2), tabula-
on where work is performed (industry) rather than
other should not have a significant impact on the
tion categories C–F (ISIC revision 3), or tabulation
type of work performed (occupation), all of an enter-
information for the three broad sectors presented
categories B–F (ISIC revision 4) and includes mining
prise’s employees are classified under the same
in the table.
and quarrying (including oil production), manufac-
industry, regardless of their trade or occupation. The
The distribution of economic wealth in the world
turing, construction, and public utilities (electricity,
categories should sum to 100 percent. Where they
remains strongly correlated with employment by
gas, and water). • Services correspond to divisions
do not, the differences are due to workers who are
economic activity. The wealthier economies are
6–9 (ISIC revision 2), tabulation categories G–P
not classified by economic activity.
those with the largest share of total employment in
(ISIC revision 3), or tabulation categories G–U (ISIC
services, whereas the poorer economies are largely
revision 4) and include wholesale and retail trade
agriculture based.
and restaurants and hotels; transport, storage, and
Data on employment are drawn from labor force surveys, household surveys, official estimates, censuses and administrative records of social insurance
The distribution of economic activity by gender
communications; financing, insurance, real estate,
schemes, and establishment surveys when no other
reveals some clear patterns. Men still make up the
and business services; and community, social, and
information is available. The concept of employment
majority of people employed in all three sectors, but
personal services.
generally refers to people above a certain age who
the gender gap is biggest in industry. Employment in
worked, or who held a job, during a reference period.
agriculture is also male-dominated, although not as
Employment data include both full-time and part-time
much as industry. Segregating one sex in a narrow
workers.
range of occupations significantly reduces economic
There are many differences in how countries define
efficiency by reducing labor market flexibility and thus
and measure employment status, particularly mem-
the economy’s ability to adapt to change. This seg-
bers of the armed forces, self-employed workers, and
regation is particularly harmful for women, who have
unpaid family workers. Where members of the armed
a much narrower range of labor market choices and
forces are included, they are allocated to the service
lower levels of pay than men. But it is also detri-
sector, causing that sector to be somewhat over-
mental to men when job losses are concentrated
stated relative to the service sector in economies
in industries dominated by men and job growth is
where they are excluded. Where data are obtained
centered in service occupations, where women have
from establishment surveys, data cover only employ-
better chances, as has been the recent experience
ees; thus self-employed and unpaid family workers
in many countries.
are excluded. In such cases the employment share
There are several explanations for the rising impor-
of the agricultural sector is severely underreported.
tance of service jobs for women. Many service jobs—
Caution should be also used where the data refer
such as nursing and social and clerical work—are
only to urban areas, which record little or no agricul-
considered “feminine” because of a perceived simi-
tural work. Moreover, the age group and area covered
larity to women’s traditional roles. Women often do
could differ by country or change over time within a
not receive the training needed to take advantage of
country. For detailed information, consult the original
changing employment opportunities. And the greater
source.
availability of part-time work in service industries
Countries also take different approaches to the treatment of unemployed people. In most countries
may lure more women, although it is unclear whether this is a cause or an effect.
unemployed people with previous job experience are classified according to their last job. But in some countries the unemployed and people seeking their first job are not classifiable by economic activity. Because of these differences, the size and distribution of employment by economic activity may not be fully comparable across countries. The ILO reports data by major divisions of the ISIC
Data sources
revision 2, revision 3, or revision 4. In the table the
Data on employment are from the ILO’s Key Indica-
reported divisions or categories are aggregated
tors of the Labour Market, 7th edition, database.
into three broad groups: agriculture, industry, and
2012 World Development Indicators
53
2.4
Decent work and productive employment Employment to population ratio
Gross enrollment ratio, secondary
Vulnerable employment
Labor productivity
Unpaid family workers and own-account workers Total
Youth
% ages 15 and older
Afghanistan Albania Algeria Angola Argentina Armenia Australia Austria Azerbaijan Bahrain Bangladesh Belarus Belgium Benin Bolivia Bosnia and Herzegovina Botswana Brazil Bulgaria Burkina Faso Burundi Cambodia Cameroon Canada Central African Republic Chad Chile China Hong Kong SAR, China Colombia Congo, Dem. Rep. Congo, Rep. Costa Rica Côte d’Ivoire Croatia Cuba Cyprus Czech Republic Denmark Dominican Republic Ecuador Egypt, Arab Rep. El Salvador Eritrea Estonia Ethiopia Finland France Gabon Gambia, The Georgia Germany Ghana Greece Guatemala Guinea Guinea-Bissau Haiti
54
% ages 15–24
% of relevant age group
Male % of male employment
1991
2010
1991
2010
1991
2010a
1990
46 52 34 66 55 46 57 54 56 61 73 59 46 72 63 39 56 60 47 82 83 80 64 59 72 67 50 75 63 46 66 62 55 63 51 53 57 60 62 52 57 43 57 75 67 76 60 50 52 71 55 56 68 46 62 69 64 58
45 52 39 64 56 41 62 58 60 65 68 50 50 72 69 35 63 65 49 81 77 81 68 61 73 67 55 71 57 59 66 66 60 64 46 56 60 54 60 56 64 44 57 78 51 80 55 51 50 72 54 55 67 48 65 70 68 60
32 40 22 47 48 26 58 61 37 32 64 39 31 66 48 21 36 54 30 76 70 71 42 57 56 49 33 72 54 35 40 40 48 50 27 41 41 49 65 37 45 22 46 69 50 70 49 33 20 58 23 58 43 31 54 51 44 32
31 36 22 46 34 18 61 54 31 32 53 31 25 57 49 17 41 53 24 73 56 70 43 55 54 49 31 57 31 35 39 39 42 48 25 40 34 25 58 37 44 25 42 67 27 71 40 31 15 56 20 47 36 21 58 52 48 29
16 89 60 12 74 .. 132 102 88 100 18 93 101 .. .. .. 49 .. 98 7 5 25 26 101 12 6 97 41 .. 53 21 46 45 .. 83 94 72 91 109 .. 55 69 38 11 100 14 116 100 40 19 95 98 35 94 23 11 5 ..
46 89 95 31 89 92 129 100 .. .. 49 .. 111 .. 80 90 .. 101 88 21 25 46 42 101 13 26 88 81 83 96 38 .. 100 .. 95 89 98 90 117 76 80 .. 65 32 104 36 108 113 .. 54 86 103 58d .. 59 38 .. ..
.. .. .. .. 25c .. 12 .. .. .. .. .. 18 .. 32c .. .. 29c .. .. .. .. .. .. .. .. .. .. .. 30 c .. .. 26 .. .. .. .. .. 8 42 33c .. .. .. 2 .. .. 11 .. .. .. 5 .. 40 .. .. .. ..
2012 World Development Indicators
2007–10b
.. .. .. .. 22c 36 11 9 47 2 .. .. 11 .. 49 .. .. 27 10 .. .. 79 .. .. .. .. 26 .. 10 48 .. .. 19 .. 17 .. 16 17 7 49 37 22 33 .. 6 .. 12 8 .. .. 62 8 .. 29 .. .. .. ..
Female % of female employment 1990
.. .. .. .. 27c .. 9 .. .. .. .. .. 17 .. 50 c .. .. 30 c .. .. .. .. .. .. .. .. .. .. .. 26 c .. .. 21 .. .. .. .. .. 6 30 41c .. .. .. 3 .. .. 11 .. .. .. 6 .. 46 .. .. .. ..
2007–10b
.. .. .. .. 17c 40 7 9 62 1 .. .. 8 .. 67 .. .. 22 8 .. .. 86 .. .. .. .. 25 .. 5 49 .. .. 20 .. 19 .. 12 10 4 30 51 49 46 .. 4 .. 7 6 .. .. 65 6 .. 27 .. .. .. ..
GDP per person employed % growth 1990–92
.. –16.6 –4.6 –5.6 9.0 –24.4 3.0 1.3 –12.6 0.8 2.3 –4.0 1.6 .. 2.6 –14.7 .. –0.3 3.1 1.6 .. 4.0 –6.6 0.8 .. .. 6.6 6.8 5.4 –0.7 –13.0 .. 2.4 –4.1 –7.7 .. –0.9 –5.2 2.5 0.9 –0.1 –1.3 .. .. –8.0 –7.9 1.7 1.4 .. .. –25.4 3.7 0.7 2.1 1.0 .. .. ..
2008–10
.. 6.6 0.8 0.8 0.2 –6.5 0.7 –0.9 5.5 1.9 3.4 3.2 –0.4 .. 1.3 –1.9 .. 1.8 1.5 0.3 .. –1.2 –0.3 0.2 .. .. 0.4 8.8 1.7 0.6 0.4 .. –0.7 0.7 –0.6 .. 0.2 0.2 0.8 2.0 –0.9 2.6 .. .. 1.9 5.8 –1.3 0.1 .. .. 0.3 –0.9 1.9 –1.6 –2.5 .. .. ..
Employment to population ratio
Gross enrollment ratio, secondary
Vulnerable employment
Labor productivity
Unpaid family workers and own-account workers Total
Youth
% ages 15 and older
Honduras Hungary India Indonesia Iran, Islamic Rep. Iraq Ireland Israel Italy Jamaica Japan Jordan Kazakhstan Kenya Korea, Dem. Rep. Korea, Rep. Kosovo Kuwait Kyrgyz Republic Lao PDR Latvia Lebanon Lesotho Liberia Libya Lithuania Macedonia, FYR Madagascar Malawi Malaysia Mali Mauritania Mauritius Mexico Moldova Mongolia Morocco Mozambique Myanmar Namibia Nepal Netherlands New Zealand Nicaragua Niger Nigeria Norway Oman Pakistan Panama Papua New Guinea Paraguay Peru Philippines Poland Portugal Puerto Rico Qatar
% ages 15–24
% of relevant age group
Male % of male employment
1991
2010
1991
2010
1991
2010a
1990
57 49 58 63 40 32 45 46 45 63 63 32 64 67 79 59 .. 58 60 80 59 39 49 57 44 56 38 83 72 60 47 32 53 57 59 51 46 77 73 45 84 53 57 55 54 53 59 52 47 49 70 68 53 60 54 59 38 79
60 45 54 63 40 34 52 54 44 56 57 36 67 60 74 58 .. 66 61 77 49 42 47 59 49 48 38 84 77 59 48 36 55 58 38 57 45 78 76 40 82 62 63 60 61 51 64 55 51 62 71 69 71 60 51 55 37 86
49 38 46 46 28 20 38 24 33 43 43 17 45 45 73 36 .. 26 41 72 43 26 38 34 25 35 18 71 47 46 35 15 41 50 38 35 39 64 51 24 79 53 54 47 47 29 49 28 38 34 57 64 35 42 32 53 20 51
47 18 34 40 24 17 31 27 20 25 39 20 44 33 57 24 .. 31 40 62 27 23 28 33 29 20 15 71 51 35 36 16 31 43 18 32 30 57 53 11 73 63 50 46 53 32 52 32 41 42 54 57 55 39 27 29 18 66
33 86 46 46 53 40 100 92 79 70 97 82 98 .. .. 91 .. 53 100 21 92 61 24 .. .. 92 76 19 17 57 7 13 55 54 90 82 36 7 23 43 34 120 92 43 7 24 103 45 23 62 12 31 67 70 87 66 .. 84
73 98 60 77 84 .. 117 91 99 93 102 91 97 60 .. 97 .. 101 84 45 95 81 46 .. .. 98 83 31 32 68 38 24 89 87 88 93 .. 25 54 .. .. 120 119 69 13 44 110 100 34 74 .. 67 92 85 97 107 82 94
48 c 8 .. .. .. .. 25 .. 29 46 15 .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. 31 .. .. 13 29 .. .. .. .. .. .. .. 7 15 .. .. .. .. .. .. 44 .. 17c 30 c .. .. 22 .. ..
2007–10b
49 8 .. 62 40 .. 18 9 21 41 10 11 30 .. .. 23 .. .. .. .. 8 32 .. 69 .. 10 24 .. .. 23 77 .. 17 27 34 60 47 .. .. .. .. 13 15 45 .. .. 8 .. 59 33 .. 42 34 c 42 20 18 .. 0
Female % of female employment 1990
50 c 7 .. .. .. .. 10 .. 24 37 26 .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. 25 .. .. 7 15 .. .. .. .. .. .. .. 12 10 .. .. .. .. .. .. 19 .. 31c 46c .. .. 28 .. ..
2.4
PEOPLE
Decent work and productive employment
2007–10b
52 5 .. 67 52 .. 5 5 15 31 11 3 34 .. .. 27 .. .. .. .. 7 16 .. 89 .. 8 22 .. .. 20 89 .. 14 32 28 54 65 .. .. .. .. 10 9 45 .. .. 3 .. 78 28 .. 48 47c 46 17 17 .. 0
GDP per person employed % growth 1990–92
.. 0.3 1.0 6.2 5.7 –32.8 2.6 1.6 0.6 0.7 0.5 –5.0 –15.1 –3.9 .. 5.1 .. –0.2 –13.1 .. –19.6 .. .. .. .. –13.9 –6.9 –5.8 –2.1 6.0 1.3 .. .. 1.0 –23.1 .. –1.6 –3.6 2.0 .. .. 0.4 0.6 .. –5.9 –2.8 3.9 0.6 6.4 .. .. .. –0.8 –3.3 1.1 2.1 .. –0.3
2012 World Development Indicators
2008–10
.. –1.3 5.6 3.2 –0.9 0.3 2.1 0.3 –1.0 –3.1 0.0 0.1 –0.9 0.6 .. 2.7 .. –4.4 –2.0 .. –0.2 .. .. .. .. –0.9 –2.0 –6.1 3.7 0.3 1.9 .. .. –1.2 –2.2 .. 2.7 4.2 .. .. .. –0.3 1.1 .. –2.5 4.6 –0.3 1.4 –0.1 .. .. .. 2.4 1.4 1.9 1.3 .. 11.1
55
2.4
Decent work and productive employment Employment to population ratio
Gross enrollment ratio, secondary
Vulnerable employment
Labor productivity
Unpaid family workers and own-account workers Total
Youth
% ages 15 and older 1991
Romania Russian Federation Rwanda Saudi Arabia Senegal Serbia Sierra Leone Singapore Slovak Republic Slovenia Somalia South Africa South Sudan Spain Sri Lanka Sudan Swaziland Sweden Switzerland Syrian Arab Republic Tajikistan Tanzania Thailand Timor-Leste Togo Trinidad and Tobago Tunisia Turkey Turkmenistan Uganda Ukraine United Arab Emirates United Kingdom United States Uruguay Uzbekistan Venezuela, RB Vietnam West Bank and Gaza Yemen, Rep. Zambia Zimbabwe World Low income Middle income Lower middle income Upper middle income Low & middle income East Asia & Pacific Europe & Central Asia Latin America & Carib. Middle East & N. Africa South Asia Sub-Saharan Africa High income Euro area
57 59 88 51 68 48e 64 64 59 50 52 37 .. 42 49 47 44 64 67 46 58 79 77 58 70 45 41 53 53 79 58 71 58 61 55 52 55 78 31 39 65 69 62 w 72 63 58 67 64 73 57 57 40 59 63 57 50
2010
52 58 85 47 69 46e 65 63 51 55 53 39 .. 47 52 49 44 58 65 39 58 79 71 54 75 63 41 44 54 75 54 76 57 58 61 54 61 75 31 42 67 83 60 w 71 60 55 65 61 70 54 62 41 55 64 55 51
% ages 15–24 1991
49 41 80 25 59 28e 39 56 46 28 42 15 .. 35 28 32 27 58 69 38 38 70 70 46 56 33 29 48 34 61 36 44 63 54 45 33 38 73 20 24 47 48 52 w 58 52 43 60 53 67 41 47 27 47 46 46 42
2010
24 36 73 12 57 19e 42 34 21 34 39 13 .. 25 30 27 26 38 61 24 38 69 46 41 57 48 23 32 35 55 34 43 48 42 44 35 40 58 16 27 51 73 43 w 55 41 36 48 43 53 34 45 24 37 46 38 34
% of relevant age group 1991
2010a
92 93 18 .. 15 .. 16 .. 88 89 .. 69 .. 105 72 20 49 90 98 48 102 5 31 .. 20 82 45 48 .. 10 94 68 87 92 84 99 56 35 .. .. 21 49 50 w 26 47 42 67 44 41 85 57 54 37 22 91 ..
95 89 32 101 37 91 .. .. 89 97 .. 94 .. 119 .. 39 58 100 95 72 87 27 77 56 .. 90 90 78 .. 28 96 .. 102 96 90 105 83 77 86 44 .. .. 68 w 39 69 58 83 64 76 89 90 72 55 36 100 107
Male % of male employment 1990
21 1 .. .. 77 .. .. 10 .. .. .. .. .. 21 .. .. .. .. 8 .. .. 86 c 67 .. .. 22 .. .. .. .. .. .. 13 .. .. .. .. .. .. .. 56 .. .. w .. .. .. .. .. .. .. 30 .. .. .. .. 16
2007–10b
33 6 .. .. .. 30 .. 12 17 15 .. 8 .. 13 38 c .. .. 9 9 34 .. .. 50 .. .. .. .. 28 .. .. .. 1 15 .. 22c .. 30 .. 26 .. .. .. .. w .. .. 70 .. .. .. 18 31 33 78 .. .. 13
a. Provisional data. b. Data are for the most recent year available. c. Limited coverage. d. Data are for 2011. e. Includes Montenegro.
56
2012 World Development Indicators
Female % of female employment 1990
33 1 .. .. 91 .. .. 6 .. .. .. .. .. 25 .. .. .. .. 11 .. .. 96 c 74 .. .. 21 .. .. .. .. .. .. 6 .. .. .. .. .. .. .. 81 .. .. w .. .. .. .. .. .. .. 29 .. .. .. .. 14
2007–10b
33 5 .. .. .. 29 .. 7 7 12 .. 12 .. 9 44 c .. .. 5 9 25 .. .. 55 .. .. .. .. 48 .. .. .. 0 8 .. 24 c .. 32 .. 31 .. .. .. .. w .. .. 77 .. .. .. 19 31 48 86 .. .. 9
GDP per person employed % growth 1990–92
2008–10
–9.3 –7.9 .. 4.8 –1.1 .. .. –2.0 –0.8 –2.3 .. –5.3 .. 1.8 5.5 1.4 .. 1.9 –0.6 6.3 –20.4 –2.3 6.8 .. .. –3.5 2.5 1.0 –13.0 –0.8 –7.9 –3.6 6.1 1.7 5.2 –7.7 4.5 4.6 .. 0.9 –2.7 –4.3 0.9 w –3.4 1.2 0.4 1.5 1.0 6.4 –9.3 1.3 1.0 3.2 –4.8 2.8 2.3
–3.2 –1.4 .. –0.7 0.0 .. .. 5.0 1.6 –1.6 .. –0.4 .. 2.5 4.8 1.7 .. –0.1 –1.1 1.5 5.8 3.3 0.7 .. .. 0.6 1.7 –1.5 4.9 3.1 –4.3 –5.2 –0.9 2.3 3.9 5.4 –3.9 3.4 .. 2.2 3.6 5.4 2.0 w –2.3 4.2 3.7 4.4 4.3 7.9 –0.7 0.1 1.1 4.8 1.5 0.7 –0.1
About the data
2.4
PEOPLE
Decent work and productive employment Definitions
Four targets were added to the UN Millennium Dec-
supplemented by official estimates and censuses
• Employment to population ratio is the proportion of
laration at the 2005 World Summit High-Level Ple-
for a small group of countries. The labor force survey
a country’s population that is employed. People ages
nary Meeting of the 60th Session of the UN General
is the most comprehensive source for internationally
15 and older are generally considered the working-
Assembly. One was full and productive employment
comparable employment, but there are still some
age population. People ages 15–24 are generally
and decent work for all, which is seen as the main
limitations for comparing data across countries and
considered the youth population. • Gross enrollment
route for people to escape poverty. The four indi-
over time even within a country. Information from
ratio, secondary, is the ratio of total enrollment in
cators for this target have an economic focus, and
labor force surveys is not always consistent in what
secondary education, regardless of age, to the popu-
three of them are presented in the table.
is included in employment. For example, informa-
lation of the age group that officially corresponds to
The employment to population ratio indicates
tion provided by the Organisation for Economic Co-
secondary education. • Vulnerable employment is
how efficiently an economy provides jobs for people
operation and Development relates only to civilian
unpaid family workers and own-account workers as a
who want to work. A high ratio means that a large
employment, which can result in an underestima-
percentage of total employment. • Labor productiv-
propor tion of the population is employed. But a lower
tion of “employees” and “workers not classified by
ity is the growth rate of gross domestic product
employment to population ratio can be seen as a pos-
status,” espe cially in countries with large armed
(GDP) divided by the number of people engaged in
itive sign, especially for young people, if it is caused
forces. While the categories of unpaid family work-
the production of goods and services.
by an increase in their education. This indicator has
ers and self-employed workers, which include own-
a gender bias because women who do not consider
account workers, would not be affected, their relative
their work employment or who are not perceived as
shares would be. Geographic coverage is another
working tend to be undercounted. This bias has dif-
factor that can limit cross-country comparisons. The
ferent effects across countries and reflects demo-
employment by status data for many Latin Ameri-
graphic, social, legal, and cultural trends and norms.
can countries covers urban areas only. Similarly, in
Comparability of employment ratios across coun-
some countries in Sub-Saharan Africa, where limited
tries is also affected by variations in definitions of
information is available anyway, the members of pro-
employment and population (see About the data for
ducer cooperatives are usually excluded from the
table 2.3). The biggest difference results from the
self-employed category. For detailed information on
age range used to define labor force activity. The
definitions and coverage, consult the original source.
population base for employment ratios can also vary
Labor productivity is used to assess a country’s
(see table 2.1). Most countries use the resident,
economic ability to create and sustain decent
noninstitutionalized population of working age living
employment opportunities with fair and equitable
in private households, which excludes members of
remuneration. Productivity increases obtained
the armed forces and individuals residing in men-
through investment, trade, technological progress, or
tal, penal, or other types of institutions. But some
changes in work organization can increase social pro-
countries include members of the armed forces in
tection and reduce poverty, which in turn reduce vul-
the population base of their employment ratio while
nerable employment and working poverty. Productiv-
excluding them from employment data (International
ity increases do not guarantee these improvements,
Labour Organization, Key Indicators of the Labour
but without them—and the economic growth they
Market, 7th edition).
bring—improvements are highly unlikely. For compa-
The proportion of unpaid family workers and own-
rability of individual sectors labor productivity is esti-
account workers in total employment is derived from
mated according to national accounts conventions.
information on status in employment. Each status
However, there are still significant limitations on the
group faces different economic risks, and unpaid
availability of reliable data. Information on consis-
family workers and own-account workers are the
tent series of output in both national currencies and
most vulnerable—and therefore the most likely to
purchasing power parity dollars is not easily avail-
fall into poverty. They are the least likely to have for-
able, especially in developing countries, because the
mal work arrangements, are the least likely to have
definition, coverage, and methodology are not always
social protection and safety nets to guard against
consistent across countries. For example, countries
Data on employment to population ratio, vulner-
economic shocks, and often are incapable of gen-
employ different methodologies for estimating the
able employment, and labor productivity are from
erating sufficient savings to offset these shocks. A
missing values for the nonmarket service sectors
the International Labour Organization’s Key Indi-
high proportion of unpaid family workers in a country
and use different definitions of the informal sector.
cators of the Labour Market, 7th edition, data-
Data sources
indicates weak development, little job growth, and
base. Data on gross enrollment ratios are from
often a large rural economy.
the United Nations Educational, Scientific, and
Data on employment by status are drawn from
Cultural Organization Institute for Statistics.
labor force surveys and household surveys,
2012 World Development Indicators
57
2.5
Unemployment Unemployment
Total % of total labor force 1990–92a 2007–10a
Afghanistan Albania Algeria Angola Argentina Armenia Australia Austria Azerbaijan Bahrain Bangladesh Belarus Belgium Benin Bolivia Bosnia and Herzegovina Botswana Brazil Bulgaria Burkina Faso Burundi Cambodia Cameroon Canada Central African Republic Chad Chile China Hong Kong SAR, China Colombia Congo, Dem. Rep. Congo, Rep. Costa Rica Côte d’Ivoire Croatia Cuba Cyprus Czech Republic Denmark Dominican Republic Ecuador Egypt, Arab Rep. El Salvador Eritrea Estonia Ethiopia Finland France Gabon Gambia, The Georgia Germany Ghana Greece Guatemala Guinea Guinea-Bissau Haiti
58
.. .. 23.0 .. 6.7b .. 10.8 3.6 .. 6.3 1.9 .. 6.7 1.5 5.5b 17.6 13.8 6.4b .. .. 0.5 .. .. 11.2b .. .. 4.4 2.3b 2.0 9.5b .. .. 4.1 6.7 11.1 .. 2.1 2.3 9.0 20.7 8.9b 9.0 7.9b .. 3.7 1.3 11.6 10.2 .. .. .. 6.6 4.7 7.8 .. .. .. 12.7
.. 13.8 11.4 .. 8.6b 28.6 5.2b 4.4 6.0 .. 5.0 .. 8.3 .. 5.2 27.2 .. 8.3 10.2 3.3 .. 1.7 2.9 8.0 b .. .. 8.1 4.3 5.2 11.6 .. .. 7.8 .. 11.8 1.6 6.2 7.3 7.4 14.3 6.5 9.4 7.3 .. 16.9 20.5 8.4 9.3 .. .. 16.5 7.1 .. 12.5 .. .. .. ..
2012 World Development Indicators
Male % of male labor force 1990–92a 2007–10a
.. .. 24.2 .. 6.4b .. 11.4 3.5 .. 5.2 2.0 .. 4.8 2.2 5.5b 15.5 11.7 5.4b .. .. 0.7 .. .. 12.0 b .. .. 3.9 .. 2.0 6.8 b .. .. 3.5 .. 11.1 .. 2.0 2.4 8.3 12.0 6.0 b 6.4 8.4b .. 3.9 1.1 13.3 8.1 .. .. .. 5.3 3.7 4.9 .. .. .. 11.9
.. 12.2 10.0 .. 7.8b 21.9 5.1b 4.6 5.2 .. 4.2 .. 8.1 .. 4.5 25.6 .. 6.1 10.9 .. .. 1.5 2.5 8.7b .. .. 7.2 .. 6.0 9.1 .. .. 6.6 .. 11.4 1.4 6.0 6.4 8.2 9.8 5.2 5.2 9.0 .. 19.5 12.1 9.0 9.0 .. .. 16.8 7.5 .. 9.9 .. .. .. ..
Female % of female labor force 1990–92a 2007–10a
.. .. 20.3 .. 7.0 b .. 10.0 3.8 .. 11.8 1.9 .. 9.5 0.6 5.6b 21.6 17.2 7.9b .. .. 0.3 .. .. 10.2b .. .. 5.3 .. 1.9 13.0 b .. .. 5.4 .. 11.2 .. 2.2 2.1 9.9 35.2 13.2b 17.0 7.2b .. 3.5 1.6 9.6 12.8 .. .. .. 8.4 5.5 12.9 .. .. .. 13.8
.. 15.9 20.0 .. 9.8b 35.0 5.4b 4.2 6.9 .. 7.4 .. 8.5 .. 6.0 30.0 .. 11.0 9.5 .. .. 1.8 3.3 7.2b .. .. 9.6 .. 4.3 15.0 .. .. 9.9 .. 12.2 2.0 6.4 8.5 6.6 21.4 8.4 22.9 4.9 .. 14.3 29.9 7.7 9.7 .. .. 16.1 6.6 .. 16.2 .. .. .. ..
Long-term unemployment
Unemployment by educational attainment
% of total unemployment Total Male Female 2007–10a 2007–10a 2007–10a
% of total unemployment Primary Secondary Tertiary 2007–10a 2007–10a 2007–10a
.. .. .. .. .. 58.2 18.5b 25.2 .. .. .. .. 48.8 .. .. .. .. .. 46.4 .. .. .. .. 12.0 b .. .. .. .. .. .. .. .. .. .. 44.4 .. 20.4 43.3 19.1 .. .. .. .. .. 27.4 .. 23.6 40.1 .. .. .. 47.4 .. 45.0 .. .. .. ..
.. .. .. .. .. 52.2 20.3b 27.8 .. .. .. .. 49.6 .. .. .. .. .. 46.3 .. .. .. .. 12.7b .. .. .. .. .. .. .. .. .. .. 41.4 .. 21.0 43.3 20.6 .. .. .. .. .. 26.8 .. 27.0 41.5 .. .. .. 48.1 .. 38.8 .. .. .. ..
.. .. .. .. .. 63.8 16.4b 22.0 .. .. .. .. 47.8 .. .. .. .. .. 46.5 .. .. .. .. 11.0 b .. .. .. .. .. .. .. .. .. .. 47.7 .. 19.8 43.3 16.9 .. .. .. .. .. 28.4 .. 19.3 38.7 .. .. .. 46.3 .. 50.3 .. .. .. ..
.. .. .. .. 48.1 11.1 47.4 37.5 9.9 22.7 .. 10.8 33.9 .. .. 94.9 .. 49.3 42.5 .. .. .. .. 26.3b .. .. 17.8 .. 38.0 21.0 .. .. 66.9 .. 16.0 46.6 26.9 b 29.6 35.5 32.0 .. .. .. .. 25.2 .. 36.6 39.5 .. .. 4.6 32.3 .. 27.7 .. .. .. ..
.. .. .. .. 36.7 68.3 33.6 54.5 75.8 38.8 .. 38.6 40.5 .. .. .. .. 35.7 47.4 .. .. .. .. 41.0 b .. .. 58.5 .. 43.8 53.7 .. .. 23.4 .. 70.4 48.5 42.1b 64.8 37.2 42.2 .. .. .. .. 58.1 .. 45.3 41.7 .. .. 56.1 56.5 .. 48.9 .. .. .. ..
.. .. .. .. 15.3 20.6 19.0 8.0 14.3 31.7 .. 50.6 19.0 .. .. 4.8 .. 4.1 10.1 .. .. .. .. 32.7b .. .. 23.5 .. 17.1 23.0 .. .. 7.9 .. 11.6 3.6 29.7b 5.7 20.8 19.5 .. .. .. .. 18.0 .. 17.4 18.3 .. .. 39.2 11.0 .. 22.4 .. .. .. ..
Unemployment
Total % of total labor force 1990–92a 2007–10a
Honduras Hungary India Indonesia Iran, Islamic Rep. Iraq Ireland Israel Italy Jamaica Japan Jordan Kazakhstan Kenya Korea, Dem. Rep. Korea, Rep. Kosovo Kuwait Kyrgyz Republic Lao PDR Latvia Lebanon Lesotho Liberia Libya Lithuania Macedonia, FYR Madagascar Malawi Malaysia Mali Mauritania Mauritius Mexico Moldova Mongolia Morocco Mozambique Myanmar Namibia Nepal Netherlands New Zealand Nicaragua Niger Nigeria Norway Oman Pakistan Panama Papua New Guinea Paraguay Peru Philippines Poland Portugal Puerto Rico Qatar
3.2b 9.9 .. 2.8 11.1 .. 15.0 11.2 9.3 15.4 2.2 .. .. .. .. 2.5 .. .. .. .. .. .. .. .. .. .. .. .. .. 3.7 .. .. 3.3 3.1 .. .. 16.0 b .. 6.0 19.0 .. 5.6 10.6b 14.4 .. .. 5.9 .. 5.2 14.7 7.7 5.0 b 9.4b 8.6 13.3 4.1b 16.9 ..
2.9 11.2 .. 7.1 10.5 .. 13.5 6.6 8.4 11.4 5.0 12.9 6.6 .. .. 3.7 45.4 .. 8.6 .. 18.7 9.0 25.3 3.7 .. 17.8 32.0 .. .. 3.7 .. .. 7.7 5.3 6.4 .. 10.0 .. .. 37.6 2.7 4.5 6.5b 5.0 .. .. 3.6 .. 5.0 6.5 .. 5.6 6.3b 7.4 9.6 10.8 13.4 0.5
Male % of male labor force 1990–92a 2007–10a
3.3b 11.0 .. 2.7 9.5 .. 14.9 9.2 6.7 9.4 2.1 .. .. .. .. 2.8 .. .. .. .. .. .. .. .. .. .. .. .. .. 3.4 .. .. 3.2 2.7 .. .. 13.0 b .. 4.7 20.0 .. 4.0 11.4b 11.3 .. .. 6.6 .. 3.8 10.8 9.0 6.0 b 7.5b 7.9 12.2 3.5b 19.1 ..
2.9 11.6 .. 6.1 9.1 .. 16.7 6.8 7.6 8.5 5.4 10.3 5.6 .. .. 4.0 40.7 .. 7.3 .. 21.7 8.6 23.0 3.4 .. 21.2 31.9 .. .. 3.6 .. .. 4.6 5.3 7.8 .. 9.8 .. .. 32.5 3.1 4.4 6.2b 4.9 .. .. 4.1 .. 4.0 5.3 .. 4.4 4.4b 7.6 9.3 9.8 14.9 0.2
Female % of female labor force 1990–92a 2007–10a
3.0 b 8.7 .. 3.0 24.4 .. 15.3 13.9 13.9 22.2 2.2 .. .. .. .. 2.1 .. .. .. .. .. .. .. .. .. .. .. .. .. 4.2 .. .. 3.6 4.0 .. .. 25.3b .. 8.8 19.0 .. 7.8 9.7b 19.5 .. .. 5.1 .. 14.0 22.3 5.9 3.7b 12.5b 9.9 14.7 5.0 b 13.3 ..
2.9 10.7 .. 8.7 16.8 .. 9.5 6.5 9.7 14.8 4.5 24.1 7.5 .. .. 3.3 56.4 .. 9.4 .. 15.7 10.1 28.0 4.1 .. 14.4 32.2 .. .. 3.8 .. .. 12.8 5.3 4.9 .. 10.5 .. .. 43.0 2.4 4.5 6.8b 5.1 .. .. 3.0 .. 8.7 8.5 .. 7.5 8.8b 6.9 10.0 11.9 11.6 2.6
2.5
PEOPLE
Unemployment Long-term unemployment
Unemployment by educational attainment
% of total unemployment Total Male Female 2007–10a 2007–10a 2007–10a
% of total unemployment Primary Secondary Tertiary 2007–10a 2007–10a 2007–10a
.. 50.6 .. .. .. .. 49.0 22.4 48.5 .. 37.6 .. .. .. .. 0.3 81.7 .. .. .. 45.0 .. .. .. .. 41.4 83.1 .. .. .. .. .. .. 2.4 .. .. .. .. .. .. .. 27.6 9.0 b .. .. .. 9.5 .. .. .. .. .. .. .. 25.5 52.3 .. 38.5
.. 51.2 .. .. .. .. 53.9 25.7 47.2 .. 44.8 .. .. .. .. 0.5 82.8 .. .. .. 48.2 .. .. .. .. 42.3 83.5 .. .. .. .. .. .. 2.7 .. .. .. .. .. .. .. 27.7 8.9b .. .. .. 10.6 .. .. .. .. .. .. .. 25.3 51.7 .. 35.3
.. 49.9 .. .. .. .. 38.2 18.5 49.9 .. 25.2 .. .. .. .. 0.3 79.8 .. .. .. 40.6 .. .. .. .. 40.2 82.4 .. .. .. .. .. .. 2.0 .. .. .. .. .. .. .. 27.4 9.0 b .. .. .. 7.7 .. .. .. .. .. .. .. 25.8 52.8 .. 40.3
.. 33.8 .. 43.4 40.4 .. 38.3 22.1 47.2 12.0 66.8 .. 45.1 .. .. 15.3 64.0 .. .. .. 23.3 45.5 57.2 .. .. 15.0 .. .. .. 10.4 .. .. 43.5 51.5 .. 28.2 .. .. .. .. .. 42.0 30.6 .. .. .. 29.9 .. 14.7 35.8 .. 53.5 31.5b 13.1 15.9 67.3 .. 19.0
.. 58.4 .. 40.6 31.0 .. 39.0 48.0 40.3 4.5 .. .. 39.7 .. .. 63.7 46.0 .. .. .. 62.2 19.7 33.5 .. .. 67.7 .. .. .. 60.9 .. .. 29.6 25.2 .. 49.0 .. .. .. .. .. 36.6 39.2 .. .. .. 49.3 .. 10.1 39.8 .. 31.4 30.5b 45.2 71.8 15.9 .. 52.7
2012 World Development Indicators
.. 7.8 .. 10.2 25.5 .. 18.5 28.1 11.2 3.9 33.2 .. 15.2 .. .. 21.1 15.0 .. .. .. 14.5 29.7 0.4 .. .. 17.3 .. .. .. 24.9 .. .. 7.9 21.0 .. 22.1 .. .. .. .. .. 18.7 25.7 .. .. .. 17.9 .. 28.0 23.8 .. 13.4 37.3b 41.2 12.1 13.5 .. 24.0
59
2.5
Unemployment Unemployment
Total % of total labor force 1990–92a 2007–10a
Romania Russian Federation Rwanda Saudi Arabia Senegal Serbia Sierra Leone Singapore Slovak Republic Slovenia Somalia South Africa South Sudan Spain Sri Lanka Sudan Swaziland Sweden Switzerland Syrian Arab Republic Tajikistan Tanzania Thailand Timor-Leste Togo Trinidad and Tobago Tunisia Turkey Turkmenistan Uganda Ukraine United Arab Emirates United Kingdom United States Uruguay Uzbekistan Venezuela, RB Vietnam West Bank and Gaza Yemen, Rep. Zambia Zimbabwe World Low income Middle income Lower middle income Upper middle income Low & middle income East Asia & Pacific Europe & Central Asia Latin America & Carib. Middle East & N. Africa South Asia Sub-Saharan Africa High income Euro area
.. 5.2 0.3 .. .. .. .. 2.7 .. 7.1 .. .. .. 18.1 14.2b .. .. 5.7 2.8 6.8 .. 3.6b 1.4 .. .. 19.6 .. 8.5 .. 1.0 .. .. 9.7 7.5b 9.0 b .. 7.7 .. .. .. 18.9 .. .. w .. .. .. 3.5 .. 2.5 .. 6.6 12.7 .. .. 7.5 9.1
7.3 7.5 .. 5.4 .. 19.2 .. 5.9 14.4 7.2 .. 23.8 .. 20.1 4.9 .. .. 8.4 4.2 8.4 .. .. 1.2 .. .. 5.3 14.2 11.9 .. 4.2 8.8 4.0 7.8 9.6b 7.3 .. 7.6 2.4 24.5 14.6 .. .. .. w .. .. .. 5.8 .. 4.7 9.5 7.8 10.6 .. .. 8.5 10.0
Male % of male labor force 1990–92a 2007–10a
.. 5.2 0.6 .. .. .. .. 2.7 .. 8.1 .. .. .. 13.9 .. .. .. 6.7 2.3 5.2 .. 2.8b 1.3 .. .. 17.0 .. 8.8 .. 1.3 .. .. 11.5 7.9b 6.8b .. 8.2 .. .. .. 16.3 .. .. w .. .. .. .. .. .. .. 5.4 10.8 .. .. 7.1 7.2
a. Data are for the most recent year available. b. Limited coverage.
60
2012 World Development Indicators
7.9 8.0 .. 3.5 .. 18.4 .. 5.4 14.2 7.4 .. 22.0 .. 19.7 3.5 .. .. 8.5 3.8 5.7 .. .. 1.2 .. .. 3.5 .. 11.4 .. 3.1 6.6 2.0 8.6 10.5b 5.3 .. 7.2 .. 17.7 11.5 .. .. .. w .. .. .. .. .. .. 10.3 6.4 8.8 .. .. 8.7 9.8
Female % of female labor force 1990–92a 2007–10a
.. 5.2 0.2 .. .. .. .. 2.6 .. 6.0 .. .. .. 25.8 .. .. .. 4.6 3.5 14.0 .. 4.3b 1.5 .. .. 23.9 .. 7.8 .. 0.6 .. .. 7.3 7.0 b 11.8b .. 6.8 .. .. .. 22.4 .. .. w .. .. .. .. .. .. .. 8.3 21.5 .. .. 8.0 11.9
6.5 6.9 .. 15.9 .. 20.2 .. 6.5 14.6 7.0 .. 25.9 .. 20.5 7.7 .. .. 8.2 4.8 22.5 .. .. 1.1 .. .. 6.2 .. 13.0 .. 5.1 6.1 12.0 6.7 8.6b 9.7 .. 8.1 .. 38.6 40.9 .. .. .. w .. .. .. .. .. .. 8.8 9.8 18.4 .. .. 8.1 10.2
Long-term unemployment
Unemployment by educational attainment
% of total unemployment Total Male Female 2007–10a 2007–10a 2007–10a
% of total unemployment Primary Secondary Tertiary 2007–10a 2007–10a 2007–10a
34.9 35.2 .. .. .. 71.1 .. .. 59.3 43.3 .. 14.4 .. 45.1 .. .. .. 16.6 34.3 .. .. .. .. .. .. .. .. 28.6 .. .. .. .. 32.6 29.0 b .. .. .. .. .. .. .. .. .. w .. .. .. .. .. .. .. .. .. .. .. 32.6 44.0
36.9 32.7 .. .. .. 70.1 .. .. 58.3 45.0 .. .. .. 44.6 .. .. .. 18.1 28.3 .. .. .. .. .. .. .. .. 24.7 .. .. .. .. 37.2 29.9b .. .. .. .. .. .. .. .. .. w .. .. .. .. .. .. .. .. .. .. .. 34.2 44.2
32.0 38.0 .. .. .. 72.1 .. .. 60.5 41.2 .. .. .. 45.6 .. .. .. 14.8 39.8 .. .. .. .. .. .. .. .. 37.0 .. .. .. .. 26.0 27.7b .. .. .. .. .. .. .. .. .. w .. .. .. .. .. .. .. .. .. .. .. 31.3 43.3
28.2 13.1 .. 7.5 .. 20.3 .. 27.2 27.8 23.3 .. 15.4 .. 58.4 45.4b .. .. 32.5 27.9 46.1 66.5 .. 41.5 .. .. 27.9 .. 52.5 .. .. 7.4 19.7 37.1 17.9 59.1 .. .. .. 53.8 .. .. .. .. w .. .. .. .. .. .. 26.1 42.3 .. .. .. 33.9 42.9
62.7 52.8 .. 48.6 .. 68.4 .. 22.7 66.2 58.1 .. 80.7 .. 22.6 22.8b .. .. 44.9 53.7 28.0 28.8 .. 49.3 .. .. 65.9 .. 26.0 .. .. 52.9 42.6 46.5 35.5 27.0 .. .. .. 14.3 .. .. .. .. w .. .. .. .. .. .. 47.8 38.1 .. .. .. 42.2 41.3
6.7 34.1 .. 43.6 .. 11.2 .. 50.1 5.9 16.3 .. 0.8 .. 17.7 31.8b .. .. 16.4 17.7 4.9 4.6 .. 0.2 .. .. 5.2 .. 13.9 .. .. 39.7 33.2 14.3 46.5 13.8 .. .. .. 24.5 .. .. .. .. w .. .. .. .. .. .. 25.6 13.0 .. .. .. 26.8 14.8
About the data
2.5
PEOPLE
Unemployment Definitions
Unemployment and total employment are the broad-
generate statistics that are more comparable inter-
• Unemployment is the share of the labor force with-
est indicators of economic activity as reflected by
nationally. But the age group, geographic coverage,
out work but available for and seeking employment.
the labor market. The International Labour Organiza-
and collection methods could differ by country or
Definitions of labor force and unemployment may
tion (ILO) defines the unemployed as members of the
change over time within a country. For detailed infor-
differ by country (see About the data). • Long-term
economically active population who are without work
mation, consult the original source.
unemployment is the number of people with continu-
but available for and seeking work, including people
Women tend to be excluded from the unemploy-
ous periods of unemployment extending for a year or
who have lost their jobs or who have voluntarily left
ment count for various reasons. Women suffer more
longer, expressed as a percentage of the total unem-
work. Some unemployment is unavoidable. At any
from discrimination and from structural, social, and
ployed. • Unemployment by educational attainment
time some workers are temporarily unemployed—
cultural barriers that impede them from seeking
is the unemployed by level of educational attainment
between jobs as employers look for the right workers
work. Also, women are often responsible for the
as a percentage of the total unemployed. The levels
and workers search for better jobs. Such unemploy-
care of children and the elderly and for household
of educational attainment accord with the ISCED97
ment, often called frictional unemployment, results
affairs. They may not be available for work during
of the United Nations Educational, Scientific, and
from the normal operation of labor markets.
the short reference period, as they need to make
Cultural Organization.
Changes in unemployment over time may reflect
arrangements before starting work. Furthermore,
changes in the demand for and supply of labor; they
women are considered to be employed when they
may also refl ect changes in reporting practices.
are working part-time or in temporary jobs, despite
Paradoxically, low unemployment rates can disguise
the instability of these jobs or their active search for
substantial poverty in a country, while high unemploy-
more secure employment.
ment rates can occur in countries with a high level of
Long-term unemployment is measured by the
economic development and low rates of poverty. In
length of time that an unemployed person has been
countries without unemployment or welfare benefits
without work and looking for a job. The data in the
people eke out a living in vulnerable employment. In
table are from labor force surveys. The underlying
countries with well developed safety nets workers
assumption is that shorter periods of joblessness
can afford to wait for suitable or desirable jobs. But
are of less concern, especially when the unem-
high and sustained unemployment indicates serious
ployed are covered by unemployment benefi ts or
inefficiencies in resource allocation.
similar forms of support. The length of time that a
The ILO definition of unemployment notwithstand-
person has been unemployed is difficult to measure,
ing, reference periods, the criteria for people consid-
because the ability to recall that time diminishes as
ered to be seeking work, and the treatment of people
the period of joblessness extends. Women’s long-
temporarily laid off or seeking work for the first time
term unemployment is likely to be lower in countries
vary across countries. In many developing countries
where women constitute a large share of the unpaid
it is especially difficult to measure employment and
family workforce.
unemployment in agriculture. The timing of a survey,
Unemployment by level of educational attainment
for example, can maximize the effects of seasonal
provides insights into the relation between the edu-
unemployment in agriculture. And informal sector
cational attainment of workers and unemployment
employment is difficult to quantify where informal
and may be used to draw inferences about changes
activities are not tracked.
in employment demand. Information on educational
Data on unemployment are drawn from labor force
attainment is the best available indicator of skill
sample surveys and general household sample
levels of the labor force. Besides the limitations to
surveys, censuses, and offi cial estimates, which
comparability raised for measuring unemployment,
are generally based on information from different
the different ways of classifying the education level
sources and can be combined in many ways. Admin-
may also cause inconsistency. Education level is
istrative records, such as social insurance statistics
supposed to be classifi ed according to Interna-
and employment office statistics, are not included
tional Standard Classifi cation of Education 1997
in the table because of their limitations in cover-
(ISCED97). For more information on ISCED97, see
age. Labor force surveys generally yield the most
About the data for table 2.11.
comprehensive data because they include groups not covered in other unemployment statistics, particularly people seeking work for the first time. These
Data sources
surveys generally use a definition of unemployment
Data on unemployment are from the ILO’s Key Indi-
that follows the international recommendations more
cators of the Labour Market, 7th edition, database.
closely than that used by other sources and therefore
2012 World Development Indicators
61
2.6
Children at work Survey year
% of children ages 7–14 in employment
% of children ages 7–14
Afghanistan Albania Algeria Angolab Argentina Armenia Australia Austria Azerbaijan Bahrain Bangladesh Belarus Belgium Benin Bolivia Bosnia and Herzegovina Botswana Brazil Bulgaria Burkina Faso Burundi Cambodiad Cameroon Canada Central African Republic Chad Chile China Hong Kong SAR, China Colombia Congo, Dem. Rep.d Congo, Rep Costa Ricad Côte d’Ivoire Croatia Cuba Cyprus Czech Republic Denmark Dominican Republicd Ecuador Egypt, Arab Rep. El Salvador Eritrea Estonia Ethiopia Finland France Gabon Gambia, The Georgia Germany Ghana Greece Guatemala Guinea Guinea-Bissau Haiti
62
2005 2001 2004
2005 2006 2005 2006 2008 2006 2008 2006 2005 2003–04 2007 2000 2004 2003
2007 2000 2005 2004 2006
2005 2006 2005 2007
2005
2005 2006 2006 2006 1994 2006 2005
Employment by economic activitya
Status in employmenta
% of children ages 7–14 in employment
% of children ages 7–14 in employment SelfUnpaid employed Wage family
Children in employment
Total
Male
Female
Work only
Study and work
.. 25.0 .. 30.1 12.9 .. .. .. 5.2 .. 16.2 11.7 .. 74.4 32.1 10.6 .. 5.2 .. 42.1 11.7 48.9 43.4 .. 67.0 60.4 4.1 .. .. 3.9 39.8 30.1 5.7 45.7 .. .. .. .. .. 5.8 14.3 7.9 7.1 .. .. 56.0 .. .. .. 43.5 31.8 .. 48.9 .. 18.2 48.3 50.5 33.4
.. 18.8 .. 30.0 15.7 .. .. .. 5.8 .. 25.7 12.1 .. 72.8 33.0 11.7 .. 6.9 .. 49.0 12.5 49.6 43.5 .. 66.5 64.4 5.1 .. .. 5.3 39.9 29.9 8.1 47.7 .. .. .. .. .. 9.0 16.9 11.5 10.1 .. .. 64.3 .. .. .. 33.9 33.6 .. 49.9 .. 24.5 47.2 52.8 37.3
.. 22.0 .. 30.1 9.8 .. .. .. 4.5 .. 6.4 11.2 .. 76.1 31.1 9.5 .. 3.5 .. 34.5 11.0 48.1 43.4 .. 67.6 56.2 3.1 .. .. 2.3 39.8 30.2 3.5 43.6 .. .. .. .. .. 2.7 11.6 4.3 3.8 .. .. 47.1 .. .. .. 52.3 29.9 .. 48.0 .. 11.7 49.5 48.1 29.6
.. 6.7 .. 26.6 4.8 .. .. .. 6.3 .. 37.8 0.0 .. 36.1 5.2 0.1 .. 4.8 .. 67.7 38.9 13.8 21.9 .. 54.9 49.1 3.2 .. .. 24.8 35.7 9.9 44.6 46.8 .. .. .. .. .. 6.2 21.0 21.0 24.9 .. .. 69.4 .. .. .. 32.1 1.0 .. 18.7 .. 28.4 98.6 36.4 17.7
.. 93.3 .. 73.4 95.2 .. .. .. 93.7 .. 62.2 100.0 .. 63.9 94.8 99.9 .. 95.2 .. 32.3 61.1 86.2 78.1 .. 45.1 50.9 96.8 .. .. 75.2 64.3 90.1 55.4 53.2 .. .. .. .. .. 93.8 79.0 79.0 75.1 .. .. 30.6 .. .. .. 67.9 99.0 .. 81.3 .. 71.6 1.4 63.6 82.3
2012 World Development Indicators
Agriculture Manufacturing
.. .. .. .. .. .. .. .. 91.7 .. .. .. .. .. 73.2 .. .. 54..7 .. 70.9
.. .. .. .. .. .. .. .. 0.7 .. .. .. .. .. 6.1 .. .. 7.6 .. 1.4
82.3 88.5 .. .. .. 24.1 .. .. 41.2 .. .. 40.3 .. .. .. .. .. .. 18.5 69.3 .. 50.1 .. .. 94.6 .. .. .. .. .. .. .. .. 63.7 .. .. ..
4.2 3.1 .. .. .. 6.9 .. .. 10.8 .. .. 9.5 .. .. .. .. .. .. 9.8 6.3 .. 13.3 .. .. 1.5 .. .. .. .. .. .. .. .. 9.7 .. .. ..
Services
.. .. .. .. .. .. .. .. 7.4 .. .. .. .. .. 19.2 .. .. 34.6 .. 24.9 .. 12.9 8.2 .. .. .. 66.9 .. .. 46.1 .. .. 49.0 .. .. .. .. .. .. 57.5 22.8 .. 35.2 .. .. 3.7 .. .. .. .. .. .. .. .. 24.7 .. .. ..
.. .. .. .. 34.2 .. .. .. 4.1 .. – .. .. .. 0.9 .. .. 5.5 .. 1.9 25.9 6.0 2.5 .. .. .. .. .. .. 22.7 .. .. 15.8 .. .. .. .. .. .. 23.8 3.6 2.2 .. .. 1.7 .. .. .. .. .. .. .. .. 2.0 .. .. ..
.. 1.4 .. 6.2 8.1 .. .. .. 3.8 .. 17.0 9.2 .. .. 9.2 1.6 .. 24.7 .. 2.2 68.6 4.1 9.5 .. 2.0 1.8 .. .. .. 29.1 6.6 4.2 57.7 2.4 .. .. .. .. .. 19.5 15.2 11.4 23.6 .. .. 2.4 .. .. .. 1.1 4.3 .. 6.1 .. 18.8 .. 4.0 1.8
.. 94.5 80.1 56.2 .. .. .. 92.1 .. 77.8 78.8 .. .. 89.9 92.1 .. 69.8 c .. 95.8 89.4 87.6 .. 56.4 77.2 .. .. .. 45.6 76.7 84.5 26.6 88.0 .. .. .. .. .. 56.2e 81.2 87.4 74.2 .. .. 95.8 .. .. .. 87.3 77.0 .. 76.2 .. 79.2 .. 87.7 79.4
Survey year
Children in employment
% of children ages 7–14 in employment
% of children ages 7–14
Honduras Hungary India Indonesia Iran, Islamic Rep. Iraq Ireland Israel Italy Jamaica Japan Jordan Kazakhstan Kenya Korea, Dem. Rep. Korea, Rep. Kosovo Kuwait Kyrgyz Republic Lao PDR Latvia Lebanon Lesotho Liberia Libya Lithuania Macedonia, FYR Madagascar Malawi Malaysia Mali Mauritania Mauritius Mexicof Moldova Mongolia Morocco Mozambiqued Myanmar Namibia Nepal Netherlands New Zealand Nicaragua Niger Nigeria Norway Oman Pakistan Panama Papua New Guinea Paraguayc Peru Philippines Poland Portugal Puerto Rico Qatar
2007 2004–05 2000 2006
2005
2006 2000
2006
2002 2007
2005 2007 2006 2006
2009 2000 2006–07 1998–99 1996 1999 1999
2005 2006
2008 2005 2007 2001 2001
Total
Male
Female
Work only
Study and work
8.7 .. 4.2 8.9 .. 14.7 .. .. .. 9.8 .. .. 3.6 37.7 .. .. .. .. 5.2 .. .. .. 2.6 37.4 .. .. 11.8 26.0 40.3 .. 49.5 .. .. 12.2 33.5 10.1 13.2 1.8 .. 15.4 47.2 .. .. 10.1 47.1 .. .. .. .. 8.9 .. 15.3 42.2 13.3 .. 3.6 .. ..
13.3 .. 4.2 8.8 .. 17.9 .. .. .. 11.3 .. .. 4.4 40.1 .. .. .. .. 5.8 .. .. .. 4.0 37.8 .. .. 14.8 27.7 41.3 .. 55.0 .. .. 16.5 34.1 11.4 13.5 1.9 .. 16.2 42.2 .. .. 16.2 49.2 .. .. .. .. 12.1 .. 22.6 44.8 16.3 .. 4.6 .. ..
4.1 .. 4.2 9.1 .. 11.3 .. .. .. 8.3 .. .. 2.8 35.2 .. .. .. .. 4.6 .. .. .. 1.3 37.1 .. .. 8.6 24.2 39.4 .. 44.1 .. .. 7.6 32.8 8.6 12.8 1.7 .. 14.7 52.4 .. .. 3.9 45.0 .. .. .. .. 5.4 .. 7.7 39.5 10.0 .. 2.6 .. ..
45.1 .. 84.9 24.9 .. 32.4 .. .. .. 2.5 .. .. 1.6 14.1 .. .. .. .. 7.9 .. .. .. 74.4 45.0 .. .. 2.8 40.9 10.5 .. 59.5 .. .. 22.6 3.8 16.4 93.2 100.0 .. 9.5 35.6 .. .. 30.8 66.5 .. .. .. .. 14.6 .. 24.2 4.0 14.8 .. 3.6 .. ..
54.9 .. 15.2 75.1 .. 67.6 .. .. .. 97.5 .. .. 98.4 85.9 .. .. .. .. 92.1 .. .. .. 25.6 55.0 .. .. 97.2 59.1 89.5 .. 40.5 .. .. 77.4 96.2 83.6 6.8 0.0 .. 90.5 64.4 .. .. 69.2 33.5 .. .. .. .. 85.4 .. 75.7 96.0 85.2 .. 96.4 .. ..
2.6
PEOPLE
Children at work Employment by economic activitya
Status in employmenta
% of children ages 7–14 in employment
% of children ages 7–14 in employment SelfUnpaid employed Wage family
Agriculture Manufacturing
61.6 .. 69.4 .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. 58.0 .. .. .. .. 87.6 .. .. .. .. .. 38.2 .. 91.3 60.6 .. .. 91.5 87.0 .. .. 70.5 .. .. .. .. .. 73.3 .. 60.8 62.6 64.3 .. 48.5 .. ..
10.4 .. 16.0 .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. 0.0 .. .. .. .. 2.9 .. .. .. .. .. 11.7 .. 0.3 8.3 .. .. 0.4 1.4 .. .. 9.7 .. .. .. .. .. 2.9 .. 6.2 5.0 4.1 .. 11.2 .. ..
Services
25.1 .. 12.4 .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. 10.4 .. .. .. .. 8.2 .. .. .. .. .. 47.0 .. 6.3 10.1 .. .. 8.0 11.1 .. .. 19.3 .. .. .. .. .. 22.9 .. 32.1 31.1 30.6 .. 33.3 .. ..
3.5 .. 7.1 .. .. .. .. .. .. .. .. .. – .. .. .. .. .. – .. .. .. 3.7 .. .. .. .. 0.1 .. .. .. .. .. 2.7 .. 5.1 2.1 .. .. 0.1 4.2 .. .. 1.2 4.8 .. .. .. .. 12.6 .. 9.3 3.8 4.1 .. .. .. ..
23.0 .. 6.8 17.8 .. 7.0 .. .. .. 16.3 .. .. 4.0 .. .. .. .. .. 3.7 .. .. .. 36.6 1.7 .. .. 3.9 10.0 6.7 .. 1.6 .. .. 34.3 2.9 0.1 10.0 .. .. 4.5 3.3 .. .. 13.8 74.5 .. .. .. .. 11.3 .. 24.8 7.6 22.8 .. .. .. ..
2012 World Development Indicators
73.5 .. 59.3 75.8e .. 85.3 .. .. .. 74.9 .. .. 75.0 .. .. .. .. .. 81.9 .. .. .. 59.7c 79.3 .. .. 89.5 89.9 75.5 .. 80.4 .. .. 63.1 82.0 94.7 81.7 .. .. 95.0 92.4 .. .. 85.0 c .. .. .. .. 76.1c .. 65.8 88.6 73.1 .. .. .. ..
63
2.6
Children at work Survey year
Children in employment
% of children ages 7–14 in employment
% of children ages 7–14
Romania Russian Federation Rwanda Saudi Arabia Senegal Serbia Sierra Leone Singapore Slovak Republic Slovenia Somalia South Africa South Sudan Spain Sri Lanka Sudang Swaziland Sweden Switzerland Syrian Arab Republic Tajikistan Tanzania Thailand Timor-Leste Togo Trinidad and Tobago Tunisia Turkeyi Turkmenistan Uganda Ukraine United Arab Emirates United Kingdom United States Uruguay Uzbekistan Venezuela, RBd Vietnam West Bank and Gaza Yemen, Rep. Zambia Zimbabwe
2000 2008 2005 2005 2007
2006 1999
1999 2000 2000
2006 2005 2005–06 2005 2006 2000 2006 2005–06 2005
2005 2006 2006 2006 2008 1999
Total
Male
Female
Work only
Study and work
1.4 .. 7.5 .. 18.5 6.9 14.9 .. .. .. 43.5 27.7 .. .. 17.0 19.1 11.2 .. .. 6.6 8.9 31.1 15.1 .. 38.7 3.9 .. 2.6 .. 38.2 17.3 .. .. .. .. 5.1 5.1 21.3 .. 18.3 34.4 14.3
1.7 .. 8.0 .. 24.4 7.2 14.9 .. .. .. 45.5 29.0 .. .. 20.4 21.5 11.4 .. .. 8.8 8.7 35.0 15.7 .. 39.8 5.2 .. 3.3 .. 39.8 18.0 .. .. .. .. 5.3 6.9 21.0 .. 20.7 35.4 15.3
1.1 .. 7.0 .. 12.6 6.6 14.9 .. .. .. 41.5 26.4 .. .. 13.4 16.8 10.9 .. .. 4.3 9.1 27.1 14.4 .. 37.4 2.8 .. 1.8 .. 36.5 16.6 .. .. .. .. 4.9 3.3 21.6 .. 15.9 33.3 13.3
20.7 .. 18.5 .. 61.9 2.1 57.7 .. .. .. 53.5 5.1 .. .. 5.4 55.9 14.0 .. .. 34.6 9.0 28.2 4.2 .. 29.8 12.8 .. 38.8 .. 7.7 0.1 .. .. .. .. 1.0 19.8 11.9 .. 30.9 18.6 12.0
79.3 .. 81.5 .. 38.1 97.9 42.3 .. .. .. 46.5 94.9 .. .. 94.6 44.1 86.0 .. .. 65.4 91.0 71.8 95.8 .. 70.2 87.2 .. 61.2 .. 92.3 99.9 .. .. .. .. 99.0 80.2 88.1 .. 69.1 81.4 88.0
Employment by economic activitya
Status in employmenta
% of children ages 7–14 in employment
% of children ages 7–14 in employment SelfUnpaid employed Wage family
Agriculture Manufacturing
97.1 .. 85.5 .. 79.1 .. 83.8 .. .. .. .. .. .. .. 71.2 .. .. .. .. .. .. 85.3 .. .. 82.9 .. .. 57.1 .. 95.5 .. .. .. .. .. .. 32.3 .. .. .. 91.9 ..
0.0 .. 0.7 .. 5.0 .. 0.8 .. .. .. .. .. .. .. 13.1 .. .. .. .. .. .. 0.7 .. .. 1.3 .. .. 14.3 .. 1.4 .. .. .. .. .. .. 7.2 .. .. .. 0.7 ..
Services
2.3 .. 10.5 .. 14.0 .. 13.4 .. .. .. .. .. .. .. 15.0 .. .. .. .. .. .. 14.0 .. .. 15.1 .. .. 27.1 .. 3.0 .. .. .. .. .. .. 55.7 .. .. .. 7.0 ..
4.5 .. 14.8 .. 6.3 .. 9.7 .. .. .. .. 7.1 .. .. 2.9 .. .. .. .. .. .. 56.3h .. .. 5.0 .. .. 2.1 .. 1.4 .. .. .. .. .. .. 31.6 .. .. .. 2.9 3.4
92.9e .. 12.8 .. 4.4 5.2 0.9 .. .. .. 1.6 7.1 .. .. 8.3 7.3 10.4 .. .. 21.5 24.2 0.9 13.5 .. 1.6 29.8 .. 34.1 .. 1.5 3.1 .. .. .. .. 3.8 33.1 5.9 .. 6.1 3.9 28.4
.. 72.3 .. 84.1 89.4 87.8 .. .. .. 94.8 85.8 .. .. 88.0 81.3 85.9 .. .. 68.8 71.3 42.8e 80.0 .. 93.4 64.9 63.8 .. 97.1 79.3 .. .. .. .. 78.6 35.3 91.2 .. 86.1 93.1 68.2
a. Shares may not sum to 100 percent because of a residual category not included in the table. b. Covers only Angola-secured territory. c. Refers to unpaid workers, regardless of whether they are family workers. d. Covers children ages 10–14. e. Refers to family workers, regardless of whether they are paid. f. Covers children ages 12–14. g. Covers northern Sudan only. h. Covers mainly workers working on their own shamba. i. Covers children ages 6–14.
64
2012 World Development Indicators
About the data
2.6
PEOPLE
Children at work Definitions
The data in the table refer to children’s work in the
defined activities —is not presented. ISIC revision 2
• Survey year is the year in which the underlying
sense of “economic activity”—that is, children in
and revision 3 are both used, depending on the coun-
data were collected. • Children in employment are
employment, a broader concept than child labor (see
try’s codification for describing economic activity.
children involved in any economic activity for at least
ILO 2009a for details on this distinction).
This does not affect the definition of the groups in
one hour in the reference week of the survey. • Work
the table.
only refers to children who are employed and not
In line with the defi nition of economic activity adopted by the 13th International Conference of
The table also aggregates the distribution of chil-
attending school. • Study and work refer to children
Labour Statisticians, the threshold set by the 1993
dren in employment by three major categories of
attending school in combination with employment.
UN System of National Accounts for classifying a
status in employment, based on the International
• Employment by economic activity is the distribu-
person as employed is to have been engaged at
Classification of Status in Employment (1993): self-
tion of children in employment by the major industrial
least one hour in any activity relating to the pro-
employed workers, wage workers (also known as
categories (ISIC revision 2 or revision 3). • Agricul-
duction of goods and services during the reference
employees), and unpaid family workers. A residual
ture corresponds to division 1 (ISIC revision 2) or
period. Children seeking work are thus excluded.
category—which includes those not classifiable by
categories A and B (ISIC revision 3) and includes
Economic activity covers all market production and
status—is not presented.
agriculture and hunting, forestry and logging, and
certain nonmarket production, including production
In most countries more boys are involved in employ-
fishing. • Manufacturing corresponds to division 3
of goods for own use. It excludes unpaid household
ment, or the gender difference is small. However, girls
(ISIC revision 2) or category D (ISIC revision 3). • Ser-
services (commonly called “household chores”)—
are often more present in hidden or underreported
vices correspond to divisions 6–9 (ISIC revision
that is, the production of domestic and personal
forms of employment such as domestic service, and
2) or categories G–P (ISIC revision 3) and include
services by household members for a household’s
in almost all societies girls bear greater responsibil-
wholesale and retail trade, hotels and restaurants,
own consumption.
ity for household chores in their own homes, work
transport, financial intermediation, real estate, pub-
Data are from household surveys by the Interna-
that lies outside the System of National Accounts
lic administration, education, health and social work,
tional Labor Organization (ILO), the United Nations
production boundary and is thus not considered in
other community services, and private household
Children’s Fund (UNICEF), the World Bank, and
estimates of children’s employment.
activity. • Self-employed workers are people whose
national statistical offices. The surveys yield data
remuneration depends directly on the profits derived
on education, employment, health, expenditure, and
from the goods and services they produce, with or
consumption indicators related to children’s work.
without other employees, and include employers,
Household survey data generally include informa-
own-account workers, and members of produc-
tion on work type—for example, whether a child is
ers cooperatives. • Wage workers (also known as
working for payment in cash or in kind or is involved
employees) are people who hold explicit (written or
in unpaid work, working for someone who is not a
oral) or implicit employment contracts that provide
member of the household, or involved in any type of
basic remuneration that does not depend directly on
family work (on the farm or in a business). Country
the revenue of the unit for which they work. • Unpaid
surveys define the ages for child labor as 5–17. The
family workers are people who work without pay in a
data in the table have been recalculated to present
market-oriented establishment operated by a related
statistics for children ages 7–14.
person living in the same household.
Although efforts are made to harmonize the definition of employment and the questions on employ-
Data sources
ment in survey questionnaires, signifi cant differ-
Data on children at work are estimates produced
ences remain in the survey instruments that collect
by the Understanding Children’s Work project
data on children in employment and in the sampling
based on household survey data sets made avail-
design underlying the surveys. Differences exist
able by the ILO’s International Programme on the
not only across different household surveys in the
Elimination of Child Labour under its Statistical
same country but also across the same type of sur-
Monitoring Programme on Child Labour, UNICEF
vey carried out in different countries, so estimates
under its Multiple Indicator Cluster Survey pro-
of working children are not fully comparable across
gram, the World Bank under its Living Standards
countries.
Measurement Study program, and national sta-
The table aggregates the distribution of children
tistical offices. Information on how the data were
in employment by the industrial categories of the
collected and some indication of their reliability
International Standard Industrial Classifi cation
can be found at www.ilo.org/public/english/
(ISIC): agriculture, manufacturing, and services.
standards/ipec/simpoc/, www.childinfo.org, and
A residual category—which includes mining and
www.worldbank.org/lsms. Detailed country statis-
quarrying; electricity, gas, and water; construction;
tics can be found at www.ucw-project.org.
extraterritorial organization; and other inadequately
2012 World Development Indicators
65
2.7
Poverty rates at national poverty lines Population below national poverty line
Survey year a
Afghanistanb Albaniab Angola Argentina Armeniab Azerbaijanb Bangladesh Belarus Benin Bhutan Bolivia Bosnia and Herzegovinab Botswana Brazil Bulgariab Burkina Faso Burundi Cambodiab Cameroon Cape Verde Central African Republic Chad Chile China Colombia Comoros Congo, Dem. Rep. Congo, Rep. Costa Rica Côte d’Ivoireb Croatiab Dominican Republic Ecuador Egypt, Arab Rep. El Salvador Ethiopia Fiji Gabon Gambia, Theb Georgiab Ghana Guatemala Guinea Guinea-Bissau Haiti Honduras India Indonesia Iraq Jamaica Jordan Kazakhstanb Kenya Kosovob Kyrgyz Republicb Lao PDRb Latviab
66
2005 2009d 2009 2001 2005 2008
2006d 2004 1993 2008d 2001 2003 2004
2006d 2004 d 2009d
2009d 2002 2002 2009d 2009d 2005 2008d,e 2000 2003
2008 1998 2000
2009d,e 2005 2010 2006d 2008 2005 2009 2002 2002
Rural %
Urban %
National %
.. 24.2 .. .. 34.9 42.5 43.8 .. .. .. 76.5 22.0 40.4 .. .. 65.5 .. 37.8 .. .. .. .. 12.3 2.8 54.3 .. .. .. 23.0 45.8 .. 47.0 57.5 26.8 49.0 45.4 40.0 .. .. 27.8 49.6 74.5 .. .. .. 64.4 41.8 16.6 .. ..
.. 11.2 .. 13.2 33.7 55.7 28.4 .. .. .. 50.3 11.3 24.7 .. .. 22.1 .. 17.6 .. .. .. .. 13.9 .. 35.8 .. .. .. 20.7 32.3 .. 28.6 25.0 10.1 35.7 36.9 28.0 .. .. 17.5 19.4 27.1 .. .. .. 52.8 25.7 9.9 .. ..
.. 18.5 .. .. 34.1 49.6 40.0 6.1 .. .. 59.9 17.7 32.9 22.6 12.8 51.0 .. 34.7 .. .. .. .. 13.7 .. 40.2 .. .. .. 21.7 40.2 11.2 34.6 36.0 19.6 39.9 44.2 35.0 .. .. 22.7 39.5 56.2 .. .. .. 58.8 37.2 13.3 .. 14.3
.. .. 49.2 .. .. 11.6
.. .. 37.4 .. .. ..
12.1 .. 45.1 31.7 33.5 7.5
2012 World Development Indicators
Poverty gap at national poverty line
Survey year a
Rural %
Urban %
National %
2008 c 2008 2000 c 2010 d 2010 2008 2010 2009 2003c 2007c 2007d 2007 2003 2009d 2007 2009 2006c 2007 2007c 2007c 2008 c 2003c 2009d 2005d 2010 d 2004 c 2006 2005 2010 d 2008 2004 2010 d 2010 d 2008 2009d,e 2005 2009 2005 2010 c 2009 2006 2006 2007c 2002 2001d 2010 d,e 2010 2011 2007 2007d 2006 2009 2005c 2009 2010 2008 2004
37.5 14.6 .. .. 36.0 18.5 35.2 .. 46.0 30.9 77.3 17.8 44.8 .. .. 52.6 68.9 34.5 55.0 44.3 69.4 58.6 12.9 2.5 50.3 48.7 75.7 57.7 .. 54.2 .. .. 53.0 30.0 46.5 39.3 43.3 44.6 73.9 30.7 39.2 70.5 63.0 69.1 88.0 65.4 33.8 15.7 39.3 .. 19.0 .. 49.1 35.3 .. 31.7 12.7
29.0 10.1 62.3 9.9 36.0 14.8 21.3 .. 29.0 1.7 50.9 8.2 19.4 .. .. 27.9 34.0 11.8 12.2 13.2 49.6 24.6 15.5 .. 33.0 34.5 61.5 .. .. 29.4 .. .. 22.5 10.6 33.3 35.1 18.6 29.8 32.7 18.4 10.8 30.0 30.5 51.6 45.0 54.3 20.9 9.2 16.1 .. 12.0 .. 33.7 33.1 .. 17.4 ..
36.0 12.4 .. .. 35.8 15.8 31.5 5.4 39.0 23.2 60.1 14.0 30.6 21.4 10.6 46.7 66.9 30.1 39.9 26.6 62.0 55.0 15.1 .. 37.2 44.8 71.3 50.1 24.2 42.7 11.1 34.4 32.8 22.0 37.8 38.9 31.0 32.7 48.4 24.7 28.5 51.0 53.0 64.7 77.0 60.0 29.8 12.5 22.9 9.9 13.0 8.2 45.9 34.5 33.7 27.6 5.9
Survey year a
Rural %
Urban %
National %
2008 c 2008
8.3 2.6 .. .. .. .. 7.4 .. 14.0 8.1 .. .. 18.4 .. .. 17.4 24.2 8.3 17.5 14.3 35.0 23.3 .. .. .. 17.8 34.9 20.6 .. 20.3 .. .. .. .. .. 8.5 14.8 16.0 .. .. 13.5 .. 22.0 27.8 .. .. 6.8 2.6 9.0 .. .. .. 17.5 .. .. .. ..
6.2 1.9 .. .. .. .. 4.3 .. 8.0 0.4 .. .. 6.5 .. .. 7.8 10.3 2.8 2.8 3.3 29.8 7.4 .. .. .. 12.1 26.2 .. .. 9.5 .. .. .. .. .. 7.7 5.4 8.5 .. .. 3.1 .. 7.7 16.9 .. .. 4.5 1.5 2.7 .. .. .. 11.4 .. .. .. ..
7.9 2.3 .. .. .. .. 6.5 .. 12.0 6.1 .. .. 11.7 .. 3.0 15.1 23.4 7.2 12.3 8.1 33.1 21.6 .. .. .. 16.3 32.2 18.9 .. 15.3 2.6 .. .. .. .. 8.3 10.1 10.0 .. .. 9.6 .. 17.6 25.0 .. .. 6.2 2.1 4.5 .. 2.8 1.3 16.3 9.6 .. .. ..
2010 2003c 2007c
2003 2007 2009 2006c 2007 2007c 2007c 2008 c 2003c
2004 c 2006 2005 2008 2004
2005 2009 2005
2006 2007c 2002
2010 2011 2007 2006 2009 2005c 2009
Population below national poverty line
Lesothob Liberiab Macedonia, FYRb Madagascar Malawi Malaysiab Mali Mauritania Mexico Moldovab Mongolia Montenegro Morocco Mozambique Namibia Nepal Nicaragua Niger Nigeria Pakistan Panama Paraguay Peru Philippines Polandb Romaniab Russian Federation Rwanda São Tomé and Príncipe Senegalb Serbiab Sierra Leone South Africa South Sudan Sri Lanka Sudan Swaziland Tajikistanb Tanzania Thailand Timor-Leste Togo Turkey Uganda Ukraineb Uruguay Venezuela, RB Vietnam West Bank and Gaza Yemen, Rep. Zambia Zimbabwe
Poverty gap at national poverty line
Survey year a
Rural %
Urban %
National %
Survey year a
Rural %
Urban %
National %
1994
68.9 .. 21.2 77.3 .. 7.1 57.0 59.0 60.3 36.3 .. 14.8 25.1 55.3 69.0 .. 67.8 .. .. 28.1 62.7 49.8 60.3 .. .. 23.5 22.7 64.2 .. .. 9.6 .. .. .. 15.7 .. .. 55.0 38.6 11.5 .. .. 34.6 34.2 8.1 9.6 .. 20.4 .. 42.5 77.3 ..
36.7 .. 19.8 53.7 .. 2.0 18.5 28.9 40.1 12.6 .. 2.6 7.6 51.5 31.0 .. 30.1 .. .. 14.9 20.0 24.7 21.1 .. .. 8.1 8.1 23.2 .. .. 4.9 .. .. .. 6.7 .. .. 49.4 23.7 3.0 .. .. 9.4 13.7 2.9 21.4 .. 3.9 .. 32.3 29.1 ..
66.6 .. 20.4 72.1 .. 3.6 47.5 46.7 47.7 26.3 .. 6.8 15.3 54.1 58.0 .. 45.8 .. .. 23.9 36.8 35.1 34.8 26.4 14.6 15.1 11.9 58.5 .. .. 6.9 .. 38.0 .. 15.2 .. .. 53.5 35.6 9.0 39.7 .. 17.1 31.1 4.6 20.9 32.6 16.0 31.2 40.1 58.4 ..
2003 2007 2006 2005 2004 2009 2010 2008 2010 d 2010 2008 c 2010 2007 2008 2004 2010 2005 2007c 2004 c 2006 2008 2010 d 2010 2009 2008 2006 2006 2011 2009c 2005c 2010 2003c 2006 2009 2010 2009 2001c 2009 2007 2009 2007 2006 2009 2009 2008 2010 d 2009d 2008 2009 2005 2006 2003c
60.5 67.7 21.3 73.5 55.9 8.4 50.6 59.4 60.8 30.3 46.6 11.3 14.5 56.9 49.0 27.4 67.9 63.9 63.8 27.0 59.8 48.9 54.2 .. .. 22.3 21.2 48.7 .. 61.9 13.6 78.5 .. 55.4 9.4 57.6 75.0 .. 37.4 10.4 .. 74.3 38.7 27.2 4.7 6.2 .. 18.7 .. 40.1 76.8 ..
41.5 55.1 17.7 52.0 25.4 1.7 18.8 20.8 45.5 10.4 26.9 4.0 4.8 49.6 17.0 15.5 29.1 36.7 43.1 13.1 17.7 24.7 19.1 .. .. 6.8 7.4 22.1 .. 35.1 5.7 47.0 .. 24.2 5.3 26.5 49.0 .. 21.8 3.0 .. 36.8 8.9 9.1 2.0 18.7 .. 3.3 .. 20.7 26.7 ..
56.6 63.8 19.0 68.7 52.4 3.8 43.6 42.0 51.3 21.9 35.2 6.6 9.0 54.7 38.0 25.2 46.2 59.5 54.7 22.3 32.7 34.7 31.3 26.5 10.6 13.8 11.1 44.9 66.2 50.8 9.2 66.4 23.0 50.6 8.9 46.5 69.2 46.7 33.4 8.1 49.9 61.7 18.1 24.5 2.9 18.6 28.5 14.5 21.9 34.8 59.3 72.0
2005 2004 2007 2006 2004 2008d 2009 2009 2001 2003 1994 2001
2005 2003 2009d 2009 2006 2007 2005 2005 2006
2009 2000 2007
2007 2000 2008 2001 2008 2005 2007 2009d 2008d 2006 2007 1998 2004
2.7
PEOPLE
Poverty rates at national poverty lines
Survey year a
2007 2006 2005 2004 2009 2010 2008 2010 2008 c 2010 2008 2004 2010 c 2007c 2004 c
2009 2006 2006 2011 2009c 2005c 2003c 2006 2009 2010 2009 2001c 2007
2006 2009 2008
2008 2009 2005 2006
Rural %
Urban %
National %
.. 26.3 7.7 28.9 8.6 1.8 15.6 22.3 .. 6.5 13.4 1.7 .. 22.2 16.0 6.0 .. 21.2 26.6 .. .. .. .. .. .. 5.3 5.5 .. .. 21.5 .. 34.6 .. 26.5 1.8 21.3 37.0 .. 11.0 .. .. 29.3 .. 7.6 0.7 .. .. 4.6 .. 10.6 38.8 ..
.. 20.2 6.9 19.3 2.8 0.3 4.7 4.9 .. 1.8 7.7 0.7 .. 19.1 6.0 3.2 .. 11.3 16.2 .. .. .. .. .. .. 1.4 1.7 .. .. 9.3 .. 16.3 .. 8.8 1.2 7.1 20.0 .. 6.5 .. .. 10.3 .. 1.8 0.3 .. .. 0.5 .. 4.5 9.4 ..
.. 24.4 7.2 26.8 8.0 0.8 13.2 14.5 .. 4.5 10.1 1.1 .. 21.2 13.0 5.4 .. 19.6 22.8 .. .. .. .. 7.2 .. 3.2 2.7 14.8 24.8 16.4 .. 27.5 7.0 23.7 1.7 16.2 32.9 .. 9.9 .. .. 22.9 .. 6.8 0.4 .. .. 3.5 4.9 8.9 28.5 ..
Note: Poverty rates are based on per capita consumption estimated from household survey data, unless otherwise noted. a. Refers to the year in which the underlying household survey data were collected or, when the data collection period bridged two calendar years, the year in which most of the data were collected. b. World Bank estimates. c. Estimates based on survey data from earlier years are available but are not comparable with the most recent year reported here; these are available at http://data.worldbank.org and http://povertydata.worldbank.org. d. Based on income per capita estimated from household survey data. e. Measured as share of households.
2012 World Development Indicators
67
2.7
Poverty rates at national poverty lines
About the data
Definitions
Estimates of poverty rates and gaps at national pov-
consumption or income from own production), from
• Survey year is the year in which the underlying
erty lines are useful for comparing poverty across
which it is possible to construct a correctly weighted
household survey data were collected or, when the
time within but not across countries. Table 2.8 shows
distribution of per capita consumption or income.
data collection period bridged two calendar years,
poverty indicators at international poverty lines that
As with any indicator measured from household
the year in which most of the data were collected.
surveys, data quality can affect the precision of
• Population below national poverty line is the per-
For countries with an active poverty monitoring pro-
poverty estimates and their comparability over time.
centage of the rural, urban, or national population liv-
gram, the World Bank—in collaboration with national
These include selective survey nonresponse, sea-
ing below the corresponding rural, urban, or national
institutions, other development agencies, and civil
sonality effects, differences in the number of income
poverty line, based on consumption estimated from
society—periodically prepares poverty assessments
or consumption items in the questionnaire, and the
household survey data, unless otherwise noted.
and other analytical reports to assess the extent
time period over which respondents are asked to
• Poverty gap at national poverty line is the mean
and causes of poverty. These reports review levels
recall their expenditures.
shortfall from the rural, urban, or national poverty
allow for comparisons across countries.
and changes in poverty indicators over time and
line (counting the nonpoor as having zero shortfall)
across regions within countries, assess the impact
National poverty lines
as a percentage of the corresponding rural, urban,
of growth and public policy on poverty and inequal-
National poverty lines are the benchmark for esti-
or national poverty line, based on consumption esti-
ity, review the adequacy of monitoring and evalua-
mating poverty indicators that are consistent with
mated from household survey data, unless otherwise
tion, and contain detailed technical overviews of
the country’s specific economic and social circum-
noted. This measure reflects the depth of poverty as
the underlying household survey data and poverty
stances. National poverty lines reflect local percep-
well as its incidence.
measurement methods used. The reports are a key
tions of the level and composition of consumption or
source of comprehensive information on poverty indi-
income needed to be nonpoor. The perceived bound-
cators at national poverty lines and generally feed
ary between poor and nonpoor typically rises with the
into country-owned processes to reduce poverty,
average income of a country and thus does not pro-
build in-country capacity, and support joint work.
vide a uniform measure for comparing poverty rates
An increasing number of countries have their
across countries. While poverty rates at national
own national programs to monitor and disseminate
poverty lines should not be used for comparing pov-
official poverty estimates at national poverty lines
erty rates across countries, they are appropriate for
along with well documented household survey data
guiding and monitoring the results of country-specific
sources and estimation methodology. Estimates
national poverty reduction strategies.
from national poverty monitoring programs and the
Almost all national poverty lines are anchored to
underlying methods used are periodically reviewed by
the cost of a food bundle—based on the prevailing
the World Bank and included in the table.
national diet of the poor—that provides adequate
The complete online database of poverty estimates
nutrition for good health and normal activity, plus
at national poverty lines (http://data. worldbank.org/
an allowance for nonfood spending. National pov-
topic/poverty) is regularly updated and may contain
erty lines must be adjusted for inflation between
more recent data or revisions not incorporated in
survey years to remain constant in real terms and
the table. In addition, the poverty and equity data
thus allow for meaningful comparisons of poverty
portal (http://povertydata.worldbank.org/poverty/
over time. Because diets and consumption baskets
home/) provides access to both the database and
change over time, countries periodically recalculate
user-friendly dashboards with graphs and interactive
the poverty line based on new survey data. In such
maps that visualize trends in key poverty and inequal-
cases the new poverty lines should be deflated to
ity indicators for different regions and countries. The
obtain comparable poverty estimates from earlier
Data on poverty rates at national poverty lines are
database is maintained by the Global Poverty Work-
years. The table reports indicators based on the two
compiled by the Global Poverty Working Group,
ing Group, a team of poverty experts from the Pov-
most recent years for which survey data are avail-
based on data from World Bank’s country poverty
erty Reduction and Equity Network, the Development
able. Countries for which the most recent indica-
assessments and analytical reports as well as
Research Group, and the Development Data Group.
tors reported are not comparable to those based
country Poverty Reduction Strategies and official
on survey data from an earlier year are footnoted
poverty estimates. Further documentation of
in the table.
the data, measurement methods and tools, and
Data quality
Data sources
Poverty estimates at national poverty lines are com-
research, as well as poverty assessments and
puted from household survey data collected from
analytical reports, are available at http://data.
nationally representative samples of households.
worldbank.org/topic/poverty, www.worldbank.org/
These data must contain sufficiently detailed infor-
poverty, and http://povertydata.worldbank.org/
mation to compute a comprehensive estimate of
poverty/home/.
total household income or consumption (including
68
2012 World Development Indicators
International poverty linea
International poverty line in local currency
Albania Algeria Angola Argentina Armenia Azerbaijan Bangladesh Belarus Belize Benin Bhutan Bolivia Bosnia and Herzegovina Botswana Brazil Bulgaria Burkina Faso Burundi Cambodia Cameroon Cape Verde Central African Republic Chad Chile China Colombia Comoros Congo, Dem. Rep. Congo, Rep. Costa Rica Croatia Czech Republic Côte d’Ivoire Djibouti Dominican Republic Ecuador Egypt, Arab Rep. El Salvador Estonia Ethiopia Fiji Gabon Gambia, The Georgia Ghana Guatemala Guinea Guinea-Bissau Guyana Haiti Honduras Hungary India Indonesia Iran, Islamic Rep. Iraq Jamaica
$1.25 a day
$2 a day
2005
2005
75.5 48.4 c 88.1 1.7 245.2 2,170.9 31.9 949.5 1.8 c 344.0 23.1 3.2 1.1 4.2 2.0 0.9 303.0 558.8 2,019.1 368.1 97.7 384.3 409.5 484.2 5.1g 1,489.7 368.0 395.3 469.5 348.7c 5.6 19.0 407.3 134.8 25.5c 0.6 2.5 6.0 c 11.0 3.4 1.9 554.7 12.9 1.0 5,594.8 5.7c 1,849.5 355.3 131.5c 24.2c 12.1c 171.9 19.5i 5,241.0i 3,393.5 799.8 54.2c
120.8 77.5c 141.0 2.7 392.4 3,473.5 51.0 1,519.2 2.9c 550.4 36.9 5.1 1.7 6.8 3.1 1.5 484.8 894.1 3,230.6 589.0 156.3 614.9 655.1 774.7 8.2g 2,383.5 588.8 632.5 751.1 557.9c 8.9 30.4 651.6 215.6 40.8 c 1.0 4.0 9.6c 17.7 5.5 3.1 887.5 20.7 1.6 8,951.6 9.1c 2,959.1 568.6 210.3c 38.7c 19.3c 275.0 31.2i 8,385.7i 5,429.6 1,279.7 86.7c
2.8
PEOPLE
Poverty rates at international poverty lines
Survey year b
2005 1988 2009d,e 2007 2001 2005 2007 1998f 2003 2007e 2004 1986 2008f 2003 2003 1998 2007 2001 2003 2006f 2005h 2009 f 2008f 2004 1993e 2002 2009 f 2009 f 2005 2008f 2003 2000 2003 1998 2007 1998 2004f 2003 1993 1993e 2008f 2004 2005h 2009h 1998 2002
Population below $1.25 a day %
<2 7.6 .. 2.0 3.5 6.3 50.5 <2 11.3 .. 26.2 13.1 <2 35.6 6.0 <2 56.5 86.4 32.2 10.8 .. 62.4 .. <2 16.3 9.7 .. .. .. 2.4 <2 <2 23.3 .. 3.0 6.4 2.0 5.4 <2 55.6 29.2 .. 65.6 15.2 39.1 24.4 56.3 52.1 6.9 .. 21.4 <2 41.6 20.4 <2 .. <2
Poverty gap at $1.25 a day %
Population below $2 a day %
Poverty gap at $2 a day %
<0.5 1.2 .. 1.2 0.7 1.1 14.2 <0.5 4.7 .. 7.0 6.6 <0.5 13.8 3.4 <0.5 20.3 47.3 7.7 2.3 .. 28.3 .. 0.5 4.0 4.7 .. .. .. 1.5 <0.5 <0.5 6.8 .. 0.7 2.9 <0.5 1.9 <0.5 16.2 11.3 .. 33.8 4.1 14.4 13.2 21.3 20.6 1.5 .. 11.8 <0.5 10.5 4.1 <0.5 .. <0.5
7.9 24.6 .. 3.4 20.5 27.1 80.3 <2 26.3 .. 49.5 24.7 <2 54.7 11.3 <2 81.2 95.4 60.1 32.5 .. 81.9 .. 3.2 36.9 18.5 .. .. .. 5.0 <2 <2 46.8 .. 10.0 13.5 18.5 14.0 2.6 86.4 48.7 .. 81.2 34.9 63.3 39.2 80.8 75.7 17.1 .. 32.6 <2 75.6 52.7 8.3 .. 8.5
1.5 6.7 .. 1.7 4.5 6.8 34.3 <0.5 10.0 .. 18.8 10.9 <0.5 25.8 5.3 <0.5 39.3 64.1 22.6 9.5 .. 45.3 .. 1.1 12.5 8.2 .. .. .. 2.3 <0.5 <0.5 17.6 .. 2.7 5.5 3.5 4.8 <0.5 37.9 21.8 .. 49.1 11.8 28.5 20.2 39.7 37.4 5.4 .. 17.5 <0.5 29.5 16.5 1.8 .. 1.5
Survey year b
2008 1995 2000 d 2010d,e 2008 2008 2010 2008 1999 f 2003 2007 2008e 2007 1994 2009 f 2007 2009 2006 2008 2007 2002 2008 2003 2009 f 2008h 2010 f 2004 2006 2005 2009 f 2008 1996e 2008 2002 2010 f 2010 f 2008 2009 f 2004 2005 2009 2005 2003 2008 2006 2006f 2007 2002 1998e 2001e 2009 f 2007 2010h 2010h 2005 2007 2004
Population below $1.25 a day %
Poverty gap at $1.25 a day %
Population below $2 a day %
Poverty gap at $2 a day %
<2 6.8 54.3 <2 <2 <2 43.3 <2 12.2 47.3 10.2 15.6 <2 31.2 6.1 <2 44.6 81.3 22.8 9.6 21.0 62.8 61.9 <2 13.1 8.2 46.1 87.7 54.1 3.1 <2 <2 23.8 18.8 2.2 4.6 <2 9.0 <2 39.0 5.9 4.8 33.6 15.3 28.6 13.5 43.3 48.9 8.7 61.7 17.9 <2 32.7 18.1 <2 2.8 <2
<0.5 1.4 29.9 0.7 <0.5 <0.5 11.2 <0.5 5.5 15.7 1.8 8.6 <0.5 11.0 3.6 <0.5 14.7 36.4 4.9 1.2 6.1 31.3 25.6 0.7 3.2 3.8 20.8 52.8 22.8 1.8 <0.5 <0.5 7.5 5.3 0.5 2.1 <0.5 4.4 <0.5 9.6 1.1 0.9 11.7 4.6 9.9 4.7 15.0 16.6 2.8 32.3 9.4 <0.5 7.5 3.3 <0.5 <0.5 <0.5
4.3 23.6 70.2 <2 12.4 2.8 76.5 <2 22.0 75.3 29.8 24.9 <2 49.4 10.8 <2 72.6 93.5 53.3 30.4 40.9 80.1 83.3 2.7 29.8 15.8 65.0 95.2 74.4 6.0 <2 <2 46.3 41.2 9.9 10.6 15.4 16.9 <2 77.6 22.9 19.6 55.9 32.2 51.8 26.3 69.6 78.0 18.0 77.5 29.8 <2 68.7 46.1 8.0 21.4 5.4
0.9 6.5 42.4 0.9 2.3 0.6 30.4 <0.5 9.9 33.5 8.5 13.1 <0.5 22.3 5.4 <0.5 31.7 56.1 17.4 8.2 15.2 46.8 43.9 1.2 10.1 6.8 34.2 67.6 38.8 2.7 <0.5 <0.5 17.8 14.6 2.4 4.1 2.8 7.6 0.5 28.9 6.0 5.0 24.4 11.7 21.3 10.5 31.0 34.9 6.7 46.7 14.9 <0.5 24.5 14.3 1.8 4.4 0.8
2012 World Development Indicators
69
2.8
Poverty rates at international poverty lines International poverty linea
International poverty line in local currency
Jordan Kazakhstan Kenya Kyrgyz Republic Lao PDR Latvia Lesotho Liberia Lithuania Macedonia, FYR Madagascar Malawi Malaysia Mali Mauritania Mexico Micronesia, Fed. Sts. Moldova Montenegro Morocco Mozambique Namibia Nepal Nicaragua Niger Nigeria Pakistan Panama Papua New Guinea Paraguay Peru Philippines Poland Romania Russian Federation Rwanda São Tomé and Príncipe Senegal Serbia Seychelles Sierra Leone Slovak Republic Slovenia South Africa Sri Lanka St. Lucia Sudan Suriname Swaziland Syrian Arab Republic Tajikistan Tanzania Thailand Timor-Leste Togo Trinidad and Tobago
70
$1.25 a day
$2 a day
2005
2005
0.6 81.2 40.9 16.2 4,677.0 0.4 4.3 0.6 2.1 29.5 945.5 71.2 2.6 362.1 157.1 9.6 0.8 c 6.0 0.6 6.9 14,532.1 6.3 33.1 9.1c 334.2 98.2 25.9 0.8c 2.1c 2,659.7 2.1 30.2 2.7 2.1 16.7 295.9 7,953.9 372.8 42.9 5.6c 1,745.3 23.5 198.2 5.7 50.0 2.4 c 154.4 2.3c 4.7 30.8 1.2 603.1 21.8 0.6c 352.8 5.8 c
1.0 129.9 65.4 26.0 7,483.2 0.7 6.9 1.0 3.3 47.2 1,512.8 113.8 4.2 579.4 251.3 15.3 1.3c 9.7 1.0 11.0 23,251.4 10.1 52.9 14.6c 534.7 157.2 41.4 1.2c 3.4 c 4,255.6 3.3 48.4 4.3 3.4 26.8 473.5 12,726.3 596.5 68.6 9.0 c 2,792.4 37.7 317.2 9.1 80.1 3.8 c 247.0 3.7c 7.5 49.3 1.9 964.9 34.9 1.0 c 564.5 9.2c
2012 World Development Indicators
Survey year b
2008 2008 1997 2008 2002 2007 1994 2004 2008 2005 1998 2007e 2006 2004 2006 2009 2007 2001 2003 1993 2003 2001e 2005 2004 2006 2009 f 2009 f 2009 f 2006 2008 2008 2008 2006 2001 2008 2000 1990 2008e 2003 2006 2002 2001 2007 2000 2008j 2001 1988e
Population below $1.25 a day %
<2 <2 19.6 6.4 44.0 <2 46.2 .. <2 <2 67.8 83.1 <2 51.4 25.4 <2 .. <2 <2 6.3 74.7 49.1 53.1 14.4 50.2 63.1 22.6 5.9 .. 7.6 5.5 22.6 <2 <2 <2 72.1 .. 44.2 <2 <2 62.8 <2 <2 17.4 14.0 .. .. .. 62.9 .. 14.7 84.6 <2 52.9 .. <2
Poverty gap at $1.25 a day %
Population below $2 a day %
Poverty gap at $2 a day %
<0.5 <0.5 4.6 1.5 12.1 <0.5 25.6 .. <0.5 <0.5 26.5 46.0 <0.5 18.8 7.0 <0.5 .. <0.5 <0.5 0.9 35.4 24.6 18.4 3.7 18.3 28.7 4.1 1.8 .. 3.2 1.6 5.5 <0.5 <0.5 <0.5 34.8 .. 14.3 <0.5 <0.5 44.8 <0.5 <0.5 3.3 2.6 .. .. .. 29.4 .. 4.4 41.6 <0.5 19.1 .. <0.5
2.1 <2 42.7 20.7 76.9 <2 59.7 .. <2 4.3 89.6 93.5 2.9 77.1 52.6 4.9 .. 7.1 <2 24.3 90.0 62.2 77.3 34.4 75.3 83.1 61.0 14.6 .. 14.2 14.0 45.0 <2 2.0 <2 87.4 .. 71.3 <2 <2 75.0 <2 <2 35.7 39.7 .. .. .. 81.0 .. 37.0 95.3 5.0 77.5 .. 8.6
<0.5 <0.5 14.7 5.9 31.1 <0.5 36.1 .. 0.5 0.7 46.9 62.3 <0.5 36.5 19.2 1.0 .. 1.2 <0.5 6.3 53.6 36.5 36.6 11.5 35.6 45.9 18.8 4.9 .. 6.0 4.6 16.4 <0.5 0.6 <0.5 52.2 .. 31.2 <0.5 <0.5 54.0 <0.5 <0.5 12.3 11.9 .. .. .. 45.8 .. 12.2 60.3 0.8 37.1 .. 1.9
Survey year b
Population below $1.25 a day %
Poverty gap at $1.25 a day %
Population below $2 a day %
Poverty gap at $2 a day %
2010 2009 2005 2009 2008 2008 2003 2007 2008 2009 2010 2004 2009e 2010 2008 2008 2000 2010 2008 2007 2008 2004 e 2010 2005e 2008 2010 2008 2010 f 1996 2010 f 2010 f 2009 2009 2009 2009 2011 2001 2005 2009 2007 2003 2009e 2004 2009 2007 1995e 2009 1999e 2010 2004 2009 2007 2009j 2007 2006 1992e
<2 <2 43.4 6.2 33.9 <2 43.4 83.8 <2 <2 81.3 73.9 <2 50.4 23.4 <2 31.2 <2 <2 2.5 59.6 31.9 24.8 11.9 43.6 68.0 21.0 6.6 35.8 7.2 4.9 18.4 <2 <2 <2 63.2 28.2 33.5 <2 <2 53.4 <2 <2 13.8 7.0 20.9 19.8 15.5 40.6 <2 6.6 67.9 <2 37.4 38.7 4.2
<0.5 <0.5 16.9 1.4 9.0 <0.5 20.8 40.9 <0.5 <0.5 43.3 32.3 <0.5 16.4 6.8 <0.5 16.3 <0.5 <0.5 0.5 25.1 9.5 5.6 2.4 12.4 33.7 3.5 2.1 12.3 3.0 1.3 3.7 <0.5 <0.5 <0.5 26.6 7.9 10.8 <0.5 <0.5 20.3 <0.5 <0.5 2.3 1.0 7.2 5.5 5.9 16.0 <0.5 1.2 28.1 <0.5 8.9 11.4 1.1
<2 <2 67.2 21.7 66.0 <2 62.3 94.9 <2 5.9 92.6 90.5 2.3 78.7 47.7 5.2 44.7 4.4 <2 14.0 81.8 51.1 57.3 31.7 75.2 84.5 60.2 13.8 57.4 13.2 12.7 41.5 <2 <2 <2 82.4 54.2 60.4 <2 <2 76.1 <2 <2 31.3 29.1 40.6 44.1 27.2 60.4 16.9 27.7 87.9 4.6 72.8 69.3 13.5
<0.5 <0.5 31.8 6.0 24.8 <0.5 33.1 59.6 <0.5 0.9 60.1 51.8 <0.5 35.2 17.7 1.3 24.5 0.7 <0.5 3.2 42.9 21.8 19.0 9.6 30.8 50.2 17.9 5.1 25.5 5.7 4.1 13.8 <0.5 0.5 <0.5 44.6 20.6 24.7 <0.5 <0.5 37.5 <0.5 <0.5 10.2 7.4 15.5 15.4 11.7 29.3 3.3 7.0 47.5 0.8 27.0 27.9 3.9
International poverty linea
International poverty line in local currency
Tunisia Turkey Turkmenistan Uganda Ukraine Uruguay Venezuela, RB Vietnam West Bank and Gaza Yemen, Rep. Zambia
$1.25 a day
$2 a day
2005
2005
0.9 1.3 5,961.1c 930.8 2.1 19.1 1,563.9 7,399.9 2.7c 113.8 3,537.9
1.4 2.0 9,537.7c 1,489.2 3.4 30.6 2,502.2 11,839.8 4.3c 182.1 5,660.7
PEOPLE
2.8
Poverty rates at international poverty lines
Survey year b
Population below $1.25 a day %
Poverty gap at $1.25 a day %
Population below $2 a day %
Poverty gap at $2 a day %
2000 2007 1993e 2006 2008 2009 f 2005f 2006 2007 1998 2004
2.6 <2 63.5 51.5 <2 <2 13.4 21.4 <2 12.9 64.3
0.5 <0.5 25.8 19.1 <0.5 <0.5 8.2 5.3 <0.5 3.0 32.8
12.8 4.5 85.7 75.6 <2 <2 21.9 48.1 2.5 36.4 81.5
3.0 1.2 44.9 36.4 <0.5 <0.5 11.6 16.3 0.5 11.1 48.3
Survey year b
Population below $1.25 a day %
Poverty gap at $1.25 a day %
Population below $2 a day %
Poverty gap at $2 a day %
2005 2008 1998 2009 2009 2010 f 2006f 2008 2009 2005 2006
<2 <2 24.8 38.0 <2 <2 6.6 16.9 <2 17.5 68.5
<0.5 <0.5 7.0 12.2 <0.5 <0.5 3.7 3.8 <0.5 4.2 37.0
8.1 4.2 49.7 64.7 <2 <2 12.9 43.4 <2 46.6 82.6
1.8 0.7 18.4 27.4 <0.5 <0.5 5.9 13.5 <0.5 14.8 51.8
a. Based on nominal per capita consumption averages and distributions estimated parametrically from grouped household survey data, unless otherwise noted. b. Refers to the year in which the underlying household survey data were collected or, when the data collection period bridged two calendar years, the year in which most of the data were collected. c. Based on purchasing power parity (PPP) dollars imputed using regression. d. Covers urban areas only. e. Based on per capita income averages and distributions estimated parametrically from grouped household survey data. f. Estimated nonparametrically from nominal income per capita distributions based on unit-record household survey data. g. PPP conversion factor based on urban prices. h. Population-weighted average of urban and rural estimates. i. Based on benchmark national PPP estimate rescaled to account for cost-of-living differences in urban and rural areas. j. Estimated nonparametrically from nominal consumption per capita distributions based on unit-record household survey data.
Regional poverty estimates and progress toward
declined from 77 percent in 1981 to 14 percent in
Most of the people who have escaped extreme
the Millennium Development Goals
2008 and the number of people living on less than
poverty remain very poor by the standards of middle-
Global poverty measured at the $1.25 a day pov-
$1.25 a day dropped more than 800 million (fi gure
income countries. The median poverty line for devel-
erty line has been decreasing since the 1980s. The
2.8b). Much of this decline was in China, where the
oping countries in 2005 was $2 a day. The poverty
share of population living on less than $1.25 a day
poverty rate fell from 84 percent to 13 percent,
rate for all developing countries measured at this line
fell almost 10 percentage points, to 43 percent,
leaving about 660 million fewer people poor. Over
fell from nearly 70 percent in 1981 to 43 percent in
in 1990 and then fell about 20 percentage points
the same period the poverty rate in South Asia
2008, but the number of people living on less than
between 1990 and 2008. The number of people liv-
fell from 61 percent to 36 percent (table 2.8c).
$2 a day has remained nearly constant at around
ing in extreme poverty fell from 1.9 billion in 1990
In contrast, the poverty rate fell only slightly in
2.5 billion. The largest decrease, in both number and
to about 1.3 billion in 2008 (figure 2.8a). This sub-
Sub-Saharan Africa—from less than 52 percent in
proportion, occurred in East Asia and Pacific, led by
stantial reduction in extreme poverty over the past
1981 to more than 59 percent in 1993 then down
China. By contrast in Sub-Saharan Africa and South
quarter century, however, disguises large regional
to 47.5 percent in 2008. But the number of people
Asia, particularly India, the number of people living
differences.
living below the poverty line has nearly doubled
on less than $2 a day increased. And globally the
over this period and started declining slightly only
number of people living on $1.25–$2 a day nearly
from 2005 onward.
doubled, to 1.2 billion (see figure 2.8a).
The greatest reduction in poverty occurred in East Asia and Pacifi c, where the poverty rate
While the number of people living on less than $1.25 a day has fallen, the number living on $1.25–$2 a day has increased 2.8a
80
2.5
People living on $1.25–$2 a day, all developing regions
People living on less than $1.25 a day, other developing regions
40 South Asia
People living on less than $1.25 a day, East Asia & Pacific 20
0.5
Sub-Saharan Africa
60
1.5 1.0
2.8b
Share of population living on less than $1.25 a day, by region (percent)
People living in poverty (billions) 3.0
2.0
Poverty rates are falling in all developing regions
People living on less than $1.25 a day, South Asia
People living on less than $1.25 a day, Sub-Saharan Africa
0 1981
1984
1987
1990
Source: PovcalNet, World Bank.
1993
1996
1999
2002
2005
2008
Europe & Central Asia
Middle East & North Africa
Latin America & Caribbean
East Asia & Pacific
0 1981
1984
1987
1990
1993
1996
1999
2002
2005
2008
Source: PovcalNet, World Bank.
2012 World Development Indicators
71
2.8
Poverty rates at international poverty lines 2.8c
Regional poverty estimates Region or country
1981
1984
1987
1990
1993
1996
1999
2002
2005
2008
People living on less than 2005 PPP $1.25 a day (millions) East Asia & Pacific
1,097
970
848
926
871
640
656
523
332
284
835
720
586
683
633
443
446
363
212
173
8
7
7
9
14
18
18
11
6
2
Latin America & Caribbean
43
53
49
53
53
54
60
63
48
37
Middle East & North Africa
16
15
15
13
12
12
14
12
10
9
568
574
593
617
632
631
619
640
598
571
China Europe & Central Asia
South Asia India Sub-Saharan Africa Total
429
427
443
448
462
463
473
484
466
445
205
239
257
290
330
349
376
390
395
386
1,938
1,858
1,768
1,909
1,910
1,704
1,743
1,639
1,389
1,289
Share of people living on less than 2005 PPP $1.25 a day (percent) East Asia & Pacific
77.2
65.0
54.1
56.2
50.7
35.9
35.6
27.6
17.1
14.3
84.0
69.4
54.0
60.2
53.7
36.4
35.6
28.4
16.3
13.1
1.9
1.6
1.5
1.9
2.9
3.9
3.8
2.3
1.3
0.5
Latin America & Caribbean
11.9
13.6
12.0
12.2
11.4
11.1
11.9
11.9
8.7
6.5
Middle East & North Africa
9.6
8.0
7.1
5.8
4.8
4.8
5.0
4.2
3.5
2.7
61.1
57.4
55.3
53.8
51.7
48.6
45.1
44.3
39.4
36.0
China Europe & Central Asia
South Asia
59.8
55.7
54.1
51.3
49.7
47.2
45.6
44.5
40.8
37.4
Sub-Saharan Africa
India
51.5
55.2
54.4
56.5
59.4
58.1
57.9
55.7
52.3
47.5
Total
52.2
47.1
42.3
43.1
41.0
34.8
34.1
30.8
25.1
22.4
People living on less than 2005 PPP $2 a day (millions) East Asia & Pacific
1,313
1,316
1,279
1,334
1,301
1,140
1,138
984
758
659
972
963
907
961
926
792
770
655
482
395
36
30
29
32
43
53
57
37
22
10
Latin America & Caribbean
87
104
92
98
100
102
111
118
92
71
Middle East & North Africa
52
51
54
53
53
57
60
57
53
44
China Europe & Central Asia
South Asia India Sub-Saharan Africa Total
811
855
906
959
1,010
1,047
1,069
1,120
1,113
1,125
621
651
689
722
760
788
818
848
857
862
288
324
350
389
434
466
503
533
559
562
2,585
2,680
2,710
2,864
2,941
2,865
2,937
2,848
2,596
2,471
Share of people living on less than 2005 PPP $2 a day (percent) East Asia & Pacific
92.4
88.3
81.6
81.0
75.8
64.0
61.7
51.9
39.0
33.2
97.8
92.9
83.7
84.6
78.6
65.1
61.4
51.2
36.9
29.8
8.3
6.7
6.3
6.9
9.2
11.2
12.1
7.9
4.6
2.2
Latin America & Caribbean
23.8
26.8
22.4
22.4
21.7
21.0
22.0
22.2
16.7
12.4
Middle East & North Africa
30.1
27.1
26.1
23.5
22.1
22.2
22.0
19.7
17.4
13.9
South Asia
87.2
85.6
84.5
83.6
82.7
80.7
77.8
77.4
73.4
70.9
86.6
84.9
84.1
82.6
81.9
80.2
78.9
77.9
75.0
72.4
Sub-Saharan Africa
72.2
74.7
74.3
76.0
78.1
77.5
77.4
76.1
74.1
69.2
Total
69.6
68.0
64.8
64.6
63.1
58.6
57.4
53.5
46.9
43.0
China Europe & Central Asia
India
Source: World Bank PovcalNet.
72
2012 World Development Indicators
2.8
PEOPLE
Poverty rates at international poverty lines About the data The World Bank produced its first global poverty esti-
Income is generally more difficult to measure accu-
1993 consumption PPP estimates produced by the
mates for developing countries for World Development
rately, and consumption comes closer to the notion
World Bank. International poverty lines were recently
Report 1990: Poverty (World Bank 1990) using house-
of living standards. And income can vary over time
revised using the new data on PPPs compiled in
hold survey data for 22 countries (Ravallion, Datt, and
even if living standards do not. But consumption data
the 2005 round of the International Comparison
van de Walle 1991). Since then there has been con-
are not always available: the latest estimates reported
Program, along with data from an expanded set of
siderable expansion in the number of countries that
here use consumption data for about two-thirds of
household income and expenditure surveys. The new
field household income and expenditure surveys. The
countries.
extreme poverty line is set at $1.25 a day in 2005
World Bank’s Development Research Group maintains
However, even similar surveys may not be strictly
PPP terms, which represents the mean of the poverty
a database that is updated annually as new survey
comparable because of differences in timing or in the
lines found in the poorest 15 countries ranked by per
data become available (and thus may contain more
quality and training of enumerators. Comparisons
capita consumption. The new poverty line maintains
recent data or revisions that are not incorporated
of countries at different levels of development also
the same standard for extreme poverty—the poverty
into the table) and conducts a major reassessment
pose a potential problem because of differences
line typical of the poorest countries in the world—but
of progress against poverty about every three years.
in the relative importance of the consumption of
updates it using the latest information on the cost of
PovcalNet (http://iresearch.worldbank.org/Povcal-
nonmarket goods. The local market value of all con-
living in developing countries.
Net/) is an interactive computational tool that allows
sumption in kind (including own production, particu-
users to replicate these internationally comparable
larly important in underdeveloped rural economies)
$1.25 and $2 a day global, regional, and country-level
should be included in total consumption expenditure
• International poverty line in local currency is
poverty estimates and to compute poverty measures
but may not be. Most survey data now include valu-
the international poverty lines of $1.25 and $2.00
for custom country groupings and for different pov-
ations for consumption or income from own produc-
a day in 2005 prices, converted to local currency
erty lines. The Poverty and Equity Data portal (http://
tion, but valuation methods vary.
using the PPP conversion factors estimated by the
Definitions
povertydata.worldbank.org/poverty/home/) provides
The statistics reported here are based on con-
International Comparison Program. • Survey year
access to the database and user-friendly dashboards
sumption data or, when unavailable, on income
is the year in which the underlying data were col-
with graphs and interactive maps that visualize trends
surveys. Analysis of some 20 countries for which
lected or, when the data collection period bridged two
in key poverty and inequality indicators for different
income and consumption expenditure data were
calendar years, the year in which most of the data
regions and countries. The country dashboards dis-
both available from the same surveys found income
were collected. • Population below $1.25 a day and
play trends in poverty measures based on the national
to yield a higher mean than consumption but also
population below $2 a day are the percentages of
poverty lines (see table 2.7) alongside the internation-
higher inequality. When poverty measures based on
the population living on less than $1.25 a day and
ally comparable estimates in the table, produced from
consumption and income were compared, the two
$2 a day at 2005 international prices. As a result of
and consistent with PovcalNet.
effects roughly cancelled each other out: there was
revisions in PPP exchange rates, poverty rates for
no significant statistical difference.
individual countries cannot be compared with poverty
Data availability
rates reported in earlier editions. • Poverty gap is
The World Bank’s internationally comparable pov-
International poverty lines
the mean shortfall from the poverty line (counting
erty monitoring database now draws on income or
International comparisons of poverty estimates
the nonpoor as having zero shortfall), expressed as a
detailed consumption data collected from interviews
entail both conceptual and practical problems. Coun-
percentage of the poverty line. This measure reflects
with 1.23 million randomly sampled households
tries have different definitions of poverty, and consis-
the depth of poverty as well as its incidence.
through more than 850 household surveys collected
tent comparisons across countries can be difficult.
by national statistical offices in nearly 130 coun-
Local poverty lines tend to have higher purchasing
tries. Despite progress in the last decade, the chal-
power in rich countries, where more generous stan-
lenges of measuring poverty remain. The timeliness,
dards are used, than in poor countries.
frequency, quality, and comparability of household
Poverty measures based on international poverty
surveys need to increase substantially, particularly in
lines attempt to hold the real value of the poverty line
the poorest countries. The availability and quality of
constant across countries, as is done when making
poverty monitoring data remains low in small states,
comparisons over time. Since World Development
countries with fragile situations, and low-income
Report 1990 the World Bank has aimed to apply a
countries and even some middle-income countries.
common standard in measuring extreme poverty,
The low frequency and lack of comparability of the
anchored to what poverty means in the world’s poor-
data available in some countries create uncertainty
est countries. The welfare of people living in different
over the magnitude of poverty reduction. The need to
countries can be measured on a common scale by
improve household survey programs for monitoring
adjusting for differences in the purchasing power of
poverty is clearly urgent. But institutional, political,
currencies. The commonly used $1 a day standard,
and financial obstacles continue to limit data collec-
measured in 1985 international prices and adjusted
tion, analysis, and public access.
to local currency using purchasing power parities (PPPs), was chosen for World Development Report
Data quality
1990 because it was typical of the poverty lines in
Besides the frequency and timeliness of survey data,
low-income countries at the time.
other data quality issues arise in measuring household
Early editions of World Development Indicators
living standards. The surveys ask detailed questions
used PPPs from the Penn World Tables to convert
on sources of income and how it was spent, which
values in local currency to equivalent purchasing
must be carefully recorded by trained personnel.
power measured in U.S dollars. Later editions used
Data sources The poverty measures are prepared by the World Bank’s Development Research Group. The international poverty lines are based on nationally representative primary household surveys by national statistical offices or by private agencies under the supervision of government or international agencies and obtained from government statistical offi ces and World Bank Group country departments. Detailed information on the methodology adopted by the Socio-Economic Database for Latin America and the Caribbean to process the income data used for countries in this region is available at http://sedlac.econo.unlp.edu.ar/eng/methodology.php. The World Bank Group has prepared an annual review of its poverty work since 1993. For details on data sources and methods used in deriving the World Bank’s latest estimates, see http://iresearch.worldbank.org/povcalnet. For further discussion of the results, see Ravallion, Chen, and Sangraula (2009) and Chen and Ravallion (2011).
2012 World Development Indicators
73
2.9
Distribution of income or consumption Survey year
Afghanistan Albania Algeria Angolac Argentinac Armenia Australia Austria Azerbaijan Bangladesh Belarus Belgium Belize Benin Bhutan Bolivia Bosnia and Herzegovina Botswana Brazil Bulgaria Burkina Faso Burundi Cambodia Cameroon Canada Cape Verde Central African Republic Chad Chile China Hong Kong SAR, China Colombia Comoros Congo, Dem. Rep. Congo, Rep. Costa Rica Côte d’Ivoire Croatia Czech Republic Denmark Djibouti Dominican Republic Ecuador Egypt, Arab Rep. El Salvador Estonia Ethiopia Finland Fiji France Gabon Gambia, The Georgia Germany Ghana Greece Guatemala
74
2008b 2008b 1995b 2000 b 2010 d 2008b 1994 d 2000 d 2008b 2010 b 2008b 2000 d 1999d 2003b 2007b 2007d 2007b 1994b 2009d 2007b 2009b 2006b 2008b 2007b 2000 d 2002b 2008b 2003b 2009d 2005d 1996d 2010 d 2004b 2006b 2005b 2009d 2008b 2008b 1996d 1997d 2002b 2010 d 2010 d 2008b 2009d 2004b 2005b 2000 d 2009b 1995d 2005b 2003b 2008b 2000 d 2006b 2000 d 2006d
2012 World Development Indicators
Gini index
27.8 34.5 35.3 58.6 44.5 30.9 35.2 29.1 33.7 32.1 27.2 33.0 53.1 38.6 38.1 56.3 36.2 61.0 54.7 28.2 39.8 33.3 37.9 38.9 32.6 50.5 56.3 39.8 52.1 42.5 43.4 55.9 64.3 44.4 47.3 50.7 41.5 33.7 25.8 24.7 40.0 47.2 49.3 30.8 48.3 36.0 29.8 26.9 42.8 32.7 41.5 47.3 41.3 28.3 42.8 34.3 55.9
Percentage share of income or consumptiona
Lowest 10%
Lowest 20%
Second 20%
Third 20%
Fourth 20%
Highest 20%
Highest 10%
4.1 3.5 2.9 0.6 1.5 3.7 2.0 3.3 3.4 4.0 3.8 3.4 0.9 3.0 2.8 0.5 2.7 1.3 0.8 3.3 2.9 4.1 3.3 2.9 2.6 1.9 1.2 2.6 1.5 1.8 2.0 0.9 0.9 2.3 2.1 1.2 2.2 3.3 4.3 2.6 2.4 1.8 1.4 4.0 1.0 2.7 4.1 4.0 2.6 2.8 2.6 2.0 2.0 3.2 2.0 2.5 1.1
9.4 8.1 7.0 2.0 4.4 8.8 5.9 8.6 8.0 8.9 9.2 8.5 3.3 7.0 6.6 2.1 6.7 3.1 2.9 8.5 6.7 9.0 7.5 6.7 7.2 4.5 3.4 6.3 4.3 5.0 5.3 3.0 2.6 5.5 5.0 3.9 5.6 8.1 10.2 8.3 6.0 4.7 4.3 9.2 3.7 6.8 9.3 9.6 6.2 7.2 6.2 4.8 5.3 8.5 5.2 6.7 3.1
13.6 12.1 11.6 5.7 9.3 12.8 12.0 13.3 12.1 12.4 13.8 13.0 8.6 10.8 10.8 6.8 11.3 5.8 7.1 13.7 10.6 11.9 11.0 10.5 12.7 7.9 6.9 10.4 7.9 9.9 9.4 6.8 5.4 9.2 8.4 8.0 10.1 12.2 14.3 14.7 10.5 8.6 8.2 13.0 8.8 11.6 13.2 14.1 9.9 12.6 10.1 8.6 10.3 13.7 9.8 11.9 6.9
17.4 15.9 16.2 10.8 14.8 16.7 17.2 17.4 16.2 16.1 17.8 16.3 11.2 15.0 15.4 11.9 16.1 9.6 12.4 17.9 14.8 15.4 14.9 14.9 17.2 12.3 11.1 15.0 11.7 15.0 13.9 11.2 8.9 13.8 13.0 12.4 14.9 16.2 17.5 18.2 15.2 13.2 13.0 16.4 13.7 16.2 16.8 17.5 14.1 17.2 14.5 13.2 15.2 17.8 14.7 16.8 11.4
22.1 20.9 22.6 19.7 22.2 21.9 23.6 22.9 21.7 21.3 22.9 20.8 19.4 21.0 22.0 19.9 22.7 16.4 19.0 23.1 20.9 21.0 20.6 21.7 23.0 19.4 18.0 21.8 18.4 22.2 20.7 18.8 15.1 20.9 20.5 19.9 21.8 21.6 21.7 22.9 21.8 20.8 20.7 21.0 20.7 22.2 21.3 22.1 20.3 22.8 21.0 20.6 22.1 23.1 21.6 23.0 18.5
37.5 43.0 42.6 61.9 49.4 39.8 41.3 37.8 42.1 41.4 36.4 41.4 57.5 46.1 45.2 59.3 43.2 65.0 58.6 36.7 47.0 42.8 45.9 46.2 39.9 55.9 60.6 46.6 57.7 47.9 50.7 60.2 68.0 50.6 53.1 55.9 47.6 42.0 36.2 35.8 46.5 52.8 53.8 40.3 53.1 43.2 39.4 36.7 49.6 40.2 48.2 52.8 47.2 36.9 48.6 41.5 60.3
23.2 29.0 26.9 44.7 32.3 25.4 25.4 23.0 27.4 27.0 21.9 28.1 42.2 31.2 29.4 43.3 27.3 51.2 42.9 22.2 32.2 28.0 31.4 30.4 24.8 40.6 46.1 30.8 42.8 32.0 34.9 44.4 55.2 34.7 37.1 39.5 31.8 27.5 22.7 21.3 30.9 36.4 38.3 26.6 37.0 28.0 25.6 22.6 34.9 25.1 33.0 36.9 31.3 22.1 32.8 26.0 44.9
Survey year
Guinea Guinea-Bissau Guyana Haiti Honduras Hungary India Indonesia Iran, Islamic Rep. Iraq Ireland Israel Italy Jamaica Japan Jordan Kazakhstan Kenya Korea, Rep. Kyrgyz Republic Lao PDR Latvia Lesotho Liberia Lithuania Macedonia, FYR Madagascar Malawi Malaysia Mali Mauritania Mexico Micronesia, Fed. Sts.c Moldova Mongolia Montenegro Morocco Mozambique Namibia Nepal Netherlands New Zealand Nicaragua Niger Nigeria Norway Pakistan Panama Papua New Guinea Paraguay Peru Philippines Poland Portugal Qatar Romania Russian Federation
2007b 2002b 1998 d 2001d 2009d 2007b 2005b 2005b 2005b 2007b 2000 d 2001d 2000 d 2004b 1993d 2010 b 2009b 2005b 1998d 2009b 2008b 2008b 2003b 2007b 2008b 2009b 2010 b 2004b 2009d 2010 b 2008b 2010 d 2000 b 2010 b 2008b 2008b 2007b 2008b 2004 d 2010 b 1999d 1997d 2005b 2008b 2010 b 2000 d 2008b 2010d 1996b 2010 d 2010 d 2009b 2009 b 1997d 2007b 2009 b 2009b
Gini index
39.4 35.5 44.5 59.2 57.0 31.2 33.4 34.0 38.3 30.9 34.3 39.2 36.0 45.5 24.9 35.4 29.0 47.7 31.6 36.2 36.7 36.6 52.5 38.2 37.6 43.2 44.1 39.0 46.2 33.0 40.5 47.7 61.1 33.0 36.5 30.0 40.9 45.7 63.9 32.8 30.9 36.2 40.5 34.6 48.8 25.8 30.0 51.9 50.9 52.4 48.1 43.0 34.1 38.5 41.1 30.0 40.1
2.9
PEOPLE
Distribution of income or consumption Percentage share of income or consumptiona
Lowest 10%
Lowest 20%
Second 20%
Third 20%
Fourth 20%
Highest 20%
Highest 10%
2.7 3.1 1.3 0.7 0.4 3.5 3.8 3.7 2.6 3.8 2.9 2.1 2.3 2.3 4.8 3.4 4.0 2.0 2.9 2.8 3.3 2.6 1.0 2.4 2.6 2.0 2.2 3.0 1.8 3.5 2.4 1.4 0.4 3.3 3.0 3.6 2.7 1.9 1.4 3.6 2.5 2.2 2.6 3.6 1.8 3.9 4.4 1.1 1.9 1.0 1.4 2.6 3.3 2.0 1.3 3.4 2.8
6.4 7.3 4.5 2.4 2.0 8.4 8.6 8.3 6.4 8.7 7.4 5.7 6.5 5.4 10.6 7.7 9.1 4.8 7.9 6.8 7.6 6.6 3.0 6.4 6.6 5.1 5.4 7.0 4.5 8.0 6.0 4.4 1.6 7.8 7.1 8.5 6.5 5.2 3.2 8.3 7.6 6.4 6.2 8.1 4.4 9.6 9.6 3.3 4.5 3.3 3.9 6.0 7.7 5.8 3.9 8.3 6.5
10.5 11.6 9.8 6.3 6.1 12.9 12.2 12.0 10.9 12.8 12.3 10.5 12.0 9.0 14.2 11.6 13.2 8.7 13.6 11.4 11.3 11.4 7.2 11.4 11.1 9.5 9.5 10.8 8.7 12.0 10.4 8.9 5.2 12.2 11.2 13.1 10.5 9.5 5.0 12.2 13.2 11.4 10.2 11.8 8.3 14.0 12.9 7.8 7.7 7.8 8.3 9.4 12.0 11.0 .. 13.1 10.4
15.1 16.0 14.7 10.4 11.4 16.9 15.8 15.8 15.5 16.7 16.3 15.9 16.8 13.5 17.6 15.7 17.1 13.2 18.0 16.0 15.3 16.1 12.5 15.7 15.7 14.5 14.1 14.9 13.7 16.3 15.1 13.3 10.2 16.5 15.6 17.2 14.5 13.7 8.2 16.2 17.2 15.8 14.8 15.8 13.0 17.2 16.4 12.5 12.1 12.8 13.6 13.9 16.2 15.5 .. 17.4 14.8
21.9 21.9 21.6 17.6 20.5 22.0 21.0 21.0 22.0 22.0 21.9 23.0 22.8 20.6 22.0 21.5 22.3 20.1 23.1 22.4 20.9 22.3 21.0 21.6 22.1 22.0 20.9 20.8 21.6 22.4 21.5 20.4 19.1 22.3 22.1 22.4 20.6 20.1 15.0 21.9 23.3 22.6 21.5 21.3 20.3 22.0 21.1 20.1 19.3 19.8 21.5 21.0 22.0 21.9 .. 22.9 21.3
46.2 43.2 49.5 63.4 59.9 39.9 42.4 42.8 45.2 39.9 42.0 44.9 42.0 51.6 35.7 43.6 38.4 53.2 37.5 43.4 44.8 43.6 56.4 45.0 44.4 48.9 50.1 46.5 51.5 41.3 47.0 53.0 64.0 41.2 44.0 38.8 47.9 51.5 68.6 41.5 38.7 43.8 47.2 43.1 54.0 37.2 40.0 56.4 56.4 56.4 52.6 49.7 42.1 45.9 52.0 38.3 47.1
30.3 28.1 34.0 47.7 42.4 25.4 28.3 28.5 29.6 25.2 27.2 28.8 26.8 35.9 21.7 28.7 23.8 38.0 22.5 27.8 30.3 28.1 39.4 30.1 29.1 32.4 34.7 31.9 34.7 25.8 31.6 37.2 47.1 26.0 28.4 24.1 33.2 36.7 54.8 26.5 22.9 27.8 31.5 28.5 38.2 23.4 26.1 40.1 40.9 41.1 36.1 33.6 27.1 29.8 35.9 23.5 31.7
2012 World Development Indicators
75
2.9
Distribution of income or consumption Survey year
Rwanda São Tomé and Príncipe Senegal Serbia Seychelles Sierra Leone Singapore Slovak Republic Slovenia South Africa South Sudan Spain Sri Lanka St. Lucia Sudan Suriname Swaziland Sweden Switzerland Syrian Arab Republic Tajikistan Tanzania Thailand Timor-Leste Togo Trinidad and Tobago Tunisia Turkey Turkmenistan Uganda Ukraine United Kingdom United States Uruguay Uzbekistan Venezuela, RB Vietnam West Bank and Gaza Yemen, Rep. Zambia Zimbabwe
2011b 2001b 2005b 2009b 2007b 2003b 1998d 2009d 2004b 2009b 2009b 2000 d 2007b 1995d 2009b 1999d 2010 b 2000 d 2000 d 2004b 2009b 2007b 2009b 2007b 2006b 1992d 2005b 2008b 1998b 2009b 2009 b 1999d 2000d 2010 d 2003b 2006d 2008b 2009b 2005b 2006b 1995b
Gini index
50.8 50.8 39.2 27.8 65.8 42.5 42.5 26.0 31.2 63.1 45.5 34.7 40.3 42.6 35.3 52.9 51.5 25.0 33.7 35.8 30.8 37.6 40.0 31.9 34.4 40.3 41.4 39.0 40.8 44.3 26.4 36.0 40.8 45.3 36.7 44.8 35.6 35.5 37.7 54.6 50.1
Percentage share of income or consumptiona
Lowest 10%
Lowest 20%
Second 20%
Third 20%
Fourth 20%
Highest 20%
Highest 10%
2.1 2.2 2.5 3.7 1.6 2.6 1.9 4.4 3.4 1.2 .. 2.6 3.1 2.0 2.7 1.1 1.7 3.6 2.9 3.4 3.5 2.8 2.8 4.0 3.3 2.1 2.4 2.1 2.6 2.4 4.2 2.1 1.9 1.9 2.9 1.2 3.2 3.2 2.9 1.5 1.8
5.2 5.2 6.2 8.9 3.7 6.1 5.0 10.1 8.2 2.7 .. 7.0 6.9 5.2 6.8 3.2 4.1 9.1 7.6 7.7 8.3 6.8 6.7 9.0 7.6 5.5 5.9 5.7 6.1 5.8 9.7 6.1 5.4 4.9 7.1 4.3 7.4 7.4 7.2 3.6 4.6
8.3 8.5 10.6 13.7 5.7 9.7 9.4 14.1 12.8 4.6 .. 12.1 10.4 9.9 11.7 7.2 7.4 14.0 12.2 11.4 12.8 11.1 10.3 12.5 11.7 10.3 10.1 10.9 10.2 9.6 14.0 11.4 10.7 9.0 11.5 9.5 11.5 11.5 11.3 6.7 8.1
11.9 12.2 15.3 17.8 8.3 14.0 14.6 17.5 17.0 8.2 .. 16.4 14.4 14.8 16.4 12.3 12.0 17.6 16.3 15.5 17.0 15.6 14.5 16.1 16.1 15.5 14.7 15.9 14.7 13.8 17.7 16.0 15.7 13.7 15.7 14.6 15.8 15.8 15.3 11.2 12.2
17.8 17.7 22.0 22.8 12.7 20.9 22.0 22.0 22.6 16.3 .. 22.5 20.5 21.8 22.7 20.4 20.0 22.7 22.6 21.4 22.6 21.7 21.4 21.2 22.2 22.7 21.3 22.4 21.5 20.0 22.4 22.5 22.4 21.5 21.5 22.2 21.8 21.8 21.0 19.2 19.3
56.8 56.4 45.9 36.9 69.6 49.3 49.0 36.2 39.4 68.2 .. 42.0 47.8 48.3 42.4 56.9 56.6 36.6 41.3 43.9 39.4 44.8 47.2 41.3 42.4 45.9 47.9 45.1 47.5 50.7 36.3 44.0 45.8 50.9 44.2 49.4 43.4 43.4 45.3 59.4 55.7
43.2 43.6 30.1 22.2 60.2 33.6 32.8 22.4 24.6 51.7 .. 26.6 32.9 32.5 26.7 40.6 40.1 22.2 25.9 28.9 24.3 29.6 31.5 27.0 27.1 29.9 32.5 29.4 31.7 36.1 22.0 28.5 29.9 34.4 29.5 33.2 28.2 28.2 30.8 43.1 40.3
a. Percentage shares by quintile may not sum to 100 percent because of rounding. b. Refers to expenditure shares by percentiles of population, ranked by per capita expenditure. c. Covers urban areas only. d. Refers to income shares by percentiles of population, ranked by per capita income.
76
2012 World Development Indicators
About the data
Inequality in the distribution of income is reflected
2.9
PEOPLE
Distribution of income or consumption Definitions
• Survey year is the year in which the underlying
in the percentage shares of income or consumption
data were collected. • Gini index measures the
accruing to portions of the population ranked by
extent to which the distribution of income (or con-
income or consumption levels. The portions ranked
sumption expenditure) among individuals or house-
lowest by personal income receive the smallest
holds within an economy deviates from a perfectly
shares of total income. The Gini index provides a con-
equal distribution. A Lorenz curve plots the cumula-
venient summary measure of the degree of inequal-
tive percentages of total income received against the
ity. Data on the distribution of income or consump-
cumulative number of recipients, starting with the
tion come from nationally representative household
poorest individual. The Gini index measures the area
surveys. Where the original data from the house-
between the Lorenz curve and a hypothetical line
hold survey were available, they have been used to
of absolute equality, expressed as a percentage of
directly calculate the income or consumption shares
the maximum area under the line. Thus a Gini index
by quintile. Otherwise, shares have been estimated
of 0 represents perfect equality, while an index of
from the best available grouped data.
100 implies perfect inequality. • Percentage share
The distribution data have been adjusted for
of income or consumption is the share of total
household size, providing a more consistent measure
income or consumption that accrues to subgroups of
of per capita income or consumption. No adjustment
population indicated by deciles or quintiles.
has been made for spatial differences in cost of living within countries, because the data needed for such calculations are generally unavailable. For further details on the estimation method for low- and middleincome economies, see Ravallion and Chen (1996). Because the underlying household surveys differ in method and type of data collected, the distribution data are not strictly comparable across countries. These problems are diminishing as survey methods improve and become more standardized, but achieving strict comparability is still impossible (see About the data for tables 2.7 and 2.8). Two sources of noncomparability should be noted in particular. First, the surveys can differ in many respects, including whether they use income or consumption expenditure as the living standard indicator. The distribution of income is typically more unequal than the distribution of consumption. In addition, the definitions of income used differ more often among surveys. Consumption is usually a much
Data sources
better welfare indicator, particularly in developing
Data on distribution are compiled by the World
countries. Second, households differ in size (num-
Bank’s Development Research Group using
ber of members) and in the extent of income sharing
PovcalNet
among members. And individuals differ in age and
PovcalNet) based on household survey data
consumption needs. Differences among countries in
obtained from government statistical agencies
these respects may bias comparisons of distribution.
and World Bank country departments. Detailed
World Bank staff have made an effort to ensure
information on the methodology adopted by the
that the data are as comparable as possible. Wher-
Socio-Economic Database for Latin America and
ever possible, consumption has been used rather
the Caribbean to process the income data used
than income. Income distribution and Gini indexes for
for countries in this region is available at http://
high-income economies are calculated directly from
sedlac.econo.unlp.edu.ar/eng/methodology.php.
the Luxembourg Income Study database, using an
Data for high-income economies are computed
estimation method consistent with that applied for
based on data from the Luxembourg Income Study
developing countries.
database.
(http://iresearch.worldbank.org/
2012 World Development Indicators
77
2.10
Assessing vulnerability and security Youth unemployment
Afghanistan Albania Algeria Angola Argentina Armenia Australia Austria Azerbaijan Bahrain Bangladesh Belarus Belgium Benin Bolivia Bosnia and Herzegovina Botswana Brazil Bulgaria Burkina Faso Burundi Cambodia Cameroon Canada Central African Republic Chad Chile China Hong Kong SAR, China Colombia Congo, Dem. Rep. Congo, Rep. Costa Rica Côte d’Ivoire Croatia Cuba Cyprus Czech Republic Denmark Dominican Republic Ecuador Egypt, Arab Rep. El Salvador Eritrea Estonia Ethiopia Finland France Gabon Gambia, The Georgia Germany Ghana Greece Guatemala Guinea Guinea-Bissau Haiti
78
Female-headed households
Pension contributors
Male % of male labor force ages 15–24
Female % of female labor force ages 15–24
% of total
2007–10a
2007–10a
2007–10a
Year
.. 16 .. 25 34 36 .. .. 25 .. 13 54 .. 23 23 .. .. .. .. .. .. 27 .. .. .. .. .. .. .. 34 21 23 .. .. 24 46 .. .. .. 35 .. 13 .. .. .. 23 .. .. .. .. .. .. 34 .. .. 17 .. 44
2006 2007 2002
.. 26 .. .. 19b 37 12b 9 13 .. .. .. 22 .. .. 45 .. 14 24 .. .. 4 .. 17b .. .. 17 .. 15 18 .. .. 10 .. 30 3 16 18 16 21 12b 17 13 .. 35 .. 22 22 .. .. 32 10 .. 27 .. .. .. ..
2012 World Development Indicators
.. 28 .. .. 25b 55 11b 9 16 .. .. .. 22 .. .. 52 .. 23 22 .. .. 3 .. 12b .. .. 22 .. 10 30 .. .. 13 .. 34 4 17 19 12 45 18b 48 8 .. 30 .. 19 23 .. .. 41 9 .. 41 .. .. .. ..
2008 2008 2005 2005 2007 2007 2004 2008 2005 2005 2008 2009 2006 2008 2008 2004 2006 2006 2007 2004 2005 2008 2007 2008 2008 2008 2008 2004 2004 2010
2007 2007 2008 2004 2009 2008 2004 2005 2005 2006 2004 2005 2004 2005 2008 1993 2004
Public expenditure on pensions
% of labor force
% of workingage population
3.7 37.9 36.7 .. 42.3 32.1 90.7 93.7 35.4 20.2 2.5 93.5 91.4 5.5 12.2 24.5 9.0 55.2 78.7 1.2 3.5 .. 16.2 66.9 1.5 2.7 59.6 26.9 78.9 31.5 14.2 9.7 55.5 12.8 82.9 .. .. 95.4 92.9 25.6 26.4 55.1 22.9 .. 94.5 .. 89.7 87.3 .. 2.7 29.2 86.9 8.1 86.0 20.3 12.1 2.0 ..
2.3 25.7 22.0 .. 33.1 24.6 69.7 68.5 24.7 13.4 1.9 66.8 61.5 4.2 9.4 29.0 7.2 45.7 54.7 1.1 3.3 .. 11.5 53.6 1.3 2.0 39.5 27.7 55.4 20.2 10.5 7.5 40.2 9.1 50.0 .. .. 67.5 86.7 19.1 18.0 27.9 15.7 .. 68.7 .. 67.4 61.4 .. 2.2 22.6 65.6 6.4 58.3 14.7 10.8 1.5 ..
Year
2005 2009 2002 2010 2006 2007 2007 2007 2004 2006 2008 2007 2006 2009 2009 2009 2010 2008 2004 2006 2005 2005 2007 2004 2009 2006 2006 2010
2009 2006 2009
2007 2007 2009 2010 2004 2010 2001 2007 2006 2007 2007 2003 2004 2007 2002 2010 2009 2005
% of GDP
0.5 6.1 3.2 .. 7.4 3.2 3.4 12.3 3.8 0.9 0.3 10.2 8.9 1.5 1.5 9.4 1.3 6.1 8.5 0.7 0.7 0.6 0.4 4.2 0.8 .. 5.0 2.5 1.6 3.5 .. .. 2.8 0.7 10.3 .. 8.5 5.6 0.7 1.8 4.1 1.7 0.3 10.9 0.3 8.3 12.5 .. 0.1 3.0 10.7 1.3 13.5 1.2 .. 2.1 ..
Year
2000 2007
2006
2002
2004
2006
2005
2005
2007
2003
Average pension % of average wage
.. .. .. .. 43.8 20.3 .. .. 24.3 .. .. 41.6 .. .. .. .. .. .. 42.9 .. .. .. .. .. .. .. 53.5 .. .. .. .. .. .. .. 32.4 .. .. 40.7 .. .. .. .. .. .. 35.4 .. .. .. .. .. 13.0 .. .. .. .. .. .. ..
Youth unemployment
Honduras Hungary India Indonesia Iran, Islamic Rep. Iraq Ireland Israel Italy Jamaica Japan Jordan Kazakhstan Kenya Korea, Dem. Rep. Korea, Rep. Kosovo Kuwait Kyrgyz Republic Lao PDR Latvia Lebanon Lesotho Liberia Libya Lithuania Macedonia, FYR Madagascar Malawi Malaysia Mali Mauritania Mauritius Mexico Moldova Mongolia Morocco Mozambique Myanmar Namibia Nepal Netherlands New Zealand Nicaragua Niger Nigeria Norway Oman Pakistan Panama Papua New Guinea Paraguay Peru Philippines Poland Portugal Puerto Rico Qatar
Female-headed households
Pension contributors
Male % of male labor force ages 15–24
Female % of female labor force ages 15–24
% of total
2007–10a
2007–10a
2007–10a
Year
26 .. 14 13 .. 11 .. .. .. 41 .. 11 .. 34 .. .. .. .. 25 .. .. .. .. 30 .. .. 8 22 .. .. 12 .. .. .. 34 29 .. .. .. 44 23 .. .. .. 19 19 .. .. 10 .. .. .. 22 17 .. .. .. ..
2008 2008 2006 2008 2001 2009 2005 2008 2005 2004 2005 2006 2004 2006
.. 28 .. 22 20 .. 34 15 27 23 10 23 7 .. .. 11 .. .. .. .. 35 22 29 4 .. 38 35 .. .. 10 .. .. 19 9 16 .. 23 .. .. .. .. 9 17b .. .. .. 11 .. 7 12 .. 9 13b 17 22 21 29 1
.. 25 .. 23 34 .. 21 13 29 33 8 46 8 .. .. 9 .. .. .. .. 34 22 42 8 .. 31 38 .. .. 12 .. .. 29 10 15 .. 19 .. .. .. .. 9 17b .. .. .. 8 .. 11 21 .. 17 16b 20 25 24 22 8
2005
2006 2004 2003 2003 2005 2004 2007 2008 2009 2008 2010 2000 2000 2008 2009 2005 2007 2006 2008 2008 2005 2008 2006 2004 2005 2008 2009 2004 2008 2007 2005 2005
2.10
Public expenditure on pensions
% of labor force
% of workingage population
17.3 92.0 10.3 7.1 34.2 35.6 88.9 .. 90.1 17.2 95.4 38.4 62.5 7.5 .. 49.5 .. .. 40.4 .. 91.7 34.5 4.4 .. 68.5 82.9 52.3 5.3 .. 49.0 7.3 13.1 53.4 27.4 56.7 33.5 23.8 1.9 .. 9.6 3.4 90.7 .. 21.7 1.9 8.1 93.2 .. 3.9 .. 4.4 12.4 21.7 25.0 81.4 92.0 .. ..
11.1 56.7 6.4 8.0 18.9 19.0 64.1 89.1 57.1 12.7 75.0 19.9 48.4 6.4 .. 34.3 .. .. 29.6 6.0 70.6 17.3 3.5 .. 37.5 56.9 33.2 4.9 .. 32.5 4.4 9.4 34.5 19.0 32.1 25.6 13.5 1.7 .. 5.8 2.6 70.7 .. 14.6 1.3 4.8 75.2 .. 2.2 .. 3.3 9.4 15.6 17.0 52.7 71.6 .. ..
Year
2010 2008 2007 2010 2000 2009 2007 2007 2007 2004 2007 2005 2009 2003 2005 2009 2010 2005 2009 2003
2001 2010 2008
2004 2010 2003 2007 2007 2009 2009 2003 2006 2004 2006 2007 2007 2006 2004 2007 2004 2005 2001 2010 2003 2009 2007
% of GDP
0.0 10.5 2.2 1.0 1.1 3.9 3.6 4.8 14.1 0.7 8.8 2.0 3.2 1.1 .. 1.6 2.7c .. 2.7 0.2 8.5 2.1 .. .. 2.1 8.6 9.4 .. .. 0.3 1.6 0.6 2.9 1.4 9.1 4.9 1.9 0.3 .. 1.3 0.2 4.7 4.3 .. 0.7 0.9 4.7 .. 0.5 .. 0.2 1.2 2.5 1.5 10.0 10.8 .. ..
Year
2005
2003
2003 2005
2005 2006
2003
2007
2012 World Development Indicators
Average pension % of average wage
.. 39.8 .. .. .. .. .. .. .. .. .. .. 24.9 .. .. .. .. .. 27.5 .. 33.1 .. .. .. .. 30.9 55.0 .. .. .. .. .. .. .. 20.9 .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. 47.1 .. .. ..
79
PEOPLE
Assessing vulnerability and security
2.10
Assessing vulnerability and security Youth unemployment
Romania Russian Federation Rwanda Saudi Arabia Senegal Serbia Sierra Leone Singapore Slovak Republic Slovenia Somalia South Africa South Sudan Spain Sri Lanka Sudan Swaziland Sweden Switzerland Syrian Arab Republic Tajikistan Tanzania Thailand Timor-Leste Togo Trinidad and Tobago Tunisia Turkey Turkmenistan Uganda Ukraine United Arab Emirates United Kingdom United States Uruguay Uzbekistan Venezuela, RB Vietnam West Bank and Gaza Yemen, Rep. Zambia Zimbabwe World Low income Middle income Lower middle income Upper middle income Low & middle income East Asia & Pacific Europe & Central Asia Latin America & Carib. Middle East & N. Africa South Asia Sub-Saharan Africa High income Euro area
Female-headed households
Pension contributors
Male % of male labor force ages 15–24
Female % of female labor force ages 15–24
% of total
2007–10a
2007–10a
2007–10a
Year
.. .. 31 .. 20 29 22 .. .. .. .. .. .. .. .. 19 48 .. .. .. .. 24 30 12 .. .. .. .. .. 30 49 .. .. .. .. 18 .. .. .. .. 24 38
2007 2007 2004
22 17 .. 24 .. 31 .. 10 35 15 .. 45 .. 43 17 .. .. 27 7 15 .. .. 4 .. .. 9 .. 21 .. .. .. 8 21 21b 16 .. 12 .. 39 .. .. .. .. w .. .. .. .. .. .. 17 12 19 .. 19 22
22 18 .. 46 .. 41 .. 17 32 14 .. 53 .. 40 28 .. .. 24 8 40 .. .. 5 .. .. 13 .. 23 .. .. .. 22 17 16b 25 .. 16 .. 47 .. .. .. .. w .. .. .. .. .. .. 18 18 36 .. .. 17 22
2003 2003 2004 2008 2003 2008 2007 2005 2006 2005 2009 2005 2005 2008 2006 2008 2003 2008 2004 2007 2004 2010 2005 2005 2007 2005 2008 2008 2009 2006 2006
Public expenditure on pensions
% of labor force
% of workingage population
67.9 66.8 4.6 .. 5.1 45.0 5.5 62.1 78.9 87.4 .. 6.3 .. 69.4 24.1 5.2 15.4 88.8 95.4 26.8 .. 4.3 22.8 .. 7.3 71.1 48.6 58.6 .. 10.3 65.3 .. 93.2 92.2 78.5 .. 33.9 19.3 14.0 10.4 10.9 ..
45.0 49.9 4.1 .. 4.1 40.2 3.8 44.6 55.3 63.8 .. 3.7 .. 48.7 14.9 2.9 10.3 72.2 78.7 14.3 .. 4.0 18.3 .. 5.7 51.2 25.5 30.5 .. 9.2 44.7 .. 71.5 71.4 61.2 86.3 24.2 15.2 6.5 5.0 8.0 ..
a. Data are for the most recent year available. b. Limited coverage. c. Includes expenditure on old-age and survivors benefi ts only.
80
2012 World Development Indicators
Year
2009 2010 2005 2006 2010
2007 2007 2010 2007 2007
2007 2007 2004 2006 2006 2003 2010 2003 2008 2003 2010 2007 2007 2010 2005 2010 2004 2009 2004 2008 2002
% of GDP
8.3 8.3 0.7 .. 1.4 14.0 .. .. 9.3 12.7 .. 2.2 .. 8.0 2.0 .. .. 7.2 6.4 1.3 .. 0.9 0.8 .. 0.8 2.8 4.3 6.2 .. 0.3 17.8 .. 5.4 6.0 8.8 6.5 5.0 2.5 4.0 1.5 1.4 2.3
Year
2005 2003
2005 2005
2006
2000 2003
2007
2007
2006 2005
Average pension % of average wage
41.5 29.2 .. .. .. .. .. .. 44.7 44.3 .. .. .. 58.6 .. .. .. .. 40.0 .. 25.7 .. .. .. .. .. .. 61.3 .. .. 48.3 .. .. 29.2 .. 40.0 .. .. .. .. .. ..
About the data
2.10
Definitions
As traditionally measured, poverty is a static con-
citizenship, residency, or income status. In contri-
• Youth unemployment is the share of the labor
cept, and vulnerability a dynamic one. Vulnerabil-
bution-related schemes, however, eligibility is usually
force ages 15–24 without work but available for and
ity reflects a household’s resilience in the face of
restricted to individuals who have contributed for a
seeking employment. • Female-headed households
shocks and the likelihood that a shock will lead to a
minimum number of years. Definitional issues—relat-
are households with a female head. • Pension con-
decline in well-being. Thus, it depends primarily on
ing to the labor force, for example—may arise in
tributors are members of the labor force or working-
the household’s assets and insurance mechanisms.
comparing coverage by contribution-related schemes
age population (here defined as ages 15 and older)
Because poor people have fewer assets and less
over time and across countries (for country-specific
covered by a pension scheme. • Public expenditure
diversified sources of income than do the better-off,
information, see Hinz and others 2011). The share
on pensions is all government expenditures on cash
fluctuations in income affect them more.
of the labor force covered by a pension scheme may
transfers to the elderly, the disabled, and survivors
be overstated in countries that do not try to count
and the administrative costs of these programs.
informal sector workers as part of the labor force.
• Average pension is the average pension payment
Enhancing security for poor people means reducing their vulnerability to such risks as ill health, providing them the means to manage risk themselves,
Public interventions and institutions can provide
of all pensioners of the main pension schemes
and strengthening market or public institutions for
services directly to poor people, although whether
(including old-age, survivors, disability, military, and
managing risk. Tools include microfinance programs,
these interventions and institutions work well for the
work accident or disease pensions) divided by the
public provision of education and basic health care,
poor is debated. State action is often ineffective,
average wage of all formal sector workers.
and old age assistance (see tables 2.11 and 2.16).
in part because governments can influence only a
Poor households face many risks, and vulnerability
few of the many sources of well-being and in part
is thus multidimensional. The indicators in the table
because of difficulties in delivering goods and ser-
focus on individual risks—youth unemployment,
vices. The effectiveness of public provision is further
female-headed households, income insecurity in
constrained by the fiscal resources at governments’
old age—and the extent to which publicly provided
disposal and the fact that state institutions may not
services may be capable of mitigating some of these
be responsive to the needs of poor people.
risks. Poor people face labor market risks, often hav-
The data on public pension spending cover the
ing to take up precarious, low-quality jobs and to
pension programs of the social insurance schemes
increase their household’s labor market participa-
for which contributions had previously been made.
tion by sending their children to work (see tables
In many cases noncontributory pensions or social
2.4 and 2.6). Income security is a prime concern
assistance targeted to the elderly and disabled are
for the elderly.
also included. A country’s pattern of spending is cor-
Youth unemployment is an important policy issue for many economies. Experiencing unemployment
related with its demographic structure—spending increases as the population ages.
may permanently impair a young person’s productive potential and future employment opportunities. The table presents unemployment among youth ages 15–24, but the lower age limit for young people in a country could be determined by the minimum age for leaving school, so age groups could differ across countries. Also, since this age group is likely to include school leavers, the level of youth unemployment varies considerably over the year as a result of different school opening and closing dates. The youth unemployment rate shares similar limitations on comparability as the general unemployment rate. For further information, see About the data for table 2.5 and the original source.
Data sources
The definition of female-headed household differs
Data on youth unemployment are from the ILO’s
greatly across countries, making cross-country com-
Key Indicators of the Labour Market, 7th edition,
parison difficult. In some cases it is assumed that a
database. Data on female-headed households
woman cannot be the head of any household with an
are from MEASURE DHS Demographic and Health
adult male, because of sex-biased stereotype. Cau-
Surveys by ICF International. Data on pension con-
tion should be used in interpreting the data.
tributors and public expenditure on pensions are
Pension scheme coverage may be broad or
from Hinz and others (2011).
even universal where eligibility is determined by
2012 World Development Indicators
81
PEOPLE
Assessing vulnerability and security
2.11
Education inputs Public expenditure per student
1999
2010a
% of GDP per capita Secondary 1999 2010a
.. .. 12.0 .. 12.9 .. 16.4 24.9 6.9 .. .. .. 18.2 11.8 14.2 .. .. 10.8 15.2 .. 14.4 5.8 .. .. .. .. 14.4 .. 12.4 15.2 .. .. 15.5 14.3 .. 25.0 17.1 11.2 24.6 .. 4.4 .. 8.6 15.0 20.9 .. 17.4 17.9 .. .. .. .. 17.6 11.7 6.7 .. .. ..
.. .. .. .. 16.8 16.5 20.2 24.1 .. .. 8.8 .. 22.4 13.0 .. .. .. 18.5 24.4 .. 19.0 6.8 6.6 .. 4.4 9.6 17.4 .. 15.1 15.7 .. 11.1 14.6 .. 21.8 44.2 28.7 13.6 24.9 7.5 .. .. 8.8 .. 25.9 18.2 18.5 18.0 .. 24.6 14.8 15.6 11.4 .. 10.4 7.0 .. ..
.. .. .. .. 18.2 .. 15.0 30.0 17.0 .. 11.5 .. 23.7 24.0 11.7 .. .. 9.5 18.4 .. 67.8 11.5 .. .. .. .. 14.8 11.5 17.7 16.1 .. .. 21.4 41.3 .. 37.1 28.2 21.7 38.1 .. 9.6 .. 7.5 37.4 27.2 .. 25.9 29.5 .. .. .. .. 40.4 15.6 4.3 .. .. ..
Primary
Afghanistan Albania Algeria Angola Argentina Armenia Australia Austria Azerbaijan Bahrain Bangladesh Belarus Belgium Benin Bolivia Bosnia and Herzegovina Botswana Brazil Bulgaria Burkina Faso Burundi Cambodia Cameroon Canada Central African Republic Chad Chile China Hong Kong SAR, China Colombia Congo, Dem. Rep. Congo, Rep. Costa Rica Côte d’Ivoire Croatia Cuba Cyprus Czech Republic Denmark Dominican Republic Ecuador Egypt, Arab Rep. El Salvador Eritrea Estonia Ethiopia Finland France Gabon Gambia, The Georgia Germany Ghana Greece Guatemala Guinea Guinea-Bissau Haiti
82
2012 World Development Indicators
.. .. .. .. 27.1 17.8 18.8 27.4 .. .. 12.0 .. 36.5 .. .. .. .. 19.5 24.2 .. 64.1 6.8 28.0 .. 14.5 19.1 17.7 .. 18.0 15.3 .. .. 14.4 .. 25.2 51.9 38.3 22.8 31.5 6.7 .. .. 9.4 .. 29.6 9.8 32.2 27.8 .. 16.2 15.5 21.8 27.2 .. 6.2 6.1 .. ..
Public expenditure on education
Tertiary 1999
.. .. .. .. 17.7 .. 26.6 51.8 19.1 .. 46.6 .. 38.2 208.2 44.1 .. .. 57.2 17.6 .. 1,036.1 42.5 .. 44.0 .. .. 19.4 90.0 .. 37.7 .. .. .. 141.0 35.8 77.6 47.9 33.7 65.9 .. .. .. 8.9 430.8 31.8 .. 40.5 30.7 .. .. .. .. .. 26.5 .. .. .. ..
2010a
.. .. .. .. 19.1 7.5 20.7 43.5 22.3 .. 27.7 14.7 36.6 .. .. .. .. 27.6 24.8 .. 477.4 .. 28.0 .. 96.0 279.1 13.7 .. 26.8 29.4 .. 134.2 .. .. 29.2 61.1 57.0 25.7 52.1 .. .. .. 11.7 .. 22.1 31.0 32.5 37.0 .. 94.4 11.4 .. 171.7 .. .. 100.0 .. ..
% of GDP 2010a
.. .. 4.3 .. 6.0 3.2 5.1 5.5 3.2 2.9 2.2 4.5 6.4 4.5 .. .. 7.8 5.4 4.4 .. 9.2 2.6 3.5 4.8 1.2 2.8 4.5 .. 3.6 4.8 .. 6.2 6.3 4.6 4.3 13.4 7.4 4.1 7.7 .. .. 3.8 3.2 .. 5.7 4.7 6.1 5.6 .. 5.0 3.2 4.6 5.5 .. 3.2 2.4 .. ..
Trained Primary teachers school in primary pupil–teacher education ratio
% of total government expenditure
% of total
pupils per teacher
2010a
2010a
2010a
.. .. 20.3 .. 14.0 13.0 12.9 11.2 10.9 11.7 14.1 8.9 12.9 18.2 .. .. 16.2 .. 12.3 .. 25.1 .. 17.9 .. 12.0 10.1 .. .. 20.2 14.9 .. .. 23.1 24.6 .. 18.3 17.4 9.5 15.0 .. .. 11.9 .. .. 14.2 25.4 12.4 10.6 .. 22.8 7.7 10.4 24.4 .. .. 19.2 .. ..
.. .. .. .. .. .. .. .. 100.0 .. 58.4 99.8 .. 42.6 .. .. .. .. .. 85.7b 91.2 99.1 57.1 .. .. 45.3 .. .. 95.6 100.0 91.7 86.8 89.5 100.0 b .. 100.0 .. .. .. 84.9 82.6 .. 92.7 93.8 .. 39.4 .. .. .. .. 94.6 .. 50.6b .. .. 65.2 38.9 ..
44 20 23 46 16 .. .. 11 11 .. 43 15 11 46 .. .. .. 23 17 48b 51 48 46 .. 84 56 23 17 15 28 37 49 18 49b 15 9 14 19 .. 26 17 27 31 38 12 54 14 19 25b 37 8 13 31b .. 28 42 52 ..
Public expenditure per student
1999
2010a
% of GDP per capita Secondary 1999 2010a
.. 17.9 11.9 .. 9.3 .. 11.0 20.5 24.0 13.4 21.1 13.7 .. 21.4 .. 18.4 .. 17.0 .. 2.2 19.5 .. 37.0 .. .. .. .. 5.7 13.4 12.6 15.3 11.6 9.3 11.9 .. .. 17.4 .. .. 22.2 9.1 15.2 20.0 .. .. .. 21.8 10.7 .. 13.7 .. 13.6 7.6 12.0 .. 18.8 .. ..
18.7 21.9 .. 11.4 15.2 .. 18.6 19.5 24.5 19.9 21.5 11.9 .. .. .. 19.4 .. 10.0 .. .. 31.4 .. 24.8 .. .. 18.1 .. 7.8 6.8 14.6 15.0 13.4 9.0 13.7 41.4 14.6 16.9 .. .. 17.8 17.8 17.2 21.9 .. 21.1 .. 18.3 12.8 .. 7.5 .. .. 7.8 9.0 25.3 19.9 .. 10.3
.. 19.0 24.7 .. 10.1 .. 16.8 21.9 27.7 21.0 20.9 15.8 .. 14.4 .. 15.7 .. .. .. 4.4 23.7 .. 82.4 .. .. .. .. .. 9.6 21.9 59.7 36.4 14.2 14.5 .. .. 45.5 .. 6.6 36.3 13.1 22.2 23.7 .. .. .. 30.4 20.9 .. 19.1 .. 18.4 10.8 10.2 10.9 26.6 .. ..
Primary
Honduras Hungary India Indonesia Iran, Islamic Rep. Iraq Ireland Israel Italy Jamaica Japan Jordan Kazakhstan Kenya Korea, Dem. Rep. Korea, Rep. Kosovo Kuwait Kyrgyz Republic Lao PDR Latvia Lebanon Lesotho Liberia Libya Lithuania Macedonia, FYR Madagascar Malawi Malaysia Mali Mauritania Mauritius Mexico Moldova Mongolia Morocco Mozambique Myanmar Namibia Nepal Netherlands New Zealand Nicaragua Niger Nigeria Norway Oman Pakistan Panama Papua New Guinea Paraguay Peru Philippines Poland Portugal Puerto Rico Qatar
279.7 23.0 .. 12.9 21.1 .. 27.5 20.4 26.7 22.6 22.3 14.3 .. .. .. 23.2 .. 13.7 .. .. 32.3 .. 55.7 .. .. 22.6 .. 11.5 23.3 20.2 37.5 31.2 14.3 13.6 39.4 .. .. .. .. 16.4 11.3 25.0 23.6 .. 41.9 .. 25.6 14.6 .. 9.9 .. .. 8.9 9.1 22.9 31.6 .. 11.0
Public expenditure on education
Tertiary 1999
.. 34.1 95.0 .. 35.5 .. 28.6 30.9 27.6 70.4 15.1 .. .. 207.9 .. 8.4 .. .. 24.3 67.7 27.9 13.8 939.7 .. 23.4 34.2 .. .. 2,503.9 81.6 256.8 80.1 25.4 48.8 .. .. 97.1 1,408.9 27.0 157.3 141.3 47.4 39.5 .. .. .. 45.8 .. .. 33.7 .. 58.9 21.1 14.4 21.1 27.1 .. ..
2010a
.. 24.8 .. 16.8 19.2 .. 32.7 21.3 25.0 50.2 20.9 .. 10.1 .. .. 10.1 .. .. 17.6 .. 14.2 10.0 .. .. .. 17.0 .. 144.8 1,937.6 60.7 135.3 193.9 16.1 38.9 44.8 6.0 83.3 .. .. .. 39.3 41.5 31.4 .. 438.8 .. 46.8 41.6 .. 21.6 .. .. .. .. 18.4 26.7 .. 337.7
% of GDP 2010a
.. 5.1 .. 4.6 4.7 .. 5.7 5.9 4.6 6.1 3.8 .. 3.1 6.7 .. 4.8 4.3 .. 6.0 3.3 5.6 1.8 13.1 2.8 .. 4.9 .. 3.2 5.7b 5.8 4.5 4.3 3.1 4.9 9.1 5.4 5.4 .. .. 8.1 4.7 5.5 7.2 .. 3.8 .. 6.5 4.4 2.4 3.8 .. .. 2.6 2.7 5.1 4.9 .. 2.4
2.11
PEOPLE
Education inputs
Trained Primary teachers school in primary pupilâ&#x20AC;&#x201C;teacher education ratio
% of total government expenditure
% of total
pupils per teacher
2010a
2010a
2010a
.. 10.4 .. 26.0 19.8 .. 13.4 13.7 9.4 11.5 9.4 .. .. 17.2 .. 15.8 17.4 .. 18.6 13.2 14.7 7.2 23.7 12.1 .. 13.1 .. 13.4 14.7b 18.9 22.0 15.2 11.4 .. 22.3 14.6 25.7 .. .. 22.4 20.2 11.9 16.1 .. 16.9 .. 16.1 .. 9.9 .. .. .. 16.4 16.9 11.8 11.0 .. 8.2
36.4 .. .. .. 98.4 .. .. .. .. .. .. .. .. 96.8 .. .. .. 100.0 68.4 96.9 .. .. 63.4 40.2 .. .. .. 90.4 95.9 .. 50.0 100.0 100.0 95.6 .. 97.6 100.0 b 75.9 99.9 95.6 80.7b .. .. 74.9 96.4b 66.1 .. 100.0 84.2 91.6 .. .. .. .. .. .. 6.6 42.9
33 10 .. 16 20 .. 16 13 .. 21 18 .. 16b 47 .. 22 .. 8 24 30 12 14 34 24 .. 13 16 40 79 13 48b 37 21 28 16 30 26b 58 28 30 30 b .. 14 30 39b 36 .. 12 40 23 .. .. 20 31 10 11 12 12
2012 World Development Indicators
83
2.11
Education inputs Public expenditure per student
1999
2010a
% of GDP per capita Secondary 1999 2010a
.. .. 11.2 .. 13.6 .. .. .. 10.2 26.1 .. 14.2 .. 18.0 .. .. 8.3 22.3 22.7 10.8 .. .. 18.1 .. 7.7 11.5 14.2 9.8 .. .. .. 5.4 13.9 17.7 7.2 .. .. .. .. .. 7.0 12.7 .. m .. .. .. 13.8 .. .. .. 12.6 .. .. .. 17.9 17.9
.. .. 8.2 .. 16.4 61.6 .. 11.5 15.6 .. .. 17.6 .. 20.3 7.4 .. 17.7 26.2 20.5 16.8 .. 21.5 24.4 .. 10.8 14.9 17.3 .. .. 7.2 .. 6.2 23.3 22.5 .. .. .. 19.4 .. .. .. .. 16.4 m .. .. .. 15.9 .. .. .. 12.4 .. 8.8 .. 19.7 19.9
.. .. 42.7 .. .. .. .. .. 18.4 25.6 .. 20.0 .. 24.4 .. .. 23.2 26.1 27.3 21.0 .. .. 16.2 .. 27.7 12.2 24.6 9.6 .. .. .. 7.2 23.8 22.3 9.9 .. .. .. .. .. 19.4 19.3 .. m .. .. .. .. .. .. .. 12.9 .. 13.1 .. 22.3 25.9
Primary
Romania Russian Federation Rwanda Saudi Arabia Senegal Serbia Sierra Leone Singapore Slovak Republic Slovenia Somalia South Africa South Sudan Spain Sri Lanka Sudan Swaziland Sweden Switzerland Syrian Arab Republic Tajikistan Tanzania Thailand Timor-Leste Togo Trinidad and Tobago Tunisia Turkey Turkmenistan Uganda Ukraine United Arab Emirates United Kingdom United States Uruguay Uzbekistan Venezuela, RB Vietnam West Bank and Gaza Yemen, Rep. Zambia Zimbabwe World Low income Middle income Lower middle income Upper middle income Low & middle income East Asia & Pacific Europe & Central Asia Latin America & Carib. Middle East & N. Africa South Asia Sub-Saharan Africa High income Euro area
a. Provisional data. b. Data are for 2011.
84
2012 World Development Indicators
.. .. 37.1 .. 28.0 14.4 .. 17.5 15.1 .. .. 19.8 .. 25.8 .. .. 37.1 30.9 31.1 14.2 .. 18.8 15.4 .. .. 16.1 24.3 .. .. 20.5 .. 8.6 28.9 24.8 .. .. .. 17.0 .. .. .. .. 22.7 m .. .. .. 15.4 .. .. .. 13.6 .. .. .. 25.1 27.5
Public expenditure on education
Tertiary 1999
32.6 10.9 1,228.1 .. .. .. .. .. 32.8 27.8 .. .. .. 19.6 .. .. 434.9 51.7 53.8 .. .. .. 36.5 .. .. 148.3 81.1 35.3 .. .. 36.5 26.2 25.6 26.8 .. .. .. .. .. .. 160.4 .. .. m .. .. .. .. .. 37.8 .. .. .. 95.0 .. 31.8 30.7
2010a
.. 14.2 186.8 .. 186.9 43.3 .. 28.7 18.3 21.2 .. .. .. 27.3 .. .. .. 41.3 43.8 .. 17.5 873.3 17.6 83.9 .. .. 46.1 .. .. 104.3 .. 19.9 22.2 21.2 .. .. .. 60.6 .. .. .. 75.4 .. m .. .. .. .. .. .. 17.3 .. .. .. .. 27.3 32.5
% of GDP 2010a
.. 4.1 4.7b 5.6 5.6 5.0 4.3 3.3 3.6 5.2 .. 6.0 .. 4.6 2.1 .. 7.4 6.8 5.4 .. 4.0 6.2 3.8 14.0 4.5 .. 6.3 .. .. 3.2 .. 1.0 5.4 5.5 .. .. .. 5.3 .. 5.2 1.3 2.5 4.6 m 3.8 4.4 4.0 4.8 4.1 3.8 4.4 4.4 4.8 2.5 5.0 5.1 5.5
Trained Primary teachers school in primary pupil–teacher education ratio
% of total government expenditure
% of total
pupils per teacher
2010a
2010a
2010a
.. .. 16.9b 19.3 24.0 9.5 18.1 10.3 10.3 11.8 .. 19.2 .. 11.2 8.1 .. 16.0 12.9 16.7 .. 14.7 18.3 22.3 11.7 17.6 .. 22.7 .. .. 15.0 .. 23.4 11.1 13.8 .. .. .. 19.8 .. 16.0 .. 8.3 15.6 m 18.7 .. .. .. .. 17.2 14.0 .. 20.0 12.6 18.9 12.7 11.5
.. .. 91.5 .. 47.9 94.2 48.0 b 94.3 .. .. .. 87.4 .. .. .. 59.7 73.1 .. .. .. 92.9 94.5 .. .. 76.7 88.0 .. .. .. 89.4 99.9 100.0 .. .. .. 100.0 b 88.4 98.3 100.0 .. .. .. .. w 82.9 .. .. .. .. .. .. .. .. .. .. .. ..
16 18 65 11 34 16 31b 17 16 17 .. 31 .. 13 24 38 32 9 .. 18 25 51 16 30 41 18 17 .. .. 49 16 17 18 14 14 16b 12 20 28 31 58 .. 24 w 45 .. .. 19 26 17 17 24 24 .. 46 15 14
About the data
2.11
PEOPLE
Education inputs Definitions
Data on education are collected by the United
class size because of the different practices coun-
• Public expenditure per student is public current
Nations Educational, Scientific, and Cultural Organi-
tries employ, such as part-time teachers, school
and capital spending on education divided by the
zation (UNESCO) Institute for Statistics from official
shifts, and multigrade classes. The comparability
number of students by level as a percentage of gross
responses to its annual education survey. The data
of pupil–teacher ratios across countries is further
domestic product (GDP) per capita. • Public expen-
are used for monitoring, policymaking, and resource
affected by the definition of teachers and by differ-
diture on education is current and capital expendi-
allocation. While international standards ensure
ences in class size by grade and in the number of
tures on education by local (including municipalities),
comparable datasets, data collection methods may
hours taught, as well as the different practices men-
regional, and national governments as a percentage
vary by country and within countries over time.
tioned above. Moreover, the underlying enrollment
of GDP and as a percentage of total government
For most countries the data on education spending
levels are subject to a variety of reporting errors (for
expenditure. • Trained teachers in primary educa-
in the table refer to public spending—government
further discussion of enrollment data, see About the
tion are the percentage of primary school teachers
spending on education at all levels plus subsidies
data for table 2.12).While the pupil–teacher ratio
who have received the minimum organized teacher
provided to households and other private entities—
is often used to compare the quality of schooling
training (pre-service or in-service) required for teach-
and generally exclude foreign aid for education that is
across countries, it is often weakly related to student
ing at the specified level of education in their country.
not included in the government budget. The data may
learning and quality of education.
• Primary school pupil–teacher ratio is the number
also exclude spending by religious schools, which
All education data published by the UNESCO
of pupils enrolled in primary school divided by the
play a significant role in many developing countries.
Institute for Statistics are mapped to the Interna-
number of primary school teachers (regardless of
Data are gathered from ministries of education and
tional Standard Classifi cation of Education 1997
their teaching assignment).
from other ministries or agencies involved in educa-
(ISCED97). This classification system ensures the
tion spending.
comparability of education programs at the interna-
The share of public expenditure devoted to educa-
tional level. UNESCO developed the ISCED to facili-
tion allows an assessment of the priority a govern-
tate comparisons of education statistics and indica-
ment assigns to education relative to other public
tors of different countries on the basis of uniform and
investments, as well as a government’s commitment
internationally agreed definitions. First developed in
to investing in human capital development. However,
the 1970s, the current version was formally adopted
returns on investment to education, especially pri-
in November 1997.
mary and lower secondary education, cannot be
The reference years in the table reflect the school
understood simply by comparing current education
year for which the data are presented. In some coun-
indicators with national income. It takes a long time
tries the school year spans two calendar years (for
before currently enrolled children can productively
example, from September 2009 to June 2010); in
contribute to the national economy (Hanushek 2002).
these cases the reference year refers to the year in
High-quality data on education finance are scarce.
which the school year ended (2010 in the example).
Improving their quality is a priority of the UNESCO Institute for Statistics. Additional resources are being allocated for technical assistance to countries in need, especially in Sub-Saharan Africa. Interagency partnerships and collaborations with national ministries in charge of education finance data are improving, and actual expenditure data are increasingly being collected. Tracking private education spending is still a challenge for all countries. The share of trained teachers in primary education reveals a country’s commitment to investing in the development of its human capital engaged in teaching, but it does not take into account differences in teachers’ experiences and status, teaching methods, teaching materials, and classroom conditions— all factors that affect the quality of teaching and learning. Some teachers without formal training may have acquired equivalent pedagogical skills through professional experience. The pupil–teacher ratio reflects the average num-
Data sources Data on education inputs are from the UNESCO Institute for Statistics.
ber of pupils per teacher. It differs from the average
2012 World Development Indicators
85
2.12
Participation in education Gross enrollment ratio
Preprimary
Afghanistan Albania Algeria Angola Argentina Armenia Australia Austria Azerbaijan Bahrain Bangladesh Belarus Belgium Benin Bolivia Bosnia and Herzegovina Botswana Brazil Bulgaria Burkina Faso Burundi Cambodia Cameroon Canada Central African Republic Chad Chile China Hong Kong SAR, China Colombia Congo, Dem. Rep. Congo, Rep. Costa Rica Côte d’Ivoire Croatia Cuba Cyprus Czech Republic Denmark Dominican Republic Ecuador Egypt, Arab Rep. El Salvador Eritrea Estonia Ethiopia Finland France Gabon Gambia, The Georgia Germany Ghana Greece Guatemala Guinea Guinea-Bissau Haiti
86
% of relevant age group Primary Secondary
Net enrollment rate
% of relevant age group Primary Secondary
Tertiary
2010a
2010a
2010a
2010a
1991
2010a
1999
.. 56 77 104 74 31 81 96 25 .. 13 99 118 18 45 17 .. 65 79 3c 9 13 28 71 4 2 56 54 .. 49 3 13 71 4c 58 100 81 106 96 38 126 24 64 14 96 5 66 110 42c 30 58 114 69 .. 71 14 7 ..
97 87 110 124 118 103 104 100 94 .. 103 100 105 126 105 88 .. 127 103 79c 156 127 120 99 93 90 106 111 102 115 94 115 110 88 c 93 103 105 106 99 108 114 106 114 45 99 102 99 111 182c 83 109 102 107c .. 116 94 123 ..
46 89 95 31 89 92 129 100 .. .. 49 .. 111 .. 80 90 .. 101 88 23c 25 46 42 101 13 26 88 81 83 96 38 .. 100 .. 95 89 98 90 117 76 80 .. 65 32 104 36 108 113 .. 54 86 103 58 c .. 59 38 .. ..
3 .. 31 4 71 52 76 60 19 .. 11 83 67 .. .. 36 .. 36 53 3 3 8 11 .. 3 2 59 26 60 39 6 6 .. .. 49 95 52 61 74 .. 40 30 23 2 63 5 92 55 .. 4 28 .. 9 .. .. 9 .. ..
28 .. 89 .. .. .. 98 90 89 99 64 .. 96 51 .. .. 89 .. .. 27 50 .. 69 98 53 .. .. 97 .. 71 56 .. 87 46 .. 94 87 .. 98 .. .. .. .. 20 .. 30 99 100 .. 50 .. 84 .. 95 .. 27 .. 21
.. 80 96 86 .. .. 97 .. 84 .. 92 92 99 94 .. 85 .. 94 98 63c .. 96 92 .. 71 .. 94 .. 94 88 .. 91 .. 61 87 99 99 .. 96 90 97 96 94 33 94 81 97 99 .. 66 100 97 84 c .. 97 77 74 ..
.. 68 .. .. 74 86 90 .. 75 86 44 82 .. 18 68 .. 53 66 85 8 .. 14 .. 95 .. 7 .. .. 70 56 .. .. .. 19 81 75 88 81 88 39 46 77 44 18 84 12 95 93 .. .. 76 .. 34 82 24 12 9 ..
2012 World Development Indicators
Adjusted net enrollment rate, primary
% of primary school– age children Male Female
Children out of school
thousand primary school– age children Male Female
2010a
2010a
2010a
2010a
2010a
.. .. .. 12 82 86 85 .. .. .. 46 87 .. .. 68 .. .. 82 83 18 c 16 .. .. .. 11 .. 83 .. 75 74 .. .. .. .. 92 86 96 84 89 62 .. .. 58 29 92 .. 94 98 .. .. 79 .. 49c .. 50 29 .. ..
.. 79 98 93 .. 78 97 .. 85 .. 91 95 99 .. .. 86 .. 96 99 65 98 96 100 .. 81 .. 94 .. 96 92 .. 92 .. 67 93 100 99 .. 95 96 .. 100 94 37 96 85 98 .. .. 68 .. .. 84 c .. 100 83 77 ..
.. 79 96 78 .. 81 98 .. 84 .. 100 97 99 .. .. 88 .. 94 100 58 100 95 88 .. 61 .. 94 .. 98 91 .. 89 .. 56 93 100 99 .. 97 90 .. 96 95 33 96 80 98 .. .. 71 .. .. 85c .. 98 70 73 ..
.. 23 28 119 .. 13 31 .. 40 .. 718 9 4 .. .. 14 .. 290 1 452 10 32 6 .. 63 .. 46 .. 7 187 .. 24 .. 497 7 0b 0b .. 11 28 .. 15 26 203 1 1,023 5 .. .. 44 .. .. 298 c .. 5 131 26 ..
.. 20 53 373 .. 10 23 .. 38 .. 1 5 3 .. .. 11 .. 396 0b 531 1 40 171 .. 135 .. 48 .. 4 188 .. 32 .. 664 6 1 0b .. 6 57 .. 184 21 215 1 1,367 4 .. .. 41 .. .. 270 c .. 27 224 30 ..
Gross enrollment ratio
Preprimary
Honduras Hungary India Indonesia Iran, Islamic Rep. Iraq Ireland Israel Italy Jamaica Japan Jordan Kazakhstan Kenya Korea, Dem. Rep. Korea, Rep. Kosovo Kuwait Kyrgyz Republic Lao PDR Latvia Lebanon Lesotho Liberia Libya Lithuania Macedonia, FYR Madagascar Malawi Malaysia Mali Mauritania Mauritius Mexico Moldova Mongolia Morocco Mozambique Myanmar Namibia Nepal Netherlands New Zealand Nicaragua Niger Nigeria Norway Oman Pakistan Panama Papua New Guinea Paraguay Peru Philippines Poland Portugal Puerto Rico Qatar
% of relevant age group Primary Secondary
Net enrollment rate
Adjusted net enrollment rate, primary
% of relevant age group Primary Secondary
Tertiary
2010a
2010a
2010a
2010a
1991
2010a
1999
44 85 54 43 43 .. .. 106 97 113 90 36 66c 52 .. 121 .. 82 19 16 84 81 33 .. .. 75 25 9 .. 67 3c .. 96 103 76 77 63c .. 10 .. .. 96 93 55 6c 14 98 45 .. 67 .. 35 78 51 66 82 96 55
116 102 118 118 108 .. 108 113 103 89 103 97 111c 113 .. 104 .. 106 100 121 101 105 103 96 .. 97 89 149 135 .. 82c 102 99 115 94 100 114 c 115 126 107 .. 108 101 118 71c 83 99 105 95 108 60 100 109 107 97 114 93 103
73 98 60 77 84 .. 117 91 99 93 102 91 100 c 60 .. 97 .. 101 84 45 95 81 46 .. .. 98 83 31 32 68 39c 24 89 87 88 93 .. 25 54 .. .. 120 119 69 13 44 110 100 34 74 .. 67 92 82 97 107 82 94
19 62 16 23 43 .. 61 62 66 29 59 42 41c 4 .. 104 .. .. 49 13 60 54 .. .. .. 77 40 4 1 40 6 4 25 27 38 53 13 .. .. 9 .. 63 83 .. 1 .. 74 24 5 45 .. 37 .. 29 71 62 86 10
88 .. .. 95 97 76 90 .. .. 97 100 .. .. .. .. 99 .. 47 .. 59 .. .. 72 .. .. .. .. 72 .. .. .. .. 93 98 .. .. 56 42 .. 82 .. 95 100 70 23 .. 100 69 .. 92 65 94 86 96 .. 98 .. 89
96 92 92 96 .. .. 95 97 98 82 100 90 88 c 83 .. 99 .. 92 87 89 95 92 73 .. .. 93 87 .. 97 .. 63c 74 93 98 88 95 96c 92 .. 85 .. 100 99 92 62c .. 99 94 74 98 .. 85 95 89 96 99 86 92
.. 83 .. 47 .. 30 91 87 85 83 99 77 88 33 .. 96 .. 98 .. 26 .. .. 17 18 .. 90 .. .. 30 66 .. 14 67 56 78 58 30 3 32 39 .. 91 90 35 6 .. 95 61 .. 59 .. 45 63 50 91 80 .. 74
% of primary schoolâ&#x20AC;&#x201C; age children Male Female
2.12
PEOPLE
Participation in education
Children out of school
thousand primary schoolâ&#x20AC;&#x201C; age children Male Female
2010a
2010a
2010a
2010a
2010a
.. 91 .. 67 .. .. 98 88 93 84 99 84 90 c 50 .. 96 .. 89 79 37 84 75 30 .. .. 91 .. 24 28 68 31c .. .. 70 79 83 .. 16 51 .. .. 87 95 46 10 .. 95 90 34 69 .. 60 78 61 91 .. .. 83
96 98 .. .. .. .. 97 97 100 84 .. 93 99c 84 .. 100 .. 97 95 91 94 94 72 .. .. 97 93 .. .. .. 71 73 92 99 90 98 97c 95 .. 84 .. .. 99 93 64 .. 99 98 81 99 .. 86 97 88 96 99 83 96
98 98 .. .. .. .. 99 97 99 82 .. 95 100 c 85 .. 99 .. 100 95 87 95 93 75 .. .. 97 95 .. .. .. 61 76 94 100 90 98 96c 89 .. 89 .. .. 100 95 52 .. 99 98 67 98 .. 86 97 90 96 100 88 97
23 4 .. .. .. .. 6 13 5 28 .. 30 3c 523 .. 0b .. 3 9 35 3 15 53 .. .. 2 5 .. .. .. 377 72 5 39 8 2 58 c 124 .. 31 .. .. 1 27 478 .. 3 3 1,884 2 .. 62 54 799 47 3 28 2
8 3 .. .. .. .. 3 10 11 32 .. 21 1c 486 .. 14 .. 0b 9 47 3 15 46 .. .. 2 3 .. .. .. 481 61 3 11 7 3 75c 243 .. 21 .. .. 1 21 607 .. 2 2 3,241 4 .. 60 43 662 47 2 18 1
2012 World Development Indicators
87
2.12
Participation in education Gross enrollment ratio
Preprimary
Romania Russian Federation Rwanda Saudi Arabia Senegal Serbia Sierra Leone Singapore Slovak Republic Slovenia Somalia South Africa South Sudan Spain Sri Lanka Sudan Swaziland Sweden Switzerland Syrian Arab Republic Tajikistan Tanzania Thailand Timor-Leste Togo Trinidad and Tobago Tunisia Turkey Turkmenistan Uganda Ukraine United Arab Emirates United Kingdom United States Uruguay Uzbekistan Venezuela, RB Vietnam West Bank and Gaza Yemen, Rep. Zambia Zimbabwe World Low income Middle income Lower middle income Upper middle income Low & middle income East Asia & Pacific Europe & Central Asia Latin America & Carib. Middle East & N. Africa South Asia Sub-Saharan Africa High income Euro area
% of relevant age group Primary Secondary
2010a
2010a
2010a
77 90 10 11 13 53 7c .. 91 86 .. 65 .. 126 42 27 23 95 102 10 9 33 100 c .. 9 .. .. 22 .. 14 97 .. 81 69 89 26c 73 82 39 1 .. .. 50 w 12 52 50 51 46 48 55 71 .. 54 17 78 107
96 99 143 106 87 96 125c .. 102 98 .. 102 .. 107 99 73 116 100 102 118 102 102 91 117 140 105 109 102 .. 121 99 .. 106 102 113 95c 103 106 91 87 115 .. 107 w 104 109 107 111 108 111 98 117 102 110 100 101 105
95 89 32 101 37 91 .. .. 89 97 .. 94 .. 119 .. 39 58 100 95 72 87 27 79c 56 .. 90 90 78 .. 28 96 .. 102 96 90 106c 83 77 86 44 .. .. 68 w 39 69 58 83 64 76 89 90 72 55 36 100 107
a. Provisional data. b. Less than 0.5. c. Data are for 2011.
88
2012 World Development Indicators
Net enrollment rate
% of relevant age group Primary Secondary
Tertiary 2010a
64 76 5 37 8 49 .. .. 54 87 .. .. .. 73 15 .. .. 71 51 .. 20 2 48 c 17 .. .. 34 46 .. 4 79 .. 59 95 63 9c 78 22 50 .. .. 6 27 w 7 24 16 33 21 25 55 37 27 11 6 70 60
Adjusted net enrollment rate, primary
1991
2010a
73 .. .. .. 45 .. .. .. .. .. .. 90 .. 100 .. .. 74 100 84 91 .. 51 .. .. 65 90 94 89 .. .. .. 97 97 97 91 .. .. .. .. .. .. .. .. w .. .. .. .. .. 96 90 .. .. 68 .. 95 ..
87 93 99 90 75 93 .. .. .. 97 .. 85 .. 100 94 .. 86 99 94 93 97 98 90 85 92 94 98 97 .. 91 91 .. 100 95 99 90 c 93 98 87 78 91 .. 88 w 80 89 85 94 87 .. 92 94 90 86 75 95 98
1999
77 .. .. .. .. .. .. .. .. 90 .. 62 .. 88 .. .. 32 96 84 39 63 5 .. .. 21 69 64 56 .. 13 91 75 94 87 .. .. 48 58 75 31 16 40 51 w 24 50 41 63 47 56 78 59 58 .. 19 88 86
2010a
82 .. .. 81 .. 90 .. .. .. 92 .. .. .. 94 .. .. 33 96 83 67 85 .. 74 c 37 .. .. .. 74 .. .. 86 .. 96 89 70 92 72 .. 84 .. .. .. 60 w 32 60 50 74 56 .. 81 73 64 .. 27 90 92
% of primary schoolâ&#x20AC;&#x201C; age children Male Female
Children out of school
thousand primary schoolâ&#x20AC;&#x201C; age children Male Female
2010a
2010a
2010a
2010a
92 95 89 90 76 95 .. .. .. 97 .. 89 .. 100 94 .. 86 100 99 100 99 91 90 86 .. 97 .. 98 .. 90 91 .. 100 96 100 94 c 95 .. 90 86 91 .. 91 w 82 92 90 95 90 .. 94 95 94 93 78 95 ..
93 96 92 89 80 94 .. .. .. 97 .. 91 .. 100 94 .. 85 99 99 98 96 93 89 86 .. 94 .. 97 .. 92 91 .. 100 98 99 91c 95 .. 88 70 94 .. 89 w 80 90 86 96 88 .. 93 95 89 89 74 96 ..
35 128 84 154 238 8 .. .. .. 2 .. 372 .. 2 56 .. 15 1 4 2 2 359 304 14 .. 2 .. 59 .. 357 73 .. 3 498 0b 62c 91 .. 23 290 108 ..
32 93 60 164 191 8 .. .. .. 2 .. 326 .. 1 51 .. 15 3 2 16 13 290 307 14 .. 4 .. 103 .. 266 64 .. 6 247 1 86c 80 .. 25 568 76 ..
About the data
2.12
PEOPLE
Participation in education Definitions
School enrollment data are reported to the United
children of primary age enrolled in preprimary edu-
• Gross enrollment ratio is the ratio of total enroll-
Nations Educational, Scientific, and Cultural Organi-
cation) are compiled from administrative data. Large
ment, regardless of age, to the population of the age
zation (UNESCO) Institute for Statistics by national
numbers of children out of school create pressure
group that officially corresponds to the level of educa-
education authorities and statistical offices. Enroll-
to enroll children and provide classrooms, teachers,
tion shown. • Preprimary education (ISCED O) refers
ment indicators help monitor whether a country is on
and educational materials, a task made difficult in
to programs at the initial stage of organized instruc-
track to achieve the Millennium Development Goal of
many countries by limited education budgets. How-
tion, designed primarily to introduce very young chil-
universal primary education by 2015, and whether
ever, getting children into school is a high priority for
dren, usually age 3, to a school-type environment and
an education system has the capacity to meet the
countries and crucial for achieving the Millennium
to provide a bridge between the home and school. On
needs of universal primary education.
Development Goal of universal primary education.
completing these programs, children continue their
Enrollment indicators are based on annual school
The reference years in the table reflect the school
education at the primary level. • Primary education
surveys, but do not necessarily reflect actual atten-
year for which the data are presented. In some coun-
(ISCED 1) refers to programs normally designed to
dance or dropout rates during the year. Also, the
tries the school year spans two calendar years (for
give students a sound basic education in reading,
length of primary education differs across coun-
example, from September 2009 to June 2010); in
writing, and mathematics along with an elementary
tries and can influence enrollment rates and ratios,
these cases the reference year refers to the year in
understanding of other subjects such as history,
although the International Standard Classification of
which the school year ended (2010 in the example).
geography, natural science, social science, art, and
Education (ISCED) tries to minimize the difference.
music. Religious instruction may also be featured. It
A shorter duration for primary education tends to
is sometimes called elementary education. • Sec-
increase the ratio; a longer one to decrease it (in
ondary education refers to programs of lower (ISCED
part because older children are more at risk of drop-
2) and upper (ISCED 3) secondary education. Lower
ping out).
secondary education continues the basic programs
Overage or underage enrollments are frequent, par-
of the primary level, but the teaching is typically more
ticularly when parents prefer children to start school
subject focused, requiring more specialized teachers
at other than the official age. Age at enrollment may
for each subject area. In upper secondary education
be inaccurately estimated or misstated, especially
instruction is often organized even more along sub-
in communities where registration of births is not
ject lines, and teachers typically need a higher or
strictly enforced.
more subject-specific qualification. • Tertiary edu-
Population data used to calculate population-
cation refers to a wide range of programs with more
based indicators are drawn from the United Nations
advanced educational content. The first stage of ter-
Population Division. Using a single source for popula-
tiary education (ISCED 5) refers to theoretically based
tion data standardizes definitions, estimations, and
programs intended to provide sufficient qualifications
interpolation methods, ensuring a consistent meth-
to enter advanced research programs or professions
odology across countries and minimizing potential
with high-skill requirements and programs that are
enumeration problems in national censuses.
practical, technical, or occupationally specific. The
Gross enrollment ratios indicate the capacity of
second stage of tertiary education (ISCED 6) refers
each level of the education system, but a high ratio
to programs devoted to advanced study and original
may reflect a substantial number of overage children
research and leading to an advanced research quali-
enrolled in each grade because of repetition or late
fication. • Net enrollment rate is the ratio of total
entry rather than a successful education system.
enrollment of children of official school age to the
The net enrollment rate excludes overage and under-
population of the age group that officially corresponds
age students and more accurately captures the sys-
to the level of education shown. • Adjusted net enroll-
tem’s coverage and internal efficiency. Differences
ment rate, primary, is the ratio of total enrollment of
between the gross enrollment ratio and the net
children of official school age for primary education
enrollment rate show the incidence of overage and
who are enrolled in primary or secondary education to
underage enrollments.
the total primary school–age population. • Children
The adjusted net enrollment rate in primary educa-
out of school are the number of primary school–age
tion captures primary school–age children who have
children not enrolled in primary or secondary school.
progressed to secondary education faster than their peers have and who are not counted in the traditional net enrollment rate. Data on children out of school (primary school– age children not enrolled in primary or secondary
Data sources Data on gross enrollment ratios, net enrollment rates, and out of school children are from the UNESCO Institute for Statistics.
school—dropouts, children never enrolled, and
2012 World Development Indicators
89
2.13
Education efficiency Gross intake ratio in first grade of primary education
Cohort survival rate
Repeaters in primary education
Transition rate to secondary education
% of grade 1 students Reaching grade 5
% of relevant age group Male Female
Afghanistan Albania Algeria Angola Argentina Armenia Australia Austria Azerbaijan Bahrain Bangladesh Belarus Belgium Benin Bolivia Bosnia and Herzegovina Botswana Brazil Bulgaria Burkina Faso Burundi Cambodia Cameroon Canada Central African Republic Chad Chile China Hong Kong SAR, China Colombia Congo, Dem. Rep. Congo, Rep. Costa Rica Côte d’Ivoire Croatia Cuba Cyprus Czech Republic Denmark Dominican Republic Ecuador Egypt, Arab Rep. El Salvador Eritrea Estonia Ethiopia Finland France Gabon Gambia, The Georgia Germany Ghana Greece Guatemala Guinea Guinea-Bissau Haiti
90
Reaching last grade of primary education
Male
Female
Male
Female
2010a
2010a
1991
2009a
1991
2009a
2009a
2009a
124 87 107 182 114 91 .. 103 91 .. 110 96 94 159 113 99 .. .. 100 91b 164 143 144 99 118 134 97 95 114 113 117 109 98 88 b 92 98 107 107 100 113 .. 105 117 44 100 145 99 .. .. 88 100 100 109b .. 131 112 169 ..
91 87 105 148 115 93 .. 101 88 .. 115 96 95 147 111 100 .. .. 99 86b 158 144 123 98 96 104 96 99 119 107 105 108 98 78b 92 98 106 108 100 101 .. 103 110 40 100 129 99 .. .. 88 102 99 111b .. 131 96 164 ..
89 .. 82 .. .. .. 98 .. .. 88 .. .. 87 30 57 .. 73 .. .. 61 66 .. 67 .. 52 43 .. .. .. 53 66 66 70 68 .. .. 96 .. 98 .. .. .. 54 .. .. .. 96 .. 47 59 .. .. 72 .. .. 43 .. 47
.. 95 93 53 96 .. .. .. .. 98 62 .. 96 62 86 73 .. .. .. 73 59 60 76 .. 61 32 .. .. 100 84 62 75 90 66 .. 97 94 99 100 .. .. .. 89 71 99 50 100 .. .. 67 94 .. 80 .. 71 74 .. ..
89 .. 79 .. .. .. 99 .. .. 87 .. .. 90 31 51 .. 81 .. .. 58 61 .. 66 .. 39 22 .. .. .. 59 55 68 73 61 .. .. 97 .. 99 .. .. .. 57 .. .. .. 97 .. 46 53 .. .. 65 .. .. 35 .. 46
.. 95 97 37 95 .. .. .. .. 98 71 .. 97 59 85 73 .. .. .. 78 66 65 77 .. 50 32 .. .. 100 85 58 79 92 66 .. 97 97 100 100 .. .. .. 90 67 99 51 100 .. .. 63 99 .. 77 .. 70 62 .. ..
.. 95 93 37 .. .. .. 96 100 98 67 96 86 .. 85 73 .. .. 94 71 56 52 67 .. 53 24 .. .. .. .. 78 71 88 .. 97 96 .. 99 99 .. .. .. 86 71 98 47 100 .. .. .. 94 98 .. .. 65 74 .. ..
.. 95 97 27 .. .. .. 99 97 98 66 100 88 .. 82 73 .. .. 94 72 64 57 65 .. 39 23 .. .. .. .. 73 71 90 .. 99 95 .. 100 100 .. .. .. 87 67 99 48 99 .. .. .. 99 99 .. .. 64 56 .. ..
2012 World Development Indicators
% of enrollment Male Female
% Male
Female
2010a
2010a
2009a
2009a
.. 1 9 10 6 0 .. 0 0 2 13 0 3 13 1 0 .. .. 2 10 34 10 14 0 21 23 1 0 1 2 14 20 7 19 0 1 0 1 0 9 .. 4 7 16 1 4 1 .. .. 6 0 1 2b .. 12 16 14 ..
.. 1 6 12 4 0 .. 0 0 2 12 0 3 13 1 0 .. .. 1 10 34 8 13 0 21 26 1 0 1 2 14 18 5 19 0 0 0 1 0 5 .. 2 5 13 0 4 0 .. .. 5 0 1 3b .. 10 18 14 ..
.. 97 90 26 96 100 .. 100 98 98 .. 98 99 .. 89 .. .. .. 95 43c 41 80 42 .. 43 77 84 .. 100 97 83 69 93 47 100 98 100 99 99 82 .. .. 95 82 99 91 100 .. .. 80 100 99 91 .. 93 62 .. ..
.. 97 92 45 97 98 .. 100 99 99 .. 100 97 .. 91 .. .. .. 96 62c 31 81 45 .. 45 66 98 .. 100 96 76 67 89 45 99 99 100 99 100 90 .. .. 94 82 98 87 100 .. .. 82 100 99 92 .. 90 51 .. ..
Gross intake ratio in ďŹ rst grade of primary education
Cohort survival rate
Repeaters in primary education
2.13
PEOPLE
Education efficiency
Transition rate to secondary education
% of grade 1 students Reaching grade 5
% of relevant age group Male Female
Honduras Hungary India Indonesia Iran, Islamic Rep. Iraq Ireland Israel Italy Jamaica Japan Jordan Kazakhstan Kenya Korea, Dem. Rep. Korea, Rep. Kosovo Kuwait Kyrgyz Republic Lao PDR Latvia Lebanon Lesotho Liberia Libya Lithuania Macedonia, FYR Madagascar Malawi Malaysia Mali Mauritania Mauritius Mexico Moldova Mongolia Morocco Mozambique Myanmar Namibia Nepal Netherlands New Zealand Nicaragua Niger Nigeria Norway Oman Pakistan Panama Papua New Guinea Paraguay Peru Philippines Poland Portugal Puerto Rico Qatar
Male
Reaching last grade of primary education Female
Male
Female
2010a
2010a
1991
2009a
1991
2009a
2009a
2009a
125 103 129 121 107 .. 106 98 101 81 103 98 112b .. .. 100 .. 101 106 136 100 108 103 120 .. 93 97 184 150 .. 82b 104 95 117 98 145 110 b 168 152 93 .. 100 .. 146 100 b 93 97 108 129 103 .. 101 103 129 99 103 97 107
120 102 125 118 108 .. 107 101 100 78 103 98 111b .. .. 98 .. 103 104 126 102 106 94 112 .. 94 97 184 159 .. 76b 107 99 117 97 138 109b 159 151 95 .. 99 .. 138 90 b 83 98 103 108 101 .. 98 102 121 99 103 94 107
50 .. .. .. 75 75 .. .. .. 92 100 93 .. .. .. 92 .. .. .. 34 .. .. 53 .. .. .. .. 31 37 86 48 52 .. 81 .. .. 70 42 .. 52 44 .. 96 39 68 .. 99 77 .. .. 55 58 .. .. .. .. .. 98
75 .. .. 83 94 .. 99 100 99 96 100 .. .. .. .. 99 .. 96 .. 66 96 90 76 64 .. .. .. 34 60 98 89c 74 99 95 .. 93 94 56 72 90 60 99 .. 48 74 c 84 100 .. 64 95 .. 81 .. 75 98 .. .. 92
43 .. .. .. 67 70 .. .. .. 94 100 89 .. .. .. 92 .. .. .. 32 .. .. 77 .. .. .. .. 31 33 87 42 47 .. 82 .. .. 64 34 .. 57 32 .. 95 48 65 .. 100 78 .. .. 52 60 .. .. .. .. .. 99
80 .. .. 89 94 .. 100 98 100 96 100 .. .. .. .. 99 .. 96 .. 68 96 91 85 56 .. .. .. 35 62 98 87c 75 97 97 .. 95 94 51 77 93 64 100 .. 55 69c 90 99 .. 59 94 .. 84 .. 82 98 .. .. 99
74 98 .. .. 94 .. .. 100 99 94 100 .. .. .. .. 99 .. 96 98 .. 95 .. .. 49 .. 98 .. .. 63 97 81 71 94 93 95 .. 78 37 .. .. .. .. .. .. 63 77 100 .. 64 94 .. 76 88 72 97 .. .. 91
79 98 .. .. 94 .. .. 98 100 96 100 .. .. .. .. 99 .. 96 97 .. 95 .. .. 43 .. 99 .. .. 57 98 77 70 98 95 96 .. 78 34 .. .. .. .. .. .. 60 83 99 .. 59 94 .. 80 88 80 98 .. .. 97
% of enrollment Male Female
% Male
Female
2010a
2010a
2009a
2009a
1 2 .. 4 2 .. 1 2 0 3 0 1 0b .. .. 0 .. 1 0 18 3 9 23 6 .. 1 0 21 19 .. 13b 3 4 4 0 0 13 8 0 18 12b .. .. 9 4b .. .. 1 5 7 .. 6 7 3 1 .. .. 0
1 2 .. 3 2 .. 1 1 0 2 0 1 0b .. .. 0 .. 1 0 16 2 7 17 7 .. 0 0 19 19 .. 13b 4 3 3 0 0 9 7 0 14 12b .. .. 7 4b .. .. 2 4 4 .. 4 6 2 1 .. .. 0
.. 97 81 91 96 .. .. 70 100 92 .. 99 100 c .. .. 100 .. 99 100 80 94 84 75 64 .. 99 98 65 78 100 74 38 65 95 99 96 84 c 52 77 80 81 .. .. .. 63c .. 100 .. 73 98 .. 89 96 99 99 .. .. 99
.. 98 81 93 97 .. .. 69 100 91 .. 99 100 c .. .. 100 .. 99 99 77 98 91 73 60 .. 99 98 63 76 99 72 31 76 94 98 98 80 c 55 77 83 81 .. .. .. 59c .. 100 .. 74 96 .. 89 93 97 98 .. .. 99
2012 World Development Indicators
91
2.13
Education efficiency Gross intake ratio in ďŹ rst grade of primary education
Cohort survival rate
Repeaters in primary education
Transition rate to secondary education
% of grade 1 students Reaching grade 5
% of relevant age group Male Female
Romania Russian Federation Rwanda Saudi Arabia Senegal Serbia Sierra Leone Singapore Slovak Republic Slovenia Somalia South Africa South Sudan Spain Sri Lanka Sudan Swaziland Sweden Switzerland Syrian Arab Republic Tajikistan Tanzania Thailand Timor-Leste Togo Trinidad and Tobago Tunisia Turkey Turkmenistan Uganda Ukraine United Arab Emirates United Kingdom United States Uruguay Uzbekistan Venezuela, RB Vietnam West Bank and Gaza Yemen, Rep. Zambia Zimbabwe World Low income Middle income Lower middle income Upper middle income Low & middle income East Asia & Pacific Europe & Central Asia Latin America & Carib. Middle East & N. Africa South Asia Sub-Saharan Africa High income Euro area
2010a
2010a
97 .. 186 104 100 92 133b .. 95 99 .. 94 .. 102 94 83 123 104 92 116 102 96 .. 141 157 104 106 99 .. 153 103 .. .. 101 106 97b 100 .. 91 109 114 .. 116 w 130 114 122 99 117 99 .. .. 105 127 122 101 101
95 .. 182 106 106 92 121b .. 95 98 .. 88 .. 102 95 75 113 103 94 117 98 97 .. 141 150 101 106 97 .. 157 104 .. .. 98 106 94b 98 .. 91 96 117 .. 113 w 123 111 116 101 114 102 .. .. 104 121 114 103 100
Male 1991
a. Provisional data. b. Data are for 2011. c. Data are for 2010.
92
2012 World Development Indicators
.. .. 49 80 78 .. .. .. .. .. .. 61 .. .. 97 .. 58 99 72 87 .. 69 .. .. 55 98 76 93 .. .. .. 78 .. .. 98 .. 69 .. .. .. .. 70 .. w .. .. .. .. .. .. .. .. .. .. .. .. ..
Reaching last grade of primary education Female
Male
Female
2009a
1991
2009a
2009a
2009a
.. .. 45 97 73 .. .. 99 .. 100 .. .. .. 99 .. 89 95 99 .. .. .. 87 .. 68 78 90 95 91 .. 56 .. .. .. 98 94 .. 93 .. .. .. 71 .. .. w 62 .. .. .. .. .. .. .. .. .. 67 .. ..
.. .. 51 76 68 .. .. .. .. .. .. 67 .. .. 98 .. 64 99 72 85 .. 71 .. .. 38 99 70 92 .. .. .. 80 .. .. 100 .. 80 .. .. .. .. 72 .. w .. .. .. .. .. .. .. .. .. .. .. .. ..
.. .. 50 91 75 .. .. 99 .. 99 .. .. .. 100 .. 100 97 99 .. .. .. 93 .. 74 77 94 97 93 .. 58 .. .. .. 89 97 .. 95 .. .. .. 70 .. .. w 63 .. .. .. .. .. .. .. .. .. 68 .. ..
95 .. .. 97 58 98 .. 99 98 100 .. .. .. 99 .. .. 81 99 .. 94 99 76 .. 63 .. 87 .. .. .. .. .. .. .. .. 94 98 c 90 .. .. .. 55 ..
96 .. .. 90 61 99 .. 99 98 99 .. .. .. 100 .. .. 87 99 .. 95 99 87 .. 70 .. 92 .. .. .. .. .. .. .. .. 97 98 c 94 .. .. .. 52 ..
% of enrollment Male Female 2010a
2 .. 14 3 6 1 15b 0 3 1 .. .. .. 3 1 4 17 0 2 9 0 3 .. 19 22 7 8 2 .. 11 0 2 0 0 7 0b 5 .. 0 7 6 .. .. w 11 .. .. 2 .. 1 .. .. 7 .. .. 0 1
2010a
1 .. 14 3 6 0 16b 0 3 0 .. .. .. 2 1 4 13 0 1 6 0 2 .. 15 22 5 5 2 .. 11 0 2 0 0 4 0b 3 .. 0 6 6 .. .. w 11 .. .. 1 .. 1 .. .. 4 .. .. 0 1
% Male
Female
2009a
2009a
98 .. 73 91 71 97 .. 86 97 99 .. .. .. 92 95 96 90 100 .. 94 99 45 .. 84 73 87 81 97 .. 60 100 94 .. .. 75 100 c 96 .. 96 .. 65 .. .. w .. .. 82 .. .. .. .. .. .. 80 .. .. ..
97 .. 72 97 66 99 .. 92 97 98 .. .. .. 95 97 92 92 100 .. 95 98 37 .. 87 67 89 87 97 .. 58 100 99 .. .. 87 98 c 97 .. 98 .. 68 .. .. w .. .. 83 .. .. .. .. .. .. 80 .. .. ..
About the data
2.13
PEOPLE
Education efficiency Definitions
The United Nations Educational, Scientific, and Cul-
low transition rate can signal such problems as an
• Gross intake ratio in first grade of primary educa-
tural Organization (UNESCO) Institute for Statistics
inadequate examination and promotion system or
tion is the number of new entrants in grade 1 regard-
calculates indicators of students’ progress through
insufficient secondary education capacity. The qual-
less of age as a percentage of the population of
school. These indicators measure an education sys-
ity of data on the transition rate is affected when
the official primary school entrance age. • Cohort
tem’s success in reaching students and efficiently
new entrants and repeaters are not correctly distin-
survival rate is the percentage of children enrolled
moving them from one grade to the next.
guished in the first grade of secondary education.
in the first grade of primary education who eventually
The gross intake ratio in the first grade of primary
Students who interrupt their studies after complet-
reach grade 5 or the last grade of primary education.
education indicates the level of access to primary
ing primary education could also affect data quality.
The estimate is based on the reconstructed cohort
education and the education system’s capacity to
The reference years in the table reflect the school
method (see About the data). • Repeaters in primary
provide access to primary education. A low gross
year for which the data are presented. In some coun-
education are the number of students enrolled in the
intake ratio in the first grade of primary education
tries the school year spans two calendar years (for
same grade as in the previous year as a percentage
reflects the fact that many children do not enter pri-
example, from September 2009 to June 2010); in
of all students enrolled in primary education. • Tran-
mary education even though school attendance, at
these cases the reference year refers to the year in
sition rate to secondary education is the number
least through the primary level, is mandatory in most
which the school year ended (2010 in the example).
of new entrants to the first grade of secondary edu-
countries. Because the gross intake ratio includes
cation (general programs only) in a given year as a
all new entrants regardless of age, it can exceed
percentage of the number of students enrolled in the
100 percent in some situations, such as immediately
final grade of primary education in the previous year.
after fees have been abolished or when the number of reenrolled children is large. The indicator is not calculated when new entrants and repeaters are not correctly distinguished in the first grade of primary education. The cohort survival rate to grade 5 and to the last grade of primary education shows the percentage of students entering primary school who are expected to reach the specified grade. It measures an education system’s holding power and internal efficiency. Cohort survival rates are calculated based on the reconstructed cohort method, which uses data on enrollment by grade for the two most recent years and data on repeaters by grade for the most recent of those two years to reflect current patterns of grade transition. Rates approaching 100 percent indicate high retention and low dropout levels. Data on repeaters are often used to indicate an education system’s internal efficiency. Repeaters not only increase the cost of education for the family and the school system, but also use limited school resources. Country policies on repetition and promotion differ. In some cases the number of repeaters is controlled because of limited capacity. In other cases the number of repeaters is almost 0 because of automatic promotion—suggesting a system that is highly efficient but that may not be endowing students with enough cognitive skills. The transition rate from primary to secondary education conveys the degree of access or transition between the two levels. As completing primary education is a prerequisite for participating
Data sources
in lower secondary education, growing numbers of
Data on education efficiency are from the UNESCO
primary completers will inevitably create pressure
Institute for Statistics.
for more available places at the secondary level. A
2012 World Development Indicators
93
2.14
Education completion and outcomes Primary completion rate
% of relevant age group Total
Afghanistan Albania Algeria Angola Argentina Armenia Australia Austria Azerbaijan Bahrain Bangladesh Belarus Belgium Benin Bolivia Bosnia and Herzegovina Botswana Brazil Bulgaria Burkina Faso Burundi Cambodia Cameroon Canada Central African Republic Chad Chile China Hong Kong SAR, China Colombia Congo, Dem. Rep. Congo, Rep. Costa Rica Côte d’Ivoire Croatia Cuba Cyprus Czech Republic Denmark Dominican Republic Ecuador Egypt, Arab Rep. El Salvador Eritrea Estonia Ethiopia Finland France Gabon Gambia, The Georgia Germany Ghana Greece Guatemala Guinea Guinea-Bissau Haiti
94
Male
1991
2010a
1991
2010a
Female 1991 2010a
28 .. 80 33 100 105 .. .. 95 97 41 94 79 22 71 .. 90 93 90 20 46 45 53 .. 28 18 .. 107 102 73 48 54 79 42 85 99 90 92 98 61 91 .. 65 18 .. 23 97 106 62 45 .. 100 64 99 .. 17 5 27
.. 86 96 47 106 101 .. 99 90 .. 65 103 90 63 99 70 94 .. 95 45 56 87 79 .. 41 33 96 .. 96 114 59 71 96 59 c 95 98 103 101 97 92 106 98 96 40 98 72 98 .. .. 71 116 100 94 c 101 84 64 68 ..
41 .. 86 .. .. .. .. .. 96 96 .. 95 76 30 78 .. 83 .. 88 25 49 .. 57 .. 37 29 .. .. .. 70 61 59 77 53 .. .. 89 91 98 .. 91 .. 64 21 .. 28 98 .. 59 56 .. 99 71 99 .. 24 7 29
.. 86 96 53 104 100 .. 99 90 .. 62 95 89 74 100 69 92 .. 95 48 57 87 85 .. 52 41 102 .. 95 113 67 73 95 65c 95 98 103 101 97 93 105 100 96 43 97 75 98 .. .. 69 116 100 97c 101 87 75 75 ..
14 .. 73 .. .. .. .. .. 94 98 .. 95 82 14 64 .. 98 .. 92 15 43 .. 49 .. 20 7 .. .. .. 76 36 49 81 32 .. .. 90 93 98 .. 92 .. 66 15 .. 18 97 .. 65 34 .. 100 56 98 .. 9 3 26
2012 World Development Indicators
.. 86 96 40 108 103 .. 100 89 .. 69 95 92 53 99 71 96 .. 96 42 55 87 72 .. 30 24 89 .. 96 115 50 69 97 52c 95 99 103 101 98 91 106 97 96 36 98 69 97 .. .. 72 116 100 91c 100 81 53 60 ..
Youth literacy rate
Adult literacy rate
% ages 15–24
% ages 15 and older
%
2005–10b
2009
.. 96 73 70 98 100 .. .. 100 91 56 100 .. 42 91 98 84 90 98 29 67 78 71 .. 55 34 99 94 .. 93 67 .. 96 55 99 100 98 .. .. 88 84 66 84 67 100 30 .. .. 88 46 100 .. 67 97 74 39 52 49
.. 40 .. .. 37 .. 5 8 11 .. .. .. 8 .. .. .. .. 38 24 .. .. .. .. 3 .. .. 22 .. 3 39 .. .. .. .. 12 .. .. 7 5 .. .. .. .. .. 3 .. 2 9 .. .. .. 6 .. 11 .. .. .. ..
Male 1985–94b 2005–10b
.. .. 86 .. 98 100 .. .. .. 97 52 100 .. 55 96 .. 86 .. .. 27 59 .. .. .. 63 26 98 97 .. 89 .. .. .. 60 100 .. 100 .. .. .. 97 71 85 .. 100 39 .. .. 94 .. .. .. .. 99 82 .. .. ..
.. 99 94 81 99 100 .. .. 100 100 74 100 .. 65 99 100 94 97 98 47 77 89 89 .. 72 54 99 99 .. 97 73 87 98 72 100 100 100 .. .. 95 97 88 95 92 100 56 .. .. 99 71 100 .. 81 99 89 68 78 ..
Female 1985–94b 2005–10b
.. .. 62 .. 99 100 .. .. .. 97 38 100 .. 27 92 .. 92 .. .. 14 48 .. .. .. 35 9 99 91 .. 92 .. .. .. 38 100 .. 100 .. .. .. 96 54 85 .. 100 28 .. .. 92 .. .. .. .. 99 71 .. .. ..
.. 99 89 66 99 100 .. .. 100 100 77 100 .. 43 99 100 97 99 97 33 76 86 77 .. 57 39 99 99 .. 98 62 78 99 61 100 100 100 .. .. 97 97 82 95 86 100 33 .. .. 97 60 100 .. 79 99 84 54 64 ..
Students at lowest proficiency on PISA mathematics
Primary completion rate
% of relevant age group Total
Honduras Hungary India Indonesia Iran, Islamic Rep. Iraq Ireland Israel Italy Jamaica Japan Jordan Kazakhstan Kenya Korea, Dem. Rep. Korea, Rep. Kosovo Kuwait Kyrgyz Republic Lao PDR Latvia Lebanon Lesotho Liberia Libya Lithuania Macedonia, FYR Madagascar Malawi Malaysia Mali Mauritania Mauritius Mexico Moldova Mongolia Morocco Mozambique Myanmar Namibia Nepal Netherlands New Zealand Nicaragua Niger Nigeria Norway Oman Pakistan Panama Papua New Guinea Paraguay Peru Philippines Poland Portugal Puerto Rico Qatar
Male
1991
2010a
1991
2010a
Female 1991 2010a
64 82 64 93 88 58 103 .. 98 94 102 101 103 .. .. 99 .. 57 .. 41 .. .. 59 .. .. .. 98 36 31 91 9 33 115 88 .. .. 48 26 .. 74 51 .. .. 42 17 .. 100 74 .. 86 46 68 .. 88 96 .. .. 71
99 98 96 105 104 65 .. 103 103 73 102 101 116c .. .. 101 .. 112 97 79 92 87 70 62 .. 96 92 72 67 .. 55c 75 96 104 93 108 85 61 104 84 .. .. .. 81 46c 74 100 101 67 97 .. 94 102 92 95 .. .. 100
67 89 76 .. 93 63 103 .. 98 90 102 101 103 .. .. 99 .. 58 .. 46 .. .. 42 .. .. .. .. 35 35 91 12 39 115 91 .. .. 57 32 .. 67 70 .. .. 43 21 .. 100 78 .. 86 51 68 .. 85 .. .. .. 71
96 98 96 104 104 74 .. 102 103 74 103 101 116c .. .. 100 .. 110 96 83 94 85 60 67 .. 97 92 72 65 .. 61c 74 96 104 94 107 87 66 101 80 .. .. .. 78 52c 79 101 102 75 97 .. 92 102 89 95 .. .. 99
61 90 52 .. 82 52 103 .. 97 98 102 101 103 .. .. 100 .. 56 .. 36 .. .. 76 .. .. .. .. 37 27 91 7 26 115 92 .. .. 39 21 .. 81 41 .. .. 53 13 .. 100 70 .. 86 42 69 .. 86 .. .. .. 72
102 97 95 105 104 55 .. 105 103 73 102 101 116c .. .. 101 .. 114 97 75 90 89 79 57 .. 94 93 73 68 .. 50 c 76 96 104 91 109 82 55 106 88 .. .. .. 84 40 c 70 100 100 59 97 .. 95 102 94 95 .. .. 100
Youth literacy rate
Adult literacy rate
% ages 15–24
% ages 15 and older
%
2005–10b
2009
84 99 63 92 85 78 .. .. 99 86 .. 92 100 87 100 .. .. 94 99 73 100 90 90 59 89 100 97 64 74 92 26 57 88 93 98 97 56 55 92 89 59 .. .. 78 29 61 .. 87 56 94 60 95 90 95 100 95 90 95
.. 8 .. 44 .. .. 7 21 9 .. 4 35 30 .. .. 2 .. .. 65 .. 6 .. .. .. .. 9 .. .. .. .. .. .. .. 22 .. .. .. .. .. .. .. 3 5 .. .. .. 6 .. .. 51 .. .. 48 .. 6 8 .. 51
Male 1985–94b 2005–10b
.. 99 74 97 92 .. .. .. .. .. .. .. 100 .. .. .. .. 91 .. .. 100 .. .. 66 99 100 99 .. 70 96 .. .. 91 96 100 .. 71 .. .. 86 68 .. .. .. .. 81 .. .. .. 95 .. 96 97 96 100 99 92 89
2.14
93 99 88 100 99 85 .. .. 100 92 .. 99 100 92 100 .. .. 99 100 89 100 98 86 70 100 100 99 66 87 98 47 71 96 99 99 95 87 78 96 91 87 .. .. 85 52 78 .. 98 79 97 65 99 98 97 100 100 87 98
Female 1985–94b 2005–10b
.. 99 49 95 81 .. .. .. .. .. .. .. 100 .. .. .. .. 84 .. .. 100 .. .. 54 96 100 99 .. 49 95 .. .. 92 95 100 .. 46 .. .. 90 33 .. .. .. .. 62 .. .. .. 95 .. 95 94 97 100 99 94 91
95 99 74 99 99 80 .. .. 100 98 .. 99 100 94 100 .. .. 99 100 79 100 99 98 81 100 100 99 64 86 99 31 64 98 98 100 97 72 64 95 95 77 .. .. 89 23 65 .. 98 61 96 70 99 97 98 100 100 88 98
PEOPLE
Education completion and outcomes
Students at lowest proficiency on PISA mathematics
2012 World Development Indicators
95
2.14
Education completion and outcomes Primary completion rate
% of relevant age group Total
Romania Russian Federation Rwanda Saudi Arabia Senegal Serbia Sierra Leone Singapore Slovak Republic Slovenia Somalia South Africa South Sudan Spain Sri Lanka Sudan Swaziland Sweden Switzerland Syrian Arab Republic Tajikistan Tanzania Thailand Timor-Leste Togo Trinidad and Tobago Tunisia Turkey Turkmenistan Uganda Ukraine United Arab Emirates United Kingdom United States Uruguay Uzbekistan Venezuela, RB Vietnam West Bank and Gaza Yemen, Rep. Zambia Zimbabwe World Low income Middle income Lower middle income Upper middle income Low & middle income East Asia & Pacific Europe & Central Asia Latin America & Carib. Middle East & N. Africa South Asia Sub-Saharan Africa High income Euro area
Male
1991
2010a
1991
2010a
Female 1991 2010a
96 92 50 .. 39 .. .. .. 95 95 .. 76 .. 104 101 .. 61 96 53 89 .. 55 .. .. 35 102 74 90 .. .. 92 103 .. .. 94 80 81 .. .. .. .. 97 79 w 44 83 68 97 78 101 92 84 .. 62 51 .. 101
91 98 70 93 59 96 74 c .. 98 95 .. .. .. 102 101 58 77 94 95 104 104 90 .. 65 74 91 91 99 .. 57 98 .. .. 104 106 93c 94 .. 95 63 103 .. 88 w 65 92 88 98 87 97 95 102 88 86 67 97 101
96 92 51 .. 48 .. .. .. 95 .. .. 72 .. 104 101 .. 57 96 53 94 .. 56 .. .. 48 99 79 93 .. .. 99 104 .. .. 91 .. 76 .. .. .. .. 99 86 w .. 89 76 101 85 105 93 84 .. 75 57 .. 100
91 .. 65 94 58 96 78 c .. 98 95 .. .. .. 102 101 61 76 93 94 104 106 88 .. 64 84 91 90 100 .. 58 97 .. .. 103 105 94 c 93 .. 97 73 98 .. 90 w 68 93 90 96 89 96 96 101 91 87 71 98 100
96 93 50 .. 31 .. .. .. 96 .. .. 80 .. 103 101 .. 64 96 54 84 .. 55 .. .. 22 105 70 86 .. .. 99 103 .. .. 96 .. 86 .. .. .. .. 96 75 w .. 77 60 94 73 97 92 85 .. 52 47 .. 100
a. Provisional data. b. Data are for the most recent year available. c. Data are for 2011.
96
2012 World Development Indicators
91 .. 74 92 61 97 71c .. 99 95 .. .. .. 102 101 55 78 94 97 103 102 92 .. 67 64 91 92 98 .. 56 98 .. .. 104 106 92c 95 .. 93 53 108 .. 87 w 63 91 86 99 86 98 94 102 85 84 63 97 101
Youth literacy rate
Adult literacy rate
% ages 15–24
% ages 15 and older
%
2005–10b
2009
Male 1985–94b 2005–10b
99 100 75 94 49 .. .. 99 .. 100 .. .. .. 100 .. .. 83 .. .. .. 100 86 .. .. .. 99 .. 97 .. 77 .. 81 .. .. 98 .. 95 94 .. 83 67 97 88 w 66 89 87 94 86 97 99 91 84 71 73 99 ..
97 100 77 99 74 .. 68 100 .. 100 .. 97 .. 100 97 .. 92 .. .. 96 100 78 98 .. 85 100 98 99 100 90 100 94 .. .. 98 100 98 97 99 96 82 98 92 w 75 94 89 99 91 99 99 97 93 85 77 99 ..
Female 1985–94b 2005–10b
99 100 75 81 28 .. .. 99 .. 100 .. .. .. 100 .. .. 84 .. .. .. 100 78 .. .. .. 99 .. 88 .. 63 .. 85 .. .. 99 .. 96 93 .. 35 66 94 79 w 52 79 75 93 75 92 98 92 67 47 58 99 ..
98 100 77 97 56 .. 48 100 .. 100 .. 98 .. 100 99 .. 95 .. .. 93 100 76 98 .. 68 100 96 97 100 85 100 97 .. .. 100 100 99 96 99 72 67 99 87 w 68 88 79 99 85 99 99 97 87 72 67 99 ..
98 100 71 86 50 .. 41 95 .. 100 .. 89 .. 98 91 .. 87 .. .. 84 100 73 94 51 57 99 78 91 100 73 100 90 .. .. 98 99 95 93 95 62 71 92 84 w 61 83 71 93 80 94 98 91 74 61 62 98 ..
Students at lowest proficiency on PISA mathematics
20 10 .. .. .. 18 .. 3 7 7 .. .. .. 9 .. .. .. 8 4 .. .. .. 22 .. .. 30 43 18 .. .. .. .. 6 8 23 .. .. .. .. .. .. ..
About the data
2.14
PEOPLE
Education completion and outcomes Definitions
Many governments publish statistics that indicate how
different lengths of school attendance or levels of
• Primary completion rate, or the gross intake ratio
their education systems are working and developing—
completion. Because definitions and methodolo-
to last grade of primary education, is the number of
statistics on enrollment and such efficiency indicators
gies of data collection differ across countries, data
new entrants in the last grade of primary education,
as repetition rates, pupil–teacher ratios, and cohort
should be used cautiously.
regardless of age, divided by the population at the
progression. The World Bank and the United Nations
The reported literacy data are compiled by the
entrance age for the last grade of primary education.
Educational, Scientific, and Cultural Organization
UNESCO Institute for Statistics based on national
• Youth literacy rate is the percentage of the popula-
(UNESCO) Institute for Statistics jointly developed the
censuses and household surveys during 1985–
tion ages 15–24 that can, with understanding, both
primary completion rate indicator. Increasingly used
2010. For countries without recent literacy data, the
read and write a short simple statement about their
as a core indicator of an education system’s perfor-
UNESCO Institute for Statistics estimates literacy
everyday life. • Adult literacy rate is the percentage
mance, it reflects an education system’s coverage
rates with the Global Age-Specific Literacy Projection
of the population ages 15 and older that can, with
and the educational attainment of students. The indi-
Model. For detailed information on sources, defini-
understanding, both read and write a short simple
cator is a key measure of education outcome at the
tions, and methodology, see www.uis.unesco.org.
statement about their everyday life. • Students at
primary level and of progress toward the Millennium
Literacy statistics for most countries cover the pop-
lowest proficiency on PISA mathematics is the per-
Development Goals and the Education for All initia-
ulation ages 15 and older, but some include younger
centage of students whose mathematics score are
tive. However, a high primary completion rate does
ages or are confined to age ranges that tend to inflate
below 357.77 (level 1) on the PISA.
not necessarily mean high levels of student learning.
literacy rates. The youth literacy rate for ages 15–24
The primary completion rate reflects the primary
reflects recent progress in education. It measures
cycle as defined by the International Standard Clas-
the accumulated outcomes of primary education over
sification of Education (ISCED97), ranging from three
the previous 10 years or so by indicating the pro-
or four years of primary education (in a very small
portion of the population who have passed through
number of countries) to fi ve or six years (in most
the primary education system and acquired basic
countries) and seven (in a small number of countries).
literacy and numeracy skills. Generally, literacy also
The primary completion rate is also called the gross
encompasses numeracy, the ability to make simple
intake ratio to last grade of primary education. It is the
arithmetic calculations.
number of new entrants in the last grade of primary
In many countries national assessments enable
education, regardless of age, divided by the population
ministries of education to monitor progress in learn-
at the entrance age for the last grade of primary educa-
ing outcomes. Of the handful of internationally or
tion. Data limitations preclude adjusting for students
regionally comparable assessments, one of the
who drop out during the final year of primary education.
largest is the Programme for International Student
Thus this rate is a proxy that should be taken as an
Assessment (PISA). Coordinated by the Organisation
upper estimate of the actual primary completion rate.
for Economic Co-operation and Development (OECD),
There are many reasons why the primary comple-
it measures the knowledge and skills of 15-year-olds,
tion rate can exceed 100 percent. The numerator may
the age at which students in most countries are near-
include late entrants and overage children who have
ing the end of their compulsory time in school. The
repeated one or more grades of primary education as
assessment tests reading, mathematical, and sci-
well as children who entered school early, while the
entific literacy in terms of general competencies—
denominator is the number of children at the entrance
that is, how well students can apply the knowledge
age for the last grade of primary education.
and skills they have learned at school to real-life
Basic student outcomes include achievements in reading and mathematics judged against established
challenges. It does not test how well a student has mastered a school’s specific curriculum.
standards. The UNESCO Institute for Statistics has
The table presents the percentage of students
established literacy as an outcome indicator based
at the lowest level of proficiency on the PISA math-
on an internationally agreed definition.
ematics scale. Student achievement is benchmarked
The literacy rate is the percentage of the popu-
in terms of levels of proficiency, ranging from level
lation who can, with understanding, both read and
1 (lowest) to level 6 (highest), as demonstrated
write a short, simple statement about their everyday
through ability to analyze, reason, and communi-
life. In practice, literacy is difficult to measure. To
cate effectively while posing, solving, and interpret-
estimate literacy using such a definition requires
ing mathematical problems that involve quantita-
Data on primary completion rates and literacy
census or survey measurements under controlled
tive, spatial, probabilistic, or other mathematical
rates are from the UNESCO Institute for Statis-
conditions. Many countries estimate the number of
concepts. The average score is 496. Because the
tics. Data on PISA mathematics results are from
literate people from self-reported data. Some use
figures are derived from samples, the data reflect a
the OECD.
educational attainment data as a proxy but apply
small measure of statistical uncertainty.
Data sources
2012 World Development Indicators
97
2.15
Education gaps by income and gender Survey year
Armenia Azerbaijan Bangladesh Belize Benin Bolivia Burundi Cambodia Cameroon Colombia Côte d’Ivoire Dominican Republic Egypt, Arab Rep. Ethiopia Georgia Ghana Guatemala Guinea Guinea-Bissau Guyana Haiti Kazakhstan Kenya Kosovo Lesotho Macedonia, FYR Madagascar Malawi Mali Mauritania Moldova Mozambique Namibia Nepal Nicaragua Niger Nigeria Panama Peru Rwanda Serbia Somalia Swaziland Syrian Arab Republic Tanzania Togo Turkey Uganda Vietnam Yemen, Rep. Zambia Zimbabwe
2005 2006 2006 2006 2006 2003 2005 2005 2006 2005 2006 2007 2005 2005 2006 2006 2000 2005 2006 2006 2005 2006 2003 2000 2004 2005 2003– 04 2006 2006 2007 2005 2003 2006 2001 2001 2006 2003 2003 2004 2005 2005 2005 2006 2006 2004 2006 2003 2006 2006 2006 2007 1999
Gross intake rate in first grade of primary education
Gross primary participation rate
Average years of schooling
Primary completion rate
Children out of school
% of relevant age group Poorest Richest quintile quintile
% of relevant age group Poorest Richest quintile quintile
Ages 15–19 Poorest Richest quintile quintile
% of relevant age group Richest quintile Male
% of relevant age group Poorest Richest quintile quintile
93 92 144 80 67 92 201 208 108 161 51 130 107 86 90 107 176 55 135 74 177 118 134 104 169 102 250 234 41 67 96 128 112 184 149 50 78 125 121 274 90 13 147 110 123 115 108 180 99 66 135 106
a. Less than 0.5.
98
2012 World Development Indicators
80 118 147 89 107 95 191 151 75 84 77 112 97 124 104 121 124 119 184 76 188 101 125 119 111 190 153 207 98 96 84 143 104 141 106 90 101 116 90 195 98 44 117 149 123 148 111 144 100 109 123 111
106 100 96 106 61 108 91 113 93 127 57 113 95 47 101 81 81 52 94 105 87 106 92 95 116 89 118 133 46 62 99 75 118 109 85 35 70 108 118 131 98 8 117 102 82 99 97 107 108 50 105 144
102 108 105 113 114 129 144 134 116 99 110 107 99 112 103 117 114 121 166 101 159 103 106 104 124 97 145 169 110 116 95 143 109 139 105 89 108 102 96 151 100 93 114 107 119 128 97 124 100 101 112 144
9 9 8 8 6 6 4 5 6 6 5 7 9 3 15 5 4 5 4 10 4 9 6 9 5 8 3 5 5 5 9 3 7 5 4 4 7 7 7 3 9 8 6 7 5 6 6 5 13 7 5 7
10 11 13 11 8 9 7 8 14 10 8 11 12 6 14 8 8 7 7 10 7 9 9 11 8 10 8 7 8 9 12 6 10 8 9 7 10 11 11 5 10 10 9 8 7 7 7 8 18 10 9 10
Poorest quintile
119 94 65 59 31 76 20 42 43 94 47 69 84 14 102 62 15 32 34 109 31 102 40 82 36 120 42 30 36 17 97 13 81 49 34 31 48 100 106 31 86 2 69 92 32 40 95 27 99 25 50 36
116 109 97 130 95 98 70 121 111 109 127 109 92 90 102 88 80 93 125 118 136 115 76 94 122 119 141 80 79 89 100 100 109 96 124 71 71 94 99 88 96 58 110 93 108 82 85 68 104 103 101 80
113 103 83 107 67 90 44 88 90 100 88 88 92 46 106 93 34 76 80 91 73 102 71 98 69 133 77 49 55 48 96 57 94 69 78 60 70 105 100 48 94 26 85 93 58 67 100 50 96 84 88 51
Female
112 105 86 72 52 81 39 85 74 103 71 106 88 33 104 86 36 48 54 112 82 97 72 83 85 78 77 52 41 52 98 43 90 62 83 30 54 88 97 42 89 20 98 92 60 56 81 42 103 31 73 57
2 20 12 5 57 22 5 37 3 11 4 12 12 74 2 22 7 60 12 2 69 0a 38 1 18 0a 33 0a 67 2 2 46 11 33 40 74 52 1 6 13 1 87 17 0a 44 1 20 25 3 2 22 22
1 11 6 7 12 5 3 13 2 2 3 4 1 30 1 12 3 16 11 1 24 1 11 4 3 0a 3 0a 20 2 1 7 2 6 4 28 6 1 1 8 0a 46 4 0a 15 1 5 7 2 2 3 8
About the data
2.15
PEOPLE
Education gaps by income and gender Definitions
The data in the table describe basic information on
facets of social exclusion. To that extent the index
• Survey year is the year in which the underlying data
school participation and educational attainment
provides only a partial view of the multidimensional
were collected. • Gross intake rate in first grade
by individuals in different socioeconomic groups
concepts of poverty, inequality, and inequity.
of primary education is the number of students in
within countries. The data are from Demographic
Creating one index that includes all asset indica-
grade 1 regardless of age as a percentage of the
and Health Surveys by Macro International with the
tors limits the types of analysis that can be per-
population of the official primary school entrance
support of the U.S. Agency for International Devel-
formed. In particular, the use of a unified index does
age. These data may differ from those in table 2.13.
opment, Multiple Indicator Cluster Surveys by the
not permit a disaggregated analysis to examine
• Gross primary participation rate is the ratio of
United Nations Children’s Fund (UNICEF), and Liv-
which asset indicators have a more or less important
total students attending primary school regardless
ing Standards Measurement Studies by the World
association with education status. In addition, some
of age to the population of the age group that offi -
Bank’s Development Economics Research Group.
asset indicators may reflect household wealth better
cially corresponds to primary education. • Average
These large-scale household sample surveys, con-
in some countries than in others—or reflect differ-
years of schooling are the years of formal school-
ducted periodically in developing countries, collect
ent degrees of wealth in different countries. Taking
ing received, on average, by youths and adults ages
information on a large number of health, nutrition,
such information into account and creating country-
15–19. • Primary completion rate is the number
and population measures as well as on respondents’
specific asset indexes with country-specific choices
of students, regardless of age, in the last grade of
social, demographic, and economic characteristics
of asset indicators might produce a more effective
primary school minus the number of repeaters in that
using detailed questionnaires. The data presented
and accurate index for each country. The asset index
grade, divided by the number of students of official
here draw on responses to individual and household
used in the table does not have this flexibility.
graduation age. These data differ from those in table
The analysis was carried out for about 80 coun-
2.14 because the source is different. • Children out
Typically, the surveys collect basic information on
tries. The table shows the most recent estimates
of school are children of official primary school age
educational attainment and enrollment levels from
for the poorest and richest quintiles by gender only;
who are not attending primary or secondary educa-
every household member ages 5 and older as part of
the full set of estimates for all indicators, other
tion. Children of official primary school age who are
household socioeconomic characteristics. The sur-
subgroups, including by urban and rural location,
attending preprimary education are considered out
veys are not intended for the collection of detailed
and older data are available in the country reports
of school. These data differ from those in table 2.12
education data; thus the education section of the
(see Data sources). The data in the table differ from
because the source is different.
surveys is not as detailed as, for instance, the health
data for similar indicators in preceding tables either
section of the Demographic and Health Survey or
because the indicator refers to a period a few years
the Multiple Indicator Cluster Survey, and the data
preceding the survey date or because the indica-
obtained from them do not replace other data on
tor definition or methodology is different. Findings
education flows. Still, the education data provide
should be used with caution because of measure-
micro-level information on education that cannot be
ment error inherent in the use of survey data.
questionnaires.
obtained from administrative data, such as information on children not attending school. Socioeconomic status as displayed in the table is based on household assets, including ownership of consumer items, features of the household dwelling, and other characteristics related to wealth. Each household asset on which information was collected was assigned a weight generated through principalcomponent analysis, which was used to create breakpoints defining wealth quintiles, expressed as quintiles of individuals in the population.
Data sources
The selection of the asset index for defining socio-
Data on education gaps by income and gender
economic status was based on pragmatic rather
are from an analysis using the ADePT Educa-
than conceptual considerations: Demographic and
tion software tool (http://go.worldbank.org/
Health Surveys do not collect consumption data but
X385KNDXM0) of MEASURE DHS Demographic
do have detailed information on household owner-
and Health Surveys by ICF International, Mul-
ship of consumer goods and access to a variety of
tiple Indicator Cluster Surveys by UNICEF, and
goods and services. Like income or consumption,
Living Standards Measurement Studies by the
the asset index defines disparities primarily in eco-
World Bank. Country reports and further updates
nomic terms. It therefore excludes other possibilities
are available at www.worldbank.org/education/
of disparities among groups, such as those based
edstats/.
on gender, education, ethnic background, or other
2012 World Development Indicators
99
2.16
Health systems Health expenditure
Afghanistan Albania Algeria Angola Argentina Armenia Australia Austria Azerbaijan Bahrain Bangladesh Belarus Belgium Benin Bolivia Bosnia and Herzegovina Botswana Brazil Bulgaria Burkina Faso Burundi Cambodia Cameroon Canada Central African Republic Chad Chile China Hong Kong SAR, China Colombia Congo, Dem. Rep. Congo, Rep. Costa Rica Côte d’Ivoire Croatia Cuba Cyprus Czech Republic Denmark Dominican Republic Ecuador Egypt, Arab Rep. El Salvador Eritrea Estonia Ethiopia Finland France Gabon Gambia, The Georgia Germany Ghana Greece Guatemala Guinea Guinea-Bissau Haiti
100
Total % of GDP
Public % of total
Out of pocket % of total
External resources % of total
2010
2010
2010
2010
7.6b 6.5 4.2 2.9c 8.1 4.4 8.7d 11.0 5.9 5.0 3.5 5.6 10.7 4.1 4.8 11.1 8.3 9.0 6.9 6.7 11.6c 5.6 5.1c 11.3 4.0 4.5 8.0 5.1 .. 7.6 7.9 2.5 10.9 5.3 7.8 10.6 6.0 7.9 11.4 6.2 8.1 4.7 6.9 2.7c 6.0 4.9 9.0 11.9 3.5c 5.7 10.1 11.6 5.2 10.2 6.9 4.9 8.5c 6.9
11.7b 39.0 77.9 82.5c 54.6 40.6 68.0 d 77.5 20.3 73.3 33.6 77.7 74.7 49.5 62.8 61.4 72.5 47.0 54.5 51.0 38.2c 37.2 29.6c 70.5 35.4 25.0 48.2 53.6 .. 72.7 42.5 46.7 68.1 21.6 84.9 91.5 41.5 83.7 85.1 43.4 37.2 37.4 61.7 48.2c 78.7 53.5 75.1 77.8 52.9c 50.8 23.6 77.1 59.5 59.4 35.8 11.3 10.0 c 21.4
83.0 b 60.8 20.9 17.5c 29.9 55.1 20.5d 14.6 69.5 14.5 64.1 19.8 20.2 46.8 28.7 38.6 8.1 30.6 44.2 36.2 37.9c 40.4 66.5c 14.7 61.4 72.5 33.3 36.6 .. 19.5 35.9 53.3 27.8 77.5 14.5 8.5 48.8 14.7 13.1 37.2 49.0 61.2 33.9 51.8 c 19.6 37.2 18.8 7.3 47.1c 23.8 68.3 13.0 26.9 38.4 53.9 88.1 66.4 c 40.2
32.0 b 1.8 0.0 2.9c 0.1 14.3 0.0 d 0.0 0.8 0.0 8.0 0.5 0.0 35.9 5.3 1.8 18.3 0.0 0.0 22.9 45.8 c 23.9 13.2c 0.0 13.4 7.9 0.0 0.1 .. 0.0 32.7 4.1 0.6 9.8 0.0 0.0 0.0 0.0 0.0 0.7 0.4 0.6 1.9 38.0 c 60.7 39.4 0.0 0.0 2.4 c 41.2 2.8 0.0 16.9 0.0 1.7 10.8 23.3c 38.3
2012 World Development Indicators
Health workers
per 1,000 people Nurses and Community Physicians midwives health workers
per 1,000 people
2010
2005–10a
2005–10a
2005–10a
44b 577 330 168 c 1,287 239 3,441d 4,388 579 1,083 57 786 4,025 65 233 972 1,145 1,028 947 93 47c 121 122c 4,404 31 62 1,199 379 .. 713 27 104 1,242 98 1,514 431 1,842 2,051 4,537 578 653 289 450 16c 1,226 51 3,281 4,021 522c 80 522 4,332 85 2,853 325 56 100 c 76
0.2 1.2 1.2 .. 3.2 3.8 3.0 4.9 3.8 1.4 0.3 5.2 3.0 0.1 .. 1.6 0.3 1.8 3.7 0.1 .. 0.2 .. 2.0 .. .. 1.0 1.4 .. 0.1 .. 0.1 .. 0.1 2.6 6.7 2.6 3.7 3.4 .. 1.7 2.8 1.6 .. 3.3 0.0 2.9 3.4 .. 0.0 4.8 3.6 0.1 6.2 .. 0.1 0.0 ..
.. .. .. .. .. .. 0.0 .. .. .. 0.3 .. .. .. .. .. 0.5 .. .. 0.1 .. .. .. .. .. .. .. 0.8 .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. 0.3 .. .. .. 0.1 .. .. 0.2 .. .. .. .. ..
0.4 2.8 .. 0.8 4.5 3.7 3.8 7.7 7.5 1.8 0.3 11.1 6.5 0.5 1.1 3.4 1.8 2.4 6.6 0.4 1.9e 0.8 1.3 3.2 1.0e 0.4 2.1 4.2 .. 1.0 0.8 1.6 1.2 0.4 5.4 5.9 3.8 7.1 3.5 1.6 1.5 1.7 1.0 0.7e 5.4 0.2 6.2 6.9 6.3 1.1e 3.1 8.2 0.9e 4.8 0.6 0.3 1.0 1.3
Per capita $ PPP $ 2010
38b 241 178 123c 742 133 4,775d 4,958 332 864 23 320 4,618 31 97 499 615 990 435 40 21c 45 61c 5,222 18 31 947 221 .. 472 16 72 811 60 1,067 607 1,705 1,480 6,422 323 328 123 237 12c 853 16 3,984 4,691 302c 26 272 4,668 67 2,729 196 23 47c 46
Hospital beds
2005–10a
0.5 3.9 1.9 .. .. 4.8 9.6 7.9 8.3 3.7 0.3 13.1 0.5 0.8 .. 5.0 2.8 6.4 4.7 0.7 .. 0.9 .. 10.4 .. .. 0.1 1.4 .. 0.6 .. 0.8 .. 0.5 5.3 9.1 4.3 8.7 16.1 .. 2.0 3.5 0.4 .. 6.6 0.2 24.0 0.3 .. 0.6 3.2 11.1 1.0 0.2 .. 0.0 0.6 ..
Health expenditure
Honduras Hungary India Indonesia Iran, Islamic Rep. Iraq Ireland Israel Italy Jamaica Japan Jordan Kazakhstan Kenya Korea, Dem. Rep. Korea, Rep. Kosovo Kuwait Kyrgyz Republic Lao PDR Latvia Lebanon Lesotho Liberia Libya Lithuania Macedonia, FYR Madagascar Malawi Malaysia Mali Mauritania Mauritius Mexico Moldova Mongolia Morocco Mozambique Myanmar Namibia Nepal Netherlands New Zealand Nicaragua Niger Nigeria Norway Oman Pakistan Panama Papua New Guinea Paraguay Peru Philippines Poland Portugal Puerto Rico Qatar
Total % of GDP
Public % of total
Out of pocket % of total
External resources % of total
2010
2010
2010
2010
65.2 69.4 29.2 49.1 40.1 81.2c,f 69.2 60.3 77.6 53.5 82.5 67.7 59.4 44.3 .. 59.0 .. 80.4 56.2 33.3 61.1 39.2 76.2 32.5 68.8 c 73.5 63.8 60.3 60.2 55.5 46.6 53.1c 41.7 48.9 45.8g 55.1 38.0 71.7 12.2 58.4 33.2 79.2 83.2 53.3 50.9 37.9c 83.9 80.1 38.5 75.1 71.5 36.4 54.0 35.3 72.6 68.1 .. 77.5
31.1 24.0 61.2 38.3 57.8 18.8 c,f 15.2 29.2 19.6 33.0 14.3 25.1 40.1 42.7 .. 31.4 .. 17.8 37.8 51.2 37.8 44.7 16.4 35.2 31.2c 25.8 35.9 27.1 11.1 34.2 53.2 44.3c 51.7 47.1 44.9g 41.4 53.6 13.7 81.1 7.4 48.3 5.2 10.5 43.3 41.3 59.2c 15.3 12.3 50.5 19.9 15.9 57.1 39.5 54.0 22.1 24.8 .. 16.0
6.3 0.0 1.2 1.3 0.0 0.8 c,f 0.0 0.0 0.0 2.1 0.0 3.7 0.6 36.1 .. 0.0 .. 0.0 12.8 15.1 0.0 4.7 19.5 55.1 0.6c 1.1 0.8 9.0 63.8 0.0 27.4 10.1c 2.0 0.0 9.6g 3.9 0.4 24.2 8.7 19.0 11.3 0.0 0.0 14.6 29.4 9.2c 0.0 0.0 4.8 0.1 24.0 2.4 1.7 1.3 0.1 0.0 .. 0.0
6.8 7.3 4.1 2.6 5.6 8.4 c,f 9.2 7.6 9.5 4.8 9.5 8.0 4.3 4.8 .. 6.9 .. 2.6 6.2 4.5 6.7 7.0 11.1 11.8 3.9c 7.0 7.1 3.8 6.6 4.4 5.0 4.4 c 6.0 6.3 11.7g 5.4 5.2 5.2 2.0 6.8 5.5 11.9 10.1 9.1 5.2 5.1c 9.5 2.8 2.2 8.1 3.6 5.9 5.1 3.6 7.5 11.0 .. 1.8
2.16
Health workers
Per capita $ PPP $ 2010
137 942 54 77 317 247c,f 4,242 2,183 3,248 247 4,065 357 393 37 .. 1,439 .. 1,223 53 46 718 651 109 29 484 c 781 317 16 26 368 32 43c 449 604 190 g 120 148 21 17 361 30 5,593 3,279 103 18 63c 8,091 574 22 616 49 163 269 77 917 2,367 .. 1,489
2010
263 1,469 132 112 836 340 c,f 3,704 2,186 3,022 372 3,204 448 541 78 .. 2,023 .. 1,133 140 97 1,093 980 170 49 713c 1,299 791 36 65 641 56 79c 803 959 360 g 218 246 49 34 436 66 5,038 3,020 253 37 121c 5,426 598 59 1,123 88 302 481 142 1,476 2,818 .. 1,622
Hospital beds
per 1,000 people Nurses and Community Physicians midwives health workers
per 1,000 people
2005–10a
2005–10a
2005–10a
.. .. 0.0 .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. 0.7 .. .. .. .. .. .. 0.0 .. .. 0.1 .. .. .. .. .. .. 0.1 .. .. 0.1 .. 0.6 .. .. .. .. .. .. ..
0.8 7.1 0.9 0.6 1.7 1.3 4.9 3.5 3.6 1.9 13.7 1.8 7.6 1.4 .. 10.3 .. 2.0 5.1 0.7 6.4 3.5 1.3 0.8 3.7 6.8 4.5 0.2 1.3e 1.8 0.1 0.4 3.4 e 1.6 6.2 5.8 1.1 0.8 0.6 2.7 5.0 4.7 .. 0.8 0.3 .. 3.3 1.8 0.6 2.2 .. 1.3 1.5 0.5 6.7 3.3 .. 1.2
.. 3.0 0.6 0.3 0.9 0.7 3.2 3.7 3.5 .. 2.1 2.5 4.1 .. .. 2.0 .. 1.8 2.3 0.3 3.0 3.5 .. 0.0 1.9 3.6 2.6 0.2 0.0 0.9 0.0 0.1 .. 2.0 2.7 2.8 0.6 0.0 0.5 0.4 .. 2.9 2.7 .. 0.0 0.4 4.2 1.9 0.8 .. 0.1 .. 0.9 .. 2.2 3.9 .. 2.8
2005–10a
.. 6.4 1.0 2.0 1.4 1.4 15.7 5.2 0.3 .. 4.1 4.0 8.3 .. .. 5.3 .. 4.6 5.7 1.0 4.8 2.2 .. 0.3 6.8 7.2 0.6 .. 0.3 2.7 0.3 0.7 .. .. 6.6 3.5 0.9 0.3 0.8 2.8 .. 0.2 10.9 .. 0.1 1.6 31.9 4.1 0.6 .. 0.5 .. 1.3 .. 5.8 5.3 .. 7.4
2012 World Development Indicators
101
PEOPLE
Health systems
2.16
Health systems Health expenditure
Romania Russian Federation Rwanda Saudi Arabia Senegal Serbia Sierra Leone Singapore Slovak Republic Slovenia Somalia South Africa South Sudan Spain Sri Lanka Sudan Swaziland Sweden Switzerland Syrian Arab Republic Tajikistan Tanzania Thailand Timor-Leste Togo Trinidad and Tobago Tunisia Turkey Turkmenistan Uganda Ukraine United Arab Emirates United Kingdom United States Uruguay Uzbekistan Venezuela, RB Vietnam West Bank and Gaza Yemen, Rep. Zambia Zimbabwe World Low income Middle income Lower middle income Upper middle income Low & middle income East Asia & Pacific Europe & Central Asia Latin America & Carib. Middle East & N. Africa South Asia Sub-Saharan Africa High income Euro area
Total % of GDP
Public % of total
Out of pocket % of total
External resources % of total
2010
2010
2010
2010
78.1 62.1 50.1 62.9 55.5 61.9 11.3 36.3 65.9 73.7 .. 44.1 .. 72.8 44.7 29.8 63.7 81.1 59.0 46.0 26.7 67.3 75.0 55.8 44.2 59.9 54.3 75.2 59.4 c 21.7 56.6 74.4 83.9 53.1 67.1 47.5 34.9 37.8 .. 24.2 60.3 .. 62.8 w 38.7 52.0 40.2 54.3 51.8 53.4 65.0 50.2 50.1 30.0 45.1 65.1 76.2
21.5 31.4 22.2 18.6 35.0 36.4 79.4 54.0 30.5 12.6 .. 16.6 .. 20.7 44.9 67.2 15.4 17.0 30.9 54.0 66.5 13.6 13.9 11.3 46.9 32.8 39.8 16.0 40.6c 49.8 40.5 18.8 10.0 11.8 13.0 42.7 59.0 57.6 .. 74.8 26.5 .. 17.7 w 48.1 36.4 52.3 33.4 36.6 36.7 29.0 34.3 47.0 60.5 31.8 13.7 14.3
0.0 0.0 47.0 0.0 18.5 0.8 20.6 0.0 0.0 0.0 .. 2.2 .. 0.0 3.0 3.3 17.2 0.0 0.0 0.7 6.1 48.8 0.3 33.7 15.2 0.1 0.3 0.0 0.3c 25.9 0.4 0.0 0.0 0.0 0.0 0.9 0.0 3.4 .. 4.3 39.2 .. 0.2 w 25.9 0.6 2.8 0.2 1.1 0.4 0.3 0.2 0.7 2.3 10.5 0.0 0.1
5.6 5.1 10.5 4.3 5.7 10.4 13.1 4.0 8.8 9.4 .. 8.9 .. 9.5 2.9 6.3 6.6 9.6 11.5 3.4 6.0 6.0 3.9 9.1 7.7 5.7 6.2 6.7 2.5c 9.0 7.7 3.7 9.6 17.9 8.4 5.8 4.9 6.8 .. 5.2 5.9 .. 10.5 w 5.4 5.7 4.2 6.1 5.7 4.7 5.8 7.8 4.7 3.8 6.5 12.7 10.8
Health workers
Per capita $ PPP $ 2010
428 525 56 680 59 546 43 1,733 1,413 2,154 .. 649 .. 2,883 70 84 203 4,710 7,812 97 49 31 179 57 41 861 238 678 106c 47 234 1,450 3,503 8,362 998 82 663 83 .. 63 73 .. 949 w 27 225 71 382 199 183 439 670 203 47 84 4,879 3,969
2010
811 998 121 968 109 1,169 107 2,273 2,060 2,552 .. 935 .. 3,027 148 141 333 3,757 5,394 174 128 83 330 84 77 1,449 483 1,029 199c 124 519 1,544 3,480 8,362 1,188 184 589 215 .. 122 90 .. 1,023 w 61 369 149 594 329 316 797 854 425 115 143 4,660 3,685
Hospital beds
per 1,000 people Nurses and Community Physicians midwives health workers
per 1,000 people
2005–10a
2005–10a
2.3 4.3 0.0 0.9 0.1 2.1 0.0 1.8 3.0 2.5 0.0 .. .. 4.0 0.5 0.3 .. 3.8 4.1 1.5 2.1 0.0 0.3 .. 0.1 1.2 1.2 1.5 2.4 0.1 3.2 1.9 2.7 2.4 3.7 2.6 .. 1.2 .. 0.3 0.1 .. 1.4 w 0.2 1.2 0.8 1.7 1.1 1.2 3.2 1.8 1.4 0.6 0.2 2.8 3.6
2005–10a
5.9 8.5 0.4 2.1 0.4 4.5 0.2 5.9 0.3 8.4 0.1 .. .. 5.1 1.9 0.8 .. 11.9 16.5 1.9 5.3 0.2 .. .. 0.3 3.6 3.3 0.6 4.4 1.3 8.6 4.1 10.1 9.8 5.5 11.1 .. 1.0 .. .. 0.7 .. 2.8 w 0.5 2.0 1.5 2.6 1.9 1.5 6.7 .. 2.3 0.9 0.8 7.1 4.7
2005–10a
.. .. .. .. .. .. 0.0 .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. w .. .. .. .. .. 0.8 .. .. .. 0.1 .. .. ..
6.6 9.7 1.6 2.2 0.3 5.4 0.4 3.1 6.5 4.6 .. 2.8 .. 3.2 .. 0.7 2.1e 2.8 5.2 1.5 5.2 0.7 2.1 5.9 0.9 2.6 2.1 2.5 4.0 0.5 8.7 1.9 3.3 3.0 1.2 4.6 1.1 3.1 .. 0.7 2.0 3.0 2.9 w .. 2.4 1.4 3.7 2.2 3.9 7.4 1.9 1.6 0.9 .. 5.7 5.8
a. Data are for the most recent year available. b. Nonprofi t institutions (such as nongovernmental organizations) serving households are accounted for in external resources, which are recorded under government expenditure. GDP includes both licit and illicit activity (for example, opium production). Public expenditures include external assistance. c. Derived from incomplete data. d. Excludes expenditure on residential facilities for care of the aged. e. Data are for 2011. f. Excludes northern Iraq. g. Excludes Transnistria.
102
2012 World Development Indicators
About the data
2.16
Definitions
Health systems—the combined arrangements of
expenditure. Thus data for this indicator from 2011
• Total health expenditure is the sum of public and
institutions and actions whose primary purpose
onward should not be compared with data from edi-
private health expenditure. It covers the provision of
is to promote, restore, or maintain health (World
tions before 2011.
health services (preventive and curative), family plan-
Health Organization, World Health Report 2000)—are
External resources for health are disbursements to
ning and nutrition activities, and emergency aid for
increasingly being recognized as key to combating
recipient countries as reported by donors, lagged one
health but excludes provision of water and sanitation.
disease and improving the health status of popu-
year to account for the delay between disbursement
• Public health expenditure is recurrent and capital
lations. The World Bank’s (2007a) Healthy Develop-
and expenditure. Except where a reliable full national
spending from central and local governments, exter-
ment: Strategy for Health, Nutrition, and Population
health account study has been done, most data are
nal borrowing and grants (including donations from
Results emphasizes the need to strengthen health
from the Organisation for Economic Co-operation and
international agencies and nongovernmental organi-
systems, which are weak in many countries, in order
Development Development Assistance Committee’s
zations), and social (or compulsory) health insurance
to increase the effectiveness of programs aimed at
Creditor Reporting System database, which compiles
funds. • Out-of-pocket health expenditure is direct
reducing specific diseases and further reduce mor-
data from government expenditure accounts, govern-
household outlays, including gratuities and in-kind
bidity and mortality. To evaluate health systems,
ment records on external assistance, routine sur-
payments, for health practitioners and pharmaceuti-
the World Health Organization (WHO) has recom-
veys of external financing assistance, and special
cal suppliers, therapeutic appliances, and other goods
mended that key components—such as financing,
services. Because of the variety of sources, caution
and services whose primary intent is to restore or
service delivery, workforce, governance, and infor-
should be used in interpreting the data.
enhance health. • External resources for health are
mation—be monitored using several key indicators
In countries where the fiscal year spans two cal-
funds or services in kind provided by entities that are
(WHO 2008b). The data in the table are a subset of
endar years, expenditure data have been allocated
not part of the country. The resources may come from
the first four indicators. Monitoring health systems
to the later year (for example, 2009 data cover fis-
international organizations, other countries through
allows the effectiveness, efficiency, and equity of
cal year 2008/09). Many low-income countries use
bilateral arrangements, or foreign nongovernmen-
different health system models to be compared.
Demographic and Health Surveys or Multiple Indica-
tal organizations and are part of public and private
Health system data also help identify weaknesses
tor Cluster Surveys funded by donors to obtain health
health expenditure. • Health expenditure per capita
and strengths and areas that need investment, such
system data.
is total health expenditure divided by population in
as additional health facilities, better health informa-
Data on health worker (physicians, nurses and
U.S. dollars and in international dollars converted
midwives, and community health workers) density
using 2005 purchasing power parity (PPP) rates from
Health expenditure data are broken down into
show the availability of medical personnel. The WHO
the World Bank’s International Comparison Project.
public and private expenditures. In general, low-
estimates that at least 2.5 physicians, nurses, and
• Physicians include generalist and specialist medi-
income economies have a higher share of private
midwives per 1,000 people are needed to provide
cal practitioners.• Nurses and midwives include pro-
health expenditure than do middle- and high-income
adequate coverage with primary care interventions
fessional nurses and midwives, auxiliary nurses and
countries, and out-of-pocket expenditure (direct pay-
associated with achieving the Millennium Develop-
midwives, enrolled nurses and midwives, and other
ments by households to providers) makes up the
ment Goals (WHO, World Health Report 2006). The
personnel, such as dental nurses and primary care
largest proportion of private expenditures. High
WHO compiles data from household and labor force
nurses. • Community health workers include tradi-
out-of-pocket expenditures may discourage people
surveys, censuses, and administrative records. Data
tional medicine practitioners, faith healers, assistant
from accessing preventive or curative care and can
comparability is limited by differences in definitions
or community health education workers, community
impoverish households that cannot afford needed
and training of medical personnel varies. In addition,
health officers, family health workers, lady health visi-
care. Health financing data are collected through
human resources tend to be concentrated in urban
tors, health extension package workers, community
national health accounts, which systematically,
areas, so that average densities do not provide a full
midwives, and traditional birth attendants. • Hospital
comprehensively, and consistently monitoring health
picture of health personnel available to the entire
beds are inpatient beds for both acute and chronic
system resource flows. To establish a national health
population.
care available in public, private, general, and special-
tion systems, or better trained human resources.
account, countries must define the boundaries of the
Availability and use of health services, such
health system and classify health expenditure infor-
as hospital beds per 1,000 people, refl ect both
mation along several dimensions, including sources
demand- and supply-side factors. In the absence of
of financing, providers of health services, functional
a consistent definition this is a crude indicator of
Data on health expenditure are from the WHO’s
use of health expenditures, and benefi ciaries of
the extent of physical, financial, and other barriers
National Global Health Expenditure database (see
expenditures. The accounting system can then pro-
to health care.
http://apps.who.int/nha/database for the most
ized hospitals and rehabilitation centers. Data sources
vide an accurate picture of resource envelopes and
recent updates), supplemented by country data.
financial flows and allow analysis of the equity and
Data on physicians, nurses and midwives, and com-
efficiency of financing to inform policy.
munity health workers are from the WHO’s Global
This year’s table, like last year’s, presents out-of-
Atlas of the Health Workforce database (http://
pocket expenditure as a percentage of total health
apps.who.int/globalatlas). Data on hospital beds
expenditure; editions before 2011 presented out-of-
are from the WHO, supplemented by country data.
pocket expenditure as a percentage of private health
2012 World Development Indicators
103
PEOPLE
Health systems
2.17
Health information Year last national health account completed
Number of national health accounts completed
Year of last health survey
Year of last census
Completeness
%
1995–2010
Afghanistan Albania Algeria Angola Argentina Armenia Australia Austria Azerbaijan Bahrain Bangladesh Belarus Belgium Benin Bolivia Bosnia and Herzegovina Botswana Brazil Bulgaria Burkina Faso Burundi Cambodia Cameroon Canada Central African Republic Chad Chile China Hong Kong SAR, China Colombia Congo, Dem. Rep. Congo, Rep. Costa Rica Côte d’Ivoire Croatia Cuba Cyprus Czech Republic Denmark Dominican Republic Ecuador Egypt, Arab Rep. El Salvador Eritrea Estonia Ethiopia Finland France Gabon Gambia, The Georgia Germany Ghana Greece Guatemala Guinea Guinea-Bissau
104
2008 2009 2003 1997 2010 2008 2009 2000 2008 2010 2009 2008 2008 2010 2002 2009 2009 2009 2007 1995 2010
2010 2009 2009 2009 2005 2003 2008 2010 2008 2009 2009 2008 2008 2008 2010 2010 2008 2010 2010 2004 2010 2009 2002 2008
2012 World Development Indicators
1 2 3 0 1 7 14 15 0 1 13 1 7 4 14 7 3 10 9 7 1 0 1 16 0 0 8 15 0 12 7 1 2 2 2 0 6 15 15 8 9 3 16 0 12 4 16 16 0 3 10 15 1 0 14 0 0
2001–11
2010 2008/09 2006 2006/07 2005
2006 2007 2005 2006 2008 2006 2000 1996 2006 2005 2010 2006 2006 2004
2010 2010 2009 1993 2006 2006 1993 2007 2004 2008 2008 2002 2005
2000 2005/06 2005 2008 2002 2005 2010
2001 2008 2010 2001 2006 2011 2009 2010 2011 2009 2011 2002 2001 2011 2010 2011 2006 2008 2008 2005 2011 2003 2009 2002 2010 2006 2006 2007 2011 2011 2002 2001 2011 2011 2010 2010 2006 2007
2007 2010 2006 2003 2003 2002 2011 2010 2011 2002 2009
Birth registration 2005–10a
Infant death reporting 2005–10a
Total death reporting 2005–10a
.. 99 99 .. .. 96 .. .. 94
.. 30 .. .. 100 43 92 100 26 84 .. 62 96 .. .. 48 32 50 98 31 .. .. .. 99 .. .. 100 .. 66 55 .. .. 96 .. 94 100 56 89 86 1 54 56 35 .. 76 .. 84 100 .. .. 49 97 100 69 63 .. ..
.. 85 91 .. 99 100 96 100 79 79 .. 100 99 .. 31 93 43 88 97 90 .. .. .. 98 .. .. 100 97 94 77 .. .. 98 .. 99 100 72 100 97 54 84 100 79 .. 95 100 99 98 .. .. 97 100 .. 91 92 .. ..
10 .. .. 60 .. 100 72 91 .. 64 60 66 70 .. 49 .. 99 .. .. 97 28 81 .. 55 .. 100 .. .. 78 90 99 99 .. .. 7 .. .. .. 55 92 .. 71 .. .. 43 24
Year last national health account completed
Number of national health accounts completed
Year of last health survey
Year of last census
2.17
Completeness
%
1995–2010
Haiti Honduras Hungary India Indonesia Iran, Islamic Rep. Iraq Ireland Israel Italy Jamaica Japan Jordan Kazakhstan Kenya Korea, Dem. Rep. Korea, Rep. Kosovo Kuwait Kyrgyz Republic Lao PDR Latvia Lebanon Lesotho Liberia Libya Lithuania Macedonia, FYR Madagascar Malawi Malaysia Mali Mauritania Mauritius Mexico Moldova Mongolia Morocco Mozambique Myanmar Namibia Nepal Netherlands New Zealand Nicaragua Niger Nigeria Norway Oman Pakistan Panama Papua New Guinea Paraguay Peru Philippines Poland Portugal
2006 2005 2009 2004 2008 2007 2010 2009 2007 2009 2000 2008 2009 2010 2010 2010
2010 2008 2008 2008 2009 2007 2006 2009 2004 2008 2004 2010 2010 2003 2006 2006 2007 2008 2010 2010 2009 2008 2009 2005 2009 1998 2006 1997 2000 2008 2005 2007 2009 2008
1 3 15 2 8 4 3 16 2 4 1 14 6 2 3 0 16 0 0 6 0 5 11 0 1 0 8 0 2 5 13 6 1 2 16 2 5 3 4 10 11 11 16 15 14 6 8 13 1 1 1 3 14 11 13 15 9
2001–11
2005/06 2005/06 2005/06 2007 2000 2006
2005 2009 2006 2010 2010
1996 2005/06 2006 2000 2009/10 2009 2000 2005 2008/09 2010 2010 2007 1995 2005 2010 2006 2009 2000 2009 2010
2006/07 2006 2008 1995 2010 2003 1996 2004 2008 2008
2003 2001 2001 2011 2010 2006 2011 2009 2012 2011 2010 2004 2009 2009 2009 2010 2010 2009 2005 2011 2006 2008 2006 2011 2010 2008 2010 2009 2000 2011 2010 2004 2010 2004 2007 2001 2001 2011 2006 2005 2001 2006 2001 2010 2010 2002 2007 2010 2011 2011
Birth registration 2005–10a
Infant death reporting 2005–10a
Total death reporting 2005–10a
81 94 .. 41 53 .. 95 .. .. .. 89 .. .. 99 60 100 .. .. .. 94 72 .. .. 45 4 .. .. 94 80 .. .. 81 56 .. .. .. 98 .. 31 72 67 35 .. .. .. 32 30 .. .. 27 .. .. .. 93 .. .. ..
.. 100 89 .. .. .. 100 86 100 99 73 92 .. 83 43 73 82 .. 100 76 .. 65 .. .. .. .. 71 61 .. .. 80 .. .. 90 82 73 60 .. .. 58 .. .. 89 97 66 .. .. 97 100 88 68 .. 34 45 38 89 57
.. 100 98 .. .. 100 100 98 96 98 88 100 82 88 42 93 98 .. 73 91 .. 96 87 .. .. .. 93 100 .. 71 99 .. .. 100 100 91 98 .. .. 100 100 .. 99 96 68 .. 3 99 70 80 90 .. 71 59 84 97 99
2012 World Development Indicators
105
PEOPLE
Health information
2.17
Health information Year last national health account completed
Number of national health accounts completed
Year of last health survey
Year of last census
Completeness
%
1995–2010
Puerto Rico Qatar Romania Russian Federation Rwanda Saudi Arabia Senegal Serbia Sierra Leone Singapore Slovak Republic Slovenia Somalia South Africa South Sudan Spain Sri Lanka Sudan Swaziland Sweden Switzerland Syrian Arab Republic Tajikistan Tanzania Thailand Timor-Leste Togo Trinidad and Tobago Tunisia Turkey Turkmenistan Uganda Ukraine United Arab Emirates United Kingdom United States Uruguay Uzbekistan Venezuela, RB Vietnam West Bank and Gaza Yemen, Rep. Zambia Zimbabwe
2010 2009 2008 2006 2008 2005 2010 2006 2009 2010 1998 2009 2008 2010 2009 2010 2010 2006 2007 2002 2000 2005 2008 2007 2008 2009 2009 2008 2010 2007 2005 2007 2006 2001
a. Data are for the most recent year available.
106
2012 World Development Indicators
0 2 12 13 5 1 2 8 3 0 13 16 0 3 0 15 14 2 0 9 16 0 4 3 13 0 1 1 5 11 0 6 6 0 13 15 13 1 0 10 1 4 12 3
2001–11
1996 1999 1996 2007/08 2007 2008/09 2005/06 2008 2005
2006 2003
2006/07 2010 2010
2006 2005 2010 2005/06 2009/10 2010 2006 2006 2003 2006 2009/10 2007
2009 2006 2000 2006 2006 2006 2007 2005/06
2010 2010 2011 2010 2002 2010 2002 2011 2004 2010 2011 2011 2001 2008 2001 2001 2008 2010 2010 2004 2010 2002 2010 2010 2010 2011 2004
2002 2001 2010 2011 2010 2004 2001 2009 2007 2004 2010 2002
Birth registration 2005–10a
Infant death reporting 2005–10a
Total death reporting 2005–10a
.. .. .. .. 82 .. 55 99 51 .. .. .. 3 92
100 77 73 76 .. 100 100 37 13 100 100 79 .. 78
100 78 100 100 100 98 100 89 .. 74 100 97 .. 82
.. 97 33 30 .. .. 95 88 16 99 55 78 96 .. 94 96 21 100 .. .. .. .. 100 .. 88 96 22 14 38
84 67 .. .. 100 100 .. 24 .. 53 .. .. 49 .. 60 .. .. 75 96 100 100 77 .. 62 71 28 .. .. ..
94 94 .. 100 98 100 100 72 .. 79 .. .. 94 99 100 .. .. 93 91 96 98 100 .. 87 86 73 16 73 ..
About the data
2.17
Definitions
According to the World Health Organization (WHO),
• Year last national health account completed
health information systems are crucial for moni-
is the latest year for which the health expenditure
toring and evaluating health systems, which are
data are available using the national health account
increasingly recognized as important for combating
approach. • Number of national health accounts
disease and improving health status. Health informa-
completed is the number of national health accounts
tion systems underpin decisionmaking through four
completed during the specified years. • Year of last
data functions: generation, compilation, analysis and
health survey is the latest year the national survey
synthesis, and communication and use. The health
that collects health information was conducted.
information system collects data from the health sec-
• Year of last census is the latest year a census was
tor and other relevant sectors; analyzes the data and
conducted in the last 10 years. • Completeness of
ensures their overall quality, relevance, and timeli-
birth registration is the percentage of children under
ness; and converts data into information for health-
age 5 whose births were registered at the time of the
related decisionmaking (WHO 2008b).
survey. The numerator of completeness of birth regis-
Numerous indicators have been proposed to
tration includes children whose birth certificate was
assess a country’s health information system.
seen by the interviewer or whose mother or caretaker
They can be grouped into two broad types: indica-
says the birth has been registered. • Completeness
tors related to data generation using core sources
of infant death reporting is the number of infant
and methods (health surveys, civil registration, cen-
deaths reported by national statistical authorities
suses, facility reporting, health system resource
to the United Nations Statistics Division’s Demo-
tracking) and indicators related to capacity for data
graphic Yearbook divided by the number of infant
synthesis, analysis, and validation. Indicators related
deaths estimated by the United Nations Population
to data generation reflect a country’s capacity to col-
Division. • Completeness of total death reporting is
lect relevant data at suitable intervals using the most
the number of total deaths from civil registration sys-
appropriate data sources. Benchmarks include peri-
tems reported by national statistical authorities to
odicity, timeliness, contents, and availability. Indi-
the United Nations Statistics Division’s Demographic
cators related to capacity for synthesis, analysis,
Yearbook divided by the number of total deaths esti-
and validation measure the dimensions of the insti-
mated by the United Nations Population Division.
tutional frameworks needed to ensure data quality, including independence, transparency, and access. Benchmarks include the availability of independent
Data sources
coordination mechanisms and micro- and meta-data
Data on year last national health account completed
(WHO 2008a).
and number of national health accounts completed
The indicators in the table are all related to data
were compiled by the World Bank’s Health, Nutri-
generation, including the years the last national
tion, and Population Unit using information on the
health account, last health survey, and latest
health expenditures provided by the WHO National
population census were completed. Frequency of
Health Accounts staff and the OECD. Data on year
data collection, a benchmark of data generation, is
of last health survey are from ICF International and
shown as the number of years for which a national
the United Nations Children’s Fund (UNICEF). Data
health account was completed during the specified
on year of last census are from United Nations Sta-
years. National health account data may be collected
tistics Division’s 2011 World Population and Hous-
using different approaches such as Organisation for
ing Census Program (http://unstats.un.org/unsd/
Economic Co-operation and Development (OECD)
demographic/sources/census/2010_PHC/default.
System of Health Accounts, WHO National Health
htm). Data on completeness of birth registration
Account producers guide approach, local national
are compiled by UNICEF in State of the World’s
health accounting methods, or Pan American
Children 2012 based mostly on household surveys
Health Organization/WHO satellite health accounts
and ministry of health data. Data used to calculate
approach.
completeness of infant death reporting and total
Indicators related to data generation include com-
death reporting are from the United Nations Statis-
pleteness of birth registration, infant death report-
tics Division’s Population and Vital Statistics Report
ing, and total death reporting.
and the United Nations Population Division’s World Population Prospects: The 2010 Revision.
2012 World Development Indicators
107
PEOPLE
Health information
2.18
Disease prevention coverage and quality Access to an improved water source
Afghanistan Albania Algeria Angola Argentina Armenia Australia Austria Azerbaijan Bahrain Bangladesh Belarus Belgium Benin Bolivia Bosnia and Herzegovina Botswana Brazil Bulgaria Burkina Faso Burundi Cambodia Cameroon Canada Central African Republic Chad Chile China Hong Kong SAR, China Colombia Congo, Dem. Rep. Congo, Rep. Costa Rica Côte d’Ivoire Croatia Cuba Cyprus Czech Republic Denmark Dominican Republic Ecuador Egypt, Arab Rep. El Salvador Eritrea Estonia Ethiopia Finland France Gabon Gambia, The Georgia Germany Ghana Greece Guatemala Guinea Guinea-Bissau Haiti
108
Access to improved sanitation facilities
% of population 1990 2010
% of population 1990 2010
.. 97 94 42 94 .. 100 100 70 .. 77 100 100 57 70 97 93 89 100 43 70 31 49 100 58 39 90 67 .. 89 45 .. 93 76 99 82 100 100 100 88 72 93 74 43 98 14 100 100 .. 74 81 100 53 96 81 51 36 59
.. 76 88 29 90 .. 100 100 .. .. 39 93 100 5 18 .. 38 68 99 8 44 9 48 100 11 8 84 24 .. 67 9 .. 93 20 99 80 100 100 100 73 69 72 75 9 95 3 100 100 .. .. 96 100 7 97 62 10 .. 26
50 95 83 51 97 98 100 100 80 .. 81 100 100 75 88 99 96 98 100 79 72 64 77 100 67 51 96 91 .. 92 45 71 97 80 99 94 100 100 100 86 94 99 88 61 98 44 100 100 87 89 98 100 86 100 92 74 64 69
2012 World Development Indicators
37 94 95 58 90 90 100 100 82 .. 56 93 100 13 27 95 62 79 100 17 46 31 49 100 34 13 96 64 .. 77 24 18 95 24 99 91 100 98 100 83 92 95 87 14 95 21 100 100 33 68 95 100 14 98 78 18 20 17
Child immunization rate
% of children ages 12–23 monthsb Measles DTP3 2010 2010
62 99 95 93 99 97 94 76 67 99 94 99 94 69 79 93 94 99 97 94 92 93 79 93 62 46 93 99 .. 88 68 76 83 70 95 99 87 98 85 79 98 96 92 99 95 81 98 90 55 97 94 96 93 99 93 51 61 59
66 99 95 91 94 94 92 83 72 99 95 98 99 83 80 90 96 98 94 95 96 92 84 80 54 59 92 99 .. 88 63 90 88 85 96 96 99 99 90 88 99 97 92 99 94 86 99 99 45 98 91 93 94 99 94 57 76 59
Children Children with with acute diarrhea who respiratory received oral infection (ARI) rehydration taken to and continuous health feeding provider
Children sleeping under treated netsa
Children with fever receiving antimalarial drugs
Tuberculosis
Treatment success rate
Case detection rate
% of children under age 5 with ARI 2005–10c
% of children under age 5 with diarrhea 2005–10c
% of children under age 5 2005–10c
% of children under age 5 with fever 2005–10c
% of new registered cases
% of new estimated cases
2009
2010
.. 70 53 .. .. 57 .. .. 33 .. 37 90 .. 36 51 91 .. 50 .. 39 38 64 35 .. 32 26 .. .. .. 64 40 48 .. 35 .. .. .. .. .. 70 .. 73 67 .. .. 19 .. .. .. 69 74 .. 51 .. .. 42 52 31
.. 63 24 .. .. 59 .. .. 31 .. 68 54 .. 42 29 53 .. .. .. 42 23 50 22 .. 47 23 .. .. .. 52 37 39 .. 45 .. .. .. .. .. 55 .. 19 .. .. .. 15 .. .. .. 38 37 .. 45 .. .. 38 53 43
.. .. .. 17.7 .. .. .. .. .. .. .. .. .. 20.1 .. .. .. .. .. 9.6 45.2 4.2 13.1 .. 15.1 9.8 .. .. .. .. 35.7 6.1 .. 3.0 .. .. .. .. .. .. .. .. .. 48.9 .. 33.1 .. .. 55.1 49.0 .. .. 28.2 .. .. 4.5 35.5 ..
.. .. .. 29.3 .. .. .. .. .. .. .. .. .. 54.0 .. .. .. .. .. 48.0 17.2 0.2 57.8 .. 57.0 35.7 .. .. .. .. 39.1 48.0 .. 36.0 .. .. .. .. .. 0.6 .. .. .. 13.1 .. 9.5 .. .. .. 63.0 .. .. 43.0 .. .. 73.9 51.2 5.1
86 89 91 72 46 73 80 66 62 98 92 64 76 90 86 99 79 72 85 76 90 95 78 75 53 76 72 95 70 77 88 78 54 79 63 90 29 67 53 85 75 88 89 85 59 84 68 .. 55 89 75 77 87 .. 83 79 67 79
47 97 70 77 66 62 84 84 63 84 46 74 87 45 62 71 70 88 79 53 70 65 69 83 47 31 75 87 87 72 53 68 78 83 73 79 68 88 93 59 51 64 96 55 85 72 87 47 42 44 100 89 70 68 37 33 62 62
Access to an improved water source
Honduras Hungary India Indonesia Iran, Islamic Rep. Iraq Ireland Israel Italy Jamaica Japan Jordan Kazakhstan Kenya Korea, Dem. Rep. Korea, Rep. Kosovo Kuwait Kyrgyz Republic Lao PDR Latvia Lebanon Lesotho Liberia Libya Lithuania Macedonia, FYR Madagascar Malawi Malaysia Mali Mauritania Mauritius Mexico Moldova Mongolia Morocco Mozambique Myanmar Namibia Nepal Netherlands New Zealand Nicaragua Niger Nigeria Norway Oman Pakistan Panama Papua New Guinea Paraguay Peru Philippines Poland Portugal Puerto Rico Qatar
Access to improved sanitation facilities
% of population 1990 2010
% of population 1990 2010
76 96 69 70 90 81 100 100 100 93 100 97 96 44 100 .. .. 99 .. .. 99 100 80 .. 54 .. 100 29 41 88 28 30 99 85 .. 54 73 36 56 64 76 100 100 74 35 47 100 80 85 84 41 52 75 85 .. 96 .. 100
50 100 18 32 79 .. 99 100 .. 80 100 97 96 25 .. 100 .. 100 .. .. .. .. .. .. 97 .. .. 9 39 84 15 16 89 64 .. .. 53 11 .. 24 10 100 .. 43 5 37 100 82 27 58 47 37 54 57 .. 92 .. 100
87 100 92 82 96 79 100 100 100 93 100 97 95 59 98 98 .. 99 90 67 99 100 78 73 .. .. 100 46 83 100 64 50 99 96 96 82 83 47 83 93 89 100 100 85 49 58 100 89 92 93 40 86 85 92 100 99 .. 100
77 100 34 54 100 73 99 100 .. 80 100 98 97 32 80 100 .. 100 93 63 78 .. 26 18 97 .. 88 15 51 96 22 26 89 85 85 51 70 18 76 32 31 100 .. 52 9 31 100 99 48 69 45 71 71 74 90 100 .. 100
Child immunization rate
% of children ages 12–23 monthsb Measles DTP3 2010 2010
99 99 74 89 99 73 90 98 90 88 94 98 99 86 99 98 .. 98 99 64 93 53 85 64 98 96 98 67 93 96 63 67 99 95 97 97 98 70 88 75 86 96 91 99 71 71 93 97 86 95 55 94 94 88 98 96 .. 99
98 99 72 83 99 65 94 96 96 99 98 98 99 83 93 94 .. 98 96 74 89 74 83 64 98 95 95 74 93 94 76 64 99 95 90 96 99 74 90 83 82 97 93 98 70 69 93 99 88 94 56 90 93 87 99 98 .. 97
Children Children with with acute diarrhea who respiratory received oral infection (ARI) rehydration taken to and continuous health feeding provider
Children sleeping under treated netsa
Children with fever receiving antimalarial drugs
2.18 Tuberculosis
Treatment success rate
Case detection rate
% of children under age 5 with ARI 2005–10c
% of children under age 5 with diarrhea 2005–10c
% of children under age 5 2005–10c
% of children under age 5 with fever 2005–10c
% of new registered cases
% of new estimated cases
2009
2010
56 .. 69 66 .. 82 .. .. .. 75 .. 75 71 56 80 .. .. .. 62 32 .. .. 66 62 .. .. 93 42 52 .. 38 45 .. .. 60 63 .. 65 .. 72 43 .. .. .. 47 45 .. .. 69 .. 63 .. 68 50 .. .. .. ..
49 .. 33 54 .. 64 .. .. .. 39 .. 32 48 43 67 .. .. .. 22 49 .. .. 48 47 .. .. 45 49 27 .. 38 32 .. .. 48 47 .. 47 .. 48 37 .. .. .. 34 25 .. .. 37 .. .. .. 60 60 .. .. .. ..
.. .. .. 3.3 .. .. .. .. .. .. .. .. .. 46.7 .. .. .. .. .. 40.5 .. .. .. 26.4 .. .. .. 45.8 56.5 .. 70.2 .. .. .. .. .. .. 22.8 .. 34.0 .. .. .. .. 63.7 29.1 .. .. .. .. .. .. .. .. .. .. .. ..
0.5 .. 8.2 0.8 .. .. .. .. .. .. .. .. .. 23.2 .. .. .. .. .. 8.2 .. .. .. 67.2 .. .. .. 19.7 30.9 .. 31.7 20.7 .. .. .. .. .. 36.7 .. 20.3 0.1 .. .. .. 33.0 49.1 .. .. 3.3 .. .. .. .. 0.0 .. .. .. ..
86 57 88 91 83 90 67 86 .. 70 52 75 62 86 89 83 .. 85 82 93 75 82 70 83 69 73 90 82 88 78 78 63 88 86 54 88 84 85 85 85 90 80 76 85 79 83 82 98 91 80 72 80 81 89 67 84 81 80
74 100 59 66 81 48 88 93 57 72 84 100 82 82 100 90 .. 86 66 72 100 71 85 56 84 76 89 44 65 80 51 21 44 110 63 72 97 34 71 82 72 85 90 100 35 40 93 85 65 89 70 77 100 65 80 79 96 87
2012 World Development Indicators
109
PEOPLE
Disease prevention coverage and quality
2.18
Disease prevention coverage and quality Access to an improved water source
Romania Russian Federation Rwanda Saudi Arabia Senegal Serbia Sierra Leone Singapore Slovak Republic Slovenia Somalia South Africa South Sudan Spain Sri Lanka Sudan Swaziland Sweden Switzerland Syrian Arab Republic Tajikistan Tanzania Thailand Timor-Leste Togo Trinidad and Tobago Tunisia Turkey Turkmenistan Uganda Ukraine United Arab Emirates United Kingdom United States Uruguay Uzbekistan Venezuela, RB Vietnam West Bank and Gaza Yemen, Rep. Zambia Zimbabwe World Low income Middle income Lower middle income Upper middle income Low & middle income East Asia & Pacific Europe & Central Asia Latin America & Carib. Middle East & N. Africa South Asia Sub-Saharan Africa High income Euro area
Access to improved sanitation facilities
% of population 1990 2010
% of population 1990 2010
75 93 66 89 61 99 38 100 100 100 .. 83 .. 100 67 65 39 100 100 86 .. 55 86 .. 49 88 81 85 .. 43 .. 100 100 99 96 90 90 57 .. 67 49 79 76 w 54 73 70 76 71 68 90 86 86 71 49 99 100
71 74 36 .. 38 .. 11 99 100 100 .. 71 .. 100 70 27 48 100 100 85 .. 7 84 .. 13 93 74 84 98 27 .. 97 100 100 94 84 82 37 .. 24 46 41 47 w 21 39 29 46 37 30 80 68 73 22 26 100 100
.. 97 65 .. 72 99 55 100 100 99 29 91 .. 100 91 58 71 100 100 90 64 53 96 69 61 94 94 100 .. 72 98 100 100 99 100 87 .. 95 85 55 61 80 88 w 65 90 87 93 86 90 96 94 89 90 61 100 100
72 70 55 .. 52 92 13 100 100 100 23 79 .. 100 92 26 57 100 100 95 94 10 96 47 13 92 85 90 98 34 94 98 100 100 100 100 .. 76 92 53 48 40 62 w 37 59 47 73 56 66 84 79 88 38 31 100 100
Child immunization rate
% of children ages 12–23 monthsb Measles DTP3 2010 2010
95 98 82 98 60 95 82 95 98 95 46 65 .. 95 99 90 94 96 90 82 94 92 98 66 84 92 97 97 99 55 94 94 93 92 95 98 79 98 .. 73 91 84 85 w 78 86 80 96 84 95 96 93 88 77 75 93 93
97 97 80 98 70 91 90 97 99 96 45 63 .. 97 99 90 89 98 96 80 93 91 99 72 92 90 98 96 96 60 90 94 96 95 95 99 78 93 .. 87 82 83 85 w 80 85 79 96 84 94 95 93 89 76 77 95 96
Children Children with with acute diarrhea who respiratory received oral infection (ARI) rehydration taken to and continuous health feeding provider % of children under age 5 with ARI 2005–10c
.. .. 28 .. 47 93 46 .. .. .. 13 .. .. .. 58 90 73 .. .. 77 64 71 84 71 23 74 59 .. 83 73 .. .. .. .. .. 68 .. 83 .. .. .. 43 .. w .. .. 67 .. .. .. .. .. .. 67 .. .. ..
% of children under age 5 with diarrhea 2005–10c
.. .. 24 .. 43 71 57 .. .. .. 7 .. .. .. 67 56 22 .. .. 34 22 50 46 63 24 32 62 22 25 39 .. .. .. .. .. 28 .. 65 .. 48 68 35 .. w 39 .. 37 .. .. .. .. .. .. 37 35 .. ..
Children sleeping under treated netsa
Children with fever receiving antimalarial drugs
% of children under age 5 2005–10c
% of children under age 5 with fever 2005–10c
.. .. 69.8 .. 29.2 .. 25.8 .. .. .. 11.4 .. .. .. 2.9 25.3 0.6 .. .. .. 1.3 63.6 .. 41.0 56.9 .. .. .. .. 32.8 .. .. .. .. .. .. .. 5.0 .. .. 56 17.3 .. w .. .. .. .. .. .. .. .. .. .. 34.0 .. ..
.. .. 10.8 .. 9.1 .. 30.1 .. .. .. 7.9 .. .. .. 0.3 35.8 0.6 .. .. .. 1.9 59.1 .. 6.0 33.8 .. .. .. .. 59.6 .. .. .. .. .. .. .. 2.6 .. .. 49.9 23.6 .. w 31.9 .. 13.8 .. .. .. .. .. .. 7.2 37.8 .. ..
Tuberculosis
Treatment success rate
Case detection rate
% of new registered cases
% of new estimated cases
2009
2010
85 55 85 65 85 86 79 82 82 87 85 73 .. .. 86 80 69 85 .. 88 81 88 86 85 81 69 83 91 84 67 60 73 82 60 80 81 84 92 82 88 90 78 86 w 86 87 87 86 87 92 65 77 87 88 79 68 ..
74 78 60 88 31 130 32 87 88 79 38 72 .. 87 69 50 66 87 55 90 44 77 70 87 10 87 91 77 96 61 73 57 91 88 97 48 66 54 16 76 90 56 65 w 58 67 61 81 65 76 73 80 73 58 60 85 ..
a. For malaria prevention only. b. Refers to children who were immunized before age 12 months or in some cases at any time before the survey (12–23 months). c. Data are for the most recent year available.
110
2012 World Development Indicators
About the data
2.18
Definitions
People’s health is influenced by the environment
progress, so it is diffi cult to accurately compare
• Access to an improved water source refers to peo-
in which they live. Lack of clean water and basic
use rates across countries. Until the current recom-
ple with access to at least 20 liters of water a person
sanitation is the main reason diseases transmitted
mended method for home management of diarrhea is
a day from an improved source, such as piped water
by feces are so common in developing countries.
adopted and applied in all countries, the data should
into a dwelling, public tap, tubewell, protected dug
Access to drinking water from an improved source
be used with caution. Also, the prevalence of diar-
well, and rainwater collection, within 1 kilometer of
and access to improved sanitation do not ensure
rhea may vary by season. Since country surveys are
the dwelling. • Access to improved sanitation facili-
safety or adequacy, as these characteristics are
administered at different times, data comparability
ties refers to people with at least adequate access
not tested at the time of the surveys. But improved
is further affected.
to excreta disposal facilities that can effectively pre-
drinking water technologies and improved sanitation
Malaria is endemic to the poorest countries in the
vent human, animal, and insect contact with excreta.
facilities are more likely than those characterized
world, mainly in tropical and subtropical regions of
Improved facilities range from protected pit latrines
as unimproved to provide safe drinking water and
Africa, Asia, and the Americas. Insecticide-treated
to flush toilets. • Child immunization rate refers to
to prevent contact with human excreta. The data
nets, properly used and maintained, are one of the
children ages 12–23 months who, before 12 months
are derived by the Joint Monitoring Programme of
most important malaria-preventive strategies to limit
or at any time before the survey, had received one
the World Health Organization (WHO) and United
human-mosquito contact.
dose of measles vaccine and three doses of diphthe-
Nations Children’s Fund (UNICEF) based on national
Prompt and effective treatment of malaria is a criti-
censuses and nationally representative household
cal element of malaria control. It is vital that suffer-
vaccine. • Children with acute respiratory infection
surveys. The coverage rates for water and sanitation
ers, especially children under age 5, start treatment
(ARI) taken to health provider are children under age
are based on information from service users on the
within 24 hours of the onset of symptoms, to pre-
5 with ARI in the two weeks before the survey who
facilities their households actually use rather than
vent progression—often rapid—to severe malaria
were taken to an appropriate health provider. • Chil-
on information from service providers, which may
and death. Data on malaria are from national- level
dren with diarrhea who received oral rehydration and
include nonfunctioning systems. While the estimates
surveys, including Multiple Indicator Cluster Surveys,
continuous feeding are children under age 5 with diar-
are based on use, the Joint Monitoring Programme
Demographic and Health Surveys, and Malaria Indi-
rhea in the two weeks before the survey who received
reports use as access, because access is the term
cator Surveys.
either oral rehydration therapy or increased fluids,
ria, pertussis (whooping cough), and tetanus (DTP3)
Data on the success rate of tuberculosis treatment
with continuous feeding. • Children sleeping under
are provided for countries that have submitted data
treated nets are children under age 5 who slept under
Governments in developing countries usually
to the WHO. The treatment success rate for tuber-
an insecticide-treated net to prevent malaria the night
finance immunization against measles and diphthe-
culosis provides a useful indicator of the quality of
before the survey. • Children with fever receiving
ria, pertussis (whooping cough), and tetanus (DTP)
health services. A low rate suggests that infectious
antimalarial drugs are children under age 5 who were
as part of the basic public health package. In many
patients may not be receiving adequate treatment.
ill with fever in the two weeks before the survey and
developing countries lack of precise information on
An important complement to the tuberculosis treat-
received any appropriate (locally defined) antimalarial
the size of the cohort of one-year-old children makes
ment success rate is the case detection rate, which
drugs. • Tuberculosis treatment success rate is new
immunization coverage diffi cult to estimate from
indicates whether there is adequate coverage by the
registered infectious tuberculosis cases that were
program statistics. The data shown here are based
recommended case detection and treatment strat-
cured or that completed a full course of treatment as
on an assessment of national immunization cover-
egy. Uncertainty bounds for the case detection rate,
a percentage of smear-positive cases registered for
age rates by the WHO and UNICEF. The assessment
not shown in the table, are available at http://data.
treatment outcome evaluation. • Tuberculosis case
considered both administrative data from service
worldbank.org and from the original source.
detection rate is newly identified tuberculosis cases
used in the Millennium Development Goal target for drinking water and sanitation.
providers and household survey data on children’s
The table shows the tuberculosis detection rate for
immunization histories. Based on the data available,
all detection methods. Editions before 2010 included
consideration of potential biases, and contributions
the tuberculosis detection rates by DOTS, the inter-
of local experts, the most likely true level of immuni-
nationally recommended strategy for tuberculosis
zation coverage was determined for each year.
control. Thus data on the case detection rate from
Data on access to water and sanitation are from
2010 onward cannot be compared with data in previ-
the WHO and UNICEF’s Progress on Drinking Water
ous editions.
and Sanitation (2012). Data on immunization are
Acute respiratory infection continues to be a leading cause of death among young children, killing
(including relapses) as a percentage of estimated incident cases (case detection, all forms). Data sources
nearly 1.5 million children under age 5 globally each
For indicators that are from household surveys, the
from WHO and UNICEF estimates (www.who.int/
year. Data are drawn mostly from household health
year in the table refers to the survey year. For more
immunization_monitoring). Data on children with ARI,
surveys in which mothers report on number of epi-
information, consult the original sources.
with diarrhea, sleeping under treated nets, and receiv-
sodes and treatment for acute respiratory infection.
ing antimalarial drugs are from UNICEF’s State of the
Most diarrhea- related deaths are due to dehydra-
World’s Children 2012, Childinfo, and MEASURE DHS
tion, and many of these deaths can be prevented with
Demographic and Health Surveys by ICF International.
the use of oral rehydration salts at home. However,
Data on tuberculosis are from the WHO’s Global Tuber-
recommendations for the use of oral rehydration
culosis Control: A Short Update to the 2011 Report.
therapy have changed over time based on scientific
2012 World Development Indicators
111
PEOPLE
Disease prevention coverage and quality
2.19
Reproductive health Total fertility Adolescent rate fertility rate
births per woman 1990 2010
Afghanistan Albania Algeria Angola Argentina Armenia Australia Austria Azerbaijan Bahrain Bangladesh Belarus Belgium Benin Bolivia Bosnia and Herzegovina Botswana Brazil Bulgaria Burkina Faso Burundi Cambodia Cameroon Canada Central African Republic Chad Chile China Hong Kong SAR, China Colombia Congo, Dem. Rep. Congo, Rep. Costa Rica Côte d’Ivoire Croatia Cuba Cyprus Czech Republic Denmark Dominican Republic Ecuador Egypt, Arab Rep. El Salvador Eritrea Estonia Ethiopia Finland France Gabon Gambia, The Georgia Germany Ghana Greece Guatemala Guinea Guinea-Bissau Haiti
112
8.0 3.2 4.7 7.2 3.0 2.5 1.9 1.5 2.7 3.7 4.5 1.9 1.6 6.7 4.9 1.7 4.7 2.8 1.8 6.8 6.5 5.7 5.9 1.8 5.8 6.7 2.6 2.3 1.3 3.1 7.1 5.4 3.2 6.3 1.6 1.8 2.4 1.9 1.7 3.5 3.7 4.4 4.0 6.2 2.0 7.1 1.8 1.8 5.2 6.1 2.2 1.5 5.6 1.4 5.6 6.7 6.6 5.4
6.3 1.5 2.3 5.4 2.2 1.7 1.9 1.4 2.3 2.5 2.2 1.4 1.8 5.3 3.3 1.1 2.8 1.8 1.5 5.9 4.3 2.6 4.5 1.7 4.6 6.0 1.9 1.6 1.1 2.1 5.8 4.5 1.8 4.4 1.5 1.5 1.5 1.5 1.9 2.6 2.5 2.7 2.3 4.5 1.6 4.2 1.9 2.0 3.3 4.9 1.6 1.4 4.2 1.4 4.0 5.2 5.1 3.3
2012 World Development Indicators
Unmet Contraceptive Pregnant need for prevalence women contraception rate receiving prenatal care
births per 1,000 women ages 15–19 2010
% of married women ages 15–49 2005–10a
Any method % of married women ages 15–49 2005–10a
% 2005–10a
107 16 7 157 55 34 14 11 32 15 73 21 12 103 76 15 47 76 39 120 20 36 120 12 102 149 57 9 4 71 183 115 63 115 13 44 6 10 5 106 81 43 79 59 19 58 9 6 85 71 42 7 66 10 104 143 102 43
.. 13 11 .. .. 13 .. .. 23 .. 17 .. .. 30 20 23 .. .. .. 31 .. 17 3 .. .. .. .. .. .. 7 24 16 .. 29 .. 8 .. .. .. 11 .. 9 .. .. .. 34 .. .. .. .. .. .. 35 .. .. 21 25 38
23 69 61 .. 78 55 .. .. 51 .. 53 73 .. 17 61 36 53 81 .. 17 22 51 29 .. 19 5 58 85 80 79 17 44 80 13 .. 78 .. .. .. 73 .. 60 73 .. .. 15 .. 71 .. .. 53 .. 24 .. 54 9 14 32
36 97 89 80 99 99 98 .. 77 100 53 99 .. 84 86 99 94 98 .. 85 99 89 82 100 69 53 .. 92 .. 97 88 86 90 85 100 100 99 .. .. 99 .. 74 94 .. .. 28 .. .. .. 98 98 .. 90 .. 93 88 93 85
Births attended by skilled health staff
% of total 1990 2005–10a
.. 93 77 .. 96 100 100 .. 97 .. .. 100 .. .. 43 97 78 70 99 .. .. .. 64 .. .. .. .. 94 .. 94 .. .. 98 .. 100 .. .. 100 .. 92 .. 37 90 .. 99 .. .. .. .. 44 97 .. 40 .. .. 31 .. 23
24 99 95 47 98 100 .. .. 88 97 27 100 .. 74 71 100 95 97 100 54 60 71 63 100 44 23 100 99 100 98 79 83 99 57 100 100 .. 100 .. 98 .. 79 96 .. 100 6 .. .. .. 57 100 100 57 .. 51 46 44 26
Maternal mortality ratio
Lifetime risk of maternal mortality
per 100,000 live births National Modeled estimates estimates a 2005–10 1990 2008
.. 21 .. .. 55 27 .. .. 24 .. 190 1 .. 400 310 3 200 75 5 310 620 206 .. .. 540 .. 17 32 .. 76 550 780 21 540 13 43 .. 2 .. 160 61 55 59 .. 7 670 .. .. .. .. 52 .. 450 .. 130 980 410 630
1,700 48 250 1,000 72 51 10 10 64 25 870 37 7 790 510 18 83 120 24 770 1,200 690 680 6 880 1,300 56 110 .. 140 900 460 35 690 8 63 17 15 7 220 230 220 200 930 48 990 7 13 260 750 58 13 630 6 140 1,200 1,200 670
1,400 31 120 610 70 29 8 5 38 19 340 15 5 410 180 9 190 58 13 560 970 290 600 12 850 1,200 26 38 .. 85 670 580 44 470 14 53 10 8 5 100 140 82 110 280 12 470 8 8 260 400 48 7 350 2 110 680 1,000 300
Probability 1 woman in: 2008
11 1,700 340 29 600 1,900 7,400 14,300 1,200 2,200 110 5,100 10,900 43 150 9,300 180 860 5,800 28 25 110 35 5,600 27 14 2,000 1,500 .. 460 24 39 1,100 44 5,200 1,400 6,600 8,500 10,900 320 270 380 350 72 5,300 40 7,600 6,600 110 49 1,300 11,100 66 31,800 210 26 18 93
Total fertility Adolescent rate fertility rate
births per woman 1990 2010
Honduras Hungary India Indonesia Iran, Islamic Rep. Iraq Ireland Israel Italy Jamaica Japan Jordan Kazakhstan Kenya Korea, Dem. Rep. Korea, Rep. Kosovo Kuwait Kyrgyz Republic Lao PDR Latvia Lebanon Lesotho Liberia Libya Lithuania Macedonia, FYR Madagascar Malawi Malaysia Mali Mauritania Mauritius Mexico Moldova Mongolia Morocco Mozambique Myanmar Namibia Nepal Netherlands New Zealand Nicaragua Niger Nigeria Norway Oman Pakistan Panama Papua New Guinea Paraguay Peru Philippines Poland Portugal Puerto Rico Qatar
5.1 1.8 3.9 3.1 4.8 6.0 2.1 2.8 1.3 2.9 1.5 5.8 2.7 6.0 2.4 1.6 3.9 2.6 3.7 6.2 2.0 3.1 4.9 6.5 4.8 2.0 2.1 6.3 6.8 3.5 7.1 5.9 2.3 3.4 2.4 4.1 4.0 6.2 3.4 5.2 5.2 1.6 2.2 4.8 7.8 6.4 1.9 7.2 6.0 3.0 4.8 4.5 3.8 4.3 2.0 1.4 2.2 4.2
3.1 1.3 2.6 2.1 1.7 4.7 2.1 3.0 1.4 2.3 1.4 3.8 2.6 4.7 2.0 1.2 2.3 2.3 2.9 2.7 1.2 1.8 3.2 5.2 2.6 1.6 1.4 4.7 6.0 2.6 6.3 4.5 1.5 2.3 1.5 2.5 2.3 4.9 2.0 3.2 2.7 1.8 2.2 2.6 7.1 5.5 2.0 2.3 3.4 2.5 4.0 3.0 2.5 3.1 1.4 1.3 1.8 2.3
Unmet Contraceptive Pregnant need for prevalence women contraception rate receiving prenatal care
births per 1,000 women ages 15–19 2010
% of married women ages 15–49 2005–10a
Any method % of married women ages 15–49 2005–10a
% 2005–10a
89 15 79 43 27 91 12 14 5 73 6 25 27 99 1 4 .. 14 33 34 15 16 66 131 3 18 19 127 111 12 176 75 33 68 31 20 13 134 14 62 93 5 24 108 199 114 8 9 30 79 64 69 51 50 13 14 52 16
17 .. 13 15 .. .. .. .. .. .. .. 11 .. 26 .. .. .. .. 1 .. .. .. .. 36 .. .. 34 19 .. .. 31 25 .. .. 7 14 .. .. .. 21 25 .. .. 8 16 20 .. .. 25 .. .. .. 8 22 .. .. .. ..
65 .. 54 56 79 50 65 .. .. 72 54 59 51 46 .. 80 .. .. 48 38 .. .. 47 11 .. .. 14 40 41 .. 8 9 .. 73 68 55 .. 16 41 55 48 69 .. 72 18 15 88 24 27 52 32 79 74 51 .. 67 .. ..
92 .. 75 95 98 84 .. .. .. 99 .. 99 100 92 100 .. .. 100 97 71 .. .. 92 79 93 .. 99 86 92 79 70 75 .. 96 98 100 .. 92 80 95 44 .. .. 90 46 58 .. 99 61 96 79 96 95 91 .. .. .. 100
Births attended by skilled health staff
2.19
Maternal mortality ratio
Lifetime risk of maternal mortality
% of total 1990 2005–10a
per 100,000 live births National Modeled estimates estimates a 2005–10 1990 2008
Probability 1 woman in: 2008
47 99 .. 41 .. 54 .. .. .. 92 100 87 99 50 .. .. .. .. 99 .. 100 .. .. .. .. 100 89 57 55 93 .. 40 91 84 100 .. 31 .. 46 68 7 .. .. .. 15 31 100 .. 19 86 .. 66 53 .. 100 98 .. ..
.. 19 250 230 25 84 .. .. .. .. .. 19 37 488 77 .. .. .. 64 410 32 .. 1,200 990 .. 9 4 500 810 29 460 690 .. 54 45 47 130 500 320 450 280 .. .. 67 650 550 .. 17 250 60 730 130 93 160 2 .. .. ..
240 5,500 140 190 1,500 300 17,800 5,100 15,200 450 12,200 510 950 38 230 4,700 .. 4,500 450 49 3,600 2,000 62 20 540 5,800 7,300 45 36 1,200 22 41 1,600 500 2,000 730 360 37 180 160 80 7,100 3,800 300 16 23 7,600 1,600 93 520 94 310 370 320 13,300 9,800 3,000 4,400
67 100 53 82 97 80 .. .. .. 98 .. 99 100 44 100 .. .. 100 99 37 100 .. 62 46 100 100 100 44 54 99 49 61 99 95 100 100 .. 55 64 81 19 .. .. 74 18 39 .. 99 39 89 53 82 84 62 100 .. .. 100
210 23 570 620 150 93 6 12 10 66 12 110 78 380 270 18 .. 10 77 1,200 57 52 370 1,100 100 34 16 710 910 56 1,200 780 72 93 62 130 270 1,000 420 180 870 10 18 190 1,400 1,100 9 49 490 86 340 130 250 180 17 15 29 15
110 13 230 240 30 75 3 7 5 89 6 59 45 530 250 18 .. 9 81 580 20 26 530 990 64 13 9 440 510 31 830 550 36 85 32 65 110 550 240 180 380 9 14 100 820 840 7 20 260 71 250 95 98 94 6 7 18 8
2012 World Development Indicators
113
PEOPLE
Reproductive health
2.19
Reproductive health Total fertility Adolescent rate fertility rate
births per woman 1990 2010
Romania Russian Federation Rwanda Saudi Arabia Senegal Serbia Sierra Leone Singapore Slovak Republic Slovenia Somalia South Africa South Sudan Spain Sri Lanka Sudan Swaziland Sweden Switzerland Syrian Arab Republic Tajikistan Tanzania Thailand Timor-Leste Togo Trinidad and Tobago Tunisia Turkey Turkmenistan Uganda Ukraine United Arab Emirates United Kingdom United States Uruguay Uzbekistan Venezuela, RB Vietnam West Bank and Gaza Yemen, Rep. Zambia Zimbabwe World Low income Middle income Lower middle income Upper middle income Low & middle income East Asia & Pacific Europe & Central Asia Latin America & Carib. Middle East & N. Africa South Asia Sub-Saharan Africa High income Euro area
1.8 1.9 7.0 5.8 6.6 1.8 5.7 1.9 2.1 1.5 6.6 3.7 .. 1.3 2.5 6.0 5.7 2.1 1.6 5.3 5.2 6.2 2.1 5.3 6.3 2.4 3.6 3.0 4.3 7.1 1.8 4.4 1.8 2.1 2.5 4.1 3.4 3.6 6.5 8.7 6.5 5.2 3.2 w 5.7 3.3 4.2 2.6 3.6 2.6 2.3 3.2 4.9 4.2 6.2 1.8 1.5
1.4 1.5 5.4 2.8 4.8 1.4 5.0 1.2 1.4 1.6 6.3 2.5 3.9 1.4 2.3 4.4 3.4 2.0 1.5 2.9 3.3 5.5 1.6 5.6 4.1 1.6 2.0 2.1 2.4 6.1 1.4 1.7 1.9 2.1 2.0 2.5 2.5 1.8 4.5 5.2 6.3 3.3 2.5 w 4.1 2.3 2.9 1.8 2.6 1.8 1.8 2.2 2.7 2.7 4.9 1.8 1.6
births per 1,000 women ages 15–19 2010
30 26 37 18 96 20 120 6 18 5 69 54 .. 12 23 57 74 6 4 39 27 129 40 58 59 33 5 34 18 136 28 25 30 33 60 13 88 24 50 71 142 58 53 w 94 51 68 29 58 19 27 72 37 73 108 18 8
a. Data are for most recent year available. b. Data are for 2011.
114
2012 World Development Indicators
Unmet Contraceptive Pregnant need for prevalence women contraception rate receiving prenatal care % of married women ages 15–49 2005–10a
.. .. 38 .. 32 29 28 .. .. .. 26 .. .. .. .. 6 24 .. .. 11 24 25 .. 31 41 27 .. 18 .. 41 10 .. .. .. .. 8 .. .. .. 24 27 13 .. w 25 .. 14 .. .. .. .. .. .. 15 25 .. ..
Any method % of married women ages 15–49 2005–10a
.. 80 52 24 12 41 8 .. .. .. 15 .. .. 66 68 8 49 .. .. 54 37 34 80 22 15 43 60 73 48 24 67 .. 84 79 78 65 .. 80 50 28 41 59b 62 w 34 65 50 81 61 78 69 75 62 51 22 .. ..
% 2005–10a
.. .. 98 97 87 98 87 .. .. .. 26 97 40 .. 99 56 97 .. .. 88 80 88 99 84 87 96 96 95 99 94 99 100 .. .. 96 99 .. 91 99 47 94 93b 84 w 69 86 78 94 83 92 .. 97 85 71 74 .. ..
Births attended by skilled health staff
% of total 1990 2005–10a
100 99 26 .. .. .. .. .. 100 100 .. .. .. .. .. 69 .. .. .. .. 90 44 .. .. 31 .. 69 .. .. 38 100 .. .. 99 .. .. .. .. .. 16 51 70 62 w .. 60 38 89 58 82 93 74 47 32 .. .. ..
Maternal mortality ratio
Lifetime risk of maternal mortality
per 100,000 live births National Modeled estimates estimates a 2005–10 1990 2008
99 21 100 17 69 .. 97 14 52 400 99 9 42 860 .. .. 100 10 100 10 33 1,000 .. 400 19 .. .. .. 99 39 49 1,100 82 589 .. .. 100 .. 96 .. 83 86 49 450 99 12 29 560 60 .. 98 .. 95 .. 95 29 100 12 42 440 99 16 100 0 .. .. .. 13 100 34 100 21 .. 57 88 69 99 .. 36 .. 47 590 60 b 730 66 w 44 71 57 98 65 91 98 90 81 48 46 .. ..
170 74 1,100 41 750 13 1,300 6 15 11 1,100 230 .. 7 91 830 260 7 8 120 120 880 50 650 650 86 130 68 91 670 49 28 10 12 39 53 84 170 .. 540 390 390 400 w 860 350 540 110 440 200 69 140 210 610 870 15 11
27 39 540 24 410 8 970 9 6 18 1,200 410 .. 6 39 750 420 5 10 46 64 790 48 370 350 55 60 23 77 430 26 10 12 24 27 30 68 56 .. 210 470 790 260 w 590 210 300 60 290 89 34 86 88 290 650 15 7
Probability 1 woman in: 2008
2,700 1,900 35 1,300 46 7,500 21 10,000 13,300 4,100 14 100 .. 11,400 1,100 32 75 11,400 7,600 610 430 23 1,200 44 67 1,100 860 1,900 500 35 3,000 4,200 4,700 2,100 1,700 1,400 540 850 .. 91 38 42 140 w 39 190 100 880 120 580 1,700 480 380 110 31 3,900 10,100
About the data
2.19
Definitions
Reproductive health is a state of physical and men-
and Health Surveys attempt to measure maternal
• Total fertility rate is the number of children that would
tal well-being in relation to the reproductive system
mortality by asking respondents about survivorship
be born to a woman if she were to live to the end of her
and its functions and processes. Means of achieving
of sisters. The main disadvantage of this method
childbearing years and bear children in accordance with
reproductive health include education and services
is that the estimates of maternal mortality that it
the age-specific fertility rate of the specified year. • Ado-
during pregnancy and childbirth, safe and effec-
produces pertain to 12 years or so before the sur-
lescent fertility rate is the number of births per 1,000
tive contraception, and prevention and treatment
vey, making them unsuitable for monitoring recent
women ages 15–19. • Unmet need for contraception is
of sexually transmitted diseases. Complications of
changes or observing the impact of interventions.
the percentage of fertile, married women of reproductive
pregnancy and childbirth are the leading cause of
In addition, measurement of maternal mortality is
age who do not want to become pregnant and are not
death and disability among women of reproductive
subject to many types of errors. Even in high-income
using contraception. • Contraceptive prevalence rate
age in developing countries.
countries with reliable vital registration systems,
is the percentage of women married or in union ages
misclassification of maternal deaths has been found
15–49 who are practicing, or whose sexual partners
to lead to serious underestimation.
are practicing, any form of contraception. • Pregnant
Total and adolescent fertility rates are based on data on registered live births from vital registration systems or, in the absence of such systems, from
The national estimates of maternal mortality
women receiving prenatal care are women attended at
censuses or sample surveys. The estimated rates
ratios in the table are based on national surveys,
least once during pregnancy by skilled health person-
are generally considered reliable measures of fertility
vital registration records, and surveillance data or
nel for pregnancy- related reasons. • Births attended by
in the recent past. Where no empirical information
are derived from community and hospital records.
skilled health staff are live births attended by person-
on age- specific fertility rates is available, a model is
The modeled estimates are based on an exercise by
nel trained to give women the necessary care during
used to estimate the share of births to adolescents.
the World Health Organization (WHO), United Nations
pregnancy, labor, and postpartum; to conduct deliveries
For countries without vital registration systems fertil-
Children’s Fund (UNICEF), United Nations Population
on their own; and to care for newborns. • Maternal mor-
ity rates are generally based on extrapolations from
Fund (UNFPA), and World Bank and include country-
tality ratio is the number of women who die from preg-
trends observed in censuses or surveys from earlier
level time series data. For countries with complete
nancy-related causes while pregnant or within 42 days
years.
vital registration systems with good attribution of
of pregnancy termination per 100,000 live births. • Life-
More couples in developing countries want to limit
cause of death, the data are used directly to esti-
time risk of maternal death is the probability (1 in the
or postpone childbearing but are not using effec-
mate maternal mortality. For countries without com-
number of women likely to die due to a maternal cause)
tive contraception. These couples have an unmet
plete registration data but with other types of data
that a 15-year-old girl will eventually die due to a mater-
need for contraception. Common reasons are lack
and for countries with no data, maternal mortality is
nal cause, if throughout her lifetime she experiences
of knowledge about contraceptive methods and
estimated with a multilevel regression model using
the maternal death risk and overall fertility and mortality
concerns about possible side effects. This indica-
available national maternal mortality data and socio-
rates of the specified year for a given population.
tor excludes women not exposed to the risk of unin-
economic information, including fertility, birth atten-
tended pregnancy because of menopause, infertility,
dants, and GDP. The methodology differs from that
or postpartum anovulation.
used for previous estimates, so data should not be
Contraceptive prevalence reflects all methods—
compared across editions. For further information
ineffective traditional methods as well as highly
on methodology, see the original source. Neither set
effective modern methods. Contraceptive prevalence
of ratios can be assumed to provide an exact esti-
rates are obtained mainly from household surveys,
mate of maternal mortality for any of the countries
including Demographic and Health Surveys, Multiple
in the table.
Indicator Cluster Surveys, and contraceptive preva-
In countries with a high risk of maternal death,
lence surveys (see Primary data documentation for
many girls die before reaching reproductive age. Life-
the most recent survey year). Unmarried women are
time risk of maternal mortality refers to the prob-
often excluded from such surveys, which may bias
ability that a 15-year-old girl will eventually die due
the estimates.
to a maternal cause.
Good prenatal and postnatal care improves mater-
For the indicators that are from household surveys,
nal health and reduces maternal and infant mortality.
the year in the table refers to the survey year. For
However, indicators on use of antenatal care ser-
more information, consult the original sources.
vices provide no information on the content or quality of the services. Data on antenatal care are obtained mostly from household surveys, which ask women who have had a live birth whether and from whom they received antenatal care. The share of births attended by skilled health staff is an indicator of a health system’s ability to provide adequate care for pregnant women. Maternal mortality ratios are generally of unknown reliability, as are many other cause-specific mortality indicators. Household surveys such as Demographic
Data sources Data on total fertility are from the United Nations Population Division’s World Population Prospects: The 2010 Revision; census reports and other statistical publications from national statistical offices; household surveys by national agencies, ICF International (for MEASURE DHS), and the U.S. Centers for Disease Control and Prevention; Eurostat’s Demographic Statistics; and the U.S. Bureau of the Census International Data Base. Data on adolescent fertility are from World Population Prospects: The 2010 Revision, with annual data linearly interpolated by the World Bank’s Development Data Group. Data on unmet need for contraception and contraceptive prevalence are from household surveys, including MEASURE DHS Demographic and Health Surveys by ICF International and Multiple Indicator Cluster Surveys by UNICEF. Data on pregnant women receiving prenatal care, births attended by skilled health staff, and national estimates of maternal mortality are from UNICEF’s State of the World’s Children 2012 and Childinfo and MEASURE DHS Demographic and Health Surveys by ICF International. Modeled estimates of maternal mortality and lifetime risk of maternal mortality are from WHO, UNICEF, UNFPA, and the World Bank’s Trends in Maternal Mortality: 1990–2008 (2010).
2012 World Development Indicators
115
PEOPLE
Reproductive health
2.20
Nutrition and growth Prevalence of undernourishment
% of population 1990–92 2006–08
Afghanistan Albania Algeria Angola Argentina Armenia Australia Austria Azerbaijan Bahrain Bangladesh Belarus Belgium Benin Bolivia Bosnia and Herzegovina Botswana Brazil Bulgaria Burkina Faso Burundi Cambodia Cameroon Canada Central African Republic Chad Chile China Hong Kong SAR, China Colombia Congo, Dem. Rep. Congo, Rep. Costa Rica Côte d’Ivoire Croatia Cuba Cyprus Czech Republic Denmark Dominican Republic Ecuador Egypt, Arab Rep. El Salvador Eritrea Estonia Ethiopia Finland France Gabon Gambia, The Georgia Germany Ghana Greece Guatemala Guinea Guinea-Bissau Haiti
116
.. <5 <5 67 <5 45 <5 <5 27 .. 38 <5 <5 20 29 <5 19 11 <5 14 44 38 33 <5 44 60 7 18 c .. 15 .. 42 <5 15 <5 6 <5 <5 <5 28 23 <5 13 67 <5 69 <5 <5 6 14 58 <5 28 <5 15 20 22 63
2012 World Development Indicators
.. <5 <5 41 <5 21 <5 <5 <5 .. 26 <5 <5 12 27 <5 25 6 <5 8 62 25 22 <5 40 39 <5 10 c .. 9 .. 13 <5 14 <5 <5 <5 <5 <5 24 15 <5 9 65 <5 41 <5 <5 <5 19 6 <5 5 <5 22 16 22 57
Prevalence of child malnutrition
Underweight Male Female 2005–10a 2005–10a
.. 6.6 3.7 16.6 2.4 3.4 .. .. 8.7 .. 40.2 1.5 .. 22.7 4.9 2.2 12.1 2.2 .. 27.1 .. 28.8b 18.9 .. .. .. 0.6 3.5 .. 3.5 30.4 12.9 0.6 30.3 .. .. .. .. .. 3.2 .. 8.1 6.5 .. .. 36.5 .. .. .. 16.7 1.3 0.9 15.7 .. 13.9 21.9 16.6 20.4
.. 6.0 3.7 14.6 2.2 5.2 .. .. 8.0 .. 42.4 1.0 .. 17.6 4.0 1.0 10.2 2.1 .. 24.7 .. 29.1b 14.3 .. .. .. 0.5 3.3 .. 3.3 26.1 10.6 1.8 28.4 .. .. .. .. .. 3.7 .. 5.4 6.7 .. .. 32.8 .. .. .. 15.0 1.0 1.3 12.9 .. 12.1 19.7 17.8 17.4
% of children under age 5 Stunting Male Female 2005–10a 2005–10a
.. 22.8 16.7 32.4 8.2 18.8 .. .. 28.5 .. 43.8 4.7 .. 47.9 28.1 12.8 34.0 8.3 .. 37.9 .. 42.3b 39.1 .. .. .. 2.2 9.9 .. 13.7 48.5 33.2 4.8 40.1 .. .. .. .. .. 11.2 .. 33.0 21.3 .. .. 51.8 .. .. .. 28.6 12.3 1.5 30.5 .. 48.7 41.5 29.7 33.2
.. 23.4 15.0 26.1 8.1 17.4 .. .. 24.9 .. 42.6 4.2 .. 41.6 26.2 10.7 28.7 5.8 .. 32.0 .. 39.4b 33.7 .. .. .. 1.8 8.9 .. 11.6 43.3 29.0 6.6 37.8 .. .. .. .. .. 8.9 .. 28.4 19.8 .. .. 49.6 .. .. .. 26.6 10.2 1.2 26.7 .. 47.3 38.5 26.3 26.5
Prevalence of overweight children
Wasting Male Female 2005–10a 2005–10a
.. 11.5 3.9 8.2 1.1 5.8 .. .. 7.8 .. 18.4 2.8 .. 9.0 2.0 3.8 7.5 1.8 .. 11.9 .. 11.2b 8.2 .. .. .. 0.3 2.4 .. 0.9 15.5 8.4 0.6 15.3 .. .. .. .. .. 2.5 .. 8.8 2.4 .. .. 13.7 .. .. .. 8.1 1.8 1.2 9.7 .. 1.1 8.9 5.3 10.2
.. 7.3 4.1 8.1 1.4 5.1 .. .. 5.7 .. 16.5 1.6 .. 7.8 0.8 4.3 6.8 1.4 .. 10.7 .. 10.5b 6.4 .. .. .. 0.2 2.1 .. 0.9 12.5 7.7 1.5 12.4 .. .. .. .. .. 2.1 .. 7.1 0.7 .. .. 10.8 .. .. .. 6.6 1.5 0.8 7.7 .. 1.1 7.8 5.8 10.4
% of children under age 5 Male Female 2005–10a 2005–10a
.. 23.3 13.4 .. 10.2 13.9 .. .. 14.9 .. 1.2 11.3 .. 11.6 9.2 27.4 11.3 6.9 .. 7.9 .. 1.9b 9.8 .. .. .. 9.8 7.5 .. 5.4 6.5 8.4 8.3 5.0 .. .. .. .. .. 9.0 .. 19.8 6.3 .. .. 5.7 .. .. .. 2.9 21.3 3.6 5.8 .. 5.3 5.2 17.6 4.4
.. 23.4 12.4 .. 9.5 9.1 .. .. 12.7 .. 1.0 8.1 .. 11.3 8.1 23.9 11.1 7.7 .. 7.6 .. 1.9b 9.5 .. .. .. 9.1 5.6 .. 4.2 7.2 8.6 7.9 4.9 .. .. .. .. .. 7.5 .. 21.2 5.0 .. .. 4.5 .. .. .. 2.5 18.3 3.3 5.9 .. 4.6 5.0 16.5 3.5
Prevalence of undernourishment
% of population 1990–92 2006–08
Honduras Hungary India Indonesia Iran, Islamic Rep. Iraq Ireland Israel Italy Jamaica Japan Jordan Kazakhstan Kenya Korea, Dem. Rep. Korea, Rep. Kosovo Kuwait Kyrgyz Republic Lao PDR Latvia Lebanon Lesotho Liberia Libya Lithuania Macedonia, FYR Madagascar Malawi Malaysia Mali Mauritania Mauritius Mexico Moldova Mongolia Morocco Mozambique Myanmar Namibia Nepal Netherlands New Zealand Nicaragua Niger Nigeria Norway Oman Pakistan Panama Papua New Guinea Paraguay Peru Philippines Poland Portugal Puerto Rico Qatar
19 <5 20 16 <5 .. <5 <5 <5 11 <5 <5 <5 33 21 <5 .. 20 17 31 <5 <5 15 30 <5 <5 <5 21 43 <5 27 12 7 <5 <5 28 6 59 .. 32 21 <5 <5 50 37 16 <5 .. 25 18 .. 16 27 24 <5 <5 .. ..
12 <5 19 13 <5 .. <5 <5 <5 5 <5 <5 <5 33 35 <5 .. 5 11 22 <5 <5 14 32 <5 <5 <5 25 27 <5 12 8 5 <5 <5 27 <5 38 .. 18 17 <5 <5 19 16 6 <5 .. 25 15 .. 10 16 13 <5 <5 .. ..
Prevalence of child malnutrition
Underweight Male Female 2005–10a 2005–10a
8.9 .. 43.1 20.7 .. 7.7 .. .. .. 1.9 .. 1.6 5.4 17.3 18.8 .. .. 2.0 2.9 32.5 .. .. 16.0 21.9 6.3 .. 1.7 .. 15.2 13.2 29.7 17.8 .. 4.3 3.0 5.3 .. 20.6 .. 18.5 37.7 .. .. 5.6 42.1 28.6 .. 8.9 .. .. 21.0 .. 4.5 20.9 .. .. .. ..
8.3 .. 43.9 18.6 .. 6.6 .. .. .. 2.6 .. 2.1 4.3 15.5 18.8 .. .. 1.5 2.5 30.6 .. .. 11.1 18.7 4.8 .. 1.9 .. 12.6 12.7 26.0 13.9 .. 2.6 3.4 5.3 .. 16.0 .. 16.5 39.8 .. .. 5.9 37.5 24.8 .. 8.3 .. .. 14.6 .. 4.5 20.6 .. .. .. ..
% of children under age 5 Stunting Male Female 2005–10a 2005–10a
31.5 .. 47.9 41.3 .. 28.7 .. .. .. 3.0 .. 7.9 17.9 37.3 32.4 .. .. 4.2 18.7 48.3 .. .. 43.1 41.9 22.2 .. 13.5 51.6 51.8 17.2 40.7 25.8 .. 16.0 11.0 29.2 .. 46.8 .. 32.0 49.1 .. .. 24.0 56.8 43.1 .. 11.3 .. .. 47.4 .. 30.5 33.5 .. .. .. ..
28.3 .. 48.0 38.8 .. 26.2 .. .. .. 4.4 .. 8.7 16.9 33.1 32.4 .. .. 3.4 17.5 46.8 .. .. 35.0 36.7 19.6 .. 9.2 46.7 44.1 17.2 36.2 20.0 .. 15.0 11.5 25.6 .. 40.7 .. 27.1 49.6 .. .. 21.9 52.6 38.8 .. 8.5 .. .. 39.6 .. 25.9 31.1 .. .. .. ..
2.20 Prevalence of overweight children
Wasting Male Female 2005–10a 2005–10a
1.6 .. 20.7 15.7 .. 6.2 .. .. .. 1.9 .. 1.6 4.5 8.2 5.0 .. .. 1.5 3.5 7.8 .. .. 4.2 7.9 6.8 .. 2.4 .. 4.4 .. 16.2 9.4 .. 2.5 6.0 2.6 .. 4.9 .. 7.3 13.0 .. .. 1.5 13.8 14.8 .. 8.1 .. .. 4.8 .. 0.9 7.4 .. .. .. ..
1.1 .. 19.3 13.8 .. 5.4 .. .. .. 2.2 .. 1.6 2.8 5.8 5.3 .. .. 2.1 3.2 6.8 .. .. 3.5 7.8 6.1 .. 4.5 .. 3.8 .. 14.3 6.8 .. 1.5 5.6 2.8 .. 3.5 .. 7.8 12.4 .. .. 1.4 10.9 14.0 .. 6.0 .. .. 4.0 .. 0.8 6.4 .. .. .. ..
% of children under age 5 Male Female 2005–10a 2005–10a
6.3 .. 2.2 11.3 .. 15.6 .. .. .. .. .. 7.9 15.1 4.7 0.0 .. .. 10.0 12.7 1.5 .. .. 7.7 4.9 23.2 .. 16.6 .. 10.3 .. 4.9 1.3 .. 8.4 8.9 15.6 .. 4.1 .. 4.9 0.6 .. .. 6.7 3.6 10.3 .. 1.5 .. .. 4.2 .. 9.8 3.6 .. .. .. ..
2012 World Development Indicators
5.2 .. 1.7 11.2 .. 14.3 .. .. .. .. .. 5.2 14.5 5.3 0.0 .. .. 8.0 8.6 1.0 .. .. 6.9 3.5 21.6 .. 15.8 .. 8.2 .. 4.6 0.6 .. 6.7 9.3 12.6 .. 3.2 .. 4.4 0.6 .. .. 5.6 3.5 10.7 .. 2.0 .. .. 2.5 .. 9.8 2.9 .. .. .. ..
117
PEOPLE
Nutrition and growth
2.20
Nutrition and growth Prevalence of undernourishment
% of population 1990–92 2006–08
Romania Russian Federation Rwanda Saudi Arabia Senegal Serbia Sierra Leone Singapore Slovak Republic Slovenia Somalia South Africa South Sudan Spain Sri Lanka Sudan Swaziland Sweden Switzerland Syrian Arab Republic Tajikistan Tanzania Thailand Timor-Leste Togo Trinidad and Tobago Tunisia Turkey Turkmenistan Uganda Ukraine United Arab Emirates United Kingdom United States Uruguay Uzbekistan Venezuela, RB Vietnam West Bank and Gaza Yemen, Rep. Zambia Zimbabwe World Low income Middle income Lower middle income Upper middle income Low & middle income East Asia & Pacific Europe & Central Asia Latin America & Carib. Middle East & N. Africa South Asia Sub-Saharan Africa High income Euro area
<5 <5 44 <5 22 <5d 45 .. <5 <5 .. <5 .. <5 28 39 12 <5 <5 <5 34 29 26 39 43 11 <5 <5 9 19 <5 <5 <5 <5 5 5 10 31 10 30 35 40 16 w 38 17 20 14 19 19 7 12 7 22 32 <5 <5
<5 <5 32 <5 19 <5d 35 .. <5 <5 .. <5 .. <5 20 22 19 <5 <5 <5 26 34 16 31 30 11 <5 <5 7 22 <5 <5 <5 <5 <5 11 7 11 21 30 44 30 13 w 29 12 17 9 14 11 6 9 7 20 22 <5 <5
Prevalence of child malnutrition
Underweight Male Female 2005–10a 2005–10a
.. .. 18.9 6.1 14.4 2.2 24.2 .. .. .. 34.2 .. .. .. 21.6 33.4 6.3 .. .. 11.5 15.9 17.8 6.9 46.8 20.5 .. 3.7 .. .. 18.2 .. .. .. .. .. 4.6 .. 20.5 2.2 .. 16.9 15.1
.. .. 17.2 4.5 14.6 1.3 18.5 .. .. .. 31.3 .. .. .. 21.6 30.0 5.9 .. .. 8.7 14.0 14.6 7.1 43.7 20.5 .. 2.9 .. .. 14.6 .. .. .. .. .. 4.3 .. 19.9 2.3 .. 13.0 12.9
% of children under age 5 Stunting Male Female 2005–10a 2005–10a
.. .. 53.1 10.8 21.3 8.2 39.5 .. .. .. 42.7 .. .. .. 19.8 39.8 33.0 .. .. 28.4 41.1 45.9 16.5 59.8 28.4 .. 9.9 .. .. 41.2 .. .. .. .. .. 19.5 .. 31.9 12.3 .. 48.8 38.6
.. .. 50.3 7.8 18.8 8.0 35.4 .. .. .. 41.3 .. .. .. 18.7 35.8 26.1 .. .. 26.5 37.2 39.2 15.0 55.6 25.2 .. 8.0 .. .. 36.1 .. .. .. .. .. 19.7 .. 29.0 11.2 .. 42.9 33.1
Prevalence of overweight children
Wasting Male Female 2005–10a 2005–10a
.. .. 5.0 12.7 8.8 4.8 10.4 .. .. .. 14.4 .. .. .. 12.1 21.9 3.5 .. .. 12.5 6.8 5.8 4.6 20.4 6.1 .. 3.6 .. .. 7.7 .. .. .. .. .. 5.3 .. 10.2 1.7 .. 6.0 7.6
1.8 .. 4.7 10.8 8.5 4.2 10.6 .. .. .. 11.9 .. .. .. 11.5 20.1 2.4 .. .. 10.5 6.7 4.0 4.8 17.4 5.9 .. 3.3 .. .. 4.9 .. .. .. .. .. 3.7 .. 9.1 1.8 .. 5.2 6.9
% of children under age 5 Male Female 2005–10a 2005–10a
.. .. 7.2 6.3 3.0 20.4 10.3 .. .. .. 4.9 .. .. .. 0.7 5.1 11.8 .. .. 17.8 7.2 6.0 8.8 6.0 4.2 .. 8.5 .. .. 5.0 .. .. .. .. .. 13.1 .. 3.4 13.4 .. 8.6 9.5
a. Data are for the most recent year available. b. Data are for 2011. c. Includes Hong Kong SAR, China; Macao SAR, China; and Taiwan, China. d. Includes Montenegro.
118
2012 World Development Indicators
.. .. 6.3 6.0 1.8 18.2 9.9 .. .. .. 4.5 .. .. .. 1.0 5.5 10.9 .. .. 18.1 6.2 5.0 7.2 5.7 5.1 .. 9.2 .. .. 4.7 .. .. .. .. .. 12.5 .. 2.5 9.4 .. 8.2 8.7
About the data
2.20
Definitions
Good nutrition is the cornerstone for survival, health
childhood obesity and a high prevalence of diabe-
• Prevalence of undernourishment is the percentage
and development. Well nourished children perform
tes, respiratory disease, high blood pressure, and
of the population whose dietary energy consump-
better in school, grow into healthy adults, and in
psychosocial and orthopedic disorders (de Onis and
tion is continuously below a minimum requirement
turn give their children a better start in life. Well
Blössner 2003). Childhood obesity is associated
for maintaining a healthy life and carrying out light
nourished women face fewer risks during pregnancy
with a higher chance of obesity, premature death,
physical activity with an acceptable minimum weight
and childbirth, and their children set off on firmer
and disability in adulthood. In addition to increased
for height. • Prevalence of child malnutrition is the
developmental paths, both physically and mentally
future risks, obese children experience breathing
percent age of children under age 5 whose weight
(United Nations Children’s Fund [UNICEF], www.
difficulties and increased risk of fractures, hyper-
for age (underweight) or height for age (stunting)
childinfo.org).
tension, early markers of cardiovascular disease,
is more than two standard deviations below the
Data on undernourishment are from the Food and
insulin resistance, and psychological effects. Chil-
median for the international reference population
Agriculture Organization (FAO) of the United Nations
dren in low- and middle-income countries are more
ages 0–59 months. Height is measured by recum-
and measure food deprivation based on average
vulnerable to inadequate nutrition before birth and
bent length for children up to two years old and by
food available for human consumption per person,
in infancy and early childhood. Many of these chil-
stature while standing for older children. Data are
the level of inequality in access to food, and the
dren are exposed to high-fat, high-sugar, high-salt,
based on the WHO child growth standards released
minimum calories required for an average person.
calorie-dense, micronutrient-poor foods, which tend
in 2006. • Prevalence of over weight children is the
From a policy and program standpoint, however,
to be lower in cost than more nutritious foods. These
percentage of children under age 5 whose weight for
this measure has its limits. First, food insecurity
dietary patterns, in conjunction with low levels of
height is more than two standard deviations above
exists even where food availability is not a problem
physical activity, result in sharp increases in child-
the median for the international reference population
because of inadequate access of poor households
hood obesity, while undernutrition continues (World
of the corresponding age as established by the WHO
to food. Second, food insecurity is an individual
Health Organization [WHO]).
child growth standards released in 2006.
or household phenomenon, and the average food
New international growth reference standards for
available to each person, even corrected for possible
infants and young children were released in 2006
effects of low income, is not a good predictor of food
by the WHO to monitor children’s nutritional status.
insecurity among the population. And third, nutrition
Differences in growth to age 5 are influenced more
security is determined not only by food security but
by nutrition, feeding practices, environment, and
also by the quality of care of mothers and children
healthcare than by genetics or ethnicity. The previ-
and the quality of the household’s health environ-
ously reported data were based on the U.S. National
ment (Smith and Haddad 2000).
Center for Health Statistics–WHO growth reference.
Undernourished children have lower resistance to
Because of the change in standards, the data in this
infection and are more likely to die from common
edition should not be compared with data in editions
childhood ailments such as diarrheal diseases and
prior to 2008.
respiratory infections. Frequent illness saps the
For indicators from household surveys, the year in
nutritional status of those who survive, locking them
the table refers to the survey year. For more informa-
into a vicious cycle of recurring sickness and falter-
tion, consult the original sources.
ing growth (UNICEF, www.childinfo.org). Estimates of child malnutrition, based on prevalence of underweight and stunting, are from national survey data. The proportion of underweight children is the most common malnutrition indicator. Being even mildly underweight increases the risk of death and inhibits cognitive development in children. And it perpetuates the problem across generations, as malnourished women are more likely to have low-birthweight babies. Stunting, or being below median height for age, is often used as a proxy for multifaceted deprivation and as an indicator of long-term changes in malnutrition.
Data sources Data on undernourishment are from the FAO’s
Estimates of overweight children are also from
The State of Food Insecurity in the World. Data on
national survey data. Once considered only a high-
malnutrition and overweight children are from the
income economy problem, overweight children have
WHO’s Global Database on Child Growth and Mal-
become a growing concern in developing coun-
nutrition (www.who.int/nutgrowthdb).
tries. Research shows an association between
2012 World Development Indicators
119
PEOPLE
Nutrition and growth
2.21
Nutrition intake and supplements Low-birthweight babies
Exclusive breastfeeding
Consumption of iodized salt
Vitamin A supplementation
Prevalence of anemia
% of births 2005–10a
% of children under 6 months 2005–10a
% of households 2005–10a
% of children 6–59 months 2010
Children under age 5 2005–10a
Pregnant women 2005–10a
.. 7 6 .. 7 7 .. .. 10 .. 22 4 .. 15 6 5 13 8 9 16 11 8 11 .. 13 .. 6 3 .. 6 10 13 7 17 5 5 .. .. .. 11 8 13 .. .. .. 20 .. .. .. 11 5 .. 13 .. 11 12 11 25
83 39 7 .. .. 35 .. .. 12 .. 43 9 .. 43 60 18 20 40 .. 16 69 74 21 .. 23 3 85 28 .. 43 37 19 15 4 98 26 .. .. .. 9 .. 53 31 .. .. 49 .. .. .. 36 11 .. 63 .. 50 48 38 41
96 .. .. 28 .. .. .. .. 89b .. 100 .. .. 100 24 .. 91 .. .. 100 73 95 89 .. 0 68 .. .. .. .. 83 84 .. 100 .. .. .. .. .. .. .. 68b .. 44 .. 84 .. .. 0 100 .. .. 93 .. 36 97 100 21
.. 31 43 .. 17 37 8 11 .. 25 .. 27 9 78 .. 27 .. 55 27 .. 56 55 .. 8 .. 71 24 .. .. 28 71 66 .. 69 23 27 19 18 9 35 38 49 .. 70 23 54 11 8 44 .. 41 8 .. 12 .. 76 75 ..
61 34 43 57 31 .. 12 15 .. 28 .. 26 13 75 .. 35 21 29 30 .. 47 44 .. 12 .. 60 28 .. .. 31 67 55 .. 55 28 39 25 22 12 40 38 34 .. 55 23 31 15 11 46 .. 42 12 .. 19 .. .. 58 50
%
Afghanistan Albania Algeria Angola Argentina Armenia Australia Austria Azerbaijan Bahrain Bangladesh Belarus Belgium Benin Bolivia Bosnia and Herzegovina Botswana Brazil Bulgaria Burkina Faso Burundi Cambodia Cameroon Canada Central African Republic Chad Chile China Hong Kong SAR, China Colombia Congo, Dem. Rep. Congo, Rep. Costa Rica Côte d’Ivoire Croatia Cuba Cyprus Czech Republic Denmark Dominican Republic Ecuador Egypt, Arab Rep. El Salvador Eritrea Estonia Ethiopia Finland France Gabon Gambia, The Georgia Germany Ghana Greece Guatemala Guinea Guinea-Bissau Haiti
120
2012 World Development Indicators
.. 76 61 45 .. 97 .. .. 54 .. 84 94 .. 67 89 62 .. 96 100 34 98 83 49 .. 62 .. .. 97 .. .. 59 82 .. .. 88 88 .. .. .. 19 .. 79 .. .. .. 20 .. .. .. 21 100 .. 32 .. 76 41 12 3
Low-birthweight babies
Exclusive breastfeeding
Consumption of iodized salt
Vitamin A supplementation
% of births 2005–10a
% of children under 6 months 2005–10a
% of households 2005–10a
% of children 6–59 months 2010
10 .. 28 11 7 15 .. .. .. 14 .. 13 6 8 6 .. .. .. 5 11 .. .. .. 14 .. .. 6 16 13 11 19 34 .. 7 6 5 .. 16 9 16 21 .. .. 9 27 12 .. 12 32 .. 10 6 8 21 .. .. .. ..
30 .. 46 15 23 25 .. .. .. 15 .. 22 17 32 .. .. .. .. 32 26 .. .. 54 34 .. .. 16 51 72 .. 38 46 .. .. 46 57 .. 37 24 24 53 .. .. 31 27 13 .. .. 37 .. 56 24 68 34 .. .. .. ..
.. .. 51 62 99 28 .. .. .. .. .. .. 92 98 25 .. .. .. 76 84 .. .. 84 .. .. .. 94 53 50 18 79 23 .. .. 60 83 21 25 93 .. .. .. .. .. 32 .. .. .. .. .. 92 94 .. 81 .. .. .. ..
2.21
Prevalence of anemia
%
Honduras Hungary India Indonesia Iran, Islamic Rep. Iraq Ireland Israel Italy Jamaica Japan Jordan Kazakhstan Kenya Korea, Dem. Rep. Korea, Rep. Kosovo Kuwait Kyrgyz Republic Lao PDR Latvia Lebanon Lesotho Liberia Libya Lithuania Macedonia, FYR Madagascar Malawi Malaysia Mali Mauritania Mauritius Mexico Moldova Mongolia Morocco Mozambique Myanmar Namibia Nepal Netherlands New Zealand Nicaragua Niger Nigeria Norway Oman Pakistan Panama Papua New Guinea Paraguay Peru Philippines Poland Portugal Puerto Rico Qatar
.. .. 34 80 .. .. .. .. .. .. .. .. .. 62 99 .. .. .. 97 83 .. .. .. 97 .. .. .. 95 96 .. 99 97 .. .. .. 61 .. 100 94 13 91 .. .. 7 98 91 .. .. 87 .. 14 .. .. 91 .. .. .. ..
Children under age 5 2005–10a
Pregnant women 2005–10a
.. 19 74 44 35 56 10 12 11 .. 11 .. .. .. .. .. .. .. .. .. 27 .. 49 .. 34 24 .. .. 73 32 .. 68 .. 24 41 .. .. .. 63 41 48 9 11 20 84 .. 6 .. .. .. 60 30 .. 21 23 13 .. ..
21 21 50 44 .. 38 15 17 15 .. 15 .. 26 .. .. 23 .. 31 34 56 25 32 25 .. 34 24 32 .. 47 .. .. 53 .. 21 36 37 .. 52 50 31 42 13 18 .. 61 .. 9 .. .. .. 55 39 .. 43 25 17 .. 29
2012 World Development Indicators
121
PEOPLE
Nutrition intake and supplements
2.21
Nutrition intake and supplements Low-birthweight babies
Exclusive breastfeeding
Consumption of iodized salt
Vitamin A supplementation
% of births 2005–10a
% of children under 6 months 2005–10a
% of households 2005–10a
% of children 6–59 months 2010
Prevalence of anemia
%
Romania Russian Federation Rwanda Saudi Arabia Senegal Serbia Sierra Leone Singapore Slovak Republic Slovenia Somalia South Africa South Sudan Spain Sri Lanka Sudan Swaziland Sweden Switzerland Syrian Arab Republic Tajikistan Tanzania Thailand Timor-Leste Togo Trinidad and Tobago Tunisia Turkey Turkmenistan Uganda Ukraine United Arab Emirates United Kingdom United States Uruguay Uzbekistan Venezuela, RB Vietnam West Bank and Gaza Yemen, Rep. Zambia Zimbabwe World Low income Middle income Lower middle income Upper middle income Low & middle income East Asia & Pacific Europe & Central Asia Latin America & Carib. Middle East & N. Africa South Asia Sub-Saharan Africa High income Euro area
.. 6 6 .. 19 6 14 .. .. .. 11 .. .. .. 17 .. 9 .. .. 10 10 10 7 .. 11 19 5 11 4 14 4 6 .. .. 9 5 8 5 7 .. 11 11 15 w 15 15 21 5 15 6 7 8 11 27 13 .. ..
.. .. 85 .. 34 15 11 .. .. .. 9 .. .. .. 76 34 44 .. .. 43 25 50 15 52 63 13 6 42 11 60 18 .. .. .. 57 26 .. 17 27 .. 61 32c 37 w 44 35 37 30 37 26 .. .. 34 47 35 .. ..
.. .. 88 .. 41 32 58 .. .. .. 1 .. .. .. 92 11 52 .. .. 79 62 59 47 60 32 28 .. 69 87 96 18 .. .. .. .. 53 .. 93 86 .. .. 91 70 w 62 71 54 91 70 86 .. .. 69 55 50 .. ..
.. .. 92 .. 97 .. 100 .. .. .. 62 39 .. .. 85 82 38 .. .. 33b 95 99 .. 48 100 .. .. .. .. 64 .. .. .. .. .. 94 .. 95b .. .. 92 49 .. w 88 .. 56 .. .. .. .. .. .. 50 86 .. ..
Children under age 5 2005–10a
40 27 56 33 83 .. 83 19 23 14 .. .. .. 13 .. 85 47 9 6 41 .. 72 .. .. 52 30 .. 33 .. 73 .. 28 .. .. 19 .. 33 .. .. 68 .. 58 .. w .. .. 65 .. .. .. 30 36 48 74 .. .. 10
Pregnant women 2005–10a
30 21 .. 32 58 .. 60 24 25 19 .. 22 .. 18 .. 58 24 13 .. 39 45 58 .. .. 50 30 .. 40 30 64 27 28 15 6 27 .. 40 .. .. 58 .. 47 .. w .. .. 48 .. .. .. 30 .. .. 50 .. 13 14
a. Data are for the most recent year available. b. Country’s vitamin A supplementation programs do not target children all the way up to 59 months of age. c. Data are for 2011.
122
2012 World Development Indicators
About the data
2.21
Definitions
Low birthweight, which is associated with maternal
newborns in low- and middle-income countries
• Low-birthweight babies are newborns weigh-
malnutrition, raises the risk of infant mortality and
remain unprotected from the lifelong consequences
ing less than 2.5 kilograms within the first hours
stunts growth in infancy and childhood. There is also
of brain damage associated with iodine deficiency
of life, before significant postnatal weight loss has
emerging evidence that low-birthweight babies are
disorders, which affect a child’s ability to learn and
occurred. • Exclusive breastfeeding is the percent-
more prone to noncommunicable diseases such as
to earn a living as an adult, thereby preventing chil-
age of children less than six months old who were
diabetes and cardiovascular diseases. Low birth-
dren, communities, and countries from fulfilling their
fed breast milk alone (no other liquids) in the past 24
weight can arise as a result of a baby being born
potential (UNICEF, www.childinfo.org). Widely used
hours. • Consumption of iodized salt is the percent-
too soon or too small for gestational age. Babies
and inexpensive, iodized salt is the best source of
age of households that use edible salt fortified with
born prematurely who are also small for their ges-
iodine, and a global campaign to iodize edible salt
iodine. • Vitamin A supplementation is the percent-
tational age have the worst prognosis. In low- and
is significantly reducing the risks. The data on con-
age of children ages 6–59 months old who received
middle-income countries low birthweight stems
sumption of iodized salt are derived from household
at least two doses of vitamin A in the previous year.
primarily from poor maternal health and nutrition.
surveys.
• Prevalence of anemia, children under age 5, is the
Three factors have the most impact: poor maternal
Vitamin A is essential for immune system function-
percentage of children under age 5 whose hemoglo-
nutritional status before conception, mother’s short
ing. Vitamin A deficiency, a leading cause of blind-
bin level is less than 110 grams per liter at sea level.
stature (due mostly to undernutrition and infections
ness, also causes a greater risk of dying from a range
• Prevalence of anemia, pregnant women, is the
during childhood), and poor nutrition during preg-
of childhood ailments such as measles, malaria, and
percentage of pregnant women whose hemoglobin
nancy (United Nations Children’s Fund [UNICEF],
diarrhea. In low- and middle-income countries, where
level is less than 110 grams per liter at sea level.
www.childinfo.org). Estimates of low-birthweight
vitamin A is consumed largely in fruits and vegeta-
infants are drawn mostly from hospital records and
bles, daily per capita intake is often insufficient to
household surveys. Many births in developing coun-
meet dietary requirements. Providing young children
tries take place at home and are seldom recorded. A
with two high-dose vitamin A capsules a year is a
hospital birth may indicate higher income and there-
safe, cost-effective, efficient strategy for eliminat-
fore better nutrition, or it could indicate a higher risk
ing vitamin A deficiency and improving child survival.
birth. Caution should therefore be used in interpret-
Giving vitamin A to new breastfeeding mothers helps
ing the data.
protect their children during the first months of life.
For optimal infant and young child feeding, mothers initiate breastfeeding within one hour of birth,
Food fortification with vitamin A is being introduced in many developing countries.
breastfeed exclusively for the first six months, and
Anemia is a condition in which the number of red
continue to breastfeed for two years or more while
blood cells or their oxygen-carrying capacity is insuf-
providing nutritionally adequate, safe, and age-
ficient to meet physiologic needs, which vary by age,
appropriate solid, semisolid, and soft foods (UNICEF,
sex, altitude, smoking status, and pregnancy sta-
www.childinfo.org). Optimal breastfeeding can save
tus. In its severe form it is associated with fatigue,
an estimated 1.4 million children a year. Breast milk
weakness, dizziness, and drowsiness (World Health
alone contains all the nutrients, antibodies, hor-
Organization [WHO], www.who.int/topics/anaemia/).
mones, and antioxidants an infant needs to thrive.
Children under age 5 and pregnant women have the
It protects babies from diarrhea and acute respira-
highest risk for anemia. Data on anemia are com-
tory infections, stimulates their immune systems and
piled by the WHO based mainly on nationally repre-
response to vaccination, and may confer cognitive
sentative surveys between 1993 and 2005, which
benefits. The data on breastfeeding are derived from
measured hemoglobin in the blood. WHO’s hemoglo-
household surveys.
bin thresholds were then used to determine anemia
Iodine defi ciency is the single most important
status based on age, sex, and physiological status.
cause of preventable mental retardation, it con-
Data should be used with caution because surveys
tributes significantly to the risk of stillbirth and mis-
differ in quality, coverage, age group interviewed,
carriage, and it increases infant mortality. A diet low
and treatment of missing values across countries
Data on low-birthweight babies, breastfeeding,
in iodine is the main cause of iodine deficiency. It
and over time.
consumption of iodized salt, and vitamin A supple-
Data sources
usually occurs among populations living in areas
For indicators from household surveys, the year in
mentation are from the United Nations Children’s
where the soil has been depleted of iodine. If soil
the table refers to the survey year. For more informa-
Fund’s The State of the World’s Children 2012 and
is deficient in iodine, so are the plants grown in it,
tion, consult the original sources.
Childinfo. Data on anemia are from the WHO’s
including the grains and vegetables that people and
Worldwide Prevalence of Anemia 1993 –2005
animals consume. There are almost no countries in
(2008c) and Integrated WHO Nutrition Global
the world where iodine deficiency has not been a
Databases.
public health problem. Every year about 40 million
2012 World Development Indicators
123
PEOPLE
Nutrition intake and supplements
2.22
Health risk factors and future challenges Prevalence of smoking
Male 2009
Female 2009
per 100,000 people 2010
.. 60 .. .. 32 51 22 47 41 34 46 49 30 15 42 47 .. 22 48 18 .. 49c 14 24 .. 22 38 51 .. .. 10 10 24 17 36 .. .. 43 30 17 .. 40 .. 10 46 8 28 36 19 31 57 33 11 63 22 25 .. ..
.. 19 .. .. 22 2 19 45 .. 8 2 9 22 1 18 36 .. 13 27 8 .. 5c 2 17 .. 3 33 2 .. .. 2 1 8 4 30 .. .. 31 28 13 .. 1 .. 2 23 1 22 27 3 3 6 25 3 41 4 2 .. ..
189 14 90 304 27 73 6 5 110 23 225 70 9 94 135 50 503 43 40 55 129 437 177 5 319 276 19 78 80 34 327 372 13 139 21 9 4 7 6 67 65 18 28 100 25 261 7 9 553 273 107 5 86 5 62 334 233 230
% of adults
Afghanistan Albania Algeria Angola Argentina Armenia Australia Austria Azerbaijan Bahrain Bangladesh Belarus Belgium Benin Bolivia Bosnia and Herzegovina Botswana Brazil Bulgaria Burkina Faso Burundi Cambodia Cameroon Canada Central African Republic Chad Chile China Hong Kong SAR, China Colombia Congo, Dem. Rep. Congo, Rep. Costa Rica Côte d’Ivoire Croatia Cuba Cyprus Czech Republic Denmark Dominican Republic Ecuador Egypt, Arab Rep. El Salvador Eritrea Estonia Ethiopia Finland France Gabon Gambia, The Georgia Germany Ghana Greece Guatemala Guinea Guinea-Bissau Haiti
124
Prevalence of HIVa
Incidence of Prevalence tuberculosis of diabetes
2012 World Development Indicators
% of population ages 20–79 2011
7.8 2.9 7.0 3.0 5.7 8.7 6.8 6.8 2.8 19.9 10.7 8.2 4.9 2.0 6.8 7.7 11.1 10.4 6.9 3.0 2.8 2.9 6.2 8.7 3.2 3.9 9.8 9.0 7.8 10.0 3.2 5.6 9.9 5.0 5.3 9.8 9.5 5.5 5.7 8.3 6.8 16.9 9.7 3.6 7.2 3.4 6.0 5.6 10.6 2.0 2.8 5.5 5.1 5.3 9.5 4.4 3.1 6.8
Female % of total Total % of population population with HIV ages 15–49 1990
.. .. <0.1 0.5 0.3 <0.1 0.1 <0.1 <0.1 .. <0.1 <0.1 <0.1 0.2 0.1 .. 3.5 .. <0.1 3.9 3.9 0.5 0.6 0.1 3.1 1.1 <0.1 .. .. 0.2 .. 5.2 <0.1 2.4 <0.1 <0.1 .. <0.1 <0.1 0.4 0.3 <0.1 0.1 0.3 <0.1 .. <0.1 0.3 0.9 0.1 <0.1 0.1 0.3 0.1 0.1 1.1 0.3 1.3
Antiretroviral therapy coverage
Youth % of population ages 15–24
2009
2009
Male 2009
Female 2009
.. .. 0.1 2.0 0.5 0.1 0.1 0.3 0.1 .. <0.1 0.3 0.2 1.2 0.2 .. 24.8 .. 0.1 1.2 3.3 0.5 5.3 0.2 4.7 3.4 0.4 0.1d .. 0.5 .. 3.4 0.3 3.4 <0.1 0.1 .. <0.1 0.2 0.9 0.4 <0.1 0.8 0.8 1.2 .. 0.1 0.4 5.2 2.0 0.1 0.1 1.8 0.1 0.8 1.3 2.5 1.9
.. .. 30 60 32 <43 31 29 60 .. 30 50 31 58 32 .. 57 .. 29 60 60 63 58 21 61 59 31 .. .. 33 .. 59 29 58 <33 31 .. <42 27 59 31 23 34 60 31 .. <36 32 58 58 43 18 59 31 33 59 60 60
.. .. 0.1 0.6 0.3 <0.1 0.1 0.3 <0.1 .. <0.1 <0.1 <0.1 0.3 0.1 .. 5.2 .. <0.1 0.5 1.0 0.1 1.6 0.1 1.0 1.0 0.2 .. .. 0.2 .. 1.2 0.2 0.7 <0.1 0.1 .. <0.1 0.1 0.3 0.2 <0.1 0.4 0.2 0.3 .. 0.1 0.2 1.4 0.9 <0.1 0.1 0.5 0.1 0.5 0.4 0.8 0.6
.. .. <0.1 1.6 0.2 <0.1 0.1 0.2 0.1 .. <0.1 0.1 <0.1 0.7 0.1 .. 11.8 .. <0.1 0.8 2.1 0.1 3.9 0.1 2.2 2.5 0.1 .. .. 0.1 .. 2.6 0.1 1.5 <0.1 0.1 .. <0.1 0.1 0.7 0.2 <0.1 0.3 0.4 0.2 .. <0.1 0.1 3.5 2.4 <0.1 <0.1 1.3 0.1 0.3 0.9 2.0 1.3
Cause of death % of population
Communicable diseases and maternal, Nonprenatal, % of population with advanced and nutrition communicable diseases Injuries conditions HIV infection 2005–10 b 2005–10b 2005–10b 2005–10b
.. .. 25 24 70 24 .. .. 21 .. 23 29 .. 53 19 .. 83 60 23 46 19 94 28 .. 19 36 63 .. .. 17 .. 23 68 28 80 <95 .. .. .. 47 30 11 53 37 .. .. .. .. 47 18 65 .. 24 .. 44 40 30 43
63 5 30 69 14 6 4 3 11 10 38 2 8 60 35 2 60 14 3 73 67 47 63 5 65 73 9 7 .. 13 72 58 7 58 3 8 4 4 6 22 20 12 17 49 2 57 2 6 52 60 5 5 53 6 35 60 67 54
29 89 63 25 80 90 90 91 85 79 52 87 86 33 57 95 31 74 94 21 26 46 31 89 27 21 83 83 .. 66 21 33 81 33 92 84 90 90 90 68 65 82 67 40 90 34 89 87 41 34 91 92 39 91 47 32 28 41
8 5 8 7 6 4 6 6 4 11 10 11 6 6 8 4 9 12 4 7 7 7 6 6 7 5 8 10 .. 21 7 9 13 9 6 8 6 6 5 10 15 6 16 12 8 9 9 7 7 6 4 4 8 4 18 7 6 5
Prevalence of smoking
Male 2009
Female 2009
per 100,000 people 2010
.. 43 26 61 26 31 .. 29 33 .. 42 47 40 26 .. 49 .. 35 45 51 50 46 .. 14 47 50 .. .. 26 50 28 29 31 24 43 48 33 18 40 30 36 31 27 .. 9 10 31 12 34 17 58 30 .. 47 36 32 .. ..
3 33 4 5 2 4 .. 13 19 .. 12 6 9 1 .. 7 .. 4 2 4 22 31 .. .. 1 22 .. .. 4 2 2 4 2 8 5 6 2 2 8 9 29 26 24 .. 1 3 28 1 6 4 31 14 9 10 25 16 .. ..
51 15 185 189 17 64 8 5 5 7 21 5 151 298 345 97 .. 41 159 90 39 17 633 293 40 69 21 266 219 82 68 337 22 16 182 224 91 544 384 603 163 7 8 42 185 133 6 13 231 48 303 46 106 275 23 29 2 38
% of adults
Honduras Hungary India Indonesia Iran, Islamic Rep. Iraq Ireland Israel Italy Jamaica Japan Jordan Kazakhstan Kenya Korea, Dem. Rep. Korea, Rep. Kosovo Kuwait Kyrgyz Republic Lao PDR Latvia Lebanon Lesotho Liberia Libya Lithuania Macedonia, FYR Madagascar Malawi Malaysia Mali Mauritania Mauritius Mexico Moldova Mongolia Morocco Mozambique Myanmar Namibia Nepal Netherlands New Zealand Nicaragua Niger Nigeria Norway Oman Pakistan Panama Papua New Guinea Paraguay Peru Philippines Poland Portugal Puerto Rico Qatar
Prevalence of HIVa
Incidence of Prevalence tuberculosis of diabetes
% of population ages 20–79 2011
6.8 6.2 9.2 5.2 11.3 9.3 5.4 7.6 5.3 16.0 7.9 12.4 7.9 5.2 8.6 7.7 .. 21.1 6.5 3.3 8.1 20.2 3.5 3.4 14.2 8.0 7.9 4.8 5.7 12.3 2.0 4.4 15.1 15.9 2.8 7.2 7.0 3.1 7.2 8.0 3.7 5.4 8.8 11.2 4.1 4.9 4.8 10.8 8.0 9.8 7.7 6.7 6.1 10.0 9.2 9.8 13.3 20.2
Female % of total Total % of population population with HIV ages 15–49 1990
1.1 0.1 0.1 <0.1 <0.1 .. <0.1 <0.1 0.3 2.1 <0.1 .. <0.1 3.9 .. <0.1 .. .. <0.1 <0.1 <0.1 <0.1 0.8 0.3 .. <0.1 .. 0.2 7.2 0.1 0.4 0.2 <0.1 0.4 <0.1 <0.1 <0.1 1.2 0.2 1.6 0.2 0.1 0.1 <0.1 0.1 1.3 <0.1 <0.1 <0.1 0.2 <0.1 <0.1 0.4 <0.1 <0.1 0.1 .. <0.1
2009
0.8 <0.1 0.3 0.2 0.2 .. 0.2 0.2 0.3 1.7 <0.1 .. 0.1 6.3 .. <0.1 .. .. 0.3 0.2 0.7 0.1 23.6 1.5 .. 0.1 .. 0.2 11.0 0.5 1.0 0.7 1.0 0.3 0.4 <0.1 0.1 11.5 0.6 13.1 0.4 0.2 0.1 0.2 0.8 3.6 0.1 0.1 0.1 0.9 0.9 0.3 0.4 <0.1 0.1 0.6 .. <0.1
Antiretroviral therapy coverage
Youth % of population ages 15–24
2009
Male 2009
Female 2009
32 <33 39 30 29 .. 29 29 33 33 34 .. 60 59 .. 31 .. .. 29 42 30 31 62 61 .. <33 .. 31 59 11 62 31 29 27 42 <29 32 61 35 59 33 30 <37 31 53 59 30 <33 29 31 58 31 25 30 31 31 .. <50
0.3 <0.1 0.1 0.1 <0.1 .. 0.1 0.1 <0.1 1.0 <0.1 .. 0.1 1.8 .. <0.1 .. .. 0.1 0.1 0.2 0.1 5.4 0.3 .. <0.1 .. 0.1 3.1 0.1 0.2 0.4 0.3 0.2 0.1 <0.1 0.1 3.1 0.3 2.3 0.2 0.1 <0.1 0.1 0.2 1.2 <0.1 <0.1 0.1 0.4 0.3 0.2 0.2 <0.1 <0.1 0.3 .. <0.1
0.2 <0.1 0.1 <0.1 <0.1 .. 0.1 <0.1 <0.1 0.7 <0.1 .. 0.2 4.1 .. <0.1 .. .. 0.1 0.2 0.1 <0.1 14.2 0.7 .. <0.1 .. 0.1 6.8 <0.1 0.5 0.3 0.2 0.1 0.1 <0.1 0.1 8.6 0.3 5.8 0.1 <0.1 <0.1 0.1 0.5 2.9 <0.1 <0.1 <0.1 0.3 0.8 0.1 0.1 <0.1 <0.1 0.2 .. <0.1
2.22 Cause of death % of population
Communicable diseases and maternal, Nonprenatal, % of population with advanced and nutrition communicable diseases Injuries conditions HIV infection 2005–10 b 2005–10b 2005–10b 2005–10b
33 27 .. 21 4 .. .. .. .. 46 .. .. 27 48 0 .. .. .. 12 67 12 18 48 14 .. 27 .. 2 46 23 50 25 22 54 17 8 27 30 18 76 11 .. .. 40 22 21 .. <95 4 37 52 37 37 37 22 .. .. ..
23 1 37 28 13 24 7 8 3 21 14 15 8 63 29 6 .. 11 14 41 3 7 63 68 12 3 2 52 63 24 75 60 7 12 5 14 19 64 33 51 43 7 3 20 81 68 7 6 46 19 47 20 30 31 4 9 .. 8
69 93 53 64 72 44 87 87 92 68 80 74 78 28 65 82 .. 76 77 48 90 84 29 28 78 86 95 42 28 67 20 32 87 78 87 72 75 28 40 38 50 89 91 69 16 27 87 83 46 69 44 69 60 61 89 86 .. 69
2012 World Development Indicators
8 6 10 9 14 31 6 5 4 11 6 11 14 9 6 12 .. 13 9 10 8 9 7 4 11 11 3 6 9 9 5 8 6 10 8 13 6 8 27 12 7 4 6 11 3 5 6 11 8 12 9 12 10 8 7 4 .. 23
125
PEOPLE
Health risk factors and future challenges
2.22
Health risk factors and future challenges Prevalence of smoking
Female 2009
per 100,000 people 2010
24 24 .. 1 1 27 8 6 19 22 .. 8 .. 27 1 2 2 .. 21 .. .. 3 3 .. .. 11 5 15 .. 3 13 2 23 25 22 3 .. 2 .. 11 4 4 8w 4 6 4 7 5 3 18 13 3 4 3 21 25
116 106 106 18 288 18 682 35 8 11 286 981 .. 16 66 119 1,287 7 8 20 206 177 137 498 455 19 25 28 66 209 101 3 13 4 21 128 33 199 5 49 462 633 128 w 264 132 174 89 150 123 90 43 42 192 271 14 ..
% of adults Male 2009
Romania Russian Federation Rwanda Saudi Arabia Senegal Serbia Sierra Leone Singapore Slovak Republic Slovenia Somalia South Africa South Sudan Spain Sri Lanka Sudan Swaziland Sweden Switzerland Syrian Arab Republic Tajikistan Tanzania Thailand Timor-Leste Togo Trinidad and Tobago Tunisia Turkey Turkmenistan Uganda Ukraine United Arab Emirates United Kingdom United States Uruguay Uzbekistan Venezuela, RB Vietnam West Bank and Gaza Yemen, Rep. Zambia Zimbabwe World Low income Middle income Lower middle income Upper middle income Low & middle income East Asia & Pacific Europe & Central Asia Latin America & Carib. Middle East & N. Africa South Asia Sub-Saharan Africa High income Euro area
46 59 .. 24 16 38 39 35 39 30 .. 24 .. 36 27 24 16 .. 31 42 .. 21 45 .. .. 27 58 47 .. 16 50 19 25 33 31 22 .. 48 .. 35 24 30 37 w 28 39 32 46 38 52 50 25 35 29 16 34 35
Prevalence of HIVa
Incidence of Prevalence tuberculosis of diabetes
% of population ages 20–79 2011
7.9 10.0 3.2 20.0 3.3 7.9 3.2 9.8 5.9 7.8 4.3 7.1 .. 6.5 7.6 8.7 3.1 4.4 6.0 10.2 6.5 2.8 7.7 7.6 3.3 13.1 9.7 8.1 2.8 2.9 2.9 19.2 5.4 9.6 5.9 6.7 10.5 3.2 9.4 9.9 4.8 9.9 8.3 w 5.9 8.7 8.0 9.4 8.4 8.3 7.7 10.5 11.6 9.0 4.6 7.9 5.8
Female % of total Total % of population population with HIV ages 15–49 1990
2009
2009
<0.1 0.1 30 <0.1 1.0 49 5.2 2.9 61 .. .. .. 0.2 0.9 59 0.1 0.1 24 <0.1 1.6 60 <0.1 0.1 30 <0.1 <0.1 <17 <0.1 <0.1 <29 0.1 0.7 47 0.7 17.8 62 .. .. .. 0.4 0.4 24 <0.1 <0.1 <32 0.1 1.1 58 2.3 25.9 58 0.1 0.1 31 0.2 0.4 32 .. .. .. <0.1 0.2 30 4.8 5.6 59 1.0 1.3 40 .. .. .. 0.6 3.2 59 0.2 1.5 33 <0.1 <0.1 <37 <0.1 <0.1 30 .. .. .. 10.2 6.5 58 0.1 1.1 49 .. .. .. 0.1 0.2 31 0.5 0.6 25 0.1 0.5 32 <0.1 0.1 29 .. .. .. <0.1 0.4 30 .. .. .. .. .. .. 12.7 13.5 57 10.1 14.3 60 0.3 w 0.8 w 37 w 1.8 2.6 46 0.2 0.7 .. 0.3 0.7 38 0.2 0.7 .. 0.3 0.9 39 0.1 0.2 .. 0.1 0.6 42 0.4 0.5 .. 0.1 0.1 28 0.1 0.3 37 2.4 5.5 58 0.2 0.3 28 0.2 0.3 27
Antiretroviral therapy coverage
Youth % of population ages 15–24 Male 2009
0.1 0.2 1.3 .. 0.3 0.1 0.6 <0.1 <0.1 <0.1 0.4 4.5 .. 0.2 <0.1 0.5 6.5 <0.1 0.2 .. <0.1 1.7 .. .. 0.9 1.0 <0.1 <0.1 .. 2.3 0.2 .. 0.2 0.3 0.3 <0.1 .. 0.1 .. .. 4.2 3.3 0.4 w 0.9 .. 0.2 .. .. .. 0.1 .. 0.1 0.1 1.5 0.2 0.1
Female 2009
<0.1 0.3 1.9 .. 0.7 0.1 1.5 <0.1 <0.1 <0.1 0.6 13.6 .. 0.1 <0.1 1.3 15.6 <0.1 0.1 .. <0.1 3.9 .. .. 2.2 0.7 <0.1 <0.1 .. 4.8 0.3 .. 0.1 0.2 0.2 <0.1 .. 0.1 .. .. 8.9 6.9 0.7 w 1.9 .. 0.5 .. .. .. 0.2 .. 0.1 0.1 3.8 0.1 0.1
Cause of death % of population
Communicable diseases and maternal, Nonprenatal, % of population with advanced and nutrition communicable diseases Injuries conditions HIV infection 2005–10 b 2005–10b 2005–10b 2005–10b
81 .. 88 .. 51 38 18 .. 62 .. 6 37 .. .. 20 5 59 .. .. .. 11 30 61 .. 29 .. 53 62 .. 39 10 .. .. .. 49 .. .. 34 .. .. 64 34
4 5 63 13 65 2 77 16 5 4 62 67 .. 5 9 43 62 5 4 13 37 66 17 60 61 12 22 9 19 64 6 13 8 6 8 15 13 16 .. 44 64 75 27 w 58 26 38 11 31 13 6 16 19 39 65 7 5
91 82 29 71 30 95 18 79 90 87 27 29 .. 91 65 44 28 90 90 77 59 27 71 34 34 78 72 85 73 25 86 67 88 87 87 79 66 75 .. 45 27 21 63 w 33 65 53 79 59 76 84 72 69 51 28 87 90
5 12 8 15 5 4 5 5 6 8 11 5 .. 4 26 13 11 5 6 10 4 8 12 5 5 10 7 6 8 10 8 21 4 7 6 6 21 9 .. 11 9 4 9w 9 10 9 10 10 10 9 12 12 10 7 6 5
a. See http://data.worldbank.org or the original source for uncertainty bands. b. Data are for the most recent year available. c. Data are for 2010. d. Includes Hong Kong SAR, China.
126
2012 World Development Indicators
About the data
2.22
Definitions
The limited availability of data on health status is a
Data on HIV are from the Joint United Nations Pro-
• Prevalence of smoking is the percentage of the
major constraint in assessing the health situation in
gramme on HIV/AIDS’s (UNAIDS) Global Report: UNAIDS
population ages 15 and older who smoke any tobacco
developing countries. Surveillance data are lacking
Report on the Global AIDS Epidemic 2010. Changes in
products. It includes daily and nondaily smoking.
for many major public health concerns. Estimates
procedures and assumptions for estimating the data
Estimates are adjusted and age- standardized preva-
of prevalence and incidence are available for some
and better coordination with countries have resulted
lence • Incidence of tuberculosis is the number of
diseases but are often unreliable and incomplete.
in improved estimates of HIV and AIDS. For example,
new and relapse cases of tuberculosis (all types)
National health authorities differ widely in capacity
improved software was used to model the course of
per 100,000 people. • Prevalence of diabetes is
and willingness to collect or report information. To
HIV epidemics and their impacts, making full use of
the percentage of people ages 20–79 who have
compensate for this and improve reliability and inter-
information on HIV prevalence trends from surveillance
type 1 or type 2 diabetes. • Prevalence of HIV is
national comparability, the World Health Organiza-
data as well as survey data. The software explicitly
the percentage of people who are infected with
tion (WHO) prepares estimates in accordance with
includes the effect of antiretroviral therapy when cal-
HIV. Total and youth rates are percentages of the
epidemiological models and statistical standards.
culating HIV incidence and models reduced infectivity
relevant age group. Female rate is as a percentage
Smoking is the most common form of tobacco use
among people receiving antiretroviral therapy, which is
of the total population living with HIV. • Antiretro-
and the prevalence of smoking is therefore a good
having a larger impact on HIV prevalence and allowing
viral therapy coverage is the percentage of adults
measure of the tobacco epidemic (Corrao and others
HIV-positive people to live longer. The software also
and children with advanced HIV infection currently
2000). Tobacco use causes heart and other vascular
allows for changes in urbanization over time—impor-
receiving antiretroviral therapy according to nation-
diseases and cancers of the lung and other organs.
tant because prevalence is higher in urban areas and
ally approved treatment protocols (or WHO/UNAIDS
Given the long delay between starting to smoke and
because many countries have seen rapid urbanization
standards) among the estimated number of people
the onset of disease, the health impact of smoking
over the past two decades. The estimates include plau-
with advanced HIV infection. • Cause of death is the
will increase rapidly only in the next few decades.
sible bounds, not shown in the table, which reflect the
share of all deaths due to the specified underlying
The data presented in the table are age- standardized
certainty associated with each of the estimates. The
cause. • Communicable diseases and maternal,
rates for adults ages 15 and older from the WHO.
bounds are available at http://data.worldbank.org and
perinatal, and nutrition conditions are infectious
from the original source.
and parasitic diseases, respiratory infections, and
Tuberculosis is one of the main causes of adult deaths from a single infectious agent in develop-
Standard antiretroviral therapy consists of the use
nutritional defi ciencies such as underweight and
ing countries. In developed countries tuberculosis
of at least three antiretroviral drugs to maximally sup-
stunting. • Noncommunicable diseases are cancer,
has reemerged largely as a result of cases among
press HIV and stop the progression of HIV disease. Anti-
diabetes mellitus, cardiovascular diseases, digestive
immigrants. Since tuberculosis incidence cannot be
retroviral therapy has led to huge reductions in death
diseases, skin diseases, musculoskeletal diseases,
directly measured, estimates are obtained by elicit-
and suffering of people with advanced HIV infection.
and congenital anomalies. • Injuries include uninten-
ing expert opinion or are derived from measurements
Data are collected through three international moni-
tional and intentional injuries.
of prevalence or mortality. These estimates include
toring and reporting processes: country responses to
uncertainty intervals, which are not shown in the
the WHO; research by the Interagency Task Team on
table but are available at http://data.worldbank.org
Prevention of HIV Infection in Women, Mothers and
and from the original source.
their Children; and country report to UNAIDS through
Diabetes, an important cause of ill health and a risk factor for other diseases in developed countries,
the United Nations General Assembly Special Session Declaration of Commitment on HIV/AIDS.
is spreading rapidly in developing countries. Highest
Data on cause of death are compiled by the WHO,
among the elderly, prevalence rates are rising among
based mainly on data from national vital registry sys-
younger and productive populations in developing
tems, as well as sample registration systems, popu-
countries. Economic development has led to the
lation laboratories, and epidemiological analysis of
Data on smoking are from the WHO’s Report on
spread of Western lifestyles and diet to develop-
specific conditions. Data are classified based on the
the Global Tobacco Epidemic 2011. Data on tuber-
ing countries, resulting in a substantial increase in
International Statistical Classification of Diseases and
culosis are from the WHO’s Global Tuberculosis
diabetes. Without effective prevention and control
Related Health Problems, 10th revision. Data have
Control Report 2011. Data on diabetes are from
programs, diabetes will likely continue to increase.
been carefully analyzed to take into account incomplete
the International Diabetes Federation’s Diabetes
Data are estimated based on sample surveys.
coverage of vital registration and the likely differences
Atlas, 5th edition. Data on HIV are from UNAIDS’s
Adult HIV prevalence rates reflect the rate of HIV
in cause of death patterns that would be expected in
Global Report: UNAIDS Report on the Global AIDS
infection in each country’s population. Low national
undercovered and often poorer subpopulations. Special
Epidemic 2010. Data on antiretroviral therapy
prevalence rates can be misleading, however. They
attention has also been paid to misattribution or mis-
coverage are from the WHO. Data on cause of
often disguise epidemics that are initially concen-
coding of causes of death in cardiovascular diseases,
death are from the WHO’s Health Statistics and
trated in certain localities or population groups and
cancer, injuries, and general ill-defined categories. For
Health Information Systems database (www.
threaten to spill over into the wider population. In
further information, consult the original source.
who.int/healthinfo/global_burden_disease/
many developing countries most new infections
For indicators from household surveys, the year in
occur in young adults, with young women especially
the table refers to the survey year. For more informa-
vulnerable.
tion, consult the original sources.
Data sources
estimates_country).
2012 World Development Indicators
127
PEOPLE
Health risk factors and future challenges
2.23 Afghanistan Albania Algeria Angola Argentina Armenia Australia Austria Azerbaijan Bahrain Bangladesh Belarus Belgium Benin Bolivia Bosnia and Herzegovina Botswana Brazil Bulgaria Burkina Faso Burundi Cambodia Cameroon Canada Central African Republic Chad Chile China Hong Kong SAR, China Colombia Congo, Dem. Rep. Congo, Rep. Costa Rica Côte d’Ivoire Croatia Cuba Cyprus Czech Republic Denmark Dominican Republic Ecuador Egypt, Arab Rep. El Salvador Eritrea Estonia Ethiopia Finland Franced Gabon Gambia, The Georgia Germany Ghana Greece Guatemala Guinea Guinea-Bissau Haiti
128
Mortality Life expectancy at birth
Neonatal mortality rate
Infant mortality rate
Under-five mortality rate
years
per 1,000 live births 1990 2010
per 1,000 live births 1990 2010
per 1,000 live births 1990 2010
1990
2010
42 72 67 41 71 68 77 76 65 72 59 71 76 49 59 67 64 66 72 48 46 55 53 77 49 51 74 69c 77 68 47 56 76 53 72 74 77 71 75 68 69 62 66 48 69 47 75 77 61 53 70 75 57 77 62 44 43 55
48 77 73 51 76 74 82 80 71 75 69 70 80 56 66 75 53 73 74 55 50 63 51 81 48 49 79 73c 83 73 48 57 79 55 76 79 79 77 79 73 75 73 72 61 75 59 80 81 62 58 73 80 64 80 71 54 48 62
2012 World Development Indicators
53 17 29 51 15 26 5 4 31 6 55 7 4 40 39 12 22 28 11 41 49 38 34 4 43 45 9 24 .. 20 48 33 10 46 8 7 5 9 4 29 20 28 18 31 13 48 4 3 31 42 27 4 38 9 28 51 48 38
45 9 18 41 7 11 3 2 19 4 27 3 2 32 23 5 19 12 7 38 42 22 34 4 42 41 5 11 .. 12 46 29 6 41 3 3 2 2 2 15 10 9 6 18 3 35 2 2 26 31 15 2 28 2 15 38 40 27
140 36 55 144 24 46 8 8 74 15 99 14 9 107 84 17 46 50 18 103 110 87 85 7 110 113 16 38 .. 30 117 74 15 105 11 11 10 12 7 48 41 68 48 87 17 111 6 7 68 78 40 7 77 11 56 135 125 104
103 16 31 98 12 18 4 4 39 9 38 4 4 73 42 8 36 17 11 93 88 43 84 5 106 99 8 16 .. 18 112 61 9 86 5 5 3 3 3 22 18 19 14 42 4 68 2 3 54 57 20 3 50 3 25 81 92 70
209 41 68 243 27 55 9 9 93 17 143 17 10 178 121 19 59 59 22 205 183 121 137 8 165 207 19 48 .. 37 181 116 17 151 13 13 11 14 9 62 52 94 62 141 21 184 7 9 93 165 47 9 122 13 78 229 210 151
149 18 36 161 14 20 5 4 46 10 48 6 4 115 54 8 48 19 13 176 142 51 136 6 159 173 9 18 .. 22 170 93 10 123 6 6 4 4 4 27 20 22 16 61 5 106 3 4 74 98 22 4 74 4 32 130 150 165
Child mortality rate
Adult mortality rate
per 1,000 per 1,000 Male Female Male Female 2005–10a,b 2005–10a,b 2006–10a 2006–10a
.. 3 .. .. .. 8 .. .. 9 .. 16 .. .. 64 18 .. .. .. .. .. 65 20 .. .. 74 .. .. .. .. 4 70 49 .. .. 1 .. .. .. .. 6 .. 5 .. .. .. 56 .. .. .. 46 5 .. 38 .. .. 89 110 33
.. 1 .. .. .. 3 .. .. 5 .. 20 .. .. 65 20 .. .. .. .. .. 65 20 .. .. 82 .. .. .. .. 3 64 43 .. .. 1 .. .. .. .. 4 .. 5 .. .. .. 56 .. .. .. 39 4 .. 28 .. .. 86 88 36
409 95 126 386 160 162 82 99 181 95 163 334 107 332 225 134 535 218 205 300 418 262 410 92 469 372 123 138 74 194 407 335 110 377 140 108 77 138 107 200 161 140 281 345 234 304 123 118 288 299 177 101 255 101 226 352 410 264
377 46 102 337 74 79 47 50 74 73 137 112 61 276 167 69 579 114 86 249 381 222 378 55 436 316 57 88 37 89 354 301 58 349 57 68 38 63 65 131 84 85 119 261 77 259 56 55 263 244 67 54 225 46 122 303 358 236
Honduras Hungary India Indonesia Iran, Islamic Rep. Iraq Ireland Israel Italy Jamaica Japan Jordan Kazakhstan Kenya Korea, Dem. Rep. Korea, Rep. Kosovo Kuwait Kyrgyz Republic Lao PDR Latvia Lebanon Lesotho Liberia Libya Lithuania Macedonia, FYR Madagascar Malawi Malaysia Mali Mauritania Mauritius Mexico Moldova Mongolia Morocco Mozambique Myanmar Namibia Nepal Netherlands New Zealand Nicaragua Niger Nigeria Norway Oman Pakistan Panama Papua New Guinea Paraguay Peru Philippines Poland Portugal Puerto Rico Qatar
Life expectancy at birth
Neonatal mortality rate
Infant mortality rate
Under-five mortality rate
years
per 1,000 live births 1990 2010
per 1,000 live births 1990 2010
per 1,000 live births 1990 2010
1990
2010
66 69 58 62 62 68 75 77 77 71 79 70 68 59 70 71 68 73 68 54 69 69 59 42 68 71 71 51 47 70 44 56 69 71 67 61 64 43 57 61 54 77 75 64 41 46 77 71 61 72 56 68 66 65 71 74 74 74
73 74 65 69 73 68 80 82 82 73 83 73 68 56 69 81 70 75 69 67 73 72 47 56 75 73 75 66 53 74 51 58 73 77 69 68 72 50 65 62 68 81 81 74 54 51 81 73 65 76 62 72 74 68 76 79 79 78
23 12 47 31 28 23 5 6 6 13 3 20 26 31 22 3 .. 9 30 39 12 18 36 53 22 10 17 40 44 9 57 42 16 17 15 27 36 51 44 25 54 5 4 25 48 49 4 22 51 14 30 24 27 23 11 7 .. 10
12 4 32 17 14 20 2 2 2 9 1 13 17 28 18 2 .. 6 19 21 5 12 35 34 10 3 8 22 27 3 48 39 9 7 9 12 19 39 32 17 28 3 3 12 32 40 2 5 41 9 23 14 9 14 4 2 .. 4
45 17 81 56 50 37 8 10 8 31 5 32 48 64 23 6 .. 13 59 100 16 31 72 151 33 14 34 97 131 15 131 80 21 38 30 76 67 146 79 49 97 7 9 52 132 126 7 36 96 26 65 40 55 42 15 11 .. 17
20 5 48 27 22 31 3 4 3 20 2 18 29 55 26 4 .. 10 33 42 8 19 65 74 13 5 10 43 58 5 99 75 13 14 16 26 30 92 50 29 41 4 5 23 73 88 3 8 70 17 47 21 15 23 5 3 .. 7
58 19 115 85 65 46 9 12 10 38 6 38 57 99 45 8 .. 15 72 145 21 38 89 227 45 17 39 159 222 18 255 124 24 49 37 107 86 219 112 73 141 8 11 68 311 213 9 47 124 33 90 50 78 59 17 15 .. 21
24 6 63 35 26 39 4 5 4 24 3 22 33 85 33 5 .. 11 38 54 10 22 85 103 17 7 12 62 92 6 178 111 15 17 19 32 36 135 66 40 50 4 6 27 143 143 3 9 87 20 61 25 19 29 6 4 .. 8
Child mortality rate
2.23 Adult mortality rate
per 1,000 per 1,000 Male Female Male Female 2005–10a,b 2005–10a,b 2006–10a 2006–10a
8 .. 9 13 .. 6 .. .. .. 5 .. 3 5 27 .. .. .. .. 8 .. .. .. .. 62 .. .. 2 30 52 .. 117 53 .. .. 7 11 .. .. .. 24 21 .. .. .. 138 91 .. .. 14 .. .. .. 13 10 .. .. .. ..
9 .. 12 12 .. 7 .. .. .. 6 .. 7 4 25 .. .. .. .. 4 .. .. .. .. 64 .. .. 1 31 54 .. 114 44 .. .. 4 10 .. .. .. 19 18 .. .. .. 135 93 .. .. 22 .. .. .. 4 9 .. .. .. ..
164 229 253 205 163 281 97 79 78 189 85 143 366 380 194 90 .. 102 304 207 247 150 578 349 137 275 126 215 409 147 361 290 206 133 302 294 144 482 235 345 186 75 87 197 313 393 82 138 189 133 315 168 158 262 198 122 133 69
2012 World Development Indicators
115 99 168 169 80 125 57 45 41 117 42 99 147 358 124 41 .. 62 132 167 94 101 613 314 85 95 78 169 411 75 297 220 102 73 146 141 91 444 187 343 160 56 58 111 271 365 50 76 158 70 239 121 97 145 76 53 51 59
129
PEOPLE
Mortality
2.23
Mortality Life expectancy at birth
Neonatal mortality rate
Infant mortality rate
Under-five mortality rate
years
per 1,000 live births 1990 2010
per 1,000 live births 1990 2010
per 1,000 live births 1990 2010
1990
Romania Russian Federation Rwanda Saudi Arabia Senegal Serbia Sierra Leone Singapore Slovak Republic Slovenia Somalia South Africa South Sudan Spain Sri Lanka Sudan Swaziland Sweden Switzerland Syrian Arab Republic Tajikistan Tanzania Thailand Timor-Leste Togo Trinidad and Tobago Tunisia Turkey Turkmenistan Uganda Ukraine United Arab Emirates United Kingdom United States Uruguay Uzbekistan Venezuela, RB Vietnam West Bank and Gaza Yemen, Rep. Zambia Zimbabwe World Low income Middle income Lower middle income Upper middle income Low & middle income East Asia & Pacific Europe & Central Asia Latin America & Carib. Middle East & N. Africa South Asia Sub-Saharan Africa High income Euro area
70 69 33 69 53 71 39 76 71 73 45 62 .. 77 70 53 59 78 77 71 63 51 72 46 53 69 70 63 63 47 70 72 76 75 72 67 71 65 68 56 47 61 65 w 53 64 59 69 63 68 68 68 64 59 50 75 76
2010
73 69 55 74 59 74 47 82 75 79 51 52 62 82 75 61 48 81 82 76 67 57 74 62 57 70 75 74 65 54 70 77 80 78 76 68 74 75 73 65 48 50 70 w 59 69 65 73 68 72 71 74 72 65 54 80 81
15 12 41 20 40 16 57 4 12 5 52 18 .. 6 18 39 24 3 4 18 37 40 17 48 40 23 23 33 33 36 9 12 5 6 11 30 17 23 .. 43 40 27 32 w 46 33 41 23 35 25 21 23 29 48 43 6 5
8 6 29 10 27 4 45 1 4 2 52 18 .. 3 10 35 21 2 3 9 25 26 8 24 32 18 9 10 23 26 6 4 3 4 6 23 10 12 .. 32 30 27 23 w 33 22 29 11 25 13 11 11 16 33 35 3 2
29 22 99 36 70 25 162 6 15 9 108 47 .. 9 26 78 70 6 7 31 91 95 26 127 87 32 39 66 78 106 18 18 8 9 20 63 28 37 36 90 109 52 62 w 103 61 78 39 68 42 42 43 56 86 105 10 8
11 9 59 15 50 6 114 2 7 2 108 41 .. 4 14 66 55 2 4 14 52 60 11 56 66 24 14 14 47 63 11 6 5 7 9 44 16 19 20 57 69 51 41 w 70 38 50 17 45 20 19 18 27 52 76 5 3
37 27 163 45 139 29 276 8 18 10 180 60 .. 11 32 125 96 7 8 38 116 155 32 169 147 37 49 80 98 175 21 22 9 11 23 77 33 51 45 128 183 78 90 w 165 85 113 49 98 56 51 54 74 120 175 12 10
14 12 91 18 75 7 174 3 8 3 180 57 .. 5 17 103 78 3 5 16 63 92 13 81 103 27 16 18 56 99 13 7 5 8 11 52 18 23 22 77 111 80 58 w 108 51 69 20 63 24 23 23 34 67 121 6 4
Child mortality rate
Adult mortality rate
per 1,000 per 1,000 Male Female Male Female 2005–10a,b 2005–10a,b 2006–10a 2006–10a
.. .. 69 3 43 4 67 .. .. .. 53 .. .. .. .. 38 32 .. .. 5 18 .. .. .. 55 5 .. 6 .. 75 4 .. .. .. .. 11 .. 5 3 10 66 21 .. w 53 .. 21 .. .. .. .. .. .. 11 68 .. ..
.. .. 55 4 39 3 61 .. .. .. 54 .. .. .. .. 30 30 .. .. 3 13 .. .. .. 43 8 .. 6 .. 62 1 .. .. .. .. 7 .. 4 3 11 55 21 .. w 51 .. 22 .. .. .. .. .. .. 15 65 .. ..
185 372 348 126 291 150e 464 77 184 124 368 567 .. 94 186 265 562 69 76 110 224 362 205 259 340 233 123 136 304 400 385 90 95 139 133 243 171 132 142 231 491 543 210 w 297 202 244 161 213 157 273 181 160 239 379 117 107
76 139 315 96 239 82e 444 45 74 54 312 560 .. 43 78 211 580 41 42 72 128 343 101 223 297 136 69 77 159 385 142 68 58 80 60 139 89 89 105 186 493 594 150 w 260 136 175 100 152 105 116 98 95 166 346 62 53
a. Data are for the most recent year available. b. Refers to a survey year. Values were estimated directly from surveys and cover the 5 or 10 years preceding the survey. c. Includes Taiwan, China. d. Excludes the French overseas departments of French Guiana, Guadeloupe, Martinique, and Réunion. e. Includes Kosovo.
130
2012 World Development Indicators
About the data
2.23
Definitions
Mortality rates for different age groups (infants, chil-
weighted polynomial regression to obtain a best esti-
• Life expectancy at birth is the number of years
dren, and adults) and overall mortality indicators (life
mate trend line by fitting a set of local regressions
a newborn infant would live if prevailing patterns of
expectancy at birth or survival to a given age) are
of mortality rates against their reference dates. (For
mortality at the time of its birth were to stay the
important indicators of health status in a country.
further discussion of childhood mortality estimates,
same throughout its life. • Neonatal mortality rate
Because data on the incidence and prevalence of
see UN Inter-agency Group for Child Mortality Estima-
is the number of neonatal infants dying before reach-
diseases are frequently unavailable, mortality rates
tion 2011; for a graphic presentation and detailed
ing 28 days of age, per 1,000 live births. • Infant
are often used to identify vulnerable populations.
background data, see www.childmortality.org).
mortality rate is the number of infants dying before
And they are among the indicators most frequently
Neonatal, infant, and child mortality rates are
reaching one year of age, per 1,000 live births.
used to compare socioeconomic development
higher for boys than for girls in countries in which
• Under-five mortality rate is the probability of a
across countries.
parental gender preferences are insignificant. Under-
child born in a specific year dying before reaching age
The main sources of mortality data are vital reg-
five and child mortality rates capture the effect of
5, if subject to the age-specific mortality rate of that
istration systems and direct or indirect estimates
gender discrimination better than neonatal and
year. The probability is derived from life tables and
based on sample surveys or censuses. A “complete”
infant mortality rates do, as malnutrition and medical
is expressed as a rate per 1,000 live births. • Child
vital registration system—covering at least 90 per-
interventions are more important in this age group.
mortality rate is the probability per 1,000 of dying
cent of vital events in the population—is the best
Where female child mortality is higher, as in some
between ages 1 and 5—that is, the probability of a
source of age-specific mortality data. Where reliable
countries in South Asia, girls probably have unequal
1-year-old dying before reaching age 5—if subject to
age-specific mortality data are available, life expec-
access to resources. Child mortality rates in the
current age-specific mortality rates. • Adult mortal-
tancy at birth is directly estimated from the life table
table are not compatible with neonatal, infant, and
ity rate is the probability per 1,000 of dying between
constructed from age-specific mortality data.
under-five mortality rates because of differences in
the ages of 15 and 60—that is, the probability of a
But complete vital registration systems are fairly
methodology and reference year. Child mortality data
15-year-old dying before reaching age 60—if sub-
uncommon in developing countries. Thus estimates
were estimated directly from surveys and cover the
ject to current age-specific mortality rates between
must be obtained from sample surveys or derived
10 years preceding the survey. In addition to esti-
those ages.
by applying indirect estimation techniques to reg-
mates from Demographic Health Surveys, estimates
istration, census, or survey data (see table 2.17
derived from Multiple Indicator Cluster Surveys have
and Primary data documentation). Survey data are
been added to the table; they cover the 5 years pre-
subject to recall error, and surveys estimating infant
ceding the survey. Data sources
deaths require large samples because households
Rates for adult mortality come from life tables.
in which a birth has occurred during a given year
Adult mortality rates increased notably in a dozen
Data on life expectancy at birth are World Bank
cannot ordinarily be preselected for sampling. Indi-
countries in Sub-Saharan Africa in the early 2000s
calculations based on male and female data from
rect estimates rely on model life tables that may be
and in several countries in Europe and Central Asia
World Population Prospects: The 2010 Revision (for
inappropriate for the population concerned. Because
in the first half of the 1990s. In Sub-Saharan Africa
more than half of countries, most of them develop-
life expectancy at birth is estimated using infant mor-
the increase stems from AIDS-related mortality
ing countries), census reports and other statistical
tality data and model life tables for many develop-
and affects both sexes, though women are more
publications from national statistical offices, Euro-
ing countries, similar reliability issues arise for this
affected. In Europe and Central Asia the causes are
stat’s Demographic Statistics, and the U.S. Bureau
indicator. Extrapolations based on outdated surveys
more diverse (high prevalence of smoking, high-fat
of the Census International Data Base. Data on
may not be reliable for monitoring changes in health
diet, excessive alcohol use, stressful conditions
neonatal, infant, and under-five mortality are from
status or for comparative analytical work.
related to the economic transition) and affect men
the UN Inter-agency Group for Child Mortality Esti-
more.
mation’s Levels and Trends in Child Mortality: Report
Estimates of neonatal, infant, and under-five mor-
PEOPLE
Mortality
tality tend to vary by source and method for a given
Annual data series from the United Nations are
2011 and are based mainly on household surveys,
time and place. Years for available estimates also
interpolated based on five-year estimates and thus
censuses, and vital registration data. Data on child
vary by country, making comparison across countries
may not reflect actual events.
mortality are from MEASURE DHS Demographic
and over time difficult. To make neonatal, infant, and
and Health Surveys by ICF International and World
under-fi ve mortality estimates comparable and to
Bank calculations based on infant and under-five
ensure consistency across estimates by different
mortality from Multiple Indicator Cluster Surveys
agencies, the United Nations Inter-agency Group
by UNICEF. Most data on adult mortality are linear
for Child Mortality Estimation, which comprises the
interpolations of five-year data from World Popula-
United Nations Children’s Fund (UNICEF), the United
tion Prospects: The 2010 Revision. Remaining data
Nations Population Division, the World Health Organi-
on adult mortality are from the Human Mortality
zation (WHO), the World Bank, and other universities
Database by the University of California, Berke-
and research institutes, developed and adopted a
ley, and the Max Planck Institute for Demographic
statistical method that uses all available information
Research (www.mortality.org).
to reconcile differences. The method uses a locally
2012 World Development Indicators
131 Text figures, tables, and boxes
2.24
Health gaps by income
Demography Survey year
Infant mortality rate
per 1,000 live births
Algeria Armenia Azerbaijan Bangladesh Bolivia Bosnia and Herzegovina Burkina Faso Cambodia Cameroon Colombia Congo, Dem. Rep. Congo, Rep. Côte d’Ivoire Dominican Republic Egypt, Arab Rep. Ethiopia Gambia, The Ghana Guinea Guinea-Bissau Haiti Honduras India Indonesia Jordan Kazakhstan Kenya Kyrgyz Republic Lao PDR Liberia Madagascar Malawi Mali Mauritania Moldova Namibia Nepal Niger Nigeria Pakistan Philippines Rwanda Senegal Serbia Sierra Leone Somalia Swaziland Syrian Arab Republic Tanzania Thailand Timor-Leste Togo Ukraine Uzbekistan Yemen, Rep. Zambia Zimbabwe
132
2005 2006 2007 2008 2006 2005 2006 2010 2007 2005 2006 2007 2008 2005 2006 2008 2005 2006 2006 2006 2007 2009 2006 2009
2009 2009 2006 2006 2007 2005 2007 2006 2006 2008 2007 2008 2008 2005 2008 2007 2006 2010 2009/10 2006 2007 2006 2006 2007 2006
2012 World Development Indicators
Under-five mortality rate
per 1,000 live births
Total fertility rate
births per woman
Teenage mothers
% of women ages 15–19
Poorest quintile
Richest quintile
Poorest quintile
Richest quintile
Poorest quintile
Richest quintile
Poorest quintile
Richest quintile
.. 42 52 66 89 .. 97 101 101 22 113 92 147 43 42 80 106 59 127 .. 78 37 82 56 32 68 66 .. .. 121 61 72 124 89 20 60 71 91 100 94 40 99 77 .. 148 .. 84 18 61 .. 62 92 19 59 94 69 48
.. 14 37 36 26 .. 78 34 51 12 58 56 59 26 17 60 58 47 68 .. 45 19 34 26 29 42 57 .. .. 95 37 62 80 57 16 24 40 67 58 53 15 45 43 .. 93 .. 84 16 63 .. 38 43 9 36 36 74 45
.. 52 63 86 116 .. 196 127 189 29 184 135 229 53 49 130 158 103 217 .. 125 50 118 77 36 82 98 .. .. 176 106 123 233 144 29 92 98 206 219 121 59 161 143 .. 211 .. 118 22 103 .. 87 150 23 72 118 124 72
.. 23 41 43 31 .. 111 43 88 13 97 85 83 28 19 92 72 60 113 .. 55 20 39 32 32 45 69 .. .. 137 48 99 124 87 17 30 47 157 87 60 17 84 56 .. 145 .. 101 20 84 .. 52 62 9 42 118 124 72
.. 1.8 2.3 3.2 6.2 .. 6.6 4.9 6.5 3.2 7.4 6.7 7.4 3.8 3.4 6.6 .. 6.5 6.5 .. 6.6 5.6 3.9 3.0 4.9 3.4 7.0 .. .. 8.0 6.8 7.1 7.6 5.4 2.1 5.1 4.7 7.9 7.1 5.8 5.2 5.8 6.7 .. 6.3 .. 5.5 .. 7.0 .. 7.3 7.3 1.7 .. .. 8.4 5.5
.. 1.5 1.6 2.2 1.9 .. 3.6 2.4 3.2 1.4 4.2 2.9 2.9 1.7 2.7 3.2 .. 2.3 4.2 .. 2.0 2.1 1.8 2.7 2.7 1.2 2.9 .. .. 3.2 2.7 4.1 4.9 3.6 1.4 2.4 1.9 6.4 4.0 3.0 1.9 4.4 3.3 .. 3.2 .. 2.6 .. 3.2 .. 4.2 2.9 1.0 .. .. 3.4 2.3
.. 5 6 42 31 .. 26 11 36 29 29 35 52 37 12 24 .. 18 39 .. 22 31 25 6 5 8 24 .. .. 53 51 43 37 14 8 22 18 40 46 16 19 5 34 .. 49 .. 33 .. 28 .. 9 36 8 .. .. 37 32
.. 0 3 20 8 .. 12 5 14 7 12 13 12 8 5 8 .. 4 20 .. 7 10 5 10 3 5 16 .. .. 20 14 20 23 11 1 5 14 24 5 4 4 4 8 .. 16 .. 15 .. 13 .. 3 7 1 .. .. 14 7
Child health Survey year
Diarrhea
Prevalence % of children under age 5
Algeria Armenia Azerbaijan Bangladesh Bolivia Bosnia and Herzegovina Burkina Faso Cambodia Cameroon Colombia Congo, Dem. Rep. Congo, Rep. Côte d’Ivoire Dominican Republic Egypt, Arab Rep. Ethiopia Gambia, The Ghana Guinea Guinea-Bissau Haiti Honduras India Indonesia Jordan Kazakhstan Kenya Kyrgyz Republic Lao PDR Liberia Madagascar Malawi Mali Mauritania Moldova Namibia Nepal Niger Nigeria Pakistan Philippines Rwanda Senegal Serbia Sierra Leone Somalia Swaziland Syrian Arab Republic Tanzania Thailand Timor-Leste Togo Ukraine Uzbekistan Yemen, Rep. Zambia Zimbabwe
2006 2005 2006 2007 2008 2006 2006 2005 2006 2010 2007 2005 2006 2007 2008 2005 2006 2008 2005 2006 2006 2006 2006 2007 2009 2006 2009 2006 2006 2007 2009 2006 2006 2007 2005 2007 2006 2006 2008 2007 2008 2008 2005 2006 2008 2006 2007 2006 2010 2006 2009/10 2006 2006 2006 2007 2006
Acute respiratory infection (ARI) Treatment % of children under age 5 with diarrhea
Prevalence % of children under age 5
Treatment Children with ARI taken to health provider % of children under age 5 with ARI
Prevalence of child malnutrition (underweight)
Child immunization
Old standards % of children under age 5
All vaccinations % of children ages 12–23 months
Poorest quintile
Richest quintile
Poorest quintile
Richest quintile
Poorest quintile
Richest quintile
Poorest quintile
Richest quintile
Poorest quintile
Richest quintile
Poorest quintile
Richest quintile
10 20 13 10 30 4 19 22 30 16 17 15 20 16 10 18 21 25 18 13 25 19 9 18 18 1 20 3 17 19 8 26 13 25 7 13 13 22 14 23 10 16 24 7 13 26 23 9 15 10 13 15 .. .. 35 14 15
8 13 10 8 20 5 16 14 10 7 14 12 13 13 7 14 15 10 17 14 18 11 8 10 14 2 13 4 9 19 10 20 8 19 12 11 12 18 5 20 7 13 23 5 9 14 9 6 16 6 17 9 .. .. 27 16 15
.. 65 66 79 58 .. 57 66 46 67 65 54 55 64 39 24 .. 58 47 .. 48 64 25 60 56 46 80 .. .. 63 52 65 41 29 50 57 27 50 25 52 68 32 51 .. 74 .. 89 .. 56 .. 81 .. .. .. .. 71 65
.. 80 55 88 75 .. 73 40 73 82 55 54 77 63 32 55 .. 69 73 .. 67 68 49 57 64 71 79 .. .. 83 67 76 68 29 70 74 59 66 61 59 82 49 52 .. 86 .. 85 .. 68 .. 76 .. .. .. .. 79 79
7 11 4 6 24 5 6 12 10 7 9 .. 8 9 10 12 6 6 10 4 10 14 6 8 5 1 11 .. 6 8 3 9 10 7 5 7 6 10 4 15 7 16 10 3 7 18 10 4 4 7 2 7 .. .. .. 5 7
5 11 2 3 17 3 2 3 6 4 6 .. 3 4 8 11 5 3 8 7 5 6 4 5 3 2 5 .. 3 9 3 8 11 7 10 1 5 11 1 13 3 14 15 2 4 12 8 6 5 3 2 8 .. .. .. 6 2
38 30 15 45 40 97 45 41 20 54 32 .. 21 63 70 19 68 45 30 32 21 46 61 48 66 37 57 .. 28 59 33 51 9 33 42 65 36 19 32 67 42 16 35 89 39 5 66 72 18 85 53 17 .. .. .. 78 9
68 42 11 73 70 96 73 58 50 67 48 .. 71 65 82 33 68 87 59 82 37 74 80 74 78 53 63 .. 12 89 68 65 50 64 68 94 54 59 66 92 64 43 61 .. 46 28 75 86 62 78 80 28 .. .. .. 56 51
5 5 17 55 11 2 44 43 35 12 33 20 26 7 9 43 26 23 28 21 27 21 61 .. 4 5 31 3 44 26 47 25 37 40 .. 27 54 48 40 .. .. 30 25 4 27 .. 9 13 25 15 .. 37 .. 6 .. 21 18
3 1 4 31 2 4 24 23 6 3 19 5 10 2 7 29 14 11 21 10 7 2 25 .. 0 1 12 3 18 17 28 16 22 13 .. 9 24 30 13 .. .. 10 7 2 15 .. 3 8 12 4 .. 15 .. 3 .. 14 6
81 66 7 80 78 66 64 56 42 64 20 29 58 48 89 14 83 75 29 43 34 77 24 39 82 98 61 .. 18 23 41 66 49 39 82 59 68 20 5 26 64 82 59 50 39 5 82 51 69 92 43 39 .. 90 18 71 43
95 68 32 88 81 71 81 76 72 67 50 73 92 76 94 36 74 84 45 64 56 68 71 75 89 96 70 .. 45 56 82 77 56 25 59 82 94 48 53 64 87 83 65 54 40 22 79 77 85 86 45 63 .. 80 73 78 64
2012 World Development Indicators
133
PEOPLE
2.24
Health gaps by income
2.24
Health gaps by income
Reproductive and women’s health Survey year
Algeria Armenia Azerbaijan Bangladesh Bolivia Bosnia and Herzegovina Burkina Faso Cambodia Cameroon Colombia Congo, Dem. Rep. Congo, Rep. Côte d’Ivoire Dominican Republic Egypt, Arab Rep. Ethiopia Gambia, The Ghana Guinea Guinea-Bissau Haiti Honduras India Indonesia Jordan Kazakhstan Kenya Kyrgyz Republic Lao PDR Liberia Madagascar Malawi Mali Mauritania Moldova Namibia Nepal Niger Nigeria Pakistan Philippines Rwanda Senegal Serbia Sierra Leone Somalia Swaziland Syrian Arab Republic Tanzania Thailand Timor-Leste Togo Ukraine Uzbekistan Yemen, Rep. Zambia Zimbabwe
134
2006 2005 2006 2007 2008 2006 2006 2005 2006 2010 2007 2005 2006 2007 2008 2005 2006 2008 2005 2006 2006 2006 2006 2007 2009 2006 2009 2006 2006 2007 2009 2006 2006 2007 2005 2007 2006 2006 2008 2007 2008 2008 2005 2006 2008 2006 2007 2006 2010 2006 2009/10 2006 2007 2006 2006 2007 2006
Knowledge of contraception
Contraceptive prevalence rate
Any method % of married women ages 15–49
Any method % of married women ages 15–49
Poorest quintile
Richest quintile
.. 97 96 100 90 .. 85 98 72 100 79 97 75 99 100 76 .. 93 87 .. 100 100 99 96 100 99 86 .. .. 72 87 98 70 53 99 97 100 68 41 92 96 99 89 .. 65 .. 100 .. 97 .. 66 94 99 .. .. 98 99
.. 100 99 100 100 .. 99 100 99 100 98 100 99 100 100 96 .. 100 97 .. 100 100 100 100 100 100 99 .. .. 99 100 100 93 93 100 100 100 89 96 99 100 99 99 .. 89 .. 100 .. 100 .. 93 99 100 .. .. 100 100
2012 World Development Indicators
Poorest quintile
56 51 55 55 46 35 9 31 11 76 14 40 9 68 55 4 6 14 5 6 20 53 42 53 54 42 20 50 .. 4 20 38 4 2 67 32 33 11 3 16 41 28 4 33 4 12 37 42 23 74 15 12 62 66 15 41 48
Richest quintile
65 60 57 60 71 35 41 54 44 80 39 46 24 73 65 37 13 31 17 23 38 73 68 64 65 59 55 51 .. 20 57 46 19 19 70 71 61 21 35 43 50 50 25 49 20 19 62 68 51 69 34 19 71 63 44 54 72
Pregnant women receiving prenatal care
Births attended by skilled health staffa
%
Problem accessing health care
%
%
Poorest quintile
Richest quintile
Poorest quintile
Richest quintile
76 86 56 32 79 98 79 58 48 92 78 75 69 87 57 .. 98 93 68 76 72 88 58 83 97 100 84 94 16 68 73 90 20 53 97 90 50 37 24 38 91 95 86 96 84 8 96 68 82 96 75 69 98 98 32 90 92
98 99 98 85 97 100 98 91 98 99 95 98 97 98 93 .. 98 100 98 89 97 99 97 99 100 100 97 99 88 96 97 95 80 94 98 97 92 82 94 92 99 97 99 100 97 51 99 94 95 100 96 100 99 99 79 98 98
88 98 76 7 39 99 56 22 19 86 62 69 29 90 58 .. 28 23 15 19 8 38 21 47 99 100 23 93 3 30 22 43 9 21 99 61 9 5 9 18 98 49 21 98 28 11 51 78 31 93 12 30 99 100 17 26 44
98 100 100 57 99 100 65 92 96 99 98 99 95 98 97 .. 89 96 87 79 71 98 90 96 100 100 82 100 81 82 90 77 76 95 100 98 64 60 86 79 99 76 91 100 72 77 93 99 88 100 71 97 100 100 74 92 97
Poorest quintile
.. 92 94 26 95 .. 88 93 92 .. 95 .. .. 75 87 98 .. 78 93 .. 98 94 67 62 81 .. 39 .. .. 91 75 90 74 .. 85 89 91 87 86 .. 88 89 88 .. 96 .. 70 .. 56 .. 94 .. .. .. .. 77 89
Richest quintile
.. 67 65 17 78 .. 65 72 69 .. 70 .. .. 43 45 82 .. 48 71 .. 75 69 15 25 62 .. 27 .. .. 45 37 53 47 .. 56 33 63 59 49 .. 50 65 54 .. 69 .. 22 .. 16 .. 70 .. .. .. .. 42 46
About the data
2.24
Definitions
Health survey data at the national level do not reveal
• Survey year is the year in which the underlying data
survey who have received at least one antenatal
within-country inequalities associated with socioeco-
were collected. The reference year of the data may be
care during pregnancy before the most recent birth
nomic status. The data in the table describe the health
preceding the survey year. • Infant mortality rate is
from any skilled personnel. • Births attended by
and demographic status as well as use of health ser-
the number of infants dying before reaching one year
skilled health staff are live births in the one, two,
vices by individuals in different socioeconomic groups
of age, per 1,000 live births. • Under-five mortality
or three years preceding the survey attended by any
within countries. The data are from MEASURE DHS
rate is the probability that a child born in a specific
skilled personnel. • Problem accessing health care
Demographic and Health Surveys by ICF International
year will die before reaching age 5, if subject to the
is the percentage of women who report they have
and Multiple Indicator Cluster Surveys by the United
age-specific mortality rate of that year. The probabil-
a big problem accessing health care when they are
Nations Children’s Fund.
ity is derived from life tables and expressed as a
sick due to inadequate knowledge of where to go
Obtaining reliable data on a household’s or individ-
rate per 1,000 live births. • Total fertility rate is the
for treatment, need to get permission or money for
ual’s socioeconomic status is challenging, and meth-
number of children that would be born to a woman if
treatment, distance to health facility, need to take
ods have evolved over time. Earlier measurements
she were to live to the end of her childbearing years
transport, desire not to go alone, or concern that a
relied on indicators such as household income and
and bear children in accordance with age-specific fer-
female provider may not be available.
consumption, which are prone to bias and are time
tility rates of a reference period. • Teenage mothers
and labor intensive when included in survey question-
are women ages 15−19 who are mothers or pregnant
naires. The wealth index, developed by MEASURE DHS
with their first child. • Diarrhea prevalence is the
with partial funding from the World Bank, is calculated
percentage of children under age 5 who had diarrhea
using easy-to-collect data on a household’s ownership
in the two weeks preceding the survey. • Diarrhea
of selected assets, such as televisions and bicycles;
treatment is the percentage of children under age 5
materials used for housing construction; and types of
with diarrhea in the two weeks preceding the survey
water access and sanitation facilities. A single asset
who received oral rehydration salts, recommended
index is developed on the basis of data from the entire
homemade fluids (rehydration salts or recommended
country sample and used. Generated with a statistical
home solution), or increased fluids. • Acute respira-
procedure known as principal components analysis,
tory infection (ARI) prevalence is the percentage
the wealth index places individual households on a
of children under age 5 who were ill with a cough
continuous scale of relative wealth. Demographic and
accompanied by rapid breathing in the two weeks
Health Surveys and Multiple Indicator Cluster Surveys
preceding the survey. • Children with ARI taken to
separate all interviewed households into five wealth
health provider are children under age 5 with ARI in
quintiles to compare the influence of wealth on various
the two weeks preceding the survey who were taken
population, health and nutrition indicators. The wealth
to a health facility. • Prevalence of child malnutri-
index is presented in the final reports of these surveys.
tion (underweight) is the percentage of children
Data disaggregated by wealth quintile provide
under age 5 whose weight for age is more than two
insights into health differentials by socioeconomic
standard deviations below the median for the inter-
status and allow problems particular to the poor,
national reference population. Data are based on the
such as unequal access to health care to be identi-
old standards of the U.S. Centers for Disease Con-
fied. If the poor have a greater disease burden than
trol National Center for Health Statistics and World
the rich, programs should focus on reaching the
Health Organization international reference popula-
poor. But this is rare. Health services too often fail
tion. • Child immunization (all vaccinations) is the
poor people in access, quality, and affordability. In
percentage of children ages 12−23 months who have
low-income countries the poor are particularly disad-
received vaccines for Bacillus Calmette-Guérin and
vantaged in using health care and experience worse
measles; three doses each of diphtheria, pertussis,
health outcomes than the rich. The table shows the
and tetanus; and polio vaccine (excluding polio 0)
estimates for the poorest and richest quintiles only;
by the time of the survey, according to the vaccina-
the full set of estimates for up to 70 indicators is
tion card or the mother’s report. • Knowledge of
available at http://data.worldbank.org and http://
contraception is the percentage of currently married
data.worldbank.org/data-catalog/health-nutrition
women who know at least one contraceptive method.
-population-statistics. The estimates in the table are
• Contraceptive prevalence rate is the percentage
Data on health gaps by income are from MEASURE
based on household survey data, which may refer to
of women ages 15-49 married or in union who are
DHS Demographic and Health Surveys, by ICF
a period preceding the survey date or use a definition
practicing, or whose sexual partners are practicing,
International, downloaded through STATcompiler,
or methodology different from the estimates in the
any form of contraception. • Pregnant women receiv-
and from Multiple Indicator Cluster Surveys by
other tables. Thus the estimates may differ, and cau-
ing prenatal care are women with one or more live
UNICEF through their final reports.
tion should be exercised in using the data.
births in the one, two, or three years preceding the
Data sources
2012 World Development Indicators
135
PEOPLE
Health gaps by income
ENVIRONMENT
T
he indicators in the Environment section measure the use of resources and the way human activities affect the natural and built environment. They include measures of environmental goods (forests, water, cultivatable land) and of degradation (pollution, deforestation, loss of habitat, and loss of biodiversity). Sustainable development and poverty reduction require efficient use of environmental resources. These indicators show that growing populations and expanding economies have placed greater demands on land, water, forests, minerals, and energy resources. But new technologies, increasing productivity, and better policies can ensure that future development is environmentally and socially sustainable. Nowhere are these risks and opportunities more intertwined than in the global effort to mitigate the effects of climate change. At the December 2011 United Nations Conference on Climate Change in Durban, South Africa, all 194 participating countries adopted the Durban Platform for Enhanced Action, which sets the direction of climate negotiations. The platform calls for parties to negotiate a legal agreement on climate change no later than 2015 that would apply to all countries and be effective by 2020. Negotiators also launched a Green Climate Fund that will eventually channel billions of dollars a year to developing countries for adaptation to and mitigation of climate change. As documented in World Development Report 2010: Development and Climate Change (World Bank 2009j), climate change is already eroding development gains and causing disruptions to social and economic systems in some countries. Continued rise in temperature, accompanied by changes in precipitation patterns, is projected for this century, and more frequent, severe, and prolonged climate-related events such as floods and droughts are also projectedâ&#x20AC;&#x201D;posing risks for agriculture, food production, and water supplies. Poor countries and the poorest people in all countries are the most vulnerable to the
impacts. Understanding climate change is thus key for development policy. Because uncertainty increases with climate change, better climate information is critical for wise development decisions. This yearâ&#x20AC;&#x2122;s Environment section includes two new tables on information related to climate change. Table 3.11 presents data on carbon dioxide emissions by economic sector, which shows how differences in industrial structure and production technologies affect the production of carbon dioxide and how these patterns have changed. Table 3.12 presents data on climate variability, exposure to impact, and resilience. Other indicators in this section describe land use, agriculture and food production, forests and biodiversity, water resources, energy use and efficiency, natural resource rents, urbanization, environmental impacts, government commitments, and threatened species. Table 3.8 adds newly available data on the share of the population with access to electricity. Where possible, the indicators come from international sources and are standardized to facilitate comparison across countries. But ecosystems span national boundaries, and access to natural resources may vary within countries. For example, water may be abundant in some parts of a country but scarce in others, and countries often share water resources. Land productivity and optimal land use may be location specific, but widely separated regions can have common characteristics. Greenhouse gas emissions and climate change are measured globally, but their effects are experienced locally, shaping peopleâ&#x20AC;&#x2122;s lives and opportunities. Measuring environmental phenomena and their effects at the subnational, national, and supranational levels and incorporating these values in national income accounts and other statistical frameworks remain major challenges for economists, environmentalists, and statisticians.
3
2012 World Development Indicators
137
Tables
3.1
Rural population and land use Rural population
% of total 1990 2010
Afghanistan Albania Algeria Angola Argentina Armenia Australia Austria Azerbaijan Bahrain Bangladesh Belarus Belgium Benin Bolivia Bosnia and Herzegovina Botswana Brazil Bulgaria Burkina Faso Burundi Cambodia Cameroon Canada Central African Republic Chad Chile China Hong Kong SAR, China Colombia Congo, Dem. Rep. Congo, Rep. Costa Rica Côte d’Ivoire Croatia Cuba Cyprus Czech Republic Denmark Dominican Republic Ecuador Egypt, Arab Rep. El Salvador Eritrea Estonia Ethiopia Finland France Gabon Gambia, The Georgia Germany Ghana Greece Guatemala Guinea Guinea-Bissau Haiti
138
82 64 48 63 13 33 15 34 46 12 80 34 4 66 44 61 58 25 34 86 94 87 59 23 63 79 17 73 1 32 72 46 49 60 46 27 33 25 15 45 45 57 51 84 29 87 39 26 31 62 45 27 64 41 59 72 72 72
75 52 34 42 8 36 11 32 48 11 72 26 3 58 34 51 39 14 28 80 89 77 42 19 61 72 11 55 .. 25 65 38 36 50 42 24 30 27 13 30 33 57 39 78 31 82 36 22 14 42 47 26 49 39 51 65 70 50
2012 World Development Indicators
Land area
Land use
average annual % growth 1990–2010
thousand sq. km 2010
Forest area 1990 2010
2.3 –0.9 –0.4 0.7 –1.7 0.5 0.1 –0.4 0.8 7.2 0.5 –1.8 0.0 2.2 0.2 –1.3 –0.7 –2.5 –1.6 2.5 2.2 0.3 0.2 0.6 1.6 2.0 –1.6 –1.1 .. 0.2 1.8 1.5 0.0 0.7 –0.7 –0.1 0.5 0.4 –1.6 –1.2 –0.6 1.7 –0.2 2.4 –0.1 1.8 –0.4 –0.5 –1.5 0.8 0.8 –0.5 0.8 –0.2 1.6 1.5 2.0 –1.4
652.2 27.4 2,381.7 1,246.7 2,736.7 28.5 7,682.3 82.5 82.6 0.8 130.2 202.9 30.3 110.6 1,083.3 51.2 566.7 8,459.4 108.6 273.6 25.7 176.5 472.7 9,093.5 623.0 1,259.2 743.5 9,327.5 1.0 1,109.5 2,267.1 341.5 51.1 318.0 56.0 106.4 9.2 77.3 42.4 48.3 248.4 995.5 20.7 101.0 42.4 1,000.0 303.9 547.7 257.7 10.0 69.5 348.6 227.5 128.9 107.2 245.7 28.1 27.6
2.1 28.8 0.7 48.9 12.7 12.2 20.1 45.8 11.2 0.3 11.5 38.4 22.4 52.1 58.0 43.3 24.2 68.0 30.1 25.0 11.3 73.3 51.4 34.1 37.2 10.4 20.5 16.8 .. 56.3 70.7 66.5 50.2 32.1 33.1 19.2 17.4 34.0 10.5 40.8 49.9 0.0 18.2 16.0 49.3 13.7 71.9 26.5 85.4 44.2 40.0 30.8 32.7 25.6 44.3 29.6 78.8 4.2
% of land area
2.1 28.3 0.6 46.9 10.7 9.2 19.4 47.1 11.3 1.3 11.1 42.5 22.4 41.2 52.8 42.7 20.0 61.4 36.2 20.6 6.7 57.2 42.1 34.1 36.3 9.2 21.8 22.2 .. 54.5 68.0 65.6 51.0 32.7 34.3 27.0 18.7 34.4 12.8 40.8 39.7 0.1 13.9 15.2 52.3 12.3 72.9 29.1 85.4 48.0 39.5 31.8 21.7 30.3 34.1 26.6 71.9 3.7
Permanent cropland 1990 2009
0.2 4.6 0.2 0.4 0.4 2.1 0.0 1.0 3.7 2.9 2.5 0.9 0.5a 0.9 0.1 2.9 0.0 0.8 2.7 0.2 14.0 0.6 2.6 0.7 0.1 0.0 0.3 0.8 .. 1.5 0.5 0.1 4.9 11.0 2.0 4.2 5.5 3.1 0.2 9.3 4.8 0.4 12.5 0.0 0.3 0.5 0.0 2.2 0.6 0.5 4.8 1.3 6.6 8.3 4.5 2.0 4.2 11.6
0.2 3.2 0.4 0.2 0.4 1.9 0.0 0.8 2.7 2.6 7.5 0.6 0.7 2.7 0.2 2.0 0.0 0.9 1.6 0.2 13.6 0.9 3.0 0.8 0.1 0.0 0.6 1.5 .. 1.4 0.3 0.2 5.9 13.5 1.6 3.5 3.6 1.0 0.1 9.7 5.4 0.8 11.1 0.0 0.2 1.0 0.0 1.9 0.6 0.5 1.7 0.6 12.3 8.9 8.8 2.8 8.9 10.9
Arable land 1990 2009
12.1 21.1 3.0 2.3 9.6 14.9 6.2 17.3 20.5 2.9 72.6 30.0 23.3a 14.6 1.9 16.7 0.7 6.0 34.9 12.9 36.2 20.9 12.6 5.0 3.1 2.6 3.8 13.3 .. 3.0 2.9 1.4 5.1 7.6 21.7 31.6 11.5 41.1 60.4 18.6 5.8 2.3 26.5 4.9 26.3 10.0 7.4 32.9 1.1 18.2 11.4 34.3 11.9 22.5 12.1 11.6 8.9 28.3
11.9 22.3 3.1 3.2 11.3 16.0 6.1 16.6 22.7 1.3 58.1 27.3 27.7 22.1 3.4 19.5 0.4 7.2 28.9 21.6 35.0 22.1 12.6 5.0 3.1 3.4 1.7 11.8 .. 1.6 3.0 1.5 3.9 8.8 15.5 34.3 9.4 41.2 57.3 16.6 4.8 2.9 32.7 6.8 14.1 13.9 7.4 33.5 1.3 40.0 6.4 34.3 19.3 19.8 14.0 11.6 10.7 38.1
Arable land hectares per 100 people 1990 2009
41.6 17.6 28.0 28.1 80.9 12.3 280.7 18.6 23.1 0.4 9.0 59.6 0.2 33.8 31.5 21.6 30.4 33.9 44.2 37.8 16.6 38.8 48.8 163.7 65.4 54.5 21.3 10.9 .. 10.0 18.3 20.1 8.5 19.4 27.1 32.1 13.8 32.1 49.8 12.5 15.6 4.0 10.3 0.1 72.7 1.4 45.5 30.9 31.8 18.8 16.3 15.1 18.3 28.5 14.6 49.5 24.6 11.0
23.3 19.2 21.5 21.6 77.4 14.8 214.8 16.4 20.9 0.1 5.2 58.3 7.8 28.5 38.2 26.5 12.6 31.7 41.4 36.9 11.0 27.9 31.1 133.7 45.2 39.3 7.5 8.3 .. 3.9 10.4 12.7 4.4 14.5 19.6 32.4 8.0 30.3 44.0 8.2 8.4 3.6 11.0 13.5 44.5 17.2 42.3 28.4 22.0 23.8 10.2 14.6 18.5 22.6 10.7 29.2 20.2 10.6
Rural population
% of total 1990 2010
Honduras Hungary India Indonesia Iran, Islamic Rep. Iraq Ireland Israel Italy Jamaica Japan Jordan Kazakhstan Kenya Korea, Dem. Rep. Korea, Rep. Kosovo Kuwait Kyrgyz Republic Lao PDR Latvia Lebanon Lesotho Liberia Libya Lithuania Macedonia, FYR Madagascar Malawi Malaysia Mali Mauritania Mauritius Mexico Moldova Mongolia Morocco Mozambique Myanmar Namibia Nepal Netherlands New Zealand Nicaragua Niger Nigeria Norway Oman Pakistan Panama Papua New Guinea Paraguay Peru Philippines Poland Portugal Puerto Rico Qatar
60 34 75 69 44 30 43 10 33 51 37 28 44 82 42 26 .. 2 62 85 31 17 86 55 24 32 42 76 88 50 77 60 56 29 53 43 52 79 75 72 91 31 15 48 85 65 28 34 69 46 85 51 31 51 39 52 28 8
51 32 70 46 31 34 38 8 32 46 33 22 42 78 37 18 .. 2 63 67 32 13 73 39 22 33 32 70 80 28 67 59 57 22 59 43 43 62 66 62 82 17 13 43 83 50 22 28 63 25 88 39 28 34 39 39 1 4
average annual % growth 1990–2010
1.1 –1.4 0.9 –1.4 –0.6 3.3 –0.2 1.6 0.0 –0.2 –0.6 2.0 1.8 2.2 –0.5 –0.9 .. 2.1 0.6 –0.3 –0.7 –0.2 0.0 2.3 0.7 –0.9 –1.4 2.4 2.5 –1.7 2.2 2.1 0.4 –0.1 0.4 1.2 0.2 1.0 –0.2 0.9 1.2 –2.6 0.3 0.7 3.4 1.1 0.9 2.4 1.1 –1.6 2.3 0.2 0.7 –0.5 0.2 –1.5 –17.9 7.7
Land area
3.1
ENVIRONMENT
Rural population and land use Land use
% of land area thousand sq. km 2010
Forest area 1990 2010
111.9 89.6 2,973.2 1,811.6 1,628.6 437.4 68.9 21.6 294.1 10.8 364.5 88.2 2,699.7 569.1 120.4 96.9 10.9b 17.8 191.8 230.8 62.2 10.2 30.4 96.3 1,759.5 62.7 25.2 581.5 94.1 328.6 1,220.2 1,030.7 2.0 1,944.0 32.9 1,553.6 446.3 786.4 653.5 823.3 143.4 33.8 263.3 120.3 1,266.7 910.8 305.5 309.5 770.9 74.3 452.9 397.3 1,280.0 298.2 304.2 91.5 8.9 11.6
72.7 20.0 21.5 65.4 6.8 1.8 6.7 6.1 25.8 31.9 68.4 1.1 1.3 6.5 68.1 64.5 .. 0.2 4.4 75.0 51.0 12.8 1.3 51.2 0.1 31.0 35.9 23.5 41.3 68.1 11.5 0.4 19.2 36.2 9.7 8.1 11.3 55.2 60.0 10.6 33.7 10.2 29.3 37.5 1.5 18.9 30.0 0.0 3.3 51.0 69.6 53.3 54.8 22.0 29.2 36.4 32.4 0.0
46.4 22.6 23.0 52.1 6.8 1.9 10.7 7.1 31.1 31.1 68.5 1.1 1.2 6.1 47.1 64.2 .. 0.3 5.0 68.2 53.9 13.4 1.4 44.9 0.1 34.5 39.6 21.6 34.4 62.3 10.2 0.2 17.2 33.3 11.7 7.0 11.5 49.6 48.6 8.9 25.4 10.8 31.4 25.9 1.0 9.9 32.9 0.0 2.2 43.7 63.4 44.3 53.1 25.7 30.7 37.8 62.2 0.0
Permanent cropland 1990 2009
3.2 2.6 2.2 6.5 0.8 0.7 0.0 4.1 10.1 9.2 1.3 0.8 0.1 0.8 1.5 1.6 .. 0.1 0.4 0.3 0.4 11.9 0.1 1.6 0.2 0.7 2.2 1.0 1.4 16.0 0.1 0.0 3.0 1.0 14.2 0.0 1.6 0.3 0.8 0.0 0.5 0.9 0.2 1.6 0.0 2.8 0.0 0.1 0.6 2.1 1.2 0.2 0.3 14.8 1.1 8.5 5.6 0.1
3.7 2.1 3.9 10.5 1.1 0.6 0.0 3.6 8.9 9.2 0.9 0.9 0.0 1.1 1.7 2.1 .. 0.2 0.4 0.5 0.1 14.0 0.1 2.2 0.2 0.4 1.4 1.0 1.3 17.6 0.1 0.0 2.0 1.4 9.2 0.0 2.2 0.3 1.7 0.0 0.8 1.0 0.3 1.9 0.0 3.3 0.0 0.1 1.1 2.0 1.5 0.3 0.6 16.9 1.3 8.5 4.5 0.3
Arable land 1990 2009
13.1 56.2 54.8 11.2 9.3 13.3 15.1 15.9 30.6 11.0 13.1 2.0 13.0 8.8 19.0 19.8 .. 0.2 6.9 3.5 27.1 17.9 10.4 3.6 1.0 46.0 23.8 4.7 23.9 5.2 1.7 0.4 49.3 12.5 52.8 0.9 19.5 4.4 14.6 0.8 16.0 26.0 10.0 10.8 8.7 32.4 2.8 0.1 26.6 6.7 0.4 5.3 2.7 18.4 47.3 25.6 7.3 0.9
9.1 50.6 53.1 13.0 10.6 10.4 15.8 14.0 23.4 11.1 11.8 2.3 8.7 9.5 22.0 16.4 27.6 0.6 6.7 5.9 18.8 14.2 11.0 4.2 1.0 32.8 16.7 5.1 38.2 5.5 5.2 0.4 42.9 12.9 55.2 0.6 18.0 6.4 16.9 1.0 16.7 31.2 1.8 15.8 11.8 37.3 2.7 0.3 26.5 7.4 0.6 9.6 2.9 18.1 41.2 12.3 6.8 1.0
Arable land hectares per 100 people 1990 2009
29.9 48.7 18.6 11.0 27.7 31.9 29.6 7.4 15.9 5.0 3.9 5.7 213.2 21.3 11.4 4.6 .. 0.2 29.0 19.1 64.6 6.2 19.3 16.5 41.6 78.0 31.3 24.1 24.0 9.3 23.7 20.0 9.4 28.8 46.8 62.5 35.1 25.5 24.4 46.7 12.0 5.9 76.7 31.6 141.7 30.3 20.3 1.9 18.3 20.7 4.6 49.7 16.1 8.9 37.8 23.5 1.8 2.1
2012 World Development Indicators
13.7 45.8 13.1 9.9 23.5 14.5 24.4 4.1 11.4 4.5 3.4 3.4 145.4 13.7 10.9 3.3 16.8 0.4 24.0 22.3 51.8 3.5 15.6 10.4 27.9 61.5 20.4 14.7 24.9 6.4 42.7 11.6 6.8 22.4 51.0 35.4 25.5 22.1 23.2 35.7 8.2 6.4 10.9 33.3 99.8 22.0 17.3 3.7 12.0 15.8 3.9 59.9 12.7 5.9 32.9 10.6 1.5 0.8
139
3.1
Rural population and land use Rural population
% of total 1990 2010
Romania Russian Federation Rwanda Saudi Arabia Senegal Serbia Sierra Leone Singapore Slovak Republic Slovenia Somalia South Africa South Sudan Spain Sri Lanka Sudan Swaziland Sweden Switzerland Syrian Arab Republic Tajikistan Tanzania Thailand Timor-Leste Togo Trinidad and Tobago Tunisia Turkey Turkmenistan Uganda Ukraine United Arab Emirates United Kingdom United States Uruguay Uzbekistan Venezuela, RB Vietnam West Bank and Gaza Yemen, Rep. Zambia Zimbabwe World Low income Middle income Lower middle income Upper middle income Low & middle income East Asia & Pacific Europe & Central Asia Latin America & Carib. Middle East & N. Africa South Asia Sub-Saharan Africa High income Euro area
47 27 95 23 61 50 67 – 44 50 70 48 .. 25 83 73 77 17 27 51 68 81 71 79 70 92 42 41 55 89 33 21 11 25 11 60 16 80 32 79 61 71 57 w 79 62 68 56 64 71 37 29 48 75 72 27 29
45 27 81 16 57 48 62 – 43 52 63 38 .. 23 85 55 75 15 26 45 74 74 66 72 57 86 33 30 51 87 32 22 10 18 8 63 6 71 28 68 64 62 49 w 72 51 60 43 54 54 36 21 42 70 63 22 26
Land area
average annual % growth 1990–2010
2012 World Development Indicators
Arable land 1990 2009
Arable land hectares per 100 people 1990 2009
41.2 8.1 35.7 1.6 16.1 .. 6.8 1.5 32.5 9.9 1.6 11.1 .. 30.7 14.4 5.4 10.5 6.9 10.3 26.6 6.1 10.2 34.2 7.4 38.6 7.0 18.7 32.0 2.9 25.0 57.6 0.4 27.4 20.3 7.2 10.5 3.2 16.4 18.1 2.9 3.9 7.5 10.9 w 7.7 11.1 14.8 9.2 10.5 12.1 11.6 6.6 5.9 42.7 6.6 11.7 27.1
40.7 88.8 12.4 21.0 42.7 .. 12.2 0.0 31.0 10.0 15.5 38.2 .. 39.5 5.2 48.3 20.9 33.2 6.1 39.6 15.6 35.3 30.7 14.8 57.3 3.0 35.7 45.5 34.8 28.3 64.0 1.9 11.6 74.4 40.5 20.9 14.3 8.1 5.5 12.8 36.8 27.6 23.7 w 23.8 18.8 18.7 18.9 19.3 12.0 68.5 30.3 22.5 17.8 32.1 42.5 22.9
% of land area thousand sq. km 2010
–0.6 229.9 0.0 16,376.9 2.6 24.7 –5.5 2,000.0 c 2.2 192.5 –0.8 88.4 1.7 71.6 .. 0.7 0.0 48.1 1.2 20.1 1.6 627.3 0.1 1,214.5 .. .. –0.2 499.1 0.9 62.7 0.9 2,376.0 1.1 17.2 0.3 410.3 0.8 40.0 1.3 183.6 1.4 140.0 2.4 885.8 0.1 510.9 1.6 14.9 0.9 54.4 0.0 5.1 –0.2 155.4 –0.2 769.6 0.4 469.9 3.0 197.1 –0.6 579.3 7.7 83.6 –0.1 241.9 –1.0 9,147.4 –1.0 175.0 1.3 425.4 –3.9 882.1 0.4 310.1 2.3 6.0 2.2 528.0 1.4 743.4 0.0 386.9 0.3 w 129,561.0 s 1.5 15,043.5 0.1 80,675.5 0.8 22,789.9 –0.9 57,885.6 0.3 95,718.9 –0.9 15,853.7 0.1 22,748.9 –0.6 20,116.2 1.1 8,643.6 1.0 4,771.2 1.7 23,585.4 –0.5 33,842.1 –0.4 2,552.0
a. Includes Luxembourg. b. Data are from national sources. c. Provisional estimate.
140
Land use
Forest area 1990 2010
27.8 49.4 12.9 0.5 48.6 26.4 43.5 3.0 40.0 59.0 13.2 6.8 .. 27.7 37.5 32.1 27.4 66.5 28.8 2.0 2.9 46.8 38.3 65.0 12.6 47.0 4.1 12.6 8.8 23.8 16.0 2.9 10.8 32.4 5.3 7.2 59.0 28.8 1.5 1.0 71.0 57.3 32.0 w 31.2 33.9 30.7 35.2 33.5 29.0 38.4 51.6 2.4 16.6 31.1 27.9 33.8
28.6 49.4 17.6 0.5 44.0 30.7 38.1 2.9 40.2 62.2 10.8 4.7 .. 36.4 29.7 29.4 32.7 68.7 31.0 2.7 2.9 37.7 37.1 49.9 5.3 44.1 6.5 14.7 8.8 15.2 16.8 3.8 11.9 33.2 10.0 7.7 52.5 44.5 1.5 1.0 66.5 40.4 31.1 w 27.6 32.8 27.9 34.6 31.9 29.6 38.6 47.0 2.4 17.1 28.0 28.8 37.3
Permanent cropland 1990 2009
2.6 0.1 12.4 0.0 0.2 26.2 1.9 1.5 1.0 1.8 0.0 0.7 .. 9.7 15.9 0.0 0.7 0.0 0.6 4.0 0.9 1.1 6.1 3.9 1.7 6.8 12.5 3.9 0.1 9.3 1.9 0.2 0.3 0.2 0.3 0.9 0.9 3.2 19.1 0.2 0.0 0.3 1.1 w 0.7 1.4 1.9 1.1 1.2 2.2 0.4 0.9 0.8 1.8 0.8 0.7 4.8
1.6 0.1 11.3 0.1 0.3 3.4 1.8 0.0 0.5 1.3 0.0 0.8 .. 9.5 15.5 0.1 0.9 0.0 0.6 5.4 1.0 1.7 7.2 4.0 3.3 4.3 14.3 3.8 0.1 11.3 1.6 2.4 0.2 0.3 0.2 0.8 0.7 10.8 19.4 0.5 0.0 0.3 1.2 w 0.9 1.4 2.7 0.9 1.3 3.4 0.4 1.0 1.0 3.1 1.0 0.7 4.2
38.2 7.4 52.7 1.5 20.0 37.7 15.1 0.0 28.7 8.7 1.6 11.8 .. 25.1 19.1 8.5 10.2 6.4 10.2 25.4 5.3 11.3 29.9 11.1 40.4 4.9 17.4 27.7 3.9 33.0 56.1 0.8 25.0 17.8 10.7 10.1 3.1 20.3 16.6 2.2 4.5 10.8 10.7 w 9.6 10.9 16.4 8.7 10.7 11.5 10.5 7.4 5.9 41.4 8.5 10.8 24.4
40.9 85.8 12.6 11.9 31.8 45.1 18.9 0.0 25.5 8.6 11.0 29.1 .. 27.2 5.8 47.5 15.0 28.3 5.3 23.3 10.9 23.0 22.3 15.0 37.3 1.9 25.9 29.7 37.2 20.4 70.5 0.9 9.8 53.1 56.2 15.5 9.7 7.3 2.5 5.0 26.3 33.5 20.4 w 18.5 17.8 15.1 20.6 17.9 9.4 59.2 25.9 15.6 12.3 24.2 33.2 18.8
About the data
3.1
ENVIRONMENT
Rural population and land use Definitions
With more than 3 billion people, including 70 percent
Satellite images show land use that differs from
• Rural population is calculated as the difference
of the world’s poor people, living in rural areas, ade-
that of ground-based measures in area under cultiva-
between the total population and the urban popula-
quate indicators to monitor progress in rural areas
tion and type of land use. Moreover, land use data
tion (see Definitions for tables 2.1 and 3.13). • Land
are essential. However, few indicators are disaggre-
in some countries (India is an example) are based
area is a country’s total area, excluding area under
gated between rural and urban areas (for some that
on reporting systems designed for collecting tax rev-
inland water bodies and national claims to the con-
are, see tables 2.7, 3.5, and 3.13). The table shows
enue. With land taxes no longer a major source of
tinental shelf and to exclusive economic zones. In
indicators of rural population and land use. Rural
government revenue, the quality and coverage of land
most cases the definition of inland water bodies
population is approximated as the midyear nonurban
use data have declined. Data on forest area may be
includes major rivers and lakes. (See table 1.1 for
population. While a practical means of identifying the
particularly unreliable because of irregular surveys
the total surface area of countries.) Variations from
rural population, it is not precise (see box 3.1a for
and differences in definitions (see About the data
year to year may be due to updated or revised data
further discussion).
for table 3.4). Forest area statistics released by the
rather than to change in area. • Land use can be
The data in the table show that land use patterns
FAO between 1948 and 1963 were based mostly on
broken into several categories, three of which are
are changing. They also indicate major differences
data from country questionnaires. Remote sensing,
presented in the table (not shown are land used as
in resource endowments and uses among countries.
statistical monitoring, and expert analysis of country
permanent pasture and land under urban develop-
True comparability of the data is limited, however,
surveys have been applied since 1980 to improve
ment). • Forest area is land under natural or planted
by variations in definitions, statistical methods, and
forest coverage estimates. The FAO’s Global Forest
stands of trees of at least 5 meters in height in situ,
quality of data. Countries use different definitions of
Resources Assessment 2010 covers 230 countries
whether productive or not, and excludes tree stands
rural and urban population and land use. The Food
and is the most comprehensive assessment of for-
in agricultural production systems (for example, in
and Agriculture Organization of the United Nations
ests, forestry, and the benefits of forest resources
fruit plantations and agroforestry systems) and trees
(FAO), the primary compiler of the data, occasion-
in both scope and number of countries and people
in urban parks and gardens. • Permanent cropland
ally adjusts its definitions of land use categories
involved. It examines status and trends for about 90
is land cultivated with crops that occupy the land for
and revises earlier data. Because the data reflect
variables on the extent, condition, uses, and values
long periods and need not be replanted after each
changes in reporting procedures as well as actual
of forests and other wooded land.
harvest, such as cocoa, coffee, and rubber. Land
changes in land use, apparent trends should be inter-
under flowering shrubs, fruit trees, nut trees, and
preted cautiously.
vines is included, but land under trees grown for wood or timber is not. • Arable land is land defined by the
What is rural? Urban?
3.1a
FAO as under temporary crops (double-cropped areas
The rural population identified in table 3.1 is approximated as the difference between total population and
are counted once), temporary meadows for mowing
urban population, calculated using the urban share reported by the United Nations Population Division.
or pasture, land under market or kitchen gardens,
There is no universal standard for distinguishing rural from urban areas, and any urban-rural dichotomy is
and land temporarily fallow. Land abandoned as a
an oversimplification (see About the data for table 3.13). The two distinct images—isolated farm, thriving
result of shifting cultivation is excluded.
metropolis—represent poles on a continuum. Life changes along a variety of dimensions, moving from the most remote forest outpost through fields and pastures, past tiny hamlets, through small towns with weekly farm markets, into intensively cultivated areas near large towns and small cities, eventually reaching the center of a megacity. Along the way access to infrastructure, social services, and nonfarm employment increase, and with them population density and income. Because rurality has many dimensions, for policy purposes the rural-urban dichotomy presented in tables 3.1, 3.5, and 3.13 is inadequate. A 2005 World Bank Policy Research Paper proposes an operational definition of rurality based on population density and distance to large cities (Chomitz, Buys, and Thomas 2005). The report argues that these criteria are important gradients along which economic behavior and appropriate development
Data sources
interventions vary substantially. Where population densities are low, markets of all kinds are thin, and the
Data on urban population shares used to estimate
unit cost of delivering most social services and many types of infrastructure is high. Where large urban
rural population are from the United Nations Popu-
areas are distant, farm-gate or factory-gate prices of outputs will be low and input prices will be high, and
lation Division’s World Urbanization Prospects: The
it will be difficult to recruit skilled people to public service or private enterprises. Thus, low population
2009 Revision, and data on total population are
density and remoteness together define a set of rural areas that face special development challenges.
World Bank estimates. Data on land area, perma-
Using these criteria and the Gridded Population of the World (CIESIN 2005), the authors’ estimates of
nent cropland, and arable land are from the FAO’s
the rural population for Latin America and the Caribbean differ substantially from those in table 3.1. Their
electronic files. The FAO gathers these data from
estimates range from 13 percent of the population, based on a population density of less than 20 people
national agencies through annual questionnaires
per square kilometer, to 64 percent, based on a population density of more than 500 people per square
and by analyzing the results of national agricultural
kilometer. Taking remoteness into account, the estimated rural population would be 13–52 percent. The
censuses. Data on forest area are from the FAO’s
estimate for Latin America and the Caribbean in table 3.1 is 21 percent.
Global Forest Resources Assessment 2010.
2012 World Development Indicators
141
3.2
Agricultural inputs Agricultural landa
% of land area 1990–92 2007–09
Afghanistan Albania Algeria Angola Argentina Armenia Australia Austria Azerbaijan Bahrain Bangladesh Belarus Belgium Benin Bolivia Bosnia and Herzegovina Botswana Brazil Bulgaria Burkina Faso Burundi Cambodia Cameroon Canada Central African Republic Chad Chile China Hong Kong SAR, China Colombia Congo, Dem. Rep. Congo, Rep. Costa Rica Côte d’Ivoire Croatia Cuba Cyprus Czech Republic Denmark Dominican Republic Ecuador Egypt, Arab Rep. El Salvador Eritrea Estonia Ethiopia Finland France Gabon Gambia, The Georgia Germany Ghana Greece Guatemala Guinea Guinea-Bissau Haiti
142
58 41 16 46 47 41 61 42 53 13 73 46 44b 21 33 43 46 29 56 35 83 26 19 7 8 38 21 57 .. 41 10 31 42 60 43 63 17 .. 65 54 29 3 70 .. 32 .. 8 55 20 61 46 49 56 71 40 57 51 57
58 44 17 47 51 62 53 38 58 9 70 44 45 30 34 42 46 31 46 44 84 31 20 7 8 39 21 56 .. 38 10 31 35 64 23 63 14 55 62 51 30 4 75 75 22 35 8 53 20 67 36 48 68 64 41 58 58 67
2012 World Development Indicators
Average annual precipitation
% irrigated 2007–09
millimeters 2007–09
4.8 16.8 2.1 .. .. 8.9 0.4 1.4 29.5 .. .. 0.5 0.4 .. .. .. .. .. 1.4 .. .. .. .. .. .. .. 5.6 .. .. .. .. .. 1.5 .. 0.4 .. 20.8 0.3 9.6 .. 12.6 .. 2.1 .. .. 0.5 .. 5.1 .. .. 4.0 .. .. 16.9 .. .. .. ..
327 1,485 89 1,010 591 562 534 1,110 447 83 2,666 618 847 1,039 1,146 1,028 416 1,782 608 748 1,274 1,904 1,604 537 1,343 322 1,522 645 .. 2,612 1,543 1,646 2,926 1,348 1,113 1,335 498 677 703 1,410 2,087 51 1,724 384 626 848 536 867 1,831 836 1,026 700 1,187 652 1,996 1,651 1,577 1,440
Land under cereal production
Fertilizer consumption
Agricultural employment
Agricultural machinery
thousand hectares 1990–92 2008–10
kilograms per hectare % of of arable fertilizer land production 2007–09 2007–09
% of total employment 1990–92 2007–09
Tractors per 100 sq. km of arable land 1990 2009
2,250.0 189.5 3,530.5 1,012.0 8,372.5 162.8 13,319.8 837.7 627.0 .. 10,860.8 2,603.0 386.2b 667.3 680.2 304.1 77.9 20,564.1 2,231.6 2,844.0 220.0 1,733.4 950.3 20,176.4 95.4 1,337.6 694.3 92,582.2 .. 1,440.7 1,921.1 9.9 76.3 1,451.0 592.7 252.3 65.1 .. 1,611.9 153.1 918.4 2,477.1 486.3 .. 453.6 .. 914.5 9,346.0 16.1 78.3 248.5 6,514.4 1,203.5 1,396.1 822.7 819.8 117.2 462.9
3,122.0 145.7 2,988.8 1,670.0 9,358.1 156.7 19,476.4 994.8 954.6 .. 12,349.1 2,380.3 329.5 1,109.3 931.6 286.2 111.3 18,674.3 1,917.5 4,290.2 232.7 3,106.8 1,639.9 13,013.6 163.1 2,467.1 523.1 90,131.6 .. 1,047.7 1,980.4 31.8 76.0 851.0 550.0 401.6 35.7 1,467.2 1,489.4 225.4 875.7 2,967.1 354.3 461.7 274.1 9,340.5 954.2 9,258.8 22.9 322.7 173.2 6,612.9 1,602.1 916.8 923.3 2,028.7 152.5 446.2
146.8 .. 487.9 .. 153.3 .. 151.7 .. .. 5.9 231.5 46.3 .. .. .. .. .. 245.0 115.1 .. .. .. .. 22.2 .. .. 94.7 96.8 .. 227.0 .. .. .. .. 48.7 445.3 .. 112.4 .. .. .. 48.5 .. .. 378.1 .. 75.8 437.6 .. .. 14.3 60.8 .. 333.5 .. .. .. ..
3.2 45.5 7.8 1.1 25.4 29.3 29.0 83.1 13.6 .. 281.7 281.1 .. .. 6.0 24.5 .. 124.9 167.4 9.1 0.9 7.1 7.4 46.8 .. .. 595.8 488.4 .. 496.8 0.5 1.1 826.6 15.9 246.8 14.2 181.9 123.3 103.2 27.2 187.3 502.8 107.4 3.5 69.5 7.9 108.0 148.3 6.1 6.8 43.0 181.4 11.9 83.7 106.8 0.6 .. ..
.. .. .. 5.1 0.4 .. 5.3 7.1 34.7 2.4 66.4 22.3 2.9 .. 2.1 .. .. 28.3 21.2 .. .. .. .. 4.1 .. .. 18.0 58.5 0.7 1.4 .. .. 24.1 .. .. 25.3 12.0 .. 5.1 18.7 6.6 38.4 35.8 .. 18.1 .. 8.9 5.9 .. .. .. 3.7 62.0 21.9 13.6 .. .. 65.6
.. 44.1 .. .. 1.2 44.2 3.3 5.3 38.6 .. .. .. 1.5 .. 36.1 .. .. 17.0 7.1 .. .. 72.2 .. 2.4 .. .. 11.2 39.6 0.2 19.7 .. .. 12.3 .. 13.9 18.6 3.9 3.1 2.5 14.5 28.7 31.6 20.9 .. 4.0 .. 4.6 2.9 .. .. 53.4 1.7 .. 11.9 .. .. .. ..
0.2 212.4 129.1 .. 100.2 345.5 .. 2,373.6 194.8 45.0 2.3 206.9 1,523.3b 1.0 24.8 235.3 140.5 143.8 135.8 2.4 1.8 3.2 0.9 164.8 .. .. 127.6 66.6 .. 96.8 .. .. .. 19.9 35.2 226.2 1,377.4 252.0 634.7 25.9 54.2 249.6 .. 5.0 455.3 .. 916.5 800.0 .. 2.4 295.6 1,309.4 7.1 744.2 .. 12.7 0.8 2.6
0.1 121.9 139.6 .. 86.9 291.6 .. 2,390.3 148.2 75.0 1.2 86.8 1,127.0 .. 20.0 .. 134.8 129.2 172.3 .. .. 5.9 .. 162.5 .. .. 425.9 81.8 .. .. .. .. .. 32.1 49.4 203.2 1,460.1 262.3 486.3 21.5 90.7 390.6 .. 8.3 604.7 .. 784.0 635.3 .. .. 216.9 838.3 4.5 1,004.7 .. 25.1 .. ..
Agricultural landa
% of land area 1990–92 2007–09
Honduras Hungary India Indonesia Iran, Islamic Rep. Iraq Ireland Israel Italy Jamaica Japan Jordan Kazakhstan Kenya Korea, Dem. Rep. Korea, Rep. Kosovo Kuwait Kyrgyz Republic Lao PDR Latvia Lebanon Lesotho Liberia Libya Lithuania Macedonia, FYR Madagascar Malawi Malaysia Mali Mauritania Mauritius Mexico Moldova Mongolia Morocco Mozambique Myanmar Namibia Nepal Netherlands New Zealand Nicaragua Niger Nigeria Norway Oman Pakistan Panama Papua New Guinea Paraguay Peru Philippines Poland Portugal Puerto Rico Qatar
30 68 61 23 39 23 64 27 54 44 15 13 82 48 21 22 .. 8 53 7 41 59 77 26 9 54 51 63 45 23 26 38 56 54 78 80 69 61 16 47 29 59 61 34 28 80 3 3 34 29 2 44 17 37 62 42 46 6
29 64 61 30 30 20 61 24 47 41 13 12 77 48 24 19 52 8 55 10 29 67 77 27 9 43 40 70 59 24 34 38 48 53 75 75 67 63 19 47 30 57 44 43 35 82 3 6 34 30 3 53 17 40 53 40 21 6
Average annual precipitation
% irrigated 2007–09
millimeters 2007–09
.. 1.8 35.1 .. 19.0 .. .. 28.4 18.8 .. 35.2 9.2 .. 0.0 .. 51.6 .. .. 9.4 .. 0.0 19.9 .. .. .. .. 7.3 2.2 0.5 .. .. .. 21.4 5.5 9.2 .. 4.6 .. 24.8 .. 27.7 10.6 .. .. .. .. .. 4.0 73.9 .. .. .. .. .. 0.4 11.4 8.5 ..
1,976 589 1,083 2,702 228 216 1,118 435 832 2,051 1,668 111 250 630 1,054 1,274 .. 121 533 1,834 641 661 788 2,391 56 656 619 1,513 1,181 2,875 282 92 2,041 752 450 241 346 1,032 2,091 285 1,500 778 1,732 2,391 151 1,150 1,414 125 494 2,692 3,142 1,130 1,738 2,348 600 854 2,054 74
3.2
ENVIRONMENT
Agricultural inputs Land under cereal production
Fertilizer consumption
Agricultural employment
Agricultural machinery
thousand hectares 1990–92 2008–10
kilograms per hectare % of of arable fertilizer land production 2007–09 2007–09
% of total employment 1990–92 2007–09
Tractors per 100 sq. km of arable land 1990 2009
516.8 2,746.0 99,499.5 14,732.7 9,787.4 3,919.5 295.3 104.9 4,225.4 2.9 2,430.9 121.1 22,152.4 1,804.8 1,517.6 1,293.2 .. 0.3 578.0 597.9 696.7 42.1 153.1 120.0 287.7 1,134.0 235.2 1,340.2 1,430.2 693.8 2,451.6 123.6 0.5 9,928.3 675.6 592.6 5,019.6 1,376.9 5,577.5 195.2 2,840.0 180.9 135.3 295.1 7,554.6 16,836.0 359.2 2.8 11,758.2 183.6 2.1 501.0 609.0 6,719.2 8,321.1 751.9 0.4 1.2
538.5 .. 2,585.7 154.0 92,610.0 166.3 17,387.5 110.9 9,440.3 125.4 2,555.5 118.6 276.4 .. 79.1 2.5 3,494.3 186.5 2.0 .. 1,941.8 114.3 44.5 32.3 15,068.4 34.1 2,542.4 .. 1,268.2 .. 970.4 87.8 .. .. 1.1 0.2 579.8 .. 1,090.6 .. 531.2 .. 64.9 3.1 190.5 .. 251.2 .. 329.0 28.2 1,037.6 8.5 161.8 .. 1,729.0 .. 1,831.2 53,107.2 680.1 126.3 3,975.1 .. 291.5 .. 0.1 .. 9,980.0 328.9 884.7 .. 259.1 .. 5,059.7 12.5 2,490.7 .. 8,732.4 185.8 309.9 .. 3,383.2 .. 210.4 16.3 135.5 238.3 464.9 .. 10,629.4 .. 13,808.9 .. 300.6 29.3 3.1 2.5 13,288.7 150.7 154.9 .. 3.4 .. 1,439.3 .. 1,248.7 98,094.6 6,853.3 1,408.8 8,423.6 101.4 324.7 91.8 0.2 .. 2.1 2.8
62.3 80.0 167.8 181.4 69.7 37.2 477.3 189.5 135.5 58.2 235.1 2,444.2 2.4 32.4 .. 388.8 .. 54.5 21.0 .. 64.9 19.9 .. .. 40.3 45.4 56.9 2.6 26.6 769.8 3.2 .. 209.4 51.7 9.4 7.9 20.8 2.9 5.4 1.6 1.4 240.9 1,231.7 22.2 0.1 2.1 191.3 236.4 217.2 46.8 119.8 66.4 104.5 140.5 144.6 159.1 .. 3,191.7
38.2 11.3 .. 54.9 .. .. 11.6 3.5 8.0 27.3 6.4 .. .. .. .. 15.8 .. .. 38.2 .. .. .. .. .. .. .. .. .. .. 21.8 .. .. 14.3 26.8 40.0 .. 3.6 .. 69.1 48.2 81.2 3.7 10.8 38.9 .. .. 5.5 .. 48.3 26.3 .. 1.9 0.8 45.4 25.0 11.5 3.4 ..
34.6 4.6 .. 39.7 21.2 23.4 5.0 1.7 3.7 20.2 3.9 3.0 29.4 .. .. 7.0 .. .. 34.0 .. 8.7 .. .. 47.6 .. 9.2 19.7 .. .. 13.5 .. .. 9.0 13.5 31.1 40.0 40.9 .. .. 16.3 .. 2.5 6.6 29.5 .. .. 2.7 .. 44.7 17.9 .. 26.5 0.8 35.2 13.3 11.2 1.5 2.3
30.9 97.7 60.7 2.2 141.5 65.8 1,623.4 798.8 1,586.5 .. 4,492.9 340.8 62.0 20.0 295.4 211.0 .. 220.0 189.4 8.5 363.7 174.9 57.7 .. 184.3 256.0 730.2 4.9 .. 152.9 10.2 8.4 .. 123.5 310.1 80.3 45.0 .. 13.6 .. 21.9 2,073.1 315.1 20.0 0.2 4.7 1,779.1 41.1 129.7 102.0 59.4 71.6 36.3 65.2 823.6 563.1 438.5 84.0
2012 World Development Indicators
48.7 261.8 128.5 2.0 152.8 92.2 1,476.4 695.3 2,117.1 .. 4,532.1 301.7 24.2 25.2 .. 1,115.4 .. 89.0 188.1 .. 501.4 .. .. .. 218.9 631.8 1,243.8 1.9 .. .. 2.2 9.8 .. 97.7 197.6 40.0 .. .. 10.7 .. 111.7 1,301.5 .. .. .. 6.6 1,539.1 58.1 153.4 147.2 .. 69.1 .. 116.8 1,257.9 1,397.7 525.0 63.1
143
3.2
Agricultural inputs Agricultural landa
% of land area 1990–92 2007–09
Romania Russian Federation Rwanda Saudi Arabia Senegal Serbia Sierra Leone Singapore Slovak Republic Slovenia Somalia South Africa South Sudan Spain Sri Lanka Sudan Swaziland Sweden Switzerland Syrian Arab Republic Tajikistan Tanzania Thailand Timor-Leste Togo Trinidad and Tobago Tunisia Turkey Turkmenistan Uganda Ukraine United Arab Emirates United Kingdom United States Uruguay Uzbekistan Venezuela, RB Vietnam West Bank and Gaza Yemen, Rep. Zambia Zimbabwe World Low income Middle income Lower middle income Upper middle income Low & middle income East Asia & Pacific Europe & Central Asia Latin America & Carib. Middle East & N. Africa South Asia Sub-Saharan Africa High income Euro area
64 14 74 58 46 .. 39 1 .. 28 70 81 .. 61 37 52 71 8 40 74 32 38 42 22 59 16 60 52 69 60 72 4 75 46 85 65 24 22 61 44 28 35 38 w 35 38 45 35 37 48 29 34 24 55 42 38 49
59 13 81 81 49 58 48 0 40 23 70 82 .. 55 42 58 71 8 38 76 34 40 39 25 62 11 63 51 69 70 71 7 72 44 85 63 24 33 61 44 31 42 38 w 38 38 47 34 38 49 28 36 23 55 45 37 46
Average annual precipitation
% irrigated 2007–09
millimeters 2007–09
2.2 2.0 .. .. .. 0.6 .. .. 1.0 0.9 .. .. .. 11.9 .. 1.0 .. .. .. 8.9 14.8 .. .. .. .. .. 4.1 13.4 .. .. 5.3 .. .. .. 1.2 .. .. .. 4.6 .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. ..
637 460 1,212 59 686 .. 2,526 2,497 824 1,162 282 495 .. 636 1,712 416 788 624 1,537 252 691 1,071 1,622 1,500 1,168 2,200 207 593 161 1,180 565 78 1,220 715 1,265 206 1,875 1,821 402 167 1,020 657 .. .. .. .. .. .. .. .. .. .. .. .. .. ..
Land under cereal production
Fertilizer consumption
Agricultural employment
Agricultural machinery
thousand hectares 1990–92 2008–10
kilograms per hectare % of of arable fertilizer land production 2007–09 2007–09
% of total employment 1990–92 2007–09
Tractors per 100 sq. km of arable land 1990 2009
5,773.9 5,016.9 69.0 48.5 32,331.0 13.0 15.6 59,541.3 386.3 .. 1.1 249.5 317.4 8.8 43.8 1,121.9 1,477.5 601.1 4.9 1,087.2 1,873.4 294.0 133.8 .. 649.1 .. .. 432.9 .. .. .. .. 701.3 59.6 95.5 .. 95.7 .. 241.9 112.5 596.3 .. .. 401.6 3,540.2 168.3 49.2 5,352.0 .. .. .. .. 5,984.2 106.1 96.9 7,402.0 1,124.7 2,970.1 257.9 802.6 7,886.4 .. 7.9 8,258.8 56.2 .. .. 61.5 959.1 264.4 69.4 1,119.2 151.8 .. 190.4 201.2 2,620.6 153.2 65.4 3,712.6 443.5 493.4 47.2 273.5 5,018.9 .. 8.7 3,253.6 12,339.5 1,397.2 125.1 10,586.6 106.8 .. .. 77.8 880.8 .. 3.3 573.2 2.2 0.7 85.2 7.1 651.8 10.3 42.3 1,469.9 12,005.1 208.5 96.5 13,731.1 983.6 .. .. 331.3 1,842.5 .. 2.1 1,139.0 14,184.5 44.7 29.7 12,542.3 0.0 30.8 1,033.0 1.4 3,008.4 183.2 239.2 3,488.6 57,488.4 103.6 109.3 65,850.8 785.8 2,741.0 131.1 569.8 1,642.1 77.5 193.3 1,225.3 1,256.3 80.7 200.2 763.1 8,641.7 403.5 402.3 6,953.4 32.5 .. .. 0.0 927.3 .. 12.0 730.0 1,216.2 .. 27.3 797.8 1,901.8 185.2 28.0 1,168.5 94.7 w 122.1 w 699,721.0 s 681,889.9 s 90,727.2 236.5 25.9 57,506.1 101.7 142.2 486,828.8 448,296.9 135.8 118.9 208,267.4 209,263.7 89.6 159.4 278,561.3 239,033.1 102.9 128.6 544,334.9 539,024.0 105.4 .. 142,379.2 150,606.8 92,762.3 35.7 38.8 125,257.0 49,996.0 212.8 92.2 48,465.9 27,682.0 46.8 79.5 31,097.6 168.5 176.0 128,100.5 125,939.2 92,037.8 192.9 10.5 69,034.7 74.3 104.3 155,386.1 142,865.9 30,496.6 98.7 143.4 32,498.0
a. Includes permanent pastures, arable land, and land under permanent crops. b. Includes Luxembourg.
144
2012 World Development Indicators
33.0 15.4 .. .. .. .. .. 0.3 .. .. .. .. .. 9.7 43.6 .. .. 3.3 4.3 28.2 46.8 84.2 60.8 .. .. 11.5 .. 44.7 .. .. 20.8 .. 2.2 2.9 4.5 .. 11.8 .. .. 52.6 49.8 .. .. w .. .. .. 48.5 .. 57.8 23.2 18.9 .. .. .. 6.2 6.7
29.1 140.6 201.2 9.7 97.8 27.1 .. 1.0 0.5 4.1 19.2 .. .. 1.6 2.1 24.0 .. 17.7 .. 4.1 .. 1.1 .. .. 3.6 197.5 154.6 9.1 4,000.0 5,895.2 .. 15.9 12.0 5.1 107.9 43.0 .. .. .. 4.2 483.1 831.2 32.6 163.1 .. .. 7.2 13.8 .. 228.9 87.1 2.2 601.9 592.4 3.1 2,783.1 2,602.9 15.2 128.1 215.0 .. 415.4 310.2 .. 8.2 23.3 41.5 33.0 280.5 .. 8.5 .. .. 0.5 0.6 3.8 1,238.9 1,972.7 .. 82.4 142.6 22.9 279.8 395.3 .. 464.7 .. 65.6 .. .. 15.8 153.3 102.7 4.2 51.4 63.3 1.1 760.6 .. 1.5 238.4 271.3 11.0 260.3 219.5 .. .. .. 8.5 .. .. .. 47.0 262.5 13.4 442.2 737.3 .. 39.0 41.0 .. 21.9 .. .. 60.1 .. .. w 202.4 w .. w .. 8.4 .. .. 84.1 109.4 .. 53.0 .. 31.3 111.9 114.5 .. 75.8 .. 39.6 53.2 .. 16.3 135.9 115.6 14.3 120.9 .. 27.2 114.6 155.8 .. 62.1 120.7 .. 19.8 .. 3.3 472.1 .. 3.6 986.1 841.4
About the data
3.2
ENVIRONMENT
Agricultural inputs Definitions
Agriculture is still a major sector in many economies,
energy price fluctuation. The FAO recently revised
• Agricultural land is permanent pastures, arable
and agricultural activities provide developing coun-
the time series for fertilizer consumption and irriga-
land, and land under permanent crops. Permanent
tries with food and revenue. But agricultural activities
tion for 2002 onward, but recent data are not avail-
pasture is land used for five or more years for for-
also can degrade natural resources. Poor farming
able for all countries. FAO collects fertilizer statistics
age, including natural and cultivated crops. Arable
practices can cause soil erosion and loss of soil
for production, imports, exports, and consumption
land includes land defi ned by the FAO as land
fertility. Efforts to increase productivity by using
through the new FAO fertilizer resources question-
under temporary crops (double-cropped areas are
chemical fertilizers, pesticides, and intensive irriga-
naire. In the previous release, the data were based
counted once), temporary meadows for mowing or
tion have environmental costs and health impacts.
on total consumption of fertilizers, but the data in
for pasture, land under market or kitchen gardens,
Excessive use of chemical fertilizers can alter the
the recent release are based on the nutrients in fer-
and land temporarily fallow. Land abandoned as a
chemistry of soil. Pesticide poisoning is common in
tilizers. Some countries compile fertilizer data on a
result of shifting cultivation is excluded. Land under
developing countries. And salinization of irrigated
calendar year basis, while others do so on a crop
permanent crops is land cultivated with crops that
land diminishes soil fertility. Thus, inappropriate use
year basis (July–June). Previous editions of World
occupy the land for long periods and need not be
of inputs for agricultural production has far-reaching
Development Indicators reported data on a crop year
replanted after each harvest, such as cocoa, cof-
effects.
basis, but this edition uses the calendar year, as
fee, and rubber. Land under flowering shrubs, fruit
adopted by the FAO. Caution should thus be used
trees, nut trees, and vines is included, but land under
The table provides indicators of major inputs to agricultural production: land, fertilizer, labor, and
when comparing data over time.
trees grown for wood or timber is not. • Irrigated
machinery. There is no single correct mix of inputs:
land refers to areas purposely provided with water,
appropriate levels and application rates vary by coun-
including land irrigated by controlled flooding. • Aver-
try and over time and depend on the type of crops,
age annual precipitation is the long-term average in
the climate and soils, and the production process
depth (over space and time) of annual precipitation
used.
in the country. Precipitation is defined as any kind
The agriculture sector is the most water-intensive
of water that falls from clouds as a liquid or a solid.
sector, and water delivery in agriculture is increas-
• Land under cereal production refers to harvested
ingly important. The table shows irrigated agricultural
areas, although some countries report only sown
land as share of total agricultural land area and data
or cultivated area. • Fertilizer consumption is the
on average precipitation to illustrate how countries
quantity of plant nutrients applied to arable land.
obtain water for agricultural use.
Fertilizer products cover nitrogen, potash, and phos-
The data here and in table 3.3 are collected by
phate fertilizers (including ground rock phosphate).
the Food and Agriculture Organization of the United
Traditional nutrients—animal and plant manures—
Nations (FAO) through annual questionnaires. The
are not included. • Fertilizer production is fertilizer
FAO tries to impose standard definitions and report-
consumption, exports, and nonfertilizer use of fertil-
ing methods, but complete consistency across
izer products minus fertilizer imports. • Agricultural
countries and over time is not possible. Thus, data
employment is employment in agriculture, forestry,
on agricultural land in different climates may not be
hunting, and fishing (see table 2.3). • Agricultural
comparable. For example, permanent pastures are
machinery refers to wheel and crawler tractors
quite different in nature and intensity in African coun-
(excluding garden tractors) in use in agriculture at
tries and dry Middle Eastern countries. Data on agri-
the end of the calendar year specified or during the
cultural employment, in particular, should be used
first quarter of the following year.
with caution. In many countries much agricultural employment is informal and unrecorded, including substantial work performed by women and children. To address some of these concerns, this indicator is heavily footnoted in the database in sources, definition, and coverage. Fertilizer consumption measures the quantity of plant nutrients. Consumption is calculated as pro-
Data sources
duction plus imports minus exports. Because some
Data on agricultural inputs are from the FAO’s
chemical compounds used for fertilizers have other
electronic files. Data on agricultural employment
industrial applications, the consumption data may
are from the International Labour Organization’s
overstate the quantity available for crops. Fertil-
Key Indicators of the Labour Market, 7th edition,
izer consumption as a share of production shows
database.
the agriculture sector’s vulnerability to import and
2012 World Development Indicators
145
3.3
Agricultural output and productivity Crop production index
2004–06 = 100 1990 2009
Afghanistan Albania Algeria Angola Argentina Armenia Australia Austria Azerbaijan Bahrain Bangladesh Belarus Belgium Benin Bolivia Bosnia and Herzegovina Botswana Brazil Bulgaria Burkina Faso Burundi Cambodia Cameroon Canada Central African Republic Chad Chile China Hong Kong SAR, China Colombia Congo, Dem. Rep. Congo, Rep. Costa Rica Côte d’Ivoire Croatia Cuba Cyprus Czech Republic Denmark Dominican Republic Ecuador Egypt, Arab Rep. El Salvador Eritrea Estonia Ethiopia Finland France Gabon Gambia, The Georgia Germany Ghana Greece Guatemala Guinea Guinea-Bissau Haiti
146
67.0 85.0 41.0 33.0 53.0 67.0 67.0 89.0 95.0 61.0 66.0 77.0 .. 45.0 53.0 75.0 83.0 59.0 149.0 44.0 98.0 46.0 58.0 82.0 76.0 51.0 60.0 56.0 .. 80.0 125.0 66.0 62.0 68.0 93.0 136.0 136.0 .. 115.0 98.0 66.0 56.0 92.0 .. 129.0 .. 110.0 96.0 78.0 56.0 138.0 93.0 34.0 80.0 57.0 59.0 60.0 107.0
125.0 121.0 121.0 153.0 83.0 125.0 101.0 103.0 110.0 116.0 122.0 111.0 100.0 117.0 106.0 99.0 122.0 117.0 98.0 95.0 61.0 147.0 113.0 109.0 113.0 105.0 101.0 116.0 .. 108.0 98.0 112.0 109.0 103.0 110.0 88.0 73.0 99.0 112.0 107.0 114.0 116.0 112.0 84.0 126.0 126.0 109.0 100.0 118.0 119.0 73.0 106.0 119.0 81.0 125.0 105.0 94.0 109.0
2012 World Development Indicators
Food production index
2004–06 = 100 1990 2009
68.0 69.0 50.0 42.0 63.0 74.0 74.0 93.0 82.0 90.0 63.0 110.0 .. 48.0 61.0 87.0 95.0 51.0 179.0 50.0 98.0 46.0 61.0 73.0 61.0 56.0 58.0 48.0 .. 70.0 120.0 63.0 59.0 66.0 105.0 160.0 98.0 .. 94.0 78.0 56.0 54.0 75.0 .. 157.0 .. 108.0 99.0 91.0 60.0 105.0 107.0 37.0 86.0 57.0 59.0 62.0 96.0
114.0 110.0 117.0 144.0 95.0 131.0 103.0 101.0 115.0 117.0 121.0 120.0 97.0 122.0 113.0 110.0 116.0 116.0 103.0 105.0 65.0 137.0 114.0 107.0 111.0 112.0 101.0 115.0 .. 112.0 99.0 116.0 113.0 108.0 114.0 98.0 87.0 98.0 107.0 116.0 115.0 118.0 109.0 99.0 115.0 122.0 101.0 98.0 115.0 119.0 72.0 105.0 119.0 86.0 122.0 107.0 98.0 106.0
Livestock production index
2004–06 = 100 1990 2009
70.0 58.0 69.0 74.0 84.0 84.0 85.0 95.0 79.0 137.0 56.0 127.0 .. 70.0 68.0 92.0 98.0 44.0 248.0 53.0 103.0 53.0 79.0 68.0 54.0 74.0 55.0 39.0 .. 68.0 99.0 53.0 70.0 80.0 106.0 185.0 69.0 .. 81.0 58.0 48.0 53.0 62.0 .. 175.0 .. 107.0 98.0 86.0 76.0 78.0 114.0 78.0 99.0 70.0 43.0 70.0 59.0
101.0 99.0 109.0 110.0 113.0 134.0 101.0 99.0 118.0 117.0 113.0 129.0 98.0 100.0 119.0 128.0 114.0 113.0 96.0 109.0 136.0 92.0 103.0 95.0 110.0 110.0 101.0 112.0 .. 117.0 102.0 126.0 116.0 111.0 106.0 136.0 95.0 95.0 101.0 126.0 114.0 114.0 107.0 111.0 109.0 113.0 97.0 96.0 102.0 106.0 68.0 103.0 120.0 95.0 103.0 120.0 115.0 102.0
Cereal yield
Agricultural productivity
kilograms per hectare 1990 2010
Agriculture value added per worker 2000 $ 1990 2010
1,201 2,794 688 321 2,232 1,843 1,716 5,577 2,113 .. 2,491 2,741 5,755a 848 1,361 3,553 266 1,755 3,954 600 1,349 1,362 1,259 2,636 810 559 3,620 4,325 .. 2,475 800 624 3,097 887 3,975 2,342 1,886 .. 6,118 3,996 1,724 5,703 1,939 .. 1,304 .. 3,543 6,083 1,750 1,004 1,998 5,411 989 3,036 1,998 1,455 1,531 1,027
1,908 4,762 1,568 617 4,937 2,074 1,721 5,358 2,019 .. 4,144 2,827 9,231 1,402 2,333 3,858 544 4,055 3,665 1,054 1,346 3,108 1,711 3,490 1,465 775 6,822 5,521 .. 3,895 771 785 3,730 1,717 5,486 1,940 1,595 4,691 5,889 4,234 3,117 6,541 2,806 536 2,444 1,674 3,136 7,093 1,782 1,127 1,271 6,716 1,814 4,908 2,299 1,409 1,555 980
.. 764 1,702 206 6,683 1,607 19,447 13,413 1,001 .. 275 2,042 .. 427 683 .. 761 1,625 3,983 107 118 .. 420 28,898 321 170 3,457 258 .. 3,123 213 .. 2,993 658 5,552 4,117 5,808 .. 14,588 1,974 2,109 1,767 1,742 .. 3,288 .. 17,163 21,454 1,216 253 2,359 13,730 .. .. 2,241 166 .. ..
.. .. 2,254 340 12,957 4,723 35,208 25,771 1,241 .. 507 5,700 .. .. 716 15,028 447 4,118 10,923 .. .. 434 .. 44,619 .. .. 6,267 545 .. 2,874 173 .. 5,596 1,056 16,423 3,618 7,927 6,415 53,407 5,083 2,040 3,265 2,752 66 3,156 226 47,514 57,973 1,972 282 1,817 32,866 .. .. 2,803 242 .. ..
Crop production index
2004–06 = 100 1990 2009
Honduras Hungary India Indonesia Iran, Islamic Rep. Iraq Ireland Israel Italy Jamaica Japan Jordan Kazakhstan Kenya Korea, Dem. Rep. Korea, Rep. Kosovo Kuwait Kyrgyz Republic Lao PDR Latvia Lebanon Lesotho Liberia Libya Lithuania Macedonia, FYR Madagascar Malawi Malaysia Mali Mauritania Mauritius Mexico Moldova Mongolia Morocco Mozambique Myanmar Namibia Nepal Netherlands New Zealand Nicaragua Niger Nigeria Norway Oman Pakistan Panama Papua New Guinea Paraguay Peru Philippines Poland Portugal Puerto Rico Qatar
70.0 100.0 73.0 61.0 55.0 102.0 91.0 95.0 84.0 98.0 123.0 66.0 144.0 72.0 99.0 91.0 .. 39.0 64.0 52.0 106.0 96.0 127.0 62.0 77.0 81.0 102.0 79.0 50.0 59.0 54.0 63.0 107.0 74.0 121.0 230.0 68.0 60.0 42.0 56.0 62.0 91.0 74.0 59.0 49.0 47.0 135.0 63.0 67.0 123.0 72.0 70.0 46.0 72.0 133.0 110.0 138.0 56.0
105.0 89.0 113.0 121.0 99.0 87.0 82.0 97.0 91.0 105.0 94.0 106.0 131.0 105.0 101.0 106.0 .. 106.0 106.0 129.0 114.0 101.0 81.0 97.0 103.0 136.0 110.0 110.0 131.0 109.0 134.0 124.0 95.0 99.0 90.0 216.0 116.0 101.0 122.0 100.0 112.0 106.0 105.0 111.0 138.0 88.0 84.0 112.0 108.0 88.0 117.0 92.0 117.0 111.0 109.0 91.0 100.0 138.0
Food production index
2004–06 = 100 1990 2009
65.0 118.0 71.0 62.0 54.0 118.0 97.0 72.0 89.0 81.0 114.0 57.0 142.0 68.0 92.0 79.0 .. 48.0 76.0 50.0 194.0 83.0 106.0 77.0 75.0 133.0 102.0 80.0 47.0 53.0 61.0 76.0 89.0 65.0 139.0 134.0 68.0 63.0 41.0 82.0 64.0 103.0 64.0 51.0 46.0 49.0 110.0 52.0 60.0 91.0 69.0 64.0 49.0 67.0 110.0 98.0 133.0 66.0
103.0 94.0 114.0 120.0 104.0 92.0 91.0 104.0 97.0 106.0 98.0 110.0 126.0 110.0 100.0 108.0 .. 110.0 101.0 125.0 118.0 104.0 100.0 121.0 104.0 110.0 114.0 112.0 127.0 114.0 146.0 107.0 98.0 101.0 91.0 146.0 117.0 98.0 126.0 90.0 110.0 108.0 98.0 115.0 132.0 92.0 103.0 97.0 115.0 102.0 108.0 110.0 123.0 112.0 104.0 98.0 103.0 168.0
Livestock production index
2004–06 = 100 1990 2009
51.0 161.0 62.0 62.0 51.0 153.0 96.0 56.0 96.0 61.0 107.0 44.0 147.0 67.0 96.0 62.0 .. 54.0 107.0 49.0 253.0 54.0 91.0 79.0 73.0 154.0 97.0 99.0 64.0 58.0 75.0 79.0 45.0 59.0 196.0 132.0 71.0 47.0 25.0 89.0 70.0 106.0 67.0 45.0 42.0 61.0 103.0 40.0 56.0 64.0 66.0 72.0 55.0 49.0 114.0 84.0 132.0 73.0
105.0 96.0 115.0 114.0 115.0 100.0 93.0 109.0 106.0 106.0 101.0 115.0 115.0 114.0 100.0 110.0 .. 112.0 102.0 118.0 114.0 112.0 112.0 124.0 106.0 95.0 114.0 113.0 151.0 109.0 135.0 104.0 109.0 105.0 91.0 142.0 116.0 95.0 138.0 86.0 107.0 109.0 97.0 119.0 122.0 112.0 106.0 77.0 117.0 114.0 91.0 117.0 128.0 114.0 102.0 106.0 104.0 192.0
3.3
ENVIRONMENT
Agricultural output and productivity Cereal yield
Agricultural productivity
kilograms per hectare 1990 2010
Agriculture value added per worker 2000 $ 1990 2010
1,468 4,521 1,891 3,800 1,445 1,061 6,577 3,486 3,945 1,116 5,846 1,220 1,338 1,562 3,916 5,853 .. 3,653 2,772 2,268 1,641 1,878 1,037 1,029 674 1,938 2,652 1,945 992 2,740 726 870 4,191 2,424 2,928 1,098 1,120 477 2,762 457 1,920 6,959 5,034 1,524 310 1,148 4,399 2,160 1,766 1,867 2,603 1,979 2,603 2,065 3,284 1,878 1,080 2,897
1,094 4,759 2,537 4,876 2,359 1,687 7,409 3,015 5,436 1,172 5,852 1,963 804 1,613 3,582 6,196 .. 3,415 2,604 3,751 2,667 2,740 909 1,179 662 2,667 3,329 2,987 2,206 3,800 1,615 946 10,000 3,499 2,696 1,370 1,548 1,006 3,989 373 2,295 8,574 7,387 2,086 479 1,413 3,810 18,987 2,592 2,131 3,839 3,457 3,899 3,232 3,220 3,463 1,878 4,795
1,184 4,177 357 493 1,988 .. .. .. 10,435 2,224 19,563 1,976 1,781 400 .. 5,338 .. .. 685 388 1,896 .. 255 .. .. .. 2,413 213 89 3,826 406 651 3,446 2,250 1,349 1,477 1,807 132 .. 1,267 248 23,743 19,767 .. 239 .. 17,454 1,029 769 2,303 559 1,660 911 854 1,619 4,516 .. ..
2012 World Development Indicators
2,041 8,522 489 730 .. .. 13,931 .. 31,254 2,758 48,794 3,401 1,782 337 .. 19,807 .. .. 996 532 3,837 41,013 202 .. .. 5,996 5,946 187 169 6,680 .. 404 6,401 3,302 1,610 1,524 3,315 234 .. 881 238 47,805 26,556 2,779 .. .. 46,480 .. 947 4,109 683 2,710 1,607 1,119 2,994 7,019 .. ..
147
3.3
Agricultural output and productivity Crop production index
2004–06 = 100 1990 2009
Romania Russian Federation Rwanda Saudi Arabia Senegal Serbia Sierra Leone Singapore Slovak Republic Slovenia Somalia South Africa South Sudan Spain Sri Lanka Sudan Swaziland Sweden Switzerland Syrian Arab Republic Tajikistan Tanzania Thailand Timor-Leste Togo Trinidad and Tobago Tunisia Turkey Turkmenistan Uganda Ukraine United Arab Emirates United Kingdom United States Uruguay Uzbekistan Venezuela, RB Vietnam West Bank and Gaza Yemen, Rep. Zambia Zimbabwe World Low income Middle income Lower middle income Upper middle income Low & middle income East Asia & Pacific Europe & Central Asia Latin America & Carib. Middle East & N. Africa South Asia Sub-Saharan Africa High income Euro area
82.0 100.0 76.0 83.0 78.0 78.0 b 69.0 90.0 .. 74.0 122.0 83.0 .. 89.0 87.0 50.0 99.0 130.0 118.0 51.0 83.0 61.0 64.0 81.0 67.0 141.0 74.0 78.0 73.0 72.0 98.0 28.0 104.0 79.0 48.0 83.0 76.0 45.0 .. 61.0 61.0 118.0 70.0c w 78.4 73.4 74.8 72.6 73.8 69.9 111.4 75.8 74.7 78.0 71.1 90.7 90.9
85.0 107.0 122.0 95.0 143.0 106.0 101.0 149.0 93.0 87.0 100.0 108.0 .. 105.0 111.0 106.0 97.0 106.0 106.0 93.0 115.0 110.0 111.0 136.0 104.0 113.0 101.0 103.0 92.0 106.0 118.0 100.0 102.0 105.0 134.0 118.0 108.0 110.0 109.0 121.0 129.0 91.0 122.2 w 132.2 128.4 127.2 129.1 128.7 133.1 129.4 128.1 127.3 119.3 128.7 103.9 96.9
Food production index
2004–06 = 100 1990 2009
94.0 117.0 72.0 70.0 76.0 99.0 b 68.0 492.0 .. 76.0 92.0 78.0 .. 83.0 85.0 47.0 90.0 109.0 104.0 51.0 99.0 62.0 69.0 95.0 64.0 84.0 69.0 79.0 51.0 71.0 128.0 29.0 107.0 77.0 60.0 76.0 71.0 47.0 .. 56.0 74.0 94.0 70.0c w 78.3 68.7 72.8 66.5 69.4 62.7 118.2 71.2 72.8 74.5 72.9 90.4 95.6
93.0 106.0 121.0 102.0 136.0 99.0 104.0 98.0 92.0 93.0 100.0 109.0 .. 104.0 112.0 103.0 101.0 101.0 103.0 96.0 118.0 106.0 112.0 129.0 114.0 107.0 101.0 108.0 113.0 105.0 106.0 113.0 101.0 109.0 109.0 123.0 106.0 114.0 101.0 126.0 115.0 92.0 123.0 w 133.2 130.3 128.2 131.4 130.5 135.1 126.3 131.2 131.6 122.7 130.0 106.3 97.7
a. Includes Luxembourg. b. Includes Montenegro. c. Food and Agriculture Organization estimate.
148
2012 World Development Indicators
Livestock production index
2004–06 = 100 1990 2009
116.0 143.0 59.0 56.0 71.0 109.0 b 81.0 535.0 .. 75.0 88.0 79.0 .. 72.0 75.0 47.0 85.0 103.0 102.0 54.0 132.0 61.0 73.0 104.0 68.0 50.0 58.0 83.0 49.0 64.0 166.0 43.0 107.0 78.0 70.0 82.0 71.0 38.0 .. 50.0 77.0 75.0 74.0c w 78.0 62.5 69.4 59.6 63.3 48.8 137.7 69.7 69.3 69.2 80.1 90.7 98.2
99.0 97.0 122.0 111.0 113.0 98.0 116.0 93.0 86.0 93.0 100.0 110.0 .. 98.0 107.0 99.0 110.0 97.0 104.0 99.0 117.0 101.0 112.0 109.0 123.0 104.0 107.0 112.0 126.0 100.0 95.0 143.0 99.0 105.0 100.0 114.0 110.0 133.0 85.0 134.0 96.0 105.0 120.3 w 131.7 131.4 131.8 131.2 131.4 135.2 119.2 132.6 134.7 132.9 125.1 104.0 100.0
Cereal yield
Agricultural productivity
kilograms per hectare 1990 2010
Agriculture value added per worker 2000 $ 1990 2010
3,011 1,743 1,043 4,245 795 2,926b 1,202 .. .. 3,279 793 1,877 .. 2,485 2,965 456 1,284 4,964 5,984 750 994 1,507 2,009 1,608 747 3,326 1,145 2,214 2,210 1,498 2,834 2,216 6,171 4,755 2,183 1,777 2,486 3,073 .. 908 1,352 1,625 2,756c w 1,565 2,556 1,928 3,177 2,429 3,796 2,596 2,089 1,471 1,926 1,033 4,138 4,490
3,331 1,844 1,930 5,621 1,196 4,959 1,554 .. 3,754 5,973 432 4,162 .. 3,231 3,974 452 1,226 4,518 6,084 1,789 2,721 1,332 2,938 2,451 1,187 2,727 1,702 2,727 3,289 1,608 2,727 2,667 6,957 6,988 4,251 4,516 4,038 5,161 1,163 1,092 2,547 752 3,568 w 2,075 3,312 2,718 3,831 3,103 4,925 2,239 3,919 2,379 2,691 1,335 5,319 5,685
2,351 1,915 174 7,863 262 .. .. 21,392 .. 13,217 .. 2,287 .. 8,938 672 512 1,025 23,307 20,556 2,698 370 219 443 .. 376 1,825 2,383 2,254 1,275 189 1,231 .. 19,880 18,703 5,648 1,427 4,458 222 .. 621 213 270 781 w 240 481 472 487 455 305 2,245 2,221 1,796 373 307 14,129 12,590
9,700 2,731 .. 20,233 256 2,057 .. 29,145 9,924 76,633 .. 3,662 .. 22,035 966 929 1,217 51,585 27,066 .. 577 292 706 .. .. 1,024 3,050 3,770 .. 200 2,500 .. 25,681 51,120 8,682 2,782 7,667 367 .. 749 214 160 992 w 288 786 677 871 728 585 3,204 3,663 .. 521 322 24,483 25,752
About the data
3.3
ENVIRONMENT
Agricultural output and productivity Definitions
The agricultural production indexes in the table are
• Crop production index is agricultural production
prepared by the Food and Agriculture Organization of
for the period specified relative to the base period
the United Nations (FAO). The FAO obtains data from
2004–06. It includes all crops except fodder crops.
official and semiofficial reports of crop yields, area
The regional and income group aggregates for the
under production, and livestock numbers. If data are
FAO’s production indexes are calculated from the
unavailable, the FAO makes estimates. The indexes
underlying values in international dollars, normal-
are calculated using the Laspeyres formula: produc-
ized to the base period 2004–06. • Food produc-
tion quantities of each commodity are weighted by
tion index covers food crops that are considered
average international commodity prices in the base
edible and that contain nutrients. Coffee and tea
period and summed for each year. Because the FAO’s
are excluded because, although edible, they have
indexes are based on the concept of agriculture as a
no nutritive value. • Livestock production index
single enterprise, estimates of the amounts retained
includes meat and milk from all sources, dairy prod-
for seed and feed are subtracted from the production
ucts such as cheese, and eggs, honey, raw silk, wool,
data to avoid double counting. The aggregates are
and hides and skins. • Cereal yield, measured in
net production available for any use except as seed
kilograms per hectare of harvested land, includes
and feed. The FAO’s indexes may differ from those
wheat, rice, maize, barley, oats, rye, millet, sorghum,
from other sources because of differences in cov-
buckwheat, and mixed grains. Production data on
erage, weights, concepts, time periods, calculation
cereals refer to crops harvested for dry grain only.
methods, and use of international prices.
Cereal crops harvested for hay or harvested green for
To facilitate cross-country comparisons, the FAO
food, feed, or silage, and those used for grazing, are
uses international commodity prices to value pro-
excluded. The FAO allocates production data to the
duction. These prices, expressed in international
calendar year in which the bulk of the harvest took
dollars (equivalent in purchasing power to the U.S.
place. But most of a crop harvested near the end of
dollar), are derived using a Geary-Khamis formula
a year will be used in the following year. • Agricul-
applied to agricultural outputs (see Inter- Secretariat
tural productivity is the ratio of agricultural value
Working Group on National Accounts 1993, sections
added, measured in 2005 U.S. dollars, to the num-
16.93–96). This method assigns a single price to
ber of workers in agriculture. Agricultural productivity
each commodity so that, for example, one metric ton
is measured by value added per unit of input. (For
of wheat has the same price regardless of where it
further discussion of the calculation of value added
was produced. The use of international prices elimi-
in national accounts, see About the data for tables
nates fluctuations in the value of output due to transi-
4.1 and 4.2.) Agricultural value added includes that
tory movements of nominal exchange rates unrelated
from forestry and fishing. Thus interpretations of land
to the purchasing power of the domestic currency.
productivity should be made with caution.
Data on cereal yield may be affected by a variety of reporting and timing differences. Millet and sorghum, which are grown as feed for livestock and poultry in Europe and North America, are used as food in Africa, Asia, and countries of the former Soviet Union. So some cereal crops are excluded from the data for some countries and included elsewhere, depending on their use.
Data sources Data on agricultural production indexes, cereal yield, and agricultural employment are from the FAO’s electronic files, available on the FAO website (www.fao.org). Data on agricultural value added are from the World Bank’s national accounts files.
2012 World Development Indicators
149
3.4
Deforestation and biodiversity Forest area
Average annual deforestationa
thousand sq. km
Afghanistan Albania Algeria Angola Argentina Armenia Australia Austria Azerbaijan Bahrain Bangladesh Belarus Belgium Benin Bolivia Bosnia and Herzegovina Botswana Brazil Bulgaria Burkina Faso Burundi Cambodia Cameroon Canada Central African Republic Chad Chile China Hong Kong SAR, China Colombia Congo, Dem. Rep. Congo, Rep. Costa Rica Côte d’Ivoire Croatia Cuba Cyprus Czech Republic Denmark Dominican Republic Ecuador Egypt, Arab Rep. El Salvador Eritrea Estonia Ethiopia Finland France Gabon Gambia, The Georgia Germany Ghana Greece Guatemala Guinea Guinea-Bissau Haiti
150
1990
2010
14 8 17 610 348 3 1,545 38 9 0c 15 78 7 58 628 22 137 5,748 33 68 3 129 243 3,101 232 131 153 1,571 .. 625 1,604 227 26 102 19 21 2 26 4 20 138 0c 4 16 21 151 219 145 220 4 28 107 74 33 47 73 22 1
14 8 15 585 294 3 1,493 39 9 0c 14 86 7 46 572 22 114 5,195 39 56 2 101 199 3,101 226 115 162 2,069 .. 605 1,541 224 26 104 19 29 2 27 5 20 99 1 3 15 22 123 222 160 220 5 27 111 49 39 37 65 20 1
2012 World Development Indicators
% 1990–2000 2000–10
0.00 0.26 0.54 0.21 0.88 1.31 –0.03 –0.16 0.00 –5.56 0.18 –0.62 0.15 1.29 0.44 0.11 0.90 0.51 –0.14 0.91 3.71 1.14 0.94 0.00 0.13 0.62 –0.37 –1.20 .. 0.16 0.20 0.08 0.76 –0.10 –0.19 –1.70 –0.63 –0.03 –0.89 0.00 1.53 –2.98 1.26 0.28 –0.71 0.97 –0.26 –0.55 0.00 –0.42 0.04 –0.31 1.99 –0.88 1.20 0.51 0.44 0.62
0.00 –0.10 0.57 0.21 0.81 1.48 0.37 –0.13 0.00 –3.55 0.18 –0.43 –0.16 1.04 0.50 0.00 0.99 0.50 –1.53 1.01 1.40 1.34 1.05 0.00 0.13 0.66 –0.25 –1.57 .. 0.17 0.20 0.07 –0.93 –0.15 –0.19 –1.66 –0.09 –0.08 –1.14 0.00 1.81 –1.73 1.45 0.28 0.12 1.08 0.14 –0.39 0.00 –0.41 0.09 0.00 2.08 –0.81 1.40 0.54 0.48 0.76
Threatened species
GEF benefits index for biodiversity
Nationally protected areas
Mammals
Birds
Fish
Higher plantsb
0–100 (no biodiversity to maximum biodiversity)
2011
2011
2011
2011
2008
1990
2010
1990
2010
11 3 14 15 38 9 55 3 7 3 34 4 3 11 20 4 7 81 7 9 11 37 38 12 8 13 20 75 2 52 30 11 9 23 7 14 5 2 2 6 43 17 5 10 1 33 1 9 14 10 10 6 16 10 16 22 12 5
14 5 9 23 49 12 52 7 14 3 30 4 2 6 34 5 10 122 11 7 11 24 20 15 9 9 34 86 20 94 35 2 19 15 10 17 4 5 2 14 73 9 5 12 3 24 4 6 4 8 10 5 13 10 10 13 5 13
5 39 36 39 37 3 103 11 10 8 18 2 11 27 0 31 2 84 19 4 17 42 112 35 3 1 20 113 13 54 83 46 50 45 60 34 19 2 15 21 50 39 14 18 5 14 6 44 61 23 9 23 44 75 25 65 32 20
2 0 12 34 35 1 27 9 0 0 15 0 0 13 72 0 0 389 5 3 2 29 378 1 17 2 34 374 6 215 80 37 112 106 5 155 16 8 1 27 1,714 2 24 4 0 24 1 27 120 4 0 13 117 52 72 22 4 26
3.4 0.2 2.9 8.3 17.7 0.2 87.7 0.3 0.8 0.0 1.4 0.0 0.0 0.2 12.5 0.4 1.4 100.0 0.8 0.3 0.3 3.5 12.5 21.5 1.5 2.2 15.3 66.6 .. 51.5 19.9 3.6 9.7 3.4 0.6 12.5 0.5 0.1 0.2 6.0 29.3 2.9 0.9 0.8 0.1 8.4 0.2 5.3 3.0 0.1 0.6 0.6 1.9 2.8 8.0 2.3 0.6 5.2
0.4 3.4 6.3 12.4 4.6 6.9 7.5 20.1 6.2 1.3 1.7 6.5 3.2 23.8 8.8 0.5 30.3 9.0 2.0 13.7 3.8 0.0 7.0 4.7 17.5 9.4 16.0 13.5 41.1 19.3 10.0 5.4 18.7 22.6 7.8 4.3 7.1 13.6 4.2 22.2 21.6 1.9 0.4 4.9 17.7 17.7 4.2 10.2 4.6 1.5 2.8 31.9 14.6 5.7 25.9 6.8 7.6 0.3
0.4 9.8 6.3 12.4 5.5 8.0 10.6 22.9 7.1 1.3 1.8 7.2 13.8 23.8 18.5 0.6 30.9 26.3 9.2 14.2 4.8 25.8 9.2 7.5 17.7 9.4 16.6 16.6 41.8 20.9 10.0 9.4 20.9 22.6 13.0 6.4 10.5 15.1 4.9 22.2 25.1 5.9 0.8 5.0 20.4 18.4 9.0 16.5 15.1 1.5 3.7 42.4 14.7 16.2 30.6 6.8 16.1 0.3
.. 0.2 0.2 0.1 0.8 .. 10.9 .. .. 0.0 0.4 .. 0.0 0.0 .. 0.7 .. 8.2 0.2 .. .. 0.0 0.4 0.6 .. .. 3.5 0.4 .. 0.9 3.8 0.0 11.9 0.1 1.3 1.3 0.3 .. 3.0 30.4 0.2 4.4 3.1 0.0 25.3 .. 3.5 0.3 0.2 0.1 0.2 35.7 0.0 0.5 0.3 0.0 2.7 0.0
.. 1.6 0.3 0.1 1.1 .. 28.3 .. .. 0.7 0.8 .. 0.0 0.0 .. 0.7 .. 16.5 3.2 .. .. 0.4 0.4 1.2 .. .. 3.7 1.3 .. 15.5 4.4 32.8 12.2 0.1 3.4 4.4 0.6 .. 3.2 30.4 75.4 9.3 3.1 0.0 26.5 .. 5.0 21.3 7.3 0.1 0.4 40.3 0.0 2.6 12.5 0.0 45.8 0.0
Terrestrial % of land area
Marine % of territorial waters
Forest area
Average annual deforestationa
thousand sq. km 1990
Honduras Hungary India Indonesia Iran, Islamic Rep. Iraq Ireland Israel Italy Jamaica Japan Jordan Kazakhstan Kenya Korea, Dem. Rep. Korea, Rep. Kosovo Kuwait Kyrgyz Republic Lao PDR Latvia Lebanon Lesotho Liberia Libya Lithuania Macedonia, FYR Madagascar Malawi Malaysia Mali Mauritania Mauritius Mexico Moldova Mongolia Morocco Mozambique Myanmar Namibia Nepal Netherlands New Zealand Nicaragua Niger Nigeria Norway Oman Pakistan Panama Papua New Guinea Paraguay Peru Philippines Poland Portugal Puerto Rico Qatar
81 18 639 1,185 111 8 5 1 76 3 250 1 34 37 82 64 .. 0c 8 173 32 1 0c 49 2 19 9 137 39 224 141 4 0c 703 3 125 50 434 392 88 48 3 77 45 19 172 91 0c 25 38 315 212 702 66 89 33 3 0c
2010
52 20 684 944 111 8 7 2 91 3 250 1 33 35 57 62 .. 0c 10 158 34 1 0c 43 2 22 10 126 32 205 125 2 0c 648 4 109 51 390 318 73 36 4 83 31 12 90 101 0c 17 33 287 176 680 77 93 35 6 0c
% 1990–2000 2000–10
2.38 –0.57 –0.22 1.75 0.00 –0.17 –3.16 –1.49 –0.98 0.11 0.03 0.00 0.17 0.35 1.67 0.13 .. –3.46 –0.26 0.46 –0.21 0.00 –0.49 0.63 0.00 –0.38 –0.49 0.42 0.88 0.36 0.58 2.66 0.03 0.52 –0.16 0.67 0.06 0.52 1.17 0.87 2.09 –0.43 –0.69 1.67 3.74 2.68 –0.19 0.00 1.76 1.18 0.45 0.88 0.14 –0.80 –0.20 –0.28 –4.92 0.00
2.06 –0.62 –0.46 0.51 0.00 –0.09 –1.53 –0.07 –0.90 0.11 –0.05 0.00 0.17 0.33 2.00 0.11 .. –2.57 –1.07 0.49 –0.34 –0.45 –0.47 0.67 0.00 –0.68 –0.41 0.45 0.97 0.54 0.61 2.66 1.00 0.30 –1.77 0.73 –0.23 0.54 0.93 0.97 0.70 –0.14 –0.01 2.01 0.98 3.67 –0.80 0.00 2.24 0.36 0.48 0.97 0.18 –0.75 –0.31 –0.11 –1.76 0.00
Threatened species
GEF benefits index for biodiversity
ENVIRONMENT
3.4
Deforestation and biodiversity
Nationally protected areas
Mammals
Birds
Fish
Higher plantsb
0–100 (no biodiversity to maximum biodiversity)
2011
2011
2011
2011
2008
1990
2010
1990
2010
7 2 94 184 16 13 5 15 7 5 28 13 16 28 9 9 .. 6 6 45 1 10 2 18 12 3 5 65 7 70 12 15 6 100 4 11 18 12 45 12 31 4 9 6 12 26 7 9 23 15 39 8 54 38 5 11 3 3
9 8 78 119 20 16 1 13 7 10 39 10 20 31 24 29 .. 8 12 23 4 8 7 11 3 4 9 35 15 45 9 11 11 56 8 20 10 24 43 25 31 2 70 12 7 14 2 11 27 17 37 27 98 74 6 8 8 4
27 9 212 140 29 11 20 36 47 21 64 13 14 68 13 19 .. 11 3 46 6 22 1 53 24 6 13 85 101 64 3 32 13 152 8 1 47 55 39 27 7 13 23 30 4 59 19 26 34 41 42 0 20 71 7 53 19 11
107 8 291 385 1 0 1 0 61 206 6 1 16 126 5 3 .. 0 14 17 0 1 4 47 2 0 0 273 12 674 7 0 88 191 2 0 28 40 37 25 2 0 19 40 2 171 2 6 2 192 142 9 268 210 8 68 51 0
7.2 0.2 39.9 81.0 7.3 1.6 0.6 0.8 3.8 4.4 36.0 0.4 5.1 8.8 0.7 1.7 .. 0.1 1.1 5.0 0.0 0.2 0.3 2.6 1.6 0.0 0.2 29.2 3.5 13.9 1.5 1.3 3.3 68.7 0.0 4.2 3.5 7.2 10.0 5.2 2.1 0.2 20.2 3.3 0.9 6.0 1.3 3.7 4.9 10.9 25.4 2.8 33.4 32.3 0.5 5.5 4.0 0.1
13.6 4.6 4.7 10.0 5.2 0.1 0.6 16.3 5.0 10.2 13.4 0.7 2.4 11.6 4.3 2.2 .. 1.6 6.4 1.5 6.5 0.5 0.5 1.6 0.1 2.0 4.2 2.2 15.0 17.1 2.3 0.5 1.7 2.2 0.9 4.1 1.2 14.8 3.1 14.4 7.7 11.2 25.4 15.4 7.1 11.6 7.0 0.0 10.1 17.2 1.9 2.9 4.7 8.7 15.3 5.8 10.0 1.7
18.2 5.1 5.0 14.1 7.1 0.1 1.8 17.8 15.1 18.9 16.5 1.9 2.5 11.8 5.9 2.4 .. 1.6 6.9 16.6 18.0 0.5 0.5 1.8 0.1 14.5 4.9 3.1 15.0 18.1 2.4 0.5 4.5 11.1 1.4 13.4 1.5 15.8 6.3 14.9 17.0 12.4 26.2 36.7 7.1 12.8 14.6 10.7 10.1 18.7 3.1 5.4 13.6 10.9 22.4 8.3 10.1 2.5
0.0 .. 1.6 0.5 1.0 0.0 0.1 0.4 0.5 0.2 2.0 0.0 .. 5.2 0.1 3.5 .. 0.0 .. .. 4.6 0.0 .. 0.0 0.0 0.8 .. 0.0 .. 1.5 .. 32.1 0.3 1.1 .. .. 0.8 1.8 0.3 0.5 .. 12.8 0.4 0.6 .. 0.2 1.2 0.0 1.8 3.1 0.3 .. 2.8 0.5 3.4 2.1 1.5 0.0
1.9 .. 1.7 2.0 1.7 0.0 0.2 0.4 17.4 4.2 5.5 30.0 .. 10.5 0.1 3.9 .. 0.0 .. .. 6.7 0.1 .. 0.0 0.1 10.7 .. 0.1 .. 2.0 .. 32.1 0.3 16.7 .. .. 1.3 3.3 0.3 8.2 .. 22.1 10.8 37.2 .. 0.2 2.4 1.3 1.8 4.0 0.3 .. 2.8 2.5 4.1 3.1 1.6 0.3
Terrestrial % of land area
Marine % of territorial waters
2012 World Development Indicators
151
3.4
Deforestation and biodiversity Forest area
Average annual deforestationa
thousand sq. km
Romania Russian Federation Rwanda Saudi Arabia Senegal Serbia Sierra Leone Singapore Slovak Republic Slovenia Somalia South Africa South Sudan Spain Sri Lanka Sudan Swaziland Sweden Switzerland Syrian Arab Republic Tajikistan Tanzania Thailand Timor-Leste Togo Trinidad and Tobago Tunisia Turkey Turkmenistan Uganda Ukraine United Arab Emirates United Kingdom United States Uruguay Uzbekistan Venezuela, RB Vietnam West Bank and Gaza Yemen, Rep. Zambia Zimbabwe World Low income Middle income Lower middle income Upper middle income Low & middle income East Asia & Pacific Europe & Central Asia Latin America & Carib. Middle East & N. Africa South Asia Sub-Saharan Africa High income Euro area
1990
2010
64 8,090 3 10 93 23 31 0c 19 12 83 82 .. 138 24 764 5 273 12 4 4 415 195 10 7 2 6 97 41 48 93 2 26 2,963 9 30 520 94 0c 5 528 222 41,582 s 4,721 27,387 7,003 20,384 32,108 4,602 8,735 10,389 207 795 7,379 9,474 859
66 8,091 4 10 85 27 27 0c 19 13 67 57 .. 182 19 699 6 282 12 5 4 334 190 7 3 2 10 113 41 30 97 3 29 3,040 17 33 463 138 0c 5 495 156 40,204 s 4,155 26,420 6,364 20,056 30,575 4,698 8,784 9,460 211 817 6,605 9,629 952
% 1990–2000 2000–10
0.01 0.00 –0.79 0.00 0.49 –0.62 0.65 0.00 0.01 –0.37 0.97 0.00 .. –2.09 1.20 0.80 –0.93 –0.04 –0.37 –1.51 –0.05 1.02 0.28 1.22 3.37 0.30 –2.67 –0.47 0.00 2.03 –0.25 –2.38 –0.68 –0.13 –4.38 –0.54 0.57 –2.28 0.00 0.00 0.32 1.58 0.18 w 0.63 0.22 0.59 0.10 0.28 0.08 –0.02 0.47 –0.10 –0.01 0.55 –0.14 –0.76
Threatened species
2012 World Development Indicators
Nationally protected areas
Mammals
Birds
Fish
Higher plantsb
0–100 (no biodiversity to maximum biodiversity)
2011
2011
2011
2011
2008
1990
0.7 34.1 0.9 3.2 1.0 0.2 1.3 0.1 0.1 0.2 6.1 20.7 .. 6.8 7.9 5.1 0.1 0.3 0.2 0.9 0.7 14.8 8.0 0.6 0.3 2.2 0.5 6.2 1.8 2.8 0.5 0.2 3.5 94.2 1.2 1.1 25.3 12.1 .. 3.2 3.8 1.9 .. .. .. .. .. .. .. .. .. .. .. .. .. ..
2.9 5.0 9.9 7.6 24.1 5.3 4.9 5.0 19.3 7.6 0.6 6.5 .. 7.7 20.3 4.2 3.0 6.0 14.5 0.3 1.9 26.6 14.7 .. 11.3 30.5 1.3 1.7 3.0 7.9 1.8 0.3 22.0 12.4 0.3 2.1 40.1 4.5 0.6 0.0 36.0 18.0 8.4 w 9.6 7.9 7.3 8.2 8.2 10.8 4.3 9.7 3.3 5.3 11.1 8.9 11.3
–0.32 7 11 19 4 0.00 32 49 35 8 –2.38 20 12 9 4 0.00 9 15 23 3 0.49 16 10 45 9 –0.99 6 9 11 2 0.69 17 10 47 48 0.00 11 15 25 57 –0.06 3 6 5 5 –0.16 4 2 29 7 1.07 15 12 27 21 0.00 24 40 87 65 .. .. .. .. .. –0.68 16 9 71 205 1.12 29 15 44 282 0.08 15 15 19 16 –0.84 5 11 4 3 –0.30 1 2 12 4 –0.38 2 1 9 2 –1.29 16 14 34 2 0.00 8 12 5 13 1.13 35 42 174 290 0.02 57 46 97 86 1.40 4 7 5 0 5.13 11 5 24 9 0.32 2 2 24 1 –1.86 13 6 35 6 –1.11 17 14 70 5 0.00 9 16 11 3 2.56 22 21 61 36 –0.21 11 11 21 16 –0.24 7 9 13 0 –0.31 5 2 43 11 –0.13 37 76 183 219 –2.14 11 24 36 0 –0.20 10 15 7 15 0.60 32 27 37 68 –1.65 54 43 68 119 –0.10 3 9 0 0 0.00 9 15 23 158 0.33 9 14 20 9 1.88 9 14 3 14 0.11 w 3,105 s 3,401 s 6,213 s 10,987 s 0.61 .. .. .. .. 0.08 .. .. .. .. 0.30 .. .. .. .. 0.01 .. .. .. .. 0.15 .. .. .. .. –0.44 .. .. .. .. –0.04 .. .. .. .. 0.45 .. .. .. .. –0.15 .. .. .. .. –0.29 .. .. .. .. 0.48 .. .. .. .. –0.04 .. .. .. .. –0.31 .. .. .. ..
a. Negative values indicate an increase in forest area. b. Flowering plants only. c. Less than 0.5.
152
GEF benefits index for biodiversity
Terrestrial % of land area 2010
7.1 9.1 10.0 31.3 24.1 6.0 4.9 5.4 23.2 13.2 0.6 6.9 .. 8.6 21.5 4.2 3.0 10.9 24.9 0.6 4.1 27.5 20.1 6.1 11.3 31.2 1.3 1.9 3.0 10.3 3.5 5.6 26.4 12.4 0.3 2.3 53.8 6.2 0.6 0.5 36.0 28.0 12.3 w 10.7 12.3 9.4 13.4 12.0 15.0 7.5 20.2 3.9 5.9 11.7 12.9 17.0
Marine % of territorial waters 1990
2010
1.6 2.2 .. 0.6 5.8 .. 0.0 0.0 .. 0.0 0.0 0.7 .. 0.6 0.1 0.0 .. 3.9 .. 0.0 .. 3.7 4.0 0.0 0.0 0.2 1.1 2.4 .. .. 4.1 0.3 4.9 21.0 0.2 .. 7.0 0.3 .. 0.0 .. .. 4.6 w .. 2.6 2.7 2.6 2.6 0.5 2.2 5.0 0.8 1.5 3.3 9.3 6.7
33.3 10.8 .. 3.4 12.4 .. 0.0 1.4 .. 0.7 0.0 6.5 .. 3.5 1.1 0.0 .. 5.3 .. 0.6 .. 10.0 4.4 6.7 0.0 2.8 1.2 2.4 .. .. 4.9 2.6 5.7 28.6 0.3 .. 15.3 1.7 .. 1.8 .. .. 10.0 w .. 7.5 4.4 8.5 7.2 1.4 10.4 13.1 2.0 1.7 5.8 16.5 15.1
About the data
3.4
ENVIRONMENT
Deforestation and biodiversity Definitions
As threats to biodiversity mount, the international
nature reserves with limited public access; national
• Forest area is land spanning more than 0.5 hect-
community is increasingly focusing on conserving
parks of national or international significance and not
are with trees higher than 5 meters and a canopy
diversity. Deforestation is a major cause of loss
materially affected by human activity; natural monu-
cover of more than 10 percent or with trees able
of biodiversity, and habitat conservation is vital
ments and natural landscapes with unique aspects;
to reach these thresholds in situ. It excludes land
for stemming this loss. Conservation efforts have
managed nature reserves and wildlife sanctuaries;
that is predominantly under agricultural or urban
focused on protecting areas of high biodiversity.
protected landscapes (which may include cultural
land use. • Average annual deforestation is the
The Food and Agriculture Organization of the United
landscapes); and areas managed mainly for the sus-
permanent conversion of natural forest area to other
Nations (FAO) Global Forest Resources Assessment
tainable use of natural systems to ensure long-term
uses, including agriculture, ranching, settlements,
2010 provides detailed information on forest cover in
protection and maintenance of biological diversity.
and infrastructure. Deforested areas exclude areas
2010 and adjusted estimates of forest cover in 1990
The data in the table cover these six categories
logged but intended for regeneration and areas
and 2000. The current survey uses a uniform defini-
as well as terrestrial protected areas that are not
degraded by fuelwood gathering, acid precipitation,
tion of forest. Because of space limitations, the table
assigned to a category by the IUCN. Designating an
or forest fires. • Threatened species are species
does not break down forest cover between natural
area as protected does not mean that protection
classified by the IUCN as endangered, vulnerable,
forest and plantation, a breakdown the FAO provides
is in force. And for small countries that have only
rare, indeterminate, out of danger, or insufficiently
for developing countries. Thus the deforestation data
protected areas smaller than 1,000 hectares, the
known. Mammals exclude whales and porpoises.
in the table may underestimate the rate at which
size limit in the definition leads to an underestimate
Birds are listed for the country where their breeding
natural forest is disappearing in some countries.
of protected areas.
or wintering ranges are located. Fish are cold-blooded
The number of threatened species is an important
Due to variations in consistency and methods of
aquatic vertebrates of the superclass Pisces. Higher
measure of the immediate need for conservation in
collection, data quality is highly variable across coun-
plants are native vascular plant species. • GEF ben-
an area. Global analyses of the status of threatened
tries. Some countries update their information more
efits index for biodiversity is a composite index of
species have been carried out for few groups of
frequently than others, some have more accurate
relative biodiversity potential based on the species
organisms. Only for mammals, birds, and amphib-
data on extent of coverage, and many underreport
in each country and their threat status and diversity
ians has the status of virtually all known species
the number or extent of protected areas.
of habitat types. • Nationally protected areas are
been assessed. Threatened species are defined
totally or partially protected areas of at least 1,000
using the International Union for Conservation of
hectares that are designated as scientific reserves
Nature’s (IUCN) classification: endangered (in danger
with limited public access, national parks, natural
of extinction and unlikely to survive if causal factors
monuments, nature reserves or wildlife sanctuar-
continue operating) and vulnerable (likely to move
ies, and protected landscapes. Terrestrial protected
into the endangered category in the near future if
areas exclude marine areas, unclassified areas, litto-
causal factors continue operating).
ral (intertidal) areas, and sites protected under local
The Global Environment Facility’s (GEF) benefi ts
or provincial law. Marine protected areas are areas of
index for biodiversity is a comprehensive indicator
intertidal or subtidal terrain—and overlying water and
of national biodiversity status and is used to guide
associated flora and fauna and historical and cultural
its biodiversity priorities. For each country the bio-
features—that have been reserved to protect part of
diversity indicator incorporates the best available
or the entire enclosed environment.
and comparable information in four relevant dimensions: represented species, threatened species, represented ecoregions, and threatened ecoregions. To combine these dimensions into one measure, the indicator uses dimensional weights that reflect the
Data sources
consensus of conservation scientists at the GEF,
Data on forest area are from the FAO’s Global
IUCN, WWF International, and other nongovernmen-
Forest Resources Assessment 2010 and website.
tal organizations.
Data on threatened species are from the elec-
The World Conservation Monitoring Centre (WCMC)
tronic files of the United Nations Environment Pro-
compiles data on protected areas, numbers of cer-
gramme (UNEP) and WCMC and the 2011 IUCN
tain species, and numbers of those species under
Red List of Threatened Species. The GEF benefits
threat from various sources. Because of differences
index for biodiversity is from Pandey and others
in definitions, reporting practices, and reporting peri-
(2006a). Data on nationally protected areas are
ods, cross-country comparability is limited.
from the UNEP and WCMC, based on data from
Nationally protected areas are defined using the six IUCN management categories for areas of at
national authorities, national legislation, and international agreements.
least 1,000 hectares: scientific reserves and strict
2012 World Development Indicators
153
3.5
Freshwater Internal renewable freshwater resourcesa
Afghanistan Albania Algeria Angola Argentina Armenia Australia Austria Azerbaijan Bahrain Bangladesh Belarus Belgium Benin Bolivia Bosnia and Herzegovina Botswana Brazil Bulgaria Burkina Faso Burundi Cambodia Cameroon Canada Central African Republic Chad Chile China Hong Kong SAR, China Colombia Congo, Dem. Rep. Congo, Rep. Costa Rica Côte d’Ivoire Croatia Cuba Cyprus Czech Republic Denmark Dominican Republic Ecuador Egypt, Arab Rep. El Salvador Eritrea Estonia Ethiopia Finland France Gabon Gambia, The Georgia Germany Ghana Greece Guatemala Guinea Guinea-Bissau Haiti
154
Annual freshwater withdrawals
Water productivity
Access to an improved water source
Flows billion cu. m
Per capita cu. m
billion cu. m
% of internal resources
% for agriculture
% for industry
% for domestic
GDP/water use 2000 $ per cu. m
% of urban population
% of rural population
2009
2009
2009b
2009b
2009b
2009b
2009b
2009b
2010
2010
35.6 4.4 52.7 0.4 4.0 36.4 4.6 4.7 35.2 219.8 2.9 7.5 34.0 0.5 0.3 0.9 1.6 0.7 28.7 7.9 2.3 0.5 0.3 1.6 0.0 0.9 1.2 19.5 .. 0.6 0.0 c 0.0 c 2.4 1.7 0.6 19.8 19.3 13.3 10.8 16.6 3.6 119.0 5.5 9.2 14.0 4.6 1.5 15.0 0.1 0.9 2.6 21.0 1.8 12.7 2.6 0.7 0.6 8.6
99 58 64 33 66 66 74 3 76 45 88 19 1 45 57 .. 41 55 16 70 77 94 76 12 1 52 70 65 .. 39 18 9 53 43 2 75 86 2 36 64 92 86 55 95 0c 94 3 12 38 28 65 0c 66 89 55 84 82 78
1 12 14 29 12 4 11 79 19 6 2 54 88 23 15 .. 18 17 68 2 6 2 7 69 16 24 20 23 .. 4 20 22 17 19 14 10 3 57 5 2 3 6 17 0 97 0c 72 69 9 24 13 84 10 2 30 3 5 4
1 30 23 38 22 30 16 18 4 50 10 27 12 32 28 .. 41 28 16 28 17 4 17 20 82 24 9 12 .. 57 63 70 29 38 85 15 10 42 58 34 6 8 28 5 3 6 25 18 53 48 22 16 24 9 15 13 13 19
42 94 79 38 80 97 100 100 71 .. 80 99 100 68 71 98 92 85 100 73 71 58 52 99 51 44 75 85 .. 72 27 32 91 68 97 89 100 100 100 84 89 99 76 57 97 34 100 100 41 85 96 100 80 99 87 65 53 51
78 96 85 60 98 99 100 100 88 100 85 100 100 84 96 100 99 100 100 95 83 87 95 100 92 70 99 98 .. 99 79 95 100 91 100 96 100 100 100 87 96 100 94 74 99 97 100 100 95 92 100 100 91 100 98 90 91 85
55 27 11 148 276 7 492 55 8 0c 105 37 12 10 304 36 2 5,418 21 13 10 121 273 2,850 141 15 884 2,813 .. 2,112 900 222 112 77 38 38 1 13 6 21 432 2 18 3 13 122 107 200 164 3 58 107 30 58 109 226 16 13
1,645 8,425 322 7,976 6,889 2,223 22,413 6,575 907 3 714 3,913 1,111 1,197 31,054 9,422 1,211 28,037 2,769 782 1,231 8,628 14,237 84,495 32,653 1,371 52,136 2,113 .. 46,261 14,018 56,324 24,484 3,971 8,512 3,385 715 1,254 1,086 2,144 30,291 23 2,881 549 9,483 1,503 20,042 3,099 110,997 1,784 13,179 1,306 1,272 5,141 7,781 23,153 10,781 1,319
2012 World Development Indicators
23.1 1.8 6.2 0.6 32.6 2.8 22.6 3.7 12.2 0.4 35.9 4.3 6.2 0.1 2.0 0.3 0.2 58.1 6.1 1.0 0.3 2.2 1.0 46.0 0.1 0.4 11.3 554.1 .. 12.7 0.6 0.0 2.7 1.4 0.6 7.6 0.2 1.7 0.7 3.5 15.3 68.3 1.4 0.6 1.8 5.6 1.6 31.6 0.1 0.1 1.6 32.3 1.0 9.5 2.9 1.6 0.2 1.2
.. 3 12 40 12 1 24 60 2 35 2 6 42 25 6 24 40 15 3 4 3 3 14 18 15 8 9 5 .. 11 10 101 9 8 45 4 66 45 253 11 2 2 11 1 5 3 86 46 46 8 3 62 8 17 9 2 1 3
Internal renewable freshwater resourcesa
Honduras Hungary India Indonesia Iran, Islamic Rep. Iraq Ireland Israel Italy Jamaica Japan Jordan Kazakhstan Kenya Korea, Dem. Rep. Korea, Rep. Kosovo Kuwait Kyrgyz Republic Lao PDR Latvia Lebanon Lesotho Liberia Libya Lithuania Macedonia, FYR Madagascar Malawi Malaysia Mali Mauritania Mauritius Mexico Moldova Mongolia Morocco Mozambique Myanmar Namibia Nepal Netherlands New Zealand Nicaragua Niger Nigeria Norway Oman Pakistan Panama Papua New Guinea Paraguay Peru Philippines Poland Portugal Puerto Rico Qatar
Annual freshwater withdrawals
Water productivity
3.5
ENVIRONMENT
Freshwater
Access to an improved water source
Flows billion cu. m
Per capita cu. m
billion cu. m
% of internal resources
% for agriculture
% for industry
% for domestic
GDP/water use 2000 $ per cu. m
% of urban population
% of rural population
2009
2009
2009b
2009b
2009b
2009b
2009b
2009b
2010
2010
96 6 1,446 2,019 129 35 49 1 183 9 430 1 75 21 67 65 .. 0c 49 190 17 5 5 200 1 16 5 337 16 580 60 0c 3 409 1 35 29 100 1,003 6 198 11 327 190 4 221 382 1 55 147 801 94 1,616 479 54 38 7 0c
12,877 599 1,197 8,504 1,757 1,132 10,989 100 3,032 3,489 3,371 115 4,686 525 2,764 1,330 .. 0c 9,199 31,151 7,424 1,144 2,433 52,139 96 4,659 2,625 16,746 1,118 20,752 4,024 118 2,158 3,651 280 12,833 917 4,388 21,071 2,747 6,734 665 75,768 33,221 234 1,431 79,110 516 323 42,578 119,492 14,822 56,179 5,223 1,405 3,574 1,790 35
1.2 5.6 761.0 113.3 93.3 66.0 .. 2.0 45.4 0.6 90.0 0.9 33.1 2.7 8.7 25.5 .. 0.9 10.1 4.3 0.4 1.3 0.1 0.2 4.3 2.4 1.0 14.7 1.0 13.2 6.5 1.6 0.7 79.8 1.9 0.5 12.6 0.7 33.2 0.3 9.8 10.6 4.8 1.3 2.4 10.3 2.9 1.3 183.5 0.5 0.4 0.5 19.3 81.6 12.0 8.5 1.0 0.4
1.2 5.4 39.8 5.6 67.7 87.3 .. 101.9 23.7 6.2 20.9 99.4 28.9 8.9 11.2 36.5 .. .. 43.7 1.3 1.2 28.1 1.7 0.1 718.0 9.6 16.1 4.4 5.6 2.3 6.5 14.0 26.4 17.5 16.4 1.4 43.4 0.3 2.8 1.7 4.7 11.7 1.5 0.7 7.0 3.6 0.8 86.6 79.5 0.3 0.0 0.1 1.0 17.0 19.4 12.3 14.0 455.2
58 6 90 82 92 79 .. 58 44 34 63 65 87 79 76 62 .. 54 94 93 12 60 20 34 83 3 12 97 84 34 90 94 68 77 40 44 87 74 89 71 98 1 74 84 88 53 29 88 94 51 0c 71 85 82 10 73 7 59
25 82 2 7 1 15 .. 6 36 22 18 4 12 4 13 12 .. 2 3 4 50 11 40 27 3 90 67 1 4 36 1 2 3 9 52 32 3 3 1 5 0c 87 4 2 1 15 43 1 1 3 43 8 8 10 60 19 2 2
17 12 7 12 7 7 .. 36 20 44 19 31 1 17 10 26 .. 44 3 3 39 29 40 40 14 7 21 2 12 30 9 5 30 14 9 24 10 23 10 24 2 12 21 14 11 31 28 10 5 46 57 20 7 8 31 8 91 39
9 .. 1 2 2 0c .. 83 25 17 54 16 1 7 .. 30 .. 43 0c 1 27 20 20 3 11 7 4 0c 3 10 1 1 9 8 1 4 5 11 .. 19 1 41 13 4 1 8 66 23 1 44 12 19 4 1 20 15 65 122
79 100 90 74 92 56 100 100 100 88 100 92 90 52 97 88 .. 99 85 62 96 100 73 60 .. .. 99 34 80 99 51 48 99 91 93 53 61 29 78 90 88 100 100 68 39 43 100 78 89 83 33 66 65 92 100 100 .. 100
95 100 97 92 97 91 100 100 100 98 100 98 99 82 99 100 .. 99 99 77 100 100 91 88 .. 98 100 74 95 100 87 52 100 97 99 100 98 77 93 99 93 100 100 98 100 74 100 93 96 97 87 99 91 93 100 99 .. 100
2012 World Development Indicators
155
3.5
Freshwater Internal renewable freshwater resourcesa
Romania Russian Federation Rwanda Saudi Arabia Senegal Serbia Sierra Leone Singapore Slovak Republic Slovenia Somalia South Africa South Sudan Spain Sri Lanka Sudan Swaziland Sweden Switzerland Syrian Arab Republic Tajikistan Tanzania Thailand Timor-Leste Togo Trinidad and Tobago Tunisia Turkey Turkmenistan Uganda Ukraine United Arab Emirates United Kingdom United States Uruguay Uzbekistan Venezuela, RB Vietnam West Bank and Gaza Yemen, Rep. Zambia Zimbabwe World Low income Middle income Lower middle income Upper middle income Low & middle income East Asia & Pacific Europe & Central Asia Latin America & Carib. Middle East & N. Africa South Asia Sub-Saharan Africa High income Euro area
Annual freshwater withdrawals
Water productivity
Access to an improved water source
Flows billion cu. m
Per capita cu. m
billion cu. m
% of internal resources
% for agriculture
% for industry
% for domestic
GDP/water use 2000 $ per cu. m
% of urban population
% of rural population
2009
2009
2009b
2009b
2009b
2009b
2009b
2009b
2010
2010
42 4,313 10 2 26 .. 160 1 13 19 6 45 .. 111 53 30 3 171 40 7 66 84 225 8 12 4 4 227 1 39 53 0c 145 2,818 59 16 722 359 1 2 80 12 42,383 s 4,197 29,152 7,995 21,157 33,348 8,773 5,076 13,425 226 1,990 3,858 9,034 977
1,969 30,405 921 90 2,131 .. 27,878 120 2,325 9,153 658 908 .. 2,422 2,555 706 2,260 18,390 5,217 356 9,774 1,930 3,268 7,469 1,949 2,874 402 3,160 273 1,205 1,153 22 2,346 9,186 17,639 588 25,451 4,178 201 90 6,303 983 6,258 w 5,381 5,944 3,227 8,718 5,867 4,506 12,887 23,323 695 1,236 4,634 8,305 2,952
6.9 66.2 0.2 23.7 2.2 4.1 0.5 .. 0.7 0.9 3.3 12.5 .. 32.5 13.0 37.1 1.0 2.6 2.6 16.8 12.0 5.2 57.3 1.2 0.2 0.2 2.9 40.1 24.9 0.3 38.5 4.0 13.0 478.4 3.7 59.6 9.1 82.0 0.4 3.6 1.7 4.2 3,908.3 s 188.6 2,791.0 1,607.4 1,183.6 2,979.6 952.0 330.4 269.5 276.5 1,026.6 124.6 928.7 185.6
3.2 1.5 1.6 943.3 5.7 .. 0.3 .. 1.4 3.0 22.4 25.0 .. 29.0 24.5 57.6 23.1 1.5 4.9 99.8 74.8 5.4 13.1 14.3 1.2 6.0 61.7 18.8 100.8 0.5 27.6 2,032.0 8.8 15.6 2.6 118.3 0.7 9.3 49.9 168.6 1.7 21.0 7.3 w 4.3 7.0 12.6 5.0 6.7 9.9 5.0 1.6 70.0 33.4 2.2 9.4 16.4
17 20 68 88 93 2 71 .. 3 0c 99 63 .. 61 87 97 97 4 2 88 92 89 90 91 45 9 76 74 97 36 51 83 10 40 87 91 44 95 45 91 76 79 70 w 90 79 88 66 79 73 63 68 86 91 84 41 32
61 60 8 3 3 82 10 .. 50 82 0c 6 .. 22 6 1 1 59 58 4 5 0 5 0c 2 25 4 11 1 18 36 2 33 46 2 3 8 4 7 2 7 7 18 w 2 11 5 20 11 16 26 11 6 2 4 42 52
22 20 24 9 4 17 19 .. 47 18 0c 31 .. 18 6 2 2 37 41 9 4 10 5 8 53 66 13 15 2 46 12 15 57 14 11 6 49 1 48 7 17 14 12 w 8 10 7 14 10 10 11 21 8 7 12 17 17
8 6 22 11 3 2 3 .. 64 27 .. 15 .. 22 2 1 2 110 110 2 0c 4 3 0c 10 61 11 9 0c 36 1 39 132 24 8 0c 18 1 9 4 3 1 10 w 2 3 1 6 3 4 3 10 2 1 4 31 36
.. 92 63 .. 56 98 35 .. 100 99 7 79 .. 100 90 52 65 100 100 86 54 44 95 60 40 93 84 99 72 68 98 100 100 94 100 81 75 93 81 47 46 69 81 w 57 84 83 86 80 84 91 81 81 88 49 98 100
a. Excludes river flows from other countries because of data unreliability. b. Data are for the most recent year available (see Primary data documentation). c. Less than 0.5.
156
2012 World Development Indicators
99 99 76 97 93 99 87 100 100 100 66 99 .. 100 99 67 91 100 100 93 92 79 97 91 89 98 99 100 97 95 98 100 100 100 100 98 94 99 86 72 87 98 96 w 86 96 93 98 95 97 99 98 94 95 83 100 100
About the data
3.5
ENVIRONMENT
Freshwater Definitions
The data on freshwater resources are based on
• Internal renewable freshwater resources are
estimates of runoff into rivers and recharge of
the average annual flows of rivers and groundwater
groundwater. These estimates are based on differ-
from rainfall in the country. Natural incoming flows
ent sources and refer to different years, so cross-
originating outside a country’s borders are excluded.
country comparisons should be made with caution.
Overlapping water resources between surface run-
Because the data are collected intermittently, they
off and groundwater recharge are also deducted.
may hide significant variations in total renewable
• Internal renewable freshwater resources per
water resources from year to year. The data also
capita are calculated using the World Bank’s popu-
fail to distinguish between seasonal and geographic
lation estimates (see table 2.1). • Annual freshwater
variations in water availability within countries. Data
withdrawals are total water withdrawals, not count-
for small countries and countries in arid and semiarid
ing evaporation losses from storage basins. With-
zones are less reliable than those for larger countries
drawals also include water from desalination plants
and countries with greater rainfall.
in countries where they are a significant source. With-
Caution should also be used in comparing data
drawals can exceed 100 percent of total renewable
on annual freshwater withdrawals, which are subject
resources where extraction from nonrenewable aqui-
to variations in collection and estimation methods.
fers or desalination plants is considerable or where
In addition, inflows and outflows are estimated at
water reuse is significant. Withdrawals for agriculture
different times and at different levels of quality and
and industry are total withdrawals for irrigation and
precision, requiring caution in interpreting the data,
livestock production and for direct industrial use
particularly for water-short countries, notably in the
(including for cooling thermoelectric plants). With-
Middle East and North Africa.
drawals for domestic uses include drinking water,
Water productivity is an indication only of the
municipal use or supply, and use for public services,
effi ciency by which each country uses its water
commercial establishments, and homes. • Water
resources. Given the different economic structure
productivity is calculated as GDP in constant prices
of each country, these indicators should be used
divided by annual total water withdrawal. • Access
carefully, taking into account the countries’ sectoral
to an improved water source is the percentage of the
activities and natural resource endowments.
population with reasonable access to an adequate
The data on access to an improved water source
amount of water from an improved source, such as
measure the percentage of the population with ready
piped water into a dwelling, plot, or yard; public tap
access to water for domestic purposes. The data
or standpipe; tubewell or borehole; protected dug
are based on surveys and estimates provided by
well or spring; and rainwater collection. Unimproved
governments to the Joint Monitoring Programme of
sources include unprotected dug wells or springs,
the World Health Organization (WHO) and the United
carts with small tank or drum, bottled water, and
Nations Children’s Fund (UNICEF). The coverage
tanker trucks. Reasonable access is defined as the
rates are based on information from service users
availability of at least 20 liters a person a day from
on actual household use rather than on informa-
a source within 1 kilometer of the dwelling.
tion from service providers, which may include nonfunctioning systems. Access to drinking water from an improved source does not ensure that the water is safe or adequate, as these characteristics are not tested at the time of survey. While information on access to an improved water source is widely used, it is extremely subjective, and such terms as safe, improved, adequate, and reasonable may have
Data sources
different meaning in different countries despite offi -
Data on freshwater resources and withdrawals
cial WHO definitions (see Definitions). Even in high-
are from the Food and Agriculture Organization’s
income countries treated water may not always be
AQUASTAT database. The GDP estimates used to
safe to drink. Access to an improved water source is
calculate water productivity are from the World
equated with connection to a supply system; it does
Bank national accounts database. Data on access
not take into account variations in the quality and
to water are from WHO and UNICEF’s Progress on
cost (broadly defined) of the service.
Drinking Water and Sanitation (2012).
2012 World Development Indicators
157
3.6
Water pollution Emissions of organic water pollutants
Afghanistan Albania Algeria Angola Argentina Armenia Australia Austria Azerbaijan Bahrain Bangladesh Belarus Belgium Benin Bolivia Bosnia and Herzegovina Botswana Brazil Bulgaria Burkina Faso Burundi Cambodia Cameroon Canada Central African Republic Chad Chile China Hong Kong SAR, China Colombia Congo, Dem. Rep. Congo, Rep. Costa Rica Côte d’Ivoire Croatia Cuba Cyprus Czech Republic Denmark Dominican Republic Ecuador Egypt, Arab Rep. El Salvador Eritrea Estonia Ethiopia Finland France Gabon Gambia, The Georgia Germany Ghana Greece Guatemala Guinea Guinea-Bissau
158
kilograms per day per worker
thousand kilograms per day 1990 2007a
1990
.. 2.4 .. .. 181.4 .. .. 90.5 41.3 .. 250.8 .. 107.8 .. 11.3 .. 2.5 .. 124.3 .. .. 3.8 .. 300.9 .. .. .. .. .. .. .. .. .. .. 48.5 .. 7.1 177.1 84.5 88.6 28.6 206.5 .. 2.4 21.7 18.5 72.5 326.5 .. 0.8 .. 806.6 .. 50.9 .. .. ..
.. 0.25 .. .. 0.21 .. .. 0.15 0.15 .. 0.15 .. 0.17 .. 0.24 .. 0.30 .. 0.17 .. .. 0.17 .. 0.17 .. .. .. .. .. .. .. .. .. .. 0.17 .. 0.21 0.14 0.18 0.18 0.24 0.19 .. 0.19 0.15 0.23 0.19 0.11 .. 0.27 .. 0.13 .. 0.19 .. .. ..
0.2 3.6 .. .. 155.5 .. .. 84.4 20.0 .. 303.0 .. 95.9 .. 11.5 .. 3.2 .. 102.1 .. .. .. .. 306.6 .. .. 92.5 9,428.9 .. 87.0 .. .. .. .. 42.9 .. 8.0 146.5 61.0 .. 44.7 .. .. 2.5 16.0 32.2 55.3 569.4 .. .. .. 936.2 16.0 60.8 .. .. ..
2012 World Development Indicators
Industry shares of emissions of organic water pollutants
% of total Stone, ceramics, Food and beverages and glass
Primary metals
Paper and pulp
Chemicals
Textiles
Wood
Other
2007a
2007a
2007a
2007a
2007a
2007a
2007a
2007a
2007a
0.21 0.25 .. .. 0.23 .. .. 0.14 0.18 .. 0.14 .. 0.17 .. 0.25 .. 0.23 .. 0.17 .. .. .. .. 0.16 .. .. 0.25 0.13 .. 0.20 .. .. .. .. 0.17 .. 0.23 0.13 0.16 .. 0.28 .. .. 0.20 0.14 0.24 0.14 0.16 .. .. .. 0.14 0.17 0.20 .. .. ..
.. .. .. .. 3.8 .. .. 5.7 8.8 .. 0.7 .. 6.4 .. 0.9 .. .. .. 3.7 .. .. .. .. 4.3 .. .. 7.6 7.2 .. 2.3 .. .. .. .. 3.1 .. 0.3 5.4 1.4 .. 1.8 .. .. 0.2 0.4 1.4 1.0 3.2 .. .. .. 3.8 3.0 3.9 .. .. ..
19.7 .. .. .. 8.4 .. .. 7.1 3.0 .. 2.3 .. 7.9 .. 9.8 .. 2.4 .. 4.3 .. .. .. .. 8.9 .. .. 6.3 3.9 .. 8.9 .. .. .. .. 7.2 .. 8.9 4.8 11.5 .. 7.8 .. .. 4.4 7.3 6.0 15.4 7.4 .. .. .. 7.1 3.8 9.0 .. .. ..
27.9 .. .. .. 15.8 .. .. 9.3 18.5 .. 3.0 .. 18.6 .. 13.1 .. .. .. 8.0 .. .. .. .. 10.9 .. .. 13.7 13.0 .. 17.3 .. .. .. .. 9.5 .. 9.3 10.9 13.1 .. 12.8 .. .. 9.5 7.1 10.9 8.7 15.0 .. .. .. 12.4 15.9 10.1 .. .. ..
14.1 39.8 .. .. 30.5 .. .. 12.2 19.6 .. 7.6 .. 16.4 .. 35.4 .. 43.8 .. 17.7 .. .. .. .. 14.0 .. .. 35.1 7.4 .. 21.3 .. .. .. .. 17.6 .. 36.3 10.9 16.4 .. 46.4 .. .. 27.3 14.6 34.7 9.0 16.6 .. .. .. 11.4 18.6 23.9 .. .. ..
11.7 .. .. .. 3.5 .. .. 5.8 8.4 .. 2.6 .. 3.1 .. 7.7 .. 0.6 .. 4.8 .. .. .. .. 2.8 .. .. 3.6 6.3 .. 5.3 .. .. .. .. 5.9 .. 9.9 6.4 4.8 .. 4.4 .. .. 9.6 5.5 8.3 4.4 3.8 .. .. .. 3.4 4.1 7.0 .. .. ..
23.3 60.2 .. .. 14.3 .. .. 4.3 11.7 .. 79.3 .. 5.5 .. 18.4 .. 3.9 .. 26.8 .. .. .. .. 7.3 .. .. 9.1 20.6 .. 24.1 .. .. .. .. 14.5 .. 5.1 7.4 1.5 .. 12.3 .. .. 29.0 8.0 27.9 2.8 4.8 .. .. .. 2.4 10.2 14.4 .. .. ..
.. .. .. .. 2.1 .. .. 6.0 1.5 .. 0.5 .. 2.2 .. 5.3 .. .. .. 3.0 .. .. .. .. 6.5 .. .. 6.9 1.7 .. 0.9 .. .. .. .. 4.9 .. 8.0 4.4 4.0 .. 2.2 .. .. 0.1 16.4 1.5 7.3 2.4 .. .. .. 1.9 33.3 2.8 .. .. ..
3.1 11.9 .. .. 21.6 .. .. 49.5 28.6 .. 4.2 .. 40.0 .. 9.5 .. 50.0 .. 31.7 .. .. .. .. 45.3 .. .. 17.7 39.9 .. 19.9 .. .. .. .. 37.2 .. 22.2 49.8 47.3 .. 12.3 .. .. 20.3 40.8 9.3 51.4 46.9 .. .. .. 57.6 11.2 28.9 .. .. ..
Emissions of organic water pollutants
thousand kilograms per day 1990 2007a
Haiti Honduras Hungary India Indonesia Iran, Islamic Rep. Iraq Ireland Israel Italy Jamaica Japan Jordan Kazakhstan Kenya Korea, Dem. Rep. Korea, Rep. Kosovo Kuwait Kyrgyz Republic Lao PDR Latvia Lebanon Lesotho Liberia Libya Lithuania Macedonia, FYR Madagascar Malawi Malaysia Mali Mauritania Mauritius Mexico Moldova Mongolia Morocco Mozambique Myanmar Namibia Nepal Netherlands New Zealand Nicaragua Niger Nigeria Norway Oman Pakistan Panama Papua New Guinea Paraguay Peru Philippines Poland Portugal
5.2 .. 122.1 .. 721.8 131.6 7.7 36.1 54.6 378.3 .. 1,455.0 15.0 123.5 .. .. 366.9 .. .. 28.9 4.3 39.8 14.7 .. .. .. 54.0 27.0 .. 37.2 .. .. .. 16.8 425.0 29.2 .. .. .. .. .. 26.4 142.3 46.7 .. .. .. 51.8 3.8 .. 10.3 .. 15.3 .. 169.0 446.7 140.6
.. .. 110.6 .. 883.0 160.8 7.7 28.4 52.7 479.2 .. 1,126.9 29.1 97.4 .. .. 319.6 .. .. 12.2 4.3 28.4 14.7 5.3 .. .. 42.2 20.3 92.8 32.7 208.3 .. .. 15.4 .. 18.8 8.8 74.0 .. .. .. 26.8 128.2 61.6 .. .. .. 46.9 7.6 153.7 13.7 .. 10.8 .. 144.6 359.7 87.7
3.6
ENVIRONMENT
Water pollution Industry shares of emissions of organic water pollutants
kilograms per day per worker
Primary metals
Paper and pulp
Chemicals
% of total Stone, ceramics, Food and beverages and glass
Textiles
Wood
Other
1990
2007a
2007a
2007a
2007a
2007a
2007a
2007a
2007a
2007a
0.20 .. 0.18 .. 0.18 0.16 0.27 0.19 0.16 0.13 .. 0.14 0.18 0.23 .. .. 0.12 .. .. 0.14 0.14 0.12 0.19 .. .. .. 0.15 0.20 .. 0.40 .. .. .. 0.16 0.18 0.44 .. .. .. .. .. 0.14 0.20 0.24 .. .. .. 0.20 0.15 .. 0.30 .. 0.20 .. 0.17 0.16 0.14
.. .. 0.15 .. 0.19 0.15 0.27 0.16 0.16 0.13 .. 0.15 0.18 0.24 .. .. 0.11 .. .. 0.20 0.14 0.18 0.19 0.13 .. .. 0.17 0.18 0.14 0.39 0.12 .. .. 0.17 .. 0.45 0.21 0.16 .. .. .. 0.16 0.19 0.23 .. .. .. 0.18 0.16 0.17 0.32 .. 0.28 .. 0.15 0.16 0.17
.. .. 2.7 .. 1.4 7.1 13.1 1.3 1.6 3.5 .. 3.3 2.3 33.3 .. .. 4.2 .. .. 9.8 1.8 2.7 0.5 0.9 .. .. 0.9 5.8 0.3 .. 2.8 .. .. 0.4 .. .. 3.7 1.0 .. .. .. 1.6 3.1 2.0 .. .. .. 4.9 4.0 2.2 0.9 .. 3.1 .. 2.6 3.3 0.2
.. .. 6.4 .. 4.1 2.8 25.6 10.2 8.9 5.2 .. 7.0 6.1 2.3 .. .. 5.4 .. .. 6.3 2.2 7.7 7.5 0.5 .. .. 5.7 4.7 1.6 1.4 4.9 .. .. 3.6 .. 3.8 5.1 2.9 .. .. .. 3.9 13.4 12.2 .. .. .. 12.1 4.6 1.9 11.6 .. 9.3 .. 4.2 5.1 8.1
.. .. 10.6 .. 12.0 12.8 29.9 17.6 13.4 10.3 .. 11.2 13.7 8.9 .. .. 12.1 .. .. 8.5 3.8 5.8 6.0 0.3 .. .. 8.3 6.3 12.4 3.7 16.5 .. .. 5.9 .. .. 3.3 7.9 .. .. .. 7.2 14.1 8.6 .. .. .. 7.5 17.8 9.1 6.9 .. 16.7 .. 9.5 11.3 3.4
.. .. 15.2 .. 23.1 16.1 16.9 14.8 16.4 9.3 .. 15.0 20.8 18.7 .. .. 6.3 .. .. 24.2 9.2 21.1 25.5 2.6 .. .. 20.5 15.1 7.6 82.1 9.1 .. .. 14.7 .. 95.2 27.2 16.3 .. .. .. 19.2 18.2 31.1 .. .. .. 19.1 20.4 15.1 55.2 .. 42.6 .. 14.4 18.1 19.8
.. .. 3.7 .. 4.0 13.8 5.4 5.9 2.9 5.4 .. 3.6 11.5 9.3 .. .. 3.0 .. .. 17.5 7.5 4.4 12.9 0.8 .. .. 4.7 3.2 2.8 0.6 3.8 .. .. .. .. .. 9.5 6.5 .. .. .. 29.9 4.0 3.1 .. .. .. 4.3 20.5 4.3 4.0 .. 5.9 .. 2.7 5.5 5.2
.. .. 9.1 .. 29.2 11.2 9.1 0.8 7.9 13.6 .. 5.3 18.6 3.9 .. .. 9.3 .. .. 9.8 49.2 11.8 16.7 93.5 .. .. 17.6 44.7 58.9 7.5 6.6 .. .. 63.9 .. .. 41.6 43.5 .. .. .. 29.4 2.1 5.8 .. .. .. 2.0 2.4 55.6 4.7 .. 11.0 .. 21.6 10.3 16.3
.. .. 3.3 .. 6.3 0.7 .. 3.8 1.2 2.9 .. 2.0 2.3 0.6 .. .. 0.9 .. .. 1.6 21.4 19.1 4.5 .. .. .. 11.4 2.9 6.3 1.1 7.8 .. .. 0.7 .. .. 5.4 2.0 .. .. .. 2.0 2.6 8.0 .. .. .. 6.0 4.0 0.4 1.6 .. 4.5 .. 2.1 4.9 8.5
.. .. 49.0 .. 19.9 35.5 .. 45.5 47.6 49.6 .. 52.5 24.5 23.0 .. .. 58.9 .. .. 22.4 4.9 27.3 26.3 1.4 .. .. 30.8 17.3 10.0 3.6 48.5 .. .. 10.9 .. 0.9 4.1 19.9 .. .. .. 6.8 42.5 29.3 .. .. .. 44.2 26.3 11.2 15.0 .. 6.9 .. 42.9 41.5 38.5
2012 World Development Indicators
159
3.6
Water pollution Emissions of organic water pollutants
thousand kilograms per day 1990 2007a
Puerto Rico Qatar Romania Russian Federation Rwanda Saudi Arabia Senegal Serbia Sierra Leone Singapore Slovak Republic Slovenia Somalia South Africa South Sudan Spain Sri Lanka Sudan Swaziland Sweden Switzerland Syrian Arab Republic Tajikistan Tanzania Thailand Timor-Leste Togo Trinidad and Tobago Tunisia Turkey Turkmenistan Uganda Ukraine United Arab Emirates United Kingdom United States Uruguay Uzbekistan Venezuela, RB Vietnam West Bank and Gaza Yemen, Rep. Zambia Zimbabwe
.. .. 411.2 1,521.4 8.1 .. 6.1 .. .. 33.1 72.8 28.1 .. 260.5 .. 348.0 .. .. .. 116.8 .. 59.7 29.1 .. 369.4 .. .. 7.0 .. 175.8 .. 3.3 .. .. 599.9 2,307.0 .. .. .. 141.0 .. 12.6 .. 29.3
.. 6.4 222.1 1,381.7 8.1 106.6 6.6 .. .. 38.3 47.9 28.8 .. 229.6 .. 378.8 266.1 38.6 .. 96.9 .. 80.4 12.8 30.3 581.4 .. .. 7.6 .. 346.4 .. 2.1 498.2 .. 521.7 1,850.8 .. .. .. 544.8 .. 46.5 .. ..
Industry shares of emissions of organic water pollutants
kilograms per day per worker
Primary metals
Paper and pulp
Chemicals
% of total Stone, ceramics, Food and beverages and glass
Textiles
Wood
Other
1990
2007a
2007a
2007a
2007a
2007a
2007a
2007a
2007a
2007a
.. .. 0.12 0.16 0.37 .. 0.30 .. .. 0.09 0.13 0.13 .. 0.17 .. 0.16 .. .. .. 0.15 .. 0.16 0.17 .. 0.15 .. .. 0.23 .. 0.18 .. 0.29 .. .. 0.16 0.14 .. .. .. 0.16 .. 0.24 .. 0.20
.. 0.12 0.15 0.17 0.37 0.18 0.29 .. .. 0.09 0.14 0.13 .. 0.17 .. 0.15 0.19 0.29 .. 0.14 .. 0.16 0.24 0.34 0.15 .. .. 0.29 .. 0.15 .. 0.23 0.19 .. 0.17 0.14 .. .. .. 0.14 .. 0.21 .. ..
.. 3.7 4.5 8.4 .. 3.2 4.9 .. .. 0.5 7.9 4.6 .. 9.9 .. 3.1 2.6 0.6 .. 5.3 .. 1.6 28.2 2.6 1.9 .. .. 4.8 .. 3.8 .. .. 13.9 .. 2.7 3.5 .. .. .. 1.4 .. .. .. ..
.. 6.7 3.5 4.9 .. 6.9 6.3 .. .. 5.5 5.4 6.1 .. 6.6 .. 8.0 4.3 1.9 .. 11.9 .. 1.9 2.7 4.8 4.2 .. .. 18.2 .. 3.8 .. 7.8 4.3 .. 12.5 8.1 .. .. .. 3.5 .. 2.1 .. ..
.. 10.5 7.1 11.6 9.0 11.6 23.8 .. .. 11.9 9.1 12.2 .. 10.6 .. 10.8 9.0 7.0 .. 9.9 .. 7.3 2.0 8.6 12.4 .. .. 21.3 .. 8.6 .. 7.3 11.2 .. 13.5 13.1 .. .. .. 6.8 .. 7.4 .. ..
.. 6.5 13.9 17.9 77.1 20.0 44.6 .. .. 5.3 10.7 7.7 .. 15.7 .. 15.3 22.4 57.5 .. 8.6 .. 19.9 18.0 61.2 16.4 .. .. 39.3 .. 12.4 .. 34.8 19.7 .. 14.9 12.0 .. .. .. 12.7 .. 35.9 .. ..
.. 18.1 4.0 8.3 4.3 10.7 3.9 .. .. 1.3 6.0 4.1 .. 5.2 .. 7.9 6.3 14.2 .. 2.6 .. 11.3 8.9 1.9 4.7 .. .. 8.0 .. 6.6 .. 13.3 6.8 .. 3.6 3.9 .. .. .. 6.4 .. 14.6 .. ..
.. 20.7 25.0 6.3 1.9 14.4 10.5 .. .. 2.3 5.0 10.8 .. 10.4 .. 8.4 43.6 8.0 .. 1.2 .. 32.0 38.4 12.7 20.5 .. .. 7.7 .. 32.2 .. 17.2 5.6 .. 4.3 4.3 .. .. .. 40.2 .. 15.5 .. ..
.. 12.5 5.3 4.2 2.9 3.3 0.8 .. .. 0.5 4.2 4.9 .. 4.2 .. 3.8 2.5 1.7 .. 5.6 .. 5.2 0.3 2.9 2.8 .. .. 8.5 .. 1.7 .. 2.3 2.1 .. 2.5 4.1 .. .. .. 3.3 .. 5.1 .. ..
.. 21.3 36.8 38.4 4.8 30.0 5.3 .. .. 72.7 51.7 49.6 .. 37.4 .. 42.7 9.3 9.1 .. 54.9 .. 20.9 1.8 5.3 37.2 .. .. 5.0 .. 30.9 .. 19.6 36.5 .. 46.1 51.1 .. .. .. 25.8 .. 19.4 .. ..
a. Data are derived using the United Nations Industrial Development Organizationâ&#x20AC;&#x2122;s (UNIDO) industry database four-digit International Standard Industrial Classification (ISIC). Data in italics are for the most recent year available and are derived using UNIDOâ&#x20AC;&#x2122;s industry database at the three-digit ISIC.
160
2012 World Development Indicators
About the data
3.6
ENVIRONMENT
Water pollution Definitions
Emissions of organic pollutants from industrial
water pollutants per day for each country and year.
• Emissions of organic water pollutants are mea-
activities are a major cause of degradation of water
The data in the table were derived by updating these
sured as biochemical oxygen demand, or the amount
quality. Water quality and pollution levels are gener-
estimates through 2007.
of oxygen that bacteria in water will consume in
ally measured as concentration or load—the rate of
breaking down waste, a standard water treatment
occurrence of a substance in an aqueous solution.
test for the presence of organic pollutants. Emis-
Polluting substances include organic matter, metals,
sions per worker are total emissions divided by the
minerals, sediment, bacteria, and toxic chemicals.
number of industrial workers. • Industry shares of
The table focuses on organic water pollution result-
emissions of organic water pollutants are emissions
ing from industrial activities. Because water pollu-
from manufacturing activities as defined by two-digit
tion tends to be sensitive to local conditions, the
divisions of the International Standard Industrial
national-level data in the table may not reflect the
Classification revision 3.
quality of water in specific locations. The data in the table come from an international study of industrial emissions that may have been the first to include data from developing countries (Hettige, Mani, and Wheeler 1998). These data were updated through 2007 by the World Bank’s Development Research Group. Unlike estimates from earlier studies based on engineering or economic models, these estimates are based on actual measurements of plant-level water pollution. The focus is on organic water pollution caused by organic waste, measured in terms of biochemical oxygen demand (BOD), because the data for this indicator are the most plentiful and reliable for cross-country comparisons of emissions. BOD measures the strength of an organic waste by the amount of oxygen consumed in breaking it down. A sewage overload in natural waters exhausts the water’s dissolved oxygen content. Wastewater treatment, by contrast, reduces BOD. Data on water pollution are more readily available than are other emissions data because most industrial pollution control programs start by regulating emissions of organic water pollutants. Such data are fairly reliable because sampling techniques for measuring water pollution are more widely understood and much less expensive than those for air pollution. Hettige, Mani, and Wheeler (1998) used plant- and sector-level information on emissions and employment from 13 national environmental protection agencies and sector-level information on output and employment from the United Nations Industrial Development Organization (UNIDO). Their econometric analysis found that the ratio of BOD to employment in each industrial sector is about the same
Data sources
across countries. This finding allowed the authors to
Data on water pollutants are from Hettige, Mani,
estimate BOD loads across countries and over time.
and Wheeler (1998). The data were updated
The estimated BOD intensities per unit of employ-
through 2007 by the World Bank Development
ment were multiplied by sectoral employment num-
Research Group using the same methodology
bers from UNIDO’s industry database for 1980–98.
as the initial study. Data on industrial sectoral
These estimates of sectoral emissions were then
employment are from UNIDO’s industry database.
used to calculate kilograms of emissions of organic
2012 World Development Indicators
161
3.7
Energy production and use Energy production
Total million metric tons of oil equivalent
Afghanistan Albania Algeria Angola Argentina Armenia Australia Austria Azerbaijan Bahrain Bangladesh Belarus Belgium Benin Bolivia Bosnia and Herzegovina Botswana Brazil Bulgaria Burkina Faso Burundi Cambodia Cameroon Canada Central African Republic Chad Chile China Hong Kong SAR, China Colombia Congo, Dem. Rep. Congo, Rep. Costa Rica Côte d’Ivoire Croatia Cuba Cyprus Czech Republic Denmark Dominican Republic Ecuador Egypt, Arab Rep. El Salvador Eritrea Estonia Ethiopia Finland France Gabon Gambia, The Georgia Germany Ghana Greece Guatemala Guinea Guinea-Bissau Haiti
162
Energy use
Total million metric tons of oil equivalent
% of total average annual % growth
1990
2009
1990
2009
1990–2009
.. 2.4 100.1 28.7 48.4 0.1 157.5 8.1 20.6 13.4 10.8 3.3 13.1 1.8 4.9 4.6 0.9 104.2 9.6 .. .. .. 11.0 273.7 .. .. 7.5 886.3 0.0 48.2 12.0 8.7 1.0 3.4 5.1 9.4 0.0 40.9 10.1 1.0 16.5 54.9 1.7 0.7 5.4 14.1 12.1 111.9 14.6 .. 2.1 186.2 4.4 9.2 3.4 .. .. 1.3
.. 1.3 152.3 101.0 80.8 0.8 310.7 11.4 64.6 17.5 24.8 4.0 15.3 2.0 14.2 4.5 0.9 230.3 9.8 .. .. 3.7 8.8 389.8 .. .. 9.3 2,084.9 0.1 99.1 23.3 15.3 2.7 11.9 4.1 5.6 0.1 31.2 23.9 1.9 27.3 88.2 3.2 0.6 4.2 30.4 16.6 129.5 13.6 .. 1.3 127.1 7.0 10.1 6.1 .. .. 1.9
.. 2.7 22.2 5.9 46.1 7.7 86.2 24.8 26.2 4.4 12.7 45.5 48.3 1.7 2.6 7.0 1.3 140.2 28.6 .. .. .. 5.0 208.6 .. .. 13.6 863.0 8.7 24.2 11.8 0.8 2.0 4.3 9.0 19.3 1.4 49.6 17.4 4.1 6.0 31.8 2.5 0.9 9.9 14.9 28.4 223.9 1.2 0.1 12.4 351.4 5.3 21.4 4.4 .. 0.1 1.6
.. 1.7 39.8 11.9 74.2 2.6 131.1 31.7 12.0 9.5 29.6 26.8 57.2 3.5 6.2 6.0 2.0 240.2 17.5 .. .. 5.2 6.9 254.1 .. .. 28.8 2,257.1 14.9 31.8 22.9 1.4 4.9 10.4 8.7 11.5 2.5 42.0 18.6 8.1 11.4 72.0 5.1 0.7 4.7 32.7 33.2 256.2 1.8 0.1 3.2 318.5 9.2 29.4 9.8 .. 0.1 2.6
.. 1.6 3.0 3.8 2.5 –2.3 2.3 1.7 –2.8 4.4 4.6 –1.7 0.9 3.7 3.9 2.9 2.7 3.0 –1.3 .. .. 3.3 1.8 1.4 .. .. 4.3 5.1 2.5 0.8 3.9 3.2 5.0 5.0 1.3 –1.8 2.9 0.1 0.1 3.6 3.9 4.8 3.8 –2.0 –2.0 3.8 1.5 0.9 2.7 .. –6.2 –0.2 3.2 2.2 4.0 .. .. 3.4
2012 World Development Indicators
Alternative and nuclear energy
Per capita kilograms of oil equivalent
Fossil fuel
Combustible renewables and waste
% of total energy use
1990
2009
1990
2009
1990
2009
1990
2009
.. 809 877 569 1,411 2,171 5,053 3,228 3,665 8,826 121 4,470 4,844 348 392 1,629 912 937 3,277 .. .. .. 409 7,505 .. .. 1,028 760 1,518 730 324 334 660 345 1,884 1,824 1,775 4,796 3,377 570 584 560 463 276 6,316 308 5,692 3,848 1,272 64 2,587 4,424 358 2,111 497 .. 73 219
.. 538 1,138 641 1,853 843 5,971 3,784 1,338 8,096 201 2,815 5,300 404 638 1,580 1,034 1,243 2,305 .. .. 371 361 7,534 .. .. 1,698 1,695 2,133 697 357 356 1,067 535 1,965 1,022 2,298 4,004 3,369 826 796 903 828 142 3,543 402 6,213 3,970 1,214 84 723 3,889 388 2,609 701 .. 67 263
.. 76.5 99.9 25.5 88.7 97.2 93.9 79.2 100.0 100.0 45.5 95.6 76.0 4.8 67.2 93.9 66.0 51.2 84.3 .. .. .. 18.7 74.6 .. .. 74.6 75.5 100.0 67.4 11.2 35.1 48.4 23.3 86.5 55.7 99.5 91.7 89.6 74.9 79.1 94.0 31.4 19.4 100.0 5.5 55.5 58.1 32.0 0.0 88.9 86.8 18.2 94.6 28.1 .. 0.0 19.7
.. 54.1 99.8 37.6 89.4 68.4 94.4 70.2 98.2 99.8 69.8 90.3 73.6 40.4 79.1 92.2 64.3 51.3 73.1 .. .. 27.8 30.9 74.9 .. .. 74.5 87.4 95.1 75.2 3.7 44.1 44.7 23.5 83.4 84.1 95.7 79.6 80.4 76.6 86.7 96.3 37.8 22.6 83.4 7.1 48.8 51.0 34.0 0.0 68.0 79.5 24.3 92.4 46.1 .. 0.0 28.1
.. 13.6 0.1 73.5 3.7 0.0 4.6 10.0 0.0 0.0 53.9 0.4 1.6 94.1 28.9 2.3 33.4 34.1 0.6 .. .. .. 76.7 3.9 .. .. 19.7 23.2 0.6 22.8 84.7 59.5 36.6 73.5 3.5 44.3 0.5 1.6 6.6 24.4 13.8 3.3 48.2 80.6 1.9 93.9 16.1 4.9 62.9 .. 3.7 1.4 73.7 4.2 68.5 .. .. 77.7
.. 12.4 0.1 60.1 3.1 0.0 4.4 17.6 0.0 0.0 29.8 6.0 4.9 57.4 17.7 3.1 23.6 31.6 4.3 .. .. 70.7 64.1 4.5 .. .. 17.5 9.0 0.4 14.0 93.7 51.1 15.8 75.2 4.2 15.8 1.8 5.6 16.2 21.8 5.4 2.1 33.8 77.4 14.7 92.0 20.9 5.9 61.8 .. 12.0 7.8 69.8 3.4 52.1 .. .. 71.2
.. 9.2 0.1 1.1 7.5 1.7 1.5 11.0 0.2 0.0 0.6 0.0 23.1 0.0 3.9 3.7 0.0 13.1 13.9 .. .. .. 4.6 21.5 .. .. 5.7 1.3 0.0 9.8 4.1 5.3 14.4 2.6 3.6 0.0 0.0 6.8 0.3 0.7 7.2 2.7 20.3 0.0 0.0 0.6 20.9 38.7 5.1 .. 5.2 11.8 9.3 1.0 3.4 .. .. 2.5
.. 26.4 0.1 2.3 6.8 31.7 1.2 12.0 1.7 0.0 0.5 0.0 21.8 0.0 3.2 9.0 0.0 15.6 24.9 .. .. 0.1 5.0 21.7 .. .. 7.6 3.7 0.0 11.1 2.9 2.0 39.5 1.8 6.8 0.1 2.5 17.5 3.3 1.6 7.0 1.7 28.2 0.0 0.4 1.0 21.8 43.9 4.2 .. 21.4 13.0 6.4 3.0 1.8 .. .. 0.7
Energy production
Total million metric tons of oil equivalent
Honduras Hungary India Indonesia Iran, Islamic Rep. Iraq Ireland Israel Italy Jamaica Japan Jordan Kazakhstan Kenya Korea, Dem. Rep. Korea, Rep. Kosovo Kuwait Kyrgyz Republic Lao PDR Latvia Lebanon Lesotho Liberia Libya Lithuania Macedonia, FYR Madagascar Malawi Malaysia Mali Mauritania Mauritius Mexico Moldova Mongolia Morocco Mozambique Myanmar Namibia Nepal Netherlands New Zealand Nicaragua Niger Nigeria Norway Oman Pakistan Panama Papua New Guinea Paraguay Peru Philippines Poland Portugal Puerto Rico Qatar
1990
2009
1.7 14.6 291.8 169.1 179.3 104.9 3.5 0.4 25.3 0.5 75.2 0.2 90.5 9.0 28.9 22.6 .. 50.4 2.5 .. 1.1 0.1 .. .. 73.2 4.9 1.3 .. .. 48.8 .. .. .. 194.7 0.1 2.7 0.8 5.6 10.7 0.2 5.5 60.5 11.5 1.5 .. 150.5 119.1 38.3 34.2 0.6 .. 4.6 10.6 17.2 103.9 3.4 .. 26.6
2.2 11.0 502.5 351.8 349.8 119.6 1.5 3.3 27.0 0.5 93.8 0.3 145.8 15.6 20.3 44.3 .. 130.2 1.2 .. 2.1 0.2 .. .. 87.1 4.2 1.6 .. .. 89.7 .. .. .. 220.0 0.1 7.7 0.8 11.9 22.4 0.3 8.8 63.0 15.2 1.7 .. 228.7 213.6 67.2 64.9 0.7 .. 7.4 15.1 23.5 67.5 4.9 .. 139.9
3.7
Energy use
Total million metric tons of oil equivalent 1990
2.4 28.7 316.7 101.3 67.9 18.1 10.0 11.5 146.6 2.8 439.3 3.3 72.7 10.9 33.2 93.1 .. 9.1 7.5 .. 7.9 2.0 .. .. 11.3 16.1 2.5 .. .. 22.0 .. .. 0.5 122.5 9.9 3.4 6.9 5.9 10.7 0.7 5.8 65.7 12.8 2.1 .. 70.6 21.0 4.2 42.8 1.5 .. 3.1 9.7 28.9 103.1 16.7 .. 6.2
2009
4.4 24.9 675.8 202.0 215.9 32.2 14.3 21.5 164.6 3.3 472.0 7.5 65.8 18.7 19.3 229.2 .. 30.2 3.0 .. 4.2 6.6 0.0 .. 20.4 8.4 2.8 .. .. 66.8 .. .. 1.2 174.6 2.4 3.2 15.1 9.8 15.1 1.7 10.0 78.2 17.4 3.1 .. 108.3 28.2 15.1 85.5 3.1 .. 4.8 15.8 38.8 94.0 24.1 .. 23.8
ENVIRONMENT
Energy production and use
Alternative and nuclear energy
% of total average annual % growth 1990–2009
3.6 0.0 3.8 3.5 6.4 3.6 2.6 3.4 1.2 2.1 0.6 4.3 –0.7 2.8 –2.0 4.6 .. 8.1 –3.4 .. –2.1 4.2 .. .. 2.3 –1.9 0.8 .. .. 5.9 .. .. .. 2.0 –5.2 –0.5 4.0 2.9 2.3 5.4 3.0 0.9 1.5 2.6 .. 2.4 1.6 6.4 3.7 3.3 .. 1.7 2.5 1.7 –0.5 2.4 .. 7.5
Per capita kilograms of oil equivalent
Fossil fuel
Combustible renewables and waste
% of total energy use
1990
2009
1990
2009
1990
2009
1990
2009
487 2,762 362 550 1,237 994 2,842 2,463 2,584 1,167 3,556 1,028 4,450 467 1,649 2,171 .. 4,364 1,693 .. 2,949 663 .. .. 2,614 4,357 1,298 .. .. 1,208 .. .. 453 1,453 2,669 1,558 280 437 271 445 303 4,393 3,720 508 .. 724 4,952 2,258 383 603 .. 724 449 469 2,705 1,677 .. 13,098
592 2,480 560 851 2,951 1,035 3,216 2,878 2,735 1,208 3,700 1,260 4,091 474 795 4,701 .. 11,402 566 .. 1,871 1,580 9 .. 3,258 2,512 1,352 .. .. 2,391 .. .. 947 1,559 687 1,194 477 427 316 764 338 4,729 4,032 540 .. 701 5,849 5,554 502 896 .. 749 550 424 2,464 2,266 .. 14,911
30.0 81.5 55.4 54.7 98.9 98.6 84.6 97.2 93.4 82.6 84.5 98.1 96.9 17.5 93.1 83.8 .. 99.9 93.5 .. 81.8 92.5 .. .. 98.9 75.8 98.0 .. .. 88.8 .. .. 0.0 87.2 100.0 96.9 93.8 5.5 14.4 62.0 5.1 95.9 67.0 28.3 .. 19.3 51.9 100.0 52.6 57.5 .. 21.3 63.3 43.4 97.8 80.4 .. 100.0
50.3 74.2 73.0 65.6 99.7 97.6 88.7 96.5 87.5 83.7 81.0 98.0 99.0 16.8 89.0 81.7 .. 100.0 72.5 .. 59.4 95.9 0.0 .. 99.2 55.7 84.2 .. .. 94.7 .. .. 0.0 88.9 91.3 96.4 92.5 7.7 27.7 70.5 11.1 93.1 63.7 44.7 .. 14.7 58.8 100.0 61.8 78.6 .. 28.5 73.5 57.0 92.8 78.0 .. 100.0
63.0 2.3 42.1 42.9 0.3 0.1 1.1 0.0 0.6 17.1 1.1 0.1 0.2 77.9 2.9 0.8 .. 0.1 0.1 .. 8.4 5.3 .. .. 1.1 1.8 0.0 .. .. 9.7 .. .. .. 7.0 0.4 2.5 4.6 93.9 84.7 15.9 93.7 1.5 5.7 53.9 .. 80.2 4.9 0.0 43.8 28.9 .. 72.5 27.5 38.5 2.2 14.8 .. 0.0
44.3 7.1 24.5 26.0 0.2 0.1 2.2 0.1 4.3 15.9 1.4 0.1 0.2 76.0 5.4 1.3 .. 0.0 0.1 .. 30.0 1.8 .. .. 0.8 9.8 7.0 .. .. 4.5 .. .. .. 4.8 3.3 3.2 3.2 81.8 69.9 12.0 85.8 4.4 6.3 45.9 .. 84.9 5.1 0.0 34.5 10.7 .. 53.7 15.4 17.9 7.1 13.6 .. 0.0
8.2 12.8 2.5 2.4 0.8 1.2 0.6 3.1 3.9 0.3 14.4 1.8 0.9 4.5 4.0 15.4 .. 0.0 11.5 .. 4.9 2.2 .. .. 0.0 28.2 1.7 .. .. 1.6 .. .. .. 5.9 0.2 0.0 1.5 0.4 1.0 17.4 1.3 1.4 27.1 17.5 .. 0.5 49.6 0.0 3.6 13.1 .. 76.0 9.2 18.1 0.1 4.8 .. 0.0
5.5 16.8 2.3 8.4 0.3 0.9 2.3 4.8 5.9 0.4 17.6 1.7 0.9 7.2 5.6 17.0 .. 0.0 28.3 .. 7.1 0.8 .. .. 0.0 34.9 4.3 .. .. 0.9 .. .. .. 6.3 0.2 0.0 1.7 14.9 2.4 7.2 2.7 2.0 29.8 9.4 .. 0.4 38.7 0.0 3.7 10.8 .. 99.5 11.1 25.0 0.3 6.6 .. 0.0
2012 World Development Indicators
163
3.7
Energy production and use Energy production
Total million metric tons of oil equivalent 1990
Romania Russian Federation Rwanda Saudi Arabia Senegal Serbia Sierra Leone Singapore Slovak Republic Slovenia Somalia South Africa South Sudan Spain Sri Lanka Sudan Swaziland Sweden Switzerland Syrian Arab Republic Tajikistan Tanzania Thailand Timor-Leste Togo Trinidad and Tobago Tunisia Turkey Turkmenistan Uganda Ukraine United Arab Emirates United Kingdom United States Uruguay Uzbekistan Venezuela, RB Vietnam West Bank and Gaza Yemen, Rep. Zambia Zimbabwe World Low income Middle income Lower middle income Upper middle income Low & middle income East Asia & Pacific Europe & Central Asia Latin America & Carib. Middle East & N. Africa South Asia Sub-Saharan Africa High income Euro area
2009
Total million metric tons of oil equivalent 1990
40.8 28.3 62.3 1,293.1 1,181.6 879.2 .. .. .. 370.2 528.4 59.8 1.0 1.3 1.7 9.4 19.3a 13.4 a .. .. .. 0.0 0.0 11.5 5.3 5.9 21.3 3.1 3.5 5.7 .. .. .. 114.5 160.6 93.9 .. .. .. 34.6 29.7 90.1 4.2 5.1 5.5 8.8 35.2 10.6 .. .. 0.3 29.7 30.3 47.2 10.3 12.8 24.3 22.3 23.6 10.5 2.0 1.5 5.3 9.1 18.0 9.7 26.6 61.7 41.9 .. .. .. 1.1 2.2 1.3 12.6 44.0 6.0 5.7 7.8 4.9 25.8 30.3 52.8 74.9 40.9 19.6 .. .. .. 135.8 76.9 251.8 110.2 168.8 20.4 208.0 158.9 205.9 1,652.5 1,686.4 1,915.0 1.1 1.5 2.3 38.6 60.7 46.4 148.8 203.5 43.5 24.7 76.6 24.3 .. .. .. 9.4 15.2 2.5 4.9 7.2 5.4 8.5 8.5 9.3 8,815.9 t 12,241.3 t 8,574.0 t 160.7 250.6 186.6 4,810.5 7,351.0 3,886.6 1,247.9 1,944.1 1,085.5 3,564.8 5,410.7 2,803.9 4,969.1 7,597.3 4,059.7 1,224.5 2,758.4 1,138.1 1,770.3 1,673.3 1,585.6 613.1 937.5 457.6 558.2 856.0 183.8 349.4 611.2 386.8 475.6 810.5 313.9 3,878.6 4,694.3 4,538.5 481.9 450.2 1,069.7
a. Includes Kosovo and Montenegro.
164
Energy use
2012 World Development Indicators
2009
34.4 646.9 .. 157.9 2.9 14.4 .. 18.5 16.7 7.0 .. 144.0 .. 126.5 9.3 15.8 0.4 45.4 27.0 22.5 2.3 19.6 103.3 0.1 2.6 20.3 9.2 97.7 19.6 .. 115.5 59.6 196.8 2,162.9 4.1 48.8 66.9 64.0 .. 7.6 7.9 9.5 11,787.1 t 263.3 6,158.2 1,656.0 4,505.9 6,407.1 2,790.9 1,137.6 716.5 454.5 817.1 511.2 5,420.3 1,169.7
Alternative and nuclear energy
% of total average annual % growth 1990–2009
Per capita kilograms of oil equivalent 1990
2009
–1.9 2,683 1,602 –1.0 5,929 4,561 .. .. .. 4.9 3,703 5,888 3.4 233 243 0.4 2,550a 1,974 .. .. .. 0.9 3,760 3,704 –0.2 4,025 3,086 1.8 2,858 3,417 .. .. .. 2.3 2,667 2,921 .. .. .. 2.7 2,319 2,756 3.2 318 449 2.4 401 372 .. 358 373 0.2 5,515 4,883 0.6 3,621 3,480 4.8 849 1,123 –2.9 1,001 342 4.1 382 451 4.9 735 1,504 .. .. 58 4.2 345 445 8.0 4,912 15,158 3.6 607 881 3.6 975 1,359 2.4 5,352 3,933 .. .. .. –3.0 4,852 2,507 5.4 11,258 8,588 0.0 3,597 3,183 1.0 7,672 7,051 2.1 724 1,224 0.5 2,261 1,758 2.0 2,205 2,357 5.3 368 745 .. .. .. 6.0 210 324 2.0 687 617 –0.2 888 763 1.9 w 1,661 w 1,788 w 2.2 386 365 2.6 1,019 1,254 2.4 607 665 2.7 1,375 1,848 2.6 963 1,162 4.8 712 1,436 –1.0 4,067 2,831 2.5 1,048 1,245 5.0 812 1,399 3.8 340 514 2.6 681 689 1.2 4,649 4,856 0.9 3,509 3,536
Fossil fuel 1990
2009
96.1 76.3 93.4 90.2 .. .. 100.0 100.0 43.3 57.8 90.6a 92.4 .. .. 100.0 99.8 81.6 69.5 71.3 69.3 .. .. 86.6 87.8 .. .. 77.4 79.9 24.1 45.3 17.4 30.2 0.0 0.0 37.3 32.0 58.6 53.3 97.7 99.3 71.3 41.2 6.9 11.1 63.8 79.4 .. 0.0 15.1 14.3 99.2 99.9 87.0 85.7 81.8 89.9 100.0 100.0 .. .. 91.8 80.0 100.0 100.0 90.7 87.3 86.4 84.1 58.7 60.3 99.2 98.4 91.5 87.7 20.3 56.2 .. .. 96.9 98.7 15.6 7.6 44.8 25.7 80.9 w 80.7 w 41.5 29.9 78.6 81.5 65.1 68.0 83.7 86.3 77.3 79.8 71.4 84.1 93.0 88.9 70.3 71.8 97.4 98.3 53.5 70.7 41.7 40.1 84.2 81.9 80.0 74.2
Combustible renewables and waste
% of total energy use
1990
2009
1990
2009
1.0 1.4 .. 0.0 56.7 6.0a .. 0.0 0.8 4.7 .. 11.1 .. 4.5 71.0 81.8 .. 11.7 5.9 0.0 0.0 91.7 35.0 .. 82.8 0.8 12.9 13.7 0.0 .. 0.1 0.0 0.3 3.3 24.2 0.0 1.2 77.8 .. 3.1 74.3 50.8 10.2 w 54.7 17.2 30.7 12.0 18.5 26.6 1.5 20.4 1.4 43.9 55.9 2.8 3.1
11.4 1.0 .. 0.0 41.1 2.0 .. 0.2 5.2 7.2 .. 9.8 .. 4.9 51.1 68.0 .. 22.9 8.2 0.0 0.0 87.7 19.9 .. 83.1 0.1 14.1 4.8 0.0 .. 0.8 0.0 2.7 3.9 26.0 0.0 0.8 39.3 .. 1.3 80.9 65.6 10.0 w 65.9 13.2 26.9 8.3 15.0 11.8 1.9 17.0 0.9 26.8 57.3 4.2 6.7
1.6 5.2 .. 0.0 0.0 4.2a .. 0.0 15.5 25.5 .. 2.4 .. 18.1 4.9 0.8 .. 50.9 36.3 2.2 26.7 1.4 1.0 .. 0.6 0.0 0.1 4.6 0.3 .. 8.2 0.0 8.5 10.3 26.8 1.2 7.3 1.9 .. 0.0 12.7 4.0 8.7 w 3.9 4.1 4.4 4.0 4.1 1.9 5.3 9.1 1.1 2.5 2.2 12.8 16.5
12.9 9.0 .. 0.0 0.7 6.4 .. 0.0 24.6 27.3 .. 2.4 .. 15.8 3.6 1.8 .. 42.9 39.2 0.7 58.6 1.2 0.6 .. 0.3 0.0 0.2 5.4 0.0 .. 19.7 0.0 9.8 11.9 11.1 1.6 11.6 4.0 .. 0.0 11.2 3.8 9.2 w 4.1 5.3 5.3 5.3 5.3 4.1 9.3 11.1 0.6 2.4 2.5 13.8 18.6
About the data
3.7
ENVIRONMENT
Energy production and use Definitions
In developing economies growth in energy use is
• Energy production refers to forms of primary
closely related to growth in the modern sectors—
energy—petroleum (crude oil, natural gas liquids,
industry, motorized transport, and urban areas—
and oil from nonconventional sources), natural gas,
but energy use also reflects climatic, geographic,
solid fuels (coal, lignite, and other derived fuels),
and economic factors (such as the relative price
and combustible renewables and waste—and pri-
of energy). Energy use has been growing rapidly in
mary electricity, all converted into oil equivalents
low- and middle-income economies, but high-income
(see About the data). • Energy use refers to the use
economies still use almost five times as much energy
of primary energy before transformation to other
on a per capita basis.
end-use fuels, which is equal to indigenous produc-
Energy data are compiled by the International
tion plus imports and stock changes, minus exports
Energy Agency (IEA). IEA data for economies that
and fuels supplied to ships and aircraft engaged in
are not members of the Organisation for Economic
international transport (see About the data). • Fos-
Co-operation and Development (OECD) are based
sil fuel comprises coal, oil, petroleum, and natural
on national energy data adjusted to conform to
gas products. • Combustible renewables and waste
annual questionnaires completed by OECD member
comprise solid biomass, liquid biomass, biogas,
governments.
industrial waste, and municipal waste. • Alternative
Total energy use refers to the use of primary energy
and nuclear energy production is noncarbohydrate
before transformation to other end-use fuels (such
energy that does not produce carbon dioxide when
as electricity and refined petroleum products). It
generated. It includes hydropower and nuclear, geo-
includes energy from combustible renewables and
thermal, and solar power, among others.
waste—solid biomass and animal products, gas and liquid from biomass, and industrial and municipal waste. Biomass is any plant matter used directly as fuel or converted into fuel, heat, or electricity. Data for combustible renewables and waste are often based on small surveys or other incomplete information and thus give only a broad impression of developments and are not strictly comparable across countries. The IEA reports include country notes that explain some of these differences (see Data sources). All forms of energy—primary energy and primary electricity—are converted into oil equivalents. A notional thermal efficiency of 33 percent is assumed for converting nuclear electricity into oil equivalents and 100 percent efficiency for converting hydroelectric power. The IEA makes these estimates in consultation with national statistical offices, oil companies, electric utilities, and national energy experts. The IEA occasionally revises its time series to reflect political changes, and energy statistics undergo continual changes in coverage or methodology as more detailed energy accounts become available. Breaks in series are therefore unavoidable. Data sources Data on energy production and use are from IEA electronic files and are published in IEA’s annual publications Energy Statistics of Non-OECD Countries, Energy Balances of Non-OECD Countries, Energy Statistics of OECD Countries, and Energy Balances of OECD Countries.
2012 World Development Indicators
165
3.8
Electricity production, sources, and access Electricity production
Afghanistan Albania Algeria Angola Argentina Armenia Australia Austria Azerbaijan Bahrain Bangladesh Belarus Belgium Benin Bolivia Bosnia and Herzegovina Botswana Brazil Bulgaria Burkina Faso Burundi Cambodia Cameroon Canada Central African Republic Chad Chile China Hong Kong SAR, China Colombia Congo, Dem. Rep. Congo, Rep. Costa Rica Côte d’Ivoire Croatia Cuba Cyprus Czech Republic Denmark Dominican Republic Ecuador Egypt, Arab Rep. El Salvador Eritrea Estonia Ethiopia Finland France Gabon Gambia, The Georgia Germany Ghana Greece Guatemala Guinea Guinea-Bissau Haiti
166
Sources of electricitya
Access to electricity
% of total
billion kilowatt hours
Coal
Natural gas
Oil
Hydropower
Renewable sourcesb
Nuclear power
2009
2009
2009
2009
2009
2009
2009
2009
.. 0.0 0.0 0.0 2.3 0.0 77.9 7.7 0.0 0.0 1.7 0.0 6.8 0.0 0.0 59.9 100.0 2.1 49.8 .. .. 0.0 0.0 15.2 .. .. 24.5 78.8 70.8 7.3 0.0 0.0 0.0 0.0 13.1 0.0 0.0 59.6 48.6 12.9 0.0 0.0 0.0 0.0 91.4 0.0 16.0 5.3 0.0 .. 0.0 43.8 0.0 56.0 8.2 .. .. 0.0
.. 0.0 97.2 0.0 51.3 20.3 13.7 18.8 85.1 100.0 89.4 81.7 32.6 0.0 59.7 0.2 0.0 2.9 4.6 .. .. 0.0 7.2 6.2 .. .. 6.5 1.4 28.9 19.3 0.4 36.0 0.0 61.9 17.4 13.4 0.0 1.2 18.5 13.5 6.9 68.7 0.0 0.0 1.2 0.0 13.6 3.9 26.4 .. 12.9 13.5 0.0 18.0 0.0 .. .. 0.0
.. 0.6 2.0 23.9 10.5 0.0 1.0 1.7 2.6 0.0 4.8 17.6 0.3 100.0 1.7 0.1 0.0 3.1 0.8 .. .. 95.6 22.7 1.4 .. .. 20.0 0.4 0.4 0.6 0.1 0.0 4.9 0.1 15.9 82.8 99.2 0.2 3.2 63.6 37.5 21.3 43.7 99.3 0.5 12.4 0.7 1.1 20.0 .. 0.5 1.6 23.2 12.6 34.5 .. .. 71.3
.. 99.4 0.8 76.1 27.8 35.6 4.7 61.4 12.2 0.0 4.1 0.1 0.4 0.0 37.5 39.8 0.0 83.8 8.2 .. .. 3.9 70.0 60.3 .. .. 41.7 16.7 0.0 71.7 99.6 64.0 77.8 35.9 53.0 0.9 0.0 3.0 0.1 9.8 53.5 9.3 26.0 0.0 0.4 87.3 17.6 10.6 53.2 .. 86.6 3.2 76.8 8.8 23.3 .. .. 28.7
.. 0.0 0.0 0.0 1.4 0.1 2.6 9.6 0.0 0.0 0.0 0.2 5.7 0.0 1.0 0.0 0.0 5.2 0.6 .. .. 0.5 0.2 1.9 .. .. 7.2 0.8 0.0 1.2 0.0 0.0 17.4 2.1 0.6 2.9 0.1 2.7 27.6 0.2 2.1 0.8 30.3 0.7 5.8 0.4 12.5 2.4 0.4 .. 0.0 12.8 0.0 4.6 34.0 .. .. 0.0
.. 0.0 0.0 0.0 6.7 44.0 0.0 0.0 0.0 0.0 0.0 0.0 52.6 0.0 0.0 0.0 0.0 2.8 36.0 .. .. 0.0 0.0 15.0 .. .. 0.0 1.9 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 33.3 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 32.6 76.2 0.0 .. 0.0 23.0 0.0 0.0 0.0 .. .. 0.0
15.6 .. 99.3 26.2 97.2 .. .. .. .. 99.4 41.0 .. .. 24.8 77.5 .. 45.4 98.3 .. 14.6 .. 24.0 48.7 .. .. .. 98.5 99.4 .. 93.6 11.1 37.1 99.3 47.3 .. 97.0 .. .. .. 95.9 92.2 99.6 86.4 32.0 .. 17.0 .. .. 36.7 .. .. .. 60.5 .. 80.5 .. .. 38.5
.. 5.3 42.8 4.2 121.9 5.7 260.9 65.6 18.9 12.1 37.9 30.4 89.8 0.1 6.1 15.7 0.4 466.5 42.4 .. .. 1.2 5.7 603.1 .. .. 60.7 3,695.9 38.7 57.3 7.8 0.5 9.3 5.9 12.7 17.7 5.2 81.7 36.4 15.0 17.2 139.0 5.8 0.3 8.8 4.1 72.1 537.4 1.7 .. 8.6 586.4 9.0 61.1 9.0 .. .. 0.7
2012 World Development Indicators
% of population
Electricity production
Honduras Hungary India Indonesia Iran, Islamic Rep. Iraq Ireland Israel Italy Jamaica Japan Jordan Kazakhstan Kenya Korea, Dem. Rep. Korea, Rep. Kosovo Kuwait Kyrgyz Republic Lao PDR Latvia Lebanon Lesotho Liberia Libya Lithuania Macedonia, FYR Madagascar Malawi Malaysia Mali Mauritania Mauritius Mexico Moldova Mongolia Morocco Mozambique Myanmar Namibia Nepal Netherlands New Zealand Nicaragua Niger Nigeria Norway Oman Pakistan Panama Papua New Guinea Paraguay Peru Philippines Poland Portugal Puerto Rico Qatar
Sources of electricitya
ENVIRONMENT
3.8
Electricity production, sources, and access
Access to electricity
% of total
billion kilowatt hours
Coal
Natural gas
Oil
Hydropower
Renewable sourcesb
Nuclear power
2009
2009
2009
2009
2009
2009
2009
2009
6.6 35.9 899.4 155.5 203.2 46.1 27.9 55.0 288.3 5.5 1,041.0 14.3 78.7 6.9 21.1 451.7 .. 53.2 11.1 .. 5.6 13.8 .. .. 30.4 14.6 6.8 .. .. 105.1 .. .. .. 261.0 3.6 4.2 21.4 17.0 5.9 1.7 3.1 113.5 43.5 3.5 .. 19.8 132.0 17.8 95.4 6.9 .. 55.0 35.4 61.9 151.1 49.5 .. 24.8
0.0 17.9 68.6 41.8 0.2 0.0 14.4 62.5 15.1 0.0 26.8 0.0 74.9 0.0 38.1 46.2 .. 0.0 2.8 .. 0.0 0.0 .. .. 0.0 0.0 77.7 .. .. 30.9 .. .. .. 11.3 0.0 96.4 52.4 0.0 0.0 17.5 0.0 23.4 7.6 0.0 .. 0.0 0.1 0.0 0.1 0.0 .. 0.0 2.5 26.6 89.1 26.1 .. 0.0
0.0 29.0 12.4 22.1 70.4 0.0 58.4 32.8 51.1 0.0 27.4 88.1 13.1 0.0 0.0 15.6 .. 28.8 8.0 .. 36.0 1.4 .. .. 41.0 14.3 0.0 .. .. 60.7 .. .. .. 53.1 95.0 0.0 13.3 0.1 19.6 0.0 0.0 60.5 20.6 0.0 .. 64.3 3.2 82.0 29.4 0.0 .. 0.0 34.3 32.1 3.2 29.7 .. 100.0
54.9 1.8 2.9 22.8 25.8 93.0 3.3 4.0 9.0 96.4 7.2 11.4 3.2 44.1 2.8 4.4 .. 71.2 0.0 .. 0.1 94.1 .. .. 59.0 5.0 3.7 .. .. 2.0 .. .. .. 17.5 1.3 3.6 20.3 0.0 8.9 0.5 0.4 1.3 0.0 69.1 .. 12.8 0.0 18.0 38.0 43.6 .. 0.0 4.1 8.7 1.8 6.6 .. 0.0
42.5 0.6 11.9 7.3 3.6 7.0 3.2 0.0 17.0 2.0 7.2 0.4 8.7 31.6 59.1 0.6 .. 0.0 89.3 .. 62.1 4.5 .. .. 0.0 2.9 18.6 .. .. 6.3 .. .. .. 10.2 1.5 0.0 12.1 99.9 71.5 82.0 99.6 0.1 55.7 8.6 .. 22.9 95.7 0.0 29.4 56.1 .. 100.0 57.6 15.8 1.6 16.7 .. 0.0
2.6 7.4 2.2 6.0 0.1 0.0 11.2 0.1 7.0 1.6 2.5 0.1 0.0 24.4 0.0 0.4 .. 0.0 0.0 .. 1.8 0.0 .. .. 0.0 1.8 0.0 .. .. 0.0 .. .. .. 3.9 0.0 0.0 1.8 0.0 0.0 0.0 0.0 9.5 15.9 22.3 .. 0.0 0.9 0.0 0.0 0.2 .. 0.0 1.4 16.8 4.2 20.2 .. 0.0
0.0 43.0 2.1 0.0 0.0 0.0 0.0 0.0 0.0 0.0 26.9 0.0 0.0 0.0 0.0 32.7 .. 0.0 0.0 .. 0.0 0.0 .. .. 0.0 74.1 0.0 .. .. 0.0 .. .. .. 4.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 3.7 0.0 0.0 .. 0.0 0.0 0.0 3.0 0.0 .. 0.0 0.0 0.0 0.0 0.0 .. 0.0
70.3 .. 66.3 64.5 98.4 86.0 .. 99.7 .. 92.0 .. 99.9 .. 16.1 26.0 .. .. 100.0 .. 55.0 .. 99.9 16.0 .. 99.8 .. .. 19.0 9.0 99.4 .. .. 99.4 .. .. 67.0 97.0 11.7 13.0 34.0 43.6 .. .. 72.1 .. 50.6 .. 98.0 62.4 88.1 .. 96.7 85.7 89.7 .. .. .. 98.7
% of population
2012 World Development Indicators
167
3.8
Electricity production, sources, and access Electricity production
Romania Russian Federation Rwanda Saudi Arabia Senegal Serbia Sierra Leone Singapore Slovak Republic Slovenia Somalia South Africa South Sudan Spain Sri Lanka Sudan Swaziland Sweden Switzerland Syrian Arab Republic Tajikistan Tanzania Thailand Timor-Leste Togo Trinidad and Tobago Tunisia Turkey Turkmenistan Uganda Ukraine United Arab Emirates United Kingdom United States Uruguay Uzbekistan Venezuela, RB Vietnam West Bank and Gaza Yemen, Rep. Zambia Zimbabwe World Low income Middle income Lower middle income Upper middle income Low & middle income East Asia & Pacific Europe & Central Asia Latin America & Carib. Middle East & N. Africa South Asia Sub-Saharan Africa High income Euro area
Sources of electricitya
Access to electricity
% of total
billion kilowatt hours
Coal
Natural gas
Oil
Hydropower
Renewable sourcesb
Nuclear power
% of population
2009
2009
2009
2009
2009
2009
2009
2009
13.2 47.4 .. 44.8 1.7 0.5 .. 81.0 7.6 3.6 .. 0.0 .. 36.9 0.0 0.0 .. 1.1 1.0 45.7 2.0 36.2 70.7 .. 0.0 99.4 89.7 49.3 100.0 .. 8.1 98.2 44.5 22.8 0.2 75.1 14.7 43.4 .. 0.0 0.0 0.0 21.4 w 20.4 19.2 22.0 18.5 19.2 7.2 39.5 20.7 58.5 16.4 4.6 23.2 23.3
1.8 1.6 .. 55.2 86.4 0.3 .. 18.8 2.4 0.2 .. 0.0 .. 6.5 60.3 52.2 .. 0.5 0.2 50.0 0.0 0.9 0.5 .. 24.6 0.3 9.2 2.5 0.0 .. 0.5 1.8 1.2 1.2 31.1 2.1 12.5 2.5 .. 100.0 0.3 0.3 4.8 w 4.7 5.7 12.1 3.9 5.7 1.5 1.9 12.1 32.9 6.6 4.1 4.1 3.7
26.9 17.6 .. 0.0 8.4 27.4 .. 0.0 16.9 28.7 .. 0.6 .. 9.0 39.5 47.8 .. 48.2 53.6 4.3 98.0 60.2 4.8 .. 73.8 0.0 0.5 18.5 0.0 .. 6.8 0.0 1.4 6.6 59.4 18.7 72.8 36.0 .. 0.0 99.7 53.3 16.1 w 45.7 20.6 16.9 21.6 21.0 16.2 18.0 55.5 4.9 13.6 18.2 11.6 10.2
0.0 0.1 .. 0.0 1.9 0.0 .. 0.1 2.1 1.2 .. 0.1 .. 16.2 0.2 0.0 .. 10.2 2.0 0.0 0.0 0.0 4.0 .. 1.6 0.2 0.6 1.1 0.0 .. 0.0 0.0 5.4 3.7 9.3 0.0 0.0 0.0 .. 0.0 0.0 0.0 3.0 0.9 1.4 2.4 1.2 1.4 1.3 0.2 3.9 0.3 1.9 0.5 4.5 9.1
20.4 16.5 .. 0.0 0.0 0.0 .. 0.0 54.3 35.0 .. 5.2 .. 18.1 0.0 0.0 .. 38.2 41.5 0.0 0.0 0.0 0.0 .. 0.0 0.0 0.0 0.0 0.0 .. 48.0 0.0 18.6 19.9 0.0 0.0 0.0 0.0 .. 0.0 0.0 0.0 13.4 w 0.0 4.6 5.3 4.4 4.5 1.6 16.1 2.4 0.0 2.0 3.1 21.3 30.8
57.7 990.0 .. 217.1 2.9 37.4 .. 41.8 25.9 16.4 .. 246.8 .. 291.0 9.9 6.8 .. 136.6 66.7 43.3 16.1 4.6 148.4 .. 0.1 7.7 15.7 194.8 16.0 .. 173.5 90.6 372.0 4,165.4 8.9 49.9 123.4 83.2 .. 6.7 10.3 7.9 20,079.3 w 185.6 9,255.1 2,007.9 7,245.8 9,461.8 4,307.5 1,785.3 1,296.7 584.2 1,054.5 419.3 10,673.5 2,244.3
37.7 16.5 .. 0.0 0.0 71.9 .. 0.0 16.5 31.3 .. 94.1 .. 12.8 0.0 0.0 .. 0.7 0.0 0.0 0.0 2.7 19.9 .. 0.0 0.0 0.0 28.6 0.0 .. 36.6 0.0 28.5 45.4 0.0 4.1 0.0 18.0 .. 0.0 0.0 46.4 40.4 w 6.9 47.9 39.6 50.2 47.0 71.6 24.0 5.0 2.0 58.5 56.5 34.3 21.6
a. Shares may not sum to 100 percent because some sources of generated electricity are not shown. b. Excludes hydropower.
168
2012 World Development Indicators
.. .. .. 99.0 42.0 .. .. 100.0 .. .. .. 75.0 .. .. 76.6 35.9 .. .. .. 92.7 .. 13.9 99.3 22.0 20.0 99.0 99.5 .. .. 9.0 .. 100.0 .. .. 98.3 .. 99.0 97.6 .. 39.6 18.8 41.5 74.1 w 23.0 81.6 67.3 98.0 73.7 90.8 .. 93.4 92.9 62.1 32.5 .. ..
About the data
Use of energy is important in improving people’s
3.8
ENVIRONMENT
Electricity production, sources, and access Definitions
• Electricity production is measured at the termi-
standard of living. But electricity generation also
nals of all alternator sets in a station. In addition to
can damage the environment. Whether such damage
hydropower, coal, oil, gas, and nuclear power gen-
occurs depends largely on how electricity is gener-
eration, it covers generation by geothermal, solar,
ated. For example, burning coal releases twice as
wind, and tide and wave energy as well as that from
much carbon dioxide—a major contributor to global
combustible renewables and waste. Production
warming—as does burning an equivalent amount of
includes the output of electric plants designed to
natural gas (see About the data for table 3.9). Nuclear
produce electricity only, as well as that of combined
energy does not generate carbon dioxide emissions,
heat and power plants. • Sources of electricity are
but it produces other dangerous waste products. The
the inputs used to generate electricity: coal, gas,
table provides information on electricity production
oil, hydropower, and nuclear power. • Coal is all coal
by source.
and brown coal, both primary (including hard coal
The International Energy Agency (IEA) compiles
and lignite-brown coal) and derived fuels (including
data on energy inputs used to generate electricity.
patent fuel, coke oven coke, gas coke, coke oven
IEA data for countries that are not members of the
gas, and blast furnace gas). Peat is also included in
Organisation for Economic Co-operation and Devel-
this category. • Natural gas is natural gas but not
opment (OECD) are based on national energy data
natural gas liquids. • Oil is crude oil and petroleum
adjusted to conform to annual questionnaires com-
products. • Hydropower is electricity produced by
pleted by OECD member governments. In addition,
hydroelectric power plants. • Renewable sources
estimates are sometimes made to complete major
are geothermal, solar photovoltaic, solar thermal,
aggregates from which key data are missing, and
tide, wind, industrial waste, municipal waste, primary
adjustments are made to compensate for differ-
solid biofuels, biogases, biogasoline, biodiesels,
ences in definitions. The IEA makes these estimates
other liquid biofuels, nonspecified primary biofuels
in consultation with national statistical offices, oil
and waste, and charcoal. • Nuclear power is electric-
companies, electric utilities, and national energy
ity produced by nuclear power plants. • Access to
experts. It occasionally revises its time series to
electricity is the percentage of the population with
reflect political changes. For example, the IEA has
access to electricity.
constructed historical energy statistics for countries of the former Soviet Union. In addition, energy statistics for other countries have undergone continuous changes in coverage or methodology in recent years as more detailed energy accounts have become available. Breaks in series are therefore unavoidable. Data on access to electricity are collected by the IEA from industry, national surveys, and international sources.
Data sources Data on electricity production and sources are from the IEA’s electronic files and its annual publications Energy Statistics of Non-OECD Countries, Energy Balances of Non-OECD Countries, Energy Statistics of OECD Countries, and Energy Balances of OECD Countries. Data on access to electricity are from the IEA’s World Energy Outlook (2011).
2012 World Development Indicators
169
3.9 Afghanistan Albania Algeria Angola Argentina Armenia Australia Austria Azerbaijan Bahrain Bangladesh Belarus Belgium Benin Bolivia Bosnia and Herzegovina Botswana Brazil Bulgaria Burkina Faso Burundi Cambodia Cameroon Canada Central African Republic Chad Chile China Hong Kong SAR, China Colombia Congo, Dem. Rep. Congo, Rep. Costa Rica Côte d’Ivoire Croatia Cuba Cyprus Czech Republic Denmark Dominican Republic Ecuador Egypt, Arab Rep. El Salvador Eritrea Estonia Ethiopia Finland France Gabon Gambia, The Georgia Germany Ghana Greece Guatemala Guinea Guinea-Bissau Haiti
170
Energy dependency and efficiency and carbon dioxide emissions Net energy importsa
GDP per unit of energy use
% of energy use 1990 2009
2005 PPP $ per kilogram of oil equivalent 1990 2009
.. 8 –351 –387 –5 98 –83 67 21 –209 16 93 73 –7 –89 34 28 26 66 .. .. .. –120 –31 .. .. 45 –3 99 –99 –2 –997 49 22 43 51 100 17 42 75 –175 –72 31 19 45 5 57 50 –1,138 .. 83 47 17 57 24 .. .. 20
.. 27 –283 –749 –9 68 –137 64 –439 –85 16 85 73 43 –128 25 54 4 44 .. .. 29 –28 –53 .. .. 68 8 100 –211 –2 –989 45 –15 53 52 97 26 –29 77 –141 –22 38 23 12 7 50 49 –657 .. 61 60 24 66 39 .. .. 28
2012 World Development Indicators
.. 4.8 7.1 5.8 5.3 1.4 4.7 7.9 1.3 2.0 6.2 1.4 5.2 3.2 7.8 .. 7.6 7.7 2.3 .. .. .. 5.1 3.6 .. .. 6.4 1.4 15.6 8.4 1.9 10.7 9.5 5.5 7.1 .. 7.7 3.4 7.5 6.7 9.3 5.8 7.9 1.8 1.6 1.8 4.1 6.3 11.8 17.6 2.4 5.9 2.5 8.2 6.7 .. 16.5 6.4
.. 13.8 6.5 8.4 7.2 5.7 5.7 9.2 6.4 2.7 7.0 4.1 6.1 3.5 6.7 4.6 11.4 7.6 4.9 .. .. 5.1 5.6 4.6 .. .. 7.7 3.7 18.4 11.8 0.8 10.1 9.5 3.2 8.3 .. 8.2 5.5 9.5 9.5 8.9 5.9 7.2 3.5 4.5 2.2 4.9 7.4 10.7 14.0 6.0 8.3 3.6 9.6 6.1 .. 15.4 4.0
Carbon dioxide emissions
Total million metric tons 1990 2008
2.7 7.5 78.9 4.4 112.6 3.7 287.3 61.0 44.2 11.9 15.5 87.5 108.5 0.7 5.5 3.9 2.2 208.9 77.7 0.6 0.3 0.5 1.7 450.1 0.2 0.1 34.9 2,460.7 27.7 57.3 4.1 1.2 3.0 5.8 16.4 33.3 4.7 139.5 50.4 9.6 16.8 75.9 2.6 .. 23.0 3.0 50.9 399.0 4.8 0.2 15.3 961.0 3.9 72.7 5.1 1.1 0.3 1.0
0.8 4.2 111.3 24.4 192.4 5.5 399.2 67.7 47.1 22.5 46.5 62.8 104.9 4.1 12.8 31.3 4.8 393.2 50.5 1.9 0.2 4.6 5.3 544.1 0.3 0.5 73.1 7,031.9 38.6 67.7 2.8 1.9 8.0 7.0 23.3 31.4 8.6 117.0 46.0 21.6 26.8 210.3 6.1 0.4 18.3 7.1 56.5 377.0 2.5 0.4 5.2 786.7 8.6 97.8 11.9 1.4 0.3 2.4
Carbon intensity kilograms per kilogram of oil equivalent energy use 1990 2008
.. 2.8 3.6 0.8 2.4 0.9 3.3 2.5 2.1 2.7 1.2 2.4 2.2 0.4 2.1 0.9 1.7 1.5 2.7 .. .. .. 0.3 2.2 .. .. 2.6 2.9 3.2 2.4 0.3 1.5 1.5 1.3 2.4 1.7 3.4 3.2 2.9 2.3 2.8 2.4 1.1 .. 3.5 0.2 1.8 1.8 4.1 3.1 1.8 2.8 0.7 3.4 1.1 .. 3.4 0.6
.. 2.0 3.0 2.1 2.5 1.9 3.1 2.0 3.5 2.4 1.7 2.2 1.8 1.2 2.1 5.2 2.2 1.6 2.6 .. .. 0.9 0.8 2.0 .. .. 2.5 3.3 2.7 2.2 0.1 1.5 1.6 0.7 2.6 3.0 3.3 2.6 2.4 2.7 2.4 3.0 1.2 0.6 3.4 0.2 1.6 1.4 1.2 3.0 1.7 2.4 0.9 3.2 1.5 .. 3.0 0.9
Per capita metric tons 1990 2008
0.1 2.3 3.1 0.4 3.4 1.1 16.8 7.9 6.0 24.1 0.1 8.6 10.9 0.1 0.8 1.0 1.6 1.4 8.9 0.1 0.1 0.0 0.1 16.2 0.1 0.0 2.6 2.2 4.8 1.7 0.1 0.5 1.0 0.5 3.7 3.2 6.1 13.5 9.8 1.3 1.6 1.3 0.5 .. 15.0 0.1 10.2 6.9 5.2 0.2 3.1 12.0 0.3 7.2 0.6 0.2 0.2 0.1
0.0 1.3 3.2 1.4 4.8 1.8 18.6 8.1 5.4 21.4 0.3 6.5 9.8 0.5 1.3 8.3 2.5 2.1 6.6 0.1 0.0 0.3 0.3 16.3 0.1 0.0 4.4 5.3 5.5 1.5 0.0 0.5 1.8 0.4 5.3 2.8 7.9 11.2 8.4 2.2 1.9 2.7 1.0 0.1 13.6 0.1 10.6 5.9 1.7 0.3 1.2 9.6 0.4 8.7 0.9 0.1 0.2 0.3
kilograms per 2005 PPP $ of GDP 1990 2008
.. 0.6 0.5 0.1 0.5 0.7 0.7 0.3 1.7 1.3 0.2 1.5 0.4 0.1 0.3 .. 0.2 0.2 1.2 0.1 0.1 .. 0.1 0.6 0.1 0.0 0.4 2.0 0.2 0.3 0.2 0.1 0.2 0.2 0.4 .. 0.4 0.9 0.4 0.3 0.3 0.4 0.1 .. 2.0 0.1 0.4 0.3 0.3 0.2 1.2 0.4 0.3 0.4 0.2 0.2 0.2 0.1
0.0 0.2 0.4 0.2 0.4 0.3 0.5 0.2 0.7 0.9 0.2 0.6 0.3 0.3 0.3 1.1 0.2 0.2 0.6 0.1 0.1 0.2 0.1 0.5 0.1 0.0 0.3 0.9 0.1 0.2 0.2 0.1 0.2 0.2 0.3 .. 0.4 0.5 0.2 0.3 0.3 0.5 0.2 0.2 0.7 0.1 0.3 0.2 0.1 0.2 0.3 0.3 0.3 0.3 0.2 0.1 0.2 0.2
Honduras Hungary India Indonesia Iran, Islamic Rep. Iraq Ireland Israel Italy Jamaica Japan Jordan Kazakhstan Kenya Korea, Dem. Rep. Korea, Rep. Kosovo Kuwait Kyrgyz Republic Lao PDR Latvia Lebanon Lesotho Liberia Libya Lithuania Macedonia, FYR Madagascar Malawi Malaysia Mali Mauritania Mauritius Mexico Moldova Mongolia Morocco Mozambique Myanmar Namibia Nepal Netherlands New Zealand Nicaragua Niger Nigeria Norway Oman Pakistan Panama Papua New Guinea Paraguay Peru Philippines Poland Portugal Puerto Rico Qatar
Net energy importsa
GDP per unit of energy use
% of energy use 1990 2009
2005 PPP $ per kilogram of oil equivalent 1990 2009
29 49 8 –67 –164 –480 65 96 83 83 83 95 –24 18 13 76 .. –453 67 .. 86 93 .. .. –546 69 49 .. .. –122 .. .. .. –59 99 20 89 5 0 67 5 8 11 29 .. –113 –467 –808 20 58 .. –49 –9 40 –1 80 .. –328
50 56 26 –74 –62 –272 89 85 84 84 80 96 –121 17 –5 81 .. –332 61 .. 50 97 .. .. –327 50 42 .. .. –34 .. .. .. –26 96 –138 95 –22 –48 81 11 19 13 45 .. –111 –656 –346 24 79 .. –56 4 40 28 80 .. –487
5.5 4.7 3.3 3.7 5.0 .. 6.4 7.3 9.2 5.1 7.3 3.2 1.6 3.0 .. 5.2 .. 2.5 1.5 .. 3.4 8.7 .. .. .. 2.9 6.6 .. .. 5.5 .. .. 13.5 6.9 1.7 1.6 9.7 0.9 1.3 9.4 2.3 6.0 5.0 3.7 .. 2.0 6.5 6.5 4.2 10.1 .. 5.5 10.0 5.4 3.0 9.6 .. ..
5.9 6.7 5.1 4.3 3.5 3.2 11.3 8.8 9.8 5.7 7.9 4.1 2.5 3.0 .. 5.4 .. 4.6 3.7 .. 6.9 7.5 142.1 .. 4.7 6.0 6.7 .. .. 5.2 .. .. 11.6 7.7 3.8 2.9 8.8 1.9 5.0 7.4 3.1 7.7 6.1 4.6 .. 2.9 8.1 4.4 4.7 13.2 .. 5.5 14.4 7.9 6.8 9.4 .. 4.9
3.9
ENVIRONMENT
Energy dependency and efficiency and carbon dioxide emissions Carbon dioxide emissions
Total million metric tons 1990 2008
2.6 63.0 690.6 149.6 227.2 52.6 30.4 33.5 424.2 8.0 1,094.7 10.4 261.3 5.8 .. 243.8 .. 45.4 10.9 0.2 14.1 9.1 .. 0.5 40.3 22.2 10.8 1.0 0.6 56.6 0.4 2.7 1.5 325.6 21.0 10.0 23.5 1.0 4.3 0.0 0.6 164.1 24.0 2.6 0.8 45.4 31.3 10.4 68.6 3.1 2.1 2.3 21.2 44.5 366.8 43.7 .. 11.8
8.7 54.6 1,742.7 406.0 538.4 102.9 43.6 37.7 445.1 12.2 1,208.2 21.4 237.0 10.4 78.4 509.2 .. 76.7 6.2 1.5 7.6 17.1 .. 0.6 58.3 15.1 11.8 1.9 1.2 208.3 0.6 2.0 4.0 475.8 4.8 10.9 47.9 2.3 12.8 4.0 3.5 173.7 33.1 4.3 0.9 95.8 50.0 45.7 163.2 6.9 2.1 4.1 40.5 83.2 316.1 56.3 .. 68.5
Carbon intensity kilograms per kilogram of oil equivalent energy use 1990 2008
1.1 2.2 2.2 1.5 3.3 2.9 3.0 2.9 2.9 2.9 2.5 3.2 3.3 0.5 .. 2.6 .. 5.0 2.2 .. 2.3 4.7 .. .. 3.6 2.0 4.0 .. .. 2.6 .. .. 3.0 2.7 3.1 2.9 3.4 0.2 0.4 0.0 0.1 2.5 1.9 1.3 .. 0.6 1.5 2.5 1.6 2.2 .. 0.7 2.2 1.5 3.6 2.6 .. 1.9
1.9 2.1 2.8 2.1 2.6 3.0 2.9 1.7 2.5 2.9 2.4 3.0 3.4 0.6 3.9 2.2 .. 2.8 2.3 .. 1.7 3.2 .. .. 3.0 1.6 3.9 .. .. 2.9 .. .. 3.3 2.6 1.5 3.5 3.2 0.2 0.8 2.2 0.4 2.2 1.9 1.4 .. 0.9 1.7 2.8 2.0 2.5 .. 0.9 2.7 2.1 3.2 2.3 .. 3.0
Per capita metric tons 1990 2008
0.5 6.1 0.8 0.8 4.1 2.9 8.7 7.2 7.5 3.3 8.9 3.3 15.9 0.2 .. 5.7 .. 21.8 2.4 0.1 5.4 3.1 .. 0.2 9.3 6.0 5.6 0.1 0.1 3.1 0.0 1.3 1.4 3.9 5.7 4.6 1.0 0.1 0.1 0.0 0.0 11.0 7.0 0.6 0.1 0.5 7.4 5.5 0.6 1.3 0.5 0.5 1.0 0.7 9.6 4.4 .. 24.9
1.2 5.4 1.5 1.7 7.4 3.4 9.9 5.2 7.4 4.5 9.5 3.7 15.1 0.3 3.2 10.5 .. 30.1 1.2 0.3 3.3 4.1 .. 0.2 9.5 4.5 5.8 0.1 0.1 7.6 0.0 0.6 3.1 4.3 1.3 4.1 1.5 0.1 0.3 1.8 0.1 10.6 7.8 0.8 0.1 0.6 10.5 17.3 1.0 2.0 0.3 0.7 1.4 0.9 8.3 5.3 .. 49.1
kilograms per 2005 PPP $ of GDP 1990 2008
0.2 0.5 0.7 0.4 0.7 .. 0.5 0.4 0.3 0.6 0.3 1.0 2.7 0.2 .. 0.5 .. 0.6 1.2 0.1 0.9 0.5 .. 0.5 .. 0.6 0.8 0.1 0.1 0.5 0.1 0.8 0.2 0.4 2.1 1.9 0.4 0.2 0.3 0.0 0.0 0.4 0.4 0.3 0.2 0.3 0.2 0.4 0.4 0.2 0.3 0.1 0.2 0.3 1.2 0.3 .. ..
2012 World Development Indicators
0.3 0.3 0.5 0.5 0.7 1.1 0.3 0.2 0.3 0.6 0.3 0.7 1.4 0.2 .. 0.4 .. 0.6 0.6 0.1 0.2 0.4 .. 0.4 0.6 0.3 0.6 0.1 0.1 0.6 0.0 0.3 0.3 0.3 0.5 1.1 0.4 0.1 0.2 0.3 0.1 0.3 0.3 0.3 0.1 0.3 0.2 0.7 0.4 0.2 0.2 0.2 0.2 0.3 0.5 0.2 .. 0.6
171
3.9 Romania Russian Federation Rwanda Saudi Arabia Senegal Serbia Sierra Leone Singapore Slovak Republic Slovenia Somalia South Africa South Sudan Spain Sri Lanka Sudan Swaziland Sweden Switzerland Syrian Arab Republic Tajikistan Tanzania Thailand Timor-Leste Togo Trinidad and Tobago Tunisia Turkey Turkmenistan Uganda Ukraine United Arab Emirates United Kingdom United States Uruguay Uzbekistan Venezuela, RB Vietnam West Bank and Gaza Yemen, Rep. Zambia Zimbabwe World Low income Middle income Lower middle income Upper middle income Low & middle income East Asia & Pacific Europe & Central Asia Latin America & Carib. Middle East & N. Africa South Asia Sub-Saharan Africa High income Euro area
Energy dependency and efficiency and carbon dioxide emissions Net energy importsa
GDP per unit of energy use
% of energy use 1990 2009
2005 PPP $ per kilogram of oil equivalent 1990 2009
34 –47 .. –519 43 31 .. 100 75 46 .. –22 .. 62 24 17 .. 37 58 –113 62 7 37 .. 17 –111 –16 51 –281 .. 46 –441 –1 14 49 17 –242 –2 .. –273 9 8 –3c w 14 –24 –15 –27 –22 –8 –12 –34 –204 10 –52 15 55
18 –83 .. –235 57 35 .. 100 65 49 .. –12 .. 77 45 –123 .. 33 53 –5 35 8 40 .. 17 –117 15 69 –109 .. 33 –183 19 22 63 –24 –204 –20 .. –101 8 10 –4c w 5 –19 –17 –20 –19 1 –47 –31 –88 25 –59 13 62
2.9 2.1 .. 5.2 6.3 4.6 .. 6.7 3.2 5.8 .. 3.0 .. 8.5 6.3 2.6 10.9 4.5 9.2 3.5 3.0 2.2 5.4 .. 2.7 2.2 7.4 8.3 0.7 .. 1.7 6.5 6.2 4.2 10.1 0.9 4.3 2.5 .. 8.6 1.8 .. 4.2 w 2.6 3.0 3.0 3.0 3.0 2.0 2.2 6.9 5.7 3.6 2.8 5.3 6.6
6.7 3.0 .. 3.4 7.1 4.8 .. 12.5 6.3 7.3 .. 3.2 .. 9.8 9.5 5.3 12.6 6.6 10.6 4.2 5.5 2.7 4.8 11.8 2.0 1.5 9.5 8.6 1.7 .. 2.3 5.3 10.1 5.9 9.6 1.5 4.8 3.7 .. 7.0 2.1 .. 5.5 w 3.4 4.5 4.6 4.4 4.4 3.9 3.6 7.7 4.7 5.2 3.2 6.7 8.3
Carbon dioxide emissions
Total million metric tons 1990 2008
158.9 94.7 2,220.7 1,708.7 0.7 0.7 215.1 433.6 3.2 5.0 49.9 45.3b 0.4 1.3 46.9 32.3 45.6 37.6 13.0 17.2 0.0 0.6 333.5 435.9 .. .. 227.6 329.3 3.8 11.8 5.6 14.1 0.4 1.1 51.7 49.0 43.0 40.4 37.5 71.6 7.2 3.1 2.4 6.5 95.8 285.7 .. 0.2 0.8 1.4 17.0 49.8 13.3 25.0 150.8 284.0 28.1 47.8 0.8 3.7 641.7 323.5 52.0 155.1 570.2 522.9 4,879.4 5,461.0 4.0 8.3 114.0 124.9 122.2 169.5 21.4 127.4 .. 2.1 9.6 23.4 2.4 1.9 15.5 9.1 22,310.9d w 32,082.6d w .. 219.1 9,256.8 16,638.0 2,001.1 3,743.9 7,255.3 12,894.1 9,398.8 16,856.9 2,895.4 8,259.3 3,893.0 3,130.1 986.2 1,584.1 579.0 1,230.3 782.0 1,970.2 464.1 684.6 11,572.3 13,284.7 2,718.9 2,633.3
Carbon intensity kilograms per kilogram of oil equivalent energy use 1990 2008
2.6 2.8 .. 3.6 1.9 3.0 b .. 4.1 2.5 2.5 .. 3.6 .. 2.5 0.7 0.5 1.4 1.1 1.8 3.6 1.7 0.2 2.3 .. 0.6 2.8 2.7 2.9 2.5 .. 2.9 2.6 2.8 2.5 1.8 2.5 2.8 0.9 .. 3.8 0.5 1.7 2.5d w 0.6 2.5 1.8 2.8 2.4 2.6 3.0 2.2 3.2 2.0 1.7 2.5 2.5
2.4 2.5 .. 2.8 1.7 3.0 .. 1.9 2.1 2.2 .. 2.9 .. 2.4 1.3 1.0 2.5 1.0 1.5 2.8 1.3 0.3 2.7 3.0 0.6 2.6 2.7 2.9 2.1 .. 2.4 2.7 2.5 2.4 2.0 2.5 2.6 2.2 .. 3.2 0.2 1.0 2.5d w 1.0 2.8 2.4 2.9 2.7 3.1 2.6 2.2 2.8 2.6 1.5 2.4 2.1
Per capita metric tons 1990 2008
6.8 14.9 0.1 13.3 0.4 5.9 b 0.1 15.4 8.6 6.5 0.0 9.5 .. 5.9 0.2 0.2 0.5 6.0 6.4 3.0 1.3 0.1 1.7 .. 0.2 14.0 1.6 2.8 7.2 0.0 12.3 28.8 10.0 19.5 1.3 5.3 6.2 0.3 .. 0.8 0.3 1.5 4.2d w .. 2.4 1.1 3.6 2.2 1.8 9.8 2.3 2.6 0.7 0.9 11.8 8.9
4.4 12.0 0.1 16.6 0.4 6.8 0.2 6.7 6.9 8.5 0.1 8.9 .. 7.2 0.6 0.3 1.1 5.3 5.3 3.6 0.5 0.2 4.2 0.2 0.2 37.4 2.4 4.0 9.7 0.1 7.0 25.0 8.5 18.0 2.5 4.6 6.1 1.5 0.5 1.0 0.2 0.7 4.8d w 0.3 3.4 1.5 5.3 3.0 4.3 7.8 2.8 3.8 1.2 0.8 11.9 8.0
kilograms per 2005 PPP $ of GDP 1990 2008
0.9 1.5 0.1 0.7 0.3 0.8b 0.1 0.6 0.9 0.5 .. 1.2 .. 0.3 0.1 0.2 0.1 0.2 0.2 1.0 0.7 0.1 0.4 .. 0.2 1.3 0.4 0.3 2.3 0.1 1.9 0.4 0.4 0.6 0.2 3.1 0.6 0.4 .. 0.4 0.2 .. 0.6d w .. 0.8 0.6 0.9 0.8 1.3 1.3 0.3 0.6 0.6 0.6 0.5 0.4
0.4 0.8 0.1 0.8 0.2 0.7 0.3 0.1 0.3 0.3 .. 0.9 .. 0.3 0.1 0.2 0.2 0.2 0.1 0.8 0.3 0.1 0.6 0.2 0.3 1.6 0.3 0.3 1.5 0.1 1.0 0.5 0.3 0.4 0.2 1.9 0.5 0.6 .. 0.5 0.1 .. 0.5d w 0.3 0.6 0.5 0.7 0.6 0.8 0.7 0.3 0.6 0.5 0.4 0.4 0.3
a. A negative value indicates that a country is a net exporter. b. Includes Kosovo and Montenegro. c. Deviation from zero is due to statistical errors and changes in stock. d. Includes emissions not allocated to specific countries.
172
2012 World Development Indicators
About the data
3.9
ENVIRONMENT
Energy dependency and efficiency and carbon dioxide emissions Definitions
Because commercial energy is widely traded, its pro-
individual values. Each year the CDIAC recalculates
• Net energy imports are estimated as energy use
duction and use need to be distinguished. Net energy
the entire time series since 1949, incorporating
less production, both measured in oil equivalents.
imports show the extent to which an economy’s use
recent findings and corrections. Estimates exclude
• GDP per unit of energy use is the ratio of gross
exceeds its production. High-income economies are
fuels supplied to ships and aircraft in international
domestic product (GDP) per kilogram of oil equiv-
net energy importers; middle-income economies are
transport because of the difficulty of apportioning
alent of energy use, with GDP converted to 2005
their main suppliers.
the fuels among benefiting countries.
international dollars using purchasing power parity
The ratio of gross domestic product (GDP) to energy
(PPP) rates. An international dollar has the same
use indicates energy efficiency. To produce compa-
purchasing power over GDP that a U.S. dollar has
rable and consistent estimates of real GDP across
in the United States. Energy use refers to the use
economies relative to physical inputs to GDP—that
of primary energy before transformation to other
is, units of energy use—GDP is converted to 2005
end-use fuel, which is equal to indigenous produc-
international dollars using purchasing power parity
tion plus imports and stock changes minus exports
(PPP) rates. Differences in this ratio over time and
and fuel supplied to ships and aircraft engaged in
across economies reflect structural changes in an
international transport (see About the data for table
economy, changes in sectoral energy efficiency, and
3.7). • Carbon dioxide emissions are emissions from
differences in fuel mixes.
the burning of fossil fuels and the manufacture of
Carbon dioxide emissions, largely by-products of
cement and include carbon dioxide produced dur-
energy production and use (see table 3.7), account
ing consumption of solid, liquid, and gas fuels and
for the largest share of greenhouse gases, which are
gas flaring.
associated with global warming. Anthropogenic carbon dioxide emissions result primarily from fossil fuel combustion and cement manufacturing. In combustion different fossil fuels release different amounts of carbon dioxide for the same level of energy use: oil releases about 50 percent more carbon dioxide than natural gas, and coal releases about twice as much. Cement manufacturing releases about half a metric ton of carbon dioxide for each metric ton of cement produced. Carbon intensity is the ratio of carbon dioxide per unit of energy, or the amount of carbon dioxide emitted as a result of using one unit of energy in production. Kilograms per 2005 PPP dollars of GDP shows the share of carbon dioxide per unit of GDP, a measure of how clean production processes are. The U.S. Department of Energy’s Carbon Dioxide Information Analysis Center (CDIAC) calculates annual anthropogenic emissions from data on fossil fuel consumption (from the United Nations Statistics Division’s World Energy Data Set) and world cement manufacturing (from the U.S. Bureau of Mines Cement Manufacturing Data Set). Carbon dioxide emissions are often calculated and reported as elemental carbon. The values in the table were converted to actual carbon dioxide mass by multiplying them by 3.667 (the ratio of the mass of carbon to
Data sources
that of carbon dioxide). Although estimates of global
Data on energy use are from the electronic files
carbon dioxide emissions are probably accurate
of the International Energy Agency. Data on car-
within 10 percent (as calculated from global aver-
bon dioxide emissions are from the CDIAC, Envi-
age fuel chemistry and use), country estimates may
ronmental Sciences Division, Oak Ridge National
have larger error bounds. Trends estimated from a
Laboratory, Tennessee, United States.
consistent time series tend to be more accurate than
2012 World Development Indicators
173
3.10
Trends in greenhouse gas emissions Carbon dioxide emissions
average annual % growtha % changeb 1990– 1990– 2008 2008
Afghanistan Albania Algeria Angola Argentina Armenia Australia Austria Azerbaijan Bahrain Bangladesh Belarus Belgium Benin Bolivia Bosnia and Herzegovina Botswana Brazil Bulgaria Burkina Faso Burundi Cambodia Cameroon Canada Central African Republic Chad Chile China Hong Kong SAR, China Colombia Congo, Dem. Rep. Congo, Rep. Costa Rica Côte d’Ivoire Croatia Cuba Cyprus Czech Republic Denmark Dominican Republic Ecuador Egypt, Arab Rep. El Salvador Eritrea Estonia Ethiopia Finland France Gabon Gambia, The Georgia Germany Ghana Greece Guatemala Guinea Guinea-Bissau Haiti
174
–6.9 2.5 1.5 10.4 2.6 3.0 1.7 1.1 0.0 3.0 6.7 –1.3 –0.4 9.3 3.6 16.6 3.7 3.3 –1.9 6.1 –4.5 15.3 4.1 1.4 1.0 11.0 4.3 5.7 1.9 0.0 –3.1 –0.1 5.0 1.4 2.6 –0.8 3.1 –0.7 –1.0 4.5 3.1 5.9 4.4 5.4 –0.6 4.7 1.1 –0.3 –3.6 4.4 –2.6 –1.1 4.7 2.0 5.6 1.5 –0.2 7.6
–69.6 –44.2 41.1 450.2 70.8 50.7 38.9 11.1 6.7 89.1 199.5 –28.2 –3.3 468.7 133.2 694.1 122.2 88.2 –35.0 216.3 –41.0 920.4 205.1 20.9 31.5 237.4 109.5 185.8 39.5 18.1 –30.8 63.0 171.2 21.0 41.7 –5.8 83.8 –16.1 –8.7 125.9 59.3 176.9 133.5 .. –20.6 135.5 10.9 –5.5 –49.0 115.4 –66.1 –18.1 118.6 34.5 134.2 31.9 11.6 145.0
2012 World Development Indicators
Methane emissions Total thousand metric tons of carbon dioxide equivalent 2005
.. 2,407 54,219 45,409 101,821 2,962 126,488 8,515 36,607 2,766 92,414 11,498 10,063 4,080 30,350 2,741 4,501 492,160 10,867 .. .. 20,215 18,518 89,338 .. .. 18,149 1,333,098 2,820 58,108 56,445 5,584 2,580 10,997 3,864 9,455 616 11,497 7,935 6,081 17,125 46,996 3,131 2,467 2,108 52,243 9,742 77,252 8,218 .. 4,410 67,582 8,990 7,289 8,306 .. .. 4,006
Nitrous oxide emissions
% of total % changeb From energy processes Agricultural 1990– 2005 2005 2005
.. –5.1 33.1 –8.3 –8.3 2.5 9.7 –15.0 110.7 54.9 6.5 –32.8 –21.8 –15.8 30.9 –53.5 –22.6 56.4 –24.8 .. .. 35.0 37.1 30.8 .. .. 49.8 28.5 84.1 13.5 –41.6 –10.4 –31.2 –2.2 –60.5 –21.0 40.0 –40.3 –0.5 3.8 31.2 68.8 18.0 30.9 –36.8 32.8 –2.8 –0.3 1.4 .. –12.4 –44.8 24.2 2.0 74.7 .. .. 34.8
.. 20.0 83.2 15.6 18.9 50.8 29.7 21.7 82.0 88.7 10.0 7.6 11.6 15.6 25.6 46.7 8.6 7.6 13.0 .. .. 4.9 39.1 32.2 .. .. 24.4 45.8 26.7 19.9 10.2 32.2 9.5 16.9 57.0 11.2 1.8 49.4 16.4 7.8 31.2 50.7 12.4 11.2 42.3 14.3 7.4 44.3 90.4 .. 36.1 32.1 23.3 26.3 12.4 .. .. 12.1
.. 70.8 8.2 27.9 70.6 36.7 55.1 48.6 13.6 0.6 70.5 70.9 56.7 47.8 34.1 42.4 84.1 61.1 18.9 .. .. 76.1 42.4 29.3 .. .. 39.4 38.8 .. 68.0 23.1 31.9 67.2 17.4 33.3 62.4 44.0 33.6 65.2 63.7 57.7 31.7 53.1 73.2 30.5 72.5 20.7 47.7 1.1 .. 50.8 43.8 39.5 50.0 48.8 .. .. 56.2
Total thousand metric tons of carbon dioxide equivalent 2005
.. 1,036 4,898 38,881 49,821 580 62,966 4,448 2,633 82 21,386 11,680 6,571 2,902 15,092 1,196 3,081 235,987 4,227 .. .. 5,794 9,127 40,171 .. .. 8,135 467,213 422 21,288 54,643 3,566 1,334 7,364 2,851 6,356 292 8,878 6,290 2,255 4,571 18,996 1,377 1,189 932 30,510 7,124 49,058 482 .. 2,019 56,560 4,899 5,977 5,376 .. .. 1,438
Other greenhouse gas emissions
% of total From energy % changeb processes Agricultural 1990– 2005 2005 2005
.. –18.7 27.5 –6.7 29.6 –27.6 –0.1 –13.5 0.4 70.8 42.1 –28.3 –27.6 –21.5 3.2 –40.8 –44.1 52.6 –55.2 .. .. 46.9 –13.3 –5.5 .. .. 57.5 48.5 –0.9 5.2 –37.3 –17.2 –26.2 –1.6 –24.5 –31.8 19.2 –10.2 –21.5 11.0 42.2 60.7 7.7 15.7 –50.7 19.4 –4.1 –30.6 58.0 .. –26.8 –23.9 –5.6 –17.1 121.1 .. .. 59.6
.. 7.1 22.6 0.4 3.9 1.2 10.3 31.0 8.3 39.7 7.5 23.1 38.1 4.0 0.7 24.7 1.4 3.4 36.0 .. .. 3.5 2.6 23.7 .. .. 16.6 12.9 38.5 4.4 2.2 1.0 4.5 2.7 36.6 15.1 13.0 53.0 18.0 7.8 3.8 8.3 8.2 3.8 21.5 5.2 42.8 24.2 10.0 .. 35.5 38.2 9.3 22.1 5.5 .. .. 6.2
.. 78.4 58.6 38.4 89.2 81.6 78.2 52.5 77.5 16.0 83.1 72.9 44.3 61.5 36.5 57.8 92.0 67.0 48.1 .. .. 66.1 75.9 58.9 .. .. 73.4 74.3 0.0 86.1 31.3 51.8 85.4 29.3 52.4 78.7 65.5 36.9 73.4 76.8 84.9 80.0 76.2 90.9 60.5 88.8 41.7 66.8 23.3 .. 56.9 52.2 70.5 58.2 56.8 .. .. 84.2
Total thousand metric tons of carbon dioxide equivalent 2005
.. 62 489 20 785 335 6,505 2,329 89 279 0 467 2,106 0 0 571 0 11,816 383 .. .. 0 419 21,943 .. .. 13 141,394 119 83 0 5 62 0 59 129 190 1,121 1,422 0 63 3,181 77 0 40 10 826 15,539 9 .. 12 31,543 15 1,842 481 .. .. 0
% changeb 1990– 2005
.. .. 50.0 .. –65.8 .. 33.5 46.2 –49.4 –89.0 .. .. 583.8 .. .. –7.5 .. 40.5 .. .. .. .. –55.0 69.7 .. .. –31.6 1,073.0 –68.6 97.6 .. .. .. .. –93.4 .. .. .. 457.6 .. .. 54.5 .. .. 1,900.0 .. 726.0 57.1 .. .. .. 8.1 –97.5 –20.8 .. .. .. ..
Carbon dioxide emissions
average annual % growtha % changeb 1990– 1990– 2008 2008
Honduras Hungary India Indonesia Iran, Islamic Rep. Iraq Ireland Israel Italy Jamaica Japan Jordan Kazakhstan Kenya Korea, Dem. Rep. Korea, Rep. Kosovo Kuwait Kyrgyz Republic Lao PDR Latvia Lebanon Lesotho Liberia Libya Lithuania Macedonia, FYR Madagascar Malawi Malaysia Mali Mauritania Mauritius Mexico Moldova Mongolia Morocco Mozambique Myanmar Namibia Nepal Netherlands New Zealand Nicaragua Niger Nigeria Norway Oman Pakistan Panama Papua New Guinea Paraguay Peru Philippines Poland Portugal Puerto Rico Qatar
7.4 –0.6 4.8 4.5 5.0 3.7 2.4 1.6 0.6 2.4 0.7 4.2 0.4 3.0 1.2 3.7 .. 7.4 –1.7 13.1 –3.3 3.0 .. 5.0 1.8 –1.8 0.4 3.4 3.1 6.3 1.9 –3.9 6.1 1.9 –8.5 –0.2 3.7 5.4 6.6 39.5 7.6 0.0 2.0 4.1 –0.2 5.4 2.7 8.5 4.9 4.0 3.9 2.7 3.7 3.2 –1.1 1.8 .. 6.8
234.5 –13.3 152.4 171.5 137.0 95.9 43.4 12.3 4.9 53.2 10.4 105.5 –9.3 78.5 .. 108.8 .. 69.0 –42.8 553.1 –46.0 87.9 .. 25.8 44.7 –31.7 9.6 93.7 100.6 268.0 40.9 –25.0 170.2 46.1 –77.2 8.5 103.5 131.1 198.8 .. 458.4 5.9 37.8 63.8 2.2 111.0 59.6 341.9 138.0 120.5 –1.5 82.0 91.5 86.7 –13.8 28.8 .. 481.6
Methane emissions Total thousand metric tons of carbon dioxide equivalent 2005
5,191 7,767 583,978 208,944 114,585 15,937 15,331 3,517 40,790 1,302 42,771 1,796 47,119 22,130 18,195 32,069 .. 14,380 3,591 .. 3,108 1,003 .. .. 14,682 5,516 1,403 .. .. 46,501 .. .. .. 128,209 3,372 6,067 10,573 12,843 77,211 5,057 22,142 21,259 27,635 6,018 .. 130,317 16,870 17,849 137,401 3,219 .. 15,388 17,187 51,889 70,023 12,173 .. 15,706
Nitrous oxide emissions
% of total % changeb From energy processes Agricultural 1990– 2005 2005 2005
31.5 –22.9 10.5 18.4 32.5 –45.8 14.3 83.8 –13.4 14.4 –36.5 111.5 –27.3 23.3 –15.0 2.4 .. 119.4 –38.2 .. –42.1 46.6 .. .. –34.7 –34.1 –36.5 .. .. 64.7 .. .. .. 26.3 –17.5 –25.9 15.8 18.2 –7.4 47.2 9.7 –30.4 3.6 26.3 .. 10.9 47.2 194.9 50.7 16.5 .. 2.0 22.7 28.6 –36.6 22.4 .. 387.2
7.2 29.1 15.9 25.5 70.6 58.4 11.9 18.4 14.7 11.4 8.1 25.0 66.2 16.9 58.6 19.9 .. 93.4 6.8 .. 53.6 9.7 .. .. 86.3 32.0 32.1 .. .. 69.3 .. .. .. 40.2 45.2 2.5 8.0 22.7 12.6 0.3 5.9 23.4 3.6 6.6 .. 68.9 74.6 94.1 23.7 4.0 .. 3.9 13.5 9.3 62.0 13.8 .. 96.5
78.4 33.6 64.4 46.4 18.2 18.6 76.7 31.2 39.8 50.3 71.2 21.8 25.3 65.5 23.5 38.6 .. 1.1 72.3 .. 27.7 25.5 .. .. 5.7 33.8 46.6 .. .. 12.4 .. .. .. 42.3 29.4 92.1 51.7 44.2 69.0 94.9 82.9 43.4 90.2 74.8 .. 19.8 12.6 3.0 63.5 79.2 .. 84.1 61.3 63.7 21.9 35.4 .. 0.4
Total thousand metric tons of carbon dioxide equivalent 2005
2,865 6,961 212,927 123,275 26,644 3,440 7,486 1,793 28,620 599 29,785 667 17,594 10,542 3,422 13,548 .. 650 1,510 .. 1,253 672 .. .. 1,285 2,451 599 .. .. 15,087 .. .. .. 42,514 849 3,489 5,814 9,501 30,932 3,797 4,516 14,596 12,930 3,340 .. 21,565 4,737 561 26,838 1,204 .. 9,067 7,560 12,950 30,198 5,958 .. 200
Other greenhouse gas emissions
% of total From energy % changeb processes Agricultural 1990– 2005 2005 2005
26.0 –31.2 33.3 43.5 41.1 –9.9 –8.3 41.5 –5.4 29.4 –17.0 39.5 –46.2 14.3 –60.6 34.7 .. 156.9 –57.7 .. –58.7 79.2 .. .. 9.3 –45.7 –33.9 .. .. 13.5 .. .. .. 8.9 –51.0 –30.0 12.2 –12.7 –23.9 47.2 26.0 –10.7 23.5 10.1 .. 12.6 –3.1 82.7 46.0 18.0 .. 0.6 35.4 34.0 4.7 24.3 .. 104.1
3.10
3.8 30.9 12.8 3.7 11.4 9.7 4.4 15.3 39.1 12.1 41.6 8.2 12.8 5.0 13.2 41.3 .. 27.7 11.2 .. 11.6 12.6 .. .. 11.2 5.0 15.9 .. .. 6.7 .. .. .. 10.6 5.5 2.2 3.0 3.4 2.6 1.1 13.0 52.5 3.5 3.3 .. 9.1 46.5 16.0 14.5 4.9 .. 1.7 2.9 9.1 33.5 22.0 .. 33.9
85.9 60.1 73.4 71.5 75.3 63.3 90.5 53.0 43.7 59.0 27.9 55.4 62.5 88.8 62.3 35.9 .. 16.9 72.6 .. 77.4 58.8 .. .. 51.9 86.0 63.9 .. .. 64.9 .. .. .. 75.2 73.5 93.2 82.6 71.4 42.9 94.3 76.8 39.5 94.2 91.7 .. 77.3 39.0 68.0 74.2 83.7 .. 82.6 81.9 73.1 57.7 43.8 .. 25.0
Total thousand metric tons of carbon dioxide equivalent 2005
0 1,552 8,433 1,027 2,569 86 1,151 1,981 13,968 51 53,786 112 339 0 2,794 10,221 .. 931 24 .. 890 0 .. .. 280 656 120 .. .. 994 .. .. .. 4,555 8 0 0 282 0 0 0 3,750 973 0 .. 669 5,202 175 819 0 .. 0 330 365 2,451 783 .. 0
2012 World Development Indicators
% changeb 1990– 2005
.. 121.1 –11.9 –40.5 –2.9 –66.0 3,097.2 88.8 211.2 .. 81.1 .. .. .. .. 66.0 .. 254.0 .. .. .. .. .. .. –0.7 .. .. .. .. 66.2 .. .. .. 53.1 .. .. .. .. .. .. .. –40.9 3.4 .. .. 176.4 –39.4 .. –18.8 .. .. .. .. 125.3 360.7 605.4 .. ..
175
ENVIRONMENT
Trends in greenhouse gas emissions
3.10
Trends in greenhouse gas emissions Carbon dioxide emissions
average annual % growtha % changeb 1990– 1990– 2008 2008
Romania Russian Federation Rwanda Saudi Arabia Senegal Serbia Sierra Leone Singapore Slovak Republic Slovenia Somalia South Africa South Sudan Spain Sri Lanka Sudan Swaziland Sweden Switzerland Syrian Arab Republic Tajikistan Tanzania Thailand Timor-Leste Togo Trinidad and Tobago Tunisia Turkey Turkmenistan Uganda Ukraine United Arab Emirates United Kingdom United States Uruguay Uzbekistan Venezuela, RB Vietnam West Bank and Gaza Yemen, Rep. Zambia Zimbabwe World Low income Middle income Lower middle income Upper middle income Low & middle income East Asia & Pacific Europe & Central Asia Latin America & Carib. Middle East & N. Africa South Asia Sub-Saharan Africa High income Euro area
Methane emissions Total thousand metric tons of carbon dioxide equivalent 2005
–2.4 –40.4 24,331 –1.0 –23.1 562,801 0.6 3.2 .. 2.8 101.6 48,152 3.0 56.3 7,129 .. .. 7,782 7.3 243.4 .. –2.1 –31.2 2,237 –0.9 –17.7 3,911 1.6 32.3 3,498 35.9 3,447.0 .. 1.4 30.7 63,785 .. .. .. 2.7 44.7 36,338 7.1 211.8 10,210 7.1 152.8 67,441 9.2 156.9 .. –0.6 –5.1 11,311 –0.3 –6.0 4,748 3.2 91.2 12,458 –2.8 –56.4 3,898 5.3 172.5 32,024 5.6 198.2 83,257 .. .. .. 3.5 83.4 2,889 4.7 193.5 10,070 3.3 88.5 8,160 3.4 88.3 64,251 3.3 70.4 27,984 8.7 358.3 .. –3.1 –49.6 70,360 6.1 198.2 23,283 –0.6 –8.3 65,788 0.7 11.9 548,074 2.3 108.5 19,589 0.6 9.6 39,602 2.6 38.8 61,183 11.3 495.0 82,978 17.3 .. .. 4.7 143.7 6,677 –1.3 –22.8 19,294 –3.4 –41.5 9,539 2.1 w 43.8 w 7,135,850 s 3.0 .. 436,333 3.0 79.7 5,160,313 3.1 87.1 1,704,859 3.0 77.7 3,455,454 3.0 79.4 5,596,645 5.6 185.3 1,928,355 –0.8 –19.6 936,608 2.5 60.6 1,008,557 4.2 112.5 287,084 4.9 151.9 846,145 2.2 47.5 589,897 0.8 14.8 1,539,204 0.1 –3.1 317,704
Nitrous oxide emissions
% of total % changeb From energy processes Agricultural 1990– 2005 2005 2005
–35.1 –18.3 .. 67.4 35.1 –58.7 .. 136.7 –39.7 0.6 .. 24.6 .. 11.9 –11.2 55.5 .. 1.3 –17.1 –10.8 –9.3 24.0 5.7 .. 5.0 32.0 106.2 46.4 –5.0 .. –42.2 58.0 –44.1 –14.4 24.1 24.0 5.9 40.1 .. 73.5 –28.4 –5.7 6.2 w –3.0 13.6 11.4 14.7 12.1 24.5 –17.3 30.3 20.0 14.6 5.4 –10.8 –18.2
42.7 79.3 .. 83.6 9.9 41.5 .. 60.1 18.2 30.7 .. 45.4 .. 10.4 5.3 7.1 .. 9.9 19.8 53.8 12.8 12.6 16.9 .. 23.5 83.9 55.6 16.0 75.2 .. 62.1 93.1 24.8 41.0 1.5 57.3 47.4 22.7 .. 17.0 6.7 11.4 37.3 w 13.7 38.8 27.2 44.6 36.9 39.2 67.5 17.3 64.7 16.1 30.5 38.9 26.0
36.0 9.1 .. 4.0 68.3 43.7 .. 1.3 39.0 32.1 .. 31.4 .. 56.8 65.2 85.2 .. 28.1 67.6 28.1 68.6 63.2 66.0 .. 39.8 0.7 25.5 33.6 21.6 .. 23.3 2.6 38.2 34.8 94.3 33.7 40.0 63.9 .. 54.9 59.3 73.3 42.6 w 61.2 42.6 52.4 37.8 44.1 43.6 17.5 58.6 20.7 65.4 44.0 37.1 46.8
Total thousand metric tons of carbon dioxide equivalent 2005
Other greenhouse gas emissions
% of total From energy % changeb processes Agricultural 1990– 2005 2005 2005
11,537 –44.0 76,121 –48.7 .. .. 6,501 17.5 4,083 37.2 4,581 –8.8 .. .. 1,068 163.1 3,354 –37.1 1,156 –12.2 .. .. 24,048 12.9 .. .. 26,529 6.5 2,056 18.0 49,472 34.9 .. .. 5,865 –13.1 2,415 –15.5 5,509 33.4 1,378 0.2 21,647 0.8 22,304 15.1 .. .. 1,738 –21.3 230 12.2 2,366 17.9 32,781 12.8 4,276 93.7 .. .. 26,097 –51.3 1,169 78.7 30,565 –44.7 317,153 1.8 7,017 16.1 10,003 9.4 14,935 23.4 23,030 98.3 .. .. 3,250 57.4 25,068 –29.7 6,114 –16.1 2,852,537 s 5.8 w 209,159 –15.0 1,830,347 17.0 686,537 17.2 1,143,810 16.9 2,039,506 12.7 707,496 38.0 214,402 –38.9 442,132 32.9 73,539 36.8 267,722 34.9 334,216 –7.6 813,031 –8.2 219,189 –18.1
32.4 27.8 .. 14.0 2.7 24.2 .. 77.6 52.0 13.2 .. 12.6 .. 18.7 12.1 1.3 .. 26.8 20.8 9.0 1.4 2.5 21.7 .. 5.6 11.5 21.4 22.7 16.4 .. 42.8 18.3 24.8 30.6 1.4 6.2 5.0 6.1 .. 11.2 2.6 3.7 15.4 w 4.1 10.6 8.8 11.7 10.0 10.5 25.3 4.6 10.7 12.6 3.7 29.1 31.7
56.2 44.3 .. 46.1 88.5 63.6 .. 2.8 37.7 70.4 .. 59.8 .. 62.6 65.1 92.6 .. 60.2 59.3 78.1 86.9 78.8 65.5 .. 67.5 60.3 66.4 66.4 78.1 .. 45.6 43.6 60.0 56.4 96.9 84.2 75.2 83.0 .. 72.5 71.7 85.2 66.2 w 62.4 70.8 71.1 70.6 70.0 72.2 56.8 72.4 74.5 74.3 66.1 56.8 55.4
Total thousand metric tons of carbon dioxide equivalent 2005
746 59,673 .. 2,193 0 4,493 .. 2,532 395 473 .. 2,552 .. 9,080 0 0 .. 2,078 2,109 0 383 0 1,104 .. 0 0 0 5,066 73 .. 693 1,075 10,403 239,517 59 608 2,468 0 .. 0 0 0 724,183 s .. 260,798 17,327 243,471 264,291 .. 75,692 20,972 6,717 9,253 .. 459,891 84,230
% changeb 1990– 2005
–62.8 130.6 .. –10.6 .. 353.8 .. 396.5 480.9 –38.5 .. 71.2 .. 47.7 .. .. .. 133.7 97.3 .. –86.4 .. –22.8 .. .. .. .. 96.9 .. .. 209.4 27.5 96.7 158.7 .. .. –24.0 .. .. .. .. .. 122.4 w .. 207.4 3.3 257.7 201.6 .. 114.6 23.5 20.7 –12.5 .. 93.3 37.2
a. Calculated using the least squares method, which accounts for ups and downs of all data points in the period (see Statistical methods). b. Calculated as the change in emissions since 1990, which is the baseline for Kyoto Protocol requirements.
176
2012 World Development Indicators
About the data
3.10
Definitions
Greenhouse gases—which include carbon dioxide,
• Carbon dioxide emissions are those from the burn-
methane, nitrous oxide, hydrofluorocarbons, per-
ing of fossil fuels and the manufacture of cement and
fluorocarbons, and sulfur hexafluoride—contribute
include carbon dioxide produced during consumption
to climate change.
of solid, liquid, and gas fuels and gas flaring. • Meth-
Carbon dioxide emissions, largely a by-product of
ane emissions are those from human activities such
energy production and use (see table 3.7), account
as agriculture and from industrial methane produc-
for the largest share of greenhouse gases. Anthro-
tion. • Methane emissions from energy processes
pogenic carbon dioxide emissions result primarily
are those from the production, handling, transmis-
from fossil fuel combustion and cement manufactur-
sion, and combustion of fossil fuels and biofuels.
ing. Burning oil releases more carbon dioxide than
• Agricultural methane emissions are those from
burning natural gas, and burning coal releases even
animals, animal waste, rice production, agricultural
more for the same level of energy use. Cement manu-
waste burning (nonenergy, on-site), and savannah
facturing releases about half a metric ton of carbon
burning. • Nitrous oxide emissions are those from
dioxide for each metric ton of cement produced.
agricultural biomass burning, industrial activities,
Methane emissions result largely from agricultural
and livestock management. • Nitrous oxide emis-
activities, industrial production landfills and waste-
sions from energy processes are those produced
water treatment, and other sources such as tropi-
by the combustion of fossil fuels and biofuels.
cal forest and other vegetation fires. The emissions
• Agricultural nitrous oxide emissions are those
are usually expressed in carbon dioxide equivalents
produced through fertilizer use (synthetic and ani-
using the global warming potential, which allows the
mal manure), animal waste management, agricultural
effective contributions of different gases to be com-
waste burning (nonenergy, on-site), and savannah
pared. A kilogram of methane is 21 times as effec-
burning. • Other greenhouse gas emissions are
tive at trapping heat in the earth’s atmosphere as a
those of hydrofluorocarbons (used as a replacement
kilogram of carbon dioxide within 100 years.
for chlorofluorocarbons, mainly in refrigeration and
Nitrous oxide emissions are mainly from fossil fuel
semiconductor manufacturing), perfluorocarbons (a
combustion, fertilizers, rainforest fires, and animal
by-product of aluminum smelting and uranium enrich-
waste. Nitrous oxide is a powerful greenhouse gas,
ment, used as a replacement for chlorofluorocarbons
with an estimated atmospheric lifetime of 114 years,
in semiconductor manufacturing), and sulfur hexaflu-
compared with 12 years for methane. The per kilo-
oride (used to insulate high-voltage electricity power
gram global warming potential of nitrous oxide is
equipment), all of which are to be curbed under the
nearly 310 times that of carbon dioxide within 100
Kyoto Protocol.
years. Other greenhouse gases covered under the Kyoto Protocol are hydrofluorocarbons, perfluorocarbons, and sulfur hexafluoride. Although emissions of these artificial gases are small, they are more powerful greenhouse gases than carbon dioxide, with much higher atmospheric lifetimes and high global warming potential. For a discussion of carbon dioxide sources and the methodology behind emissions calculation, see About the data for table 3.9. Data sources Data on carbon dioxide emissions are from the Carbon Dioxide Information Analysis Center, Environmental Sciences Division, Oak Ridge National Laboratory, Tennessee, United States. Data on methane, nitrous oxide, and other greenhouse gas emissions are compiled by the International Energy Agency.
2012 World Development Indicators
177
ENVIRONMENT
Trends in greenhouse gas emissions
3.11
Carbon dioxide emissions by sector Carbon dioxide emissions
% of total fuel combustion Electricity and heat production 1990 2008
Afghanistan Albania Algeria Angola Argentina Armenia Australia Austria Azerbaijan Bahrain Bangladesh Belarus Belgium Benin Bolivia Bosnia and Herzegovina Botswana Brazil Bulgaria Burkina Faso Burundi Cambodia Cameroon Canada Central African Republic Chad Chile China Hong Kong SAR, China Colombia Congo, Dem. Rep. Congo, Rep. Costa Rica Côte d’Ivoire Croatia Cuba Cyprus Czech Republic Denmark Dominican Republic Ecuador Egypt, Arab Rep. El Salvador Eritrea Estonia Ethiopia Finland France Gabon Gambia, The Georgia Germany Ghana Greece Guatemala Guinea Guinea-Bissau Haiti
178
.. 12.0 42.9 12.5 34.0 29.6 54.1 35.0 44.3 56.2 32.7 47.4 28.9 12.0 29.0 40.6 55.3 14.2 63.4 .. .. .. 1.1 32.9 .. .. 33.0 32.2 72.4 24.3 3.4 0.0 7.3 22.8 37.3 45.2 45.3 42.7 51.7 41.2 15.2 32.5 8.3 .. 73.2 13.6 36.2 18.1 34.4 .. 46.3 42.4 3.0 52.3 7.3 .. .. 25.5
2012 World Development Indicators
.. 5.2 39.2 3.7 34.9 19.6 62.9 34.1 50.3 54.2 43.7 52.9 25.4 3.1 34.8 69.5 25.0 19.0 64.3 .. .. 36.7 34.3 33.5 .. .. 39.0 51.9 68.1 18.7 1.1 3.4 10.2 43.9 33.1 53.3 51.0 56.9 50.1 50.0 20.7 43.0 26.6 42.2 75.8 6.6 47.7 18.9 28.5 .. 19.5 45.2 26.2 53.4 27.6 .. .. 9.8
Manufacturing industries and construction 1990 2008
.. 44.2 14.1 46.1 16.2 22.3 17.7 17.9 24.4 34.3 33.3 28.1 28.4 12.0 11.6 26.4 18.1 29.8 17.9 .. .. .. 8.2 19.8 .. .. 24.9 40.9 9.5 27.4 29.4 8.6 26.8 16.3 28.2 18.4 20.1 30.0 10.9 10.3 16.9 35.8 25.9 .. 12.4 27.1 26.7 22.7 15.6 .. 22.4 18.9 17.7 14.8 22.7 .. .. 21.3
.. 16.1 13.6 24.1 22.2 37.6 12.6 18.1 7.4 31.1 24.5 19.9 24.7 5.2 17.2 6.6 26.8 29.7 15.1 .. .. 3.7 7.0 17.8 .. .. 19.1 33.3 15.7 31.7 35.3 4.1 17.3 9.3 21.2 28.3 15.1 17.7 10.0 6.3 17.5 23.4 23.5 4.4 8.2 24.5 21.5 19.2 38.9 .. 15.5 14.7 15.3 9.8 15.5 .. .. 22.6
Residential buildings and commercial and public services 1990 2008
1990
2008
Other sectors 1990 2008
.. 4.8 20.3 15.7 17.2 30.9 3.3 20.7 7.2 1.0 16.2 10.3 22.6 12.0 10.5 1.2 3.4 8.4 3.9 .. .. .. 25.5 16.8 .. .. 9.2 16.5 4.5 8.1 10.8 10.0 4.2 11.4 10.5 9.1 4.7 19.0 12.5 12.1 14.5 11.9 7.4 .. 4.1 5.4 11.9 24.1 13.3 .. 16.5 20.4 15.9 7.3 14.2 .. .. 8.5
.. 11.2 22.6 25.2 28.1 14.3 23.6 24.2 5.4 8.5 12.2 8.1 18.5 64.0 39.3 9.2 22.2 41.9 8.5 .. .. .. 65.2 28.6 .. .. 31.7 5.3 13.6 37.0 18.9 78.6 60.2 44.9 18.2 16.0 29.9 4.6 20.3 35.5 51.7 19.8 57.9 .. 6.6 41.6 21.3 31.9 35.6 .. 13.1 16.7 59.0 21.5 52.4 .. .. 44.7
.. 59.6 22.4 50.3 24.6 15.8 20.1 31.9 17.9 13.8 14.2 10.2 24.4 61.2 36.0 14.2 43.6 41.0 16.9 .. .. 25.0 50.6 29.4 .. .. 35.1 7.0 10.2 38.5 22.6 87.2 65.5 24.3 29.5 3.0 26.0 15.3 28.3 29.1 49.2 21.9 40.9 24.4 12.9 56.7 22.4 33.9 21.1 .. 37.6 18.5 49.8 23.6 51.3 .. .. 57.3
.. 27.8 0.0 0.7 4.5 2.9 1.3 2.2 18.7 0.0 5.7 6.2 1.5 0.0 9.5 22.6 1.4 5.7 6.2 .. .. .. 0.0 1.9 .. .. 1.2 5.1 0.0 3.2 37.8 0.0 1.5 4.9 5.9 11.2 0.0 3.6 4.6 1.0 1.8 0.0 0.0 .. 3.8 12.2 3.8 3.2 2.2 .. 1.7 1.7 4.4 4.1 3.0 .. .. 0.0
.. 9.8 24.9 21.7 12.9 0.0 3.0 14.7 22.1 0.9 11.9 12.4 23.5 30.9 10.6 0.6 3.1 5.4 2.7 .. .. 26.5 8.4 16.4 .. .. 6.2 6.2 6.0 8.5 10.6 5.4 3.6 12.1 12.6 10.0 4.5 8.8 7.7 13.9 11.4 8.1 8.9 28.9 2.3 12.4 4.8 22.8 9.7 .. 18.5 20.5 6.0 10.5 5.7 .. .. 10.3
Transport
.. 9.1 0.0 0.3 5.3 26.8 1.5 1.2 2.4 0.0 5.7 4.6 1.9 0.0 1.5 9.0 1.3 4.9 1.0 .. .. 7.8 0.0 2.9 .. .. 0.6 1.6 0.0 2.7 30.7 0.0 3.5 10.5 3.7 5.4 3.6 1.3 3.9 0.7 1.2 3.6 0.0 0.0 0.9 0.0 3.5 5.3 1.7 .. 8.9 1.1 2.7 2.6 0.1 .. .. 0.0
3.11
ENVIRONMENT
Carbon dioxide emissions by sector Carbon dioxide emissions
% of total fuel combustion Electricity and heat production 1990 2008
Honduras Hungary India Indonesia Iran, Islamic Rep. Iraq Ireland Israel Italy Jamaica Japan Jordan Kazakhstan Kenya Korea, Dem. Rep. Korea, Rep. Kosovo Kuwait Kyrgyz Republic Lao PDR Latvia Lebanon Lesotho Liberia Libya Lithuania Macedonia, FYR Madagascar Malawi Malaysia Mali Mauritania Mauritius Mexico Moldova Mongolia Morocco Mozambique Myanmar Namibia Nepal Netherlands New Zealand Nicaragua Niger Nigeria Norway Oman Pakistan Panama Papua New Guinea Paraguay Peru Philippines Poland Portugal Puerto Rico Qatar
0.9 34.8 44.5 37.3 22.7 28.3 36.1 57.6 35.9 27.8 38.4 38.6 47.9 8.0 13.9 27.4 .. 62.7 17.3 .. 52.3 43.3 .. .. 59.7 41.3 64.8 .. .. 35.7 .. .. .. 34.9 44.8 51.3 40.0 10.2 39.9 .. 0.0 38.2 21.6 30.6 .. 28.5 26.9 54.8 27.0 22.8 .. 1.6 20.7 35.5 65.1 41.6 .. 53.3
34.4 37.6 59.9 37.7 29.0 35.6 33.7 66.6 38.3 51.1 44.6 48.0 47.4 32.4 16.2 52.4 .. 68.2 23.1 .. 25.8 49.2 .. .. 62.1 35.3 68.1 .. .. 49.6 .. .. .. 40.2 47.7 62.6 37.1 1.0 20.8 22.6 0.3 38.2 32.8 40.1 .. 36.5 33.4 56.7 32.3 27.0 .. 0.0 26.8 44.0 55.8 39.9 .. 52.1
Manufacturing industries and construction 1990 2008
35.2 23.2 28.6 23.7 26.8 27.3 15.8 12.9 21.1 8.3 27.0 14.2 34.9 25.8 67.4 23.4 .. 21.7 36.9 .. 13.6 5.0 .. .. 13.2 21.6 18.8 .. .. 30.7 .. .. .. 23.9 6.7 22.1 25.7 13.0 28.1 .. 22.7 21.9 28.8 18.0 .. 17.3 24.5 21.8 34.0 21.1 .. 7.9 22.4 21.0 13.7 24.8 .. 35.9
19.9 13.2 19.6 34.0 22.4 24.0 11.5 2.8 15.8 3.3 21.5 14.1 22.3 15.8 62.9 19.1 .. 15.6 28.5 .. 13.8 11.5 .. .. 9.4 21.2 14.2 .. .. 24.2 .. .. .. 14.9 9.8 13.2 17.0 20.7 25.4 6.6 33.9 21.2 18.4 14.7 .. 10.1 21.3 23.1 32.4 18.8 .. 3.0 26.6 18.0 12.6 16.1 .. 31.5
Residential buildings and commercial and public services 1990 2008
1990
2008
Other sectors 1990 2008
15.5 25.2 9.8 13.7 22.3 9.2 29.5 4.2 16.7 4.0 12.9 11.3 0.2 10.9 0.5 26.9 .. 0.7 0.0 .. 13.4 22.7 .. .. 4.8 16.1 4.9 .. .. 4.4 .. .. .. 7.8 2.9 6.4 7.3 7.4 0.3 .. 28.4 18.5 6.0 7.1 .. 14.3 8.7 1.9 14.4 9.3 .. 6.8 18.1 6.7 12.9 5.5 .. 0.6
48.4 12.5 13.8 22.7 21.5 35.1 16.5 19.6 24.0 15.0 19.7 28.9 6.0 48.3 4.1 18.8 .. 15.0 13.3 .. 16.6 28.8 .. .. 22.4 16.5 8.9 .. .. 29.2 .. .. .. 31.5 7.9 12.1 18.7 55.6 31.7 .. 38.6 16.6 39.1 40.4 .. 39.9 35.1 16.8 23.1 46.7 .. 83.8 35.5 34.9 6.0 24.6 .. 10.1
37.7 24.2 9.2 19.7 21.8 31.1 30.6 16.4 27.2 21.6 19.7 24.8 7.0 37.2 1.7 16.8 .. 15.5 24.2 .. 44.9 28.5 .. .. 22.8 35.0 13.3 .. .. 23.3 .. .. .. 37.1 14.7 13.6 25.7 69.9 28.1 48.6 27.9 19.7 41.7 35.3 .. 48.6 37.3 15.3 23.5 47.6 .. 92.1 37.8 31.3 14.8 35.7 .. 16.0
0.0 4.4 3.3 2.7 6.6 0.0 2.2 5.7 2.2 44.9 1.9 7.1 11.0 6.9 14.2 3.4 .. 0.0 32.6 .. 4.1 0.0 .. .. 0.0 4.5 2.6 .. .. 0.0 .. .. .. 1.9 37.6 8.1 8.2 13.9 0.0 .. 11.4 4.8 4.4 3.8 .. 0.0 4.9 4.7 1.6 0.0 .. 0.0 3.4 2.0 2.3 3.5 .. 0.0
3.3 22.7 6.2 6.3 24.2 9.3 22.3 4.4 16.8 3.3 13.3 10.9 0.7 9.6 0.1 10.0 .. 0.7 0.0 .. 11.4 10.8 .. .. 5.6 6.9 4.0 .. .. 2.5 .. .. .. 5.8 23.5 7.3 9.5 7.8 2.6 0.3 31.5 17.9 4.2 6.5 .. 4.8 3.2 1.3 11.4 5.8 .. 4.9 6.7 5.7 13.3 6.8 .. 0.4
Transport
2012 World Development Indicators
4.9 2.2 5.1 2.3 2.5 0.0 1.8 9.9 2.0 20.5 0.9 2.2 22.6 5.1 19.1 1.7 .. 0.0 24.3 .. 4.2 0.0 .. .. 0.0 1.5 0.6 .. .. 0.4 .. .. .. 2.0 4.4 3.2 10.7 1.0 23.1 21.9 6.6 3.0 2.8 3.1 .. 0.0 4.8 3.6 0.3 0.8 .. 0.0 2.2 1.1 3.4 1.6 .. 0.0
179
3.11
Carbon dioxide emissions by sector Carbon dioxide emissions
% of total fuel combustion Electricity and heat production 1990 2008
Romania Russian Federation Rwanda Saudi Arabia Senegal Serbia Sierra Leone Singapore Slovak Republic Slovenia Somalia South Africa South Sudan Spain Sri Lanka Sudan Swaziland Sweden Switzerland Syrian Arab Republic Tajikistan Tanzania Thailand Timor-Leste Togo Trinidad and Tobago Tunisia Turkey Turkmenistan Uganda Ukraine United Arab Emirates United Kingdom United States Uruguay Uzbekistan Venezuela, RB Vietnam West Bank and Gaza Yemen, Rep. Zambia Zimbabwe World Low income Middle income Lower middle income Upper middle income Low & middle income East Asia & Pacific Europe & Central Asia Latin America & Carib. Middle East & N. Africa South Asia Sub-Saharan Africa High income Euro area
47.3 55.9 .. 50.3 36.8 64.6 .. 78.7 30.3 43.0 .. 56.1 .. 37.4 4.5 9.1 .. 18.4 4.1 25.2 14.0 17.5 36.5 .. 12.3 41.6 33.0 30.2 31.6 .. 51.4 26.4 44.2 43.9 13.9 39.1 40.1 27.7 .. 24.6 6.2 42.8 41.9 w 18.1 42.5 42.6 42.4 41.9 32.0 51.7 29.1 31.0 42.4 48.1 41.8 36.7
50.4 59.5 .. 51.4 28.4 63.3 .. 71.4 36.9 37.4 .. 64.5 .. 37.7 33.1 27.0 .. 22.8 7.0 49.9 17.5 18.5 41.1 .. 1.8 35.9 39.5 42.7 43.4 .. 45.2 50.8 44.5 47.7 39.8 33.3 36.3 29.3 .. 34.3 5.0 56.8 47.6 w 25.5 49.1 48.1 49.4 48.8 50.0 53.4 31.9 36.4 56.8 53.3 46.1 37.8
Note: Shares may not sum to 100 percent because of rounding.
180
2012 World Development Indicators
Manufacturing industries and construction 1990 2008
35.8 13.2 .. 17.8 12.4 16.6 .. 6.7 32.6 20.7 .. 26.9 .. 22.1 13.1 16.9 .. 24.2 14.3 20.6 0.0 22.2 18.8 .. 3.5 45.6 27.6 26.6 0.0 .. 29.0 51.4 15.2 14.4 19.5 4.9 28.0 32.6 .. 3.1 51.5 29.4 22.3 w 50.4 26.5 25.6 26.7 27.0 39.8 19.2 24.4 24.9 29.1 25.8 18.4 21.0
22.3 14.4 .. 22.9 17.4 16.9 .. 11.9 25.7 14.5 .. 13.5 .. 17.4 10.6 10.0 .. 21.0 14.9 20.9 0.0 13.3 29.2 .. 7.3 56.6 17.7 14.6 0.0 .. 29.4 29.8 11.5 11.3 11.4 19.0 28.0 35.1 .. 11.2 49.1 16.1 21.0 w 36.7 26.0 23.0 26.8 26.1 33.1 16.9 22.4 20.6 20.8 13.8 15.1 16.8
Residential buildings and commercial and public services 1990 2008
1990
2008
Other sectors 1990 2008
5.2 13.1 .. 1.6 6.5 3.3 .. 0.6 26.7 15.4 .. 4.4 .. 7.9 2.7 2.5 .. 17.3 44.3 5.0 0.0 19.9 3.1 .. 10.5 1.2 13.4 16.8 0.0 .. 7.8 0.6 17.2 11.1 15.5 0.0 5.0 11.2 .. 10.6 8.5 5.9 12.7 w 2.8 11.7 8.9 12.6 11.5 14.8 10.1 9.2 15.6 10.3 6.0 13.6 18.5
6.9 13.6 .. 30.4 35.8 7.2 .. 14.0 7.1 20.8 .. 11.5 .. 30.6 66.0 71.3 .. 37.5 35.4 30.6 6.6 40.4 34.5 .. 73.7 11.7 20.4 21.9 5.2 .. 7.9 21.6 20.8 29.2 40.0 4.7 26.9 24.3 .. 61.7 29.6 12.9 19.5 w 10.2 13.7 14.8 13.3 13.6 8.1 11.3 33.6 24.1 14.9 18.0 24.3 21.4
16.6 15.3 .. 24.7 46.2 13.1 .. 16.2 19.5 35.5 .. 13.6 .. 34.3 48.2 55.6 .. 50.7 39.5 22.2 9.2 56.8 22.3 .. 78.2 6.1 23.1 17.1 5.9 .. 10.5 17.1 24.4 30.2 34.3 7.8 31.0 24.5 .. 27.1 32.1 12.5 19.6 w 17.5 14.1 16.0 13.6 14.1 8.9 14.1 35.6 23.2 10.9 23.6 25.9 25.8
4.8 4.2 .. 0.0 8.5 8.3 .. 0.0 3.2 0.2 .. 1.2 .. 2.1 13.9 0.2 .. 2.6 2.0 18.6 79.4 0.0 7.1 .. 0.0 0.0 5.5 4.6 63.3 .. 3.8 0.0 2.5 1.3 11.2 51.3 0.0 4.0 .. 0.0 4.2 9.0 3.7 w 18.4 5.6 8.0 4.9 5.9 5.3 7.7 3.7 4.3 3.2 2.0 1.9 2.4
9.1 9.7 .. 1.0 7.3 4.9 .. 0.5 16.9 10.9 .. 7.3 .. 8.1 3.4 5.7 .. 3.7 37.1 2.9 0.0 10.4 2.8 .. 12.7 1.4 14.6 19.3 0.0 .. 13.5 2.3 18.5 9.9 7.1 36.6 4.4 9.5 .. 9.3 2.5 4.0 9.6 w 6.9 8.0 8.6 7.9 8.0 6.0 11.1 6.9 17.0 6.8 7.6 11.5 17.2
Transport
1.6 1.2 .. 0.0 0.8 1.8 .. 0.0 1.0 1.7 .. 1.1 .. 2.5 4.6 1.5 .. 1.7 1.5 4.2 73.3 1.2 4.6 .. 0.0 0.0 5.0 6.3 50.8 .. 1.4 0.0 1.0 0.8 7.5 3.3 0.2 1.6 .. 18.0 11.9 10.6 2.2 w 13.5 2.7 4.4 2.3 2.9 1.9 4.4 3.2 2.9 4.7 1.7 1.4 2.3
About the data
3.11
Definitions
Carbon dioxide emissions account for the largest
Carbon dioxide emissions from transport are emis-
• Carbon dioxide emissions from electricity and
share of greenhouse gases, which are associated
sions from fuel combustion for all transport activity
heat production are those from main activity pro-
with global warming. In 2010 the International Energy
(IPCC source/sink category 1A3), including domestic
ducers of electricity and heat, unallocated auto-
Agency (IEA) released data on carbon dioxide emis-
aviation, domestic navigation, road, rail, and pipeline
producers, and other energy industries. • Carbon
sions by sector for the first time, allowing a more
transport but excluding international marine bunkers
dioxide emissions from manufacturing industries
comprehensive understanding of each sector’s con-
and international aviation. The IEA data do not allow
and construction are those from fuel combustion
tribution to total emissions. The sectoral approach
energy consumption to be categorized by end-use,
in industry (IPCC source/sink category 1A2). • Car-
yields data on carbon dioxide emissions from fuel
and thus emissions from autoproducers are listed
bon dioxide emissions from residential buildings
combustion (Intergovernmental Panel on Climate
separately under unallocated autoproducers.
and commercial and public services are those from
Change [IPCC] source/sink category 1A) as calcu-
Carbon dioxide emissions from other sectors are
fuel combustion in household and all activities of
lated using the IPCC tier 1 sectoral approach. The
emissions from commercial and institutional activi-
ISIC divisions 41, 50–52, 55, 63–67, 70–75, 80,
table does not include all sectors.
ties and from residential, agriculture and forestry,
85, 90–93, and 99. • Carbon dioxide emissions
Carbon dioxide emissions from electricity and heat
fishing, and other processes not specified elsewhere
from transport are those from fuel combustion for all
production are the sum of emissions from main activ-
that are included in IPCC source/sink categories 1A4
transport activities (IPCC source/sink category 1A3).
ity producers of electricity and heat, unallocated auto-
and 1A5. Although in the 1996 IPCC guidelines, this
• Carbon dioxide emissions from other sectors are
producers, and other energy industries. Main activity
category included emissions from autoproducers in
those from commercial and institutional activities
producers (formerly known as public supply under-
the commercial, residential, and agricultural sectors
and from residential, agriculture and forestry, fish-
takings) generate electricity or heat for sale to third
that generate electricity or heat, the IEA data do not
ing, and other emissions not specified elsewhere
parties as their primary activity and may be privately
allow energy consumption to be classified by end-
that are included in IPCC source/sink categories
or publicly owned. Emissions from own onsite use
use, and thus emissions from autoproducers are
1A4 and 1A5.
of fuel in power plants are also included in this cat-
listed separately under unallocated autoproducers.
egory. Unallocated autoproducers are undertakings that generate electricity or heat, wholly or partly for their own use as an activity that supports their primary activity and may be privately or publicly owned. In the 1996 IPCC guidelines these emissions were allocated among industry, transport, and “other” sectors. Emissions from other energy industries are emissions from fuel combusted in petroleum refineries, the manufacture of solid fuels, coal mining, oil and gas extraction, and other energy-producing industries. Carbon dioxide emissions from manufacturing industries and construction are the emissions from fuel combustion in industry (IPCC source/sink Category 1A2). Although in the 1996 IPCC guidelines, this category included emissions from industry autoproducers that generate electricity or heat, the IEA data do not allow energy consumption to be categorized by end-use, and thus emissions from autoproducers are listed separately under unallocated autoproducers. Emissions from manufacturing industries and construction include those from coke inputs into blast furnaces, which may be reported under the transformation sector, the industry sector, or industrial processes (IPCC source/sink category 2). Carbon emissions from residential buildings and commercial and public services are the sum of emissions from fuel combustion in households (IPCC source/sink category 1A4b) and emissions from all
Data sources
activities of International Standard Industrial Clas-
Data on carbon dioxide emissions by sector are
sification divisions 41, 50–52, 55, 63–67, 70–75,
from IEA electronic files.
80, 85, 90–93, and 99.
2012 World Development Indicators
181
ENVIRONMENT
Carbon dioxide emissions by sector
3.12
Climate variability, exposure to impact, and resilience Climate variability
Exposure to impact
Resilience
% of population Average daily minimum/ maximum temperature degrees Celsius 1961–1990
Afghanistan Albania Algeria Angola Argentina Armenia Australia Austria Azerbaijan Bahrain Bangladesh Belarus Belgium Benin Bolivia Bosnia and Herzegovina Botswana Brazil Bulgaria Burkina Faso Burundi Cambodia Cameroon Canada Central African Republic Chad Chile China Hong Kong SAR, China Colombia Congo, Dem. Rep. Congo, Rep. Costa Rica Côte d’Ivoire Croatia Cuba Cyprus Czech Republic Denmark Dominican Republic Ecuador Egypt, Arab Rep. El Salvador Eritrea Estonia Ethiopia Finland France Gabon Gambia, The Georgia Germany Ghana Greece Guatemala Guinea Guinea-Bissau Haiti
182
5.5 / 19.7 6.3 / 16.5 15.4 / 29.6 14.8 / 28.3 8.2 / 21.4 1.3 / 13.0 14.9 / 28.4 2.3 / 10.4 6.7 / 17.2 20.5 / 33.8 20.4 / 29.6 2.0 / 10.3 5.5 / 13.6 21.7 / 33.4 15.2 / 27.9 5.0 / 14.7 13.6 / 29.4 19.6 / 30.3 5.6 / 15.5 21.5 / 35.0 13.8 / 25.8 22.3 / 31.3 19.1 / 30.1 –10.1 / –0.6 18.3 / 31.5 18.6 / 34.5 3.5 / 13.4 0.9 / 13.0 19.3 / 26.0 19.6 / 29.4 18.4 / 29.6 19.8 / 29.3 19.6 / 30.0 21.1 / 31.6 6.2 / 15.6 20.4 / 30.0 13.1 / 23.8 3.3 / 11.8 4.3 / 10.7 19.9 / 29.2 15.6 / 28.1 14.8 / 29.4 18.4 / 30.5 18.9 / 32.1 1.5 / 8.7 15.5 / 28.9 –2.4 / 5.8 6.5 / 14.9 20.9 / 29.2 20.4 / 34.6 0.4 / 11.2 4.6 / 12.4 22.1 / 32.3 10.5 / 20.3 18.2 / 28.7 19.6 / 31.8 20.9 / 32.6 20.1 / 29.7
2012 World Development Indicators
change in average number of days/nights Projected annual temperature degrees Celsius 2045–2065
2.3 to 3.6 1.9 to 2.9 2.4 to 3.0 2.1 to 2.7 1.4 to 2.1 2.1 to 3.3 1.7 to 2.4 1.9 to 2.9 1.9 to 3.1 2.1 to 3.0 1.7 to 2.4 2.2 to 3.1 1.4 to 2.4 2.0 to 2.5 2.1 to 3.0 1.9 to 3.0 2.5 to 3.3 1.9 to 2.6 1.9 to 3.1 2.2 to 2.8 2.1 to 2.4 1.6 to 2.0 2.1 to 2.4 2.6 to 3.5 2.1 to 2.4 2.3 to 2.6 1.2 to 1.9 2.1 to 3.0 1.4 to 1.9 1.8 to 2.5 2.1 to 2.4 2.0 to 2.3 1.6 to 2.3 1.8 to 2.4 1.9 to 2.9 1.4 to 1.8 1.7 to 2.3 1.9 to 2.8 1.6 to 2.5 1.5 to 1.8 1.8 to 2.3 1.9 to 2.4 1.7 to 2.9 2.1 to 2.7 2.2 to 3.2 2.1 to 2.5 2.4 to 3.7 1.6 to 2.6 1.8 to 2.2 2.1 to 2.7 1.9 to 3.0 1.7 to 2.6 1.8 to 2.4 1.7 to 2.6 1.7 to 2.8 2.0 to 2.6 1.8 to 2.2 1.5 to 1.8
Projected annual Projected annual hot days/ cool days/ warm nights cold nights 2045–2065 2045–2065
–1.4 / –1.5 –1.5 / –1.4 –1.7 / –2.0 –2.2 / –2.6 –1.2 / –1.3 –1.3 / –1.4 –1.8 / –1.9 –1.4 / –1.5 –1.3 / –1.3 –1.5 / –1.9 –1.7 / –2.1 –1.5 / –1.7 –1.4 / –1.5 –2.3 / –2.6 –2.0 / –2.1 –1.5 / –1.5 –1.8 / –2.1 –2.1 / –2.5 –1.4 / –1.3 –2.1 / –2.6 –2.1 / –2.8 –1.8 / –1.7 –2.4 / –2.4 –1.6 / –1.7 –2.3 / –2.4 –1.8 / –2.3 –1.7 / –1.7 –1.5 / –1.6 –1.2 / –1.4 –2.4 / –2.9 –2.1 / –2.7 –2.3 / –2.7 –2.8 / –3.0 –2.4 / –2.5 –1.6 / –1.6 –1.9 / –1.8 –1.9 / –1.8 –1.5 / –1.6 –1.6 / –1.7 –2.8 / –2.9 –2.6 / –2.8 –1.7 / –2.1 –2.2 / –2.3 –2.0 / –2.4 –1.7 / –1.8 –2.0 / –2.7 –1.7 / –1.9 –1.5 / –1.5 –2.6 / –2.9 –2.3 / –2.6 –1.3 / –1.3 –1.5 / –1.5 –2.4 / –2.5 –1.7 / –1.7 –1.7 / –1.7 –2.4 / –2.5 –2.7 / –2.7 –2.8 / –2.8
3.1 / 7.0 3.2 / 6.5 3.9 / 7.8 7.0 / 20.2 2.9 / 6.5 2.8 / 6.3 3.6 / 9.3 2.9 / 5.2 2.7 / 6.2 4.6 / 8.6 3.4 / 11.8 2.1 / 4.5 2.9 / 5.5 5.5 / 19.2 5.1 / 16.9 3.1 / 6.1 4.7 / 12.8 5.6 / 19.6 2.9 / 6.0 5.3 / 16.6 7.8 / 26.2 4.5 / 18.2 6.2 / 22.0 1.9 / 4.4 4.7 / 22.5 5.1 / 13.7 3.8 / 7.3 2.9 / 5.6 7.4 / 10.3 7.9 / 26.6 6.4 / 24.7 9.1 / 26.5 11.8 / 26.8 7.1 / 22.9 3.2 / 6.0 10.5 / 17.6 4.0 / 7.3 2.5 / 4.7 2.6 / 5.2 12.1 / 23.6 9.5 / 24.4 4.0 / 8.1 8.9 / 24.5 6.6 / 14.4 2.1 / 4.8 6.6 / 21.4 1.7 / 4.1 3.6 / 6.3 13.7 / 26.9 6.7 / 16.8 2.8 / 5.9 2.6 / 5.0 6.6 / 22.1 3.5 / 6.5 7.8 / 21.6 6.9 / 20.2 9.9 / 22.2 12.6 / 23.1
Projected annual precipitation millimeters 2045–2065
–58 to 13 –147 to –44 –39 to 4 –89 to 75 –57 to 52 –75 to –7 –67 to 54 –70 to 27 –58 to 3 –19 to 4 –126 to 120 8 to 70 –64 to 29 –207 to 97 –91 to 137 –116 to –7 –106 to 24 –95 to 136 –127 to –21 –229 to 88 –21 to 206 –109 to 95 –71 to 115 17 to 88 –73 to 100 –79 to 41 –118 to 24 –37 to 86 –83 to 73 –80 to 199 –48 to 128 –40 to 134 –239 to 60 –125 to 73 –112 to –2 –108 to 39 –98 to –44 –43 to 55 21 to 87 –128 to 30 22 to 273 –20 to 1 –205 to 14 –55 to 26 33 to 93 –42 to 79 37 to 103 –112 to 2 –51 to 148 –87 to 26 –77 to –3 –38 to 57 –159 to 83 –110 to –40 –186 to 22 –122 to 104 –69 to 130 –125 to 34
Population Land area living in with an areas with elevation of 5 meters an elevation of 5 meters or less or less % of land area 2000 2000
0.0 5.0 0.4 0.2 1.2 0.0 1.1 0.0 20.0 39.0 14.1 0.0 9.2 1.2 0.0 0.1 0.0 1.2 0.4 0.0 0.0 3.8 0.1 2.4 0.0 0.0 3.1 1.4 24.6 0.9 0.0 0.1 2.1 0.2 3.0 12.7 6.4 0.0 17.7 4.1 2.0 4.0 2.5 3.1 3.4 0.7 1.1 2.1 0.5 16.6 1.4 4.9 0.8 6.3 0.6 1.1 9.5 3.9
0.0 8.2 3.5 2.1 4.5 0.0 7.2 0.0 29.8 66.6 14.0 0.0 14.3 14.1 0.0 0.1 0.0 4.9 1.5 0.0 0.0 10.6 0.3 4.0 0.0 0.0 1.6 8.1 26.2 2.0 0.0 1.0 0.8 3.2 3.4 10.0 9.7 0.0 18.5 3.0 7.3 25.6 1.7 1.2 7.2 0.4 4.4 4.0 5.9 33.4 3.3 4.4 2.3 9.9 0.3 3.6 18.8 5.4
Population affected by droughts, floods, and extreme temperature 1990–2009
Disaster risk reduction progress score 1, worst, to 5, best 2011
1.1 5.3 0.0 1.0 0.2 0.5 3.0 0.0 1.1 .. 4.6 0.0 0.0 0.9 1.3 0.5 0.7 0.5 0.0 1.3 2.4 6.6 0.1 0.0 0.2 2.7 0.3 8.0 0.0 0.7 0.0 0.3 0.7 0.0 0.0 0.7 0.0 0.2 0.0 0.1 0.3 0.0 0.4 7.3 0.0 3.3 0.0 0.0 .. 0.2 0.8 0.0 1.0 0.0 1.3 0.2 0.5 0.8
.. .. 3.5 .. 3.3 3.0 4.0 .. .. .. 4.0 .. .. .. 2.3 .. 3.0 4.5 3.5 .. 3.3 .. .. 4.3 .. .. 2.8 .. .. 3.8 .. .. 4.5 2.5 .. 4.5 .. 2.8 .. 3.0 4.8 .. 3.3 .. .. .. 3.5 .. .. .. 2.8 4.3 3.3 .. 3.3 .. 1.0 ..
Climate variability
Exposure to impact
Resilience
% of population
Honduras Hungary India Indonesia Iran, Islamic Rep. Iraq Ireland Israel Italy Jamaica Japan Jordan Kazakhstan Kenya Korea, Dem. Rep. Korea, Rep. Kosovo Kuwait Kyrgyz Republic Lao PDR Latvia Lebanon Lesotho Liberia Libya Lithuania Macedonia, FYR Madagascar Malawi Malaysia Mali Mauritania Mauritius Mexico Moldova Mongolia Morocco Mozambique Myanmar Namibia Nepal Netherlands New Zealand Nicaragua Niger Nigeria Norway Oman Pakistan Panama Papua New Guinea Paraguay Peru Philippines Poland Portugal Puerto Rico Qatar
change in average number of days/nights
Average daily minimum/ maximum temperature degrees Celsius 1961–1990
Projected annual temperature degrees Celsius 2045–2065
18.9 / 28.1 4.8 / 14.7 17.7 / 29.6 21.1 / 30.6 10.1 / 24.4 14.1 / 28.7 5.9 / 12.7 13.4 / 25.0 9.5 / 17.4 20.6 / 29.3 7.0 / 15.3 11.2 / 25.4 0.6 / 12.2 18.9 / 30.6 0.3 / 11.1 6.5 / 16.5 .. 18.5 / 32.2 –4.6 / 7.7 18.2 / 27.4 1.9 / 9.3 10.7 / 22.1 5.2 / 18.5 20.0 / 30.6 14.4 / 29.2 2.3 / 10.1 4.5 / 15.1 18.0 / 27.3 16.6 / 27.2 21.2 / 29.6 20.9 / 35.6 20.2 / 35.1 18.9 / 25.9 13.5 / 28.5 5.0 / 13.9 –7.7 / 6.3 11.1 / 23.1 18.5 / 29.1 17.7 / 8.4 12.7 / 27.2 1.5 / 14.7 5.5 / 13.0 6.0 / 15.1 20.3 / 29.5 19.4 / 34.9 20.8 / 32.8 –2.0 / 5.0 20.8 / 30.4 13.2 / 27.2 21.3 / 29.5 20.1 / 30.4 17.7 / 29.4 13.5 / 25.7 21.6 / 30.1 3.8 / 11.9 10.2 / 20.1 21.2 / 29.3 21.3 / 33.0
1.6 to 2.6 2.0 to 3.0 1.9 to 2.6 1.5 to 1.8 2.2 to 3.3 2.3 to 3.2 0.9 to 1.4 1.9 to 2.7 1.8 to 2.6 1.3 to 1.8 1.8 to 2.3 2.1 to 2.9 2.2 to 3.1 1.9 to 2.1 2.1 to 2.7 1.8 to 2.3 2.0 to 3.1 2.2 to 3.1 2.3 to 3.2 1.6 to 2.4 2.1 to 3.1 2.0 to 2.9 2.0 to 2.5 1.7 to 2.2 2.0 to 2.4 2.0 to 3.0 1.9 to 3.0 1.7 to 2.1 2.0 to 2.5 1.6 to 1.9 2.5 to 3.1 2.4 to 3.1 1.3 to 1.7 1.7 to 2.8 2.0 to 3.3 2.3 to 3.1 2.1 to 3.0 1.9 to 2.4 1.7 to 2.3 2.2 to 2.9 2.2 to 3.4 1.4 to 2.3 1.1 to 1.7 1.6 to 2.4 2.4 to 2.8 2.0 to 2.5 1.8 to 3.3 2.0 to 2.4 2.4 to 3.4 1.6 to 2.2 1.4 to 1.8 1.9 to 2.6 2.1 to 2.7 1.4 to 1.8 1.9 to 2.8 1.3 to 2.3 1.4 to 1.8 2.3 to 2.9
Projected annual Projected annual hot days/ cool days/ warm nights cold nights 2045–2065 2045–2065
–2.2 / –2.5 –1.5 / –1.7 –2.0 / –2.2 –2.8 / –2.9 –1.4 / –1.4 –1.5 / –1.5 –1.3 / –1.4 –1.6 / –1.8 –1.8 / –1.8 –2.9 / –2.8 –1.9 / –2.2 –1.5 / –1.6 –1.2 / –1.4 –2.0 / –2.9 –1.4 / –1.8 –1.4 / –1.8 –1.5 / –1.3 –1.5 / –1.7 –1.2 / –1.4 –1.1 / –1.3 –1.7 / –1.8 –1.7 / –1.6 –1.6 / –1.9 –2.7 / –2.7 –1.7 / –2.1 –1.6 / –1.8 –1.4 / –1.3 –2.5 / –2.7 –2.1 / –2.5 –2.5 / –2.8 –2.0 / –2.5 –2.0 / –2.4 –2.9 / –2.9 –1.6 / –1.5 –1.5 / –1.6 –1.3 / –1.4 –1.8 / –2.0 –2.2 / –2.5 –1.7 / –1.8 –1.9 / –2.2 –2.1 / –2.1 –1.6 / –1.6 –2.2 / –2.1 –2.6 / –2.8 –1.8 / –2.4 –2.2 / –2.5 –1.8 / –1.8 –1.9 / –2.1 –1.8 / –1.9 –2.9 / –3.0 –2.8 / –2.9 –0.8 / –1.2 –2.5 / –2.7 –2.5 / –2.5 –1.5 / –1.7 –2.0 / –1.9 –3.0 / –2.9 –1.6 / –1.9
10.2 / 24.5 3.0 / 5.3 4.6 / 13.3 16.5 / 26.7 3.3 / 7.1 3.4 / 6.8 3.0 / 5.6 3.7 / 7.7 4.0 / 6.7 14.0 / 23.7 3.5 / 6.0 3.7 / 7.4 2.3 / 5.0 8.1 / 25.0 2.6 / 4.6 3.2 / 5.2 3.1 / 6.3 3.9 / 7.4 3.1 / 6.6 3.1 / 12.7 2.1 / 5.0 3.6 / 7.5 3.7 / 10.1 10.2 / 25.3 3.5 / 7.6 2.0 / 4.8 3.2 / 6.4 7.6 / 15.7 3.6 / 15.9 13.0 / 26.3 5.5 / 12.8 5.1 / 10.6 8.5 / 13.7 5.6 / 12.1 2.8 / 5.2 2.2 / 4.2 3.6 / 7.9 5.0 / 15.0 3.8 / 13.9 5.8 / 13.1 2.5 / 8.0 2.9 / 5.3 4.1 / 7.9 10.4 / 26.0 5.4 / 12.2 5.5 / 19.0 2.2 / 4.8 4.4 / 11.8 3.4 / 8.1 14.0 / 26.9 15.5 / 25.5 3.5 / 11.8 7.7 / 22.9 13.5 / 23.8 2.3 / 4.8 4.5 / 8.7 12.8 / 24.2 4.6 / 8.8
Projected annual precipitation millimeters 2045–2065
–204 to 15 –75 to 23 –91 to 135 –160 to 234 –51 to 6 –38 to –2 –13 to 78 –41 to –11 –108 to –11 –102 to 28 –47 to 80 –28 to –3 –1 to 37 0 to 144 –9 to 129 0 to 181 –148 to –34 –19 to 4 –32 to 35 –95 to 70 31 to 89 –75 to –27 –66 to 89 –115 to 81 –18 to 5 22 to 83 –150 to –43 –104 to 56 –53 to 91 –160 to 117 –142 to 43 –113 to 9 –165 to 67 –178 to 10 –72 to 11 1 to 50 –62 to –5 –69 to 61 –84 to 97 –67 to 23 –116 to 231 –18 to 50 –44 to 53 –220 to 19 –71 to 36 –128 to 89 41 to 131 –16 to 31 –60 to 36 –228 to 107 –163 to 421 –48 to 112 –57 to 181 –136 to 140 –12 to 64 –113 to –7 –133 to 37 –23 to 8
Population Land area living in with an areas with elevation of 5 meters an elevation of 5 meters or less or less % of land area 2000 2000
3.0 0.0 1.4 5.5 1.6 4.0 4.0 7.8 5.2 7.1 5.9 2.0 6.7 0.2 2.4 4.3 .. 8.9 0.0 0.0 3.0 1.7 0.0 0.4 0.8 1.9 0.0 1.3 0.0 3.0 0.0 1.0 7.1 2.9 1.3 0.0 0.7 1.8 4.6 0.3 0.0 58.5 2.7 3.6 0.0 0.5 4.9 1.1 1.4 3.7 1.8 0.0 0.4 6.0 1.7 2.3 7.7 13.4
2.2 0.0 3.8 11.2 5.0 6.5 6.6 8.3 7.5 5.8 16.2 4.2 3.9 1.4 5.3 5.0 .. 22.8 0.0 0.0 23.9 9.1 0.0 3.3 4.7 4.0 0.0 2.0 0.0 9.5 0.0 20.4 5.6 2.7 0.9 0.0 3.8 6.5 14.0 2.9 0.0 61.3 12.6 1.5 0.0 3.0 9.3 5.5 1.3 4.0 2.0 0.0 1.7 10.5 2.5 5.2 11.3 23.1
Population affected by droughts, floods, and extreme temperature 1990–2009
Disaster risk reduction progress score 1, worst, to 5, best 2011
1.3 0.1 4.4 0.2 3.1 0.0 0.0 0.0 0.0 1.1 0.0 0.4 0.2 6.5 2.5 0.1 .. 0.0 2.1 2.7 0.0 0.0 3.4 1.9 0.0 0.0 0.3 0.9 8.8 0.1 0.7 3.1 0.0 0.1 0.3 2.6 0.1 3.7 0.1 3.4 0.7 0.0 0.0 0.8 7.5 0.1 0.0 .. 1.1 0.2 0.7 0.7 2.0 0.8 0.0 0.0 0.0 ..
3.8 .. 3.3 3.3 .. .. .. .. 3.5 3.8 4.5 .. .. 4.0 .. .. .. .. .. 2.3 .. 3.0 2.5 .. .. .. 3.3 3.8 1.8 3.8 .. .. 3.5 4.3 .. 2.8 3.0 4.0 .. .. 2.8 .. 3.8 3.8 .. 4.0 3.8 .. 3.5 3.0 .. 3.8 3.0 .. 3.3 .. .. ..
2012 World Development Indicators
183
ENVIRONMENT
3.12
Climate variability, exposure to impact, and resilience
3.12
Climate variability, exposure to impact, and resilience Climate variability
Exposure to impact
Resilience
% of population Average daily minimum/ maximum temperature degrees Celsius 1961–1990
Romania Russian Federation Rwanda Saudi Arabia Senegal Serbia Sierra Leone Singapore Slovak Republic Slovenia Somalia South Africa South Sudan Spain Sri Lanka Sudan Swaziland Sweden Switzerland Syrian Arab Republic Tajikistan Tanzania Thailand Timor-Leste Togo Trinidad and Tobago Tunisia Turkey Turkmenistan Uganda Ukraine United Arab Emirates United Kingdom United States Uruguay Uzbekistan Venezuela, RB Vietnam West Bank and Gaza Yemen, Rep. Zambia Zimbabwe World Low income Middle income Lower middle income Upper middle income Low & middle income East Asia & Pacific Europe & Central Asia Latin America & Carib. Middle East & N. Africa South Asia Sub-Saharan Africa High income Euro area
184
3.8 / 13.8 –10.1 / –0.1 11.9 / 23.8 18.2 / 31.1 20.8 / 34.9 .. 21.0 / 31.1 22.6 / 30.3 2.1 / 11.5 4.2 / 13.6 21.2 / 32.9 10.6 / 24.9 .. 7.9 / 18.7 23.3 / 30.6 19.4 / 34.4 15.5 / 27.3 –2.1 / 6.3 2.0 / 9.0 10.8 / 24.7 –3.9 / 7.9 16.7 / 28.0 21.2 / 31.4 .. 21.8 / 32.5 21.5 / 30.0 13.5 / 24.9 5.4 / 16.8 8.7 / 21.5 16.6 / 29.0 4.0 / 12.6 21.0 / 33.0 5.1 / 11.8 2.2 / 14.9 12.2 / 22.9 5.8 / 18.3 20.3 / 30.4 20.6 / 28.3 .. 18.6 / 29.1 14.5 / 28.3 14.2 / 27.8
2012 World Development Indicators
change in average number of days/nights Projected annual temperature degrees Celsius 2045–2065
2.0 to 3.1 2.6 to 3.7 2.1 to 2.4 2.3 to 2.9 2.0 to 2.7 2.0 to 3.1 1.8 to 2.3 1.5 to 1.8 2.0 to 2.9 2.0 to 3.0 1.9 to 2.2 1.9 to 2.7 .. 1.5 to 2.6 1.5 to 1.8 2.1 to 2.6 2.0 to 2.4 2.1 to 3.1 1.7 to 2.8 2.2 to 3.1 2.3 to 3.4 1.9 to 2.2 1.7 to 2.1 1.3 to 1.7 1.9 to 2.5 1.5 to 2.1 1.9 to 2.6 1.9 to 2.9 1.8 to 3.0 2.1 to 2.3 2.1 to 3.0 2.2 to 2.8 1.1 to 1.8 2.0 to 3.0 1.4 to 1.8 2.0 to 3.0 1.8 to 2.9 1.4 to 2.1 2.0 to 2.6 2.0 to 2.6 2.1 to 2.7 2.2 to 2.9
Projected annual Projected annual hot days/ cool days/ warm nights cold nights 2045–2065 2045–2065
–1.5 / –1.6 –1.6 / –1.8 –2.2 / –2.8 –1.5 / –1.7 –2.3 / –2.6 –1.5 / –1.4 –2.7 / –2.6 –2.6 / –2.7 –1.5 / –1.8 –1.4 / –1.5 –2.2 / –2.8 –1.7 / –2.0 .. –1.9 / –1.8 –2.8 / –2.9 –1.8 / –2.2 –1.6 / –2.1 –1.7 / –1.8 –1.4 / –1.5 –1.6 / –1.3 –1.4 / –1.5 –2.1 / –2.8 –1.6 / –1.6 –3.0 / –3.0 –2.4 / –2.5 –2.9 / –3.0 –1.9 / –2.0 –1.5 / –1.4 –1.0 / –1.1 –2.1 / –2.8 –1.4 / –1.6 –1.6 / –2.0 –1.4 / –1.5 –1.4 / –1.5 –0.8 / –1.0 –1.1 / –1.3 –2.5 / –2.9 –1.5 / –1.5 –1.7 / –1.8 –2.0 / –2.2 –2.0 / –2.5 –1.9 / –2.3
3.0 / 5.5 1.7 / 4.0 8.3 / 26.4 4.6 / 9.5 7.1 / 16.9 3.2 / 6.1 9.2 / 24.0 12.4 / 25.9 2.6 / 4.7 3.1 / 5.6 9.5 / 21.2 4.1 / 10.4 .. 4.0 / 8.0 7.9 / 23.9 5.2 / 16.6 3.8 / 10.9 2.0 / 4.4 3.1 / 5.6 3.3 / 6.7 3.2 / 7.0 6.2 / 21.6 3.6 / 17.0 16.1 / 25.9 6.0 / 21.5 14.7 / 26.5 3.4 / 6.8 3.1 / 6.3 2.7 / 5.7 6.4 / 25.6 2.6 / 4.8 4.2 / 9.4 3.0 / 5.4 3.1 / 6.4 2.0 / 5.7 2.6 / 5.7 9.1 / 26.5 5.7 / 14.4 3.8 / 7.9 5.6 / 13.1 4.0 / 17.7 3.8 / 13.2
Projected annual precipitation millimeters 2045–2065
–92 to 2 24 to 79 –12 to 211 –27 to 9 –124 to 37 –125 to –15 –96 to 128 –134 to 49 –44 to 45 –93 to 5 –4 to 111 –78 to 33 .. –112 to –18 11 to 196 –69 to 44 –82 to 34 35 to 107 –89 to 11 –57 to –13 –46 to 27 –9 to 167 –109 to 76 –123 to 206 –175 to 100 –266 to 8 –57 to –16 –103 to –29 –32 to 12 –13 to 224 –43 to 31 –26 to 17 –10 to 66 –26 to 98 0 to 130 –26 to 17 –150 to 85 –107 to 61 –40 to –13 –24 to 51 –54 to 94 –81 to 24
Population Land area living in with an areas with elevation of 5 meters an elevation of 5 meters or less or less % of land area 2000 2000
2.9 1.9 0.0 0.5 4.5 0.2 3.0 8.1 0.0 0.2 0.6 0.1 .. 1.3 3.9 0.1 0.0 1.5 0.0 0.1 0.0 0.2 4.2 2.9 0.6 8.0 2.8 1.0 5.3 0.0 1.5 4.6 7.6 1.7 1.9 0.1 2.3 17.5 14.8 0.6 0.0 0.0 1.8 t 0.7 1.8 1.7 1.8 1.6 2.5 2.5 1.5 1.4 1.5 0.4 2.2 3.6
2.9 2.9 0.0 1.0 14.8 0.1 5.1 12.1 0.0 1.3 2.2 0.5 .. 6.6 5.4 0.2 0.0 6.3 0.0 0.3 0.0 1.3 13.8 4.4 6.1 7.5 9.5 2.4 5.6 0.0 2.1 7.3 8.6 4.1 4.7 0.0 3.7 42.8 11.2 1.8 0.0 0.0 6.5 t 5.1 6.5 6.4 6.5 6.3 10.3 3.0 3.8 9.7 4.4 2.0 7.7 8.5
Population affected by droughts, floods, and extreme temperature 1990–2009
Disaster risk reduction progress score 1, worst, to 5, best 2011
0.1 0.1 1.3 0.0 0.6 0.0 0.2 .. 0.0 0.0 4.6 1.8
3.3 .. .. .. 2.8 .. 3.0 .. .. .. .. ..
0.7 2.2 2.8 9.2 0.0 0.0 0.5 5.4 1.5 3.8 0.0 0.5 0.0 0.1 0.1 0.0 0.9 0.3 .. 0.0 0.2 0.3 0.1 0.2 1.6 .. 0.1 4.2 .. .. .. .. .. .. .. .. .. .. .. .. .. .. ..
.. 3.5 .. .. 3.8 4.8 3.5 .. 3.5 3.8 .. .. .. .. .. .. .. .. .. .. 3.5 .. .. 2.8 .. .. 2.3 3.8 .. .. .. .. .. .. .. .. .. .. .. .. .. .. ..
3.12
About the data
Scientists use the terms climate change and global
wave can be both a prolonged period of excessively
at an elevation of 5 meters or less is the percent-
warming to refer to the gradual increase in the
cold weather and the sudden invasion of very cold
age of the population living in areas with an eleva-
Earth’s surface temperature that has accelerated
air over a large area. Accompanied by frost, it can
tion of 5 meters or less. • Population affected by
since the industrial revolution and especially over
damage agriculture, infrastructure, and property.
droughts, floods, and extreme temperature is the
the past two decades. Most global warming has been
A heat wave is a prolonged period of excessively
average annual percentage of the population that is
caused by human activities that have changed the
hot and sometimes humid weather. Population
affected by natural disasters classified as droughts,
chemical composition of the atmosphere through
affected by these natural disasters is the num-
floods, or extreme temperature events. • Disaster
a buildup of greenhouse gases—primarily carbon
ber of people injured, left homeless, or requiring
risk reduction progress score is the average of self-
dioxide, methane, and nitrous oxide. Rising global
immediate assistance and can include displaced
assessment scores, ranging from 1 to 5, where 1 is
temperatures will cause sea level rise and alter local
or evacuated people.
the worst and 5 is the best, submitted by countries
climate conditions, affecting forests, crop yields,
• Resilience is measured by the disaster risk reduc-
and water supplies, and may affect human health,
tion progress score, an average of self-assess-
animals, and many types of ecosystems. The 2007
ment scores submitted by countries under Prior-
Intergovernmental Panel on Climate Change’s (IPCC)
ity 1 of the Hyogo Framework National Progress
assessment report concluded that global warming
Reports. The Hyogo Framework is a global blueprint
is “unequivocal” and gave the strongest warning yet
for disaster risk reduction efforts that was adopted
about the role of human activities. The report esti-
by 168 countries in 2005. Assessments of Priority
mated that sea levels would rise approximately 49
1 include four indicators that reflect the degree to
centimeters over the next 100 years, with a range of
which countries have prioritized disaster risk reduc-
uncertainty of 20–86 centimeters. That will lead to
tion and the strengthening of relevant institutions.
under Priority 1 of the Hyogo Framework National Progress Reports.
increased coastal flooding through direct inundation and a higher base for storm surges, allowing flooding
Definitions
of larger areas and higher elevations. Climate model
• Average daily minimum and maximum tempera-
simulations predict an increase in average surface
ture are the minimum and maximum daily tem-
air temperature of about 2.5°C by 2100 (Kattenberg
peratures in the country averaged over the period
and others 1996) and increase of “killer” heat waves
specified, based on gridded climatologies from the
during the warm season (Karl and others 1997).
Climatic Research Unit of the University of East
Data in the table are categorized into three climate
Anglia. • Projected change in annual temperature
Data sources
change topics.
is the projected change in annual temperature during
Data on average daily minimum and maximum
• Climate variability contains such indicators as aver-
the period specified, relative to the control period
temperature are from Mitchell and others (2003).
age daily minimum and maximum temperature,
1961–2000. • Projected change in annual cool
Data on projected change in annual temperature
projected change in annual temperature, projected
days and cold nights are the projected change in
are from IPCC (2007). Data on projected change
change in annual cool days and cold nights and
the annual number of cool days and cold nights dur-
in annual cool days and cold nights and projected
hot days and warm nights, and projected change
ing the period specified, relative to the control period
change in annual hot days and warm nights are
in annual precipitation. These indicators are use-
1961–2000. Cool days are those that fall below the
from University of Cape Town Climate Systems
ful for understanding critical thresholds related
10th percentile of maximum temperature in the con-
Analysis Group calculations based on data from
to heat stress in such sectors as agriculture and
trol period, and cold nights are those that fall below
Meehl and others (2007). Data on projected
energy.
the 10th percentile of minimum temperature in the
change in annual precipitation are from Meehl and
• Exposure to impact contains indicators that mea-
control period. • Projected change in annual hot
others (2007). Data on land area with an elevation
sure vulnerability to the impacts of climate change,
days and warm nights are the projected change in
of 5 meters or less and population living in areas
such as land area with an elevation of 5 meters
the annual number of hot days and warm nights dur-
with an elevation 5 meters or less are from CIESIN
or less and population living in such areas as well
ing the period specified, relative to the control period
(2007). Data on population affected by droughts,
as population affected by droughts, floods, and
1961–2000. Hot days are those that exceed the
floods, and extreme temperature are from the
extreme temperature. A drought is an extended
90th percentile of maximum temperatures in the con-
United States Agency for International Develop-
period of deficiency in a region’s water supply as
trol period, and warm nights are those that exceed
ment Office of Foreign Disaster Assistance/Center
a result of below average precipitation. A drought
the 90th percentile of minimum temperatures in the
for Research on the Epidemiology of Disasters
can lead to losses in agriculture, affect inland
control period. • Projected change in annual precipi-
International Disaster Database (www.emdat.be),
navigation and hydropower plants, reduce access
tation is the projected change in annual precipitation
Université Catholique de Louvain, and the World
to drinking water, and cause famines. A flood is
during the period specified, relative to the control
Bank. Data on disaster risk reduction progress
a significant rise of water level in a stream, lake,
period 1961–2000. • Land area at an elevation
score are from UNISDR (2009, 2010, 2011) Prog-
reservoir, or coastal region. Extreme temperature
of 5 meters or less is the percentage of land area
ress Reports.
events are either cold waves or heat waves. A cold
with an elevation of 5 meters or less. • Population
2012 World Development Indicators
185
ENVIRONMENT
Climate variability, exposure to impact, and resilience
3.13
Urbanization Urban population
Afghanistan Albania Algeria Angola Argentina Armenia Australia Austria Azerbaijan Bahrain Bangladesh Belarus Belgium Benin Bolivia Bosnia and Herzegovina Botswana Brazil Bulgaria Burkina Faso Burundi Cambodia Cameroon Canada Central African Republic Chad Chile China Hong Kong SAR, China Colombia Congo, Dem. Rep. Congo, Rep. Costa Rica Côte d’Ivoire Croatia Cuba Cyprus Czech Republic Denmark Dominican Republic Ecuador Egypt, Arab Rep. El Salvador Eritrea Estonia Ethiopia Finland France Gabon Gambia, The Georgia Germany Ghana Greece Guatemala Guinea Guinea-Bissau Haiti
186
millions 1990 2010
% of total population 1990 2010
3 1 13 4 28 2 15 5 4 0 21 7 10 2 4 2 1 112 6 1 0 1 5 21 1 1 11 311 6 23 10 1 2 5 3 8 1 8 4 4 6 25 3 0 1 6 3 43 1 0 3 58 5 6 4 2 0 2
18 36 52 37 87 68 85 66 54 88 20 66 96 35 56 39 42 75 66 14 6 13 41 77 37 21 83 27 100 68 28 54 51 40 54 73 67 75 85 55 55 44 49 16 71 13 61 74 69 38 55 73 36 59 41 28 28 29
9 2 24 11 37 2 20 6 5 1 42 7 11 4 7 2 1 169 5 3 1 3 11 28 2 3 15 601 7 35 23 3 3 10 3 9 1 8 5 7 10 35 4 1 1 15 3 50 1 1 2 60 13 7 7 4 0 5
2012 World Development Indicators
25 48 67 59 92 64 89 68 52 89 28 74 97 42 67 49 61 87 72 20 11 23 58 81 39 28 89 45 100 75 35 62 64 50 58 76 70 74 87 71 67 43 61 22 70 18 64 78 86 58 53 74 52 61 50 35 30 50
average annual % growth 1990–2010
4.3 1.7 2.4 4.3 1.1 0.1 1.8 0.6 1.5 7.6 2.8 0.4 0.9 3.8 2.3 1.0 2.5 1.4 –0.3 5.1 5.3 3.9 3.6 1.3 2.3 4.3 1.2 2.5 0.9 1.8 4.5 3.2 2.3 3.3 0.2 0.0 1.5 0.3 0.7 2.4 2.4 1.8 1.0 5.1 0.0 3.9 0.9 0.8 2.4 4.2 1.1 0.0 3.8 0.6 3.4 3.6 2.3 4.1
Population in urban agglomerations of more than 1 million
Population in largest city
% of total population 1990 2010
% of urban population 1990 2010
% of urban population 1990 2010
% of rural population 1990 2010
7 .. 7 15 39 33 60 20 24 .. 9 16 17 .. 25 .. .. 35 14 6 .. 6 14 40 .. .. 35 9 100 31 13 29 24 17 .. 20 .. 12 20 21 26 21 18 .. .. 4 17 22 .. .. 25 8 13 30 9 16 .. 16
37 21 14 41 37 49 25 30 45 29 32 24 17 31 29 24 21 13 21 42 66 51 19 18 43 38 42 3 100 21 35 54 47 42 27 27 33 16 24 38 28 37 37 72 43 29 28 22 62 62 46 6 22 51 22 55 54 56
.. 94 99 67 93 95 100 100 .. 100 58 91 100 14 28 98 61 80 100 43 41 36 63 100 21 21 91 48 .. 79 23 .. 94 38 99 86 100 100 100 83 86 91 88 58 96 20 100 100 .. .. 97 100 12 100 81 19 .. 44
.. 66 77 6 73 .. 100 100 .. .. 34 96 100 0 6 .. 22 33 98 2 44 5 37 99 5 4 48 15 .. 40 4 .. 91 8 98 64 100 98 100 61 48 57 62 0 94 1 100 100 .. .. 95 100 4 93 48 6 4 19
11 .. 8 25 39 36 58 20 22 .. 14 20 17 .. 33 .. .. 41 16 12 .. 11 20 44 .. .. 35 18 105 38 18 33 31 21 .. 19 .. 11 21 22 31 19 25 .. .. 4 21 22 .. .. 25 8 17 29 8 17 .. 21
44 29 12 43 35 56 22 30 42 16 35 26 18 23 25 22 16 12 22 57 52 48 19 20 42 27 39 3 100 24 38 53 49 42 27 25 31 15 25 31 28 32 41 60 43 20 33 21 49 45 48 6 19 47 16 47 68 43
Access to improved sanitation facilities
60 95 98 85 91 95 100 100 86 100 57 91 100 25 35 99 75 85 100 50 49 73 58 100 43 30 98 74 .. 82 24 20 95 36 99 94 100 99 100 87 96 97 89 52 96 29 100 100 33 70 96 100 19 99 87 32 44 24
30 93 88 19 77 80 100 100 78 .. 55 97 100 5 10 92 41 44 100 6 46 20 36 99 28 6 83 56 .. 63 24 15 96 11 98 81 100 97 100 75 84 93 83 4 94 19 100 100 30 65 93 100 8 97 70 11 9 10
Urban population
millions 1990 2010
Honduras Hungary India Indonesia Iran, Islamic Rep. Iraq Ireland Israel Italy Jamaica Japan Jordan Kazakhstan Kenya Korea, Dem. Rep. Korea, Rep. Kosovo Kuwait Kyrgyz Republic Lao PDR Latvia Lebanon Lesotho Liberia Libya Lithuania Macedonia, FYR Madagascar Malawi Malaysia Mali Mauritania Mauritius Mexico Moldova Mongolia Morocco Mozambique Myanmar Namibia Nepal Netherlands New Zealand Nicaragua Niger Nigeria Norway Oman Pakistan Panama Papua New Guinea Paraguay Peru Philippines Poland Portugal Puerto Rico Qatar
2 7 223 56 31 13 2 4 38 1 78 2 9 4 12 32 .. 2 2 1 2 2 0 1 3 2 1 3 1 9 2 1 0 60 2 1 12 3 10 0 2 10 3 2 1 34 3 1 34 1 1 2 15 30 23 5 3 0
4 7 369 129 51 21 3 7 41 1 85 5 10 9 15 40 .. 3 2 2 2 4 1 2 5 2 1 6 3 21 5 1 1 88 1 2 18 9 16 1 5 14 4 3 3 79 4 2 64 3 1 4 21 62 23 6 4 2
% of total population 1990 2010
40 66 26 31 56 70 57 90 67 49 63 72 56 18 58 74 .. 98 38 15 69 83 14 45 76 68 58 24 12 50 23 40 44 71 47 57 48 21 25 28 9 69 85 52 15 35 72 66 31 54 15 49 69 49 61 48 72 92
49 68 30 54 70 66 62 92 68 54 67 79 59 22 63 82 .. 98 37 33 68 87 27 62 78 67 68 30 20 72 33 41 43 78 41 58 57 38 34 38 18 83 87 57 17 50 78 72 37 75 13 62 72 66 61 61 99 96
average annual % growth 1990–2010
2.9 0.4 2.3 3.1 1.9 2.8 0.8 1.8 0.7 0.6 0.2 2.3 1.9 4.0 1.0 0.5 .. 3.4 1.6 5.0 –0.6 0.9 3.7 5.2 1.7 –1.4 0.9 4.0 5.7 2.9 4.7 2.9 0.6 1.6 –0.8 1.9 1.6 4.4 2.7 3.4 4.4 1.2 1.3 1.8 4.0 4.0 1.3 2.6 2.9 2.6 2.1 2.7 1.2 2.8 0.0 1.1 0.5 9.7
3.13
Population in urban agglomerations of more than 1 million
Population in largest city
% of total population 1990 2010
% of urban population 1990 2010
% of urban population 1990 2010
% of rural population 1990 2010
29 29 4 14 21 32 46 48 9 49 42 37 12 32 21 33 .. 68 38 70 49 53 49 108 26 23 40 36 24 12 37 53 30 25 38 46 22 27 30 36 23 9 30 34 36 14 22 27 21 65 31 53 39 27 7 53 60 54
71 100 51 56 83 .. 100 100 .. 78 100 98 96 27 .. 100 .. 100 94 .. .. 100 .. .. 97 95 92 15 48 88 33 29 91 76 .. .. 81 36 .. 62 37 100 .. 59 19 39 100 96 72 73 78 61 71 69 96 97 .. 100
36 100 7 21 74 .. 98 100 .. 81 100 95 97 25 .. 100 .. 100 .. .. .. .. .. .. 96 .. .. 7 38 81 10 8 88 34 .. .. 27 4 .. 9 7 100 88 26 2 36 100 55 7 40 42 15 17 45 .. 87 .. 100
12 19 10 10 24 27 26 56 19 .. 46 27 7 6 13 51 .. 67 .. .. .. 44 .. .. 20 .. .. 8 .. 8 9 .. .. 33 .. .. 18 6 9 .. .. 13 25 18 6 12 .. .. 15 35 .. 26 27 14 4 37 44 ..
14 17 12 9 24 23 25 57 17 .. 49 18 8 9 12 48 .. 84 .. .. .. 46 .. .. 17 .. .. 9 .. 9 11 .. .. 35 .. .. 19 7 11 .. .. 12 32 23 7 15 .. .. 18 39 .. 31 31 14 4 39 69 ..
28 25 6 7 14 28 40 47 8 40 43 23 14 39 18 24 .. 86 43 40 46 53 39 34 22 24 35 30 29 7 33 51 28 22 44 61 18 18 27 41 19 8 37 28 40 13 23 33 20 52 37 51 43 19 7 44 70 28
Access to improved sanitation facilities
85 100 58 73 100 76 100 100 .. 78 100 98 97 32 86 100 .. 100 94 89 82 100 32 29 97 95 92 21 49 96 35 51 91 87 89 64 83 38 83 57 48 100 .. 63 34 35 100 100 72 75 71 90 81 79 96 100 .. 100
2012 World Development Indicators
69 100 23 39 100 67 98 100 .. 82 100 98 98 32 71 100 .. 100 93 50 71 .. 24 7 96 .. 82 12 51 95 14 9 88 79 82 29 52 5 73 17 27 100 .. 37 4 27 100 95 34 51 41 40 37 69 80 100 .. 100
187
ENVIRONMENT
Urbanization
3.13
Urbanization Urban population
millions 1990 2010
Romania Russian Federation Rwanda Saudi Arabia Senegal Serbia Sierra Leone Singapore Slovak Republic Slovenia Somalia South Africa South Sudan Spain Sri Lanka Sudan Swaziland Sweden Switzerland Syrian Arab Republic Tajikistan Tanzania Thailand Timor-Leste Togo Trinidad and Tobago Tunisia Turkey Turkmenistan Uganda Ukraine United Arab Emirates United Kingdom United States Uruguay Uzbekistan Venezuela, RB Vietnam West Bank and Gaza Yemen, Rep. Zambia Zimbabwe World Low income Middle income Lower middle income Upper middle income Low & middle income East Asia & Pacific Europe & Central Asia Latin America & Carib. Middle East & N. Africa South Asia Sub-Saharan Africa High income Euro area
188
12 109 0 12 3 4 1 3 3 1 2 18 .. 29 3 7 0 7 5 6 2 5 17 0 1 0 5 32 2 2 35 1 51 188 3 8 17 13 1 2 3 3 2,262 s 108 1,456 560 896 1,564 463 246 309 117 286 144 698 215
12 103 2 23 5 4 2 5 3 1 3 31 .. 36 3 20 0 8 6 11 2 12 24 0 3 0 7 51 2 4 31 6 56 255 3 10 27 25 3 8 5 5 3,482 s 225 2,401 992 1,409 2,626 901 259 462 192 492 319 856 244
2012 World Development Indicators
% of total population 1990 2010
53 73 5 77 39 50 33 100 57 50 30 52 .. 75 17 27 23 83 73 49 32 19 29 21 30 9 58 59 45 11 67 79 89 75 89 40 84 20 68 21 39 29 43 w 21 38 32 44 36 29 63 71 52 25 28 73 71
55 73 19 84 43 52 38 100 57 48 37 62 .. 77 15 45 26 85 74 55 27 26 34 28 43 14 67 70 50 13 68 78 90 82 93 37 94 29 72 32 36 38 51 w 28 48 39 57 46 46 64 79 58 30 37 78 74
average annual % growth 1990–2010
0.1 –0.1 4.5 4.0 3.3 –0.1 3.0 1.8 0.4 –0.2 3.5 2.1 .. 0.5 0.9 4.5 2.6 0.9 1.1 2.6 1.5 4.7 1.6 3.6 3.7 2.9 1.6 1.9 2.1 4.4 –0.3 8.0 0.8 1.2 0.5 1.8 1.9 2.7 2.8 4.9 2.0 2.0 2.0 w 3.6 2.2 2.7 1.9 2.3 2.7 0.5 1.6 2.2 2.5 3.9 1.0 0.6
Population in urban agglomerations of more than 1 million
Population in largest city
% of total population 1990 2010
% of urban population 1990 2010
9 17 .. 34 19 15 .. 99 .. .. 16 28 .. 22 .. 9 .. 12 15 31 .. 5 10 .. 17 .. .. 24 .. 4 12 26 26 42 50 10 34 9 .. 5 10 10 17 w 8 14 12 16 13 .. 16 32 20 10 11 .. 18
9 18 .. 39 23 15 .. 95 .. .. 16 34 .. 24 .. 12 .. 14 15 34 .. 7 10 .. 28 .. .. 29 .. 5 14 21 26 45 49 8 32 13 .. 10 11 13 20 w 11 18 14 22 17 .. 18 35 20 13 14 .. 18
17 8 57 19 50 30 40 99 .. 27 53 10 .. 15 21 33 22 15 20 26 35 27 35 79 56 45 14 20 25 38 7 33 15 9 56 26 17 25 .. 26 24 35 17 w 34 15 16 14 16 9 15 24 26 9 27 20 16
17 10 57 21 54 29 40 95 .. 26 43 12 .. 16 22 26 25 16 20 27 39 28 30 55 64 32 11 21 26 36 9 27 15 8 53 21 11 25 .. 31 31 34 16 w 33 13 14 12 15 7 16 22 22 11 26 19 15
Access to improved sanitation facilities
% of urban population 1990 2010
% of rural population 1990 2010
88 80 69 100 62 96 22 99 100 100 .. 82 .. 100 85 51 62 100 100 95 93 10 94 .. 26 93 95 96 99 32 97 98 100 100 95 95 89 63 91 70 61 54 76 w 38 68 61 71 66 54 88 80 89 54 42 100 100
52 58 34 .. 22 .. 5 .. 100 100 .. 60 .. 100 67 18 44 100 100 75 .. 6 80 .. 8 93 44 66 97 26 .. 95 100 99 83 76 45 30 .. 12 37 35 27 w 17 22 17 27 21 20 68 38 57 11 19 99 99
88 74 52 100 70 96 23 100 100 100 52 86 .. 100 88 44 64 100 100 96 95 20 95 73 26 92 96 97 99 34 96 98 100 100 100 100 .. 94 92 93 57 52 79 w 47 75 66 82 72 76 87 84 94 60 42 100 100
54 59 56 .. 39 88 6 .. 99 100 6 67 .. 100 93 14 55 100 100 93 94 7 96 37 3 92 64 75 97 34 89 95 100 99 99 100 .. 68 92 34 43 32 47 w 32 45 34 62 43 57 80 59 80 28 23 99 100
About the data
3.13
Definitions
There is no consistent and universally accepted
• Urban population is the midyear population of
standard for distinguishing urban from rural areas,
areas defined as urban in each country and reported
in part because of the wide variety of situations
to the United Nations (see About the data). • Popula-
across countries (see About the data for table 3.1).
tion in urban agglomerations of more than 1 million
Most countries use an urban classification related
is the percentage of a country’s population living in
to the size or characteristics of settlements. Some
metropolitan areas that in 2005 had a population of
define urban areas based on the presence of cer-
more than 1 million. • Population in largest city is
tain infrastructure and services. And other countries
the percentage of a country’s urban population living
designate urban areas based on administrative
in that country’s largest metropolitan area. • Access
arrangements.
to improved sanitation facilities is the percentage
The population of a city or metropolitan area
of the urban or rural population with access to at
depends on the boundaries chosen. For example, in
least adequate excreta disposal facilities (private or
1990 Beijing, China, contained 2.3 million people in
shared but not public) that can effectively prevent
87 square kilometers of “inner city” and 5.4 million
human, animal, and insect contact with excreta.
in 158 square kilometers of “core city.” The popula-
Improved facilities range from simple but protected
tion of “inner city and inner suburban districts” was
pit latrines to flush toilets with a sewerage connec-
6.3 million and that of “inner city, inner and outer
tion. To be effective, facilities must be correctly con-
suburban districts, and inner and outer counties”
structed and properly maintained.
was 10.8 million. (Most countries use the last definition.) For further discussion of urban-rural issues see box 3.1a in About the data for table 3.1. Estimates of the world’s urban population would change significantly if China, India, and a few other populous nations were to change their definition of urban centers. According to China’s State Statistical Bureau, by the end of 1996 urban residents accounted for about 43 percent of China’s population, more than double the 20 percent considered urban in 1994. In addition to the continuous migration of people from rural to urban areas, one of the main reasons for this shift was the rapid growth in the hundreds of towns reclassified as cities in recent years. Because the estimates in the table are based on national definitions of what constitutes a city or metropolitan area, cross-country comparisons should be made with caution. To estimate urban populations, UN ratios of urban to total population were applied to the World Bank’s estimates of total population (see table 2.1). The table shows access to improved sanitation facilities for both urban and rural populations to
Data sources
allow comparison of access. Definitions of access
Data on urban population and the population in
and urban areas vary, however, so comparisons
urban agglomerations and in the largest city are
between countries can be misleading.
from the United Nations Population Division’s World Urbanization Prospects: The 2010 Revision. Data on total population are World Bank estimates. Data on access to sanitation are from the World Health Organization and United Nations Children’s Fund’s Progress on Drinking Water and Sanitation (2012).
2012 World Development Indicators
189
ENVIRONMENT
Urbanization
3.14
Urban housing conditions Census year
Household size
number of people National Urban
Afghanistan Albania Algeria Angola Argentina Armenia Australia Austria Azerbaijan Bahrain Bangladesh Belarus Belgium Benin Bolivia Bosnia and Herzegovina Botswana Brazil Bulgaria Burkina Faso Burundi Cambodia Cameroon Canada Central African Republic Chad Chile China Hong Kong SAR, China Colombia Congo, Dem. Rep. Congo, Rep. Costa Rica Côte d’Ivoire Croatia Cuba Cyprus Czech Republic Denmark Dominican Republic Ecuador Egypt, Arab Rep. El Salvador Eritrea Estonia Ethiopia Finland France Gabon Gambia, The Georgia Germany Ghana Greece Guatemala Guinea Guinea-Bissau Haiti
190
2001 1998 2001 2001 2001 2001 1999 2001 1999 2001 1992 2001 2001 2000 2001 1996 1990 2005 1987 2001 2003 1993 2002 2000 1993 1984 1984 2000 1998 2001 2002 2001 2001 2002 2001 1996 1992 2000 1994 2000 1999 2003 1993 2002 2001 2000 2001 2002 1996 1982
.. 4.2 4.9 .. 3.6 4.1 3.8 2.4 4.7 .. 4.8 .. 2.6 5.9 4.2 .. 4.2 3.8 2.7 6.2 4.7 5.0 5.2 2.6 5.2 5.1 3.4 3.4 .. 4.8 5.4 10.5 4.0 5.4 3.0 3.1 .. 2.4 2.2 3.9 3.5 4.7 .. .. 2.4 4.8 2.2 2.5 5.2 8.9 3.5 2.3 5.1 3.0 4.4 6.7 .. 4.2
2012 World Development Indicators
.. 3.9 .. .. .. 4.0 .. .. 4.4 .. 4.8 .. .. .. 4.3 .. 3.9 3.7 2.7 5.8 .. 4.9 5.1 .. 5.8 5.1 3.5 3.2 .. .. .. .. .. .. .. .. .. .. .. .. 3.7 .. .. .. 2.3 4.7 .. .. .. .. 3.5 .. 5.1 .. 4.7 .. .. ..
Overcrowding
Households living in overcrowded dwellingsa % of total National Urban
.. .. .. .. 19 4 1 2 .. .. .. .. 0b .. 40 .. 27 .. .. 30 .. 35 67 .. 32 .. .. .. .. 27b 55 .. 22 .. .. 5 .. .. .. .. 30 .. 63 .. 3 .. .. .. .. .. .. .. .. 1 .. 63 .. 26
.. .. .. .. .. 6 .. .. .. .. .. .. .. .. .. .. 47 .. .. 53 .. 32 77 .. 36b .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. ..
Durable dwelling units
Home ownership
Buildings with durable structure % of total National Urban
Privately owned dwellings % of total National Urban
.. .. .. .. 97 93 .. .. .. .. 21b .. .. 26 43 .. 88 .. 79 .. .. 79 77 .. 78 .. 91 82 .. 83b .. .. 88 .. .. .. .. .. .. 97 81 .. 67 .. .. .. .. .. .. 18 .. .. 45 .. 67 .. .. ..
.. .. .. .. .. 93 .. .. .. .. 42b .. .. .. 58 .. 90 b .. 89 .. .. 88 .. .. 92 .. 92 .. .. .. .. .. .. .. .. .. .. .. .. .. 88 .. 83 .. .. 23 .. .. .. .. .. .. .. .. 80 .. .. ..
.. 65b 67 .. .. 95 .. 48 74 .. 88b .. 67 59 70 .. 61 74 98 .. .. 58 73 64 85 .. 66 88 .. 68b .. 76 72 .. .. .. .. 52 .. .. 68 b .. 70 .. .. .. 64 55 .. 68 .. 43 57 .. 81 76 .. 92
.. 30 b .. .. .. 90 .. .. 62 .. 61b .. .. .. 59 .. 47 75 98 .. .. 57 48 .. 74 .. 65 74 .. .. .. .. .. .. .. .. .. .. .. .. 58b .. 68 .. .. 54 .. .. .. .. .. .. .. .. 74 .. .. 68
Multiunit dwellings
Vacancy rate
% of total National Urban
Unoccupied dwellings % of total National Urban
.. .. .. .. 4 1 .. .. 4 .. .. .. 32b .. 3b .. 1 .. .. .. .. 27 27 32 .. .. 13 .. .. 13 .. .. 2 .. .. .. .. 49 .. 8 9 75 3 .. 72 .. 44 .. .. .. .. .. 53 .. 2 .. .. ..
.. .. .. .. .. 1 .. .. 5 .. .. .. .. .. 5b .. .. .. .. .. .. 32 42 .. .. .. 15 .. .. .. .. .. 3 .. .. .. .. .. .. .. 14 .. 6 .. .. .. .. .. .. .. .. .. .. .. 4 .. .. ..
.. 12 19 .. 16b .. .. .. .. .. .. .. .. .. 6 .. .. .. 23 .. .. .. .. 8 .. .. 11 1 .. 10 b .. .. 9 .. 12 .. .. 12 .. 11 12 .. 11 .. 13 .. .. 7 .. .. .. 7 5 .. 13 .. .. 9
.. 13 .. .. .. .. .. .. .. .. .. .. .. .. 4 .. .. .. 17 .. .. .. .. .. .. .. 10 .. .. .. .. .. 6 .. .. .. .. .. .. .. 7 .. 11 .. .. .. .. .. .. .. .. .. .. .. 11 .. .. 19
Census year
Household size
number of people National Urban
Honduras Hungary India Indonesia Iran, Islamic Rep. Iraq Ireland Israel Italy Jamaica Japan Jordan Kazakhstan Kenya Korea, Dem. Rep. Korea, Rep. Kosovo Kuwait Kyrgyz Republic Lao PDR Latvia Lebanon Lesotho Liberia Libya Lithuania Macedonia, FYR Madagascar Malawi Malaysia Mali Mauritania Mauritius Mexico Moldova Mongolia Morocco Mozambique Myanmar Namibia Nepal Netherlands New Zealand Nicaragua Niger Nigeria Norway Oman Pakistan Panama Papua New Guinea Paraguay Peru Philippines Poland Portugal Puerto Rico Qatar
2001 2001 2001 2000 1996 1997 2002 1995 2001 2001 2000 2004 1999 2000 1993 1995 1999 1995 2000 2001 1974 2001 2002 1993 1998 2000 1998 1988 2000 2005 2003 2000 1982 1997 2001 2001 2001 1995 2001 1991 1980 2003 1998 2000 1990 2002 2007 2000 1988 2001 2005
4.4 2.6 5.3 4.0 4.8 7.7 3.0 3.5 2.8 3.5 2.7 5.3 .. 4.6 3.8 4.4 .. 6.4 4.4 6.1 3.0 .. 5.0 4.8 6.4 2.6 3.6 4.9 4.4 4.5 5.6 .. 3.9 4.0 .. 4.4 5.9 4.4 .. 5.3 5.4 .. 2.8 5.3 6.4 5.0 2.7 7.1 6.8 4.1 4.5b 4.6 3.9 4.9 3.2 2.8 2.8 ..
.. .. 5.3 .. 4.6 7.2 .. .. .. .. .. 5.1 .. 3.4 .. .. .. .. 3.6 6.1 2.6 .. .. .. .. .. 3.6b 4.8 4.4 4.4 .. .. 3.8 3.9 .. 4.5 5.3 4.9 .. .. 4.9 .. .. .. 6.0 4.7 .. .. 6.8 .. 6.5 4.5 3.9 .. .. .. . ..
Overcrowding
Households living in overcrowded dwellingsa % of total National Urban
.. 2 77 .. 33b .. .. .. .. .. .. 35 .. .. 23 .. .. .. .. .. 4 .. 10 b 31 .. 7 8b 64 30 .. .. .. 6 24 .. .. .. 37 .. .. .. .. 1b .. .. .. 1 .. .. 28b .. 38b 35 .. .. .. 1 ..
.. .. 71 .. 26b .. .. .. .. .. .. 34 .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. 57 .. .. .. .. 7 20 .. .. .. 28 .. .. .. .. .. .. .. .. .. .. .. .. .. .. 31 .. .. .. .. ..
Durable dwelling units
Home ownership
Buildings with durable structure % of total National Urban
Privately owned dwellings % of total National Urban
69 .. 83 .. 72 88 .. .. .. 98b .. .. .. 35 .. .. .. .. .. 49 88 .. .. 20 .. .. 95b .. 48 .. .. .. 91 .. .. .. .. 7 .. .. .. .. .. 79 .. .. .. .. 58 88 .. 95b .. .. .. .. .. ..
85 .. 81 .. 76 96 .. .. .. .. .. .. .. 72 .. .. .. .. .. 77 .. .. .. .. .. .. 95b .. 84 .. .. .. 94 .. .. .. .. 20 .. .. .. .. .. 87 .. .. .. .. 86 98b .. 98b .. .. .. .. .. ..
.. .. 87 .. 73 70 .. .. .. 58b 61 64 .. 72 50 .. .. .. .. 96 58 .. 84 1 .. .. 48b 81 86 .. .. .. 87 .. .. .. .. 92 .. .. 88 .. 65 84 77 .. 67 .. 81 80 .. 79 .. 71 .. 76 75 ..
.. .. 67 .. 67 66 .. .. .. .. .. 60 .. 25 .. .. .. .. .. 86 .. .. .. .. .. .. .. 59 47 .. .. .. 81 .. .. .. .. 83 .. .. .. .. .. 86 40 .. .. .. .. 66b 44 75 .. .. .. .. .. ..
Multiunit dwellings
Vacancy rate
% of total National Urban
Unoccupied dwellings % of total National Urban
.. .. .. .. .. 4 8b .. .. 2b 37 72 .. .. 15 .. .. 9b .. .. 74 .. 0 .. .. .. .. .. .. 10 b .. .. .. .. .. 48 .. 1 .. .. .. .. 17 0 .. .. 38 .. .. 10 b .. 1b .. 12 .. 86 .. ..
.. .. .. .. .. 5 .. .. .. .. .. 80 .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. 16b .. .. .. .. .. 56 .. 1 .. .. .. .. .. 0 .. .. .. .. .. 10 b 8 2b ..
14 4 6 .. .. 13 .. .. 21 .. .. .. .. 39 .. .. .. 11 .. .. 0 .. .. .. 7 .. 7b .. .. .. .. .. 7 3 .. .. .. 0 .. .. 0 .. 10 8 .. .. .. .. .. 14 .. 6b ..
.. .. 9 .. .. 15 .. .. .. .. .. .. .. 17 .. .. .. .. .. .. .. .. .. .. .. .. 3b .. .. .. .. .. 6 2 .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. 6b ..
.. .. .. ..
1 .. .. ..
.. .. .. ..
2012 World Development Indicators
191
ENVIRONMENT
3.14
Urban housing conditions
3.14
Urban housing conditions Census year
Household size
number of people National Urban
Romania Russian Federation Rwanda Saudi Arabia Senegal Serbia Sierra Leone Singapore Slovak Republic Slovenia Somalia South Africa South Sudan Spain Sri Lanka Sudan Swaziland Sweden Switzerland Syrian Arab Republic Tajikistan Tanzania Thailand Timor-Leste Togo Trinidad and Tobago Tunisia Turkey Turkmenistan Uganda Ukraine United Arab Emirates United Kingdom United States Uruguay Uzbekistan Venezuela, RB Vietnam West Bank and Gaza Yemen, Rep. Zambia Zimbabwe
2002 2002 2002 2004 2002 2001 1985 2000 2002 1975 2007 2001 2001 1993 1997 1990 2000 1981 2000 2002 2000 2000 1994 1990 2002 2003 2001 2005 1996 2001 1999 1997 1994 2000 1992
2.9 2.8 4.4 5.5 9.2 2.9 6.8 4.4 .. 2.8 .. 3.0 .. 2.9 3.8 5.8 5.4 2.0 2.2 6.3 .. 4.9 3.8 .. .. 3.7 8.0 5.0 .. 4.7 .. .. .. 2.5 3.3 .. 4.4 4.6 7.1 6.7 5.3 4.8
Overcrowding
Households living in overcrowded dwellingsa % of total National Urban
2.8 2.7 3.7 .. 8.0 2.2 .. .. .. 2.7 .. 2.8 .. .. .. 6.0 3.7 .. .. 6.0 .. 4.5b .. .. .. .. .. .. .. 3.9 .. .. 2.4 .. 3.4b .. .. 4.5 .. 6.8 5.9 4.2
a. More than two people per room. b. Data are from a previous census.
192
2012 World Development Indicators
20 7 43 .. 72 .. .. .. .. 14 .. 16 .. 1 .. .. .. .. 1 .. .. 33b .. .. .. 9b .. .. .. .. .. .. .. 0 22b .. .. .. .. 54b .. ..
20 5 36 .. 68 .. .. .. .. 17 .. 15 .. .. .. .. .. .. .. .. .. 7b .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. 6b .. ..
Durable dwelling units
Home ownership
Buildings with durable structure % of total National Urban
Privately owned dwellings % of total National Urban
.. .. 13 92b .. .. 34 .. .. .. .. .. .. .. 93b .. .. .. .. .. .. .. 93 .. .. 98b 99 .. .. 19 .. .. .. .. .. .. .. 77 .. .. .. ..
.. .. 31 .. .. .. .. .. .. .. .. .. .. .. 92b .. .. .. .. .. .. .. 93 .. .. .. .. .. .. 61 .. .. .. .. .. .. .. 89 .. .. .. ..
84 .. 79 43 74 .. 68 .. .. 91 .. 43 .. 82 70 b 86b .. .. 34 .. .. 82b 81 .. .. 74b 71 70 .. 76 .. .. .. 74 57b .. 78 95 78 88b 94 94
72 .. 41 .. 54 .. .. .. .. 87 .. 40 .. .. 58b 58b .. .. .. .. .. 43b 62 .. .. .. 89b .. .. 28 .. .. 69 .. 57b .. .. 86 .. 68b 30 30
Multiunit dwellings
Vacancy rate
% of total National Urban
Unoccupied dwellings % of total National Urban
.. 73 36 .. .. .. .. .. .. 33 .. .. .. .. 1 0b .. 54 77 .. .. .. 3 .. .. 17b 6 .. .. 37 .. .. .. 26 .. .. 14 .. 45 3b .. 6
.. 86 60 .. .. .. .. .. .. 56 .. .. .. .. 14b 1b .. .. .. .. .. .. .. .. .. .. 10 b .. .. 71 .. .. 19 .. .. .. .. .. .. 11b .. ..
.. .. .. .. .. .. .. .. .. .. .. .. .. .. 13 .. .. 1 .. .. .. .. 3 .. .. .. 15 .. .. .. .. .. .. .. 13b .. 16 .. .. .. .. ..
.. .. .. .. .. .. .. .. .. .. .. .. .. .. 1b .. .. .. .. .. .. .. .. .. .. .. 12b .. .. .. .. .. .. .. 13b .. .. .. .. .. .. ..
About the data
3.14
Definitions
Urbanization can yield important social benefi ts,
• Census year is the year in which the underlying
improving access to public services and the job mar-
data were collected. • Household size is the aver-
ket. It also leads to significant demands for services.
age number of people within a household, calcu-
Inadequate living quarters and demand for housing
lated by dividing total population by the number
and shelter are major concerns for policymakers.
of households in the country and in urban areas.
The unmet demand for affordable housing, along
• Overcrowding refers to the number of households
with urban poverty, has led to the emergence of
living in dwellings with two or more people per room
slums in many poor countries. Improving the shel-
as a percentage of total households in the country
ter situation requires a better understanding of the
and in urban areas. • Durable dwelling units are
mechanisms governing housing markets and the pro-
the number of housing units in structures made of
cesses governing housing availability. That requires
durable building materials (concrete, stone, cement,
good data and adequate policy-oriented analysis so
brick, asbestos, zinc, and stucco) expected to main-
that housing policy can be formulated in a global
tain their stability for 20 years or longer under local
comparative perspective and drawn from lessons
conditions with normal maintenance and repair, tak-
learned in other countries. Housing policies and
ing into account location and environmental hazards
outcomes affect such broad socioeconomic condi-
such as floods, mudslides, and earthquakes, as a
tions as the infant mortality rate, performance in
percentage of total dwellings. • Home ownership
school, household saving, productivity levels, capital
refers to the number of privately owned dwellings as
formation, and government budget deficits. A good
a percentage of total dwellings. When the number
understanding of housing conditions thus requires
of private dwellings is not available from the census
an extensive set of indicators within a reasonable
data, the share of households that own their housing
framework.
unit is used. Privately owned and owner-occupied
There is a strong demand for quantitative indi-
units are included, depending on the definition used
cators that can measure housing conditions on a
in the census data. State- and community-owned
regular basis to monitor progress. However, data
units and rented, squatted, and rent-free units are
deficiencies and lack of rigorous quantitative analy-
excluded. • Multiunit dwellings are the number
sis hamper informed decisionmaking on desirable
of multiunit dwellings, such as apartments, flats,
policies to improve housing conditions. The data
condominiums, barracks, boardinghouses, orphan-
in the table are from housing and population cen-
ages, retirement houses, hostels, hotels, and col-
suses, collected using similar definitions. The table
lective dwellings, as a percentage of total dwellings.
will incorporate household survey data in future edi-
• Vacancy rate is the percentage of completed dwell-
tions. The table focuses attention on urban areas,
ing units that are currently unoccupied. It includes
where housing conditions are typically most severe.
all vacant units, whether on the market or not (such
Not all the compiled indicators are presented in the
as second homes).
table because of space limitations.
Data sources Data on urban housing conditions are from national population and housing censuses.
2012 World Development Indicators
193
ENVIRONMENT
Urban housing conditions
3.15
Traffic and congestion Motor vehicles
Afghanistan Albania Algeria Angola Argentina Armenia Australia Austria Azerbaijan Bahrain Bangladesh Belarus Belgium Benin Bolivia Bosnia and Herzegovina Botswana Brazil Bulgaria Burkina Faso Burundi Cambodia Cameroon Canada Central African Republic Chad Chile China Hong Kong SAR, China Colombia Congo, Dem. Rep. Congo, Rep. Costa Rica Côte d’Ivoire Croatia Cuba Cyprus Czech Republic Denmark Dominican Republic Ecuador Egypt, Arab Rep. El Salvador Eritrea Estonia Ethiopia Finland France Gabon Gambia, The Georgia Germany Ghana Greece Guatemala Guinea Guinea-Bissau Haiti
194
Passenger Road cars density
km. of road per 100 sq. km. of % of total land area consumption
per 1,000 people
per kilometer of road
per 1,000 people
2009
2009
2009
2009
29 120 112 38 314 103 688 569 104 537 3 282 552 22 68 138 133 209 375 11 6 21 14 607 0 6 174 47 74 71 5 27 166 20 384 38 675 482 478 128 57 45 54 11 471 3 532 598 .. 8 151 564 30 573 98 .. 33 ..
11 .. 35 .. .. 42 18 45 13 104 .. .. 39 .. 9 20 7 18 72 1 .. 7 .. 15 .. 1 38 16 254 25 .. .. 20 5 58 .. 48 39 36 .. 18 37 .. .. 11 5 36 39 .. 3 28 72 6 55 .. .. .. ..
21 89 74 8 .. 94 550 521 86 451 2 240 483 18 18 121 69 178 330 7 2 18 10 420 0 2 118 34 56 53 .. 16 130 16 346 21 529 423 380 87 36 33 49 6 407 1 459 496 .. 5 126 510 18 455 37 .. 27 ..
6 63 5 4 8 26 11 127 61 575 166 46 504 17 7 43 4 21 36 34 44 21 6 14 4 3 10 40 188 15 7 5 76 25 52 55 134 166 170 26 17 10 48 3 129 4 23 173 3 33 29 180 46 89 13 18 12 15
2012 World Development Indicators
Road sector energy consumption
Fuel price
Particulate matter concentration
Total
Diesel fuel
Gasoline fuel
Super grade gasoline
Diesel
Urban-populationweighted PM10 micrograms per cubic meter
2009
2009
2009
2009
2010
2010
1990
2009
.. 23 23 13 17 22 18 23 12 12 7 7 15 27 32 15 31 24 15 .. .. 7 12 17 .. .. 21 5 13 21 1 27 30 4 22 4 30 14 22 17 38 18 17 5 14 4 12 16 6 .. 22 16 16 24 21 .. .. 9
.. 123 265 85 307 182 1,103 878 155 955 14 192 814 110 206 234 318 298 336 .. .. 26 42 1,313 .. .. 355 92 285 144 4 98 319 21 435 40 682 557 727 144 305 159 140 7 493 17 728 642 78 .. 156 612 62 632 145 .. .. 25
.. 120 177 45 140 38 433 594 45 369 10 107 657 41 98 148 131 149 192 .. .. 13 23 405 .. .. 208 56 224 77 0 68 156 17 268 16 294 338 456 59 174 86 62 6 292 14 409 471 53 .. 69 323 37 269 70 .. .. 19
.. 15 67 36 105 60 610 208 109 535 3 50 125 63 68 81 174 73 81 .. .. 12 18 886 .. .. 147 45 50 54 3 26 147 6 153 22 351 186 292 78 148 61 71 1 218 2 299 123 22 .. 79 236 27 358 68 .. .. 23
1.15 1.46 0.32 0.65 0.96 1.08 1.27 1.63 0.75 0.21 1.09 1.08 1.87 1.04 0.70 1.42 0.93 1.58 1.51 1.44 1.43 1.15 1.20 1.21 1.71 1.32 1.38 1.11 1.92 1.41 1.28 1.27 1.14 1.68 1.59 1.72 1.47 1.75 2.00 1.23 0.53 0.48 0.92 2.54 1.54 0.91 1.94 1.98 .. .. 1.13 1.90 0.82 2.05 0.95 0.95 .. ..
1.00 1.40 0.19 0.43 1.05 0.99 1.23 1.55 0.56 0.13 0.63 0.86 1.62 1.21 0.54 1.42 0.97 1.14 1.58 1.28 1.42 0.98 1.10 1.08 1.69 1.31 1.02 1.04 1.32 0.95 1.27 0.84 0.97 1.30 1.49 1.24 1.47 1.69 1.79 1.03 0.28 0.32 0.89 1.07 1.57 0.78 1.60 1.72 .. .. 1.13 1.68 0.83 1.78 0.85 0.95 .. ..
69 92 112 112 104 367 22 39 133 75 228 24 31 77 105 36 92 39 108 143 60 107 122 25 60 209 89 115 .. 38 71 130 43 87 45 31 60 41 28 43 36 214 44 196 44 108 22 18 9 136 224 27 38 64 69 103 114 68
32 37 75 56 60 61 14 27 29 45 121 7 21 48 60 19 66 19 45 63 28 37 58 16 33 82 53 60 .. 20 38 60 29 29 25 15 28 17 16 16 20 88 28 64 9 51 15 12 5 61 54 16 21 31 68 54 45 33
kilograms of oil equivalent per capita
$ per liter
Motor vehicles
Honduras Hungary India Indonesia Iran, Islamic Rep. Iraq Ireland Israel Italy Jamaica Japan Jordan Kazakhstan Kenya Korea, Dem. Rep. Korea, Rep. Kosovo Kuwait Kyrgyz Republic Lao PDR Latvia Lebanon Lesotho Liberia Libya Lithuania Macedonia, FYR Madagascar Malawi Malaysia Mali Mauritania Mauritius Mexico Moldova Mongolia Morocco Mozambique Myanmar Namibia Nepal Netherlands New Zealand Nicaragua Niger Nigeria Norway Oman Pakistan Panama Papua New Guinea Paraguay Peru Philippines Poland Portugal Puerto Rico Qatar
Passenger Road cars density
Road sector energy consumption
km. of road per 100 sq. km. of % of total land area consumption
per 1,000 people
per kilometer of road
per 1,000 people
2009
2009
2009
2009
95 347 18 79 128 77 513 316 672 120 589 154 199 23 .. 355 .. 495 59 20 459 .. .. 3 290 555 155 26 8 350 14 .. 166 276 146 72 70 12 7 103 5 522 718 58 8 31 578 215 13 141 9 91 68 33 508 509 596 532
.. 18 5 38 51 55 24 128 80 15 63 117 33 14 .. 165 .. 212 9 3 15 .. .. .. .. 23 23 .. .. 69 8 .. 102 81 41 2 38 9 10 5 8 63 33 15 6 .. 30 12 8 35 .. 16 16 .. 50 67 92 ..
29 301 12 45 113 27 434 265 596 84 454 113 167 13 .. 267 .. 412 44 2 401 .. .. 2 225 508 138 7 4 313 8 .. 129 191 107 48 53 9 5 46 3 459 603 18 6 31 465 166 10 101 6 39 41 8 432 495 582 335
12 212 125 25 11 9 137 83 162 201 320 9 4 11 21 105 .. 37 17 17 107 67 20 10 5 125 54 8 13 30 2 1 101 19 38 3 13 4 4 5 14 329 35 17 1 21 29 18 32 19 4 8 10 67 123 90 301 67
Fuel price
Particulate matter concentration
Total
Diesel fuel
Gasoline fuel
Super grade gasoline
Diesel
Urban-populationweighted PM10 micrograms per cubic meter
2009
2009
2009
2009
2010
2010
1990
2009
23 17 7 14 18 32 28 27 22 16 14 23 6 7 2 12 .. 13 28 .. 20 26 .. .. 20 16 15 .. .. 21 .. .. .. 28 11 11 25 5 6 37 6 14 24 17 .. 8 12 13 13 18 .. 27 29 19 16 25 .. 9
136 433 37 119 531 334 900 766 602 189 530 290 235 31 13 569 .. 1,492 160 .. 370 418 .. .. 640 405 203 .. .. 492 .. .. .. 439 75 135 119 20 19 281 19 673 949 90 .. 53 704 721 63 162 .. 200 162 79 400 572 .. 1,331
65 259 26 44 223 185 519 347 376 65 173 108 39 21 7 286 .. 491 103 .. 245 5 .. .. 405 242 116 .. .. 178 .. .. .. 123 52 42 98 17 8 89 13 380 394 49 .. 4 549 59 38 124 .. 150 113 47 225 406 .. 675
64 149 11 75 237 134 347 380 172 177 332 173 205 11 5 158 .. 920 51 .. 137 386 .. .. 206 103 59 .. .. 295 .. .. .. 295 23 119 16 4 9 174 4 240 531 38 .. 45 258 617 11 151 .. 37 34 29 105 137 .. 591
1.04 1.67 1.15 0.79 0.10 0.78 1.78 1.85 1.87 0.98 1.60 1.04 0.71 1.33 .. 1.52 1.63 0.23 0.85 1.26 1.48 1.13 0.97 0.98 0.17 1.59 1.52 1.52 1.71 0.59 1.42 1.16 1.55 0.81 1.21 1.11 1.23 1.11 0.80 1.06 1.18 2.13 1.47 1.09 1.07 0.44 2.12 0.31 0.86 0.85 .. 1.28 1.41 1.05 1.57 1.85 .. 0.19
0.92 1.61 0.82 0.51 0.02 0.56 1.69 1.87 1.69 0.98 1.37 0.73 0.51 1.27 .. 1.35 1.60 0.21 0.79 0.97 1.49 0.77 1.07 0.96 0.13 1.42 1.27 1.26 1.54 0.56 1.25 0.99 1.23 0.72 1.08 1.04 0.88 0.86 0.80 1.09 0.91 1.71 0.97 0.99 1.16 0.77 2.01 0.38 0.92 0.77 .. 1.01 1.10 0.84 1.50 1.58 .. 0.19
43 34 110 132 92 168 23 66 41 56 42 107 43 64 169 51 .. 113 79 92 38 39 108 68 101 52 45 80 82 35 258 145 21 66 118 181 39 112 107 50 67 44 14 46 199 195 22 133 216 55 35 107 96 56 58 49 23 54
34 15 57 68 55 110 12 23 21 29 25 29 17 30 56 33 .. 95 35 45 12 28 43 31 81 15 18 30 33 19 106 68 14 33 33 101 23 24 41 45 30 30 12 24 92 42 15 82 101 33 16 65 43 17 34 20 14 31
kilograms of oil equivalent per capita
$ per liter
2012 World Development Indicators
195
ENVIRONMENT
3.15
Traffic and congestion
3.15
Traffic and congestion Motor vehicles
Romania Russian Federation Rwanda Saudi Arabia Senegal Serbia Sierra Leone Singapore Slovak Republic Slovenia Somalia South Africa South Sudan Spain Sri Lanka Sudan Swaziland Sweden Switzerland Syrian Arab Republic Tajikistan Tanzania Thailand Timor-Leste Togo Trinidad and Tobago Tunisia Turkey Turkmenistan Uganda Ukraine United Arab Emirates United Kingdom United States Uruguay Uzbekistan Venezuela, RB Vietnam West Bank and Gaza Yemen, Rep. Zambia Zimbabwe World Low income Middle income Lower middle income Upper middle income Low & middle income East Asia & Pacific Europe & Central Asia Latin America & Carib. Middle East & N. Africa South Asia Sub-Saharan Africa High income Euro area
196
Passenger Road cars density
km. of road per 100 sq. km. of % of total land area consumption
per 1,000 people
per kilometer of road
per 1,000 people
2009
2009
2009
2009
230 271 5 192 22 252 6 156 348 566 .. 162 .. 596 47 27 89 519 563 63 38 7 134 .. 2 353 114 142 106 8 167 313 523 802 200 .. 147 13 30 35 21 114 170 w 10 66 33 99 61 54 219 178 87 17 28 618 593
20 39 .. 20 16 42 2 232 43 30 .. .. .. 41 .. .. 25 8 61 20 .. .. 50 .. 1 .. 61 29 .. .. 45 271 77 38 .. .. .. 7 22 .. .. .. 30 w .. 18 10 25 15 18 36 23 40 5 .. 37 47
198 233 2 139 16 224 5 121 293 522 .. 110 .. 478 19 19 45 462 519 30 29 4 57 .. 2 .. 76 95 80 3 142 293 460 439 179 .. 107 13 25 16 13 98 125 w 7 54 21 84 50 38 183 143 67 11 23 447 418
83 6 53 11 8 50 .. 473 89 192 3 30 .. 132 148 1 21 129 173 37 19 11 35 .. 21 162 12 46 5 29 28 5 172 67 44 18 11 48 93 14 12 25 30 w .. 23 48 17 21 37 9 18 9 102 .. 46 140
2012 World Development Indicators
Road sector energy consumption
2009
14 7 .. 21 21 13 .. 15 11 24 .. 11 .. 24 18 16 .. 16 21 22 4 6 18 .. 11 5 16 14 4 .. 6 14 19 23 23 4 27 14 .. 24 2 4 15 w 6 11 11 11 10 7 8 24 20 7 9 19 18
Fuel price
Particulate matter concentration
Total
Diesel fuel
Gasoline fuel
Super grade gasoline
Diesel
Urban-populationweighted PM10 micrograms per cubic meter
2009
2009
2009
2010
2010
1990
2009
1.46 0.84 1.63 0.16 1.57 1.50 0.94 1.42 1.70 1.67 .. 1.19 .. 1.56 1.19 0.62 1.07 1.87 1.66 0.96 1.02 1.22 1.41 1.40 1.18 .. 0.94 2.52 0.22 1.42 1.01 0.47 1.92 0.76 1.49 0.92 0.02 0.88 1.71 0.35 1.66 1.29 1.21 w 1.18 1.08 1.08 1.13 1.12 1.08 1.21 1.04 0.94 1.12 1.22 1.63 1.78
1.46 0.72 1.62 0.07 1.34 1.48 0.94 1.04 1.53 1.62 .. 1.14 .. 1.47 0.66 0.43 1.10 1.82 1.77 0.45 0.91 1.19 0.95 0.90 1.17 .. 0.82 2.03 0.20 1.11 0.92 0.71 1.98 0.84 1.44 0.83 0.01 0.77 1.54 0.23 1.52 1.15 1.07 w 1.15 0.97 0.91 1.02 0.98 0.93 1.13 0.98 0.56 0.83 1.15 1.55 1.62
42 41 53 160 94 .. 87 108 46 39 82 39 .. 41 93 285 30 15 34 135 84 56 77 .. 56 136 71 77 196 29 71 273 24 30 229 110 21 123 .. 133 125 55 79 w 127 96 124 78 98 112 57 57 125 130 119 37 33
14 16 23 103 80 .. 37 23 12 26 28 26 .. 25 71 137 38 10 22 71 30 20 53 .. 28 98 23 37 41 11 17 62 13 18 142 37 9 50 .. 43 30 37 43 w 56 49 58 43 50 56 23 30 66 68 46 23 19
kilograms of oil equivalent per capita
223 330 .. 1,241 50 265 .. 545 339 836 .. 320 .. 660 79 58 .. 773 737 248 14 26 275 .. 51 704 145 185 172 .. 153 1,229 616 1,640 281 67 626 108 .. 77 10 27 259 w 20 132 70 195 120 105 230 299 283 38 61 939 646
147 114 .. 543 41 162 .. 347 194 514 .. 134 .. 528 54 39 .. 353 286 154 0 19 163 .. 17 313 96 120 0 .. 46 606 351 384 165 11 102 59 .. 2 4 17 110 w 13 65 37 93 60 58 100 123 142 26 26 357 418
67 221 .. 635 9 70 .. 174 105 288 .. 176 .. 126 22 17 .. 357 424 83 12 6 74 .. 31 355 41 34 164 .. 102 563 255 1,134 99 53 459 46 .. 66 10 10 134 w 7 61 30 92 55 51 125 137 115 10 33 519 186
$ per liter
About the data
3.15
Definitions
Traffic congestion in urban areas constrains eco-
Considerable uncertainty surrounds estimates
• Motor vehicles include cars, buses, and freight
nomic productivity, damages people’s health, and
of particulate matter concentrations, and caution
vehicles but not two-wheelers. Population figures are
degrades the quality of life. In recent years owner-
should be used in interpreting them. They allow for
midyear population in the year for which data are
ship of passenger cars has increased, and the expan-
cross-country comparisons of the relative risk of
available. Roads refer to motorways (a road designed
sion of economic activity has led to more goods and
particulate matter pollution facing urban residents.
and built for motor traffic that separates the traf-
services being transported by road over greater dis-
Major sources of urban outdoor particulate matter
fic flowing in opposite directions), highways, main
tances (see table 5.10). These developments have
pollution are traffic and industrial emissions, but
or national roads, and secondary or regional roads.
increased demand for roads and vehicles, adding to
nonanthropogenic sources such as dust storms may
• Passenger cars are road motor vehicles, other than
urban congestion, air pollution, health hazards, and
also be a substantial contributor for some cities.
two-wheelers, intended for the carriage of passen-
traffic accidents and injuries.
Country technology and pollution controls are impor-
gers and designed to seat no more than nine people
The data in the table on motor vehicles, passenger
tant determinants of particulate matter. Data on par-
(including the driver). • Road density is the ratio of
cars, and road density are compiled by the Interna-
ticulate matter for selected cities are in table 3.16.
the length of the country’s total road network to the
tional Road Federation (IRF) through questionnaires
country’s land area. The road network includes all
sent to national organizations. The IRF uses a hier-
roads in the country— motorways, highways, main
archy of sources to gather as much information as
or national roads, secondary or regional roads, and
possible. Primary sources are national road asso-
other urban and rural roads. • Road sector energy
ciations. If they lack data or do not respond, other
consumption is the total energy used in the road
agencies are contacted, including road directorates,
sector, including energy from petroleum products,
ministries of transport or public works, and central
natural gas, combustible and renewable waste,
statistical offices. As a result, data quality is uneven.
and electricity (see table 3.7). • Total energy con-
Coverage of each indicator may differ across coun-
sumption is the country’s total energy consumption
tries because of different definitions. Comparability
from all sources (see table 3.7). • Diesel is heavy
is also limited when time series data are reported.
oils used in internal combustion in diesel engines.
The IRF is taking steps to improve the quality of the
• Gasoline fuel is light hydrocarbon oil used in inter-
data in its World Road Statistics. Because this effort
nal combustion engines such as motor vehicles,
covers only 2003–09, time series data may not be
excluding aircraft. • Fuel price is the pump price of
comparable. Another reason is coverage. Road den-
super grade gasoline and of diesel fuel, converted
sity is a rough indicator of accessibility and does not
from the local currency to U.S. dollars (see About
capture road width, type, or condition. Thus com-
the data). • Particulate matter concentration is fine
parisons over time and across countries should be
suspended particulates of less than 10 microns in
made with caution.
diameter (PM10) that are capable of penetrating
Road sector energy consumption includes energy
deep into the respiratory tract and causing severe
from petroleum products, natural gas, renewable and
health damage. Data are urban-population-weighted
combustible waste, and electricity. Biodiesel and bio-
PM10 levels in residential areas of cities with more
gasoline, forms of renewable energy, are biodegrad-
than 100,000 residents. The estimates represent
able and emit less sulfur and carbon monoxide than
the average annual exposure level of the average
petroleum-derived ones. They can be produced from
urban resident to outdoor particulate matter.
vegetable oils, such as soybean, corn, palm, peanut, or sunflower oil, and can be used directly only in a modified internal combustion engine. Data on fuel prices are compiled by the German Agency for International Cooperation (GIZ), from its global network and other sources, including the
Data sources
Allgemeiner Deutscher Automobile Club (for Europe)
Data on vehicles and road density are from the
and the Latin American Energy Organization (for Latin
IRF’s electronic files and its annual World Road
America). Local prices are converted to U.S. dollars
Statistics, except where noted. Data on road sec-
using the exchange rate in the Financial Times inter-
tor energy consumption are from the IRF and the
national monetary table on the survey date. When
International Energy Agency. Data on fuel prices
multiple exchange rates exist, the market, parallel,
are from the GIZ’s electronic files. Data on par-
or black market rate is used. Prices were compiled
ticulate matter concentrations are from Pandey
in mid-November 2010, based on the crude oil price
and others (2006b).
of $81 a barrel Brent.
2012 World Development Indicators
197
ENVIRONMENT
Traffic and congestion
3.16
Air pollution City
City population
Particulate matter concentration
Sulfur dioxide
Nitrogen dioxide
About the data
Indoor and outdoor air pollution place a major burden
Argentina Australia Austria Belgium Brazil Bulgaria Canada Chile China Colombia Croatia Cuba Czech Republic Denmark Ecuador Egypt, Arab Rep. Finland France Germany Ghana Greece Hungary Iceland India
198
Buenos Aires Córdoba Melbourne Perth Sydney Vienna Brussels Rio de Janeiro São Paulo Sofia Montréal Toronto Vancouver Santiago Anshan Beijing Changchun Chengdu Chongqing Dalian Foshan Guangzhou Guiyang Harbin Jinan Kunming Lanzhou Liupanshui Nanchang Shanghai Shenyang Shenzhen Tianjin Wuhan Xi’an Zhengzhou Zibo Bogotá Zagreb Havana Prague Copenhagen Guayaquil Quito Cairo Helsinki Paris Berlin Frankfurt Munich Accra Athens Budapest Reykjavik Ahmadabad Bengaluru
2012 World Development Indicators
Urbanpopulation-weighted PM10 micrograms micrograms per per micrograms per cubic meter cubic meter cubic meter thousands 2010 1990 2009 2001 a 2001 a
on world health. More than half the world’s people
13,074 1,493 3,853 1,599 4,429 1,706 1,904 11,950 20,262 1,196 3,783 5,449 2,220 5,952 1,663 12,385 3,597 4,961 9,401 3,306 4,969 8,884 2,154 4,251 3,237 3,116 2,285 1,221b 2,701 16,575 5,166 9,005 7,884 7,681 4,747 2,966 2,456 8,500 779 b 2,130 1,162 1,186 2,690 1,846 11,001 1,117 10,485 3,450 680 b 1,349 2,342 3,257 1,706 319 b 5,717 7,218
pollution, which is responsible for 1.6 million deaths a
159 78 17 16 27 45 33 50 57 118 24 29 17 100 132 141 117 136 194 79 107 99 111 121 148 111 145 94 124 115 160 89 198 125 221 154 117 51 48 35 42 30 33 44 274 24 14 30 27 27 37 69 35 23 126 68
92 45 11 11 17 32 23 25 28 49 15 18 11 60 68 73 61 71 101 41 56 52 58 63 77 58 75 49 65 60 83 46 103 65 115 80 61 27 26 17 17 17 18 24 112 17 10 18 16 16 21 33 16 16 66 35
.. .. .. 5 28 14 20 129 43 39 10 17 14 29 115 90 21 77 340 61 .. 57 424 23 132 19 102 102 69 53 99 .. 82 40 .. 63 198 .. 31 1 14 7 15 22 69 4 14 18 11 8 .. 34 39 5 30 ..
.. 97 30 19 81 42 48 .. 83 122 42 43 37 81 88 122 64 74 70 100 .. 136 53 30 45 33 104 .. 29 73 73 .. 50 43 .. 95 43 .. .. 5 33 54 .. .. .. 35 57 26 45 53 .. 64 51 42 21 ..
rely on dung, wood, crop waste, or coal to meet basic energy needs. Cooking and heating with these fuels on open fires or stoves without chimneys lead to indoor air year—one every 20 seconds. In many urban areas air pollution exposure is the main environmental threat to health. Long-term exposure to high levels of soot and small particles contributes to a range of health effects, including respiratory diseases, lung cancer, and heart disease. Particulate pollution, alone or with sulfur dioxide, creates an enormous burden of ill health. Sulfur dioxide and nitrogen dioxide emissions lead to deposition of acid rain and other acidic compounds over long distances, which can lead to the leaching of trace minerals and nutrients critical to trees and plants. Sulfur dioxide emissions can damage human health, particularly that of the young and old. Nitrogen dioxide is emitted by bacteria, motor vehicles, industrial activities, nitrogen fertilizers, fuel and biomass combustion, and aerobic decomposition of organic matter in soils and oceans. Where coal is the primary fuel for power plants without effective dust controls, steel mills, industrial boilers, and domestic heating, high levels of urban air pollution are common— especially particulates and sulfur dioxide. Elsewhere the worst emissions are from petroleum product combustion. Sulfur dioxide and nitrogen dioxide concentration data are based on average observed concentrations at urban monitoring sites, which not all cities have. The data on particulate matter are estimated average annual concentrations in residential areas away from air pollution “hotspots,” such as industrial districts and transport corridors. The data are from the World Bank’s Development Research Group and Environment Department estimates of annual ambient concentrations of particulate matter in cities with populations exceeding 100,000 (Pandey and others 2006b). A country’s technology and pollution controls are important determinants of particulate matter concentrations. Pollutant concentrations are sensitive to local conditions, and even monitoring sites in the same city may register different levels. Thus these data should be considered only a general indication of air quality, and comparisons should be made with caution. Current World Health Organization (WHO) air quality guidelines are annual mean concentrations of 20 micrograms per cubic meter for particulate matter less than 10 microns in diameter and 40 micrograms for nitrogen dioxide and daily mean concentrations of 20 micrograms per cubic meter for sulfur dioxide.
City
City population
Particulate matter concentration
Sulfur dioxide
Nitrogen dioxide
3.16
Definitions
• City population is the number of residents of
India (continued) Indonesia Iran, Islamic Rep. Ireland Italy Japan Kenya Korea, Rep Malaysia Mexico Netherlands New Zealand Norway Philippines Poland Portugal Romania Russian Federation Singapore Slovak Republic South Africa Spain Sweden Switzerland Thailand Turkey Ukraine United Kingdom United States Venezuela, RB
Chennai Delhi Hyderabad Kanpur Kolkata Lucknow Mumbai Nagpur Pune Jakarta Tehran Dublin Milan Rome Turin Osaka-Kobe Tokyo Yokohama Nairobi Busan Seoul Daegu Kuala Lumpur Mexico City Amsterdam Auckland Oslo Manila Katowice Lódz Warsaw Lisbon Bucharest Moscow Omsk Singapore Bratislava Cape Town Durban Johannesburg Barcelona Madrid Stockholm Zurich Bangkok Ankara Istanbul Kiev Birmingham London Manchester Chicago Los Angeles New York Caracas
Urbanpopulation-weighted PM10 micrograms micrograms per per micrograms per cubic meter cubic meter cubic meter thousands 2010 1990 2009 2001 a 2001 a
the city or metropolitan area as defined by national
7,547 22,157 6,751 3,364 15,552 2,873 20,041 2,607 5,002 9,210 7,241 1,099 2,967 3,362 1,665 11,337 36,669 3,654b 3,523 3,425 9,773 2,458 1,519 19,460 1,049 1,404 888 11,628 309b 742b 1,712 2,824 1,934 10,550 1,124 4,837 500 b 3,405 2,879 3,670 5,083 5,851 1,285 1,150 6,976 3,906 10,525 2,805 2,302 8,631 2,253 9,204 12,762 19,425 3,090
diameter (PM10) that are capable of penetrating
56 227 62 165 193 165 95 84 71 137 92 24 46 44 66 48 54 42 67 52 55 59 36 88 45 13 27 78 60 60 65 44 47 42 44 108 44 24 47 49 43 37 14 32 88 75 88 91 33 27 24 33 46 28 31
29 118 32 86 101 86 50 44 37 70 55 13 24 23 35 28 32 25 31 33 35 38 19 43 30 11 19 24 35 34 38 18 16 16 17 23 12 16 32 33 27 23 9 21 60 35 42 21 20 17 12 20 28 17 13
15 24 12 15 49 26 33 6 .. .. 209 20 31 .. .. 19 18 100 .. 60 44 81 24 74 10 3 8 33 83 21 16 8 10 109 20 20 21 21 31 19 11 24 3 11 11 55 120 14 9 25 26 14 9 26 33
17 41 17 14 34 25 39 13 .. .. .. .. 248 .. .. 63 68 13 .. 51 60 62 .. 130 58 20 43 .. 79 43 32 52 71 .. 34 30 27 72 .. 31 43 66 20 39 23 46 .. 51 45 77 49 57 74 79 57
authorities and reported to the United Nations. • Particulate matter concentration is fi ne suspended particulates of less than 10 microns in deep into the respiratory tract and causing severe health damage. Data are urban- population-weighted PM10 levels in residential areas of cities with more than 100,000 residents. The estimates represent the average annual exposure level of the average urban resident to outdoor particulate matter. • Sulfur dioxide is an air pollutant produced when fossil fuels containing sulfur are burned. • Nitrogen dioxide is a poisonous, pungent gas formed when nitric oxide combines with hydrocarbons and sunlight, producing a photochemical reaction. These conditions occur in both natural and anthropogenic activities.
Data sources Data on city population are from the United Nations Population Division’s World Urbanization Prospects: The 2010 Revision. Data on particulate matter concentrations are from Pandey and others (2006b). Data on sulfur dioxide and nitrogen dioxide concentrations are from the WHO’s Healthy Cities Air Management Information System and the World Resources Institute.
a. Data are for the most recent year available. b. Data are from national sources.
2012 World Development Indicators
199
ENVIRONMENT
Air pollution
3.17
Government commitment Biodiversity Environmental assessments, strategies strategies, or or action action plans plans
Participation in treatiesa
Climate changeb
Afghanistan Albania Algeria Angola Argentina Armenia Australia Austria Azerbaijan Bahrain Bangladesh Belarus Belgium Benin Bolivia Bosnia and Herzegovina Botswana Brazil Bulgaria Burkina Faso Burundi Cambodia Cameroon Canada Central African Republic Chad Chile China Hong Kong SAR, China Colombia Congo, Dem. Rep. Congo, Rep. Costa Rica Côte d’Ivoire Croatia Cuba Cyprus Czech Republic Denmark Dominican Republic Ecuador Egypt, Arab Rep. El Salvador Eritrea Estonia Ethiopia Finland France Gabon Gambia, The Georgia Germany Ghana Greece Guatemala Guinea Guinea-Bissau Haiti
200
1993 2001 1992 1992 1998 1991 1993 1994 1990 1993 1994 1999 1990 1990 1994
1994 1990 1988 1991 1988 1994 1989 1989 1994 1993 1994
1998 1990 1994 2001
1988 1990 1990 1992 1991 2000
1994 1994 1993 1992 1994 1995 1998 1994 1995 1990 1992 1998 1992 1994 1994 1993 1999
1995 1995 1988 1988 1991 1990 1989 1988 1988 1988 1991
2012 World Development Indicators
Ozone layer
CFC control
Law of the Seac
Biological diversity b
Kyoto Protocol 1997
1992
1985
1987
1982
1992
2002 1994 d 1993 2000 1994 1993e 1992 1994 1995 1994 1994 2000 f 1996 1994 1994 2000 d 1994 1994 1995 1993 1997 1995d 1994 1992 1995 1994 1995 1993
2004 d 1999d 1992d 2000 d 1990 1999d 1987d 1987 1996d 1990d 1990 d 1986e 1988 1993d 1994 d 1993g 1991d 1990 d 1990d 1989 1997d 2001d 1989d 1986 1993d 1989d 1990 1989d
2004 d 1999d 1992d 2000 d 1990 1999d 1989 1989 1996d 1990 d 1990 d 1988f 1988 1993d 1994 d 1992g 1991d 1990 d 1990 d 1989 1997d 2001d 1989d 1988 1993d 1994 1990 1991d
2003 1996 1990 1995 2002 1994 1995 1985 2001 2006 1998 1997 1995 1994 1990 1988 1996 2005 1985 2003 2009 1997 1996
2002 1994 d 1995 1998 1994 1993e 1993 1994 2000 f 1996 1994 1993 1996 1994 1994 2002e 1995 1994 1996 1993 1997 1995d 1994 1992 1995 1994 1994 1993
1995 1995 1996 1994 1994 1996e 1994 1997 1993f 1995 1998 1993 1994 1995 1995d 1994 1994 1994 e 1994 1998 1994 1994 d 1993 1995 1994 1995 1993 1995 1996
1990 d 1994 d 1994 d 1991d 1993d 1992g 1992d 1992d 1993g 1988 1993d 1990d 1988 1992 2005d 1996d 1994 d 1986 1987f 1994 d 1990d 1996d 1988 1989d 1988 1987d 1992d 2002d 2000 d
1993d 1994 d 1994 1991d 1993d 1992g 1992d 1992d 1993g 1988 1993d 1990 d 1988 1992d 2005d 1996d 1994 d 1988 1988f 1994 d 1990 d 1996d 1988 1992 1988 1989d 1992d 2002d 2000 d
1989 2008 1992 1984 1995 1984 1988 1996 2004 2009 1983 2005 1996 1996 1998 1984 1996 1994 1983 1995 1997 1985 1986 1996
1994 1994 1996 1994 1994 1996 1994 1996 1993f 1993 1996 1993 1994 1994 1996d 1994 1994 e 1994 1997 1994 1994 d 1993 1994 1994 1995 1993 1995 1996
CITES
CCD
Stockholm Convention
1973
1994
2001
2005d 2005d 2007 2001 2003d 2007 2002 2000 d 2006d 2001d 2005d 2002 2002d 1999 2007d 2003d 2002 2002 2005d 2001d 2002d 2002d 2002 2008d 2009d 2002 2002f
1985d 2003d 1983d 1981 2008d 1976 1982d 1998d 1981 1995d 1983 1984 d 1979 2009d 1977d 1975 1991d 1989d 1988d 1997 1981d 1975 1980 d 1989d 1975 1981d
1995d 2000 d 1996 1997 1997 1997 2000 1997d 1998d 1997d 1996 2001d 1997d 1996 1996 2002d 1996 1997 2001d 1996 1997 1997 1997 1995 1996 1996 1997 1997
2004 2006 2006d 2005 2003 2004 2002 2004 d 2006 2007 2004 d 2006 2004 2003 2010 2002d 2004 2004 2004 2005 2006 2009 2001 2008 2004 2005 2004
2001d 2005d 2007d 2002 2007d 2007 2002 1992d 2001f 2002 2002d 2000 2005 1998 2005d 2002 2005d 2002 2002f 2006d 2001d 1999d 2002 2003d 2002 1999 2000 d 2005d 2005d
1981 1976d 1983d 1975 1994 d 2000 d 1990 d 1974 1993 1977 1986d 1975 1978 1987d 1994 d 1992d 1989d 1976d 1978f 1989d 1977d 1996d 1976 1975 1992d 1979 1981d 1990 d
1999 1997 1999 1998 1997 2000 d 1997 2000 d 2000 d 1995 1997d 1995 1995 1997d 1996 1997 1995d 1997 1996d 1996 1999 1996 1996 1997 1998d 1997 1995 1996
2008 2005d 2007 2007 2004 2007 2007 2005d 2002 2003 2007 2004 2003 2008 2005d 2008d 2003 2002d 2004f 2007 2006 2006 2002 2003 2006 2008 2007 2008
Biodiversity Environmental assessments, strategies strategies, or or action action plans plans
Participation in treatiesa
Climate changeb
Honduras Hungary India Indonesia Iran, Islamic Rep. Iraq Ireland Israel Italy Jamaica Japan Jordan Kazakhstan Kenya Korea, Dem. Rep. Korea, Rep. Kosovo Kuwait Kyrgyz Republic Lao PDR Latvia Lebanon Lesotho Liberia Libya Lithuania Macedonia, FYR Madagascar Malawi Malaysia Mali Mauritania Mauritius Mexico Moldova Mongolia Morocco Mozambique Myanmar Namibia Nepal Netherlands New Zealand Nicaragua Niger Nigeria Norway Oman Pakistan Panama Papua New Guinea Paraguay Peru Philippines Poland Portugal Puerto Rico Qatar
3.17
Ozone layer
CFC control
Law of the Seac
Biological diversity b
Kyoto Protocol
CITES
CCD
Stockholm Convention
1992
1985
1987
1982
1992
1997
1973
1994
2001
1993 1995 1993 1993 1994 1991 1994
1994 1993 1992
1995 1994 1993 1994 1996 2009d 1994 1996 1994 1995 1993e 1993 1995 1994 1994f 1993
1993d 1988d 1991d 1992d 1990 d 2008d 1988d 1992d 1988 1993d 1988d 1989d 1998d 1988d 1995d 1992d
1993d 1989d 1992d 1992 1990 d 2008d 1988 1992 1988 1993d 1988e 1989d 1998d 1988 1995d 1992
1993 2002 1995 1986 1985 1996 1995 1983 1996 1995 1989 1996
1995 1994 1994 1994 1996 2009d 1996 1995 1994 1995 1993e 1993 1994 1994 1994f 1994
2000 2002d 2002d 2004 2005d 2009d 2002 2004 2002 1999d 2002d 2003d 2009 2005d 2005d 2002
1985d 1985d 1976 1978d 1976 2002 1979 1979 1997d 1980e 1978d 2000 d 1978 1993d
1997 1999d 1996 1998 1997 2010 d 1997 1996 1997 1997d 1998e 1996 1997 1997 2003d 1999
2005 2008 2006 2009 2006
1995 1995 1989 1988 1994 1991 1988 1990 2002 1995 1994 1992 1993 1994 1994 1994 1990 1994 1990 1992 1989 1993 1995
1991 1988 1989 1988 1988 1989 1991 1992 1994 1991 1993 1988 1989 1991
1994 d 2000 1995d 1995 1994 1995 2002 1999 1995 1998d 1999 1994 1994 1995 1994 1992 1993 1995 1993 1995 1995 1994 1995 1994 1993e 1993 1995 1995 1994 1993 1995 1994 1995 1993 1994 1993 1994 1994 1993
1992d 2000 d 1998d 1995d 1993d 1994 d 1996d 1990 d 1995d 1994g 1996d 1991d 1989d 1994d 1994 d 1992d 1987 1996d 1996d 1995 1994 d 1993d 1993d 1994 d 1988e 1987 1993d 1992d 1988d 1986 1999d 1992d 1989d 1992d 1992d 1989 1991d 1990 d 1988d
1992d 2000 d 1998d 1995d 1993d 1994 d 1996d 1990 d 1995d 1994g 1996d 1991d 1989d 1994 d 1994 d 1992d 1988e 1996d 1996d 1995 1994 d 1993d 1993d 1994 d 1988e 1988 1993d 1992d 1988d 1988 1999d 1992d 1989 1992d 1992d 1993d 1991 1990 d 1988
1986 1998 2004 1995 2007 2008 2003 1994 2001 2010 1996 1985 1996 1994 1983 2007 1996 2007 1997 1996 1983 1998 1996 1996 2000 1986 1996 1989 1997 1996 1997 1986 1984 1998 1997
2002 1996d 1996d 1995 1994 1995 2000 2001 1996 1997d 1996 1994 1995 1996 1992 1993 1995 1993 1995 1995 1994 1997 1993 1994 e 1993 1995 1995 1994 1993 1995 1994 1995 1993 1994 1993 1993 1996 1993
2005d 2003d 2003d 2002 2006d 2000d 2002d 2006d 2003 2004 d 2003d 2001d 2002 2002 2005d 2001d 2000 2008d 1999d 2002d 2005d 2003d 2003d 2005d 2002e 2002 1999 2004 2004 d 2002 2005d 2005d 1999 2002 1999 2002 2003 2002 2002f
2002 2007d 2004 d 1997d 2003 1981d 2003d 2001d 2000 d 1975 1982d 1977d 1994 d 1998d 1975 1991d 2001d 1996d 1975 1981d 1997d 1990 d 1975d 1984 1989d 1977d 1975 1974 1976 2008d 1976d 1978 1975d 1976 1975 1981 1989 1980
1997 1997d 1996d 2002d 1996 1995 1998d 1996 2003d 2002d 1997 1996 1997 1995 1996 1996 1995 1999d 1996 1996 1997 1997d 1997 1996 1995e 2000 d 1998 1996 1997 1996 1996d 1997 1996 2000 d 1997 1995 2000 2001d 1996
2006 2006 2006 2004 2003 2002 2002d 2005d 2006 2004 2005 2009 2003 2005 2004 2003 2004 2004 2004 2005 2004 d 2005d 2007 2002e 2004 2005 2006 2004 2002 2005 2008 2003 2003 2004 2005 2004 2008 2004 e
1996d
1996d
1996d
2002
1996
2005a
2001d
1999d
2004 d
2012 World Development Indicators
2010
2007 2002d 2004 2007 2004 2002d 2007
201
ENVIRONMENT
Government commitment
3.17
Government commitment Biodiversity Environmental assessments, strategies strategies, or or action action plans plans
Participation in treatiesa
Climate changeb
Romania Russian Federation Rwanda Saudi Arabia Senegal Serbia Sierra Leone Singapore Slovak Republic Slovenia Somalia South Africa South Sudan Spain Sri Lanka Sudan Swaziland Sweden Switzerland Syrian Arab Republic Tajikistan Tanzania Thailand Togo Trinidad and Tobago Tunisia Turkey Turkmenistan Uganda Ukraine United Arab Emirates United Kingdom United States Uruguay Uzbekistan Venezuela Vietnam West Bank and Gaza Yemen, Rep. Zambia Zimbabwe
Ozone layer
CFC control
Law of the Seac
Biological diversity b
Kyoto Protocol
CITES
CCD
Stockholm Convention
1992
1985
1987
1982
1992
1997
1973
1994
2001
1995 1999 1991 1984 1994 1993 1994 1993
1994 1991 1995
1994 1994 1998 1994 d 1994 2001a 1995 1997 1994f 1995 2009d 1997
1993d 1986e 2001d 1993d 1993d 2001g 2001d 1989d 1993g 1992g 2001d 1990d
1993d 1988e 2001d 1993d 1993 2001g 2001d 1989d 1993g 1992g 2001d 1990 d
1996 1997 1996 1984 2001 1994 1994 1996 1995 1989 1997
1994 1995 1996 2001d 1994 2002 1994 d 1995 1994f 1996 2009d 1995
2001 2008 2004 d 2005d 2001d 2007d 2006d 2006d 2002 2002 2010 2002d
1994 d 1992h 1980 d 1996d 1977d 2006h 1994 d 1986d 1993g 2000 d 1985d 1975
1998d 2003d 1998 1997d 1995 2007a 1997 1999d 2002d 2001d 2002d 1997
2004 2011 2002d
1994 1999 1994 1991 1994 1998 1994 1999 1995 1995
1991 1988 1988 1988 1994 1995 1993
1993 1993 1993 1996 1993 1993 1996d 1998d 1996 1994 1995e 1994 1993 2004 d 1995d 1993 1997 1995d 1993 1992 1994 1993d 1994 1994
1988d 1989d 1993d 1992d 1986 1987 1989d 1996d 1993d 1989d 1991d 1989d 1989d 1991d 1993d 1988d 1986e 1989d 1987 1986 1989d 1993d 1988d 1994 d
1988 1989d 1993d 1992d 1988 1988 1989d 1998d 1993d 1989 1991 1989d 1989d 1991d 1993d 1988 1988e 1989d 1988 1988 1991d 1993d 1989 1994 d
1997 1994 1985 2009 1996 1985 2011 1985 1986 1985 1990 1999 1997 1992 1994
1993 1994 1995 1994 1993 1994 1996 1997d 1996 2003 1995e 1996 1993 1997 1996d 1993 1995 2000 1994 1993 1995d 1994 1994
2002 2002d 2004 d 2006d 2002 2003 2006d 2008d 2002d 2002 2004 d 1999 2003d 2006d 1999d 2002d 2004 2005d 2002 2001 1999 2005d 2002
1986d 1979d 1982 1997d 1974 1974 2003d 1979 1983 1978 1984 d 1974 1996d 1991d 1999d 1990 d 1976 1974 1975 1997d 1977 1994 d
1996 1998d 1995 1996 1995 1996 1997 1997d 1997 2001d 1995e 2000 d 1995 1998 1996 1997 2002d 1998d 1996 2000 1999d 1995 1998d 1998d
1996 1994 1987
1992
1996 1993 1992
1996d 1990 d 1992d
1996d 1990 d 1992d
1987 1983 1993
1996 1993 1994
2004 d 2006 2009d
1997d 1980 d 1981d
1997d 1996 1997
2003 2009 2003d 2005 2002 2004 2010 d 2002 2004 2005 2006 2006d 2002 2003 2005 2007 2004 2005 2004 2002d 2004 2009 2004 d 2007 2002 2005 2004 2005 2002 2004 2006
a. Ratification of the treaty. b. Year the treaty entered into force in the country. c. Convention of December 10, 1982. d. Accession. e. Acceptance. f. Approval. g. Succession. h. Continuation.
202
2012 World Development Indicators
About the data
3.17
Definitions
National environmental strategies and participation
Environment and Development (the Earth Summit) in
• Environmental strategies or action plans pro-
in international treaties on environmental issues pro-
Rio de Janeiro, which produced Agenda 21—an array
vide a comprehensive analysis of conservation and
vide some evidence of government commitment to
of actions to address environmental challenges:
resource management issues that integrate envi-
sound environmental management. But the signing
• The Framework Convention on Climate Change
ronmental concerns with development. They include
of these treaties does not always imply ratification,
aims to stabilize atmospheric concentrations of
national conservation strategies, environmental
nor does it guarantee that governments will comply
greenhouse gases at levels that will prevent human
action plans, environmental management strategies,
with treaty obligations.
activities from interfering dangerously with the
and sustainable development strategies. The date
global climate.
is the year a country adopted a strategy or action
In many countries efforts to halt environmental degradation have failed, primarily because govern-
• The Vienna Convention for the Protection of the
plan. • Biodiversity assessments, strategies, or
ments have neglected to make this issue a priority, a
Ozone Layer aims to protect human health and the
action plans include biodiversity profiles (see About
reflection of competing claims on scarce resources.
environment by promoting research on the effects
the data). • Participation in treaties covers nine
To address this problem, many countries are prepar-
of changes in the ozone layer and on alternative
international treaties (see About the data). • Cli-
ing national environmental strategies—some focus-
substances (such as substitutes for chlorofluoro-
mate change refers to the Framework Convention
ing narrowly on environmental issues, and others
carbon) and technologies, monitoring the ozone
on Climate Change (signed in 1992). • Ozone layer
integrating environmental, economic, and social
layer, and taking measures to control the activities
refers to the Vienna Convention for the Protection
concerns. Among such initiatives are conservation
that produce adverse effects.
of the Ozone Layer (signed in 1985). • CFC control
strategies and environmental action plans. Some
• The Montreal Protocol for Chlorofl uorocarbon
countries have also prepared country environmen-
Control requires that countries help protect the
the Ozone Layer (the Montreal Protocol for Chloro-
tal profiles and biodiversity strategies and profiles.
earth from excessive ultraviolet radiation by cut-
fluorocarbon Control) (signed in 1987). • Law of
refers to the Protocol on Substances That Deplete
National conservation strategies—promoted by
ting chlorofluorocarbon consumption by 20 percent
the Sea refers to the United Nations Convention on
the World Conservation Union (IUCN)—provide a
over their 1986 level by 1994 and by 50 percent
the Law of the Sea (signed in 1982). • Biological
comprehensive, cross-sectoral analysis of conser-
over their 1986 level by 1999, with allowances for
diversity refers to the Convention on Biological Diver-
vation and resource management issues to help inte-
increases in consumption by developing countries.
sity (signed at the Earth Summit in 1992). • Kyoto
grate environmental concerns with the development
• The United Nations Convention on the Law of the
Protocol refers to the protocol on climate change
process. Such strategies discuss current and future
Sea, which became effective in November 1994,
adopted at the third conference of the parties to the
needs, institutional capabilities, prevailing technical
establishes a comprehensive legal regime for seas
United Nations Framework Convention on Climate
conditions, and the status of natural resources in
and oceans, establishes rules for environmental
Change in December 1997. • CITES is the Conven-
a country.
standards and enforcement provisions, and devel-
tion on International Trade in Endangered Species of
ops international rules and national legislation to
Wild Fauna and Flora, an agreement among govern-
prevent and control marine pollution.
ments to ensure that the survival of wild animals
National environmental action plans, supported by the World Bank and other development agencies, describe a country’s main environmental concerns,
• The Convention on Biological Diversity promotes
and plants is not threatened by uncontrolled exploita-
identify the principal causes of environmental prob-
conservation of biodiversity through scientifi c
tion. Adopted in 1973, it entered into force in 1975.
lems, and formulate policies and actions to deal with
and technological cooperation among countries,
• CCD is the United Nations Convention to Combat
them. These plans are a continuing process in which
access to financial and genetic resources, and
Desertification, an international convention address-
governments develop comprehensive environmental
transfer of ecologically sound technologies.
ing the problems of land degradation in the world’s
policies, recommend specific actions, and outline
But 10 years after the Earth Summit in Rio de
drylands. Adopted in 1994, it entered into force in
the investment strategies, legislation, and institu-
Janeiro the World Summit on Sustainable Develop-
1996. • Stockholm Convention is an international
tional arrangements required to implement them.
ment in Johannesburg recognized that many of the
legally binding instrument to protect human health
Biodiversity profiles—prepared by the World Con-
proposed actions had yet to materialize. To help
and the environment from persistent organic pollut-
servation Monitoring Centre and the IUCN—provide
developing countries comply with their obligations
ants. Adopted in 2001, it entered into force in 2004.
basic background on species diversity, protected
under these agreements, the Global Environment
areas, major ecosystems and habitat types, and
Facility (GEF) was created to focus on global improve-
legislative and administrative support. In an effort
ment in biodiversity, climate change, international
to establish a scientific baseline for measuring prog-
waters, and ozone layer depletion. The UNEP, United
Data on environmental strategies and participa-
ress in biodiversity conservation, the United Nations
Nations Development Programme, and World Bank
tion in international environmental treaties are
Environment Programme (UNEP) coordinates global
manage the GEF according to the policies of its gov-
from the Secretariat of the United Nations Frame-
biodiversity assessments.
erning body of country representatives. The World
work Convention on Climate Change, the Ozone
Bank is responsible for the GEF Trust Fund and chairs
Secretariat of the UNEP, the World Resources
the GEF.
Institute, the UNEP, the Center for International
To address global issues, many governments have also signed international treaties and agreements
Data sources
launched in the wake of the 1972 United Nations
Earth Science Information Network, and the
Conference on the Human Environment in Stock-
United Nations Treaty Series.
holm and the 1992 United Nations Conference on
2012 World Development Indicators
203
ENVIRONMENT
Government commitment
3.18 Afghanistan Albania Algeria Angola Argentina Armenia Australia Austria Azerbaijan Bahrain Bangladesh Belarus Belgium Benin Bolivia Bosnia and Herzegovina Botswana Brazil Bulgaria Burkina Faso Burundi Cambodia Cameroon Canada Central African Republic Chad Chile China Hong Kong SAR, China Colombia Congo, Dem. Rep. Congo, Rep. Costa Rica Côte d’Ivoire Croatia Cuba Cyprus Czech Republic Denmark Dominican Republic Ecuador Egypt, Arab Rep. El Salvador Eritrea Estonia Ethiopia Finland France Gabon Gambia, The Georgia Germany Ghana Greece Guatemala Guinea Guinea-Bissau Haiti
204
Contribution of natural resources to gross domestic product Total natural resources rents
Oil rents
Natural gas rents
Coal rents
Mineral rents
Forest rents
% of GDP
% of GDP
% of GDP
% of GDP
% of GDP
% of GDP
2010
2010
2010
2010
2010
2010
2.3 3.6 25.7 46.3 6.1 1.7 8.3 0.3 46.5 23.2 3.3 1.9 0.0 1.7 18.1 2.8 4.7 5.3 2.3 7.1 13.3 1.2 9.3 3.8 5.9 43.3 18.9 4.0 0.0 7.9 29.8 64.1 0.4 7.1 1.2 5.1 0.0 0.3 2.1 0.3 20.6 10.1 0.5 0.6 1.1 4.7 0.8 0.1 49.8 2.5 0.8 0.1 10.5 0.2 2.4 21.2 4.6 0.6
.. 3.0 17.6 46.1 4.0 0.0 0.9 0.1 42.6 16.4 0.0 1.3 0.0 0.0 4.6 0.0 0.0 2.2 0.0 .. .. 0.0 7.1 2.3 .. 41.2 0.0 1.5 0.0 6.5 3.9 61.6 0.0 4.3 0.5 3.0 0.0 0.0 1.8 0.0 20.2 5.9 0.0 0.0 0.0 0.0 0.0 0.0 46.4 .. 0.2 0.0 0.0 0.0 0.8 .. .. 0.0
.. 0.0 7.8 0.1 1.4 0.0 0.7 0.1 3.9 6.9 2.8 0.0 0.0 0.0 9.0 0.0 0.0 0.1 0.0 .. .. 0.0 0.2 0.3 .. .. 0.1 0.1 0.0 0.5 0.0 0.1 0.0 1.0 0.5 0.6 0.0 0.0 0.3 0.0 0.1 3.9 0.0 0.0 0.0 0.0 0.0 0.0 0.2 .. 0.0 0.0 0.0 0.0 0.0 .. .. 0.0
0.0 0.0 0.0 .. 0.0 .. 1.8 0.0 .. .. 0.1 .. 0.0 .. .. 2.4 0.4 0.0 1.1 .. .. .. 0.0 0.1 .. .. 0.0 3.8 .. 1.4 0.1 .. .. .. 0.0 .. .. 0.7 0.0 .. .. 0.0 .. .. 1.9 .. .. 0.0 .. .. 0.1 0.1 .. 0.3 .. .. .. ..
0.0 0.5 0.3 0.0 0.6 1.7 6.6 0.0 0.0 0.0 0.0 0.0 0.0 0.0 4.2 2.3 4.6 2.7 2.0 4.1 1.3 0.0 0.2 0.8 0.0 0.0 18.3 2.2 0.0 0.8 16.4 0.0 0.1 0.7 0.0 1.5 0.0 0.0 0.0 0.2 0.0 0.3 0.0 0.0 0.0 0.3 0.2 0.0 0.1 0.0 0.6 0.0 8.8 0.1 0.8 17.0 0.0 0.0
2.3 0.1 0.1 0.2 0.1 0.0 0.1 0.1 0.0 0.0 0.5 0.5 0.0 1.7 0.4 0.6 0.1 0.3 0.3 2.9 12.0 1.2 1.8 0.5 5.8 2.1 0.5 0.2 0.0 0.1 9.5 2.5 0.3 1.1 0.2 0.1 0.0 0.3 0.0 0.0 0.3 0.1 0.5 0.6 1.1 4.4 0.7 0.1 3.1 2.5 0.1 0.0 1.6 0.0 0.8 4.2 4.6 0.6
2012 World Development Indicators
Honduras Hungary India Indonesia Iran, Islamic Rep. Iraq Ireland Israel Italy Jamaica Japan Jordan Kazakhstan Kenya Korea, Dem. Rep. Korea, Rep. Kosovo Kuwait Kyrgyz Republic Lao PDR Latvia Lebanon Lesotho Liberia Libya Lithuania Macedonia, FYR Madagascar Malawi Malaysia Mali Mauritania Mauritius Mexico Moldova Mongolia Morocco Mozambique Myanmar Namibia Nepal Netherlands New Zealand Nicaragua Niger Nigeria Norway Oman Pakistan Panama Papua New Guinea Paraguay Peru Philippines Poland Portugal Puerto Rico Qatar
3.18
Total natural resources rents
Oil rents
Natural gas rents
Coal rents
Mineral rents
Forest rents
% of GDP
% of GDP
% of GDP
% of GDP
% of GDP
% of GDP
2010
2010
2010
2010
2010
2010
1.8 0.6 4.0 6.0 31.6 69.3 0.2 0.3 0.2 1.1 0.0 1.6 27.6 1.3 .. 0.0 1.5 43.0 9.2 14.1 1.5 0.0 1.3 14.6 46.1 1.8 7.4 3.1 4.1 10.9 15.0 54.7 0.0 7.3 0.2 23.4 2.6 8.5 .. 1.0 3.4 1.2 2.4 3.0 3.3 32.6 13.1 39.2 3.9 0.1 36.3 1.3 11.3 2.6 1.0 0.3 0.0 27.9
0.0 0.3 1.0 2.4 23.7 69.1 0.0 0.0 0.1 0.0 0.0 0.0 22.4 0.0 .. 0.0 .. 41.2 0.7 .. 0.0 0.0 .. .. 42.3 0.1 0.0 .. .. 6.3 .. .. .. 6.0 0.1 2.0 0.0 0.0 .. 0.0 0.0 0.1 0.7 0.0 .. 29.5 10.1 31.6 0.8 0.0 18.4 0.0 1.1 0.1 0.1 0.0 .. 13.4
0.0 0.2 0.4 0.9 6.8 0.2 0.0 0.2 0.0 0.0 0.0 0.1 2.7 0.0 .. 0.0 .. 1.8 0.0 .. 0.0 0.0 .. .. 3.8 0.0 0.0 .. .. 3.9 .. .. .. 0.5 0.0 0.0 0.0 5.0 .. 0.0 0.0 1.1 0.5 0.0 .. 2.2 2.9 7.6 2.3 0.0 .. 0.0 0.7 0.3 0.1 0.0 .. 14.5
.. 0.1 2.6 3.4 0.0 .. 0.0 0.0 0.0 .. 0.0 .. 5.5 .. .. 0.0 .. .. 0.4 0.0 .. .. .. .. .. .. 1.5 .. .. 0.1 .. .. .. 0.1 .. 22.9 0.0 0.0 .. .. 0.0 0.0 0.1 .. 0.0 0.0 0.0 .. 0.1 .. .. .. 0.0 0.2 1.3 0.0 .. ..
0.7 0.0 2.1 1.9 1.0 0.0 0.1 0.1 0.0 1.0 0.0 1.5 2.5 0.1 .. 0.0 1.5 0.0 8.5 12.6 0.0 0.0 0.0 2.1 0.0 0.0 7.3 1.3 0.0 0.1 14.0 54.2 0.0 0.6 0.0 21.2 2.5 0.1 .. 1.0 0.0 0.0 0.4 1.3 1.3 0.0 0.0 0.0 0.1 0.0 32.2 0.0 9.3 2.0 0.6 0.2 0.0 0.0
1.1 0.1 0.6 0.7 0.0 0.0 0.0 0.0 0.0 0.1 0.0 0.0 0.0 1.2 .. 0.0 .. 0.0 0.0 1.5 1.5 0.0 1.3 12.5 0.0 1.6 0.1 1.8 4.1 0.6 1.1 0.5 0.0 0.1 0.1 0.2 0.1 3.3 .. 0.1 3.4 0.0 0.8 1.7 2.0 0.9 0.1 0.0 0.7 0.1 4.1 1.3 0.1 0.2 0.2 0.1 .. ..
2012 World Development Indicators
205
ENVIRONMENT
Contribution of natural resources to gross domestic product
3.18 Romania Russian Federation Rwanda Saudi Arabia Senegal Serbia Sierra Leone Singapore Slovak Republic Slovenia Somalia South Africa South Sudan Spain Sri Lanka Sudan Swaziland Sweden Switzerland Syrian Arab Republic Tajikistan Tanzania Thailand Timor-Leste Togo Trinidad and Tobago Tunisia Turkey Turkmenistan Uganda Ukraine United Arab Emirates United Kingdom United States Uruguay Uzbekistan Venezuela, RB Vietnam West Bank and Gaza Yemen, Rep. Zambia Zimbabwe World Low income Middle income Lower middle income Upper middle income Low & middle income East Asia & Pacific Europe & Central Asia Latin America & Carib. Middle East & N. Africa South Asia Sub-Saharan Africa High income Euro area
Contribution of natural resources to gross domestic product Total natural resources rents
Oil rents
Natural gas rents
Coal rents
Mineral rents
Forest rents
% of GDP
% of GDP
% of GDP
% of GDP
% of GDP
% of GDP
2010
2010
2010
2010
2010
2010
2.2 19.9 3.4 53.7 2.4 0.9 4.5 0.0 0.4 0.2 .. 4.6 .. 0.1 0.5 19.3 1.7 1.2 0.0 16.5 0.9 6.8 2.7 0.3 3.9 37.3 6.7 0.5 43.9 4.9 2.9 20.5 1.5 1.0 1.0 29.4 19.6 10.4 .. 21.9 28.1 3.4 4.0 w 6.5 9.8 11.4 9.3 9.8 7.9 14.2 8.0 18.7 6.2 16.7 1.4 0.3
1.2 14.2 .. 50.5 0.0 0.8 .. 0.0 0.0 0.0 .. 0.0 .. 0.0 0.0 18.5 .. 0.0 0.0 14.4 0.2 0.0 1.1 .. 0.0 11.5 4.4 0.2 19.7 .. 0.9 18.0 1.2 0.7 0.0 3.3 18.0 8.0 .. 19.0 0.0 0.0 1.8 w 1.2 4.6 5.7 4.3 4.5 1.7 8.9 4.7 14.9 0.9 12.0 0.6 0.0
0.7 3.6 .. 3.2 0.0 0.1 .. 0.0 0.0 0.0 .. 0.0 .. 0.0 0.0 0.0 .. 0.0 0.0 1.9 0.1 0.5 1.2 .. 0.0 25.8 1.0 0.0 24.2 .. 1.7 2.4 0.3 0.1 0.0 18.1 0.9 1.3 .. 3.0 0.0 0.0 0.4 w 1.3 0.9 1.2 0.8 0.9 0.4 2.5 0.4 3.2 0.6 0.5 0.2 0.1
Note: Components may not sum to 100 percent because of rounding.
206
2012 World Development Indicators
0.3 1.4 .. .. .. 2.0 .. .. 0.0 0.2 .. 5.1 .. 0.0 .. .. 0.0 0.0 .. .. 0.3 0.0 0.1 .. 0.0 .. .. 0.2 .. .. 3.1 .. 0.0 0.4 .. 0.2 0.1 3.1 .. .. 0.0 2.9 0.8 w .. 2.0 2.2 2.0 2.0 3.4 1.3 0.1 0.0 2.3 .. 0.2 0.0
0.0 1.7 0.1 0.0 1.2 0.0 0.9 0.0 0.0 0.0 .. 3.8 .. 0.0 0.0 0.1 0.0 0.7 0.0 0.2 0.6 4.2 0.1 0.0 1.9 0.0 1.1 0.2 0.0 0.0 0.1 0.0 0.0 0.1 0.2 8.1 0.7 0.3 .. 0.0 26.7 1.2 0.8 w 1.8 2.0 1.8 2.1 2.0 2.1 1.2 2.6 0.6 1.7 2.8 0.3 0.0
0.2 0.3 3.3 0.0 1.1 .. 3.6 0.0 0.3 0.2 .. 0.8 .. 0.0 0.5 0.7 1.7 0.5 0.0 0.0 0.0 2.1 0.3 0.3 2.0 0.0 0.1 0.1 .. 4.9 0.3 .. 0.0 0.1 0.9 0.0 0.0 0.7 .. 0.0 1.5 2.2 0.2 w 2.3 0.3 0.6 0.2 0.3 0.2 0.3 0.2 0.1 0.7 1.3 0.1 0.1
About the data
3.18
Definitions
Accounting for the contribution of natural resources
• Total natural resources rents are the sum of oil
to economic output is important in building an ana-
rents, natural gas rents, coal rents (hard and soft),
lytical framework for sustainable development. In
mineral rents, and forest rents. • Oil rents are the
some countries earnings from natural resources,
difference between the value of crude oil produc-
especially from fossil fuels and minerals, account
tion at world prices and total costs of production.
for a sizable share of GDP, and much of these earn-
• Natural gas rents are the difference between the
ings come in the form of economic rents—revenues
value of natural gas production at world prices and
above the cost of extracting the resources. Natural
total costs of production. • Coal rents are the dif-
resources give rise to economic rents because they
ference between the value of both hard and soft
are not produced. For produced goods and services
coal production at world prices and their total costs
competitive forces expand supply until economic
of production. • Mineral rents are the difference
profits are driven to zero, but natural resources in
between the value of production for a stock of miner-
fixed supply often command returns well in excess
als at world prices and their total costs of production.
of their cost of production. Rents from nonrenew-
Minerals included in the calculation are tin, gold,
able resources—fossil fuels and minerals—as well
lead, zinc, iron, copper, nickel, silver, bauxite, and
as rents from overharvesting of forests indicate the
phosphate. • Forest rents are roundwood harvest
liquidation of a country’s capital stock. When coun-
times the product of average prices and a region-
tries use such rents to support current consumption
specific rental rate (based on a number of reviews;
rather than to invest in new capital to replace what is
World Bank 2011c).
being used up, they are, in effect, borrowing against their future. The estimates of natural resources rents shown in the table are calculated as the difference between the price of a commodity and the average cost of producing it. This is done by estimating the world price of units of specific commodities and subtracting estimates of average unit costs of extraction or harvesting costs (including a normal return on capital). These unit rents are then multiplied by the physical quantities countries extract or harvest to determine the rents for each commodity as a share of gross domestic product (GDP). This definition of economic rent differs from that used in the System of National Accounts, where rents are a form of property income, consisting of payments to landowners by a tenant for the use of the land or payments to the owners of subsoil assets by institutional units permitting them to extract subsoil deposits. The Environment section of previous editions of World Development Indicators included a table “Toward a broader measure of savings,” which showed the derivation of adjusted net savings, taking into account consumption of fixed and natural capital, pollution damage, and additions to human capital. Adjusted net savings measures the net additions or subtractions from a country’s stock of tangible and intangible capital. This table is included
Data sources
in the Economy section as table 4.11, along with
Data on contributions of natural resources to GDP
the closely related table 4.10, “Toward a broader
are estimates based on sources and methods
measure of income.”
described in World Bank (2011c).
2012 World Development Indicators
207
ENVIRONMENT
Contribution of natural resources to gross domestic product
ECONOMY
T
he data in the Economy section provide a picture of the global economy and the economic activity of more than 200 countries and territories that produce, trade, and consume the world’s output. The indicators measure changes in the size and structure of the global economy and the effects of these changes on national economies. They include measures of macroeconomic performance (gross domestic product [GDP], consumption, investment, and international trade), stability (central government budgets, prices, the money supply, and the balance of payments), and broader measures of income and savings adjusted for pollution, depreciation, and depletion of resources. In 2010 the world economy grew 4.2 percent, a quick rebound from 2.3 percent in 2009 and well above the annual average of 2.9 percent since 2000. Total output measured in GDP at current prices increased more than $10 trillion. Upper middle-income economies, including China, were affected by slowing investment and widespread uncertainty in financial markets but still grew 7.8 percent. Lower middle-income economies grew 6.9 percent, and low-income economies grew 5.9 percent. High-income economies, accounting for 68 percent of the world’s GDP, grew 3.1 percent in 2010. Developing economies grew faster over the last decade than in the previous two and faster than high-income economies. World output in 2010 reached $63 trillion, measured in GDP at current prices—a nominal increase of 96 percent increase over 2000. Developing economies’ share of global output increased from 18 percent to 31 percent. The developing economies in East Asia and Pacific grew the most, quadrupling their output and more than doubling their share of global output from 5 percent to 12 percent.
The accuracy of GDP estimates and their comparability across countries depend on timely revisions to data on GDP and its components. The frequency of revisions to GDP data varies: some countries revise numbers monthly, others quarterly or annually, and others less frequently. Such revisions are usually small and based on additional information received during the year. However, in some cases larger revisions are required because of new methodologies, changes to the base year, or changes in coverage. Comprehensive revisions of GDP data usually result in upward adjustments as improved data sources increase the coverage of the economy and as new weights for growing industries more accurately reflect their contributions to the economy. Revisions to data can lead to changes in income or lending classification, but such changes have been rare. Revisions to GDP data may cause breaks in series unless they are applied consistently to historical data. For constant price series a break caused by rebasing can be eliminated by linking the old series to the new using historical growth rates. But for nominal GDP data a break in the time series cannot be avoided unless the statistics office revises historical series. Other data series are affected by revisions to GDP. Because rebasing real GDP and its components leaves the pre−base year current price series unchanged, the GDP deflator calculated from these two series is skewed for pre−base years. Other series affected by the break in GDP are fiscal indicators expressed as a percentage of GDP. When nominal GDP is revised upward, the ratio of revenue and expenditure to GDP look smaller than previously reported. Information on significant revisions and breaks in series will be included in the next release of the World Development Indicators database.
4
2012 World Development Indicators
209
4.a Albania Algeria Angola Antigua and Barbuda Argentina Armenia Australia Austria Azerbaijan Bangladesh Belarus Belgium Belize Benin Bolivia Botswana Brazil Bulgaria Burkina Faso Burundi Cambodia Cameroon Canada Cape Verde Central African Republic Chad Chile China Hong Kong SAR, China Colombia Comoros Congo, Dem. Rep. Congo, Rep. Costa Rica Côte d’Ivoire Croatia Czech Republic
210
Recent economic performance Gross domestic product
Exports of goods and services
Imports of goods and services
GDP deflator
average annual % growth 2010 2011 a
average annual % growth 2010 2011 a
average annual % growth 2010 2011 a
average annual % growth 2010 2011 a
3.5 3.3 2.3 –5.2 9.2 2.1 2.3 2.3 5.0 6.1 7.6 2.3 2.9 3.0 4.1 7.2 7.5 0.2 9.2 3.9 6.0 2.6 3.2 5.4 3.3 4.3 5.2 10.4 7.0 4.3 2.1 7.2 8.8 4.2 3.0 –1.2 2.3
4.2 .. .. .. 14.6 21.7 5.3 8.3 24.2 0.9 7.1 9.9 .. .. 9.9 1.2 11.5 16.2 .. .. 20.6 –0.3 6.4 .. .. .. –0.3 28.4 16.8 1.2 .. 9.4 .. 5.4 –0.5 6.0 18.0
–8.5 .. .. .. 34.0 13.8 5.1 8.0 1.3 0.7 11.9 8.7 .. .. 11.0 5.0 36.2 4.5 .. .. 16.8 4.6 13.1 .. .. .. 26.3 20.1 17.3 18.1 .. 17.0 .. 15.2 7.6 –1.3 18.0
3.5 16.2 26.6 2.0 15.4 9.2 0.1 1.8 11.2 6.5 10.2 1.8 0.9 1.8 8.8 14.7 7.3 2.9 4.0 7.8 3.1 3.2 2.9 3.3 3.2 11.6 14.4 6.6 0.5 3.1 3.8 22.4 19.8 7.8 0.7 1.0 –1.2
3.0 3.0 7.0 3.0 7.5 4.2 1.7 2.8 0.2 6.5 3.8 2.1 2.1 3.4 4.5 6.8 2.9 2.0 5.8 4.4 6.1 3.8 2.3 5.8 4.0 6.0 6.2 9.1 4.7 4.9 2.3 6.7 5.1 3.8 –5.8 1.2 2.1
2012 World Development Indicators
5.0 .. .. .. 5.5 6.0 5.2 6.6 1.0 12.3 7.2 4.5 .. .. 5.6 7.2 4.4 5.0 .. .. 5.2 7.4 4.2 .. .. .. 5.7 11.3 4.9 7.0 .. 16.0 .. 6.1 –2.0 5.8 5.0
3.2 .. .. .. 9.9 7.5 9.2 5.9 2.5 10.5 2.0 5.0 .. .. 5.4 5.1 9.7 5.0 .. .. 3.4 7.3 6.0 .. .. .. 10.5 14.4 5.0 6.8 .. 13.6 .. 7.6 5.1 5.5 6.8
1.9 9.6 12.4 –3.9 23.4 6.6 4.1 2.0 17.7 9.9 35.2 1.9 5.2 1.3 11.9 12.0 6.9 –0.3 9.5 4.6 4.6 4.5 4.1 –4.4 2.6 19.9 4.6 4.9 12.1 3.8 5.0 15.2 14.7 4.9 –6.5 2.0 2.4
Current account balance
Gross international reserves
% of GDP 2010 2011 a
$ millions 2011 a
months of import coverage 2011 a
2,421 183,122 28,348 148 43,321 1,959 42,921 11,482 10,274 8,533 6,076 18,311 237 976 9,984 8,337 350,414 15,321 843 282 3,471 3,199 65,658 278 155 951 41,932 3,204,610 285,306 30,504 161 1,273 5,641 4,756 4,192 14,484 39,692
4.1 36.2 6.3 2.5 6.1 4.8 1.7 0.6 9.3 2.9 1.7 0.5 3.1 4.3 17.4 16.7 14.0 5.6 3.1 5.3 4.6 5.2 1.4 2.4 5.6 4.1 6.0 19.7 6.2 6.7 11.6 2.3 6.2 3.2 4.7 7.0 2.9
–11.9 .. 8.8 –13.7 0.8 –14.7 –2.8 3.0 29.1 2.1 –15.2 1.4 –3.3 .. 4.4 0.3 –2.3 –1.5 .. –18.7 –7.8 –3.8 –3.1 –11.2 .. .. 1.8 5.2 5.7 –3.1 .. .. .. –4.0 .. –1.6 –3.1
–11.7 .. 7.0 –13.9 0.0 –12.8 –2.2 2.5 26.6 0.7 –15.7 –0.5 –3.3 .. 4.7 –3.3 –2.5 1.3 .. –13.4 –12.1 –2.9 –2.7 –16.7 .. .. –0.4 3.7 –0.5 –2.5 .. .. .. –5.2 .. –2.8 –3.7
Denmark Dominica Dominican Republic Ecuador Egypt, Arab Rep. El Salvador Equatorial Guinea Eritrea Estonia Ethiopia Fiji Finland France Gabon Gambia, The Georgia Germany Ghana Greece Guatemala Guinea Guinea-Bissau Guyana Haiti Honduras Hungary Iceland India Indonesia Ireland Israel Italy Jamaica Japan Jordan Kazakhstan Kenya
Gross domestic product
Exports of goods and services
Imports of goods and services
GDP deflator
average annual % growth 2010 2011 a
average annual % growth 2010 2011 a
average annual % growth 2010 2011 a
average annual % growth 2010 2011 a
1.3 0.1 7.8 3.6 5.1 1.4 0.9 2.2 3.1 10.1 0.3 3.7 1.5 5.7 5.0 6.4 3.7 6.6 –3.5 2.8 1.9 3.5 3.6 –5.1 2.8 1.3 –4.0 8.8 6.1 –0.4 4.7 1.5 –0.6 4.0 3.1 7.3 5.3
3.2 .. 11.6 2.3 –3.0 12.3 .. .. 10.7 14.4 .. 8.6 9.7 3.0 4.1 .. 13.7 53.7 4.2 4.4 1.5 .. .. –7.3 6.0 14.3 0.4 17.9 14.9 6.3 13.4 12.2 .. 23.9 7.6 1.9 6.1
3.5 .. 14.4 16.3 –3.2 11.1 .. .. 8.8 15.9 .. 7.4 8.8 9.3 7.0 .. 11.7 69.4 –7.2 12.4 0.1 .. .. 19.7 10.2 12.8 4.0 9.2 17.3 2.7 12.5 12.7 .. 9.8 7.1 –4.0 3.0
3.9 0.0 5.1 7.6 10.1 1.2 19.1 11.6 1.5 3.8 8.1 0.4 0.8 18.0 8.4 8.7 0.6 14.0 1.7 5.0 19.7 1.7 5.9 5.4 5.7 3.1 6.9 10.5 8.0 –2.4 1.1 0.4 10.6 –2.2 6.3 19.5 3.9
1.3 0.9 4.9 5.1 0.5 1.5 2.8 8.2 7.6 7.7 1.3 3.1 1.6 6.0 5.3 5.4 3.0 13.6 –5.4 2.8 4.3 4.8 4.6 6.7 3.4 3.4 2.8 7.0 6.5 2.2 4.5 0.7 1.3 –0.2 2.5 6.6 4.3
6.6 .. 6.8 4.6 –4.8 7.5 .. .. 6.1 9.0 .. 0.0 5.7 3.7 4.7 .. 5.9 41.0 –1.0 4.5 1.8 .. .. 14.5 2.7 8.7 6.2 23.0 15.9 7.8 4.3 6.2 .. 0.5 –3.7 11.0 8.9
5.4 .. 5.1 7.0 –11.7 7.0 .. .. 6.5 11.0 .. 3.5 5.0 9.6 4.3 .. 6.8 35.9 –10.0 5.5 5.1 .. .. 0.2 2.9 7.9 6.7 14.0 13.6 6.8 12.5 4.8 .. 3.5 –0.5 11.0 8.6
3.2 4.9 8.4 7.2 15.1 3.4 6.9 14.7 5.4 10.8 0.4 2.2 1.5 11.4 5.5 –1.7 1.9 22.7 2.3 8.7 10.1 –9.2 3.9 3.2 7.3 5.3 1.7 9.1 7.1 2.0 2.6 2.0 7.3 –1.2 1.4 12.9 10.1
4.a
ECONOMY
Recent economic performance Current account balance
Gross international reserves
% of GDP 2010 2011 a
$ millions 2011 a
months of import coverage 2011 a
81,794 81 3,755 1,710 15,046 2,165 3,054 113 195 .. 832 7,942 52,819 2,157 223 2,818 72,796 .. 1,442 5,847 .. 225 798 1,389 2,751 48,686 8,454 272,249 106,665 1,410 74,874 53,421 2,273 1,259,494 11,489 25,316 4,264
6.1 3.5 2.4 0.9 3.3 2.4 7.8 1.8 0.1 .. 4.6 0.9 0.8 5.0 8.2 4.6 0.5 .. 0.2 3.8 .. 9.4 5.4 3.5 2.9 5.4 14.9 5.7 6.4 0.1 9.5 0.9 3.8 16.0 6.8 5.7 3.4
5.5 –15.3 –8.6 –3.1 –2.1 –2.3 .. .. 3.5 –1.4 –12.9 1.9 –1.7 .. 6.5 –11.5 5.7 –8.6 –10.3 –1.5 –7.2 .. –7.2 –2.5 –6.2 1.1 –8.0 –3.0 0.8 0.5 2.9 –3.5 –6.6 3.6 –4.8 2.0 –8.0
5.8 –21.9 –8.2 –2.7 1.8 –3.8 .. .. 0.9 –10.6 –7.2 1.0 –2.0 .. 1.9 –10.4 5.3 –7.0 –8.6 –2.2 –14.2 .. –10.6 –13.4 –6.4 2.8 –8.1 –3.4 0.4 0.6 0.6 –3.9 –11.2 2.2 –8.5 6.3 –9.2
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211
4.a Korea, Rep. Kyrgyz Republic Lao PDR Latvia Lebanon Lesotho Lithuania Luxembourg Macedonia, FYR Madagascar Malawi Malaysia Mali Mauritania Mauritius Mexico Moldova Morocco Mozambique Namibia Nepal Netherlands Nicaragua Niger Nigeria Norway Pakistan Panama Papua New Guinea Paraguay Peru Philippines Poland Portugal Romania Russian Federation
212
Recent economic performance Gross domestic product
Exports of goods and services
Imports of goods and services
GDP deflator
average annual % growth 2010 2011 a
average annual % growth 2010 2011 a
average annual % growth 2010 2011 a
average annual % growth 2010 2011 a
6.2 –1.4 9.4 –0.3 7.0 3.3 1.3 2.7 1.8 1.6 7.1 7.2 4.5 5.0 4.0 5.4 6.9 3.7 7.2 4.8 4.6 1.7 7.6 8.8 7.9 0.7 4.1 4.8 8.0 15.0 8.8 7.6 3.9 1.4 0.9 4.0
3.8 5.5 7.9 3.8 3.0 3.1 5.8 1.9 3.8 2.6 5.6 4.8 5.4 5.1 4.1 4.0 6.0 4.3 7.4 3.9 4.0 1.4 4.1 6.0 7.0 1.6 3.2 8.1 9.0 4.9 6.3 3.7 4.0 –1.5 2.4 4.1
2012 World Development Indicators
14.5 –4.2 29.3 10.3 0.4 2.2 16.3 2.8 23.4 .. .. 9.9 .. .. –4.2 25.6 12.8 16.3 2.2 –42.3 –13.7 10.8 13.2 .. .. 1.8 15.8 6.2 .. 34.3 2.5 21.0 12.1 8.8 10.5 7.1
9.1 6.5 6.5 7.0 –9.1 5.9 5.8 6.0 7.2 .. .. 4.1 .. .. 1.9 8.6 20.0 7.3 7.3 10.9 22.1 6.2 6.1 .. .. 6.3 11.7 7.1 .. 4.0 7.2 –7.7 6.1 6.0 14.7 5.0
16.9 1.6 4.1 8.6 –7.8 9.6 17.6 4.6 10.9 .. .. 15.1 .. .. –0.6 23.5 13.7 3.3 1.7 –60.0 26.7 10.6 10.8 .. .. 9.9 4.4 21.2 .. 29.3 23.8 22.5 13.9 5.4 10.5 25.6
6.3 5.5 5.0 5.0 –4.1 5.4 9.0 6.2 5.8 .. .. 5.2 .. .. 13.5 7.2 10.0 8.8 6.0 1.9 13.5 5.7 7.9 .. .. 6.7 8.8 7.7 .. 5.1 9.1 1.6 7.1 –1.5 11.9 10.0
3.7 6.9 9.4 –2.3 4.4 3.7 2.0 4.9 2.2 8.1 7.7 5.1 3.6 19.3 1.6 4.4 11.2 0.6 12.7 9.3 13.4 1.3 2.9 1.7 7.5 6.4 12.0 3.0 9.3 6.7 6.9 4.2 1.4 1.0 3.6 11.4
1.5 17.8 7.7 6.4 2.4 –0.7 10.0 2.4 0.7 9.2 9.2 4.1 2.1 5.6 1.6 2.7 3.7 –0.7 9.5 3.9 11.6 1.8 7.9 3.7 23.5 6.3 11.2 5.3 9.8 10.6 4.2 5.0 2.9 2.3 5.4 11.7
Current account balance
Gross international reserves
% of GDP 2010 2011 a
months of import coverage 2011 a
2.8 –8.3 0.4 3.0 –22.8 –19.8 1.5 7.7 –2.2 .. .. 11.5 .. .. –8.2 –0.6 –8.3 –4.3 –11.6 0.3 .. 6.6 –14.7 .. 1.3 12.3 –0.8 –10.7 –6.7 –3.5 –1.5 4.5 –4.7 –10.0 –4.0 4.7
2.5 –6.4 –14.0 –0.7 –20.6 –24.5 –2.3 6.1 –5.1 .. .. 9.7 .. .. –11.1 –0.8 –9.4 –6.7 –13.6 –0.5 .. 7.3 –16.3 .. 14.3 16.9 0.5 –12.3 –24.0 –3.1 –2.7 2.1 –5.1 –7.6 –4.5 5.1
$ millions 2011 a
304,349 1,707 .. 6,011 34,236 .. 7,925 904 2,343 1,279 212 131,867 1,418 485 2,589 144,174 1,965 19,572 2,473 1,796 3,567 21,322 1,892 659 35,249 49,273 14,636 1,792 4,172 4,951 47,266 67,565 92,824 2,635 43,118 455,474
5.8 4.2 .. 4.5 12.7 .. 3.4 0.2 4.0 4.2 1.1 7.0 5.2 3.3 4.3 4.5 4.1 4.7 6.5 3.3 6.1 0.4 3.1 3.3 4.8 4.3 3.7 1.0 6.2 4.8 12.9 9.7 4.6 0.3 6.0 13.9
Rwanda Saudi Arabia Senegal Seychelles Sierra Leone Singapore Slovak Republic Slovenia South Africa Spain Sri Lanka St. Lucia St. Vincent & Grenadines Sudan Swaziland Sweden Switzerland Syrian Arab Republic Tanzania Thailand Togo Trinidad and Tobago Tunisia Turkey Uganda Ukraine United Kingdom United States Uruguay Uzbekistan Vanuatu Venezuela, RB Vietnam Yemen, Rep. Zambia Zimbabwe
Gross domestic product
Exports of goods and services
Imports of goods and services
GDP deflator
average annual % growth 2010 2011 a
average annual % growth 2010 2011 a
average annual % growth 2010 2011 a
average annual % growth 2010 2011 a
.. .. 3.5 .. .. 16.6 16.3 7.2 5.5 5.4 13.0 .. .. .. 0.9 12.7 7.3 8.3 10.9 21.5 .. .. 3.8 20.7 7.8 11.1 8.6 12.5 16.2 .. .. –2.9 14.1 –6.8 .. 6.6
2.1 12.4 1.4 –0.8 14.4 –0.5 0.5 –1.1 8.1 1.0 7.3 5.1 2.1 17.6 6.1 1.2 0.1 6.3 7.7 3.7 1.4 4.4 4.0 6.3 9.1 15.0 2.9 0.8 5.1 18.5 2.8 46.7 11.9 24.7 11.7 17.5
7.5 3.8 4.2 6.2 4.9 14.5 4.2 1.4 2.8 –0.1 8.0 3.1 –1.3 4.5 1.1 5.6 2.7 3.2 7.0 7.8 3.4 0.1 3.7 9.0 5.2 4.2 2.1 3.0 8.5 8.5 3.0 –1.5 6.8 8.0 7.6 9.0
7.2 5.0 4.2 4.0 5.6 4.8 3.0 2.8 3.2 0.7 7.7 2.7 –0.2 5.3 –2.1 4.1 1.7 –3.0 6.4 2.0 3.7 2.8 –0.5 7.9 6.3 4.5 1.0 1.7 5.5 7.5 3.9 3.1 5.6 –6.0 6.8 5.0
.. .. 5.7 .. .. 19.2 16.5 9.5 16.5 10.3 5.8 .. .. .. –2.4 11.1 8.4 5.7 10.7 14.7 .. .. 4.8 3.4 5.6 4.5 7.4 11.3 8.5 .. .. –12.9 14.7 15.8 .. 21.5
.. .. 5.2 .. .. 4.0 8.1 3.0 5.0 5.9 15.0 .. .. .. 5.3 6.8 7.4 –3.5 3.9 8.8 .. .. –1.2 6.0 6.4 6.5 6.0 6.6 7.1 .. .. 6.1 18.2 0.2 .. 14.1
.. .. 4.4 .. .. 5.0 8.7 –2.6 11.0 0.5 16.5 .. .. .. 4.7 7.0 5.9 5.8 6.7 13.1 .. .. –2.2 13.0 16.0 9.0 0.3 3.9 7.1 .. .. 7.9 14.5 –1.0 .. 8.0
3.8 15.3 4.4 2.0 9.1 4.0 4.0 –3.0 6.8 2.1 7.0 –3.5 6.2 4.1 5.0 0.9 –0.1 7.5 8.3 1.9 3.3 10.3 –5.9 8.9 4.3 6.5 3.8 2.5 6.8 19.6 5.5 59.8 12.0 19.8 16.9 3.3
4.a
ECONOMY
Recent economic performance Current account balance
Gross international reserves
% of GDP 2010 2011 a
months of import coverage 2011 a
–7.5 15.4 .. –24.0 –16.8 23.7 –3.4 –0.8 –2.8 –4.6 –2.9 –14.6 –29.2 0.3 –10.7 6.6 14.0 –0.6 –8.6 4.1 .. .. –4.8 –6.4 –10.2 –2.2 –3.3 –3.2 –0.4 .. .. 3.1 –4.0 –3.9 3.8 ..
–6.1 23.3 .. –33.8 –13.6 20.5 –2.3 –0.6 –3.0 –3.5 –3.8 –21.4 –29.1 –7.2 –15.8 6.6 13.0 –2.2 –9.1 0.7 .. .. –5.8 –9.8 –12.1 –4.4 –1.5 –3.4 –2.0 .. .. 7.9 –4.9 9.6 4.9 ..
$ millions 2011 a
825 541,234 2,536 252 432 237,874 908 836 42,811 33,330 6,095 213 90 220 601 44,243 281,187 16,714 3,726 167,652 746 10,106 7,652 78,660 2,617 30,458 79,808 150,964 10,289 .. 176 10,562 .. 4,519 2,324 ..
4.8 34.3 4.2 2.0 7.0 5.9 0.1 0.3 4.1 0.9 3.8 3.0 2.6 0.2 2.5 2.3 10.0 9.5 4.9 7.7 4.6 13.7 3.5 3.8 4.0 4.0 1.2 0.7 10.3 .. 5.8 2.4 .. 4.4 4.3 ..
a. Data are preliminary estimates based on World Bank staff estimates and national sources. Source: World Development Indicators data files, the World Bank’s Global Economic Prospects 2012, and the International Monetary Fund’s International Financial Statistics.
2012 World Development Indicators
213
4.1
Growth of output Gross domestic product
average annual % growth 1990–2000 2000–10
Afghanistan Albania Algeria Angolaa Argentina Armenia Australia Austria Azerbaijan Bahrain Bangladesh Belarus Belgium Benina Bolivia Bosnia and Herzegovina Botswana Brazil Bulgaria Burkina Faso Burundi Cambodia Cameroon Canada Central African Republic Chad Chile Chinaa Hong Kong SAR, China Colombia Congo, Dem. Rep. Congo, Rep.a Costa Rica Côte d’Ivoirea Croatia Cuba Cyprus Czech Republic Denmark Dominican Republica Ecuador Egypt, Arab Rep. El Salvador Eritrea Estonia Ethiopia Finland France Gabona Gambia, The Georgia Germany Ghana Greece Guatemala Guinea Guinea-Bissau Haiti
214
.. 3.8 1.9 1.6 4.3 –1.9 3.7 2.5 –6.3 5.0 4.8 –1.6 2.2 4.8 4.0 .. 5.7 2.7 –1.1 5.5 –2.9 7.0 1.7 3.1 2.0 2.2 6.6 10.6 3.6 2.8 –4.9 1.0 5.3 3.2 0.5 –0.7 4.2 1.1 2.7 6.3 1.9 4.4 4.8 5.7 0.4 3.8 2.7 1.9 2.3 3.0 –7.1 1.6 4.3 2.2 4.2 4.4 1.2 0.5
2012 World Development Indicators
11.3 5.4 3.9 12.9 5.6b 9.2 3.2 1.8 17.1 6.6 5.9 8.0 1.6 4.0 4.1 4.6 4.1 3.7 4.8 5.5 3.2 8.7 3.2 2.0 1.0 9.0 4.0 10.8 4.6 4.5 5.3 4.3 4.9 1.1 3.2 6.7 3.1 3.8 0.9 5.6 4.8 5.1 2.2 0.2 4.6 8.8 2.1 1.3 2.2 3.7 6.9 1.0 5.9 2.6 3.6 2.9 1.5 0.6
Agriculture
average annual % growth 1990–2000 2000–10
.. 4.3 3.6 –1.4 3.5 0.5 3.4 –0.1 –1.7 .. 2.9 –4.0 .. 5.8 2.9 .. –0.7 3.6 –3.9 5.9 –1.9 3.7 5.4 1.1 3.8 4.9 2.2 4.1 .. –2.7 1.4 .. 4.1 3.5 –5.5 –3.3 1.4 0.0 4.6 1.9 –1.7 3.1 1.2 1.5 –6.2 2.6 –0.3 2.0 2.0 3.3 –11.0 0.1 .. .. 2.8 4.3 .. ..
6.3 1.4 4.2 14.2 2.9 6.0 1.7 1.3 4.7 .. 3.5 5.5 .. 4.6 2.9 4.3 0.9 3.6 –2.3 6.2 –1.5 5.6 3.4 1.5 0.3 .. 4.2 4.4 –3.9 2.2 1.7 .. 3.3 1.6 1.9 –0.9 –3.3 0.4 –0.7 3.4 4.4 3.3 3.1 2.7 –2.9 7.1 2.7 0.3 1.5 3.2 0.0 –0.2 .. .. 2.9 5.0 .. ..
Industry
average annual % growth 1990–2000 2000–10
.. –0.5 1.8 4.4 3.8 –7.4 2.8 2.5 –2.1 .. 7.3 –1.8 1.8 4.1 4.1 .. 5.4 2.4 –19.5 5.9 –4.3 14.3 –0.9 3.2 0.7 0.6 5.6 13.7 .. 1.4 –8.0 .. 6.2 6.3 –2.2 –1.0 0.6 0.2 2.4 7.1 2.6 5.1 5.1 15.0 –2.4 4.1 3.4 0.9 1.6 1.0 –8.1 –0.1 .. 1.0 4.3 4.9 .. ..
14.5 4.7 3.2 12.7 6.0 9.2 2.8 2.0 22.4 .. 7.7 12.0 0.6 3.8 5.4 6.3 3.2 2.8 5.3 7.3 –6.2 10.6 –0.4 0.1 –0.4 .. 2.3 11.8 –2.7 4.5 8.7 .. 4.8 0.3 2.8 2.3 3.0 5.5 –0.8 2.6 5.4 5.5 1.4 0.6 8.6 9.3 3.0 0.5 0.8 7.3 9.3 0.1 .. 0.8 2.5 4.6 .. ..
Manufacturing
average annual % growth 1990–2000 2000–10
.. .. –2.1 –0.3 2.7 –4.3 1.7 2.5 –15.7 .. 7.2 –0.7 .. 5.8 3.8 .. 3.5 2.0 .. 5.9 .. 18.6 1.4 4.5 –0.2 .. 4.4 12.9 .. –2.5 –8.7 .. 6.8 5.5 –3.5 0.8 0.2 4.3 2.2 7.0 1.5 6.3 5.2 10.6 7.3 3.9 6.4 .. 3.0 0.9 .. 0.1 .. .. 2.8 4.0 .. ..
9.5 .. 2.0 19.3 5.9 5.8 0.9 2.6 8.3 .. 7.8 10.7 .. 2.7 4.4 6.5 5.1 2.5 5.8 6.3 .. 10.2 .. –1.6 –0.1 .. 2.7 11.6 –3.6 3.9 6.3 .. 4.4 –1.1 1.6 –1.5 0.1 6.9 0.2 2.8 5.2 4.9 1.7 –6.0 8.9 7.6 3.3 0.1 3.0 .. 10.2 0.5 .. .. 2.6 3.1 .. ..
Services
average annual % growth 1990–2000 2000–10
.. 6.9 1.8 –2.2 4.5 6.7 4.0 2.7 –1.6 .. 4.5 –0.4 .. 4.2 4.3 .. 7.5 3.8 .. 3.9 –2.8 7.1 0.2 3.1 0.2 0.8 6.9 11.0 .. 4.1 –13.0 .. 4.7 2.0 2.2 –0.7 6.5 1.2 2.7 5.9 2.4 4.1 4.0 5.7 3.2 5.2 2.8 2.2 3.1 3.7 –0.3 2.6 .. .. 4.7 3.6 .. ..
14.2 8.1 5.2 12.9 5.0 10.4 3.6 2.0 14.5 .. 6.1 5.4 .. 3.2 3.1 3.9 4.7 3.9 5.6 5.5 10.4 9.2 6.2 3.0 –2.5 .. 4.4 11.5 4.9 4.7 11.2 .. 5.4 1.2 3.9 8.3 3.9 3.2 1.2 7.1 2.8 5.4 2.4 0.6 7.1 10.9 1.5 1.7 3.3 6.2 8.4 1.7 .. .. 4.4 –2.1 .. ..
Gross domestic product
average annual % growth 1990–2000 2000–10
Honduras Hungary India Indonesiaa Iran, Islamic Rep. Iraq Ireland Israela Italy Jamaica Japan Jordan Kazakhstan Kenya Korea, Dem. Rep. Korea, Rep. Kosovo Kuwait a Kyrgyz Republic Lao PDR Latvia Lebanon Lesotho Liberia Libya Lithuania Macedonia, FYR Madagascar Malawi Malaysiaa Mali Mauritania Mauritius Mexico Moldova Mongolia Morocco Mozambique Myanmar a Namibia Nepal Netherlands New Zealand Nicaragua Niger a Nigeria Norway Omana Pakistan Panama Papua New Guinea Paraguaya Peru Philippinesa Poland Portugal Puerto Ricoa Qatar
3.2 1.0 5.9 4.2 3.1 .. 7.4 5.5 1.5 1.6 1.0 5.0 –4.1 2.2 .. 5.8 .. 4.9 –4.1 6.4 –1.5 5.3 4.0 4.1 .. –2.5 –0.8 2.0 3.7 7.0 4.1 2.9 5.2 3.1 –9.6 1.0 2.4 6.1 .. 4.0 4.9 3.2 3.3 3.7 2.4 2.5 3.9 4.5 3.8 4.7 3.8 2.2 4.7 3.3 4.7 2.9 4.3 ..
4.6 2.2 8.0 5.3 5.4 0.4 2.8 3.6 0.5 1.2 0.9 6.7 8.3 4.3 .. 4.1 5.3 8.4 4.4 7.2 4.8 4.9 3.5 0.9 5.4 5.3 3.3 3.4 5.2 5.0 5.2 4.4 3.9 2.1 5.2 7.2 4.9 7.8 .. 5.0 3.8 1.6 2.6 3.6 4.2 6.7 1.7 4.7 5.1 6.8 3.8 3.8 6.1 4.9 4.3 0.7 0.0 14.2
Agriculture
average annual % growth 1990–2000 2000–10
2.2 –1.9 3.2 2.0 3.2 .. 0.0 .. 2.1 –0.6 –0.4 –3.0 –8.0 1.9 .. 1.6 .. 1.0 1.5 4.8 –5.2 2.9 2.8 .. .. –0.4 0.2 1.8 8.6 0.3 2.6 –0.2 0.0 1.5 –11.2 0.3 –0.4 5.2 .. 3.8 2.5 1.8 2.9 4.7 3.0 .. 2.6 5.0 4.4 3.1 4.5 3.3 5.5 1.9 0.5 –0.6 .. ..
3.2 3.4 3.0 3.5 5.9 .. –4.6 .. –0.1 –0.2 –0.8 8.2 3.8 1.9 .. 2.0 .. .. 0.0 3.4 2.8 0.8 –1.9 .. .. 1.9 1.9 2.4 2.9 3.3 4.8 1.3 0.1 1.7 –0.9 4.6 5.9 8.3 .. –2.4 3.2 1.6 2.0 2.9 .. .. 2.6 .. 3.4 2.9 2.4 5.2 4.1 3.2 1.1 –0.2 .. ..
Industry
average annual % growth 1990–2000 2000–10
3.6 3.7 6.1 5.2 2.6 .. 11.5 .. 1.0 –0.8 –0.4 5.2 –8.6 1.2 .. 6.0 .. 0.3 –10.3 11.1 –8.3 –0.2 5.5 .. .. 3.3 –2.3 2.4 2.0 8.6 6.4 3.4 5.4 3.8 –13.6 1.5 3.2 12.3 .. 2.4 7.1 1.7 2.5 5.5 2.0 .. 3.8 3.9 4.1 6.0 5.4 0.6 5.4 3.2 6.7 3.1 .. ..
3.6 2.1 8.5 4.1 6.9 .. 3.7 .. –0.8 –0.3 0.5 7.9 9.0 4.9 .. 5.4 .. .. 1.1 13.1 3.7 4.3 3.4 .. .. 5.8 3.3 4.2 6.2 3.3 4.5 4.4 1.8 1.3 –1.7 5.8 3.8 8.5 .. 1.1 2.5 0.8 1.1 3.4 .. .. –0.6 .. 6.7 6.0 4.3 2.1 6.6 4.2 5.8 –0.9 .. ..
4.1
Manufacturing
average annual % growth 1990–2000 2000–10
4.0 7.9 6.7 6.7 5.1 .. .. .. 1.6 –1.8 0.5 5.6 .. 1.3 .. 7.3 .. –0.1 –7.5 11.7 –7.3 1.9 7.9 .. .. 6.6 –5.3 2.0 0.5 9.5 –1.4 5.8 5.3 4.3 –7.1 –6.6 2.6 10.2 .. 7.4 8.9 2.6 .. 5.3 2.6 .. 1.5 6.0 3.8 2.7 4.6 1.4 3.8 3.0 8.1 2.7 .. ..
4.1 3.4 8.7 4.6 9.9 .. .. .. –1.4 –1.5 1.6 8.9 6.3 4.3 .. 6.3 .. .. –0.6 –0.2 2.5 0.9 4.7 .. .. 7.1 1.9 5.1 5.7 4.1 5.1 –0.9 0.6 1.1 1.0 6.1 3.0 7.1 .. 0.2 0.7 1.1 .. 4.7 .. .. 2.3 .. 8.0 1.7 3.9 1.3 6.2 3.7 8.7 –0.4 .. ..
ECONOMY
Growth of output
Services
average annual % growth 1990–2000 2000–10
3.8 0.5 7.7 4.0 3.8 .. .. .. 1.7 3.8 1.7 5.0 1.1 3.2 .. 5.6 .. 3.5 –5.2 6.6 2.7 1.5 4.5 .. .. 5.8 0.5 2.3 1.6 7.3 3.0 4.9 6.3 2.9 0.7 –0.9 3.1 5.0 .. 4.2 6.2 3.5 3.6 5.0 1.9 .. 3.9 5.0 4.4 4.5 –0.6 2.5 4.0 3.7 5.2 2.4 .. ..
2012 World Development Indicators
5.9 2.1 9.6 7.3 5.3 .. 3.8 .. 1.0 1.7 1.3 6.1 8.3 4.5 .. 3.6 .. .. 10.9 7.3 5.7 4.9 3.8 .. .. 5.3 3.6 3.6 6.5 6.9 6.5 5.2 5.6 2.5 10.1 8.9 4.9 6.9 .. 6.9 4.4 2.1 3.1 4.3 .. .. 3.1 .. 5.7 7.3 3.9 3.8 6.2 5.8 3.6 1.6 .. ..
215
4.1
Growth of output Gross domestic product
average annual % growth 1990–2000 2000–10
Romania Russian Federation Rwandaa Saudi Arabiaa Senegal Serbia Sierra Leone Singapore Slovak Republic Slovenia Somalia South Africa South Sudan Spain Sri Lankaa Sudan Swaziland Sweden Switzerland Syrian Arab Republic Tajikistan Tanzaniac Thailanda Timor-Lestea Togoa Trinidad and Tobago Tunisiaa Turkey Turkmenistan Uganda Ukraine United Arab Emirates United Kingdom United States Uruguay Uzbekistan Venezuela, RB Vietnama West Bank and Gaza Yemen, Rep.a Zambia Zimbabwe World Low income Middle income Lower middle income Upper middle income Low & middle income East Asia & Pacific Europe & Central Asia Latin America & Carib. Middle East & N. Africa South Asia Sub-Saharan Africa High income Euro area
–0.6 –4.7 –0.2 2.1 3.0 –4.2 –5.0 7.2 1.9 2.7 .. 2.1 .. 2.7 5.3 5.5 3.4 2.3 1.0 5.1 –10.4 3.0 4.2 .. 3.5 3.2 4.7 3.9 –4.9 7.0 –9.3 4.8 3.2 3.6 3.9 –0.2 1.6 7.9 7.3 5.6 0.5 2.3 2.9 w 3.0 3.9 3.8 3.9 3.9 8.5 –1.8 3.2 3.8 5.5 2.5 2.7 2.0
5.0 5.4 7.6 3.6 4.2 4.1 8.8 6.0 5.4 3.3 .. 3.9 .. 2.4 5.6 6.7 2.4 2.2 1.9 5.0 8.6 7.1 4.5 3.4 2.7 6.5 4.7 4.7 13.6 7.7 4.8 5.1 1.8 1.8 3.6 7.1 4.7 7.5 –0.9 4.1 5.6 –6.0 2.7 w 5.5 6.4 6.3 6.5 6.4 9.4 5.4 3.8 4.7 7.4 5.0 1.8 1.3
Agriculture
average annual % growth 1990–2000 2000–10
–1.9 –4.9 2.5 1.6 2.4 .. .. –2.8 0.4 0.4 .. 1.0 .. 3.1 1.9 7.4 0.9 –0.8 –0.9 6.0 –6.8 3.2 1.0 .. 4.0 2.7 2.6 1.3 –4.7 3.4 –5.6 .. –0.3 3.8 3.9 0.5 1.2 4.3 .. 5.1 4.2 4.3 2.0 w 2.9 2.4 2.6 2.3 2.4 3.4 –2.1 2.0 2.9 3.3 3.2 1.5 1.6
7.0 1.5 .. 1.2 2.5 1.1 .. –3.2 6.2 –0.6 .. 1.5 .. –0.1 3.1 2.6 1.4 3.3 0.5 .. 7.4 4.4 2.2 .. 2.8 –8.3 2.5 1.6 12.3 2.2 2.9 .. 0.2 1.9 2.1 6.4 2.5 3.7 .. 2.7 1.1 –9.6 2.5 w 3.7 3.5 3.3 3.6 3.5 4.1 2.7 2.9 4.6 3.1 3.2 0.7 0.2
Industry
average annual % growth 1990–2000 2000–10
–1.2 –7.1 –3.8 2.2 3.8 .. .. 7.7 4.1 1.6 .. 1.0 .. 2.2 6.9 8.5 3.2 4.3 0.2 9.2 –11.4 3.1 5.7 .. 1.8 3.2 4.4 4.7 –2.7 12.3 –12.6 .. 1.5 3.7 1.3 –3.4 1.2 11.9 .. 5.2 –4.2 0.4 2.4 w 3.6 4.4 4.1 4.5 4.4 10.9 –4.4 3.0 4.1 6.0 1.9 1.9 1.0
5.8 4.1 .. 3.2 3.2 2.0 .. 5.6 7.9 3.3 .. 2.9 .. 0.8 5.7 10.6 1.7 2.2 2.0 .. 8.9 9.3 5.4 .. 8.1 9.2 3.0 5.2 20.2 9.2 3.2 .. –0.7 0.3 3.2 5.0 2.2 9.3 .. 2.0 9.4 –4.9 2.5 w 7.2 7.2 6.0 7.5 7.2 10.2 5.4 3.1 3.5 8.1 4.9 0.7 0.4
Manufacturing
average annual % growth 1990–2000 2000–10
.. .. –5.8 5.6 3.1 .. .. 7.0 .. 1.8 .. 1.6 .. 5.2 8.1 7.5 2.8 8.9 1.0 .. –12.6 2.8 6.9 .. 1.8 4.9 5.7 4.7 .. 14.2 –11.2 .. .. .. –0.4 0.7 4.5 11.2 .. 1.8 0.8 0.4 .. w 3.6 6.2 4.7 6.5 6.2 10.9 .. 2.9 4.3 6.4 2.2 .. 1.2
.. .. .. 5.6 1.4 .. .. 6.1 9.7 3.2 .. 3.1 .. –0.2 4.6 4.7 1.8 2.7 2.3 .. 8.2 8.7 5.6 .. 7.5 8.9 2.8 5.1 .. 6.8 6.2 .. .. 1.9 4.9 2.6 2.4 10.9 .. 5.1 5.1 –5.9 3.1 w 6.6 7.5 6.3 7.8 7.5 10.1 .. 2.8 5.4 8.4 3.4 1.9 0.3
Services
average annual % growth 1990–2000 2000–10
–0.3 –4.7 –0.9 2.2 3.0 .. .. 7.2 4.4 3.0 .. 3.0 .. 2.7 6.0 1.9 3.9 2.0 1.2 1.5 –10.8 2.6 3.7 .. 3.9 3.2 5.5 4.0 –5.8 8.2 –8.1 .. 4.1 3.6 1.7 0.4 –0.1 7.5 .. 6.1 2.5 3.0 3.1 w 2.8 4.0 4.5 3.9 4.0 8.4 –1.2 3.5 3.3 6.9 2.6 3.0 2.4
6.0 6.5 .. 4.3 5.9 5.4 .. 6.3 4.2 3.6 .. 4.1 .. 3.2 6.2 7.9 3.4 2.2 1.9 .. 7.2 7.8 4.0 .. –0.7 5.6 6.6 5.0 21.8 8.4 4.8 .. 2.6 2.2 3.6 9.0 6.4 7.5 .. 6.0 5.5 –4.2 2.8 w 6.1 6.6 7.6 6.4 6.6 10.0 5.9 4.0 5.1 8.8 4.8 2.1 1.8
a. Components are at producer prices. b. Private analysts estimate that consumer price index inflation was considerably higher for 2007–09 and believe that GDP volume growth has been significantly lower than official reports indicate since the last quarter of 2008. c. Covers mainland Tanzania only.
216
2012 World Development Indicators
About the data
4.1
ECONOMY
Growth of output Definitions
An economy’s growth is measured by the change in
Rebasing national accounts
• Gross domestic product (GDP) at purchaser prices
the volume of its output or in the real incomes of
When countries rebase their national accounts, they
is the sum of gross value added by all resident pro-
its residents. The 1993 United Nations System of
update the weights assigned to various components
ducers in the economy plus any product taxes (less
National Accounts (1993 SNA) offers three plausible
to better reflect current patterns of production or
subsidies) not included in the valuation of output. It
indicators for calculating growth: the volume of gross
uses of output. The new base year should represent
is calculated without deducting for depreciation of
domestic product (GDP), real gross domestic income,
normal operation of the economy—it should be a
fabricated capital assets or for depletion and degra-
and real gross national income. The volume of GDP
year without major shocks or distortions. Some
dation of natural resources. Value added is the net
is the sum of value added, measured at constant
developing countries have not rebased their national
output of an industry after adding up all outputs and
prices, by households, government, and industries
accounts for many years. Using an old base year
subtracting intermediate inputs. The industrial origin
operating in the economy.
can be misleading because implicit price and vol-
of value added is determined by the International
ume weights become progressively less relevant
Standard Industrial Classifi cation (ISIC) revision
and useful.
3. • Agriculture is the sum of gross output less
Each industry’s contribution to growth in the economy’s output is measured by growth in the industry’s value added. In principle, value added in constant
To obtain comparable series of constant price data,
the value of intermediate input used in production
prices can be estimated by measuring the quantity
the World Bank rescales GDP and value added by
for industries classified in ISIC divisions 1–5 and
of goods and services produced in a period, valu-
industrial origin to a common reference year. This
includes forestry and fishing. • Industry is the sum
ing them at an agreed set of base year prices, and
year’s World Development Indicators continues to
of gross output less the value of intermediate input
subtracting the cost of intermediate inputs, also in
use 2000 as the reference year. Because rescaling
used in production for industries classified in ISIC
constant prices. This double-deflation method, rec-
changes the implicit weights used in forming regional
divisions 10–45, which cover mining, manufactur-
ommended by the 1993 SNA and its predecessors,
and income group aggregates, aggregate growth
ing (also reported separately), construction, electric-
requires detailed information on the structure of
rates in this year’s edition are not comparable with
ity, water, and gas. • Manufacturing is the sum of
prices of inputs and outputs.
those from earlier editions with different base years.
gross output less the value of intermediate input
In many industries, however, value added is
Rescaling may result in a discrepancy between
used in production for industries classified in ISIC
extrapolated from the base year using single volume
the rescaled GDP and the sum of the rescaled com-
divisions 15–37. • Services correspond to ISIC divi-
indexes of outputs or, less commonly, inputs. Par-
ponents. Because allocating the discrepancy would
sions 50–99. This sector is derived as a residual
ticularly in the services industries, including most of
cause distortions in the growth rates, the discrep-
(from GDP less agriculture and industry) and may not
government, value added in constant prices is often
ancy is left unallocated. As a result, the weighted
properly reflect the sum of services output, including
imputed from labor inputs, such as real wages or
average of the growth rates of the components gen-
banking and financial services. For some countries
number of employees. In the absence of well defined
erally will not equal the GDP growth rate.
it includes product taxes (minus subsidies) and may
measures of output, measuring the growth of services remains difficult.
also include statistical discrepancies. Computing growth rates
Moreover, technical progress can lead to improve-
Growth rates of GDP and its components are calcu-
ments in production processes and in the quality of
lated using the least squares method and constant
goods and services that, if not properly accounted
price data in the local currency. Constant price U.S.
for, can distort measures of value added and thus
dollar series are used to calculate regional and
of growth. When inputs are used to estimate output,
income group growth rates. Local currency series are
as for nonmarket services, unmeasured technical
converted to constant U.S. dollars using an exchange
progress leads to underestimates of the volume of
rate in the common reference year. The growth rates
output. Similarly, unmeasured improvements in qual-
in the table are average annual compound growth
ity lead to underestimates of the value of output and
rates. Methods of computing growth are described
value added. The result can be underestimates of
in Statistical methods.
growth and productivity improvement and overesti-
Data sources Data on national accounts for most developing
Changes in the System of National Accounts
countries are collected from national statistical
Informal economic activities pose a particular
World Development Indicators adopted the termi-
organizations and central banks by visiting and
measurement problem, especially in developing
nology of the 1993 SNA in 2001. Although many
resident World Bank missions. Data for high-
countries, where much economic activity is unre-
countries continue to compile their national accounts
income economies are from Organisation for
corded. A complete picture of the economy requires
according to the SNA version 3 (referred to as the
Economic Co-operation and Development (OECD)
estimating household outputs produced for home
1968 SNA), more and more are adopting the 1993
data files. The United Nations Statistics Division
use, sales in informal markets, barter exchanges,
SNA. Some low-income countries still use concepts
publishes detailed national accounts for UN mem-
and illicit or deliberately unreported activities. The
from the even older 1953 SNA guidelines, including
ber countries in National Accounts Statistics: Main
consistency and completeness of such estimates
valuations such as factor cost, in describing major
Aggregates and Detailed Tables and publishes
depend on the skill and methods of the compiling
economic aggregates. Countries that use the 1993
updates in the Monthly Bulletin of Statistics.
statisticians.
SNA are identified in Primary data documentation.
mates of inflation.
2012 World Development Indicators
217
4.2
Structure of output Gross domestic product
Agriculture
$ billions
Afghanistan Albania Algeria Angolaa Argentina Armenia Australia Austria Azerbaijan Bahrain Bangladesh Belarus Belgium Benina Bolivia Bosnia and Herzegovina Botswana Brazil Bulgaria Burkina Faso Burundi Cambodia Cameroon Canada Central African Republic Chad Chile Chinaa Hong Kong SAR, China Colombia Congo, Dem. Rep. Congo, Rep.a Costa Rica Côte d’Ivoirea Croatia Cuba Cyprus Czech Republic Denmark Dominican Republica Ecuador Egypt, Arab Rep. El Salvador Eritrea Estonia Ethiopia Finland France Gabona Gambia, The Georgia Germany Ghana Greece Guatemala Guinea Guinea-Bissau Haiti
218
Industry
% of GDP
Manufacturing
% of GDP
Services
% of GDP
% of GDP
2000
2010
2000
2010
2000
2010
2000
2010
2000
2010
2.5 3.7 54.8 9.1 284.2 1.9 416.9 192.1 5.3 8.0 47.1 12.7 232.7 2.3 8.4 5.5 5.6 644.7 12.9 2.6 0.7 3.7 10.1 724.9 1.0 1.4 75.2 1,198.5 169.1 100.4 4.3 3.2 15.9 10.4 21.5 30.6 9.3 56.7 160.1 24.0 15.9 99.8 13.1 0.6 5.7 8.2 121.8 1,326.3 5.1 0.4 3.1 1,886.4 5.0 124.4 19.3 3.1 0.2 3.7
17.2 11.8 162.0 84.4 368.7 9.4 1,131.6 379.1 51.8 20.6 100.4 54.7 469.4 6.6 19.6 16.6 14.9 2,087.9 47.7 8.8 1.6 11.2 22.4 1,577.0 2.0 7.6 212.7 5,926.6 224.5 288.2 13.1 11.9 35.8 22.8 60.9 62.7 23.1 192.0 312.0 51.8 58.0 218.9 21.2 2.1 19.2 29.7 238.0 2,560.0 13.0 0.8 11.7 3,280.5 31.3 301.1 41.2 4.5 0.9 6.7
45 29 9 6 5 26 4 2 17 .. 26 14 1 37 15 11 3 6 14 29 40 38 22 2 53 42 6 15 0 9 50 5 9 24 6 8 4 4 3 7 9 17 10 15 5 50 3 3 6 36 22 1 39 .. 15 20 56 ..
30 20 7 10 10 20 2 2 6 .. 19 9 1 .. 13 8 3 6 5 .. .. 36 .. .. 56 14 3 10 0 7 43 4 7 23 6 5 2 2 1 6 7 14 13 15 3 48 3 2 4 27 8 1 30 .. 13 13 .. ..
20 19 59 72 28 39 27 31 45 .. 25 39 27 14 30 23 53 28 26 24 19 23 36 33 16 11 38 46 12 29 20 72 32 25 29 28 19 38 27 36 32 33 32 23 28 12 35 23 56 13 22 30 28 21 29 33 13 ..
22 19 62 63 31 36 20 29 65 .. 28 44 22 .. 37 28 45 27 31 .. .. 23 .. .. 15 49 43 47 7 36 24 80 26 27 27 20 20 38 22 32 38 38 27 22 29 14 29 19 54 16 23 28 19 18 19 47 .. ..
15 11 7 3 18 19 13 20 6 .. 15 32 19 9 15 10 5 17 18 16 9 17 21 19 7 9 19 32 3 15 5 3 25 22 20 18 10 27 16 26 11 19 25 11 18 6 26 16 4 5 9 23 10 .. 21 4 11 ..
13 19 6 6 21 11 9 19 6 .. 18 31 14 .. 14 14 3 16 16 .. .. 16 .. .. .. 7 12 30 2 15 5 4 17 19 18 10 8 24 12 24 10 16 21 6 17 5 19 11 4 5 13 21 6 .. 19 5 .. ..
35 52 33 22 67 35 70 67 38 .. 49 47 72 50 55 66 45 67 61 47 41 39 42 65 31 46 55 39 88 62 30 23 58 51 65 64 77 58 71 57 59 50 58 62 68 38 62 74 38 51 56 68 32 .. 56 47 31 ..
48 61 31 27 59 44 78 69 30 .. 53 47 78 .. 50 64 52 67 63 .. .. 41 .. .. 29 38 54 43 93 57 33 16 67 50 67 75 78 60 77 62 55 48 60 63 68 38 68 79 42 57 68 71 51 .. 68 40 .. ..
2012 World Development Indicators
Gross domestic product
Agriculture
$ billions
Honduras Hungary India Indonesiaa Iran, Islamic Rep. Iraq Ireland Israela Italy Jamaica Japan Jordan Kazakhstan Kenya Korea, Dem. Rep. Korea, Rep. Kosovo Kuwait a Kyrgyz Republic Lao PDR Latvia Lebanon Lesotho Liberia Libya Lithuania Macedonia, FYR Madagascar Malawi Malaysiaa Mali Mauritania Mauritius Mexico Moldova Mongolia Morocco Mozambique Myanmar a Namibia Nepal Netherlands New Zealand Nicaragua Niger a Nigeria Norway Omana Pakistan Panama Papua New Guinea Paraguaya Peru Philippinesa Poland Portugal Puerto Ricoa Qatar
Industry
% of GDP
4.2
Manufacturing
% of GDP
ECONOMY
Structure of output
Services
% of GDP
% of GDP
2000
2010
2000
2010
2000
2010
2000
2010
2000
2010
7.1 46.4 460.2 165.0 101.3 25.9 97.5 124.7 1,104.0 9.0 4,667.4 8.5 18.3 12.7 .. 533.4 1.8 37.7 1.4 1.7 7.8 17.3 0.7 0.6 33.9 11.4 3.6 3.9 1.7 93.8 2.4 1.1 4.6 581.4 1.3 1.1 37.0 4.2 .. 3.9 5.5 385.1 51.6 3.9 1.8 46.0 168.3 19.9 74.0 11.6 3.5 7.1 53.3 81.0 171.3 117.3 61.7 17.8
15.4 128.6 1,727.1 706.6 331.0 82.2 206.6 217.3 2,061.0 14.3 5,458.8 27.6 149.1 31.4 .. 1,014.5 5.6 109.5 4.6 7.3 24.0 39.0 2.1 1.0 62.4 36.3 9.2 8.7 5.1 237.8 9.3 3.6 9.7 1,034.8 5.8 6.2 90.8 9.6 .. 12.2 15.7 779.4 126.7 6.6 5.5 193.7 417.5 46.9 176.9 26.7 9.5 18.3 157.1 199.6 469.4 228.6 96.3 98.3
16 6 23 16 14 5 3 .. 3 7 2 2 9 32 .. 5 .. 0 37 53 5 7 12 72 5 6 12 29 40 9 42 28 7 4 29 31 15 24 57 12 41 3 9 21 38 49 2 2 26 7 36 17 8 14 5 4 1 ..
13 4 19 15 .. .. 1 .. 2 6 1 3 5 19 .. 3 12 .. 21 33 4 6 8 61 2 4 11 29 31 11 .. 20 4 4 14 16 15 32 36 8 36 2 .. 21 .. .. 2 .. 21 5 36 22 8 12 4 2 1 ..
32 32 26 46 37 84 42 .. 28 26 32 26 40 17 .. 38 .. 59 31 23 24 23 32 12 66 30 34 14 18 48 21 30 31 28 22 25 29 25 10 28 22 25 25 28 18 31 42 57 23 19 41 22 30 34 32 28 42 ..
27 31 26 47 .. .. 32 .. 25 22 27 31 42 14 .. 39 20 .. 28 30 22 21 34 17 78 28 28 16 16 44 .. 37 29 34 13 38 30 23 26 20 15 24 .. 30 .. .. 40 .. 25 17 45 20 34 33 32 23 50 ..
23 24 16 28 13 1 33 .. 21 11 22 16 18 12 .. 28 .. 3 19 17 14 13 14 9 3 19 21 12 13 31 4 9 23 20 16 8 17 12 7 13 9 16 17 17 7 3 11 5 15 10 8 15 16 24 19 18 39 ..
18 23 14 25 .. .. 24 .. 17 9 18 19 13 8 .. 31 17 .. 18 8 12 8 16 13 4 16 16 14 10 26 .. 4 19 18 13 7 15 13 20 8 7 13 .. 20 .. .. 9 .. 17 6 6 12 17 21 18 13 46 ..
52 62 50 38 50 10 55 .. 69 67 66 72 51 51 .. 57 .. 40 32 25 72 70 56 16 29 64 54 57 43 43 38 43 62 68 49 44 56 51 33 60 37 72 66 51 44 21 56 41 51 74 23 61 62 52 63 68 57 ..
61 65 55 38 .. .. 67 .. 73 71 72 66 53 67 .. 58 68 .. 51 37 74 72 58 22 20 68 61 55 53 45 .. 43 67 62 73 46 55 45 38 73 48 74 .. 49 .. .. 58 .. 53 78 19 57 57 55 65 74 49 ..
2012 World Development Indicators
219
4.2
Structure of output Gross domestic product
Agriculture
$ billions
Romania Russian Federation Rwandaa Saudi Arabiaa Senegal Serbia Sierra Leone Singapore Slovak Republic Slovenia Somalia South Africa South Sudan Spain Sri Lankaa Sudan Swaziland Sweden Switzerland Syrian Arab Republic Tajikistan Tanzaniab Thailanda Timor-Lestea Togoa Trinidad and Tobago Tunisiaa Turkey Turkmenistan Uganda Ukraine United Arab Emirates United Kingdom United States Uruguay Uzbekistan Venezuela, RB Vietnama West Bank and Gaza Yemen, Rep.a Zambia Zimbabwe World Low income Middle income Lower middle income Upper middle income Low & middle income East Asia & Pacific Europe & Central Asia Latin America & Carib. Middle East & N. Africa South Asia Sub-Saharan Africa High income Euro area
% of GDP
2000
2010
37.1 259.7 1.7 188.4 4.7 6.1 0.6 95.9 28.7 20.0 .. 132.9 .. 580.7 16.3 12.4 1.5 247.3 249.9 19.3 0.9 10.2 122.7 0.3 1.3 8.2 21.5 266.6 2.9 6.2 31.3 104.3 1,477.2 9,898.8 22.8 13.8 117.1 31.2 4.1 9.6 3.2 6.6 32,248.5 w 164.8 5,708.1 1,258.9 4,449.2 5,874.8 1,727.2 709.9 2,054.4 433.5 608.2 342.1 26,375.3 6,256.1
161.6 1,479.8 5.6 434.7 13.0 38.4 1.9 208.8 87.3 46.9 .. 363.7 .. 1,407.4 49.6 62.0 3.6 458.6 527.9 59.1 5.6 23.1 318.5 0.7 3.2 20.6 44.3 734.4 20.0 17.0 137.9 297.6 2,261.7 14,586.7 39.1 39.0 391.8 106.4 .. 31.3 16.2 7.5 63,242.1 w 416.5 19,632.1 4,312.3 15,317.0 20,071.7 7,630.5 3,059.0 4,980.8 1,207.0 2,090.4 1,097.9 43,240.0 12,149.1
2000
13 6 37 5 19 20 58 0 4 3 .. 3 .. 4 20 42 12 2 2 24 27 33 9 26 34 1 11 11 24 29 17 2 1 1 7 34 4 25 .. 14 22 18 4w 34 11 20 9 12 15 11 6 13 24 16 2 2
a. Components are at producer prices. b. Covers mainland Tanzania only.
220
2012 World Development Indicators
Industry
Manufacturing
% of GDP 2010
7 4 34 3 17 9 49 0 4 2 .. 3 .. 3 13 24 7 2 1 23 21 28 12 .. .. 1 8 10 12 24 8 1 1 1 9 20 .. 21 .. 8 9 17 3w 25 10 17 8 10 11 7 6 .. 19 13 1 2
2000
36 38 14 54 23 30 28 35 36 36 .. 32 .. 29 27 22 45 29 27 38 39 19 42 19 18 49 30 31 44 23 36 50 27 23 25 23 50 37 .. 46 25 25 29 w 21 36 34 36 35 44 35 30 43 26 29 28 28
Services
% of GDP 2010
26 37 14 62 22 27 21 28 35 32 .. 31 .. 26 29 33 50 27 27 31 22 25 45 .. .. 52 32 27 54 25 31 54 22 20 26 35 .. 41 .. 29 37 29 25 w 25 36 31 37 35 45 32 31 .. 26 30 24 26
2000
15 17 7 10 15 24 4 27 25 26 .. 19 .. 19 17 9 39 22 19 7 34 9 34 3 8 7 18 23 11 8 19 13 17 16 14 9 20 19 .. 6 11 16 19 w 12 21 17 23 21 31 18 18 13 15 15 19 20
% of GDP 2010
22 16 6 10 13 16 .. 22 21 21 .. 15 .. 13 18 6 45 16 19 .. 10 10 36 .. .. 5 18 18 .. 8 17 10 11 13 14 9 .. 20 .. 6 9 15 16 w 14 20 16 22 20 29 17 17 .. 15 13 15 16
2000
51 56 49 41 58 50 13 65 59 61 .. 65 .. 66 53 37 43 69 71 38 34 47 49 56 48 49 58 57 31 48 47 48 72 75 69 43 46 39 .. 40 52 57 68 w 45 53 46 55 53 41 55 65 44 50 54 71 70
2010
67 59 52 35 61 64 30 72 61 66 .. 66 .. 72 58 43 42 71 72 46 57 47 43 .. .. 47 60 64 34 50 61 46 78 79 65 45 .. 38 .. 63 54 53 72 w 50 55 52 55 54 43 61 63 .. 54 57 75 72
About the data
4.2
ECONOMY
Structure of output Definitions
An economy’s gross domestic product (GDP) rep-
Ideally, industrial output should be measured
• Gross domestic product (GDP) at purchaser prices
resents the sum of value added by all its produc-
through regular censuses and surveys of fi rms.
is the sum of gross value added by all resident pro-
ers. Value added is the value of the gross output of
But in most developing countries such surveys are
ducers in the economy plus any product taxes (less
producers less the value of intermediate goods and
infrequent, so earlier survey results must be extrapo-
subsidies) not included in the valuation of output.
services consumed in production, before accounting
lated using an appropriate indicator. The choice of
It is calculated without deducting for depreciation
for consumption of fixed capital in production. The
sampling unit, which may be the enterprise (where
of fabricated assets or for depletion and degrada-
United Nations System of National Accounts calls
responses may be based on financial records) or
tion of natural resources. Value added is the net
for value added to be valued at either basic prices
the establishment (where production units may be
output of an industry after adding up all outputs and
(excluding net taxes on products) or producer prices
recorded separately), also affects the quality of
subtracting intermediate inputs. The industrial origin
(including net taxes on products paid by producers
the data. Moreover, much industrial production is
of value added is determined by the International
but excluding sales or value added taxes). Both valu-
organized in unincorporated or owner-operated ven-
Standard Industrial Classifi cation (ISIC) revision
ations exclude transport charges that are invoiced
tures that are not captured by surveys aimed at the
3. • Agriculture is the sum of gross output less
separately by producers. Total GDP shown in the
formal sector. Even in large industries, where regu-
the value of intermediate input used in production
table and elsewhere in this volume is measured at
lar surveys are more likely, evasion of excise and
for industries classified in ISIC divisions 1–5 and
purchaser prices. Value added by industry is normally
other taxes and nondisclosure of income lower the
includes forestry and fishing. • Industry is the sum
measured at basic prices. When value added is mea-
estimates of value added. Such problems become
of gross output less the value of intermediate input
sured at producer prices, this is noted in Primary data
more acute as countries move from state control of
used in production for industries classified in ISIC
documentation and footnoted in the table.
industry to private enterprise, because new firms and
divisions 10–45, which cover mining, manufactur-
While GDP estimates based on the production
growing numbers of established firms fail to report.
ing (also reported separately), construction, electric-
approach are generally more reliable than estimates
In accordance with the System of National Accounts,
ity, water, and gas. • Manufacturing is the sum of
compiled from the income or expenditure side, dif-
output should include all such unreported activity
gross output less the value of intermediate input
ferent countries use different definitions, methods,
as well as the value of illegal activities and other
used in production for industries classified in ISIC
and reporting standards. World Bank staff review the
unrecorded, informal, or small-scale operations.
divisions 15–37. • Services correspond to ISIC divi-
quality of national accounts data and sometimes
Data on these activities need to be collected using
sions 50–99. This sector is derived as a residual
make adjustments to improve consistency with
techniques other than conventional surveys of firms.
(from GDP less agriculture and industry) and may not
international guidelines. Nevertheless, significant
In industries dominated by large organizations
properly reflect the sum of services output, including
discrepancies remain between international stan-
and enterprises, such as public utilities, data on
banking and financial services. For some countries
dards and actual practice. Many statistical offices,
output, employment, and wages are usually read-
it includes product taxes (minus subsidies) and may
especially those in developing countries, face severe
ily available and reasonably reliable. But in the
also include statistical discrepancies.
limitations in the resources, time, training, and bud-
services industry the many self-employed workers
gets required to produce reliable and comprehensive
and one-person businesses are sometimes difficult
series of national accounts statistics.
to locate, and they have little incentive to respond to surveys, let alone to report their full earnings.
Data problems in measuring output
Compounding these problems are the many forms
Among the difficulties faced by compilers of national
of economic activity that go unrecorded, including
accounts is the extent of unreported economic activ-
the work that women and children do for little or no
ity in the informal or secondary economy. In develop-
pay. For further discussion of the problems of using
ing countries a large share of agricultural output is
national accounts data, see Srinivasan (1994) and
either not exchanged (because it is consumed within
Heston (1994). Data sources
the household) or not exchanged for money. Dollar conversion
Data on national accounts for most developing
indirectly, using a combination of methods involv-
Agricultural production often must be estimated
To produce national accounts aggregates that are
countries are collected from national statistical
ing estimates of inputs, yields, and area under cul-
measured in the same standard monetary units,
organizations and central banks by visiting and
tivation. This approach sometimes leads to crude
the value of output must be converted to a single
resident World Bank missions. Data for high-
approximations that can differ from the true values
common currency. The World Bank conventionally
income economies are from Organisation for
over time and across crops for reasons other than
uses the U.S. dollar and applies the average official
Economic Co-operation and Development (OECD)
climate conditions or farming techniques. Similarly,
exchange rate reported by the International Monetary
data files. The United Nations Statistics Division
agricultural inputs that cannot easily be allocated to
Fund for the year shown. An alternative conversion
publishes detailed national accounts for UN mem-
specific outputs are frequently “netted out” using
factor is applied if the official exchange rate is judged
ber countries in National Accounts Statistics: Main
equally crude and ad hoc approximations. For further
to diverge by an exceptionally large margin from the
Aggregates and Detailed Tables and publishes
discussion of the measurement of agricultural pro-
rate effectively applied to transactions in foreign cur-
updates in the Monthly Bulletin of Statistics.
duction, see About the data for table 3.3.
rencies and traded products.
2012 World Development Indicators
221
4.3
Structure of manufacturing Manufacturing value added
Food, beverages, and tobacco
Textiles and clothing
Machinery and transport equipment
Chemicals
Other manufacturinga
$ billions
% of total 2000 2008
% of total 2000 2008
% of total 2000 2008
% of total 2000 2008
% of total 2000 2008
2000
Afghanistan Albania Algeria Angola Argentina Armenia Australia Austria Azerbaijan Bahrain Bangladesh Belarus Belgium Benin Bolivia Bosnia and Herzegovina Botswana Brazil Bulgaria Burkina Faso Burundi Cambodia Cameroon Canada Central African Republic Chad Chile China Hong Kong SAR, China Colombia Congo, Dem. Rep. Congo, Rep. Costa Rica Côte d’Ivoire Croatia Cuba Cyprus Czech Republic Denmark Dominican Republic Ecuador Egypt, Arab Rep. El Salvador Eritrea Estonia Ethiopia Finland France Gabon Gambia, The Georgia Germany Ghana Greece Guatemala Guinea Guinea-Bissau Haiti
222
0.64 0.37 3.86 0.26 46.88 0.32 49.08 35.36 0.28 .. 6.92 3.44 39.90 0.20 1.11 0.47 0.25 96.17 1.98 0.40 0.05 0.59 1.94 129.47 0.06 0.12 13.25 384.94 5.54 14.44 0.21 0.11 3.68 2.26 3.62 4.57 0.85 13.79 22.25 5.64 2.17 17.97 3.03 0.07 0.90 0.42 28.07 190.45 0.19 0.02 0.26 392.47 0.45 .. 2.54 0.12 0.02 ..
2010
1.96 1.95 7.32 4.89 69.44 0.89 98.34 65.50 2.81 .. 17.36 14.90 59.03 .. 2.21 1.87 0.44 280.65 6.67 .. .. 1.65 .. .. .. 0.38 22.65 1,756.82 3.76 40.07 0.59 0.46 5.68 4.38 9.43 4.96 1.66 42.25 32.86 11.49 5.41 32.98 4.04 0.10 2.39 1.46 39.11 253.61 0.49 0.03 1.31 614.23 1.95 .. 7.52 0.20 .. ..
2012 World Development Indicators
.. 20 .. .. 29 .. 21 10 42 .. 24 .. 12 .. 37 .. 20 17 20 .. .. 7 36 12 .. .. 32 14 7 30 .. .. 45 .. .. .. 37 12 18 .. 60 20 29 60 17 54 6 13 .. .. 37 8 .. 24 .. .. .. ..
.. 15 .. .. .. .. 19 9 15 .. .. .. 13 .. .. .. 22 18 20 .. .. .. .. 1 .. .. 14 12 14 .. .. .. 42 .. .. .. 32 9 16 .. 40 18 .. .. 14 46 6 14 .. .. 25 7 .. 22 .. .. .. ..
.. 27 .. .. 8 .. 4 4 3 .. 40 .. 5 .. 5 .. 5 7 14 .. .. 87 19 4 .. .. 4 11 20 11 .. .. 6 .. .. .. 7 6 2 .. 3 10 28 12 15 12 2 4 .. .. 1 2 .. 12 .. .. .. ..
.. 23 .. .. .. .. 3 2 1 .. .. .. 4 .. .. .. 5 6 14 .. .. .. .. 2 .. .. 2 10 11 .. .. .. 4 .. .. .. 3 3 2 .. 3 12 .. 31 8 7 1 3 .. .. 1 2 .. 8 .. .. .. ..
.. 3 .. .. 11 .. 13 25 11 .. 3 .. 19 .. 0 .. .. 19 14 .. .. 0 1 32 .. .. 4 14 12 4 .. .. 3 .. .. .. 4 24 20 .. 1 7 2 1 10 7 33 26 .. .. 12 33 .. 11 .. .. .. ..
.. 3 .. .. .. .. 14 26 6 .. .. .. 17 .. .. .. .. 21 18 .. .. .. .. 26 .. .. 2 24 21 .. .. .. 3 .. .. .. 5 30 19 .. 2 6 .. 1 18 2 37 25 .. .. 8 37 .. 10 .. .. .. ..
.. 5 .. .. 15 .. 8 7 6 .. 11 .. 20 .. 4 .. .. 12 11 .. .. 0 2 8 .. .. 14 12 4 16 .. .. 12 .. .. .. 6 7 10 .. 5 22 16 6 4 5 5 12 .. .. 7 10 .. 10 .. .. .. ..
.. 11 .. .. .. .. 7 7 4 .. .. .. 21 .. .. .. .. 11 5 .. .. .. .. 8 .. .. 14 11 5 .. .. .. 9 .. .. .. 6 5 12 .. 4 14 .. 11 6 6 6 13 .. .. 7 10 .. 6 .. .. .. ..
.. 46 .. .. 37 .. 55 54 37 .. 21 .. 44 .. 54 .. 75 45 41 .. .. 7 43 45 .. .. 46 48 58 39 .. .. 33 .. .. .. 46 51 48 .. 31 41 25 21 53 22 54 45 .. .. 43 47 .. 43 .. .. .. ..
.. 48 .. .. .. .. 58 56 75 .. .. .. 45 .. .. .. 73 44 44 .. .. .. .. 64 .. .. 69 43 48 .. .. .. 43 .. .. .. 54 53 51 .. 51 50 .. 56 55 39 50 44 .. .. 59 44 .. 54 .. .. .. ..
Manufacturing value added
Food, beverages, and tobacco
Textiles and clothing
Machinery and transport equipment
Chemicals
Other manufacturinga
$ billions
% of total 2000 2008
% of total 2000 2008
% of total 2000 2008
% of total 2000 2008
% of total 2000 2008
2000
Honduras Hungary India Indonesia Iran, Islamic Rep. Iraq Ireland Israel Italy Jamaica Japan Jordan Kazakhstan Kenya Korea, Dem. Rep. Korea, Rep. Kosovo Kuwait Kyrgyz Republic Lao PDR Latvia Lebanon Lesotho Liberia Libya Lithuania Macedonia, FYR Madagascar Malawi Malaysia Mali Mauritania Mauritius Mexico Moldova Mongolia Morocco Mozambique Myanmar Namibia Nepal Netherlands New Zealand Nicaragua Niger Nigeria Norway Oman Pakistan Panama Papua New Guinea Paraguay Peru Philippines Poland Portugal Puerto Rico Qatar
4.3
ECONOMY
Structure of manufacturing
1.46 9.28 65.75 45.79 13.24 0.24 28.22 .. 205.51 0.85 1,034.09 1.14 3.02 1.31 .. 134.56 .. 0.97 0.25 0.29 0.96 1.97 0.10 0.05 0.64 1.96 0.62 0.43 0.20 28.95 0.09 0.09 0.94 107.20 0.18 0.08 5.74 0.45 .. 0.46 0.49 53.51 7.99 0.59 0.12 1.97 15.70 1.08 10.10 1.10 0.25 1.09 7.69 19.83 28.21 17.99 24.08 ..
2010
2.63 25.26 226.79 175.39 .. .. 48.71 .. 308.22 1.16 905.54 4.78 18.54 3.02 .. 279.44 0.77 .. 0.74 0.52 2.62 2.85 0.30 0.10 3.88 5.43 1.25 1.11 0.48 62.10 .. 0.12 1.60 179.11 0.62 0.40 12.50 1.09 .. 0.88 0.85 92.47 .. 1.14 .. .. 34.12 .. 28.24 1.57 0.50 2.24 23.56 42.80 76.44 26.97 44.64 ..
.. 15 13 18 10 .. 14 12 9 .. 11 30 .. 29 .. 8 .. 8 33 46 27 26 .. .. .. 27 32 0 71 8 .. .. 20 25 65 49 34 .. .. .. 45 17 33 .. .. .. 21 12 21 56 .. 66 33 29 5 13 8 4
.. 11 9 26 .. .. 17 10 9 .. 11 21 .. 30 .. 6 .. .. 19 .. 23 .. .. .. .. 22 18 0 .. 9 .. .. 28 25 40 43 29 .. .. .. .. 19 27 .. .. .. 20 8 22 .. .. .. 30 22 17 14 9 1
.. 5 13 17 6 .. 1 5 12 .. 3 7 .. 8 .. 8 .. 4 2 22 11 10 .. .. .. 18 15 35 5 4 .. .. 52 4 9 40 18 .. .. .. 19 2 .. .. .. .. 1 5 33 5 .. 6 16 7 6 18 3 8
.. 3 8 12 .. .. 1 3 10 .. 2 10 .. 4 .. 5 .. .. 10 .. 6 .. .. .. .. 9 17 30 .. 2 .. .. 31 3 14 17 13 .. .. .. .. 2 2 .. .. .. 1 0 29 .. .. .. 12 5 4 11 1 2
.. 27 16 20 20 .. 20 32 23 .. 34 3 .. 2 .. 41 .. 2 1 8 8 3 .. .. .. 12 9 0 0 38 .. .. 1 24 5 0 4 .. .. .. 1 21 .. .. .. .. 21 1 5 3 .. 0 3 27 15 16 9 0
.. 30 16 18 .. .. 14 22 23 .. 37 4 .. 2 .. 46 .. .. 3 .. 16 .. .. .. .. 13 4 1 .. 30 .. .. 1 18 5 0 6 .. .. .. .. 20 13 .. .. .. 25 1 8 .. .. .. 2 33 19 7 9 0
.. 10 21 11 19 .. 36 10 8 .. 10 18 .. 5 .. 10 .. 3 1 3 3 6 .. .. .. 3 6 2 9 8 .. .. .. 15 .. 2 12 .. .. .. 10 14 .. .. .. .. 12 5 17 7 .. 10 11 9 8 5 60 21
.. 10 14 6 .. .. 36 20 7 .. 10 18 .. 4 .. 8 .. .. 2 .. 5 .. .. .. .. 10 6 2 .. 12 .. .. .. 19 .. 5 18 .. .. .. .. 14 .. .. .. .. 9 12 14 .. .. .. 12 6 8 6 62 17
.. 43 38 35 45 .. 29 41 48 .. 41 43 .. 55 .. 33 .. 82 63 22 51 55 .. .. .. 40 38 62 15 42 .. .. 26 31 22 9 32 .. .. .. 24 46 67 .. .. .. 45 76 25 32 .. 18 37 28 66 48 20 67
2012 World Development Indicators
.. 46 54 38 .. .. 33 44 51 .. 40 47 .. 62 .. 35 .. .. 66 .. 50 .. .. .. .. 46 55 67 .. 47 .. .. 39 35 41 35 34 .. .. .. .. 45 58 .. .. .. 45 79 26 .. .. .. 44 33 53 61 20 80
223
4.3
Structure of manufacturing Manufacturing value added
Food, beverages, and tobacco
Textiles and clothing
Machinery and transport equipment
Chemicals
Other manufacturinga
$ billions
% of total 2000 2008
% of total 2000 2008
% of total 2000 2008
% of total 2000 2008
% of total 2000 2008
2000
Romania Russian Federation Rwanda Saudi Arabia Senegal Serbia Sierra Leone Singapore Slovak Republic Slovenia Somalia South Africa South Sudan Spain Sri Lanka Sudan Swaziland Sweden Switzerland Syrian Arab Republic Tajikistan Tanzaniab Thailand Timor-Leste Togo Trinidad and Tobago Tunisia Turkey Turkmenistan Uganda Ukraine United Arab Emirates United Kingdom United States Uruguay Uzbekistan Venezuela, RB Vietnam West Bank and Gaza Yemen, Rep. Zambia Zimbabwe World Low income Middle income Lower middle income Upper middle income Low & middle income East Asia & Pacific Europe & Central Asia Latin America & Carib. Middle East & N. Africa South Asia Sub-Saharan Africa High income Euro area
2010
4.77 32.50 52.13 209.23 0.12 0.33 18.21 43.85 0.61 1.49 1.32 5.04 0.02 .. 24.01 43.63 6.32 16.44 4.48 8.60 .. .. 22.93 38.85 .. .. 97.78 172.43 2.46 8.92 1.02 3.30 0.48 1.37 47.75 65.64 44.62 95.68 1.29 .. 0.27 0.48 0.89 2.05 41.23 113.47 0.01 .. 0.11 .. 0.58 1.10 3.50 7.27 53.51 113.76 0.29 .. 0.44 1.33 5.10 21.06 9.47 28.93 226.97 230.62 1,468.08 1,814.34 2.86 4.81 1.14 3.10 21.71 .. 5.79 20.94 .. .. 0.55 1.88 0.33 1.43 0.90 0.88 5,737.81 t 9,989.14 t 18.46 46.11 1,188.59 3,868.14 203.74 639.71 985.43 3,226.81 1,207.17 3,916.69 530.45 2,185.11 .. .. 338.85 712.08 51.72 115.45 86.21 283.82 41.20 82.72 4,535.77 5,562.68 1,115.92 1,754.91
a. Includes unallocated data. b. Covers mainland Tanzania only.
224
2012 World Development Indicators
32 19 75 .. 22 .. .. 3 10 10 .. 15 .. 14 39 66 .. 7 .. .. .. 45 18 .. .. 21 19 18 .. 64 .. .. 14 13 39 .. 22 30 32 43 .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. ..
16 15 .. 19 .. .. .. 3 7 7 .. 19 .. 15 30 .. .. 7 .. .. .. 62 16 .. .. 11 17 12 .. .. .. .. 15 13 42 .. .. .. 27 60 .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. ..
12 2 2 .. 4 .. .. 1 4 10 .. 5 .. 7 31 4 .. 1 .. .. .. 0 12 .. .. 1 35 16 .. 4 .. .. 4 3 9 .. 2 21 21 4 .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. ..
12 2 .. 5 .. .. .. 1 3 5 .. 3 .. 4 31 .. .. 1 .. .. .. 8 9 .. .. 1 24 19 .. .. .. .. 2 2 7 .. .. .. 13 9 .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. ..
13 19 .. .. 2 .. .. 57 20 18 .. 14 .. 19 4 4 .. 35 .. .. .. 2 26 .. .. 0 3 15 .. .. .. .. 25 30 3 .. .. 12 1 1 .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. ..
24 10 .. 6 .. .. .. 54 30 20 .. 13 .. 18 7 .. .. 35 .. .. .. 1 35 .. .. 0 .. 20 .. .. .. .. 24 25 4 .. .. .. 1 0 .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. ..
5 8 6 .. 29 .. .. 14 7 11 .. 7 .. 10 4 4 .. 9 .. .. .. 7 6 .. .. 27 10 10 .. 11 .. .. 10 12 8 .. 34 6 4 5 .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. ..
5 10 .. 27 .. .. .. 21 4 15 .. 6 .. 9 4 .. .. 11 .. .. .. .. 6 .. .. 39 9 7 .. .. .. .. 12 16 8 .. .. .. 4 4 .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. ..
38 51 17 .. 43 .. .. 25 59 52 .. 59 .. 51 22 21 .. 48 .. .. .. 46 38 .. .. 51 33 42 .. 21 .. .. 47 42 41 .. 41 31 41 48 .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. ..
43 63 .. 43 .. .. .. 21 56 53 .. 58 .. 54 27 .. .. 47 .. .. .. 29 34 .. .. 49 49 42 .. .. .. .. 48 44 39 .. .. .. 55 27 .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. ..
About the data
4.3
ECONOMY
Structure of manufacturing Definitions
The data on the distribution of manufacturing value
resulting in a homogeneous set of products” (United
• Manufacturing value added is the sum of gross
added by industry are provided by the United Nations
Nations 1990 [ISIC, series M, no. 4, rev. 3], p. 9).
output less the value of intermediate inputs used in
Industrial Development Organization (UNIDO). UNIDO
Firms typically use multiple processes to produce
production for industries classified in ISIC major divi-
obtains the data from a variety of national and inter-
a product. For example, an automobile manufac-
sion D. • Food, beverages, and tobacco correspond
national sources, including the United Nations Sta-
turer engages in forging, welding, and painting as
to ISIC divisions 15 and 16. • Textiles and clothing
tistics Division, the World Bank, the Organisation for
well as advertising, accounting, and other service
correspond to ISIC divisions 17–19. • Machinery and
Economic Co-operation and Development, and the
activities. Collecting data at such a detailed level
transport equipment correspond to ISIC divisions
International Monetary Fund. To improve comparabil-
is not practical, nor is it useful to record produc-
29, 30, 32, 34, and 35. • Chemicals correspond to
ity over time and across countries, UNIDO supple-
tion data at the highest level of a large, multiplant,
ISIC division 24. • Other manufacturing, a residual,
ments these data with information from industrial
multiproduct firm. The ISIC has therefore adopted as
covers wood and related products (ISIC division 20),
censuses, statistics from national and international
the definition of an establishment “an enterprise or
paper and related products (ISIC divisions 21 and
organizations, unpublished data that it collects in the
part of an enterprise which independently engages in
22), petroleum and related products (ISIC division
field, and estimates by the UNIDO Secretariat. Nev-
one, or predominantly one, kind of economic activity
23), basic metals and mineral products (ISIC divi-
ertheless, coverage may be incomplete, particularly
at or from one location . . . for which data are avail-
sion 27), fabricated metal products and professional
for the informal sector. When direct information on
able . . .” (United Nations 1990, p. 25). By design,
goods (ISIC division 28), and other industries (ISIC
inputs and outputs is not available, estimates may
this definition matches the reporting unit required
divisions 25, 26, 31, 33, 36, and 37).
be used, which may result in errors in industry totals.
for the production accounts of the United Nations
Moreover, countries use different reference periods
System of National Accounts. The ISIC system is
(calendar or fiscal year) and valuation methods (basic
described in the United Nations’ International Stan-
or producer prices) to estimate value added. (See
dard Industrial Classification of All Economic Activi-
About the data for table 4.2.)
ties, Third Revision (1990). The discussion of the ISIC
The data on manufacturing value added in U.S. dol-
draws on Ryten (1998).
lars are from the World Bank’s national accounts files and may differ from those UNIDO uses to calculate shares of value added by industry, in part because of differences in exchange rates. Thus value added in a particular industry estimated by applying the shares to total manufacturing value added will not match those from UNIDO sources. Classification of manufacturing industries in the table accords with the United Nations International Standard Industrial Classifi cation (ISIC) revision 3. Editions of World Development Indicators prior to 2008 used revision 2, first published in 1948. Revision 3 was completed in 1989, and many countries now use it. But revision 2 is still widely used for compiling cross-country data. UNIDO has converted these data to accord with revision 3. Concordances matching ISIC categories to national classifi cation systems and to related systems such as the Standard International Trade Classification are available. In establishing classifi cations systems compilers must define both the types of activities to be described and the units whose activities are to
Data sources
be reported. There are many possibilities, and the
Data on manufacturing value added are from the
choices affect how the statistics can be interpreted
World Bank’s national accounts files. Data used
and how useful they are in analyzing economic
to calculate shares of industry value added are
behavior. The ISIC emphasizes commonalities in the
provided to the World Bank in electronic files
production process and is explicitly not intended to
by UNIDO. The most recent published source is
measure outputs (for which there is a newly devel-
UNIDO’s International Yearbook of Industrial Sta-
oped Central Product Classification). Nevertheless,
tistics 2011.
the ISIC views an activity as defined by “a process
2012 World Development Indicators
225
4.4 Afghanistan Albania Algeria Angola Argentina Armenia Australia Austria Azerbaijan Bahrain Bangladesh Belarus Belgium Benin Bolivia Bosnia and Herzegovina Botswana Brazil Bulgaria Burkina Faso Burundi Cambodia Cameroon Canada Central African Republic Chad Chile China† Hong Kong SAR, Chinaa Colombia Congo, Dem. Rep. Congo, Rep. Costa Rica Côte d’Ivoire Croatia Cuba Cyprus Czech Republic Denmark Dominican Republic Ecuador Egypt, Arab Rep. El Salvador Eritrea Estonia Ethiopia Finland France Gabon Gambia, The Georgia Germany Ghana Greece Guatemala Guinea Guinea-Bissau Haiti †Data for Taiwan, China
226
Structure of merchandise exports Merchandise exports
Food
Agricultural raw materials
Fuels
Ores and metals
Manufactures
$ millions
% of total 2000 2010
% of total 2000 2010
% of total 2000 2010
% of total 2000 2010
% of total 2000 2010
2000
2010
137 258 22,031 7,921 26,341 294 63,870 67,710 1,745 6,195 6,389 7,326 188,371 392 1,230 1,069 2,675 55,086 4,852 209 50 1,389 1,833 276,635 161 183 19,210 249,203 202,683 13,040 807 2,489 5,865 3,888 4,432 1,676 951 29,094 51,292 5,737 4,927 5,276 2,941 37 3,830 486 46,102 327,611 2,598 15 323 551,810 1,671 11,751 2,696 666 62 318 151,357
430 1,550 57,053 53,500 68,133 1,011 212,554 152,313 26,476 13,647 19,191 25,226 412,223 1,200 6,290 4,803 4,693 201,915 20,666 1,288 100 5,030 4,000 388,019 140 3,450 71,028 1,577,824 401,022 39,820 5,300 8,200 9,385 10,320 11,807 3,900 1,412 132,852 97,681 6,598 17,490 26,438 4,499 12 11,605 2,238 69,630 520,661 9,371 15 1,583 1,268,874 7,896 21,409 8,466 1,250 125 580 203,675
2012 World Development Indicators
.. 7 0 .. 44 14 21 5 3 1 8 7 9 21 30 .. 3 23 10 19 91 1 15 6 11 .. 25 5 2 19 .. .. 30 50 9 50 33 4 20 41 37 8 19 54 8 71 2 11 1 81 28 4 48 22 56 3 .. .. 1
40 4 1 .. 51 17 11 7 3 7 .. 13 9 .. 15 7 5 31 16 33 81 1 24 10 4 .. 17 3 7 12 .. .. 35 50 11 .. 34 4 18 27 30 17 22 .. 10 79 3 12 1 78 22 5 61 24 42 2 .. .. 1
.. 6 0 .. 2 5 6 2 2 0 1 4 2 72 3 .. 0 5 3 59 8 3 9 6 13 .. 10 1 0 5 .. .. 3 14 5 0 1 2 3 2 4 5 0 10 9 19 6 1 12 1 3 1 10 3 4 3 .. .. 1
11 2 0 .. 1 1 2 2 0 0 .. 2 1 .. 1 6 0 4 1 56 5 2 15 4 32 .. 5 0 3 4 .. .. 3 10 4 .. 2 1 3 1 4 3 1 .. 5 9 6 1 9 2 1 1 7 3 4 5 .. .. 1
.. 2 97 .. 18 11 22 1 85 0 0 20 4 0 13 .. 0 2 12 3 .. 0 54 13 0 .. 1 3 0 43 .. .. 1 21 11 3 6 3 7 16 49 42 3 0 4 0 3 3 83 0 8 1 8 15 6 0 .. .. 1
.. 18 97 .. 8 3 31 3 95 0 .. 28 9 .. 44 15 0 10 13 0 2 0 50 26 0 .. 0 2 3 60 .. .. 1 24 12 .. 0 4 8 2 55 30 3 .. 16 0 8 4 83 0 6 2 0 11 5 2 .. .. 6
.. 4 0 .. 3 27 17 3 2 16 0 1 3 0 25 .. 7 10 13 0 1 0 6 4 8 .. 45 2 2 1 .. .. 1 0 3 37 5 2 1 2 0 4 1 8 5 1 3 2 2 0 29 2 19 7 2 63 .. .. 1
0 13 0 .. 4 52 34 4 0 70 .. 1 4 .. 34 12 15 18 17 2 5 0 3 8 62 .. 65 1 9 2 .. .. 1 0 5 .. 14 2 2 4 1 6 2 .. 3 1 5 2 3 10 21 3 11 9 6 59 .. .. 2
.. 82 2 .. 32 43 29 80 8 10 91 65 78 7 29 .. 90 58 57 18 0 96 3 64 68 .. 16 88 95 32 .. .. 66 14 73 9 49 88 64 34 10 38 21 28 73 10 85 81 2 17 31 84 15 50 32 30 .. .. 95
20 62 2 .. 33 24 17 80 2 22 .. 53 75 .. 6 57 80 37 49 9 6 96 8 49 3 .. 13 94 77 23 .. .. 61 16 68 .. 50 86 60 65 10 43 73 .. 62 9 77 78 4 10 50 82 21 50 43 32 .. .. 89
Merchandise exports
Food
Agricultural raw materials
Fuels
Ores and metals
Manufactures
$ millions
% of total 2000 2010
% of total 2000 2010
% of total 2000 2010
% of total 2000 2010
% of total 2000 2010
2000
Honduras Hungary India Indonesia Iran, Islamic Rep. Iraq Ireland Israel Italy Jamaica Japan Jordan Kazakhstan Kenya Korea, Dem. Rep. Korea, Rep. Kosovo Kuwait Kyrgyz Republic Lao PDR Latvia Lebanon Lesotho Liberia Libya Lithuania Macedonia, FYR Madagascar Malawi Malaysia Mali Mauritania Mauritius Mexico Moldova Mongolia Morocco Mozambique Myanmar Namibia Nepal Netherlands New Zealand Nicaragua Niger Nigeria Norway Oman Pakistan Panama Papua New Guinea Paraguay Peru Philippines Poland Portugal Puerto Rico Qatar
4.4
ECONOMY
Structure of merchandise exports
3,343 28,192 42,379 65,403 28,739 20,603 77,413 31,404 240,518 1,304 479,249 1,899 8,812 1,734 708 172,267 .. 19,436 505 330 1,868 715 220 329 13,380 3,810 1,323 824 379 98,229 545 355 1,557 166,367 472 536 7,432 364 1,646 1,320 804 233,130 13,272 645 283 20,975 60,058 11,319 9,028 859 2,096 869 7,028 39,783 31,747 24,363 .. 11,594
2010
5,742 95,437 219,959 157,818 100,524 52,800 116,801 58,393 447,535 1,337 769,839 7,028 59,217 5,151 3,010 466,384 .. 67,014 1,760 1,600 9,489 5,021 820 231 47,400 20,835 3,302 1,090 1,066 198,801 2,350 2,033 2,239 298,305 1,582 2,899 17,579 3,200 8,749 4,052 860 573,360 31,396 1,851 930 82,000 131,395 36,601 21,410 832 5,612 4,534 35,565 51,496 155,752 48,748 .. 62,000
72 7 13 9 3 1 8 2 6 23 0 16 7 59 .. 2 .. 0 17 .. 6 20 5 .. 1 12 15 38 89 6 4 21 18 5 62 4 21 42 .. 29 10 13 46 88 44 0 6 4 11 74 15 65 30 5 8 7 .. 0
54 7 8 16 6 0 9 3 8 25 1 17 4 48 .. 1 .. 0 30 .. 17 15 14 .. .. 17 18 27 76 12 30 58 37 6 72 .. 19 16 .. 23 19 14 56 88 21 3 7 3 17 73 .. 86 20 7 11 11 .. 0
5 1 1 4 0 0 0 1 1 0 0 0 1 9 .. 1 .. 0 14 .. 29 2 0 .. 0 5 2 3 3 3 91 0 1 1 3 28 2 11 .. 1 0 3 14 2 3 0 1 0 3 1 2 15 3 1 2 3 .. 0
1 1 2 7 0 0 1 1 1 0 1 0 0 11 .. 1 .. 0 5 .. 12 1 9 .. .. 2 1 3 3 3 48 0 1 0 1 .. 2 4 .. 0 4 3 11 1 3 2 1 0 2 2 .. 3 1 1 1 3 .. 0
0 2 3 25 89 97 0 0 2 0 0 0 54 8 .. 5 .. 94 27 .. 2 0 0 .. 93 21 5 4 0 10 0 .. 0 10 0 0 4 21 .. 2 0 8 3 2 2 100 64 83 1 7 29 0 7 1 5 2 .. 91
4 3 17 30 71 99 1 1 5 23 2 1 71 4 .. 7 .. 93 15 .. 5 0 0 .. .. 23 1 7 0 16 0 0 0 14 0 .. 1 20 .. 0 0 10 5 1 2 87 64 81 6 0 .. 0 12 2 4 6 .. 73
6 2 3 5 1 0 0 1 1 4 1 15 19 3 .. 1 .. 0 11 .. 6 7 0 .. 0 2 9 2 0 1 0 46 0 1 1 41 9 17 .. 11 0 2 5 0 41 0 6 1 0 2 51 0 39 2 5 2 .. 0
4 2 7 10 3 0 1 1 2 12 3 9 11 2 .. 2 .. 0 4 .. 4 11 2 .. .. 1 3 9 11 2 1 30 0 3 4 .. 12 54 .. 31 5 2 4 2 60 1 6 3 2 11 .. 1 52 4 5 4 .. 0
17 86 78 57 7 0 86 82 89 73 94 69 19 21 .. 91 .. 4 31 .. 56 71 95 .. 7 60 69 52 7 80 5 0 81 84 33 26 64 7 .. 56 67 59 31 8 9 0 18 12 85 16 2 19 20 92 80 85 .. 9
2012 World Development Indicators
35 82 64 37 16 0 85 93 82 40 89 74 14 35 .. 89 .. 6 38 .. 59 64 74 .. .. 54 51 48 9 67 20 0 60 76 23 .. 66 2 .. 45 72 57 21 7 14 7 18 12 74 13 .. 11 14 86 79 74 .. 5
227
4.4
Structure of merchandise exports Merchandise exports
Food
Agricultural raw materials
Fuels
Ores and metals
Manufactures
$ millions
% of total 2000 2010
% of total 2000 2010
% of total 2000 2010
% of total 2000 2010
% of total 2000 2010
2000
Romania Russian Federation Rwanda Saudi Arabia Senegal Serbia Sierra Leone Singaporea Slovak Republic Slovenia Somalia South Africa South Sudan Spain Sri Lanka Sudan Swaziland Sweden Switzerland Syrian Arab Republic Tajikistan Tanzania Thailand Timor-Leste Togo Trinidad and Tobago Tunisia Turkey Turkmenistan Uganda Ukraine United Arab Emirates United Kingdom United States Uruguay Uzbekistan Venezuela, RB Vietnam West Bank and Gaza Yemen, Rep. Zambia Zimbabwe World Low income Middle income Lower middle income Upper middle income Low & middle income East Asia & Pacific Europe & Central Asia Latin America & Carib. Middle East & N. Africa South Asia Sub-Saharan Africa High income Euro area
2010
10,412 49,401 105,565 400,132 52 297 77,583 249,700 920 2,161 .. 9,795 13 338 137,804 351,867 11,832 65,345 8,770 29,446 .. .. 29,983 81,821 .. .. 115,251 245,637 5,430 8,500 1,807 11,443 910 1,550 87,132 158,314 80,500 195,392 4,634 13,500 785 1,195 734 3,687 69,057 195,319 .. 17 363 800 4,274 10,590 5,850 16,427 27,775 113,981 2,506 6,500 403 1,612 14,573 51,478 49,835 220,000 285,425 405,666 781,918 1,278,263 2,295 6,733 2,817 11,857 33,529 65,786 14,483 72,192 .. .. 4,079 8,700 892 7,200 1,925 2,500 6,456,422 t 15,211,311 t 23,852 79,667 1,350,497 4,848,882 299,735 960,037 1,050,746 3,889,297 1,374,348 4,928,568 544,009 2,281,768 201,167 830,383 356,697 862,436 114,670 352,565 64,379 271,099 93,392 332,645 5,082,101 10,278,808 1,920,244 4,007,130
3 1 57 1 52 17 19 2 3 4 .. 8 .. 14 21 17 34 2 3 9 4 66 14 .. 20 6 9 13 0 71 9 1 5 7 47 .. 1 25 .. 2 9 47 7w 26 9 12 9 10 8 5 16 4 12 15 6 8
8 2 52 1 29 .. .. 2 4 4 .. 9 .. 15 27 6 .. 5 4 22 .. 32 13 .. 15 3 8 11 .. 67 19 1 6 10 64 .. 0 21 .. 6 6 20 8w .. 10 15 9 11 8 7 16 .. 12 15 8 9
5 3 3 0 2 6 1 0 2 2 .. 3 .. 1 2 5 11 5 1 5 13 13 3 .. 23 0 1 1 10 15 2 0 0 2 9 .. 0 2 .. 0 4 13 2w 11 2 2 2 2 2 3 2 1 2 5 2 1
2 2 3 0 1 .. .. 0 1 2 .. 2 .. 1 4 1 .. 4 0 1 .. 7 5 .. 5 0 1 0 .. 7 1 0 1 3 8 .. 0 2 .. 0 1 7 2w .. 2 3 2 2 2 2 2 .. 2 4 2 1
Note: Components may not sum to 100 percent because of unclassified trade. Exports of gold are excluded. a. Includes re-exports.
228
2012 World Development Indicators
7 51 0 92 14 0 .. 7 7 1 .. 10 .. 4 0 69 1 3 0 76 14 0 3 .. 1 65 12 1 81 6 5 94 8 2 2 .. 86 26 .. 97 1 1 10 w 3 21 29 19 21 7 34 17 77 3 37 8 3
5 64 0 87 26 .. .. 16 5 4 .. 10 .. 5 0 92 .. 7 3 39 .. 3 5 .. 0 66 14 4 .. 1 7 65 13 7 1 .. 93 15 .. 92 1 2 12 w .. 22 22 21 21 8 42 21 .. 13 32 9 5
7 9 37 0 5 16 1 1 3 4 .. 11 .. 2 0 0 0 2 6 1 56 1 1 .. 26 0 2 3 0 5 12 3 2 2 0 .. 3 0 .. 0 74 11 3w 8 4 4 5 4 2 9 6 2 2 7 2 2
4 6 37 0 4 .. .. 1 3 4 .. 33 .. 3 1 0 .. 5 4 4 .. 34 1 .. 6 0 2 4 .. 2 7 1 4 4 0 .. 2 1 .. 0 86 35 5w .. 6 7 6 6 3 6 10 .. 6 18 4 3
77 24 3 7 27 61 10 86 84 90 .. 54 .. 78 77 8 54 82 91 8 13 20 75 .. 31 29 77 81 7 3 69 2 77 83 42 .. 9 43 .. 0 11 28 75 w 51 61 52 63 61 80 42 58 16 80 31 78 80
79 15 8 11 40 .. .. 73 87 85 .. 47 .. 73 67 0 .. 74 89 33 .. 24 75 .. 74 31 76 79 .. 23 65 4 70 66 26 .. 4 60 .. 2 6 36 69 w .. 59 52 60 58 79 36 51 .. 66 31 72 77
About the data
4.4
ECONOMY
Structure of merchandise exports Definitions
Data on merchandise trade are from customs
data for countries in Europe and Central Asia has
• Merchandise exports are the f.o.b. value of goods
reports of goods moving into or out of an economy
also improved.
provided to the rest of the world. • Food corresponds
or from reports of financial transactions related to
Export shares by major commodity group are from
to the commodities in SITC sections 0 (food and live
merchandise trade recorded in the balance of pay-
Comtrade. The values of total exports reported
animals), 1 (beverages and tobacco), and 4 (animal
ments. Because of differences in timing and defi -
here have not been fully reconciled with the esti-
and vegetable oils and fats) and SITC division 22
nitions, trade flow estimates from customs reports
mates from the national accounts or the balance
(oil seeds, oil nuts, and oil kernels). • Agricultural
and balance of payments may differ. Several inter-
of payments.
raw materials correspond to SITC section 2 (crude
national agencies process trade data, each correct-
The classification of commodity groups is based
materials except fuels), excluding divisions 22, 27
ing unreported or misreported data, leading to other
on the Standard International Trade Classification
(crude fertilizers and minerals excluding coal, petro-
differences.
(SITC) revision 3. Previous editions contained data
leum, and precious stones), and 28 (metalliferous
The most detailed source of data on international
based on the SITC revision 1. Data for earlier years in
ores and scrap). • Fuels correspond to SITC section
trade in goods is the United Nations Statistics Divi-
previous editions may differ because of this change
3 (mineral fuels). • Ores and metals correspond to
sion’s Commodity Trade Statistics (Comtrade) data-
in methodology. Concordance tables are available
the commodities in SITC divisions 27, 28, and 68
base. The International Monetary Fund (IMF) also col-
to convert data reported in one system to another.
(nonferrous metals). • Manufactures correspond to
lects customs-based data on trade in goods. Exports
the commodities in SITC sections 5 (chemicals), 6
are recorded as the cost of the goods delivered to the
(basic manufactures), 7 (machinery and transport
frontier of the exporting country for shipment—the
equipment), and 8 (miscellaneous manufactured
free on board (f.o.b.) value. Many countries report
goods), excluding division 68.
trade data in U.S. dollars. When countries report in local currency, the United Nations Statistics Division applies the average official exchange rate to the U.S. dollar for the period shown. Countries may report trade according to the general or special system of trade. Under the general system exports comprise outward-moving goods that are (a) goods wholly or partly produced in the country; (b) foreign goods, neither transformed nor declared for domestic consumption in the country, that move outward from customs storage; and (c) goods previously included as imports for domestic consumption but subsequently exported without transformation. Under the special system exports comprise categories a and c. In some compilations categories b and c are classified as re-exports. Because of differences in reporting practices, data on exports may not be fully comparable across economies. The data on total exports of goods (merchandise)
Data sources
are from the World Trade Organization (WTO), which
Data on merchandise exports are from the WTO.
obtains data from national statistical offices and the
Data on shares of exports by major commodity
IMF’s International Financial Statistics, supplemented
group are from the United Nations Statistics Divi-
by the Comtrade database and publications or data-
sion’s Comtrade database. The WTO publishes
bases of regional organizations, specialized agen-
data on world trade in its Annual Report. The IMF
cies, economic groups, and private sources (such as
publishes estimates of total exports of goods in
Eurostat, the Food and Agriculture Organization, and
its International Financial Statistics and Direction
country reports of the Economist Intelligence Unit).
of Trade Statistics, as does the United Nations Sta-
Country websites and email contact have improved
tistics Division in its Monthly Bulletin of Statistics.
collection of up-to-date statistics, reducing the pro-
And the United Nations Conference on Trade and
portion of estimates. The WTO database now covers
Development publishes data on the structure of
most major traders in Africa, Asia, and Latin America,
exports in its Handbook of Statistics. Tariff line
which together with high-income countries account
records of exports are compiled in the United
for nearly 95 percent of world trade. Reliability of
Nations Statistics Division’s Comtrade database.
2012 World Development Indicators
229
4.5 Afghanistan Albania Algeria Angola Argentina Armenia Australia Austria Azerbaijan Bahrain Bangladesh Belarus Belgium Benin Bolivia Bosnia and Herzegovina Botswana Brazil Bulgaria Burkina Faso Burundi Cambodia Cameroon Canada Central African Republic Chad Chile China† Hong Kong SAR, China Colombia Congo, Dem. Rep. Congo, Rep. Costa Rica Côte d’Ivoire Croatia Cuba Cyprus Czech Republic Denmark Dominican Republic Ecuador Egypt, Arab Rep. El Salvador Eritrea Estonia Ethiopia Finland France Gabon Gambia, The Georgia Germany Ghana Greece Guatemala Guinea Guinea-Bissau Haiti †Data for Taiwan, China
230
Structure of merchandise imports Merchandise imports
Food
Agricultural raw materials
Fuels
Ores and metals
Manufactures
$ millions
% of total 2000 2010
% of total 2000 2010
% of total 2000 2010
% of total 2000 2010
% of total 2000 2010
2000
2010
1,176 1,090 9,171 3,040 25,154 882 71,529 72,394 1,172 4,633 8,883 8,646 177,511 613 1,830 3,107 2,081 59,053 6,544 611 148 1,939 1,489 244,786 117 317 18,507 225,094 214,042 11,539 683 465 6,372 2,482 7,887 4,843 31,974 3,846 45,557 9,479 3,721 14,578 4,947 471 5,052 1,260 34,443 338,940 950 187 709 497,197 2,973 33,480 4,791 612 59 1,036 140,642
4,400 4,601 40,212 21,500 56,503 3,783 201,640 158,752 6,746 10,000 27,819 34,868 390,443 2,200 5,361 9,223 5,657 191,491 25,403 2,048 509 7,500 4,850 402,280 340 2,600 58,956 1,395,099 442,035 40,683 4,500 2,900 13,570 7,830 20,054 11,300 126,222 8,499 84,848 15,299 20,591 52,923 8,498 690 12,252 8,552 68,510 605,706 2,983 276 5,096 1,066,839 10,703 63,173 13,837 1,100 220 3,150 174,371
2012 World Development Indicators
.. 22 28 .. 5 25 5 5 19 10 16 12 9 22 14 .. 14 7 5 13 23 10 18 5 29 .. 7 4 4 12 .. .. 7 17 8 16 5 19 11 12 9 25 12 37 10 7 5 8 18 35 23 7 13 11 12 24 .. .. 4
14 18 16 .. 3 18 5 7 19 12 .. 8 8 .. 8 18 12 5 10 15 14 7 18 7 39 .. 7 5 4 10 .. .. 9 19 10 .. 5 15 14 14 8 19 17 .. 11 11 7 8 17 36 18 7 15 12 13 13 .. .. 5
.. 1 3 .. 1 1 1 3 2 1 6 2 2 5 2 .. 1 2 1 1 2 3 2 1 4 .. 1 5 1 3 .. .. 1 1 2 1 2 1 3 2 3 5 2 1 3 1 2 2 1 1 1 2 2 2 2 1 .. .. 2
0 1 2 .. 1 1 1 2 2 1 .. 1 1 .. 1 2 1 1 1 1 1 2 2 1 2 .. 1 4 1 1 .. .. 1 1 1 .. 1 1 2 1 1 3 2 .. 2 0 2 1 0 1 1 2 1 1 1 0 .. .. 1
.. 9 1 .. 4 21 8 5 5 1 7 30 9 19 5 .. 5 15 26 25 12 13 23 5 8 .. 18 9 2 2 .. .. 8 34 15 24 10 13 6 23 7 8 12 2 7 20 12 10 4 12 20 9 21 13 13 25 .. .. 9
21 14 2 .. 7 18 14 11 1 2 .. 35 14 .. 12 19 15 17 22 22 2 7 27 10 1 .. 21 15 4 5 .. .. 12 24 19 .. 9 20 8 25 17 13 16 .. 16 19 18 14 7 20 18 11 1 24 18 33 .. .. 21
.. 2 1 .. 2 1 1 3 4 3 2 4 4 1 1 .. 2 3 6 1 2 0 1 2 4 .. 1 6 2 2 .. .. 2 1 2 1 4 1 2 1 2 2 1 1 3 1 6 3 1 1 1 4 1 3 1 1 .. .. 5
0 4 2 .. 3 3 2 5 2 19 .. 3 4 .. 1 2 2 3 9 1 1 2 1 3 2 .. 2 14 2 2 .. .. 2 1 2 .. 4 1 2 1 1 4 1 .. 1 1 8 3 1 1 2 5 1 3 1 0 .. .. 7
.. 67 67 .. 87 52 84 81 71 41 68 48 76 53 79 .. 75 73 59 61 60 73 56 84 54 .. 71 75 91 80 .. .. 82 46 73 58 80 65 76 62 77 56 43 58 76 71 73 77 76 51 55 68 62 70 72 49 .. .. 79
19 64 78 .. 85 56 75 74 76 65 .. 47 71 .. 78 58 68 74 55 61 82 82 52 77 56 .. 69 61 90 80 .. .. 73 55 67 .. 77 61 73 60 72 60 64 .. 63 69 61 73 74 41 60 68 81 60 66 53 .. .. 65
Merchandise imports
Food
Agricultural raw materials
Fuels
Ores and metals
Manufactures
$ millions
% of total 2000 2010
% of total 2000 2010
% of total 2000 2010
% of total 2000 2010
% of total 2000 2010
2000
Honduras Hungary India Indonesia Iran, Islamic Rep. Iraq Ireland Israel Italy Jamaica Japan Jordan Kazakhstan Kenya Korea, Dem. Rep. Korea, Rep. Kosovo Kuwait Kyrgyz Republic Lao PDR Latvia Lebanon Lesotho Liberia Libya Lithuania Macedonia, FYR Madagascar Malawi Malaysia Mali Mauritania Mauritius Mexico Moldova Mongolia Morocco Mozambique Myanmar Namibia Nepal Netherlands New Zealand Nicaragua Niger Nigeria Norway Oman Pakistan Panama Papua New Guinea Paraguay Peru Philippines Poland Portugal Puerto Rico Qatar
ECONOMY
4.5
Structure of merchandise imports
3,988 32,172 51,523 43,595 13,898 13,384 51,041 37,686 238,757 3,326 379,511 4,597 5,040 3,105 1,686 160,481 .. 7,157 554 535 3,202 6,230 809 668 3,732 5,457 2,094 1,097 532 81,963 806 454 2,093 179,464 777 615 11,534 1,158 2,401 1,550 1,573 218,267 13,906 1,805 395 8,721 34,392 5,131 10,864 3,379 1,151 2,193 7,415 37,027 49,029 39,952 .. 3,252
2010
8,550 88,120 327,230 131,737 65,021 42,500 60,032 61,209 483,814 5,195 694,052 15,402 29,760 12,090 4,420 425,212 .. 22,446 3,223 1,800 11,593 18,460 2,200 700 10,500 23,399 5,451 2,650 1,900 164,733 2,850 1,822 4,402 310,618 3,855 3,278 35,277 4,500 4,807 5,360 5,280 516,927 30,617 4,173 2,150 44,235 77,252 19,870 39,044 9,145 3,850 10,040 30,126 58,229 173,648 75,648 .. 23,240
22 3 5 10 19 1 6 5 9 15 13 21 9 14 .. 5 .. 14 15 .. 12 19 18 .. 28 10 12 13 10 4 15 19 14 5 13 17 14 14 7 17 13 9 8 16 39 20 6 22 14 12 18 17 12 7 6 11 .. 12
19 5 4 8 15 .. 12 7 9 18 9 16 9 12 .. 5 .. 15 17 .. 15 16 20 .. .. 12 13 14 14 8 12 19 21 6 15 .. 11 12 .. 14 14 10 11 16 15 10 8 12 13 8 .. 7 10 11 8 13 .. 8
2 1 3 7 3 0 1 1 4 2 3 2 1 2 .. 3 .. 0 2 .. 2 2 1 .. 1 3 2 0 2 1 1 0 2 1 2 1 3 1 0 1 4 2 1 1 4 1 2 1 3 0 1 1 2 1 2 3 .. 1
1 1 2 3 2 .. 1 1 2 1 2 1 1 2 .. 2 .. 1 1 .. 1 1 2 .. .. 2 1 1 1 2 0 0 2 1 1 .. 2 1 .. 1 2 1 1 1 2 1 1 1 5 0 .. 1 2 1 2 1 .. 0
18 5 39 19 2 0 4 10 10 18 20 5 12 22 .. 24 .. 1 23 .. 12 18 19 .. 0 22 14 23 16 5 24 23 12 3 32 19 18 13 19 3 16 10 10 18 15 2 4 2 33 19 22 16 16 11 11 10 .. 0
19 11 36 20 3 .. 12 18 19 30 29 22 10 22 .. 29 .. 1 27 .. 15 21 11 .. .. 32 5 15 10 10 26 26 19 8 21 .. 23 20 .. 14 18 16 15 22 13 1 7 7 30 1 .. 12 14 17 11 14 .. 1
1 3 5 3 2 0 1 2 4 1 6 2 3 1 .. 6 .. 1 2 .. 2 2 2 .. 1 2 2 0 1 3 1 0 1 2 1 0 3 1 1 1 3 2 2 1 2 2 5 3 2 1 1 1 1 2 3 2 .. 3
1 3 5 3 2 .. 2 2 5 0 8 2 1 2 .. 8 .. 3 1 .. 2 2 1 .. .. 2 1 0 1 5 1 0 1 3 1 .. 3 1 .. 1 4 3 2 0 1 1 7 4 3 0 .. 1 1 4 3 3 .. 4
50 84 47 61 73 8 82 81 69 61 57 66 74 60 .. 62 .. 34 59 .. 71 59 49 .. 70 61 45 63 72 85 59 41 70 83 51 63 63 68 72 78 49 65 79 65 41 75 81 70 47 68 58 66 70 78 78 73 .. 84
2012 World Development Indicators
60 72 51 65 71 .. 66 71 64 49 51 56 80 63 .. 57 .. 81 54 .. 59 59 53 .. .. 50 62 70 74 74 61 53 56 80 62 .. 59 50 .. 70 64 57 71 61 69 86 76 51 49 90 .. 79 72 67 74 67 .. 84
231
4.5
Structure of merchandise imports Merchandise imports
Food
Agricultural raw materials
Fuels
Ores and metals
Manufactures
$ millions
% of total 2000 2010
% of total 2000 2010
% of total 2000 2010
% of total 2000 2010
% of total 2000 2010
2000
Romania Russian Federation Rwanda Saudi Arabia Senegal Serbia Sierra Leone Singapore Slovak Republic Slovenia Somalia South Africa South Sudan Spain Sri Lanka Sudan Swaziland Sweden Switzerland Syrian Arab Republic Tajikistan Tanzania Thailand Timor-Leste Togo Trinidad and Tobago Tunisia Turkey Turkmenistan Uganda Ukraine United Arab Emirates United Kingdom United States Uruguay Uzbekistan Venezuela, RB Vietnam West Bank and Gaza Yemen, Rep. Zambia Zimbabwe World Low income Middle income Lower middle income Upper middle income Low & middle income East Asia & Pacific Europe & Central Asia Latin America & Carib. Middle East & N. Africa South Asia Sub-Saharan Africa High income Euro area
2010
13,148 61,995 44,659 248,738 211 1,431 30,238 97,077 1,519 4,782 .. 16,734 149 770 134,545 310,791 12,760 66,557 10,147 30,037 .. .. 29,695 94,040 .. .. 156,143 314,320 7,177 13,512 1,553 10,045 1,046 1,700 72,880 148,710 82,521 175,933 3,815 16,900 675 2,900 1,524 7,830 61,924 182,400 .. 298 562 1,550 3,308 6,575 8,567 22,218 54,503 185,542 1,786 5,600 1,536 4,550 13,956 60,911 35,009 160,000 348,058 560,097 1,259,300 1,969,184 3,466 8,622 2,697 8,386 16,213 40,800 15,638 84,801 .. .. 2,324 9,700 888 5,321 1,863 3,800 6,659,168 t 15,264,186 t 38,879 134,286 1,250,130 4,613,566 282,691 1,083,885 967,323 3,529,396 1,289,014 4,747,794 475,589 2,046,331 182,530 786,004 375,652 872,376 92,037 329,532 81,760 419,140 81,357 302,642 5,370,223 10,519,458 1,904,622 3,949,668
7 20 21 18 23 9 33 3 6 6 .. 5 .. 9 14 23 19 6 6 19 10 15 4 .. 18 8 8 4 12 14 6 11 8 4 11 .. 12 5 .. 36 8 4 7w 16 8 10 7 8 5 10 7 17 8 12 7 8
Note: Components may not sum to 100 percent because of unclassified trade.
232
2012 World Development Indicators
8 13 13 16 22 .. .. 3 6 8 .. 6 .. 10 15 15 .. 9 6 14 .. 10 5 .. 16 11 9 4 .. 12 9 7 10 5 10 .. 15 8 .. 28 5 19 7w .. 8 10 7 8 6 9 7 15 7 10 7 9
1 2 3 1 2 4 4 0 2 4 .. 1 .. 2 1 1 2 2 1 3 1 3 3 .. 2 1 3 4 0 2 2 1 2 1 3 .. 2 3 .. 2 3 1 2w 3 3 3 3 3 4 3 2 3 4 2 2 2
1 1 2 1 2 .. .. 0 1 3 .. 1 .. 1 1 1 .. 1 1 3 .. 1 2 .. 1 1 2 3 .. 1 1 0 1 1 2 .. 1 3 .. 1 1 3 1w .. 2 2 2 2 3 2 1 2 2 1 1 2
12 4 14 0 23 20 28 12 18 9 .. 14 .. 12 9 7 13 9 5 4 37 19 12 .. 19 32 11 14 1 17 43 1 4 11 15 .. 4 14 .. 12 12 42 10 w 16 11 20 9 11 10 15 7 7 34 14 10 9
10 2 8 0 30 .. .. 26 13 13 .. 20 .. 18 17 4 .. 13 8 31 .. 28 18 .. 14 33 13 15 .. 20 32 1 11 19 24 .. 1 11 .. 21 12 11 16 w .. 15 23 13 15 15 14 11 13 32 17 16 15
4 3 2 3 1 4 1 2 3 5 .. 2 .. 3 1 1 1 3 6 2 0 1 3 .. 2 2 2 4 1 2 5 2 3 2 1 .. 2 2 .. 1 3 3 3w 2 3 3 3 3 4 4 2 2 4 1 3 3
3 1 1 5 2 .. .. 2 4 6 .. 2 .. 4 2 1 .. 4 4 4 .. 1 5 .. 2 5 4 8 .. 1 4 5 4 2 1 .. 1 4 .. 1 21 14 4w .. 6 4 6 6 10 4 3 3 5 2 4 4
76 59 60 76 51 63 35 82 72 76 .. 69 .. 73 74 68 64 74 83 65 51 63 77 .. 59 56 76 70 80 65 41 85 78 77 69 .. 81 73 .. 49 73 48 74 w 63 72 57 76 71 76 63 79 56 49 66 75 72
75 69 76 77 44 .. .. 66 76 70 .. 65 .. 65 65 78 .. 69 81 47 .. 60 70 .. 67 50 72 63 .. 65 53 73 68 70 62 .. 81 74 .. 50 62 52 68 w .. 68 60 70 68 66 63 77 66 52 67 68 67
About the data
4.5
ECONOMY
Structure of merchandise imports Definitions
Data on imports of goods are derived from the
• Merchandise imports are the c.i.f. value of goods
same sources as data on exports. In principle, world
purchased from the rest of the world valued in U.S.
exports and imports should be identical. Similarly,
dollars. • Food corresponds to the commodities in
exports from an economy should equal the sum of
SITC sections 0 (food and live animals), 1 (beverages
imports by the rest of the world from that economy.
and tobacco), and 4 (animal and vegetable oils and
But differences in timing and definitions result in dis-
fats) and SITC division 22 (oil seeds, oil nuts, and oil
crepancies in reported values at all levels. For further
kernels). • Agricultural raw materials correspond to
discussion of indicators of merchandise trade, see
SITC section 2 (crude materials except fuels), exclud-
About the data for tables 4.4 and 6.1.
ing divisions 22, 27 (crude fertilizers and minerals
The value of imports is generally recorded as the
excluding coal, petroleum, and precious stones),
cost of the goods when purchased by the importer
and 28 (metalliferous ores and scrap). • Fuels cor-
plus the cost of transport and insurance to the fron-
respond to SITC section 3 (mineral fuels). • Ores
tier of the importing country—the cost, insurance,
and metals correspond to the commodities in SITC
and freight (c.i.f.) value, corresponding to the landed
divisions 27, 28, and 68 (nonferrous metals). • Man-
cost at the point of entry of foreign goods into the
ufactures correspond to the commodities in SITC
country. A few countries, including Australia, Canada,
sections 5 (chemicals), 6 (basic manufactures), 7
and the United States, collect import data on a free
(machinery and transport equipment), and 8 (miscel-
on board (f.o.b.) basis and adjust them for freight and
laneous manufactured goods), excluding division 68.
insurance costs. Many countries report trade data in U.S. dollars. When countries report in local currency, the United Nations Statistics Division applies the average official exchange rate to the U.S. dollar for the period shown. Countries may report trade according to the general or special system of trade. Under the general system imports include goods imported for domestic consumption and imports into bonded warehouses and free trade zones. Under the special system imports comprise goods imported for domestic consumption (including transformation and repair) and withdrawals for domestic consumption from bonded warehouses and free trade zones. Goods transported through a country en route to another are excluded. The data on total imports of goods (merchandise) in the table come from the World Trade Organization (WTO). For further discussion of the WTO’s sources
Data sources
and methodology, see About the data for table 4.4.
Data on merchandise imports are from the WTO.
The import shares by major commodity group are
Data on shares of imports by major commodity
from the United Nations Statistics Division’s Com-
group are from the United Nations Statistics Divi-
modity Trade Statistics (Comtrade) database. The
sion’s Comtrade database. The WTO publishes
values of total imports reported here have not
data on world trade in its Annual Report. The Inter-
been fully reconciled with the estimates of imports
national Monetary Fund publishes estimates of
of goods and services from the national accounts
total imports of goods in its International Finan-
(shown in table 4.8) or those from the balance of
cial Statistics and Direction of Trade Statistics,
payments (table 4.17).
as does the United Nations Statistics Division in
The classification of commodity groups is based
its Monthly Bulletin of Statistics. And the United
on the Standard International Trade Classification
Nations Conference on Trade and Development
(SITC) revision 3. Previous editions contained data
publishes data on the structure of imports in
based on the SITC revision 1. Data for earlier years in
its Handbook of Statistics. Tariff line records of
previous editions may differ because of this change
imports are compiled in the United Nations Sta-
in methodology. Concordance tables are available
tistics Division’s Comtrade database.
to convert data reported in one system to another.
2012 World Development Indicators
233
4.6
Structure of service exports Commercial service exports
Transport
$ millions
Afghanistan Albania Algeria Angola Argentina Armenia Australia Austria Azerbaijan Bahrain Bangladesh Belarus Belgium Benin Bolivia Bosnia and Herzegovina Botswana Brazil Bulgaria Burkina Faso Burundi Cambodia Cameroon Canada Central African Republic Chad Chile China Hong Kong SAR, China Colombia Congo, Dem. Rep. Congo, Rep. Costa Rica Côte d’Ivoire Croatia Cuba Cyprus Czech Republic Denmark Dominican Republic Ecuador Egypt, Arab Rep. El Salvador Eritrea Estonia Ethiopia Finland France Gabon Gambia, The Georgia Germany Ghana Greece Guatemala Guinea Guinea-Bissau Haiti
234
Travel
% of total
Insurance and financial services
% of total
Computer, information, communications, and other commercial services
% of total
% of total
2000
2010
2000
2010
2000
2010
2000
2010
2000
2010
.. 429 .. 267 4,775 130 19,413 22,865 234 933 283 989 36,285 126 207 448 306 8,961 2,129 28 2 423 666 39,271 .. .. 3,995 30,146 40,362 1,984 .. 130 1,911 415 4,056 .. 6,751 3,798 23,721 3,143 793 9,687 673 54 1,458 387 7,669 82,115 171 73 320 79,659 490 19,181 702 27 4 158
.. 2,192 2,794 857 12,931 750 48,490 54,161 2,017 4,047 1,209 4,470 85,339 204 530 1,280 385 30,294 6,750 142 7 1,671 1,105 67,432 .. .. 10,685 170,249 106,432 4,357 .. 303 4,149 816 11,034 .. 20,911 8,044 60,405 4,998 1,371 23,618 944 .. 4,485 1,991 27,729 143,896 .. 88 1,514 233,338 1,344 37,336 2,192 61 32 183
.. 4 .. 6 24 49 22 18 51 30 32 59 24 14 24 6 17 16 30 13 43 17 21 19 .. .. 55 12 32 30 .. 26 13 20 14 .. 21 19 45 2 37 27 37 18 49 56 22 22 56 27 48 25 20 41 12 58 2 2
.. 11 28 5 16 21 18 24 32 20 14 67 30 9 13 23 10 16 19 26 10 14 43 17 .. .. 61 20 27 28 .. 4 7 29 10 .. 24 24 .. 8 26 34 32 .. 40 59 11 25 .. 42 46 25 27 55 13 6 0 ..
.. 91 .. 18 61 29 48 43 27 61 18 9 19 61 33 52 73 20 50 67 37 72 9 27 .. .. 21 54 15 52 .. 9 68 12 68 .. 44 51 15 91 51 45 32 64 35 15 18 40 12 67 44 23 68 48 69 7 77 81
.. 74 10 84 38 54 56 35 33 34 7 10 12 64 58 46 57 20 53 47 24 75 14 23 .. .. 15 27 19 48 .. 18 48 14 73 .. 34 27 .. 84 57 53 41 .. 24 26 10 32 .. 36 44 15 46 33 63 3 38 91
.. 0 .. 13 0 3 5 8 1 .. 6 1 7 2 22 4 4 8 1 0 0 0 3 7 .. .. 3 1 12 4 .. 2 0 13 1 .. 6 6 .. 0 0 1 10 1 1 1 0 3 0 1 3 5 1 1 3 0 2 ..
.. 0 9 .. 0 3 3 4 1 22 4 0 5 2 13 1 1 8 3 2 22 0 4 11 .. .. 3 2 14 1 .. 31 1 13 1 .. 2 15 .. 1 .. 1 3 .. 2 0 2 3 .. 0 4 8 1 2 2 16 9 ..
.. 5 .. 81 15 19 25 31 22 8 44 31 50 22 21 38 7 56 18 19 20 11 68 46 .. .. 22 33 42 15 .. 63 18 55 17 .. 30 25 39 7 13 27 20 17 15 28 60 34 32 6 5 46 11 9 16 34 21 19
.. 15 54 11 46 22 22 37 35 24 75 23 53 24 16 30 32 56 25 26 44 11 38 48 .. .. 21 51 40 23 .. 47 44 57 16 .. 40 34 .. 7 17 12 23 .. 34 14 76 40 .. 22 7 53 26 10 22 74 53 9
2012 World Development Indicators
Commercial service exports
Transport
$ millions
Honduras Hungary India Indonesia Iran, Islamic Rep. Iraq Ireland Israel Italy Jamaica Japan Jordan Kazakhstan Kenya Korea, Dem. Rep. Korea, Rep. Kosovo Kuwait Kyrgyz Republic Lao PDR Latvia Lebanon Lesotho Liberia Libya Lithuania Macedonia, FYR Madagascar Malawi Malaysia Mali Mauritania Mauritius Mexico Moldova Mongolia Morocco Mozambique Myanmar Namibia Nepal Netherlands New Zealand Nicaragua Niger Nigeria Norway Oman Pakistan Panama Papua New Guinea Paraguay Peru Philippines Poland Portugal Puerto Rico Qatar
Travel
% of total
Insurance and ďŹ nancial services
% of total
4.6
ECONOMY
Structure of service exports
Computer, information, communications, and other commercial services
% of total
% of total
2000
2010
2000
2010
2000
2010
2000
2010
2000
2010
487 5,836 16,031 5,061 1,357 .. 18,326 15,619 55,998 1,988 68,303 1,602 905 727 .. 30,650 .. 1,571 57 134 1,131 4,412 20 .. 119 1,052 290 314 34 13,812 92 24 1,066 13,291 155 74 2,854 325 459 163 410 48,361 4,352 187 35 1,833 17,528 452 1,284 1,961 243 573 1,445 3,377 10,395 8,905 .. ..
1,003 19,288 123,277 16,211 .. 1,721 97,833 24,209 97,368 2,600 138,875 4,782 3,890 2,920 .. 81,556 .. 7,137 679 489 3,657 15,706 44 40 410 4,064 903 .. 126 32,760 335 .. 2,656 14,935 663 483 12,138 576 334 835 584 93,361 8,908 430 100 2,613 39,506 1,761 2,949 5,659 279 1,324 3,816 14,358 32,700 22,957 .. ..
12 9 12 16 49 .. 8 16 17 17 37 19 51 57 .. 45 .. 88 29 15 68 0 3 .. 13 47 42 16 26 20 36 3 21 8 54 41 17 30 17 23 15 35 28 16 24 12 55 43 65 59 5 12 17 14 24 16 .. ..
5 20 11 16 .. 22 5 18 15 11 28 18 58 54 .. 47 .. 45 22 10 50 4 2 56 64 60 32 .. 18 14 6 .. 14 7 37 36 18 28 45 16 7 27 20 11 8 75 40 36 48 53 7 17 22 9 27 27 .. ..
53 64 22 98 37 .. 14 26 49 67 5 45 39 39 .. 22 .. 6 27 85 12 97 88 .. 63 37 13 39 74 36 44 83 51 62 25 49 71 23 35 68 38 15 52 69 64 6 12 49 6 23 3 13 58 64 55 59 .. ..
65 28 11 43 .. 0 4 20 39 77 10 71 26 27 .. 12 .. 3 42 78 17 50 79 31 15 25 22 .. 55 56 57 .. 48 79 26 51 55 34 22 53 59 14 55 72 66 22 12 44 10 30 1 16 60 18 29 44 .. ..
3 3 3 0 11 .. 17 0 2 1 4 0 2 1 .. 3 .. 6 3 0 4 1 0 .. 17 1 2 1 2 2 3 .. 6 14 2 0 1 3 .. 0 0 2 1 2 4 1 3 3 1 9 2 5 9 3 3 3 .. ..
2 1 6 2 .. 0 19 0 6 2 4 .. 3 5 .. 4 .. 2 1 2 7 14 0 .. 18 1 1 .. 0 2 2 .. 3 12 1 2 2 1 .. 1 0 2 1 1 3 1 4 1 3 9 3 2 6 1 2 1 .. ..
32 24 63 2 3 .. 61 58 32 15 53 36 8 4 .. 31 .. 0 42 0 17 1 9 .. 7 16 44 44 0 41 18 15 22 16 19 10 10 47 48 8 47 48 19 13 8 82 30 5 27 9 90 70 17 20 19 22 .. ..
28 52 71 38 .. 78 72 63 40 10 59 11 13 14 .. 37 .. 50 35 9 26 32 19 13 3 14 45 .. 26 30 35 .. 34 2 36 11 25 36 34 30 34 57 24 16 23 3 45 18 38 8 89 64 12 71 42 28 .. ..
2012 World Development Indicators
235
4.6
Structure of service exports Commercial service exports
Transport
$ millions 2000
Romania Russian Federation Rwanda Saudi Arabia Senegal Serbia Sierra Leone Singapore Slovak Republic Slovenia Somalia South Africa South Sudan Spain Sri Lanka Sudan Swaziland Sweden Switzerland Syrian Arab Republic Tajikistan Tanzania Thailand Timor-Leste Togo Trinidad and Tobago Tunisia Turkey Turkmenistan Uganda Ukraine United Arab Emirates United Kingdom United States Uruguay Uzbekistan Venezuela, RB Vietnam West Bank and Gaza Yemen, Rep. Zambia Zimbabwe World Low income Middle income Lower middle income Upper middle income Low & middle income East Asia & Pacific Europe & Central Asia Latin America & Carib. Middle East & N. Africa South Asia Sub-Saharan Africa High income Euro area
236
% of total 2010
1,720 8,728 9,565 44,605 41 243 4,779 10,346 330 909 .. 3,525 39 60 28,420 112,061 2,218 5,817 1,883 6,120 .. .. 4,888 13,617 .. .. 52,112 122,773 915 2,448 24 224 271 250 22,193 64,835 29,443 81,649 1,480 7,040 60 182 575 2,047 13,785 34,058 .. .. 46 265 543 758 2,680 5,471 19,267 34,247 269 .. 205 984 3,800 16,466 .. .. 118,567 253,287 275,881 522,510 1,249 2,458 .. .. 1,057 1,626 2,702 7,460 453 554 174 1,460 114 312 .. .. 1,530,963 t 3,806,462 t 6,240 20,076 223,261 773,203 63,035 276,733 161,130 504,513 229,067 792,143 70,928 280,031 44,988 144,429 51,355 109,176 .. .. 19,358 131,836 15,245 40,508 1,305,653 3,018,606 472,024 1,145,715
2012 World Development Indicators
Travel
2000
37 37 34 .. 10 .. 46 41 45 26 .. 24 .. 16 44 63 7 21 15 17 76 10 24 .. 23 38 22 15 50 15 77 .. 16 16 30 .. 35 .. 8 12 37 .. 24 w 30 21 23 21 22 16 39 18 .. 24 21 25 23
Insurance and ďŹ nancial services
% of total 2010
29 33 11 20 5 22 40 29 31 26 .. 12 .. 17 47 2 8 15 7 8 27 22 17 .. 34 28 28 28 .. 5 47 .. 12 14 18 .. 35 .. 5 13 49 .. 22 w 24 21 23 21 22 17 36 17 28 19 31 22 23
2000
21 36 57 .. 44 .. 27 18 20 51 .. 55 .. 57 27 22 8 18 23 73 3 65 54 .. 18 39 63 40 12 81 10 .. 18 37 57 .. 40 .. 62 42 58 .. 32 w 35 48 50 48 48 56 33 51 .. 20 35 27 32
Computer, information, communications, and other commercial services
% of total 2010
13 20 83 65 51 23 43 13 38 42 .. 67 .. 43 24 42 20 17 18 88 2 61 59 .. 26 48 48 61 .. 74 23 .. 13 26 61 .. 45 .. 74 80 40 .. 26 w 26 41 32 44 41 38 27 57 29 13 50 21 23
2000
8 1 .. .. 2 .. 0 8 2 1 .. 9 .. 3 4 8 0 6 41 2 2 3 1 .. 9 8 2 2 .. 1 1 .. 22 7 8 .. 0 .. 0 .. 4 .. 6w 4 4 2 4 4 1 2 9 .. 3 6 7 4
% of total 2010
2 3 0 12 2 2 1 13 1 2 .. 8 .. 5 3 6 16 3 25 1 5 2 1 .. 5 14 3 4 .. 3 3 .. 24 16 5 .. 0 .. 0 .. 2 .. 8w 4 4 3 5 4 2 3 8 3 5 5 9 5
2000
35 26 8 .. 45 .. 27 32 33 22 .. 12 .. 24 25 15 86 55 21 10 20 22 22 .. 51 14 13 43 38 4 12 .. 43 40 5 .. 24 .. 29 46 0 .. 38 w 33 28 32 27 28 29 27 23 .. 52 41 41 40
2010
56 43 6 3 42 53 15 45 30 30 .. 13 .. 36 26 49 55 65 49 3 65 15 23 .. 35 9 21 8 .. 18 27 .. 51 45 16 .. 19 .. 21 7 9 .. 45 w 47 33 42 31 33 43 34 19 40 63 14 48 49
About the data
4.6
ECONOMY
Structure of service exports Definitions
Balance of payments statistics, the main source of
lodging, and transport (within the economy visited),
• Commercial service exports are total service
information on international trade in services, have
including car rental.
exports minus exports of government services not
many weaknesses. Disaggregation of important
included elsewhere. • Transport covers all transport
components may be limited and varies considerably
services (sea, air, land, internal waterway, space,
across countries. There are inconsistencies in the
and pipeline) performed by residents of one economy
methods used to report items. And the recording of
for those of another and involving the carriage of
major flows as net items is common (for example,
passengers, movement of goods (freight), rental of
insurance transactions are often recorded as premi-
carriers with crew, and related support and auxiliary
ums less claims). These factors contribute to a down-
services. Excluded are freight insurance, which is
ward bias in the value of the service trade reported
included in insurance services; goods procured in
in the balance of payments.
ports by nonresident carriers and repairs of trans-
Efforts are being made to improve the coverage,
port equipment, which are included in goods; repairs
quality, and consistency of these data. Eurostat and
of harbors, railway facilities, and airfield facilities,
the Organisation for Economic Co-operation and
which are included in construction services; and
Development, for example, are working together
rental of carriers without crew, which is included
to improve the collection of statistics on trade in
in other services. • Travel covers goods and ser-
services in member countries. In addition, the Inter-
vices acquired from an economy by travelers in that
national Monetary Fund (IMF) has implemented
economy for their own use during visits of less than
the new classifi cation of trade in services intro-
one year for business or personal purposes. • Insur-
duced in the fifth edition of its Balance of Payments
ance and financial services cover freight insurance
Manual (1993).
on goods exported and other direct insurance such
Still, difficulties in capturing all the dimensions of
as life insurance; financial intermediation services
international trade in services mean that the record
such as commissions, foreign exchange transac-
is likely to remain incomplete. Cross-border intrafirm
tions, and brokerage services; and auxiliary services
service transactions, which are usually not captured
such as financial market operational and regulatory
in the balance of payments, have increased in recent
services. • Computer, information, communica-
years. An example is transnational corporations’ use
tions, and other commercial services cover such
of mainframe computers around the clock for data
activities as international telecommunications and
processing, exploiting time zone differences between
postal and courier services; computer data; news-
their home country and the host countries of their
related service transactions between residents and
affiliates. Another important dimension of service
nonresidents; construction services; royalties and
trade not captured by conventional balance of pay-
license fees; miscellaneous business, professional,
ments statistics is establishment trade—sales in
and technical services; and personal, cultural, and
the host country by foreign affiliates. By contrast,
recreational services.
cross-border intrafirm transactions in merchandise may be reported as exports or imports in the balance of payments. The data on exports of services in the table and on imports of services in table 4.7, unlike those in editions before 2000, include only commercial services and exclude the category “government services not included elsewhere.” The data are compiled by the IMF based on returns from national sources. Data on total trade in goods and services from the IMF’s Balance of Payments database are shown in table 4.17. International transactions in services are defined by the IMF’s Balance of Payments Manual (1993) as
Data sources
the economic output of intangible commodities that
Data on commercial service exports are from the
may be produced, transferred, and consumed at the
IMF, which publishes balance of payments data in
same time. Definitions may vary among reporting
its International Financial Statistics and Balance of
economies. Travel services include the goods and
Payments Statistics Yearbook.
services consumed by travelers, such as meals,
2012 World Development Indicators
237
4.7
Structure of service imports Commercial service imports
Transport
$ millions
Afghanistan Albania Algeria Angola Argentina Armenia Australia Austria Azerbaijan Bahrain Bangladesh Belarus Belgium Benin Bolivia Bosnia and Herzegovina Botswana Brazil Bulgaria Burkina Faso Burundi Cambodia Cameroon Canada Central African Republic Chad Chile China Hong Kong SAR, China Colombia Congo, Dem. Rep. Congo, Rep. Costa Rica Côte d’Ivoire Croatia Cuba Cyprus Czech Republic Denmark Dominican Republic Ecuador Egypt, Arab Rep. El Salvador Eritrea Estonia Ethiopia Finland France Gabon Gambia, The Georgia Germany Ghana Greece Guatemala Guinea Guinea-Bissau Haiti
238
Travel
% of total
Insurance and financial services
% of total
Computer, information, communications, and other commercial services
% of total
% of total
2000
2010
2000
2010
2000
2010
2000
2010
2000
2010
.. 413 .. 2,271 8,960 177 18,554 16,383 475 757 1,523 524 35,288 186 450 256 538 15,573 1,660 132 36 321 994 43,597 .. .. 4,664 35,858 24,588 3,242 .. 728 1,261 1,142 1,782 .. 1,563 5,364 21,063 1,340 1,225 7,161 912 24 870 479 8,323 64,400 846 32 271 135,812 514 10,918 786 183 30 270
.. 1,992 11,203 16,028 13,769 985 51,470 36,926 3,762 1,905 4,128 2,878 78,377 488 1,128 581 867 59,746 4,164 545 156 1,084 1,717 89,963 .. .. 11,568 192,174 50,869 7,893 .. 3,523 1,769 2,324 3,389 .. 3,114 16,925 51,894 2,044 2,950 12,991 1,024 .. 2,770 2,534 27,650 131,391 .. 72 996 262,245 2,444 19,892 2,362 381 85 1,223
.. 23 .. 14 27 66 34 23 30 57 66 21 22 67 60 48 42 28 44 64 53 53 22 21 .. .. 47 29 25 40 .. 11 33 45 21 .. 56 13 44 62 36 31 44 28 48 60 31 28 32 78 38 19 53 37 54 61 90 91
.. 16 26 19 27 45 31 33 21 40 83 48 25 59 41 39 51 19 22 56 71 57 36 23 .. .. 58 33 28 36 .. 15 38 58 16 .. 41 24 .. 59 59 51 45 .. 35 65 20 27 .. 48 56 24 46 54 48 60 36 47
.. 66 .. 6 49 22 34 38 28 30 19 41 29 7 17 28 37 25 32 15 38 10 21 29 .. .. 13 37 51 33 .. 7 38 17 32 .. 26 24 22 23 24 15 18 50 23 15 22 35 10 11 41 39 20 42 23 5 9 7
.. 68 4 1 35 41 39 27 21 27 6 21 24 11 28 33 3 27 30 12 13 18 11 33 .. .. 17 29 36 23 .. 5 24 15 25 .. 37 24 .. 19 19 17 21 .. 23 6 15 29 .. 15 20 30 24 14 33 2 30 5
.. 8 .. 3 4 6 5 7 2 3 8 3 8 9 14 10 4 6 5 18 4 4 4 10 .. .. 9 7 5 10 .. 5 4 11 5 .. 7 9 .. 7 3 7 17 2 1 4 0 2 8 10 8 2 6 3 8 4 0 ..
.. 5 2 7 5 7 3 4 3 23 2 3 4 5 13 5 5 5 6 14 3 6 7 11 .. .. 8 9 9 9 .. 5 8 9 6 .. 8 3 .. 9 8 11 13 .. 2 4 3 4 .. 7 14 4 5 10 9 7 5 0
.. 3 .. 78 20 6 27 32 42 10 6 35 41 17 9 14 17 41 18 3 4 32 53 40 .. .. 31 27 18 17 .. 77 25 27 42 .. 10 54 33 8 36 48 21 21 27 21 46 36 50 1 13 40 22 18 15 30 1 3
.. 11 67 73 33 7 27 36 55 11 9 28 47 25 18 22 42 48 43 18 14 19 46 33 .. .. 17 30 27 32 .. 75 30 27 53 .. 14 49 .. 13 14 21 21 .. 40 25 61 40 .. 30 11 42 25 22 10 31 28 47
2012 World Development Indicators
Commercial service imports
Transport
$ millions
Honduras Hungary India Indonesia Iran, Islamic Rep. Iraq Ireland Israel Italy Jamaica Japan Jordan Kazakhstan Kenya Korea, Dem. Rep. Korea, Rep. Kosovo Kuwait Kyrgyz Republic Lao PDR Latvia Lebanon Lesotho Liberia Libya Lithuania Macedonia, FYR Madagascar Malawi Malaysia Mali Mauritania Mauritius Mexico Moldova Mongolia Morocco Mozambique Myanmar Namibia Nepal Netherlands New Zealand Nicaragua Niger Nigeria Norway Oman Pakistan Panama Papua New Guinea Paraguay Peru Philippines Poland Portugal Puerto Rico Qatar
Travel
% of total
Insurance and ďŹ nancial services
% of total
4.7
ECONOMY
Structure of service imports
Computer, information, communications, and other commercial services
% of total
% of total
2000
2010
2000
2010
2000
2010
2000
2010
2000
2010
688 4,708 18,898 15,381 1,577 .. 31,212 11,849 54,632 1,391 115,686 1,463 1,831 665 .. 33,128 .. 4,115 144 13 680 3,340 247 .. 815 655 260 395 167 16,603 324 130 748 16,242 190 158 1,520 439 310 308 193 49,941 4,404 334 125 3,144 14,832 1,758 2,109 1,096 772 390 2,165 5,175 8,862 6,787 .. ..
1,312 15,376 116,140 25,601 .. 7,565 107,270 17,787 108,616 1,767 155,800 4,164 11,142 1,816 .. 92,936 .. 12,260 915 258 2,211 13,262 479 234 5,251 2,718 816 .. 79 32,216 813 .. 1,956 21,818 729 760 5,724 1,102 761 697 846 84,384 9,227 660 735 20,163 42,358 6,525 6,481 2,569 2,737 707 5,843 11,188 29,473 14,237 .. ..
60 15 46 26 72 .. 8 36 24 42 30 47 18 51 .. 33 .. 37 35 38 31 14 7 .. 43 34 50 48 53 35 74 37 35 38 32 54 41 38 82 29 34 26 32 46 67 20 35 37 72 55 21 61 40 40 17 29 .. ..
45 21 40 34 .. 53 2 33 24 41 30 51 17 51 .. 31 .. 40 46 6 30 14 14 54 45 52 39 .. 19 37 57 .. 28 49 39 38 46 28 60 32 33 22 30 50 73 43 28 41 58 60 22 68 42 44 21 30 .. ..
17 35 14 21 13 .. 8 24 29 15 28 24 22 20 .. 22 .. 61 11 62 36 80 78 .. 49 39 13 29 30 12 12 33 24 34 38 33 28 25 8 24 38 24 33 23 21 19 31 27 12 17 7 21 20 32 37 33 .. ..
24 16 9 25 .. 10 7 19 25 11 18 34 11 12 .. 19 .. 55 30 79 29 36 65 27 39 29 11 .. 59 25 12 .. 20 33 36 35 21 23 7 21 48 23 33 31 7 28 33 15 14 15 4 22 22 31 29 27 .. ..
2 7 11 2 14 .. 9 3 3 8 3 6 3 10 .. 1 .. 1 9 â&#x20AC;&#x201C;3 10 4 3 .. 1 2 3 2 0 3 4 .. 7 14 2 1 2 6 .. 6 7 3 3 7 3 3 6 6 4 8 5 15 8 4 6 5 .. ..
8 2 10 6 .. 27 13 2 8 11 6 7 5 10 .. 2 .. 3 3 5 6 10 4 1 12 2 6 .. 7 4 6 .. 8 15 3 4 4 3 .. 7 4 3 1 11 4 3 3 11 4 16 13 8 9 3 4 4 .. ..
21 43 29 51 1 .. 75 38 44 35 39 22 57 19 .. 44 .. 1 46 0 22 2 12 .. 8 26 34 21 17 49 10 30 34 14 28 13 29 32 10 40 29 47 32 23 9 59 28 30 12 20 67 4 33 25 39 33 .. ..
23 61 41 35 .. 10 78 46 44 37 46 7 67 27 .. 48 .. 2 21 10 35 41 17 18 4 17 44 .. 15 38 25 .. 44 3 21 24 29 46 33 40 16 52 36 8 17 26 36 33 24 9 61 3 27 21 46 38 .. ..
2012 World Development Indicators
239
4.7
Structure of service imports Commercial service imports
Transport
$ millions 2000
Romania Russian Federation Rwanda Saudi Arabia Senegal Serbia Sierra Leone Singapore Slovak Republic Slovenia Somalia South Africa South Sudan Spain Sri Lanka Sudan Swaziland Sweden Switzerland Syrian Arab Republic Tajikistan Tanzania Thailand Timor-Leste Togo Trinidad and Tobago Tunisia Turkey Turkmenistan Uganda Ukraine United Arab Emirates United Kingdom United States Uruguay Uzbekistan Venezuela, RB Vietnam West Bank and Gaza Yemen, Rep. Zambia Zimbabwe World Low income Middle income Lower middle income Upper middle income Low & middle income East Asia & Pacific Europe & Central Asia Latin America & Carib. Middle East & N. Africa South Asia Sub-Saharan Africa High income Euro area
240
% of total 2010
1,948 9,341 16,230 72,278 113 442 10,927 50,996 396 1,110 .. 3,477 82 134 29,968 96,255 1,779 6,781 1,423 4,305 .. .. 5,657 18,023 .. .. 32,837 86,752 1,592 3,084 632 2,195 300 650 24,127 47,316 14,533 39,435 1,468 3,377 103 389 620 1,840 15,329 44,592 .. .. 116 374 363 335 1,119 3,165 7,624 18,343 669 .. 459 1,809 2,590 12,137 .. .. 96,893 162,086 203,169 367,016 842 1,365 .. .. 4,236 10,548 3,252 9,921 459 777 757 2,263 322 901 .. .. 1,485,017 t 3,472,677 t 9,090 29,773 264,736 906,395 82,866 296,871 182,790 613,085 273,647 935,741 94,144 323,841 39,655 160,613 67,954 155,546 23,663 70,149 24,653 132,213 25,073 97,316 1,211,620 2,536,783 477,111 1,044,273
2012 World Development Indicators
Travel
2000
32 14 73 21 61 .. 21 42 24 25 .. 43 .. 31 62 88 12 14 36 48 79 33 44 .. 72 48 49 32 23 33 15 .. 25 30 47 .. 43 .. 12 45 63 .. 29 w 58 36 40 35 36 33 26 38 48 53 41 27 24
Insurance and ďŹ nancial services
% of total 2010
30 17 73 25 54 29 70 30 28 22 .. 39 .. 24 66 46 11 17 21 47 51 39 50 .. 62 39 50 45 .. 55 34 .. 18 21 45 .. 38 .. 11 47 56 .. 27 w 61 39 43 38 39 37 33 42 42 49 40 24 25
2000
22 55 19 .. 12 .. 39 16 17 36 .. 37 .. 18 15 9 10 34 37 46 2 54 18 .. 2 41 23 22 19 13 18 .. 39 33 33 .. 25 .. 66 9 14 .. 31 w 20 28 20 30 28 28 33 30 22 15 27 32 31
Computer, information, communications, and other commercial services
% of total 2010
18 37 17 41 14 27 10 17 29 28 .. 31 .. 19 15 51 9 28 28 45 5 45 12 .. 13 31 17 26 .. 14 31 .. 31 23 31 .. 17 .. 62 8 7 .. 25 w 14 26 22 27 26 25 28 29 20 11 25 25 25
2000
7 3 .. 2 10 .. 4 7 5 2 .. 7 .. 4 6 0 2 3 7 2 6 3 5 .. 15 0 7 13 2 3 6 .. 6 8 6 .. 5 .. 2 7 6 .. 6w 7 8 6 8 8 5 7 10 .. 9 6 5 3
% of total 2010
7 4 1 5 12 3 5 6 8 4 .. 4 .. 7 6 1 6 1 7 4 7 4 5 .. 9 7 9 10 .. 13 10 .. 7 21 4 .. 5 .. 2 10 10 .. 9w 5 8 7 9 8 7 7 11 7 8 5 10 5
2000
39 28 8 77 18 .. 36 34 54 37 .. 13 .. 47 17 3 76 50 20 7 13 9 33 .. 12 12 20 32 57 64 61 .. 30 29 13 .. 27 .. 20 38 18 .. 34 w 18 28 34 27 28 34 33 21 23 23 27 36 41
2010
46 43 9 28 21 41 15 47 35 46 .. 26 .. 49 13 2 74 54 43 4 37 12 32 .. 16 22 23 18 .. 18 26 .. 44 36 20 .. 39 .. 25 35 26 .. 39 w 21 27 29 27 27 31 32 18 31 32 30 42 45
About the data
4.7
ECONOMY
Structure of service imports Definitions
Trade in services differs from trade in goods because
• Commercial service imports are total service
services are produced and consumed at the same
imports minus imports of government services not
time. Thus services to a traveler may be consumed
included elsewhere. • Transport covers all transport
in the producing country (for example, use of a hotel
services (sea, air, land, internal waterway, space,
room) but are classified as imports of the traveler’s
and pipeline) performed by residents of one economy
country. In other cases services may be supplied
for those of another and involving the carriage of
from a remote location; for example, insurance
passengers, movement of goods (freight), rental of
services may be supplied from one location and
carriers with crew, and related support and auxiliary
consumed in another. For further discussion of the
services. Excluded are freight insurance, which is
problems of measuring trade in services, see About
included in insurance services; goods procured in
the data for table 4.6.
ports by nonresident carriers and repairs of trans-
The data on imports of services in the table and on
port equipment, which are included in goods; repairs
exports of services in table 4.6, unlike those in edi-
of harbors, railway facilities, and airfield facilities,
tions before 2000, include only commercial services
which are included in construction services; and
and exclude the category “government services not
rental of carriers without crew, which is included
included elsewhere.” The data are compiled by the
in other services. • Travel covers goods and ser-
International Monetary Fund (IMF) based on returns
vices acquired from an economy by travelers in that
from national sources.
economy for their own use during visits of less than
International transactions in services are defined
one year for business or personal purposes. • Insur-
by the IMF’s Balance of Payments Manual (1993) as
ance and financial services cover freight insurance
the economic output of intangible commodities that
on goods imported and other direct insurance such
may be produced, transferred, and consumed at the
as life insurance; financial intermediation services
same time. Definitions may vary among reporting
such as commissions, foreign exchange transac-
economies.
tions, and brokerage services; and auxiliary services
Travel services include the goods and services
such as financial market operational and regulatory
consumed by travelers, such as meals, lodging, and
services. • Computer, information, communica-
transport (within the economy visited), including car
tions, and other commercial services cover such
rental.
activities as international telecommunications, and postal and courier services; computer data; newsrelated service transactions between residents and nonresidents; construction services; royalties and license fees; miscellaneous business, professional, and technical services; and personal, cultural, and recreational services.
Data sources Data on commercial service imports are from the IMF, which publishes balance of payments data in its International Financial Statistics and Balance of Payments Statistics Yearbook.
2012 World Development Indicators
241
4.8 Afghanistan Albania Algeria Angola Argentina Armenia Australia Austria Azerbaijan Bahrain Bangladesh Belarus Belgium Benin Bolivia Bosnia and Herzegovina Botswana Brazil Bulgaria Burkina Faso Burundi Cambodia Cameroon Canada Central African Republic Chad Chile China Hong Kong SAR, China Colombia Congo, Dem. Rep. Congo, Rep. Costa Rica Côte d’Ivoire Croatia Cuba Cyprus Czech Republic Denmark Dominican Republic Ecuador Egypt, Arab Rep. El Salvador Eritrea Estonia Ethiopia Finland France Gabon Gambia, The Georgia Germany Ghana Greece Guatemala Guinea Guinea-Bissau Haiti
242
Structure of demand Household final consumption expenditure
General government final consumption expenditure
Gross capital formation
Exports of goods and services
Imports of goods and services
Gross savings
% of GDP 2000 2010
% of GDP 2000 2010
% of GDP 2000 2010
% of GDP 2000 2010
% of GDP 2000 2010
% of GDP 2000 2010
31 19 41 90 11 23 19 46 39 89 14 69 78 15 18 29 53 10 50 9 8 50 23 46 20 17 32 23 143 16 22 80 49 40 42 14 55 63 47 37 37 16 27 15 85 12 44 29 69 48 23 33 49 26 20 24 32 13
62 37 21 63 12 51 21 44 38 64 19 72 75 28 27 76 41 12 56 25 20 62 20 40 24 35 30 21 139 17 21 44 46 33 45 17 55 66 40 46 31 23 42 82 88 24 34 28 33 57 40 33 67 40 29 28 52 33
.. 24 .. 24 13 4 21 24 17 20 27 23 25 10 11 13 41 14 12 5 4 14 15 23 .. .. 21 37 32 14 .. 31 13 8 18 .. 15 25 23 18 26 18 14 4 23 16 29 22 42 .. 22 20 15 14 12 15 –15 ..
112 85 42 .. 71 97 58 55 69 47 78 58 53 82 76 104 31 64 68 79 88 89 70 55 81 87 64 47 59 69 88 29 67 75 60 61 65 52 48 78 64 76 88 79 55 74 49 56 32 78 82 58 84 70 84 78 95 86
111 88 35 .. 60 78 54 55 37 31 77 57 53 .. 62 80 46 61 61 .. .. 82 .. 58 93 73 59 35 62 62 75 33 65 73 56 54 68 51 48 88 68 75 93 .. 53 89 55 58 43 78 77 57 80 75 86 77 .. ..
2012 World Development Indicators
8 9 14 .. 14 12 18 19 9 18 5 19 21 12 15 25 25 19 19 21 18 5 9 19 14 8 12 16 9 17 8 12 13 7 24 29 16 21 25 8 10 11 10 64 20 18 21 23 10 14 9 19 10 19 7 7 14 8
11 8 14 .. 15 13 18 19 11 13 5 16 24 .. 14 21 21 21 16 .. .. 6 .. 22 5 15 12 13 8 16 5 10 18 9 22 33 20 22 29 8 12 11 11 .. 21 10 25 25 10 15 21 20 11 18 10 8 .. ..
12 25 25 15 16 19 26 25 21 10 23 25 23 19 18 21 32 18 18 17 6 18 17 20 10 23 22 35 27 15 3 23 17 11 19 13 18 29 21 23 20 20 17 24 28 20 21 20 22 17 27 22 24 25 18 20 11 27
16 26 41 15 22 33 28 22 17 33 24 41 20 26 17 20 36 19 25 .. .. 17 .. 22 11 37 21 48 24 24 29 25 20 14 23 11 18 23 17 16 26 19 13 .. 20 21 19 19 26 26 20 17 22 16 15 20 .. 25
15 30 31 58 22 21 20 54 55 97 18 55 80 14 41 36 29 11 58 .. .. 54 28 29 15 44 39 30 223 16 15 82 38 41 38 20 40 79 50 22 33 21 26 5 78 11 40 25 52 29 35 47 25 22 25 35 .. 12
54 52 21 44 18 45 20 50 20 74 25 68 77 28 34 57 32 12 60 .. .. 60 33 31 23 69 32 26 217 18 34 50 41 36 39 18 47 75 45 34 39 26 44 20 72 32 39 28 31 49 52 41 38 30 36 39 .. 57
.. 13 54 16 22 19 24 25 46 43 38 25 23 13 25 15 26 17 24 .. .. 13 .. 18 .. .. 23 53 30 19 .. .. 15 15 22 .. 9 21 23 7 23 18 11 .. 24 17 21 17 .. 13 10 23 20 5 13 10 .. 23
Honduras Hungary India Indonesia Iran, Islamic Rep. Iraq Ireland Israel Italy Jamaica Japan Jordan Kazakhstan Kenya Korea, Dem. Rep. Korea, Rep. Kosovo Kuwait Kyrgyz Republic Lao PDR Latvia Lebanon Lesotho Liberia Libya Lithuania Macedonia, FYR Madagascar Malawi Malaysia Mali Mauritania Mauritius Mexico Moldova Mongolia Morocco Mozambique Myanmar Namibia Nepal Netherlands New Zealand Nicaragua Niger Nigeria Norway Oman Pakistan Panama Papua New Guinea Paraguay Peru Philippines Poland Portugal Puerto Rico Qatar
4.8
ECONOMY
Structure of demand Household final consumption expenditure
General government final consumption expenditure
Gross capital formation
Exports of goods and services
Imports of goods and services
Gross savings
% of GDP 2000 2010
% of GDP 2000 2010
% of GDP 2000 2010
% of GDP 2000 2010
% of GDP 2000 2010
% of GDP 2000 2010
54 75 13 41 23 .. 97 37 27 .. 11 42 57 22 .. 39 .. 56 42 30 42 14 34 21 36 45 49 31 26 120 27 46 61 31 50 54 28 16 0 41 23 70 35 24 18 54 47 59 13 73 66 38 16 51 27 29 75 67
66 78 14 30 17 .. 84 37 26 .. 10 68 49 32 .. 36 .. 30 48 44 49 36 103 26 15 51 64 38 35 101 39 74 62 33 75 68 33 37 1 45 32 65 33 51 26 32 29 31 15 70 49 49 18 53 34 40 98 22
71 55 64 61 48 .. 49 53 60 74 56 81 62 78 .. 55 .. 42 66 93 63 84 83 89 46 65 74 83 82 44 79 83 60 67 91 70 61 81 .. 63 76 50 60 84 83 .. 43 40 75 60 45 79 71 72 64 64 91 15
80 53 57 57 .. .. 51 58 60 80 59 85 49 78 .. 53 95 28 84 69 63 78 87 202 23 65 75 79 66 48 .. 70 76 65 91 53 57 82 .. 53 82 45 58 91 .. .. 43 40 82 71 70 69 63 72 61 66 94 21
13 21 13 7 14 .. 14 26 18 14 17 24 12 15 .. 12 .. 21 20 7 21 17 42 14 21 23 18 9 15 10 9 26 14 11 10 15 18 9 .. 24 9 22 17 12 13 .. 19 21 9 13 17 13 11 11 17 19 11 20
18 22 12 9 .. .. 19 24 21 17 20 21 11 13 .. 15 18 21 19 9 17 12 44 19 9 20 18 12 19 13 .. 18 14 12 24 14 18 12 .. 22 11 28 21 10 .. .. 22 20 8 6 9 9 10 10 19 22 11 25
28 27 24 22 33 .. 24 21 21 .. 25 22 19 17 .. 31 .. 11 20 14 24 20 44 5 13 19 22 15 14 27 25 19 26 24 24 29 26 31 12 17 24 22 21 30 11 .. 20 12 17 24 22 19 20 18 25 28 18 20
23 18 35 32 .. .. 11 16 20 21 20 15 25 21 .. 29 30 14 28 26 21 33 34 20 28 17 25 33 24 21 .. 28 22 25 24 41 35 24 23 23 35 19 20 28 .. .. 22 30 15 27 18 19 24 21 21 20 9 39
44 87 22 25 .. .. 101 37 27 26 15 45 44 26 .. 52 19 56 58 36 53 21 49 31 67 68 47 29 26 97 .. 48 45 30 40 55 33 25 0 39 10 78 29 41 .. 39 41 53 14 65 56 57 25 35 42 31 78 47
65 80 25 23 .. .. 82 35 29 43 14 66 29 39 .. 50 62 28 89 41 54 44 114 173 27 70 66 53 36 79 .. 63 58 32 78 62 43 43 0 38 37 71 27 70 .. 27 29 41 19 69 53 55 22 37 43 38 92 31
21 19 25 25 39 .. 24 17 21 17 28 23 21 14 .. 33 .. 50 15 2 19 –4 37 .. 22 13 22 9 10 36 16 21 26 20 16 23 24 10 .. 25 22 28 18 8 5 .. 35 29 20 23 32 11 17 23 19 18 .. ..
2012 World Development Indicators
16 20 34 32 .. .. 12 18 17 12 24 9 28 16 .. 32 .. 59 20 20 24 12 34 –2 67 19 24 .. 13 33 .. .. 16 24 16 27 31 11 .. 34 37 23 16 13 .. .. 36 38 22 18 20 23 23 27 17 10 .. ..
243
4.8
Structure of demand
Romania Russian Federation Rwanda Saudi Arabia Senegal Serbia Sierra Leone Singapore Slovak Republic Slovenia Somalia South Africa South Sudan Spain Sri Lanka Sudan Swaziland Sweden Switzerland Syrian Arab Republic Tajikistan Tanzaniaa Thailand Timor-Leste Togo Trinidad and Tobago Tunisia Turkey Turkmenistan Uganda Ukraine United Arab Emirates United Kingdom United States Uruguay Uzbekistan Venezuela, RB Vietnam West Bank and Gaza Yemen, Rep. Zambia Zimbabwe World Low income Middle income Lower middle income Upper middle income Low & middle income East Asia & Pacific Europe & Central Asia Latin America & Carib. Middle East & N. Africa South Asia Sub-Saharan Africa High income Euro area
Household final consumption expenditure
General government final consumption expenditure
Gross capital formation
Exports of goods and services
Imports of goods and services
Gross savings
% of GDP 2000 2010
% of GDP 2000 2010
% of GDP 2000 2010
% of GDP 2000 2010
% of GDP 2000 2010
% of GDP 2000 2010
33 44 9 44 28 24 18 192 70 54 .. 28 .. 29 39 15 76 47 46 35 99 13 67 .. 31 59 40 20 96 11 62 49 28 11 17 25 30 55 16 41 27 39 25 w 17 27 27 27 27 35 37 20 27 14 32 24 37
38 24 26 25 37 40 39 180 73 57 .. 25 .. 32 50 18 90 40 41 29 101 20 58 .. 51 45 43 23 81 22 57 41 29 15 20 22 18 57 71 34 41 36 25 w 25 26 27 25 25 31 31 21 25 16 31 25 36
78 46 88 37 76 88 100 43 56 57 .. 63 .. 60 72 76 78 49 60 64 84 78 57 112 92 57 61 71 36 77 54 61 66 69 77 62 52 66 95 60 87 60 61 w 79 60 67 58 61 50 60 66 60 67 69 61 57
60 49 81 34 82 74 84 37 58 56 .. 57 .. 58 66 61 77 49 58 71 94 66 54 .. .. 51 63 71 33 75 63 57 64 71 67 55 57 65 .. 81 55 101 62 w 80 56 64 54 56 42 60 63 .. 62 64 63 58
a. Covers mainland Tanzania only.
244
2012 World Development Indicators
7 15 11 26 13 20 14 11 20 19 .. 18 .. 17 11 8 19 26 11 12 8 12 11 35 10 9 17 12 14 15 21 9 19 14 12 19 12 6 27 14 10 24 16 w 10 14 12 15 14 14 14 15 15 11 16 17 20
15 19 15 23 9 19 12 11 20 21 .. 20 .. 21 16 15 25 27 11 10 29 18 13 .. .. 10 16 14 11 12 20 8 23 17 13 18 11 7 .. 12 13 17 19 w 10 14 11 15 14 13 17 15 .. 11 17 19 22
20 19 18 19 20 9 7 33 26 27 .. 16 .. 26 28 18 17 19 23 17 9 17 23 26 18 20 26 21 35 19 20 22 18 21 14 16 24 30 33 19 17 14 22 w 19 24 22 24 24 31 20 20 24 23 17 22 22
31 23 22 22 29 23 16 24 23 23 .. 25 .. 23 28 23 17 18 19 19 23 31 26 .. .. 11 26 20 59 24 19 25 15 15 19 26 21 39 .. 12 22 1 20 w 23 29 28 30 29 41 23 22 .. 32 24 18 19
23 30 12 57 25 35 17 211 81 65 .. 26 .. 26 22 20 58 50 54 35 15 24 71 .. .. 65 49 21 52 24 50 78 30 13 27 31 29 78 .. 30 44 37 28 w 20 29 28 29 28 37 31 22 .. 20 30 28 41
30 22 29 35 44 51 30 183 82 65 .. 27 .. 28 31 19 77 44 42 36 61 38 64 .. .. 38 54 27 55 34 53 69 33 16 26 31 17 88 .. 34 35 56 28 w 32 28 31 27 28 33 31 22 .. 25 32 28 39
16 36 13 29 14 .. –4 44 23 25 .. 16 .. 23 22 9 13 23 35 22 9 13 30 .. 1 26 22 18 .. 14 24 .. 15 18 11 .. 34 31 9 33 –1 .. 22 w 19 25 23 25 25 34 26 17 .. 25 16 22 22
26 28 15 33 19 16 13 46 20 22 .. 16 .. 19 25 18 3 25 36 17 3 21 31 .. .. 35 20 14 .. 19 17 .. 12 11 17 .. 31 32 .. 9 22 .. 19 w 27 30 28 30 30 46 22 21 .. 33 17 17 20
About the data
4.8
ECONOMY
Structure of demand Definitions
Gross domestic product (GDP) from the expenditure
1993 SNA guidelines are capital outlays on defense
• Household final consumption expenditure is the
side is made up of household final consumption
establishments that may be used by the general pub-
market value of all goods and services, including
expenditure, general government final consumption
lic, such as schools, airfields, and hospitals, and
durable products (such as cars and computers),
expenditure, gross capital formation (private and
intangibles such as computer software and mineral
purchased by households. It excludes purchases
public investment in fixed assets, changes in inven-
exploration outlays. Data on capital formation may
of dwellings but includes imputed rent for owner-
tories, and net acquisitions of valuables), and net
be estimated from direct surveys of enterprises and
occupied dwellings. It also includes government fees
exports (exports minus imports) of goods and ser-
administrative records or based on the commodity
for permits and licenses. Expenditures of nonprofit
vices. Such expenditures are recorded in purchaser
flow method using data from production, trade, and
institutions serving households are included, even
prices and include net taxes on products.
construction activities. The quality of data on govern-
when reported separately. Household consumption
Because policymakers have tended to focus on
ment fixed capital formation depends on the quality
expenditure may include any statistical discrepancy
fostering the growth of output, and because data on
of government accounting systems (which tend to
in the use of resources relative to the supply of
production are easier to collect than data on spend-
be weak in developing countries). Measures of fixed
resources. • General government fi nal consump-
ing, many countries generate their primary estimate
capital formation by households and corporations—
tion expenditure is all government current expendi-
of GDP using the production approach. Moreover,
particularly capital outlays by small, unincorporated
tures for purchases of goods and services (including
many countries do not estimate all the components
enterprises—are usually unreliable.
compensation of employees). It also includes most
of national expenditures but instead derive some
Estimates of changes in inventories are rarely
expenditures on national defense and security but
of the main aggregates indirectly using GDP (based
complete but usually include the most important
excludes military expenditures with potentially wider
on the production approach) as the control total.
activities or commodities. In some countries these
public use that are part of government capital forma-
Household final consumption expenditure (private
estimates are derived as a composite residual along
tion. • Gross capital formation is outlays on addi-
consumption in the 1968 United Nations System of
with household fi nal consumption expenditure.
tions to fixed assets of the economy, net changes in
National Accounts, or SNA) is often estimated as
According to national accounts conventions, adjust-
inventories, and net acquisitions of valuables. Fixed
a residual, by subtracting all other known expendi-
ments should be made for appreciation of the value
assets include land improvements (fences, ditches,
tures from GDP. The resulting aggregate may incor-
of inventory holdings due to price changes, but this
drains); plant, machinery, and equipment purchases;
porate fairly large discrepancies. When household
is not always done. In highly inflationary economies
and construction (roads, railways, schools, buildings,
consumption is calculated separately, many of the
this element can be substantial.
and so on). Inventories are goods held to meet tem-
estimates are based on household surveys, which
Data on exports and imports are compiled from
porary or unexpected fluctuations in production or
tend to be one-year studies with limited coverage.
customs reports and balance of payments data.
sales, and “work in progress.” • Exports and imports
Thus the estimates quickly become outdated and
Although the data from the payments side provide
of goods and services are the value of all goods and
must be supplemented by estimates using price- and
reasonably reliable records of cross-border transac-
other market services provided to or received from
quantity-based statistical procedures. Complicating
tions, they may not adhere strictly to the appropriate
the rest of the world. They include the value of mer-
the issue, in many developing countries the distinc-
definitions of valuation and timing used in the bal-
chandise, freight, insurance, transport, travel, royal-
tion between cash outlays for personal business
ance of payments or correspond to the change-of-
ties, license fees, and other services (communica-
and those for household use may be blurred. World
ownership criterion. This issue has assumed greater
tion, construction, financial, information, business,
Development Indicators includes in household con-
significance with the increasing globalization of inter-
personal, government services, and so on). They
sumption the expenditures of nonprofit institutions
national business. Neither customs nor balance of
exclude compensation of employees and investment
serving households.
payments data usually capture the illegal transac-
income (factor services in the 1968 SNA) and trans-
General government final consumption expenditure
tions that occur in many countries. Goods carried
fer payments. • Gross savings are gross national
(general government consumption in the 1968 SNA)
by travelers across borders in legal but unreported
income less total consumption, plus net transfers.
includes expenditures on goods and services for
shuttle trade may further distort trade statistics.
individual consumption as well as those on services
Gross savings represent the difference between
for collective consumption. Defense expenditures,
disposable income and consumption and replace
including those on capital outlays (with certain excep-
gross domestic savings, a concept used by the World
tions), are treated as current spending.
Bank and included in World Development Indicators Data sources
Gross capital formation (gross domestic invest-
editions before 2006. The change was made to con-
ment in the 1968 SNA) consists of outlays on
form to SNA concepts and definitions. For further
Data on national accounts indicators for most
additions to the economy’s fixed assets plus net
discussion of the problems in compiling national
developing countries are collected from national
changes in the level of inventories. It is generally
accounts, see Srinivasan (1994), Heston (1994),
statistical organizations and central banks by vis-
obtained from industry reports of acquisitions and
and Ruggles (1994). For an analysis of the reliability
iting and resident World Bank missions. Data for
distinguishes only the broad categories of capital
of foreign trade and national income statistics, see
high-income economies are from Organisation for
formation. The 1993 SNA recognizes a third cat-
Morgenstern (1963).
Economic Co-operation and Development (OECD)
egory of capital formation: net acquisitions of valu-
data files.
ables. Included in gross capital formation under the
2012 World Development Indicators
245
4.9
Growth of consumption and investment Household final consumption expenditure
General government final consumption expenditure
average annual % growth Total Per capita 1990–2000 2000–10 1990–2000 2000–10
Afghanistan Albania Algeria Angola Argentina Armenia Australia Austria Azerbaijan Bahrain Bangladesh Belarus Belgium Benin Bolivia Bosnia and Herzegovina Botswana Brazil Bulgaria Burkina Faso Burundi Cambodia Cameroon Canada Central African Republic Chad Chile China Hong Kong SAR, China Colombia Congo, Dem. Rep. Congo, Rep. Costa Rica Côte d’Ivoire Croatia Cuba Cyprus Czech Republic Denmark Dominican Republic Ecuador Egypt, Arab Rep. El Salvador Eritrea Estonia Ethiopia Finland France Gabon Gambia, The Georgia Germany Ghana Greece Guatemala Guinea Guinea-Bissau Haiti
246
.. 1.3 –0.1 .. 2.8 –0.5 3.3 1.7 2.0 .. 2.6 –0.5 1.9 2.6 3.6 .. 3.2 3.7 –2.6 5.7 –4.9 6.0 3.1 2.7 .. 1.5 7.3 8.9 3.8 2.4 –1.1 –1.8 5.1 4.1 1.9 4.0 6.1 3.0 2.2 6.1 2.1 3.7 5.3 –5.0 0.6 3.6 1.8 1.6 –0.3 3.6 .. 1.7 .. 2.2 4.2 5.2 .. ..
.. 5.2 3.6 .. 5.0 7.3 3.7 1.4 12.4 .. 4.6 11.3 1.4 2.3 3.4 .. 8.5 3.9 5.2 4.5 .. 8.0 4.5 3.3 –0.9 2.7 5.5 7.7 3.6 4.0 .. .. 4.2 .. 2.9 5.0 4.3 3.3 1.9 6.9 5.2 4.6 2.4 1.6 5.5 10.8 2.8 1.7 4.2 .. .. 0.4 .. 3.3 3.7 4.2 .. ..
2012 World Development Indicators
.. 2.2 –1.9 .. 1.5 1.1 2.1 1.4 1.0 .. 0.5 –0.3 1.6 –0.6 1.3 .. 0.8 2.2 –2.0 2.8 .. 3.5 0.6 1.7 .. –1.7 5.6 7.7 2.0 0.6 –3.7 .. 2.5 1.2 2.7 3.5 4.2 3.0 1.7 4.2 0.2 1.9 4.2 –6.7 2.1 0.4 1.4 1.2 –3.1 0.7 .. 1.3 .. 1.4 1.9 1.4 .. ..
.. 4.8 2.1 .. 4.1 7.2 2.1 0.9 11.1 .. 3.2 11.9 0.8 –0.9 1.6 .. 7.1 2.8 5.9 1.5 .. 6.7 2.2 2.3 –2.5 –0.8 4.4 7.1 3.0 2.4 .. .. 2.4 .. 2.9 4.8 2.6 3.1 1.5 5.3 3.5 2.7 2.0 –2.2 5.8 8.2 2.4 1.0 2.2 .. .. 0.4 .. 2.9 1.2 2.4 .. ..
average annual % growth 1990–2000 2000–10
.. 14.5 3.6 .. 2.2 –1.5 3.0 2.6 7.4 .. 4.7 –1.9 1.6 4.4 3.6 .. 6.1 1.0 –8.0 2.9 –2.6 7.2 0.7 0.3 .. –8.3 3.7 9.6 3.7 10.9 –20.4 –4.4 2.0 0.8 2.6 –2.9 2.3 –0.9 2.4 7.0 –1.5 4.4 2.8 22.6 5.7 9.0 0.9 1.4 3.7 –2.2 .. 2.0 .. 2.1 5.1 –0.5 .. ..
.. 6.3 4.8 .. 4.2 8.2 3.1 1.6 22.4 .. 8.5 0.1 1.5 8.3 3.5 .. 4.8 3.3 1.4 8.7 .. 9.9 2.8 2.8 –1.3 2.7 4.8 9.2 1.2 4.1 .. .. 2.4 2.9 2.6 7.6 3.6 1.9 1.7 4.8 4.3 2.7 1.6 1.2 2.6 1.8 1.4 1.6 2.7 .. .. 1.1 .. 2.2 3.9 0.9 .. ..
Gross capital formation
Goods and services
average annual % growth average annual Exports Imports % growth 1990–2000 2000–10 1990–2000 2000–10 1990–2000 2000–10
.. 25.8 –0.6 .. 7.4 –1.9 5.3 2.6 41.7 .. 9.2 –7.5 2.4 12.2 8.5 .. 4.6 4.2 –5.3 3.1 –0.5 10.3 0.4 4.6 .. 4.0 9.3 10.8 4.8 2.1 2.6 10.4 5.1 8.1 7.2 0.7 –2.6 4.6 5.7 11.7 –0.6 5.8 7.1 19.1 0.5 6.5 3.2 1.9 3.0 1.9 .. 1.1 .. 4.1 6.1 0.1 .. 9.0
.. 5.1 8.8 .. 11.0 16.3 6.9 0.6 13.9 .. 7.7 18.1 2.1 7.7 4.8 5.3 3.1 4.7 11.2 9.0 .. 12.6 4.4 4.3 –0.1 –2.4 8.1 13.3 2.7 9.6 .. .. 5.6 4.3 7.1 8.8 4.7 2.5 0.4 2.4 8.2 7.2 0.4 –1.0 3.9 11.1 1.5 1.4 5.5 .. .. 0.2 .. 0.0 –0.1 1.8 .. 1.2
.. 18.9 3.2 .. 8.7 –18.4 7.8 5.8 13.4 .. 13.1 –4.8 5.3 1.8 4.5 .. 4.4 5.9 4.3 4.4 –1.2 21.7 3.2 8.7 .. 2.3 9.4 15.5 7.9 5.0 –0.5 3.0 10.9 1.9 6.3 –9.0 6.3 8.7 5.0 8.3 5.3 3.5 13.4 –2.5 11.0 7.1 10.3 6.8 2.1 0.1 .. 5.8 .. 7.6 6.1 0.3 .. 10.1
.. 9.2 2.3 .. 6.2 5.3 2.5 4.4 22.5 .. 10.6 5.1 2.6 2.7 6.9 8.7 1.6 6.4 7.6 10.9 .. 14.2 –0.7 –0.6 –3.6 33.6 4.9 19.1 7.8 5.2 6.4 .. 5.9 2.0 3.0 12.2 1.4 10.0 3.0 1.2 5.4 15.1 3.0 –6.3 4.8 9.7 3.7 1.4 –1.8 1.6 .. 5.7 .. 2.0 2.3 2.4 .. 4.1
.. 15.7 –1.0 .. 15.6 –12.7 7.6 4.8 15.5 .. 9.7 –8.7 5.0 2.1 6.0 .. 4.3 11.6 2.9 1.9 –1.6 14.8 5.1 7.1 .. –1.8 11.7 16.7 8.4 9.3 –2.4 2.0 9.2 8.2 4.9 –2.9 4.6 12.0 6.0 9.9 2.8 3.0 11.6 7.5 12.0 5.8 6.7 5.7 0.1 0.1 .. 5.9 .. 7.4 9.2 –1.1 .. 19.4
.. 11.8 7.8 .. 9.7 7.7 8.4 3.5 17.1 .. 8.0 10.3 2.7 1.8 5.4 3.4 4.8 8.5 9.0 7.2 .. 13.8 3.6 3.2 –3.9 –3.7 10.3 16.2 7.1 9.6 15.0 .. 4.6 4.1 4.3 10.1 3.7 8.8 4.7 2.7 8.5 12.9 2.5 –3.7 4.7 16.7 4.7 2.7 4.0 1.7 .. 4.7 .. 1.8 2.1 1.2 .. 3.1
Household final consumption expenditure
General government final consumption expenditure
average annual % growth Total Per capita 1990–2000 2000–10 1990–2000 2000–10
Honduras Hungary India Indonesia Iran, Islamic Rep. Iraq Ireland Israel Italy Jamaica Japan Jordan Kazakhstan Kenya Korea, Dem. Rep. Korea, Rep. Kosovo Kuwait Kyrgyz Republic Lao PDR Latvia Lebanon Lesotho Liberia Libya Lithuania Macedonia, FYR Madagascar Malawi Malaysia Mali Mauritania Mauritius Mexico Moldova Mongolia Morocco Mozambique Myanmar Namibia Nepal Netherlands New Zealand Nicaragua Niger Nigeria Norway Oman Pakistan Panama Papua New Guinea Paraguay Peru Philippines Poland Portugal Puerto Rico Qatar
3.0 0.3 4.8 6.6 3.2 .. 5.6 5.0 1.6 .. 1.4 4.9 –7.5 3.6 .. 4.9 .. 4.5 –6.4 .. –3.9 –0.2 1.8 .. .. 5.3 2.2 2.2 5.4 5.3 3.0 .. 5.1 3.9 9.9 .. 1.8 5.8 .. 4.8 .. 3.1 3.2 6.1 1.8 .. 3.6 5.4 4.9 6.4 2.5 2.6 4.0 3.9 5.4 3.0 .. ..
4.6 1.9 7.1 4.3 7.4 .. 3.1 3.4 0.6 .. 0.9 7.6 9.2 3.9 .. 2.9 .. .. 8.1 5.0 6.1 3.6 8.2 .. .. 6.5 4.6 2.2 .. 7.4 0.9 7.4 5.6 2.8 7.2 .. 4.7 5.8 .. 4.9 4.2 0.5 3.1 3.7 .. .. 3.7 .. 4.7 7.2 .. 3.3 5.3 4.5 3.7 1.3 1.9 ..
0.6 0.5 2.8 5.0 1.5 .. 4.8 2.5 1.6 .. 1.1 1.1 –6.4 0.6 .. 3.9 .. 0.6 –7.3 .. –2.7 –1.9 –0.1 .. .. 6.1 1.7 –0.9 3.7 2.6 0.3 .. 3.9 2.1 10.1 .. 0.3 2.6 .. 1.8 .. 2.5 2.0 3.9 .. .. 3.0 3.4 2.2 4.2 –0.1 0.3 2.2 1.6 5.3 2.7 .. ..
2.5 2.2 5.5 3.1 6.1 .. 1.2 1.5 –0.1 .. 0.8 5.2 8.2 1.3 .. 2.5 .. .. 7.0 3.4 6.7 2.3 7.1 .. .. 7.1 4.3 –0.9 .. 5.3 –2.2 4.4 4.7 1.6 7.5 .. 3.5 3.2 .. 3.0 .. 0.1 1.8 2.4 .. .. 2.9 .. 2.8 5.4 .. 1.4 4.1 2.5 3.8 0.9 .. ..
average annual % growth 1990–2000 2000–10
2.0 0.3 6.6 0.1 1.6 .. 4.1 2.7 –0.3 .. 2.9 4.7 –7.1 6.9 .. 4.7 .. –2.4 –8.8 .. 1.8 10.9 8.1 .. .. 1.9 –0.4 0.0 –4.4 4.8 3.2 .. 3.6 1.8 –12.4 .. 3.9 3.2 .. 3.3 .. 2.0 2.4 –1.5 0.8 .. 2.7 2.4 0.7 1.7 2.5 2.5 5.2 2.6 3.2 3.0 .. ..
6.0 1.1 6.2 8.1 3.6 .. 3.5 1.7 1.5 .. 1.7 6.7 7.1 2.5 .. 4.8 .. .. 1.0 4.2 1.1 2.4 6.3 .. .. 3.6 0.5 5.5 .. 7.4 .. 3.1 4.0 1.1 4.9 .. 3.9 –2.9 .. 4.6 5.7 3.3 3.9 1.6 .. .. 2.3 .. 7.5 3.6 .. 4.2 5.7 3.9 4.3 1.6 0.3 ..
Gross capital formation
4.9
ECONOMY
Growth of consumption and investment
Goods and services
average annual % growth average annual Exports Imports % growth 1990–2000 2000–10 1990–2000 2000–10 1990–2000 2000–10
6.9 8.2 6.9 –0.6 –0.1 .. 9.7 2.0 1.6 .. –0.8 0.3 –19.0 6.1 .. 3.4 .. 1.0 –4.5 .. –3.7 –5.8 0.2 .. .. 11.1 3.6 3.3 –8.4 5.3 0.4 .. 4.8 4.7 –15.5 .. 2.5 8.6 .. 7.3 .. 4.4 6.1 11.3 4.0 .. 6.0 4.0 1.8 10.4 1.9 0.7 7.4 2.1 10.6 5.9 .. ..
2.8 –0.6 12.9 6.1 8.3 .. –1.1 2.5 0.1 .. –1.6 5.1 15.2 9.1 .. 2.1 .. .. 9.1 16.6 3.7 10.3 1.6 .. .. 5.6 4.8 14.1 .. 3.0 6.2 23.8 4.7 0.4 7.5 .. 8.3 8.1 .. 8.5 10.2 0.8 3.0 4.3 .. .. 4.6 .. 5.0 10.6 .. 3.6 10.7 2.8 5.8 –2.1 –3.6 ..
1.6 9.3 12.3 5.9 1.2 .. 15.7 10.9 5.9 .. 4.3 2.6 –1.9 1.0 .. 16.0 .. –1.6 –1.6 .. 4.3 18.6 10.3 .. .. 4.9 4.2 3.8 4.0 12.0 9.9 –1.3 5.6 14.6 0.7 .. 5.9 13.1 .. 3.8 .. 7.3 5.2 9.3 3.1 .. 5.5 6.2 1.7 –0.4 5.1 3.1 8.5 8.2 11.3 5.7 1.6 ..
4.1 9.5 14.7 7.6 5.0 .. 3.9 4.9 1.5 .. 5.1 5.0 4.7 6.1 .. 10.3 .. .. 5.9 9.4 6.7 9.6 7.9 .. .. 8.9 2.6 6.7 .. 4.8 6.3 –2.1 1.6 4.6 8.9 .. 6.0 14.3 .. 2.5 –1.6 3.9 2.4 8.4 .. .. 0.2 .. 6.7 7.8 .. 7.6 7.2 5.2 8.7 3.1 1.1 ..
3.8 11.0 14.4 5.7 –6.8 .. 14.5 7.6 4.5 .. 4.3 1.5 –12.7 9.4 .. 10.0 .. 0.8 –8.2 .. 7.6 –1.1 2.7 .. .. 7.5 7.5 4.1 –1.1 10.3 3.5 0.6 5.1 12.3 5.6 .. 5.1 7.6 .. 5.4 .. 7.6 6.2 12.2 –2.1 .. 5.8 5.9 2.5 1.2 3.4 2.9 9.0 8.5 16.7 7.6 4.5 ..
2012 World Development Indicators
3.8 7.9 15.9 8.2 13.2 .. 3.5 2.9 2.2 .. 2.2 6.3 4.3 7.9 .. 8.1 .. .. 11.4 8.8 6.3 6.8 11.3 .. .. 9.5 3.8 9.3 .. 5.8 3.9 14.1 2.0 4.7 10.0 .. 7.8 6.2 .. 5.1 6.1 3.7 4.4 5.2 .. .. 4.8 .. 6.3 7.4 .. 6.7 9.2 3.5 7.9 2.5 1.6 ..
247
4.9
Growth of consumption and investment Household final consumption expenditure
General government final consumption expenditure
average annual % growth Total Per capita 1990–2000 2000–10 1990–2000 2000–10
Romania Russian Federation Rwanda Saudi Arabia Senegal Serbia Sierra Leone Singapore Slovak Republic Slovenia Somalia South Africa South Sudan Spain Sri Lanka Sudan Swaziland Sweden Switzerland Syrian Arab Republic Tajikistan Tanzaniaa Thailand Timor-Leste Togo Trinidad and Tobago Tunisia Turkey Turkmenistan Uganda Ukraine United Arab Emirates United Kingdom United States Uruguay Uzbekistan Venezuela, RB Vietnam West Bank and Gaza Yemen, Rep. Zambia Zimbabwe World Low income Middle income Lower middle income Upper middle income Low & middle income East Asia & Pacific Europe & Central Asia Latin America & Carib. Middle East & N. Africa South Asia Sub-Saharan Africa High income Euro area
1.3 –0.9 0.4 .. 2.6 .. –4.4 5.8 4.9 4.0 .. 2.9 .. 2.4 .. 3.7 7.3 1.5 1.1 3.0 –11.8 5.1 3.7 .. 5.0 0.7 4.2 3.8 .. 6.6 –6.9 .. 3.6 3.8 5.0 .. 0.6 5.4 5.3 .. 2.4 .. 3.0 w 2.9 4.0 4.1 4.0 4.0 7.4 0.5 3.6 2.8 4.6 3.3 2.9 1.9
6.4 9.3 .. 5.3 4.9 1.0 .. 3.9 4.7 2.9 .. 4.1 .. 2.4 .. 5.9 2.2 2.2 1.5 .. 6.0 6.3 3.8 .. 0.5 13.3 4.5 5.0 .. 4.4 11.2 7.0 1.7 2.1 3.5 .. 7.6 7.8 –1.5 .. 0.1 .. 2.5 w 5.4 5.6 5.9 5.6 5.6 6.9 7.2 4.1 4.9 6.6 4.6 1.8 1.2
a. Covers mainland Tanzania only.
248
2012 World Development Indicators
1.6 –0.7 .. .. –0.2 .. .. 2.7 4.7 4.1 .. 0.6 .. 2.0 .. 1.1 5.5 1.2 0.5 0.3 –13.1 2.0 2.7 .. 2.2 0.1 2.5 2.2 .. 3.4 –6.4 .. 3.3 2.5 4.3 .. –1.5 3.8 1.1 .. –0.3 .. 1.6 w 0.4 2.5 2.2 2.9 2.4 6.1 0.3 1.9 0.7 2.5 0.6 2.1 1.6
6.8 9.7 .. 1.6 2.1 1.3 .. 1.5 4.6 2.6 .. 2.8 .. 0.9 .. 3.4 1.8 1.7 0.6 .. 4.9 3.4 2.9 .. –1.9 12.9 3.5 3.6 .. 1.1 12.0 .. 1.2 1.1 3.4 .. 5.7 6.6 –4.9 .. –2.2 .. 1.3 w 3.1 4.4 4.2 4.8 4.3 6.0 7.0 2.8 3.1 4.9 2.1 1.1 0.7
average annual % growth 1990–2000 2000–10
0.8 –2.2 –2.6 .. 0.9 .. 10.4 9.3 1.1 2.8 .. 0.3 .. 2.7 7.6 5.5 7.1 0.6 0.5 2.0 –15.7 –8.8 5.1 .. 0.0 0.3 4.5 4.6 .. 6.4 –4.1 .. 1.7 0.7 2.3 .. 3.7 3.2 12.7 3.8 –8.1 .. 1.8 w –1.0 3.3 3.5 3.3 3.3 7.9 –0.8 1.9 3.6 5.8 0.3 1.5 1.5
3.8 2.0 .. 7.6 –0.9 5.9 .. 4.7 3.6 3.2 .. 5.6 .. 4.8 6.8 8.4 6.6 0.9 1.4 9.1 1.6 12.8 5.4 .. 1.3 4.3 5.1 4.1 .. 3.7 2.0 4.7 2.2 2.0 1.1 .. 7.7 7.9 1.3 3.6 24.9 .. 2.7 w 6.0 5.7 5.7 5.7 5.7 8.7 3.2 3.5 3.7 6.5 5.5 2.1 1.9
Gross capital formation
Goods and services
average annual % growth average annual Exports Imports % growth 1990–2000 2000–10 1990–2000 2000–10 1990–2000 2000–10
–5.1 –19.1 0.4 .. 3.5 .. –5.6 6.6 7.4 10.4 .. 4.7 .. 3.2 6.6 22.0 –4.7 2.0 0.7 3.3 –17.6 –1.1 –4.0 .. –0.1 12.5 3.1 4.7 .. 8.4 –18.5 .. 5.0 7.6 6.1 –2.5 11.0 19.8 9.2 10.0 3.9 .. 3.3 w 5.5 2.6 2.8 2.6 2.6 7.7 –11.2 5.4 1.2 6.5 4.6 3.5 2.2
10.6 7.6 .. 11.4 9.6 18.1 .. 5.4 4.2 3.4 .. 7.7 .. 1.9 8.2 11.2 0.2 3.0 0.2 7.9 5.8 12.4 4.5 .. 5.9 .. 3.9 6.7 5.7 11.0 1.6 11.9 1.6 –0.6 6.6 4.7 10.7 12.0 –3.0 –0.6 6.6 .. 2.8 w 8.4 9.6 9.5 9.6 9.6 12.0 8.0 5.2 7.6 11.9 7.8 0.4 0.8
8.1 0.8 –6.4 .. 4.1 .. –11.2 11.4 8.0 1.7 .. 5.8 .. 10.5 7.5 11.6 6.4 8.6 4.1 12.0 –5.3 11.7 9.5 .. 1.2 6.9 5.3 11.1 –2.4 13.8 –3.6 .. 7.8 7.3 6.0 2.5 1.0 19.2 8.7 22.9 6.7 3.9 7.2 w 5.6 7.5 6.0 7.9 7.5 11.8 1.8 8.1 4.1 10.0 .. 7.1 6.8
9.0 6.5 .. 6.9 3.9 9.7 .. 9.2 8.8 6.7 .. 2.6 .. 2.7 3.1 14.3 4.2 4.2 4.5 1.9 9.2 11.8 5.6 .. 6.0 5.8 3.4 5.8 17.5 19.7 0.9 12.5 3.2 4.5 7.8 4.9 –2.9 11.2 –3.1 1.8 21.9 –9.2 5.8 w 9.0 9.9 9.1 10.1 9.9 13.9 6.5 4.8 6.9 13.0 .. 4.6 3.7
6.0 –6.1 6.1 .. 2.0 .. –0.2 11.4 8.5 5.2 .. 7.1 .. 9.4 8.6 8.4 6.2 6.4 4.3 4.4 –6.0 4.7 4.5 .. 1.1 9.9 3.7 10.8 7.2 10.2 –6.6 .. 8.2 9.8 9.9 –0.4 8.2 19.5 7.5 11.9 15.5 3.1 7.2 w 5.2 6.5 5.9 6.6 6.4 11.0 –2.3 10.5 0.0 11.2 5.7 7.4 6.3
12.1 14.5 .. 16.9 7.1 8.6 .. 8.7 7.3 6.3 .. 7.0 .. 3.8 4.7 12.0 4.2 4.0 3.5 7.5 10.3 15.5 5.4 .. 3.1 9.5 3.0 8.2 9.4 11.8 4.3 18.7 3.1 3.0 7.2 4.2 12.2 13.2 –2.3 5.4 15.6 –3.8 5.5 w 8.9 10.2 9.7 10.4 10.2 12.1 10.6 6.8 9.6 13.7 8.8 4.1 3.6
About the data
4.9
ECONOMY
Growth of consumption and investment Definitions
Measures of growth in consumption and capital for-
• Household final consumption expenditure is the
mation are subject to two kinds of inaccuracy. The
market value of all goods and services, including
first stems from the difficulty of measuring expendi-
durable products (such as cars and computers),
tures at current price levels, as described in About
purchased by households. It excludes purchases
the data for table 4.8. The second arises in deflat-
of dwellings but includes imputed rent for owner-
ing current price data to measure volume growth,
occupied dwellings. It also includes government fees
where results depend on the relevance and reliabil-
for permits and licenses. Expenditures of nonprofit
ity of the price indexes and weights used. Measur-
institutions serving households are included, even
ing price changes is more difficult for investment
when reported separately. Household consumption
goods than for consumption goods because of the
expenditure may include any statistical discrepancy
one-time nature of many investments and because
in the use of resources relative to the supply of
the rate of technological progress in capital goods
resources. • Household final consumption expen-
makes capturing change in quality diffi cult. (An
diture per capita is household final consumption
example is computers—prices have fallen as qual-
expenditure divided by midyear population. • Gen-
ity has improved.) Several countries estimate capital
eral government final consumption expenditure is
formation from the supply side, identifying capital
all government current expenditures for goods and
goods entering an economy directly from detailed
services (including compensation of employees). It
production and international trade statistics. This
also includes most expenditures on national defense
means that the price indexes used in deflating pro-
and security but excludes military expenditures with
duction and international trade, reflecting delivered
potentially wider public use that are part of govern-
or offered prices, will determine the deflator for capi-
ment capital formation. • Gross capital formation is
tal formation expenditures on the demand side.
outlays on additions to fixed assets of the economy,
Growth rates of household fi nal consumption
net changes in inventories, and net acquisitions
expenditure, household final consumption expen-
of valuables. Fixed assets include land improve-
diture per capita, general government fi nal con-
ments (fences, ditches, drains); plant, machinery,
sumption expenditure, gross capital formation, and
and equipment purchases; and construction (roads,
exports and imports of goods and services are esti-
railways, schools, buildings, and so on). Inventories
mated using constant price data. (Consumption, cap-
are goods held to meet temporary or unexpected
ital formation, and exports and imports of goods and
fluctuations in production or sales, and “work in prog-
services as shares of GDP are shown in table 4.8.)
ress.” • Exports and imports of goods and services
To obtain government consumption in constant
are the value of all goods and other market services
prices, countries may defl ate current values by
provided to or received from the rest of the world.
applying a wage (price) index or extrapolate from
They include the value of merchandise, freight, insur-
the change in government employment. Neither
ance, transport, travel, royalties, license fees, and
technique captures improvements in productivity
other services (communication, construction, finan-
or changes in the quality of government services.
cial, information, business, personal, government
Deflators for household consumption are usually cal-
services, and so on). They exclude compensation of
culated on the basis of the consumer price index.
employees and investment income (factor services in
Many countries estimate household consumption
the 1968 System of National Accounts) and transfer
as a residual that includes statistical discrepancies
payments.
associated with the estimation of other expenditure items, including changes in inventories; thus these estimates lack detailed breakdowns of household consumption expenditures.
Data sources Data on national accounts indicators for most developing countries are collected from national statistical organizations and central banks by visiting and resident World Bank missions. Data for high-income economies are from Organisation for Economic Co-operation and Development (OECD) data files.
2012 World Development Indicators
249
4.10
Toward a broader measure of national income Gross domestic product
Gross national income
Adjustments
Adjusted net national income
Gross domestic product
Gross national income
Adjusted net national income
% of GNI
Afghanistan Albania Algeria Angola Argentina Armenia Australia Austria Azerbaijan Bahrain Bangladesh Belarus Belgium Benin Bolivia Bosnia and Herzegovina Botswana Brazil Bulgaria Burkina Faso Burundi Cambodia Cameroon Canada Central African Republic Chad Chile China Hong Kong SAR, China Colombia Congo, Dem. Rep. Congo, Rep. Costa Rica Côte d’Ivoire Croatia Cuba Cyprus Czech Republic Denmark Dominican Republic Ecuador Egypt, Arab Rep. El Salvador Eritrea Estonia Ethiopia Finland France Gabon Gambia, The Georgia Germany Ghana Greece Guatemala Guinea Guinea-Bissau Haiti
250
$ billions
$ billions
Consumption of fi xed capital
$ billions
% growth
% growth
% growth
2010
2010
2010
2010
2010
2000–10
2000–10
2000–10
17.2 11.8 162.0 84.4 368.7 9.4 1,131.6 379.1 51.8 20.6 100.4 54.7 469.4 6.6 19.6 16.6 14.9 2,087.9 47.7 8.8 1.6 11.2 22.4 1,577.0 2.0 7.6 212.7 5,926.6 224.5 288.2 13.1 11.9 35.8 22.8 60.9 62.7 23.1 192.0 312.0 51.8 58.0 218.9 21.2 2.1 19.2 29.7 238.0 2,560.0 13.0 0.8 11.7 3,280.5 31.3 301.1 41.2 4.5 0.9 6.7
15.2 11.7 155.5 75.5 358.6 9.7 1,094.5 377.1 48.3 21.0 109.7 53.4 477.6 6.6 18.8 17.0 14.8 2,049.2 46.0 8.8 1.6 10.7 22.0 1,549.7 2.0 6.7 197.3 5,957.0 229.2 276.1 12.3 8.6 34.9 21.7 58.8 61.8 22.5 179.4 319.3 50.0 56.9 214.5 20.8 2.1 18.4 29.6 242.0 2,606.8 11.5 0.7 11.5 3,341.4 30.8 292.9 40.0 4.2 0.9 6.5
8.9 10.6 11.3 12.1 12.1 9.9 14.6 14.1 12.0 6.7 7.5 11.4 13.7 8.4 10.2 10.5 11.6 12.2 14.3 7.9 6.6 8.9 9.1 14.3 7.7 9.3 13.2 10.8 13.2 11.8 7.0 14.1 11.9 9.4 12.8 11.3 13.3 13.6 14.0 11.4 10.9 10.3 10.6 7.6 13.0 7.4 16.0 13.6 13.3 8.4 9.7 13.6 9.3 13.6 10.5 8.2 8.0 8.2
2.6 2.5 18.1 35.1 4.9 1.0 6.5 0.2 34.5 30.0 2.3 1.0 0.0 0.3 12.3 .. 3.4 3.3 2.0 4.3 12.7 0.1 4.8 2.3 0.0 29.0 12.4 5.1 0.0 7.7 13.7 59.6 0.1 3.9 0.9 3.2 0.0 0.5 1.7 0.2 12.9 7.1 0.4 0.0 1.6 4.2 0.1 0.0 33.1 0.8 0.6 0.1 8.0 0.3 1.7 14.3 0.5 0.6
13.4 10.1 109.8 39.9 297.6 8.6 863.2 323.4 25.9 13.3 99.0 46.7 412.3 6.1 14.6 .. 12.6 1,729.7 38.5 7.7 1.3 9.7 19.0 1,292.3 1.9 4.2 146.8 5,013.1 198.9 222.2 9.7 2.3 30.7 18.8 50.8 52.8 19.5 154.0 269.3 44.2 43.4 177.2 18.5 1.9 15.7 26.2 203.1 2,253.0 6.1 0.7 10.3 2,883.3 25.5 252.0 35.1 3.3 0.8 5.9
.. 5.4 3.9 12.9 5.6 9.2 3.2 1.8 17.1 6.6 5.9 8.0 1.6 4.0 4.1 4.6 4.1 3.7 4.8 5.5 3.2 8.7 3.2 2.0 1.0 9.0 4.0 10.8 4.6 4.5 5.3 4.3 4.9 1.1 3.2 6.7 3.1 3.8 0.9 5.6 4.8 5.1 2.2 0.2 4.6 8.8 2.1 1.3 2.2 3.7 6.9 1.0 5.9 2.6 3.6 2.9 1.5 0.6
.. 5.8 4.4 .. 5.3 9.2 3.3 1.7 18.1 .. 5.3 8.3 1.7 3.9 4.2 5.3 3.5 3.6 5.4 6.0 .. 8.9 2.7 1.8 –0.9 20.2 4.7 10.6 4.4 4.7 5.6 .. 4.5 0.9 3.4 6.6 3.0 4.1 0.4 5.5 4.3 5.2 2.2 1.4 4.6 8.7 1.9 1.2 2.3 3.5 .. 0.6 .. 3.1 3.8 3.7 .. ..
.. 6.6 5.0 .. 5.8 9.6 4.0 1.8 21.6 .. 5.9 9.9 1.2 3.6 3.2 .. 3.5 3.8 4.4 5.2 .. 9.5 4.4 2.6 –1.2 –2.5 4.9 9.6 3.8 4.5 7.4 .. 4.0 1.1 4.3 6.7 3.3 4.2 1.7 5.4 5.7 3.2 1.7 4.1 5.3 10.8 1.6 1.1 4.3 2.3 .. 1.5 .. 2.0 3.1 0.3 .. ..
2012 World Development Indicators
Natural resource depletion
Gross domestic product
Gross national income
Adjustments
Adjusted net national income
Gross domestic product
4.10
Gross national income
Adjusted net national income
% of GNI
Honduras Hungary India Indonesia Iran, Islamic Rep. Iraq Ireland Israel Italy Jamaica Japan Jordan Kazakhstan Kenya Korea, Dem. Rep. Korea, Rep. Kosovo Kuwait Kyrgyz Republic Lao PDR Latvia Lebanon Lesotho Liberia Libya Lithuania Macedonia, FYR Madagascar Malawi Malaysia Mali Mauritania Mauritius Mexico Moldova Mongolia Morocco Mozambique Myanmar Namibia Nepal Netherlands New Zealand Nicaragua Niger Nigeria Norway Oman Pakistan Panama Papua New Guinea Paraguay Peru Philippines Poland Portugal Puerto Rico Qatar
$ billions
$ billions
Consumption of fi xed capital
$ billions
% growth
% growth
% growth
2010
2010
2010
2010
2010
2000–10
2000–10
2000–10
14.8 122.4 1,712.6 686.6 328.6 77.8 171.3 210.4 2,051.4 13.6 5,601.6 27.8 131.9 31.3 .. 1,014.8 5.7 117.2 4.3 7.0 24.1 39.1 2.6 0.8 62.0 35.7 9.0 8.6 5.0 229.6 8.9 3.7 9.8 1,020.3 6.3 5.6 88.6 9.4 .. 12.1 15.8 772.7 121.4 6.3 5.5 176.8 427.2 44.1 183.6 25.0 9.3 18.0 147.0 199.9 452.3 221.1 63.3 ..
10.1 12.9 9.3 10.5 10.9 10.6 16.9 13.8 13.7 11.6 13.6 10.8 13.3 7.3 .. 12.9 10.0 7.2 9.2 9.4 18.0 11.8 7.3 8.4 12.0 12.3 11.1 7.7 7.5 12.1 8.4 8.8 11.5 12.0 8.7 10.9 10.5 7.7 .. 11.1 7.8 14.2 14.0 9.3 3.2 9.9 14.5 13.5 8.5 12.3 9.4 10.4 11.8 9.8 12.7 18.4 20.0 ..
0.5 0.5 4.3 6.6 19.9 45.7 0.2 0.2 0.1 0.6 0.0 1.0 23.4 1.1 .. 0.0 .. 25.1 6.9 8.3 0.5 0.0 1.0 6.4 29.0 0.6 5.9 1.0 1.8 6.9 9.8 34.3 0.0 5.7 0.2 32.3 1.6 3.3 .. 0.7 2.5 0.8 1.0 1.6 2.4 22.0 10.2 28.5 2.8 0.0 22.2 0.0 8.1 2.1 1.4 0.1 .. ..
13.2 106.0 1,478.5 568.9 227.5 34.0 142.0 180.9 1,768.9 11.9 4,841.4 24.6 83.4 28.6 .. 883.5 .. 79.3 3.6 5.7 19.6 34.4 2.3 0.7 36.6 31.1 7.5 7.9 4.5 185.9 7.3 2.1 8.7 839.9 5.7 3.2 77.9 8.4 .. 10.7 14.2 656.9 103.3 5.6 5.2 120.4 321.5 25.6 162.9 22.0 6.3 16.1 117.7 176.2 388.7 180.1 .. ..
4.6 2.2 8.0 5.3 5.4 0.4 2.8 3.6 0.5 1.2 0.9 6.7 8.3 4.3 .. 4.1 5.3 8.4 4.4 7.2 4.8 4.9 3.5 0.9 5.4 5.3 3.3 3.4 5.2 5.0 5.2 4.4 3.9 2.1 5.2 7.2 4.9 7.8 .. 5.0 3.8 1.6 2.6 3.6 4.2 6.7 1.7 4.7 5.1 6.8 3.8 3.8 6.1 4.9 4.3 0.7 0.0 14.2
4.6 2.2 7.9 5.1 6.2 .. 2.8 2.8 0.5 .. 0.7 6.5 9.3 4.3 .. 4.0 .. .. 4.4 7.1 4.6 4.4 13.4 .. .. 5.2 3.4 3.2 .. 4.5 5.9 6.3 3.6 2.0 4.9 .. 4.8 7.4 .. 5.4 .. 2.0 2.7 3.3 .. .. 1.6 .. 4.7 7.1 .. 4.0 6.7 4.9 4.7 0.9 0.3 ..
2.5 2.4 7.7 4.7 6.7 .. 1.8 3.6 0.3 .. 1.2 6.9 9.2 4.8 .. 3.3 .. .. 2.7 6.2 6.0 5.0 12.7 .. .. 6.5 2.6 2.3 .. 7.0 4.9 6.9 2.0 1.7 5.6 .. 4.3 6.5 .. .. .. 1.2 2.6 2.8 .. .. 3.6 .. 4.3 6.1 .. 3.9 5.3 4.1 4.3 0.3 .. ..
15.4 128.6 1,727.1 706.6 331.0 82.2 206.6 217.3 2,061.0 14.3 5,458.8 27.6 149.1 31.4 .. 1,014.5 5.6 109.5 4.6 7.3 24.0 39.0 2.1 1.0 62.4 36.3 9.2 8.7 5.1 237.8 9.3 3.6 9.7 1,034.8 5.8 6.2 90.8 9.6 .. 12.2 15.7 779.4 126.7 6.6 5.5 193.7 417.5 46.9 176.9 26.7 9.5 18.3 157.1 199.6 469.4 228.6 96.3 98.3
Natural resource depletion
2012 World Development Indicators
251
ECONOMY
Toward a broader measure of national income
4.10
Toward a broader measure of national income Gross domestic product
Gross national income
Adjustments
Adjusted net national income
Gross domestic product
Gross national income
Adjusted net national income
% of GNI
Romania Russian Federation Rwanda Saudi Arabia Senegal Serbia Sierra Leone Singapore Slovak Republic Slovenia Somalia South Africa South Sudan Spain Sri Lanka Sudan Swaziland Sweden Switzerland Syrian Arab Republic Tajikistan Tanzaniaa Thailand Timor-Leste Togo Trinidad and Tobago Tunisia Turkey Turkmenistan Uganda Ukraine United Arab Emirates United Kingdom United States Uruguay Uzbekistan Venezuela, RB Vietnam West Bank and Gaza Yemen, Rep. Zambia Zimbabwe World Low income Middle income Lower middle income Upper middle income Low & middle income East Asia & Pacific Europe & Central Asia Latin America & Carib. Middle East & N. Africa South Asia Sub-Saharan Africa High income Euro area
$ billions
$ billions
Consumption of fi xed capital
2010
2010
2010
2010
161.6 1,479.8 5.6 434.7 13.0 38.4 1.9 208.8 87.3 46.9 .. 363.7 .. 1,407.4 49.6 62.0 3.6 458.6 527.9 59.1 5.6 23.1 318.5 0.7 3.2 20.6 44.3 734.4 20.0 17.0 137.9 297.6 2,261.7 14,586.7 39.1 39.0 391.8 106.4 .. 31.3 16.2 7.5 63,242.1 w 416.5 19,632.1 4,312.3 15,317.0 20,071.7 7,630.5 3,059.0 4,980.8 1,207.0 2,090.4 1,097.9 43,240.0 12,149.1
159.0 1,431.1 5.6 381.3 12.9 37.5 1.9 201.1 86.1 46.2 .. 356.5 .. 1,388.7 48.9 55.9 3.6 466.9 568.6 57.3 5.6 23.0 304.8 2.7 2.8 19.3 42.0 727.1 18.1 16.7 135.9 273.5 2,271.6 14,635.6 37.7 39.0 389.0 102.0 .. 29.5 14.3 7.0 63,087.6 w 422.4 19,525.6 4,370.0 15,152.4 19,971.7 7,614.8 2,965.9 4,867.8 1,334.2 2,089.3 1,044.6 43,247.5 12,161.9
11.7 12.4 8.0 12.1 8.9 11.3 7.2 14.4 12.7 13.3 .. 13.5 .. 13.6 10.1 10.3 10.6 13.2 13.5 10.6 8.6 7.9 11.4 2.1 8.8 13.4 11.3 12.1 11.8 8.0 10.4 13.6 13.6 14.0 12.6 9.2 12.4 9.4 .. 9.7 10.3 8.6 13.0 w 7.7 11.2 9.8 11.6 11.1 10.7 12.2 12.1 10.7 9.2 10.9 13.9 13.8
1.6 14.3 3.1 29.1 0.8 .. 2.1 0.0 0.4 0.3 .. 6.0 .. 0.0 0.3 12.9 0.1 0.4 0.0 11.9 0.8 3.2 2.4 .. 3.4 32.0 5.1 0.4 .. 4.5 3.7 .. 1.3 0.9 0.6 19.2 12.4 9.4 .. 14.5 18.9 2.7 2.6 w 3.8 6.5 7.4 6.2 6.4 5.2 9.4 5.5 12.8 4.0 11.8 0.9 0.1
a. Covers mainland Tanzania only.
252
2012 World Development Indicators
Natural resource depletion
$ billions
% growth
% growth
% growth
2010
2000–10
2000–10
2000–10
5.0 5.4 7.6 3.6 4.2 4.1 8.8 6.0 5.4 3.3 .. 3.9 .. 2.4 5.6 6.7 2.4 2.2 1.9 5.0 8.6 7.1 4.5 3.4 2.7 6.5 4.7 4.7 13.6 7.7 4.8 5.1 1.8 1.8 3.6 7.1 4.7 7.5 –0.9 4.1 5.6 –6.0 2.7 w 5.5 6.4 6.3 6.5 6.4 9.4 5.4 3.8 4.7 7.4 5.0 1.8 1.3
5.4 5.6 .. 3.4 4.1 4.3 .. 6.1 5.5 3.5 .. 3.9 .. 2.5 5.6 6.7 3.2 1.9 2.3 4.8 8.1 6.9 4.6 .. .. 8.3 4.9 4.6 16.1 7.7 4.7 .. 1.7 1.9 3.9 5.0 4.4 7.8 0.2 3.8 7.7 –6.0 2.6 w 5.6 6.4 6.3 6.4 6.4 9.3 5.4 3.7 4.9 7.3 4.4 1.7 1.3
6.5 8.4 .. 6.0 4.3 .. .. 5.4 5.6 3.3 .. 3.8 .. 2.2 5.5 5.6 2.1 2.3 1.8 7.4 6.2 6.4 4.5 .. 1.1 5.4 4.1 3.9 .. 7.6 6.8 .. 1.7 1.2 3.1 –8.7 8.1 7.3 .. 5.6 5.1 –7.1 2.5 w 5.7 6.2 5.9 6.3 6.2 8.5 6.2 3.9 4.9 7.2 4.2 1.5 1.3
137.8 1,049.8 5.0 224.0 11.6 .. 1.7 172.1 74.8 40.0 .. 286.8 .. 1,198.9 43.8 43.0 3.2 403.3 491.9 44.4 5.0 20.5 263.0 .. 2.5 10.5 35.1 636.8 .. 14.6 116.7 .. 1,931.7 12,453.3 32.7 27.9 292.4 82.8 .. 22.3 10.1 6.2 53,010.4 w 373.6 16,053.8 3,663.4 12,388.6 16,442.8 6,401.9 2,320.4 4,016.0 1,023.9 1,814.7 807.6 36,615.4 10,459.5
About the data
4.10
Definitions
An economy’s growth is typically measured by the
economic growth that is strikingly different from the
• Gross domestic product is the sum of value added
change in the volume of its output, as shown in
one provided by GDP.
by all resident producers plus any product taxes (less
table 4.1. But gross domestic product (GDP), though
The key to increasing future consumption and
subsidies) not included in the valuation of output.
widely tracked, may not always be the most relevant
thus the standard of living lies in increasing national
• Gross national income is GDP plus net receipts
summary of aggregated economic performance for
wealth—including not only the traditional measures
of primary income (compensation of employees
all economies, especially when production occurs at
of capital (such as produced and human capital),
and property income) from abroad. • Consumption
the expense of consuming capital stock. For coun-
but also natural capital. Natural capital comprises
of fixed capital is the replacement value of capital
tries with significant exhaustible natural resources
such assets as land, forests, and subsoil resources.
used up in production. • Natural resource deple-
and important foreign-investor presence, adjusted
All three types of capital are key to sustaining eco-
tion is the sum of net forest depletion, energy deple-
net national income complements GDP in assessing
nomic growth. By accounting for the consumption
tion, and mineral depletion. Net forest depletion is
economic progress (Hamilton and Ley 2010).
of fixed and natural capital depletion, adjusted net
unit resource rents times the excess of roundwood
The table presents three measures of economic
national income better measures the income avail-
harvest over natural growth. Energy depletion is the
progress: GDP, gross national income (GNI), and
able for consumption or for investment to increase
ratio of the value of the stock of energy resources to
adjusted net national income. GDP accounts for
a country’s future consumption. For a measure of
the remaining reserve lifetime (capped at 25 years).
all domestic production, regardless of whether the
how comprehensive wealth is changing over time,
It covers coal, crude oil, and natural gas. Mineral
income accrues to domestic or foreign institutions.
see table 4.11.
depletion is the ratio of the value of the stock of
GNI accounts for the operation of foreign inves-
Methods of computing growth are described in Sta-
mineral resources to the remaining reserve lifetime
tors, who may be repatriating some of the income
tistical methods. For a detailed note on methodology,
(capped at 25 years). It covers tin, gold, lead, zinc,
produced domestically. GNI comprises GDP plus
see http://data.worldbank.org.
iron, copper, nickel, silver, bauxite, and phosphate.
net receipts of primary income from nonresident
• Adjusted net national income is GNI minus con-
sources. Adjusted net national income goes a step
sumption of fi xed capital and natural resources
further by subtracting from GNI a charge for the con-
depletion.
sumption of fixed capital (a calculation that yields net national income) and for the depletion of natural resources. The deduction for the depletion of natural resources, which covers net forest depletion, energy depletion, and mineral depletion, reflects the decline in asset values associated with the extraction and harvest of natural resources. For more discussion of the estimates and methodology of produced capital consumption and natural capital depletion, see About the data for table 4.11. The United Nations System of National Accounts includes nonproduced natural assets (such as land, mineral resources, and forests) within the asset boundary when they are under the effective control of institutional units. The calculation of adjusted net national income, which accounts for net forest, energy, and mineral depletion, thus remains within
Data sources
the System of National Accounts boundaries. This
GNI and GDP are estimated by World Bank staff
point is critical because it allows for comparisons
based on national accounts data collected by
across GDP, GNI, and adjusted net national income;
World Bank staff during economic missions or
such comparisons reveal the impact of natural
reported by national statistical offices to other
resource depletion, which is otherwise ignored by
international organizations such as the Organisa-
the popular economic indicators.
tion for Economic Co-operation and Development.
Adjusted net national income is particularly useful
Data on consumption of fi xed capital are from
in monitoring low-income, resource-rich economies,
the United Nations Statistics Division’s National
like many countries in Sub-Saharan Africa, because
Accounts Statistics: Main Aggregates and Detailed
such economies often see large natural resources
Tables, extrapolated to 2010. Data on energy, min-
depletion as well as substantial exports of resource
eral, and forest depletion are estimates based
rents to foreign mining companies. For recent years
on sources and methods in World Bank (2011a).
adjusted net national income gives a picture of
2012 World Development Indicators
253
ECONOMY
Toward a broader measure of national income
4.11
Toward a broader measure of savings Gross savings
Afghanistan Albania Algeria Angola Argentina Armenia Australia Austria Azerbaijan Bahrain Bangladesh Belarus Belgium Benin Bolivia Bosnia and Herzegovina Botswana Brazil Bulgaria Burkina Faso Burundi Cambodia Cameroon Canada Central African Republic Chad Chile China Hong Kong SAR, China Colombia Congo, Dem. Rep. Congo, Rep. Costa Rica Côte d’Ivoire Croatia Cuba Cyprus Czech Republic Denmark Dominican Republic Ecuador Egypt, Arab Rep. El Salvador Eritrea Estonia Ethiopia Finland France Gabon Gambia, The Georgia Germany Ghana Greece Guatemala Guinea Guinea-Bissau Haiti
254
Consumption Education of fixed expenditure capital
Net forest depletion
Energy depletion
Mineral depletion
Carbon dioxide damage
Local pollution
Adjusted net savings
% of GNI
% of GNI
% of GNI
% of GNI
% of GNI
% of GNI
% of GNI
% of GNI
% of GNI
2010
2010
2010
2010
2010
2010
2010
2010
2010
.. 13.6 53.6 17.9 23.1 18.1 25.1 24.9 49.5 45.4 35.2 25.7 22.5 12.8 26.1 14.5 26.8 16.8 24.6 .. .. 13.2 .. 18.8 .. .. 24.9 52.7 29.3 19.8 .. .. 15.7 15.4 22.6 .. 9.5 22.5 22.4 7.5 23.1 18.2 11.2 .. 25.3 16.7 20.3 17.2 .. 13.7 10.0 22.7 20.8 4.8 13.3 10.5 .. 23.2
8.9 10.6 11.3 12.1 12.1 9.9 14.6 14.1 12.0 6.7 7.5 11.4 13.7 8.4 10.2 10.5 11.6 12.2 14.3 7.9 6.6 8.9 9.1 14.3 7.7 9.3 13.2 10.8 13.2 11.8 7.0 14.1 11.9 9.4 12.8 11.3 13.3 13.6 14.0 11.4 10.9 10.3 10.6 7.6 13.0 7.4 16.0 13.6 13.3 8.4 9.7 13.6 9.3 13.6 10.5 8.2 8.0 8.2
.. 2.8 4.5 2.3 6.0 2.2 4.5 5.3 3.4 3.0 1.8 4.4 6.1 4.3 5.2 .. 7.6 5.2 4.1 4.3 8.7 1.6 3.1 4.5 1.1 2.3 4.6 1.8 3.1 3.9 0.9 2.5 6.2 4.3 4.3 13.4 6.9 3.8 7.3 1.9 1.4 4.4 3.0 1.7 5.5 2.9 5.7 5.1 3.1 3.1 2.8 4.4 4.7 3.2 2.9 2.3 2.3 1.5
2.6 0.0 0.1 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.4 0.0 0.0 0.3 0.0 .. 0.0 0.0 0.0 1.5 11.8 0.1 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.1 0.0 0.2 0.0 0.0 0.0 0.0 0.0 0.0 0.1 0.4 0.0 0.3 4.0 0.0 0.0 0.0 0.8 0.0 0.0 1.5 0.0 0.7 2.3 0.5 0.6
0.0 2.2 17.8 35.1 4.5 0.0 2.2 0.1 34.5 30.0 1.8 1.0 0.0 0.0 9.4 1.4 0.2 1.6 0.7 0.0 0.0 0.0 4.6 1.8 0.0 29.0 0.1 3.7 0.0 7.2 2.7 59.6 0.0 3.4 0.7 2.3 0.0 0.5 1.7 0.0 12.9 6.8 0.0 0.0 1.3 0.0 0.0 0.0 33.0 0.0 0.2 0.1 0.0 0.2 0.5 0.0 0.0 0.0
0.0 0.3 0.2 0.0 0.4 1.0 4.3 0.0 0.0 0.0 0.0 0.0 0.0 0.0 2.9 1.4 3.2 1.7 1.3 2.8 0.9 0.0 0.1 0.6 0.0 0.0 12.3 1.4 0.0 0.5 11.0 0.0 0.0 0.5 0.0 0.9 0.0 0.0 0.0 0.2 0.0 0.2 0.0 0.0 0.0 0.2 0.1 0.0 0.0 0.0 0.4 0.0 6.5 0.1 0.6 12.0 0.0 0.0
0.1 0.3 0.6 0.3 0.4 0.4 0.3 0.1 0.9 0.8 0.4 1.0 0.2 0.5 0.5 1.3 0.2 0.2 0.8 0.2 0.1 0.3 0.2 0.3 0.1 0.0 0.3 1.1 0.1 0.2 0.2 0.2 0.2 0.3 0.3 0.4 0.3 0.5 0.1 0.4 0.4 0.8 0.2 0.2 0.7 0.2 0.2 0.1 0.1 0.4 0.4 0.2 0.3 0.2 0.3 0.3 0.3 0.3
0.8 0.2 0.3 1.8 1.6 2.2 0.0 0.2 0.4 0.3 0.6 0.0 0.1 0.5 1.1 0.1 0.3 0.2 1.2 0.8 0.1 0.4 0.7 0.1 0.2 1.4 0.6 1.2 .. 0.1 0.7 0.9 0.1 0.3 0.3 0.0 0.3 0.0 0.0 0.0 0.1 0.7 0.2 0.4 0.0 0.2 0.0 0.0 0.0 0.5 1.5 0.0 0.0 0.5 0.2 0.6 0.8 0.5
.. 2.7 29.8 –29.2 10.1 6.8 8.2 15.6 5.1 10.6 26.2 16.6 14.6 7.4 7.3 .. 18.6 6.1 10.4 .. .. 5.1 .. 6.3 .. .. 3.0 36.3 .. 3.8 .. .. 9.7 5.3 12.7 .. 2.5 11.6 13.9 –2.6 0.2 3.6 2.7 .. 15.6 7.5 9.7 8.5 .. 6.7 0.7 13.2 8.6 –6.7 3.4 –10.6 .. 15.1
2012 World Development Indicators
Gross savings
Honduras Hungary India Indonesia Iran, Islamic Rep. Iraq Ireland Israel Italy Jamaica Japan Jordan Kazakhstan Kenya Korea, Dem. Rep. Korea, Rep. Kosovo Kuwait Kyrgyz Republic Lao PDR Latvia Lebanon Lesotho Liberia Libya Lithuania Macedonia, FYR Madagascar Malawi Malaysia Mali Mauritania Mauritius Mexico Moldova Mongolia Morocco Mozambique Myanmar Namibia Nepal Netherlands New Zealand Nicaragua Niger Nigeria Norway Oman Pakistan Panama Papua New Guinea Paraguay Peru Philippines Poland Portugal Puerto Rico Qatar
Consumption Education of fixed expenditure capital
4.11
ECONOMY
Toward a broader measure of savings Net forest depletion
Energy depletion
Mineral depletion
Carbon dioxide damage
Local pollution
Adjusted net savings
% of GNI
% of GNI
% of GNI
% of GNI
% of GNI
% of GNI
% of GNI
% of GNI
% of GNI
2010
2010
2010
2010
2010
2010
2010
2010
2010
16.9 21.5 34.0 32.9 .. .. 14.8 18.9 17.1 12.9 23.2 9.1 31.6 15.6 .. 31.6 .. 54.9 21.3 20.5 23.7 12.0 27.4 –2.7 66.8 18.8 24.8 .. 13.0 34.1 .. .. 16.1 24.6 14.7 29.8 31.5 11.0 .. 34.2 36.8 23.6 17.1 13.4 .. .. 35.2 39.7 21.0 19.5 20.8 23.0 24.4 27.2 17.5 10.8 .. ..
10.1 12.9 9.3 10.5 10.9 10.6 16.9 13.8 13.7 11.6 13.6 10.8 13.3 7.3 .. 12.9 10.0 7.2 9.2 9.4 18.0 11.8 7.3 8.4 12.0 12.3 11.1 7.7 7.5 12.1 8.4 8.8 11.5 12.0 8.7 10.9 10.5 7.7 .. 11.1 7.8 14.2 14.0 9.3 3.2 9.9 14.5 13.5 8.5 12.3 9.4 10.4 11.8 9.8 12.7 18.4 20.0 ..
3.5 5.2 3.1 4.3 4.1 .. 5.9 5.7 4.4 5.8 3.2 5.6 4.4 5.9 .. 4.3 .. 3.2 6.0 1.1 5.6 1.6 9.8 3.1 .. 4.7 4.9 2.7 4.4 4.1 3.9 3.7 3.1 4.8 7.7 5.1 5.2 4.0 0.8 8.0 4.2 4.7 7.2 3.0 3.5 0.9 6.0 4.2 1.6 3.5 .. 3.7 2.1 2.4 4.9 4.9 .. 1.8
0.0 0.0 0.5 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.1 .. 0.0 .. 0.0 0.0 0.0 0.5 0.0 1.0 4.7 0.0 0.5 0.1 0.2 1.8 0.0 0.0 0.4 0.0 0.0 0.1 0.0 0.0 0.0 .. 0.0 2.5 0.0 0.0 0.7 1.5 0.3 0.0 0.0 0.7 0.0 0.0 0.0 0.0 0.1 0.1 0.0 .. ..
0.0 0.5 2.5 5.3 19.2 45.7 0.0 0.1 0.1 0.0 0.0 0.1 21.6 0.0 .. 0.0 0.0 25.1 0.7 0.0 0.0 0.0 0.0 0.0 29.0 0.1 1.0 0.0 0.0 6.9 0.0 0.0 0.0 5.4 0.1 17.5 0.0 3.2 .. 0.0 0.0 0.8 0.7 0.0 0.0 21.7 10.2 28.5 2.1 0.0 0.0 0.0 1.3 0.4 1.0 0.0 0.0 ..
0.5 0.0 1.3 1.3 0.6 0.0 0.1 0.1 0.0 0.6 0.0 0.9 1.8 0.1 .. 0.0 0.9 0.0 6.2 8.3 0.0 0.0 0.0 1.7 0.0 0.0 4.8 0.8 0.0 0.1 9.8 33.9 0.0 0.3 0.0 14.8 1.6 0.1 .. 0.7 0.0 0.0 0.3 0.9 0.9 0.0 0.0 0.0 0.1 0.0 22.2 0.0 6.8 1.5 0.4 0.1 0.0 ..
0.4 0.3 0.9 0.5 1.2 1.1 0.2 0.2 0.2 0.6 0.2 0.7 1.4 0.3 .. 0.4 .. 0.5 1.1 0.2 0.2 0.4 .. 0.8 0.7 0.3 1.0 0.2 0.2 0.7 0.1 0.4 0.3 0.3 0.6 1.6 0.4 0.2 .. 0.2 0.2 0.2 0.2 0.6 0.1 0.5 0.1 0.8 0.7 0.2 0.4 0.2 0.2 0.3 0.6 0.2 .. ..
0.2 0.0 0.7 0.8 0.8 4.0 0.0 0.1 0.1 0.2 0.3 0.2 0.2 0.1 0.8 0.5 .. 0.5 0.5 0.8 0.0 0.2 0.1 0.4 1.8 0.1 0.1 0.1 0.1 0.0 1.7 0.6 0.0 0.4 0.9 2.8 0.1 0.1 0.5 0.1 0.1 0.4 0.0 0.1 1.4 0.7 0.0 0.0 1.1 0.2 0.0 1.1 0.4 0.1 0.4 0.0 .. 0.1
9.2 12.9 21.8 18.8 .. .. 3.5 10.3 7.5 5.6 12.3 2.0 –2.3 13.1 .. 22.1 .. 14.9 9.5 2.9 10.5 1.2 .. –14.0 .. 10.4 11.6 .. 7.5 18.5 .. .. 7.6 11.0 11.9 –12.6 24.1 3.7 .. 30.1 29.2 12.8 8.5 4.9 .. .. 16.4 –8.5 9.4 10.3 .. 15.1 5.9 17.3 7.4 –3.1 .. ..
2012 World Development Indicators
255
4.11
Toward a broader measure of savings Gross savings
Romania Russian Federation Rwanda Saudi Arabia Senegal Serbia Sierra Leone Singapore Slovak Republic Slovenia Somalia South Africa South Sudan Spain Sri Lanka Sudan Swaziland Sweden Switzerland Syrian Arab Republic Tajikistan Tanzaniaa Thailand Timor-Leste Togo Trinidad and Tobago Tunisia Turkey Turkmenistan Uganda Ukraine United Arab Emirates United Kingdom United States Uruguay Uzbekistan Venezuela, RB Vietnam West Bank and Gaza Yemen, Rep. Zambia Zimbabwe World Low income Middle income Lower middle income Upper middle income Low & middle income East Asia & Pacific Europe & Central Asia Latin America & Carib. Middle East & N. Africa South Asia Sub-Saharan Africa High income Euro area
Consumption Education of fixed expenditure capital
Energy depletion
Mineral depletion
Carbon dioxide damage
Local pollution
Adjusted net savings
% of GNI
% of GNI
% of GNI
% of GNI
% of GNI
% of GNI
% of GNI
% of GNI
% of GNI
2010
2010
2010
2010
2010
2010
2010
2010
2010
26.7 28.6 15.1 32.5 19.7 15.9 13.0 47.7 20.4 22.4 .. 16.8 .. 19.1 25.1 19.7 2.7 24.5 33.4 17.4 2.6 21.1 32.3 .. .. 36.4 21.4 13.7 .. 19.1 17.5 .. 11.9 10.9 17.3 .. 31.7 33.2 .. 9.6 25.4 .. 22.5 w 24.6 33.8 29.2 35.1 33.7 48.4 23.9 20.9 19.2 32.7 18.0 17.6 19.4
11.7 12.4 8.0 12.1 8.9 11.3 7.2 14.4 12.7 13.3 .. 13.5 .. 13.6 10.1 10.3 10.6 13.2 13.5 10.6 8.6 7.9 11.4 2.1 8.8 13.4 11.3 12.1 11.8 8.0 10.4 13.6 13.6 14.0 12.6 9.2 12.4 9.4 .. 9.7 10.3 8.6 13.0 w 7.7 11.2 9.8 11.6 11.1 10.7 12.2 12.1 10.7 9.2 10.9 13.9 13.8
1.6 13.2 0.0 29.1 0.0 1.9 0.0 0.0 0.0 0.1 .. 3.3 .. 0.0 0.0 12.9 0.0 0.0 0.0 11.7 0.4 0.3 2.2 0.0 0.0 32.0 4.1 0.2 35.1 0.0 3.7 10.9 1.3 0.8 0.0 13.7 12.0 9.0 .. 14.5 0.0 1.9 2.1 w 1.4 5.1 6.0 4.8 5.0 3.8 8.6 3.8 12.4 2.4 9.4 0.7 0.1
0.0 1.1 0.1 0.0 0.8 0.0 0.6 0.0 0.0 0.0 .. 2.6 .. 0.0 0.0 0.0 0.0 0.4 0.0 0.1 0.4 2.9 0.1 0.0 1.3 0.0 0.9 0.1 0.0 0.0 0.0 0.0 0.0 0.1 0.1 5.5 0.4 0.2 .. 0.0 18.9 0.8 0.5 w 1.2 1.3 1.2 1.3 1.3 1.3 0.8 1.6 0.4 1.1 1.9 0.2 0.0
a. Covers mainland Tanzania only.
256
Net forest depletion
2012 World Development Indicators
3.4 3.5 4.2 7.2 5.2 5.0 3.4 3.0 3.5 4.9 .. 5.4 .. 4.2 1.7 0.9 6.9 6.2 5.3 2.6 3.2 2.4 4.1 3.3 4.4 4.0 6.0 2.6 .. 3.0 5.9 .. 5.1 4.8 2.3 9.4 3.6 2.8 .. 4.2 1.3 2.5 4.2 w 2.9 3.3 3.3 3.3 3.3 2.2 3.6 4.7 4.3 2.8 3.6 4.6 4.7
0.0 0.0 3.0 0.0 0.0 .. 1.5 0.0 0.3 0.2 .. 0.2 .. 0.0 0.3 0.0 0.1 0.0 0.0 0.0 0.0 0.0 0.1 .. 2.1 0.0 0.1 0.0 .. 4.5 0.0 .. 0.0 0.0 0.5 0.0 0.0 0.2 .. 0.0 0.0 0.0 0.0 w 1.2 0.1 0.3 0.0 0.1 0.0 0.0 0.0 0.1 0.6 0.5 0.0 0.0
0.5 0.9 0.1 0.8 0.3 1.0 0.6 0.2 0.4 0.3 .. 0.9 .. 0.2 0.2 0.2 0.2 0.1 0.1 1.0 0.5 0.2 0.8 0.1 0.4 1.5 0.5 0.3 2.5 0.2 1.7 0.4 0.2 0.3 0.2 2.9 0.3 1.0 .. 0.6 0.1 1.0 0.4 w 0.3 0.7 0.7 0.7 0.7 1.0 0.8 0.3 0.7 0.8 0.5 0.2 0.2
0.0 0.1 0.1 1.2 0.7 .. 1.1 0.4 0.0 0.1 0.5 0.2 .. 0.2 0.3 0.9 0.1 0.0 0.1 1.3 0.2 0.1 0.3 .. 0.1 0.3 0.1 0.9 0.8 0.0 0.1 0.8 0.0 0.1 1.8 0.4 0.0 0.5 .. 0.4 0.3 0.2 0.3 w 0.5 0.7 0.7 0.7 0.7 1.1 0.4 0.4 0.9 0.7 0.5 0.1 0.1
16.2 4.5 7.7 –3.6 13.7 .. 5.4 35.7 10.4 13.3 .. 1.6 .. 9.2 15.9 –3.6 –1.4 17.0 25.0 –4.8 –4.3 12.1 21.5 .. .. –26.0 10.3 2.7 .. 9.4 7.4 .. 1.8 0.4 4.4 .. 10.1 15.8 .. –11.5 –2.8 .. 6.4 w .. 14.4 13.3 14.7 14.5 31.9 4.5 7.0 .. 20.1 –1.0 5.3 7.3
About the data
4.11
ECONOMY
Toward a broader measure of savings Definitions
Adjusted net savings measure the change in value of
of production. Natural resources give rise to rents
• Gross savings are the difference between gross
a specified set of assets, excluding capital gains. If
because they are not produced; in contrast, for pro-
national income and public and private consump-
a country’s net savings are positive and the account-
duced goods and services competitive forces will
tion, plus net current transfers. • Consumption of
ing includes a sufficiently broad range of assets,
expand supply until economic profits are driven to
fi xed capital is the replacement value of capital
economic theory suggests that the present value
zero. For each type of resource and each country, unit
used up in production. • Education expenditure is
of social welfare is increasing. Conversely, persis-
resource rents are derived by taking the difference
public current operating expenditures in education,
tently negative adjusted net savings indicate that an
between world prices (to reflect the social oppor-
including wages and salaries and excluding capi-
economy is on an unsustainable path.
tunity cost of resource extraction) and the average
tal investments in buildings and equipment. • Net
The table shows the extent to which today’s rents
unit extraction or harvest costs (including a “normal”
forest depletion is unit resource rents times the
from a number of natural resources and changes
return on capital). Unit rents are then multiplied by
excess of roundwood harvest over natural growth.
in human capital are balanced by net savings —that
the physical quantity extracted or harvested to arrive
• Energy depletion is the ratio of the value of the
is, this generation’s bequest to future generations.
at total rent. To estimate the value of the resource,
stock of energy resources to the remaining reserve
Adjusted net savings are derived from standard
rents are assumed to be constant over the life of the
lifetime (capped at 25 years). It covers coal, crude
national accounting measures of gross savings
resource (the El Serafy approach), and the present
oil, and natural gas. • Mineral depletion is the ratio
by making four adjustments. First, estimates of
value of the rent flow is calculated using a 4 percent
of the value of the stock of mineral resources to the
fixed capital consumption of produced assets are
social discount rate. For details on the estimation of
remaining reserve lifetime (capped at 25 years). It
deducted to obtain net savings. Second, current
natural wealth see World Bank (2011c).
covers tin, gold, lead, zinc, iron, copper, nickel, silver,
public expenditures on education are added to net
A positive net depletion figure for forest resources
bauxite, and phosphate. • Carbon dioxide damage is
savings (in standard national accounting these
implies that the harvest rate exceeds the rate of
estimated at $20 per ton of carbon (the unit damage
expenditures are treated as consumption). Third,
natural growth; this is not the same as deforesta-
in 1995 U.S. dollars) times tons of carbon emitted.
estimates of the depletion of a variety of natural
tion, which represents a change in land use (see
• Local pollution damage is the willingness to pay
resources are deducted to reflect the decline in asset
Definitions for table 3.4). In principle, there should
to avoid illness and death attributable to particulate
values associated with their extraction and harvest.
be an addition to savings in countries where growth
emissions.• Adjusted net savings are net savings
And fourth, deductions are made for damages from
exceeds harvest, but empirical estimates suggest
plus education expenditure minus energy depletion,
carbon dioxide emissions and local pollution.
that most of this net growth is in forested areas that
mineral depletion, net forest depletion, and carbon
The exercise treats public education expenditures
cannot currently be exploited economically. Because
dioxide and particulate emissions damage.
as an addition to savings. However, because of the
the depletion estimates reflect only timber values,
wide variability in the effectiveness of public edu-
they ignore all the external and nontimber benefits
cation expenditures, these figures cannot be con-
associated with standing forests.
strued as the value of investments in human capital.
Pollution damage from emissions of carbon dioxide
A current expenditure of $1 on education does not
is calculated as the marginal social cost per unit mul-
necessarily yield $1 of human capital. The calcula-
tiplied by the increase in the stock of carbon dioxide.
tion should also consider private education expen-
The unit damage figure represents the present value
diture, but data are not available for a large number
of global damage to economic assets and to human
of countries.
welfare over the time the unit of pollution remains
Data on gross savings are from World Bank
in the atmosphere.
national accounts data files (see table 4.8).
While extensive, the accounting of natural resource
Data sources
depletion and pollution costs still has some gaps.
Local pollution damage is estimated by valuing the
Data on consumption of fi xed capital are from
Key estimates missing on the resource side include
human health effects from exposure to particulate
the United Nations Statistics Division’s National
the value of fossil water extracted from aquifers, net
matter pollution in urban areas. The estimates are
Accounts Statistics: Main Aggregates and Detailed
depletion of fish stocks, and depletion and degrada-
calculated as willingness to pay to avoid illness and
Tables, extrapolated to 2010. Data on education
tion of soils. Important pollutants affecting human
death from cardiopulmonary disease and lung cancer
expenditure are from the United Nations Educa-
health and economic assets are excluded because no
in adults and acute respiratory infections in children
tional, Scientific, and Cultural Organization Insti-
internationally comparable data are widely available
that is attributable to particulate emissions.
tute for Statistics online database; missing data
on damage from ground-level ozone or sulfur oxides.
Adjusted net savings aims to be as comprehensive
are estimated by World Bank staff. Data on for-
Estimates of resource depletion are based on the
a measure as possible to provide a better under-
est, energy, and mineral depletion are estimates
“change in real wealth” method described in Hamil-
standing of the rate of county wealth creation or
based on sources and methods in World Bank
ton and Ruta (2008), which estimates depletion as
depletion. To do so, it treats education as investment
(2011c). Data on carbon dioxide damage are
the ratio between the total value of the resource
and accounts for pollution damages to assets and
from Fankhauser (1995). Data on local pollution
and the remaining reserve lifetime. The total value
human welfare, which goes outside the boundaries
damage are from Pandey and others (2006c). The
of the resource is the present value of current and
of the United Nations System of National Accounts.
conceptual underpinnings of the savings measure
future rents from resource extractions. An economic rent represents an excess return to a given factor
For a detailed note on methodology, see http://
appear in Hamilton and Clemens (1999).
data.worldbank.org.
2012 World Development Indicators
257
4.12
Central government finances Revenuea
Expense
Cash surplus or deficit
Net incurrence of liabilities
Debt and interest payments
% of GDP
Afghanistanb Albaniab Algeria Angola Argentina Armeniab Australia Austria Azerbaijanb Bahrain Bangladeshb Belarusb Belgium Beninb Bolivia Bosnia and Herzegovina Botswanab Brazilb Bulgariab Burkina Faso Burundib Cambodia Cameroonb Canadab Central African Republicb Chad Chile Chinab Hong Kong SAR, China Colombia Congo, Dem. Rep.b Congo, Rep.b Costa Rica Côte d’Ivoire Croatiab Cuba Cyprus Czech Republicb Denmark Dominican Republic Ecuador b Egypt, Arab Rep.b El Salvador Eritrea Estonia Ethiopiab Finland France Gabon Gambia, Theb Georgiab Germany Ghanab Greece Guatemalab Guineab Guinea-Bissau Haiti
258
% of GDP 2000 2010
% of GDP 2000 2010
% of GDP 2000 2010
Domestic 2000 2010
2000
2010
.. 23.4 .. .. 14.1 17.7 25.8 37.7 .. 32.9 9.8 28.7 42.8 16.5 18.4 37.1 .. 19.9 32.9 11.4 15.8 10.3 14.1 20.9 .. .. 21.6 7.1 14.9 15.1 3.7 28.6 .. 16.9 35.7 .. .. 30.5 36.2 .. .. 24.3 16.0 .. 31.0 11.9 40.9 42.9 .. .. 10.4 30.6 18.1 41.9 10.2 12.0 .. ..
.. 24.3 .. .. 19.7 16.4 24.0 40.4 .. 22.6 9.0 25.3 42.9 11.9 28.8 35.6 .. 21.7 31.6 10.9 20.6 9.4 12.0 18.7 .. .. 21.0 10.7 21.3 19.6 9.1 19.9 .. 17.4 39.1 .. 61.7 33.4 34.8 .. .. 27.2 17.9 .. 29.5 14.6 34.9 44.9 .. .. 11.6 32.0 18.7 44.7 10.9 13.5 .. ..
.. –6.7 .. .. –5.7 –0.7 2.0 –2.0 .. 8.5 –0.7 0.1 0.0 0.7 –8.7 0.7 .. –1.8 –0.4 –4.5 –2.4 –3.4 0.1 2.2 .. .. –0.7 –2.6 –6.8 –5.8 –4.2 1.9 .. –2.5 –5.3 .. .. –3.6 1.6 .. .. –6.7 –4.7 .. 0.2 –4.2 6.7 –1.7 .. .. –1.6 1.4 –6.5 –3.8 –1.8 –2.4 .. ..
.. 3.1 .. .. 1.9 0.1 .. .. .. 4.8 3.3 0.3 –8.2 –3.3 2.4 1.0 .. .. 0.0 0.6 3.3 –0.2 .. .. .. .. –0.4 4.2 1.6 3.5 4.1 .. .. 2.2 0.5 .. .. 2.5 .. .. .. 14.4 –2.1 .. .. 0.8 –5.2 .. .. .. 1.9 –0.8 –0.3 .. 0.5 0.2 .. ..
.. 2.6 .. .. 2.0 2.4 .. .. .. 0.7 1.1 –0.5 8.9 3.1 4.2 0.5 .. .. –1.6 4.1 2.9 3.6 .. .. .. .. –0.3 0.0 .. 4.3 –0.1 .. .. 1.1 3.9 .. .. 0.0 .. .. .. 1.7 9.4 .. .. 3.4 0.9 .. .. .. –0.8 0.0 5.0 .. 1.2 2.4 .. ..
0.7 .. 0.0 .. .. 2.8 .. .. 0.2 .. 0.4 2.8 1.7 2.0 –0.1 3.3 .. –0.1 0.5 3.4 .. 2.1 .. .. .. .. 0.7 0.0 –0.1 0.0 5.5 .. .. .. 1.3 .. .. 2.6 .. 2.2 .. 0.2 2.0 .. .. .. –0.6 .. .. .. 5.5 –0.2 2.6 .. 1.3 .. .. ..
10.1 .. 37.3 .. .. 22.4 24.6 36.6 25.8 28.8 11.1 31.7 40.7 18.2 23.3 39.7 .. 23.1 32.4 15.6 .. 12.2 .. 17.2 .. .. 22.0 11.9 20.7 18.2 23.4 .. 24.7 18.9 32.9 .. 39.7 29.6 39.8 14.5 .. 24.8 19.2 .. 36.8 .. 38.8 40.9 .. .. 23.8 29.7 15.4 37.1 10.9 .. .. ..
2012 World Development Indicators
46.1 .. 25.5 .. .. 22.7 26.6 39.6 14.6 18.8 11.3 31.6 44.1 15.0 21.8 40.6 .. 25.6 31.7 12.1 .. 11.3 .. 19.2 .. .. 21.0 .. 19.2 18.3 13.7 .. 26.0 17.8 36.6 .. 42.7 36.7 42.2 15.6 .. 28.9 21.0 .. 36.5 .. 34.8 48.1 .. .. 26.3 32.0 18.0 52.0 12.4 .. .. ..
1.4 .. –4.5 .. .. –4.9 –2.4 –2.6 0.3 4.0 –1.7 –1.5 –3.3 –1.0 1.2 –2.3 .. –3.5 –0.1 –5.7 .. –3.7 .. –2.0 .. .. –0.4 .. 1.1 –3.5 3.8 .. –3.4 0.9 –4.3 .. –5.9 –4.9 –2.1 –3.6 .. –7.7 –2.7 .. –1.3 .. 4.6 –7.3 .. .. –4.4 –2.2 –5.6 –15.6 –3.1 .. .. ..
0.2 .. 6.0 .. .. 0.8 .. .. 0.0 .. 3.1 –0.2 1.8 –0.3 –0.2 –1.4 .. 8.3 –0.4 2.6 .. 1.1 .. .. .. .. 1.8 2.0 2.0 8.2 –4.7 .. .. .. 3.9 .. .. 2.0 .. 1.9 .. 9.2 –0.8 .. .. .. –0.2 .. .. .. 0.6 3.1 2.8 .. 1.4 .. .. ..
Foreign
Total debt % of GDP 2010
.. .. .. .. .. .. 24.1 70.5 .. 19.2 .. 18.7 91.8 .. .. .. .. 61.0 .. .. .. .. .. 52.6 .. .. .. .. 34.0 62.9 .. .. .. .. .. .. 97.3 36.2 40.8 .. .. 85.8 50.0 .. 9.0 .. 36.0 83.5 .. .. 36.7 47.6 .. 142.0 23.0 .. .. ..
Interest % of revenue 2010
0.0 .. 1.0 .. .. 3.7 3.7 7.0 0.3 3.2 21.7 1.8 7.8 2.7 8.0 1.1 .. 20.7 2.2 2.4 .. 1.4 .. 9.8 .. .. 2.1 .. 0.3 15.3 1.3 .. 8.8 7.1 5.9 .. 7.7 3.3 4.9 12.9 .. 20.5 11.7 .. 0.6 .. 3.2 5.4 .. .. 3.7 5.5 15.2 14.3 12.6 .. .. ..
Revenuea
Expense
Cash surplus or deficit
Net incurrence of liabilities
Debt and interest payments
% of GDP
Honduras Hungary Indiab Indonesiab Iran, Islamic Rep.b Iraq Ireland Israel Italy Jamaica Japan Jordanb Kazakhstanb Kenyab Korea, Dem. Rep. Korea, Rep.b Kosovo Kuwaitb Kyrgyz Republicb Lao PDR Latviab Lebanon Lesothob Liberiab Libya Lithuania Macedonia, FYRb Madagascar Malawi Malaysiab Mali Mauritania Mauritius Mexicob Moldovab Mongoliab Moroccob Mozambique Myanmar b Namibiab Nepalb Netherlands New Zealand Nicaraguab Niger Nigeriab Norway Omanb Pakistanb Panamab Papua New Guineab Paraguay b Perub Philippinesb Poland Portugal Puerto Rico Qatar b
% of GDP 2000 2010
% of GDP 2000 2010
20.0 38.6 11.9 18.3 23.4 .. 33.0 40.7 36.9 31.9 .. 25.1 11.3 19.7 .. 22.3 .. 48.3 14.2 .. 26.1 16.0 50.7 .. .. 25.9 .. 11.7 .. 17.5 13.4 .. .. 14.7 24.5 24.4 30.0 .. 5.3 30.1 10.6 40.7 33.7 15.1 .. .. 48.4 23.9 13.9 23.1 24.2 17.0 17.4 14.2 31.5 34.6 .. ..
20.9 42.0 15.7 16.2 16.9 .. 27.6 47.1 38.9 34.5 .. 27.1 13.7 16.8 .. 16.6 .. 39.4 15.8 .. 28.1 30.5 43.7 .. .. 26.9 .. 10.6 .. 16.5 11.6 .. .. 15.4 28.9 22.4 30.4 .. 3.1 28.5 .. 39.3 32.1 16.5 .. .. 32.6 26.2 17.2 22.1 30.0 17.5 17.9 16.3 35.6 36.7 .. ..
21.1 41.3 11.4 15.1 31.9 .. 31.0 35.3 38.4 25.9 .. 21.8 9.9 20.3 .. 22.7 .. 55.5 20.2 14.2 25.0 22.2 66.2 0.4 .. 27.4 32.9 14.2 .. 20.8 17.1 .. 22.7 .. 31.6 33.7 31.8 .. .. 29.2 15.2 40.9 36.1 19.7 13.5 9.7 47.7 .. 13.8 .. .. 18.1 18.5 13.4 30.0 34.5 .. 47.2
23.4 46.2 15.0 14.4 24.7 .. 44.2 41.1 43.8 39.8 .. 25.9 16.2 22.4 .. 19.9 .. 32.6 22.0 10.8 35.6 29.2 51.9 0.3 .. 37.5 30.3 11.8 .. 19.6 14.7 .. 22.7 .. 35.0 26.6 30.6 .. .. 24.1 .. 45.5 32.1 20.0 11.6 7.2 35.7 .. 17.5 .. .. 15.1 16.4 16.9 35.7 43.0 .. 19.3
% of GDP 2000 2010
–3.0 –2.8 –3.9 –3.7 1.8 .. 4.9 –3.5 –0.7 –2.4 .. –2.0 0.1 2.0 .. 4.4 .. 4.9 –2.9 .. –2.2 –18.4 –2.8 .. .. –2.8 .. –2.0 .. –4.1 –3.4 .. .. –1.2 –1.5 0.2 –3.1 .. –2.7 –1.6 .. 2.0 1.7 –3.6 .. .. 15.7 –4.4 –4.1 –0.8 –1.9 –3.9 –2.1 –3.7 –2.8 –2.6 .. ..
–3.1 –4.1 –3.7 –0.6 0.6 .. –14.1 –4.0 –4.9 –15.3 .. –5.4 –1.1 –5.9 .. 1.7 .. 18.7 –5.0 –0.8 –6.8 –8.2 5.7 0.0 .. –7.2 –0.8 –1.9 .. –5.4 –2.1 .. –2.4 .. –2.6 3.0 –2.6 .. .. 2.0 .. –4.8 3.1 –1.0 –0.9 –1.7 11.7 .. –5.0 .. .. 1.4 0.3 –3.5 –6.1 –8.7 .. 15.2
Domestic 2000 2010
2000
2010
1.7 –1.7 5.1 .. 1.2 .. .. .. .. .. .. 1.8 –0.7 0.1 .. –0.9 .. .. .. .. 1.5 13.3 0.0 .. .. 0.7 .. 1.3 .. 1.6 –1.0 .. .. .. 1.5 1.3 1.7 .. 2.7 1.0 1.0 .. 1.4 .. .. .. 0.0 –1.3 .. .. 1.6 2.7 0.6 1.3 4.9 –0.2 .. ..
2.0 2.8 0.4 1.4 0.0 .. .. .. .. .. .. –1.7 1.2 1.2 .. –0.1 .. .. .. .. –0.2 9.3 9.0 .. .. 2.0 .. 1.7 .. 2.1 3.0 .. .. –0.7 –0.2 4.3 –2.8 .. 0.0 0.7 2.1 .. –1.0 .. .. .. 7.1 –0.7 .. .. 1.8 0.9 2.3 2.4 –1.7 2.0 .. ..
2.6 6.0 0.3 0.1 .. .. .. .. .. 4.6 .. 2.8 1.0 1.9 .. –0.1 .. .. 3.1 1.7 5.9 0.3 1.6 0.0 .. 8.6 0.2 3.0 .. 4.8 2.6 .. 1.8 .. 2.9 –0.9 2.1 .. .. –0.1 0.1 .. .. .. 2.4 .. 4.7 .. .. .. .. 0.3 –0.6 1.5 3.6 5.8 .. ..
0.4 –1.9 4.2 1.0 .. .. .. .. .. 7.1 .. 3.3 1.7 4.9 .. 1.4 .. .. –0.1 0.2 0.5 11.6 –0.4 0.0 .. 1.0 –0.6 0.6 .. 0.5 –4.4 .. –0.2 .. 0.4 1.2 2.3 .. .. –0.8 2.0 .. .. .. –1.9 0.1 –2.5 .. .. .. .. 0.1 1.6 2.5 1.6 3.3 .. ..
4.12
Foreign
Total debt % of GDP 2010
.. 83.2 46.1 26.1 .. .. 70.5 .. 118.4 115.8 174.4 59.0 10.2 .. .. .. .. .. .. .. 49.9 .. .. .. .. 43.2 .. .. .. 53.1 .. .. 37.8 .. 26.3 45.0 50.3 .. .. .. 43.8 58.2 37.9 .. .. 3.0 35.4 .. .. .. .. .. 21.6 .. 48.1 84.0 .. ..
2012 World Development Indicators
Interest % of revenue 2010
3.9 10.6 27.1 9.4 0.6 .. 6.9 12.7 11.1 64.5 .. 8.5 2.6 10.4 .. 5.0 .. 0.0 3.5 3.3 4.5 48.7 1.3 2.1 .. 6.1 1.9 3.9 .. 9.8 1.7 .. 10.6 .. 2.2 1.4 3.8 .. .. 6.3 4.6 4.6 3.4 6.5 1.8 6.6 1.8 .. 37.4 .. .. 2.1 6.0 25.1 8.1 7.7 .. 3.6
259
ECONOMY
Central government finances
4.12
Central government finances Revenuea
Expense
Cash surplus or deficit
Net incurrence of liabilities
Debt and interest payments
% of GDP 2000 2010
% of GDP 2000 2010
% of GDP 2000 2010
Domestic 2000 2010
2000
2010
Total debt % of GDP 2010
25.8 31.8 .. .. 16.9 .. 11.4 26.2 35.3 38.9 .. 26.3 .. 31.1 16.8 8.0 25.6 42.2 24.3 24.0 10.6 .. 19.5 .. .. 27.1 26.5 .. .. 10.8 26.8 6.5 37.1 20.1 24.7 .. 21.2 .. .. 23.4 19.7 29.1 25.8 m 10.4 14.6 13.8 14.9 14.5 8.2 .. 18.1 .. 12.3 .. 27.5 36.3
25.9 22.6 .. .. 12.8 .. 28.7 16.0 39.4 38.8 .. 27.9 .. 31.3 23.0 7.6 22.1 .. 25.5 .. 9.0 .. 15.9 .. .. 23.9 25.0 .. .. 15.5 26.9 6.2 36.2 19.6 26.6 .. 21.6 .. .. 21.6 15.9 33.2 26.7 m 11.0 18.2 18.7 18.1 18.1 11.6 .. 19.2 .. 16.1 .. 27.3 37.5
–2.0 7.0 .. .. –0.9 .. –9.3 11.2 –3.2 –1.1 .. –2.0 .. –0.5 –8.4 –0.4 –0.8 .. 2.2 .. –0.8 .. 1.5 .. .. 2.0 –2.4 .. .. –1.9 –0.6 0.1 1.6 0.5 –3.0 .. –1.2 .. .. –2.3 1.8 –5.2 –0.2 m –1.7 –2.3 –4.0 –1.8 –2.3 –2.6 .. –1.5 .. –4.0 .. 0.1 0.1
0.4 –4.1 .. .. 0.3 .. 4.8 7.8 2.9 –0.4 .. 1.6 .. .. 9.5 1.0 .. .. –2.8 .. –0.5 .. 0.9 .. .. .. 0.5 .. .. 0.6 1.5 .. .. 0.9 –7.4 .. 3.9 .. .. .. .. –0.6 .. m .. .. .. 0.9 .. 2.7 0.7 1.6 2.6 5.1 .. .. ..
1.7 –1.9 .. .. 0.5 .. .. .. –0.2 1.6 .. 0.3 .. .. 0.0 .. .. .. .. .. 0.5 .. –0.6 .. .. .. –0.2 .. .. 2.0 –0.3 .. .. 1.9 2.7 .. –0.5 .. .. .. 4.6 –0.1 .. m .. 1.0 .. 0.8 .. 2.4 0.4 2.0 0.7 0.4 .. .. ..
0.9 0.2 .. .. .. 1.1 .. .. 3.0 –1.4 .. 1.0 .. 2.2 –0.1 .. .. .. .. .. .. .. –0.1 .. 1.9 0.3 –0.4 0.9 .. 1.9 4.9 .. .. 5.1 –0.4 .. .. .. .. .. .. .. .. m .. 1.2 .. 0.9 0.4 1.5 2.9 1.2 0.1 0.3 .. .. ..
.. 9.4 .. .. .. 38.7 .. 109.2 38.2 .. .. .. .. 47.8 85.0 .. .. 44.2 28.8 .. .. .. 28.8 .. .. 21.4 40.5 50.5 .. 33.1 .. .. 73.3 76.1 45.6 .. .. .. .. .. .. .. .. m .. .. .. .. .. .. .. .. .. 56.5 .. 58.2 70.5
% of GDP
Romania Russian Federation Rwandab Saudi Arabia Senegalb Serbiab Sierra Leoneb Singaporeb Slovak Republic Sloveniab Somalia South Africa South Sudan Spain Sri Lankab Sudanb Swazilandb Sweden Switzerlandb Syrian Arab Republicb Tajikistanb Tanzania Thailand Timor-Leste Togo Trinidad and Tobagob Tunisiab Turkey b Turkmenistan Ugandab Ukraineb United Arab Emiratesb United Kingdom United States Uruguay b Uzbekistan Venezuela, RBb Vietnam West Bank and Gaza Yemen, Rep.b Zambiab Zimbabweb World Low income Middle income Lower middle income Upper middle income Low & middle income East Asia & Pacific Europe & Central Asia Latin America & Carib. Middle East & N. Africa South Asia Sub-Saharan Africa High income Euro area
30.9 26.8 .. .. .. 37.5 11.8 18.1 28.6 36.9 .. 28.4 .. 24.9 14.9 .. .. 34.8 18.3 .. .. .. 20.3 .. 17.6 33.5 29.0 24.4 .. 12.6 34.6 .. 36.0 16.9 30.9 .. .. .. .. .. 17.4 .. 23.8 m .. 18.8 14.9 19.7 18.6 13.5 25.6 .. 30.5 11.7 24.3 24.9 34.7
33.8 28.2 .. .. .. 39.8 22.9 13.4 37.7 42.7 .. 33.2 .. 30.5 19.2 .. .. .. 17.0 .. .. .. 18.6 .. 14.5 32.3 27.0 25.4 .. 13.9 40.7 .. 46.4 26.8 30.3 .. .. .. .. .. 17.2 .. 31.1 m .. .. 17.6 .. .. .. 27.3 .. 27.2 15.5 24.2 32.2 40.0
–4.6 –1.9 .. .. .. –3.9 –3.2 8.0 –7.3 –5.5 .. –4.9 .. –5.2 –6.6 .. .. .. 1.3 .. .. .. –0.6 .. 0.6 –4.8 –1.3 –2.2 .. –0.9 –5.6 .. –10.9 –10.0 –0.9 .. .. .. .. .. –1.5 .. –7.0 m .. .. –3.6 .. .. .. –2.2 .. –3.0 –3.8 –1.0 –7.5 –5.2
a. Excludes grants. b. Data were reported on a cash basis and have been adjusted to the accrual framework.
260
2012 World Development Indicators
2.4 1.4 .. .. .. 2.3 .. 10.3 2.9 3.7 .. 7.0 .. 2.4 6.9 .. .. .. 2.0 .. .. .. 2.6 .. –2.7 –0.4 –0.6 2.8 .. 1.6 6.8 .. .. 6.5 –0.8 .. .. .. .. .. .. .. .. m .. 2.8 .. 3.0 0.7 1.1 0.7 2.0 6.8 2.0 .. .. ..
Foreign
Interest % of revenue 2010
2.0 1.6 .. .. .. 2.9 8.3 0.0 4.7 4.0 .. 8.4 .. 5.8 31.0 .. .. .. 3.5 .. .. .. 6.1 .. 4.5 8.4 6.2 18.3 .. 7.7 3.1 .. 5.3 11.4 7.9 .. .. .. 1.1 .. 9.0 .. 5.5 m .. 7.0 6.0 7.0 5.8 6.1 3.2 9.3 6.9 15.9 .. 5.4 6.5
About the data
4.12
Definitions
Tables 4.12–4.14 present an overview of the size
data documentation. Because budgetary accounts
• Revenue is cash receipts from taxes, social con-
and role of central governments relative to national
may not include all central government units (such
tributions, and other revenues such as fines, fees,
economies. The tables are based on the concepts
as social security funds), they usually provide an
rent, and income from property or sales. Grants, usu-
and recommendations of the second edition of the
incomplete picture.
ally considered revenue, are excluded. • Expense is
International Monetary Fund’s (IMF) Government
Data on government revenue and expense are col-
cash payments for government operating activities in
Finance Statistics Manual 2001. Before 2005 World
lected by the IMF through questionnaires to mem-
providing goods and services. It includes compensa-
Development Indicators reported data derived on the
ber countries and by the Organisation for Economic
tion of employees, interest and subsidies, grants,
basis of the 1986 manual’s cash-based method. The
Co- operation and Development. Despite IMF efforts
social benefi ts, and other expenses such as rent
2001 manual, harmonized with the 1993 United
to standardize data collection, statistics are often
and dividends. • Cash surplus or deficit is revenue
Nations System of National Accounts, recommends
incomplete, untimely, and not comparable across
(including grants) minus expense, minus net acquisi-
an accrual accounting method, focusing on all eco-
countries.
tion of nonfinancial assets. In editions before 2005
nomic events affecting assets, liabilities, revenues,
Government fi nance statistics are reported in
nonfinancial assets were included under revenue
and expenses, not only those represented by cash
local currency. The indicators here are shown as
and expenditure in gross terms. This cash surplus
transactions. It takes all stocks into account, so
percentages of GDP. Many countries report govern-
or deficit is close to the earlier overall budget balance
that stock data at the end of an accounting period
ment finance data by fiscal year; see Primary data
(still missing is lending minus repayments, which are
equal stock data at the beginning of the period plus
documentation for information on fiscal year end by
included as a financing item under net acquisition
flows over the period. The 1986 manual considered
country.
of financial assets). • Net incurrence of liabilities
only the debt stock data. Further, the new manual no
is domestic financing (obtained from residents) and
longer distinguishes between current and capital rev-
foreign financing (obtained from nonresidents), or
enue or expenditures, and it introduces the concepts
the means by which a government provides financial
of nonfinancial and financial assets. Most countries
resources to cover a budget deficit or allocates finan-
still follow the 1986 manual, however. The IMF has
cial resources arising from a budget surplus. The net
reclassified historical Government Finance Statistics
incurrence of liabilities should be offset by the net
Yearbook data to conform to the 2001 manual’s for-
acquisition of financial assets (a third financing item).
mat. Because of reporting differences, the reclas-
The difference between the cash surplus or deficit
sified data understate both revenue and expense.
and the three financing items is the net change in
The 2001 manual describes government’s eco-
the stock of cash. • Total debt is the entire stock of
nomic functions as the provision of goods and ser-
direct government fixed-term contractual obligations
vices on a nonmarket basis for collective or individual
to others outstanding on a particular date. It includes
consumption, and the redistribution of income and
domestic and foreign liabilities such as currency and
wealth through transfer payments. Government
money deposits, securities other than shares, and
activities are financed mainly by taxation and other
loans. It is the gross amount of government liabili-
income transfers, though other financing such as
ties reduced by the amount of equity and financial
borrowing for temporary periods can also be used.
derivatives held by the government. Because debt
Government excludes public corporations and quasi
is a stock rather than a flow, it is measured as of
corporations (such as the central bank).
a given date, usually the last day of the fiscal year.
Units of government at many levels meet this defini-
• Interest payments are interest payments on gov-
tion, from local administrative units to the national
ernment debt—including long-term bonds, long-term
government, but inadequate statistical coverage pre-
loans, and other debt instruments —to domestic and
cludes presenting subnational data. Although data
foreign residents.
for general government under the 2001 manual are available for a few countries, only data for the central government are shown to minimize disparities.
Data sources
Still, different accounting concepts of central govern-
Data on central government finances are from the
ment make cross-country comparisons potentially
IMF’s Government Finance Statistics database.
misleading.
Each country’s accounts are reported using the
Central government can refer to consolidated or
system of common definitions and classifications
budgetary accounting. For most countries central
in the IMF’s Government Finance Statistics Manual
government finance data have been consolidated
2001. See these sources for complete and author-
into one account, but for others only budgetary
itative explanations of concepts, definitions, and
central government accounts are available. Coun-
data sources.
tries reporting budgetary data are noted in Primary
2012 World Development Indicators
261
ECONOMY
Central government finances
4.13 Afghanistana Albaniaa Algeria Angola Argentina Armeniaa Australia Austria Azerbaijana Bahrain Bangladesha Belarusa Belgium Benina Bolivia Bosnia and Herzegovina Botswanaa Brazila Bulgariaa Burkina Faso Burundia Cambodia Cameroona Canadaa Central African Republica Chad Chile Chinaa Hong Kong SAR, China Colombia Congo, Dem. Rep.a Congo, Rep.a Costa Rica Côte d’Ivoire Croatiaa Cuba Cyprus Czech Republica Denmark Dominican Republic Ecuador a Egypt, Arab Rep.a El Salvador Eritrea Estonia Ethiopiaa Finland France Gabon Gambia, Thea Georgiaa Germany Ghanaa Greece Guatemalaa Guineaa Guinea-Bissau Haiti
262
Central government expenses Goods and services
Compensation of employees
Interest payments
Subsidies and other transfers
Other expense
% of expense 2000 2010
% of expense 2000 2010
% of expense 2000 2010
% of expense 2000 2010
% of expense 2000 2010
.. 15 .. .. 4 53 11 5 .. 8 14 14 3 33 16 26 .. 18 24 25 .. 35 23 8 .. .. 9 .. 23 7 56 26 .. 30 24 .. 11 7 9 .. .. 8 15 .. 19 22 9 7 .. .. 17 4 14 13 15 15 .. ..
2012 World Development Indicators
75 .. 11 .. .. 13 10 6 9 26 12 10 3 16 14 24 .. 13 9 17 .. 29 .. 8 .. .. 10 .. 28 1 19 .. 11 29 8 .. 12 6 9 17 .. 8 17 .. 13 .. 10 6 .. .. 16 5 16 12 15 .. .. ..
.. 31 .. .. 11 5 11 13 .. 58 27 11 7 38 25 31 .. 21 9 41 .. 37 37 11 .. .. 21 .. 26 21 27 28 .. 39 26 .. 36 10 14 .. .. 30 43 .. 26 23 11 23 .. .. 11 6 36 22 31 30 .. ..
23 .. 34 .. .. 24 10 14 12 54 19 12 7 48 22 28 .. 19 19 46 .. 39 .. 12 .. .. 20 .. 23 11 43 .. 46 38 26 .. 35 8 13 36 .. 25 38 .. 21 .. 10 21 .. .. 18 5 40 24 29 .. .. ..
.. 16 .. .. 34 4 7 9 .. 7 18 3 14 6 8 2 .. 17 12 7 .. 2 22 22 .. .. 6 5 0 20 6 35 .. 16 4 .. 15 3 10 .. .. 20 11 .. 0 11 8 6 .. .. 24 7 39 17 10 32 .. ..
0 .. 1 .. .. 4 3 7 1 5 22 2 7 4 10 1 .. 19 2 4 .. 2 .. 9 .. .. 2 .. 0 15 3 .. 8 9 5 .. 7 3 5 12 .. 18 11 .. 1 .. 4 5 .. .. 4 5 16 10 11 .. .. ..
.. 36 .. .. 43 34 67 70 .. 8 26 68 74 2 45 38 .. 45 53 8 .. 15 17 59 .. .. 59 65 27 3 1 11 .. 16 43 .. 30 74 24 .. .. 24 3 .. 42 44 68 60 .. .. 48 81 4 42 22 12 .. ..
2 .. 45 .. .. 39 73 71 18 10 35 69 82 30 47 42 .. 49 64 12 .. 19 .. 69 .. .. 51 .. 19 61 34 .. 21 16 55 .. 33 74 17 28 .. 42 24 .. 48 .. 71 54 .. .. 51 81 28 50 33 .. .. ..
.. 3 .. .. 7 4 6 5 .. .. 14 4 3 21 6 3 .. 0 2 19 14 11 .. 1 .. .. 10 0 26 1 16 1 .. .. 3 .. 1 7 2 .. .. 18 27 .. 3 0 8 6 .. .. .. 4 .. 10 21 1 .. ..
0 .. 8 .. .. 20 6 5 61 5 12 7 3 2 7 5 .. 0 6 21 .. 11 .. 4 .. .. 19 .. 33 16 1 .. 14 7 6 .. 1 10 2 7 .. 8 12 .. 4 .. 8 2 .. .. 11 4 12 7 12 .. .. ..
Honduras Hungary Indiaa Indonesiaa Iran, Islamic Rep.a Iraq Ireland Israel Italy Jamaica Japan Jordana Kazakhstana Kenyaa Korea, Dem. Rep. Korea, Rep.a Kosovo Kuwait a Kyrgyz Republica Lao PDR Latviaa Lebanon Lesothoa Liberiaa Libya Lithuania Macedonia, FYRa Madagascar Malawi Malaysiaa Mali Mauritania Mauritius Mexicoa Moldovaa Mongoliaa Moroccoa Mozambique Myanmar a Namibiaa Nepala Netherlands New Zealand Nicaraguaa Niger Nigeriaa Norway Omana Pakistana Panamaa Papua New Guineaa Paraguaya Perua Philippinesa Poland Portugal Puerto Rico Qatar a
4.13
Goods and services
Compensation of employees
Interest payments
Subsidies and other transfers
Other expense
% of expense 2000 2010
% of expense 2000 2010
% of expense 2000 2010
% of expense 2000 2010
% of expense 2000 2010
13 9 12 11 18 .. 6 24 4 16 .. 6 25 21 .. 14 .. 23 40 .. 13 3 30 .. .. 16 .. 18 .. 15 38 .. .. 8 10 23 13 .. .. 21 .. 7 31 17 .. .. 10 50 48 15 37 8 23 26 7 9 .. ..
14 10 10 11 11 .. 10 26 4 6 .. 9 22 14 .. 11 .. 15 25 29 9 3 42 37 .. 10 28 15 .. 16 31 .. 12 .. 20 20 9 .. .. 20 .. 8 30 13 30 15 11 .. 22 .. .. 10 22 27 5 7 .. 28
51 13 10 11 57 .. 14 23 16 33 .. 70 9 55 .. 11 .. 32 37 .. 12 24 38 .. .. 21 .. 41 .. 28 37 .. .. 17 10 13 42 .. .. 51 .. 8 25 23 .. .. 11 34 4 37 25 54 22 31 11 32 .. ..
56 13 9 16 40 .. 23 24 15 14 .. 52 7 38 .. 10 .. 24 28 43 12 21 35 36 .. 15 17 40 .. 31 34 .. 35 .. 14 29 40 .. .. 45 .. 7 25 38 30 24 16 .. 4 .. .. 53 18 31 12 24 .. 28
7 13 30 24 0 .. 7 17 16 47 .. 13 10 18 .. 7 .. 1 9 .. 3 50 8 .. .. 6 .. 13 .. 15 8 .. .. 13 22 7 13 .. .. 7 .. 8 6 13 .. .. 3 5 36 22 14 7 13 24 8 8 .. ..
4 10 21 10 1 .. 5 11 10 43 .. 8 3 10 .. 6 .. 0 4 6 4 38 2 2 .. 5 2 7 .. 10 2 .. 11 .. 2 2 4 .. .. 8 .. 4 4 7 3 9 2 .. 31 .. .. 3 7 20 7 6 .. 7
18 57 43 53 24 .. 29 30 61 2 .. 8 55 3 .. 53 .. 32 14 .. 67 21 21 .. .. 55 .. 10 .. 48 0 .. .. .. 55 56 29 .. .. 10 .. 76 37 35 .. .. 74 11 3 26 24 31 37 19 70 46 .. ..
6 63 60 55 34 .. 40 31 66 6 .. 29 66 37 .. 58 .. 30 42 13 72 36 14 24 .. 68 49 25 .. 42 15 .. 31 .. 59 42 33 .. .. 13 .. 79 38 37 9 53 68 .. 25 .. .. 27 48 20 71 51 .. 16
12 13 7 0 1 .. 1 7 5 2 .. 3 1 2 .. 15 .. 11 .. .. 4 3 .. .. .. 2 .. 19 .. 3 17 .. .. .. 3 2 3 .. .. 11 .. 4 4 12 .. .. 4 0 .. 1 .. 1 5 2 8 7 .. ..
2012 World Development Indicators
21 8 1 9 14 .. 1 9 6 31 .. 2 2 1 .. 15 .. 19 2 9 3 2 6 .. .. 6 4 14 .. 1 17 .. 10 .. 5 7 14 .. .. 14 .. 4 7 5 28 .. 5 .. 17 .. .. 7 6 3 7 1 .. 22
263
ECONOMY
Central government expenses
4.13 Romania Russian Federation Rwandaa Saudi Arabia Senegala Serbiaa Sierra Leonea Singaporea Slovak Republic Sloveniaa Somalia South Africa South Sudan Spain Sri Lankaa Sudana Swazilanda Sweden Switzerlanda Syrian Arab Republica Tajikistana Tanzania Thailand Timor-Leste Togo Trinidad and Tobagoa Tunisiaa Turkeya Turkmenistan Ugandaa Ukrainea United Arab Emiratesa United Kingdom United States Uruguaya Uzbekistan Venezuela, RBa Vietnam West Bank and Gaza Yemen, Rep.a Zambiaa Zimbabwea World Low income Middle income Lower middle income Upper middle income Low & middle income East Asia & Pacific Europe & Central Asia Latin America & Carib. Middle East & N. Africa South Asia Sub-Saharan Africa High income Euro area
Central government expenses Goods and services
Compensation of employees
Interest payments
Subsidies and other transfers
Other expense
% of expense 2000 2010
% of expense 2000 2010
% of expense 2000 2010
% of expense 2000 2010
% of expense 2000 2010
22 19 .. .. 26 .. 15 33 11 16 .. 11 .. 4 22 41 26 .. 23 .. 31 .. 27 .. .. 15 9 .. .. 55 19 46 17 13 12 .. 6 .. .. 17 35 13 15 m .. 15 15 13 17 23 19 13 8 22 .. 9 7
13 13 .. .. .. 13 24 36 7 13 .. 13 .. 4 14 .. .. .. 6 .. .. .. 32 .. 26 19 6 10 .. 31 12 .. 18 15 13 .. .. .. 12 .. 24 .. 12 m .. 13 15 11 14 27 13 13 9 22 .. 10 7
16 17 .. .. 41 .. 23 29 12 18 .. 16 .. 11 24 41 45 .. 5 .. 22 .. 35 .. .. 37 40 .. .. 12 15 36 14 12 15 .. 21 .. .. 49 39 38 21 m .. 22 29 21 27 31 17 23 41 10 .. 13 14
19 16 .. .. .. 25 28 30 12 19 .. 13 .. 8 28 .. .. .. 6 .. .. .. 40 .. 36 26 36 25 .. 14 13 .. 14 12 23 .. .. .. 67 .. 43 .. 23 m .. 26 31 22 28 31 17 29 36 9 .. 15 17
Note: Components may not sum to 100 percent because of rounding or missing data. a. Data were reported on a cash basis and have been adjusted to the accrual framework.
264
2012 World Development Indicators
8 9 .. .. 11 .. 22 2 6 4 .. 18 .. 9 25 10 2 .. 3 .. 4 .. 7 .. .. 18 12 .. .. 5 9 .. 7 13 7 .. 12 .. .. 11 7 21 8m .. 12 15 9 10 10 6 13 12 30 .. 7 8
2 2 .. .. .. 3 7 0 4 4 .. 7 .. 5 25 .. .. .. 4 .. .. .. 7 .. 7 9 7 18 .. 9 3 .. 4 7 8 .. .. .. 1 .. 10 .. 5m .. 7 8 7 6 7 3 9 7 21 .. 5 5
43 54 .. .. 19 .. 5 36 64 59 .. 53 .. 44 21 8 27 .. 67 .. 43 .. 24 .. .. 29 36 .. .. 27 56 .. 55 62 66 .. 59 .. .. 21 3 28 43 m .. 41 29 48 34 44 50 43 .. 26 .. 58 60
60 67 .. .. .. 58 23 0 68 62 .. 63 .. 81 23 .. .. .. 83 .. .. .. 21 .. 26 45 39 44 .. 45 70 .. 53 64 46 .. .. .. 18 .. 15 .. 45 m .. 44 33 46 36 21 58 33 36 25 .. 57 64
12 1 .. .. .. .. 34 .. 12 3 .. 2 .. 2 9 .. 21 .. 2 .. 0 .. 6 .. .. 1 5 .. .. .. 1 .. 9 2 0 .. 2 .. .. .. 16 .. 6m .. 4 .. 5 .. 0 5 4 .. 7 .. 4 4
8 9 .. .. .. 1 18 .. 14 3 .. 4 .. 5 10 .. .. .. 3 .. .. .. 3 .. 4 2 12 6 .. 1 2 .. 12 4 10 .. .. .. 1 .. 8 .. 6m .. 7 8 6 7 7 6 12 9 1 .. 5 4
About the data
4.13
Definitions
The term expense has replaced expenditure in the
• Goods and services are all government payments
table since the 2005 edition of World Development
in exchange for goods and services used for the
Indicators in accordance with use in the International
production of market and nonmarket goods and ser-
Monetary Fund’s (IMF) Government Finance Statis-
vices. Own-account capital formation is excluded.
tics Manual 2001. Government expenses include all
• Compensation of employees is all payments in
nonrepayable payments, whether current or capital,
cash, as well as in kind (such as food and hous-
requited or unrequited. The concept of total central
ing), to employees in return for services rendered,
government expense as presented in the IMF’s Gov-
and government contributions to social insurance
ernment Finance Statistics Yearbook is comparable to
schemes such as social security and pensions that
the concept used in the 1993 United Nations System
provide benefits to employees. • Interest payments
of National Accounts.
are payments made to nonresidents, to residents,
Expenses can be measured either by function
and to other general government units for the use of
(health, defense, education) or by economic type
borrowed money. (Repayment of principal is shown
(interest payments, wages and salaries, purchases
as a financing item, and commission charges are
of goods and services). Functional data are often
shown as purchases of services.) • Subsidies and
incomplete, and coverage varies by country because
other transfers include all unrequited, nonrepayable
functional responsibilities stretch across levels of
transfers on current account to private and public
government for which no data are available. Defense
enterprises; grants to foreign governments, inter-
expenses, usually the central government’s respon-
national organizations, and other government units;
sibility, are shown in table 5.7. For more information
and social security, social assistance benefits, and
on education expenses, see table 2.11; for more on
employer social benefits in cash and in kind. • Other
health expenses, see table 2.16.
expense is spending on dividends, rent, and other
The classification of expenses by economic type in the table shows whether the government produces
miscellaneous expenses, including provision for consumption of fixed capital.
goods and services and distributes them, purchases the goods and services from a third party and distributes them, or transfers cash to households to make the purchases directly. When the government produces and provides goods and services, the cost is reflected in compensation of employees, use of goods and services, and consumption of fixed capital. Purchases from a third party and cash transfers to households are shown as subsidies and other transfers, and other expenses. The economic classification can be problematic. For example, subsidies to public corporations or banks may be disguised as capital financing or hidden in special contractual pricing for goods and services. For further discussion of government finance statistics, see About the data for tables 4.12 and 4.14.
Data sources Data on central government expenses are from the IMF’s Government Finance Statistics database. Each country’s accounts are reported using the system of common definitions and classifications in the IMF’s Government Finance Statistics Manual 2001. See these sources for complete and authoritative explanations of concepts, definitions, and data sources.
2012 World Development Indicators
265
ECONOMY
Central government expenses
4.14 Afghanistana Albaniaa Algeria Angola Argentina Armeniaa Australia Austria Azerbaijana Bahrain Bangladesha Belarusa Belgium Benina Bolivia Bosnia and Herzegovina Botswanaa Brazila Bulgariaa Burkina Faso Burundia Cambodia Cameroona Canadaa Central African Republica Chad Chile Chinaa Hong Kong SAR, China Colombia Congo, Dem. Rep.a Congo, Rep.a Costa Rica Côte d’Ivoire Croatiaa Cuba Cyprus Czech Republica Denmark Dominican Republic Ecuador a Egypt, Arab Rep.a El Salvador Eritrea Estonia Ethiopiaa Finland France Gabon Gambia, Thea Georgiaa Germany Ghanaa Greece Guatemalaa Guineaa Guinea-Bissau Haiti
266
Central government revenues Taxes on income, profits, and capital gains
Taxes on goods and services
Taxes on international trade
Other taxes
Social contributions
Grants and other revenue
% of revenue 2000 2010
% of revenue 2000 2010
% of revenue 2000 2010
% of revenue 2000 2010
% of revenue 2000 2010
% of revenue 2000 2010
.. 14 .. .. 13 10 67 24 .. 11 3 11 37 20 7 2 .. 25 11 18 18 6 21 54 .. .. 22 8 38 28 10 7 .. 18 9 .. .. 13 37 .. .. 20 20 .. 13 14 26 26 .. .. 8 18 22 22 24 6 .. ..
3 .. 60 .. .. 19 65 23 33 19 1 7 35 16 10 6 .. 30 16 15 .. 10 .. 53 .. .. 27 25 36 21 12 .. 17 15 7 .. 26 15 45 22 .. 25 25 .. 8 .. 20 22 .. .. 31 16 23 21 29 .. .. ..
2012 World Development Indicators
.. 43 .. .. 28 43 18 24 .. 25 2 39 24 35 41 28 .. 34 38 37 38 30 26 16 .. .. 47 65 13 32 14 15 .. 18 46 .. .. 34 42 .. .. 22 39 .. 38 10 32 25 .. .. 57 20 15 29 60 3 .. ..
3 .. 28 .. .. 43 23 23 23 29 0 32 25 38 43 45 .. 33 45 36 .. 35 .. 15 .. .. 45 59 9 32 14 .. 32 20 46 .. 34 28 36 53 .. 22 40 .. 39 .. 32 23 .. .. 51 24 29 29 56 .. .. ..
.. 9 .. .. 14 3 2 0 .. 31 6 5 .. 21 4 18 .. 4 2 13 17 22 28 3 .. .. 6 10 0 6 14 5 .. 46 6 .. .. 2 .. .. .. 8 7 .. 0 26 0 0 .. .. 7 .. 32 0 12 48 .. ..
5 .. 4 .. .. 4 2 0 4 24 4 11 .. 22 3 0 .. 2 1 11 .. 14 .. 1 .. .. 1 4 0 6 14 .. 4 33 1 .. 1 0 .. 9 .. 5 5 .. .. .. .. 0 .. .. 1 .. 16 0 7 .. .. ..
.. 1 .. .. 15 10 2 4 .. 4 2 3 2 7 8 5 .. 8 1 2 1 0 4 .. .. .. 2 6 10 7 20 0 .. 3 1 .. .. 1 6 .. .. 3 1 .. .. 1 2 3 .. .. .. .. .. 4 1 2 .. ..
0 .. 1 .. .. 8 0 5 1 3 .. 3 1 6 9 0 .. 2 0 2 .. 0 .. .. .. .. 8 1 17 5 0 .. 3 8 2 .. 4 1 5 4 .. 4 0 .. .. .. 2 4 .. .. 1 .. .. 3 2 .. .. ..
.. 17 .. .. 20 13 .. 41 .. .. .. 35 34 .. 9 34 .. 25 27 .. 6 .. 2 19 .. .. 7 .. 0 0 .. 3 .. 8 32 .. .. 47 6 .. .. .. 15 .. 35 0 29 41 .. .. 21 57 .. 30 2 1 .. ..
0 .. .. .. .. 13 .. 42 .. .. .. 37 36 2 7 39 .. 26 23 .. .. .. .. 23 .. .. 6 .. 0 0 .. .. 34 6 35 .. 22 44 3 3 .. .. 12 .. 36 .. 31 45 .. .. 17 55 .. 36 3 .. .. ..
.. 17 .. .. 11 20 11 7 .. 28 87 7 2 18 32 13 .. 5 20 30 19 42 18 8 .. .. 16 11 39 32 41 76 .. 7 5 .. .. 3 9 .. .. 47 19 .. .. 49 12 5 .. .. 7 4 31 15 6 40 .. ..
89 .. 6 .. .. 15 10 7 39 24 95 9 4 15 28 10 .. 6 16 36 .. 41 .. 8 .. .. 13 12 38 36 60 .. 10 18 8 .. .. 12 .. 9 .. 44 17 .. .. .. 15 .. .. .. 15 4 32 12 4 .. .. ..
Honduras Hungary Indiaa Indonesiaa Iran, Islamic Rep.a Iraq Ireland Israel Italy Jamaica Japan Jordana Kazakhstana Kenyaa Korea, Dem. Rep. Korea, Rep.a Kosovo Kuwait a Kyrgyz Republica Lao PDR Latviaa Lebanon Lesothoa Liberiaa Libya Lithuania Macedonia, FYRa Madagascar Malawi Malaysiaa Mali Mauritania Mauritius Mexicoa Moldovaa Mongoliaa Moroccoa Mozambique Myanmar a Namibiaa Nepala Netherlands New Zealand Nicaraguaa Niger Nigeriaa Norway Omana Pakistana Panamaa Papua New Guineaa Paraguaya Perua Philippinesa Poland Portugal Puerto Rico Qatar a
4.14
Taxes on income, proďŹ ts, and capital gains
Taxes on goods and services
Taxes on international trade
Other taxes
Social contributions
Grants and other revenue
% of revenue 2000 2010
% of revenue 2000 2010
% of revenue 2000 2010
% of revenue 2000 2010
% of revenue 2000 2010
% of revenue 2000 2010
15 20 27 31 13 .. 39 34 35 15 .. 9 24 29 .. 26 .. 0 14 .. 12 12 17 .. .. 11 .. 12 .. 43 12 .. .. 34 3 12 22 .. 19 32 15 25 54 13 .. .. 28 24 19 18 31 11 19 40 15 25 .. ..
20 23 47 36 19 .. 33 26 32 25 .. 13 24 41 .. 28 .. 1 21 13 7 15 17 28 .. 6 13 12 .. 46 19 .. 20 .. 1 27 26 .. .. 28 14 26 57 27 12 1 30 .. 25 .. .. 13 31 41 14 23 .. 40
44 34 29 26 7 .. .. 26 23 33 .. 31 40 41 .. 28 .. 0 61 .. 40 26 12 .. .. 44 .. 22 .. 25 42 .. .. 62 45 36 30 .. 33 23 30 26 29 54 .. .. 28 1 30 9 9 37 40 27 33 32 .. ..
40 32 23 29 3 .. .. 33 20 37 .. 43 20 40 .. 27 .. 0 37 42 35 44 12 15 .. 37 40 15 .. 17 29 .. 50 .. 48 29 36 .. .. 19 38 27 26 52 18 2 24 .. 31 .. .. 47 39 29 37 31 .. ..
6 3 19 3 6 .. 0 1 .. 8 .. 17 5 15 .. 4 .. 2 3 .. 1 26 41 .. .. 1 .. 40 .. 7 11 .. .. 4 5 7 11 .. 4 35 23 .. 3 7 .. .. 0 3 11 9 24 11 8 19 2 0 .. ..
4 0 13 2 6 .. 0 1 .. 7 .. 6 10 11 .. 4 .. 1 9 7 0 6 57 39 .. .. 5 31 .. 2 10 .. 2 .. 4 6 6 .. .. 44 16 .. 3 4 26 .. 0 .. 8 .. .. 9 2 21 0 0 .. 3
1 1 0 3 1 .. 4 5 5 20 .. 8 7 0 .. 10 .. 0 0 .. 0 11 0 .. .. 0 .. 1 .. 3 5 .. .. 1 0 1 3 .. .. 1 3 4 0 0 .. .. 1 2 8 4 2 3 3 4 0 2 .. ..
2 1 0 4 1 .. 2 5 7 10 .. 2 0 1 .. 8 .. 0 .. 1 0 10 3 1 .. 0 0 6 .. 4 10 .. 7 .. 0 2 6 .. .. 1 5 2 0 0 3 .. 1 .. 5 .. .. 1 6 .. 1 2 .. ..
12 34 0 .. 11 .. 15 15 33 2 .. 1 0 0 .. 13 .. .. .. .. 34 2 .. .. .. 36 .. .. .. .. .. .. .. 10 23 18 13 .. .. 1 .. 39 0 .. .. .. 18 .. .. 18 0 7 10 .. 36 30 .. ..
13 32 0 .. 19 .. 22 18 36 3 .. 0 .. .. .. 16 .. .. .. .. 29 1 .. .. .. 39 29 4 .. .. .. .. 7 .. 30 13 13 .. .. 0 .. 35 0 .. .. .. 20 .. .. .. .. 7 9 .. 37 33 .. ..
22 8 25 37 61 .. .. 20 4 21 .. 35 24 15 .. 18 .. 98 22 .. 13 24 29 .. .. 8 .. 26 .. 22 30 .. .. 10 23 26 22 .. 44 9 28 6 14 27 .. .. 25 70 32 38 33 30 20 10 14 .. .. ..
2012 World Development Indicators
21 12 16 28 52 .. .. 17 5 18 .. 36 46 7 .. 17 .. 98 34 37 28 23 11 18 .. 18 13 32 .. 31 31 .. 14 .. 17 23 14 .. .. 7 27 10 15 16 41 97 24 .. 32 .. .. 23 14 9 10 .. .. 57
267
ECONOMY
Central government revenues
4.14 Romania Russian Federation Rwandaa Saudi Arabia Senegala Serbiaa Sierra Leonea Singaporea Slovak Republic Sloveniaa Somalia South Africa South Sudan Spain Sri Lankaa Sudana Swazilanda Sweden Switzerlanda Syrian Arab Republica Tajikistana Tanzania Thailand Timor-Leste Togo Trinidad and Tobagoa Tunisiaa Turkeya Turkmenistan Ugandaa Ukrainea United Arab Emiratesa United Kingdom United States Uruguaya Uzbekistan Venezuela, RBa Vietnam West Bank and Gaza Yemen, Rep.a Zambiaa Zimbabwea World Low income Middle income Lower middle income Upper middle income Low & middle income East Asia & Pacific Europe & Central Asia Latin America & Carib. Middle East & N. Africa South Asia Sub-Saharan Africa High income Euro area
Central government revenues Taxes on income, proďŹ ts, and capital gains
Taxes on goods and services
Taxes on international trade
Other taxes
Social contributions
Grants and other revenue
% of revenue 2000 2010
% of revenue 2000 2010
% of revenue 2000 2010
% of revenue 2000 2010
% of revenue 2000 2010
% of revenue 2000 2010
9 5 .. .. 21 .. 15 29 17 13 .. 52 .. 27 13 15 24 17 14 30 3 .. 29 .. .. 36 20 .. .. 10 12 .. 38 57 15 .. 27 .. .. 18 36 43 20 m .. 20 18 14 18 25 10 18 17 17 .. 26 25
22 2 .. .. .. 9 17 34 9 10 .. 53 .. 23 18 .. .. 11 24 .. .. .. 33 .. 11 47 27 24 .. 22 10 .. 36 50 18 .. .. .. 2 .. 44 .. 23 m .. 24 23 22 21 33 8 26 27 19 .. 26 23
33 28 .. .. 34 .. 8 18 31 34 .. 33 .. 25 57 35 13 29 22 21 55 .. 40 .. .. 23 37 .. .. 29 33 17 30 3 34 .. 25 .. .. 9 45 23 30 m .. 34 32 36 32 30 37 40 30 30 .. 27 25
35 21 .. .. .. 45 25 26 33 34 .. 32 .. 20 45 .. .. 37 26 .. .. .. 40 .. 37 15 31 51 .. 47 34 .. 28 3 40 .. .. .. 21 .. 32 .. 32 m .. 36 33 37 36 29 40 39 31 27 .. 26 27
3 9 .. .. 30 .. 29 1 1 2 .. 3 .. 0 11 29 50 .. 1 11 14 .. 10 .. .. 5 11 .. .. 22 4 .. .. 1 3 .. 7 .. .. 10 13 20 6m .. 7 13 6 9 9 6 6 10 15 .. 1 0
0 26 .. .. .. 4 14 0 0 0 .. 3 .. .. 14 .. .. .. 6 .. .. .. 5 .. 19 4 6 1 .. 10 2 .. .. 1 4 .. .. .. 11 .. 8 .. 4m .. 5 7 4 6 6 4 4 6 10 .. 0 0
Note: Components may not sum to 100 percent because of missing data or adjustment to tax revenue. a. Data were reported on a cash basis and have been adjusted to the accrual framework.
268
2012 World Development Indicators
1 0 .. .. 3 .. 0 9 1 3 .. 3 .. 0 4 1 4 6 3 6 1 .. 0 .. .. 17 4 .. .. 0 2 .. 8 1 8 .. 4 .. .. 2 0 2 2m .. 1 1 3 2 3 0 4 4 4 .. 3 3
0 0 .. .. .. 0 .. 16 0 0 .. 2 .. 0 8 .. .. 13 3 .. .. .. 1 .. 6 11 4 8 .. 0 0 .. 7 1 2 .. .. .. 0 .. 0 .. 2m .. 2 1 2 1 1 0 2 3 3 .. 2 3
42 37 .. .. .. .. .. .. 39 39 .. 2 .. 41 2 .. .. 30 41 0 20 .. 4 .. .. 5 17 .. .. .. 30 0 20 35 29 .. 4 .. .. .. 0 3 .. m .. .. .. 16 .. .. 30 9 .. .. .. 33 35
33 20 .. .. .. 34 .. .. 43 42 .. 2 .. 51 1 .. .. 25 36 .. .. .. 6 .. .. 6 21 .. .. .. 37 .. 23 39 30 .. .. .. 0 .. .. .. .. m .. .. .. 19 .. .. 30 9 6 0 .. 35 37
13 20 .. .. 12 .. 48 42 13 9 .. 7 .. .. 14 21 9 .. 19 32 7 .. 17 .. .. 13 10 .. .. 39 19 82 4 3 12 .. 34 .. .. 61 6 9 15 m .. 17 20 14 20 24 17 16 30 26 .. 8 6
10 31 .. .. .. 8 44 24 15 14 .. 8 .. 6 14 .. .. .. 5 .. .. .. 15 .. 27 16 10 16 .. 22 17 .. 6 6 7 .. .. .. 66 .. 15 .. 17 m .. 17 18 14 18 28 16 16 23 30 .. 12 7
About the data
4.14
Definitions
The International Monetary Fund (IMF) classifi es
• Taxes on income, profits, and capital gains are
government revenues as taxes, grants, and property
levied on the actual or presumptive net income
income. Taxes are classified by the base on which
of individuals, on the profi ts of corporations and
the tax is levied, grants by the source, and property
enterprises, and on capital gains, whether real-
income by type (for example, interest, dividends,
ized or not, on land, securities, and other assets.
or rent). The most important source of revenue is
Intragovernmental payments are eliminated in con-
taxes. Grants are unrequited, nonrepayable, non-
solidation. • Taxes on goods and services include
compulsory receipts from other government units
general sales and turnover or value added taxes,
and foreign governments or from international orga-
selective excises on goods, selective taxes on ser-
nizations. Transactions are generally recorded on an
vices, taxes on the use of goods or property, taxes
accrual basis.
on extraction and production of minerals, and prof-
The IMF’s Government Finance Statistics Manual
its of fiscal monopolies. • Taxes on international
2001 describes taxes as compulsory, unrequited
trade include import duties, export duties, profi ts
payments made to governments by individuals, busi-
of export or import monopolies, exchange profi ts,
nesses, or institutions. Taxes are classified in six
and exchange taxes. • Other taxes include employer
major groups by the base on which the tax is levied:
payroll or labor taxes, taxes on property, and taxes
income, profits, and capital gains; payroll and work-
not allocable to other categories, such as penalties
force; property; goods and services; international
for late payment or nonpayment of taxes. • Social
trade and transactions; and other. However, the dis-
contributions include social security contributions by
tinctions are not always clear. Taxes levied on the
employees, employers, and self-employed individu-
income and profits of individuals and corporations
als, and other contributions whose source cannot
are classified as direct taxes, and taxes and duties
be determined. They also include actual or imputed
levied on goods and services are classified as indi-
contributions to social insurance schemes operated
rect taxes. This distinction may be a useful simplifica-
by governments. • Grants and other revenue include
tion, but it has no particular analytical significance
grants from other foreign governments, international
except with respect to the capacity to fix tax rates.
organizations, and other government units; interest;
Direct taxes tend to be progressive, whereas indirect
dividends; rent; requited, nonrepayable receipts
taxes are proportional.
for public purposes (such as fines, administrative
Social security taxes do not reflect compulsory
fees, and entrepreneurial income from government
payments made by employers to provident funds
ownership of property); and voluntary, unrequited,
or other agencies with a like purpose. Similarly,
nonrepayable receipts other than grants.
expenditures from such funds are not reflected in government expenses (see table 4.13). For further discussion of taxes and tax policies, see About the data for table 5.6. For further discussion of government revenues and expenditures, see About the data for tables 4.12 and 4.13.
Data sources Data on central government revenues are from the IMF’s Government Finance Statistics database. Each country’s accounts are reported using the system of common definitions and classifications in the IMF’s Government Finance Statistics Manual 2001. The IMF receives additional information from the Organisation for Economic Co-operation and Development on the tax revenues of some of its members. See the IMF sources for complete and authoritative explanations of concepts, definitions, and data sources.
2012 World Development Indicators
269
ECONOMY
Central government revenues
4.15
Monetary indicators Broad money
annual % growth 2000 2010
Afghanistan Albania Algeria Angolaa Argentinaa Armenia Australiaa Austriab Azerbaijan Bahrain Bangladesh Belarus Belgiumb Benina Bolivia Bosnia and Herzegovinaa Botswana Brazil Bulgaria Burkina Fasoa Burundi Cambodia Cameroona Canada Central African Republica Chada Chile Chinaa Hong Kong SAR, Chinaa Colombia Congo, Dem. Rep.a Congo, Rep.a Costa Rica Côte d’Ivoirea Croatia Cuba Cyprus Czech Republic Denmark Dominican Republic Ecuador Egypt, Arab Rep. El Salvador Eritrea Estonia Ethiopiaa Finlandb Franceb Gabona Gambia, Thea Georgia Germany b Ghana Greeceb Guatemala Guineaa Guinea-Bissaua
270
.. 12.0 14.1 303.7 1.5 38.6 3.7 .. 73.4 10.2 19.3 219.3 .. 26.0 1.6 11.3 1.4 19.7 30.8 7.5 15.5 26.9 19.1 6.6 2.4 19.4 9.1 12.3 9.3 3.6 51.2 58.5 24.0 –1.8 29.1 .. 10.4 16.0 –12.1 16.8 47.0 11.6 1.6 17.3 30.5 13.1 .. .. 18.3 34.8 39.2 .. 54.2 .. 21.4 12.9 60.7
26.9 12.5 10.5 14.0 33.1 10.6 9.4 .. 24.3 10.5 21.1 31.9 .. 7.1 14.8 7.2 10.7 15.4 6.3 19.3 19.9 21.3 12.8 15.1 14.2 26.1 10.6 18.9 7.4 11.5 30.8 37.6 0.8 18.2 4.3 .. 19.8 1.9 –3.1 12.2 18.6 12.4 –0.1 15.6 2.0 23.4 .. .. 19.2 13.7 34.8 .. 31.9 .. 9.1 .. 24.4
2012 World Development Indicators
Claims on domestic economy
Claims on central government
Annual growth % of broad money 2000 2010
Annual growth % of broad money 2000 2010
.. 0.9 8.4 .. .. 0.3 .. .. –23.9 .. 10.7 59.9 .. .. –1.3 19.9 10.3 8.3 6.5 .. 15.0 5.4 6.6 3.6 .. .. 4.1 .. .. 8.9 .. .. 14.1 .. 21.3 .. 11.1 –11.0 26.1 13.2 –11.4 4.1 2.6 3.7 39.4 .. .. .. .. .. 18.7 .. 7.5 .. 4.2 .. ..
9.0 4.9 2.4 .. .. 24.9 .. .. 12.9 .. 19.7 79.2 .. .. 9.6 3.6 6.3 20.7 1.5 .. 15.7 14.8 7.1 23.3 .. .. 2.1 .. .. 16.2 .. .. 4.7 .. 7.0 .. 35.6 2.1 5.0 13.0 20.2 3.9 –0.4 1.6 –8.6 .. .. .. .. .. 24.7 .. 13.0 .. 2.6 .. ..
.. 4.8 –11.6 –413.7 –0.8 –5.7 –1.8 .. 15.4 –0.4 5.6 22.2 .. 0.6 3.1 –0.4 –56.2 13.5 8.5 6.6 –22.6 –6.9 –12.3 2.4 6.8 15.1 4.0 0.0 0.4 6.0 –1.6 –11.7 –0.2 –7.6 2.0 .. 0.1 2.6 3.0 2.8 –28.1 7.7 2.3 25.7 –4.1 19.8 .. .. –42.2 2.7 19.8 .. 32.9 .. 10.2 7.9 16.2
–6.0 0.2 –2.7 –13.9 13.1 8.3 0.2 .. 8.2 –0.5 2.7 –4.7 .. –5.5 –6.5 2.3 20.2 4.9 3.6 3.4 5.3 0.4 3.2 4.7 11.6 8.7 0.4 0.3 0.6 –0.7 –44.7 –34.4 0.2 4.7 1.4 .. 3.5 1.9 0.0 –0.1 –5.9 2.5 2.0 12.6 3.4 2.5 .. .. 21.4 15.4 –2.0 .. 11.7 .. 2.3 .. 9.4
Interest rate
Deposit 2000 2010
.. 8.3 7.5 39.6 8.3 18.1 5.1 2.2 12.9 5.8 8.6 37.6 3.6 3.5 11.0 14.7 9.4 17.2 3.1 3.5 .. 6.8 5.0 3.5 5.0 5.0 9.2 2.3 4.8 12.1 .. 5.0 13.4 3.5 3.7 .. 6.5 3.4 3.2 17.7 8.8 9.5 9.3 .. 3.8 6.0 1.6 2.6 5.0 12.5 10.2 3.4 28.6 6.1 10.2 7.5 3.5
.. 6.4 1.8 12.8 9.2 9.0 4.2 .. 11.6 1.2 7.1 9.1 .. 3.5 1.0 3.2 5.6 8.9 4.1 3.5 .. 1.3 3.3 0.1 3.3 3.3 1.8 2.8 0.0 3.7 16.8 3.3 5.3 3.5 1.8 .. 3.4 1.1 .. 4.9 3.9 6.2 .. .. 1.1 4.7 .. 1.5 3.3 14.6 9.2 .. 17.1 .. 5.5 .. 3.5
% Lending 2000 2010
.. 22.1 10.0 103.2 11.1 31.6 9.3 5.6 19.7 11.6 15.5 67.7 8.0 .. 34.6 30.5 15.5 56.8 11.3 .. 15.8 .. 22.0 7.3 22.0 22.0 14.8 5.9 9.5 18.8 .. 22.0 24.9 .. 12.1 .. 8.0 7.2 8.1 26.8 17.1 13.2 14.0 .. 7.4 10.9 5.6 6.7 22.0 24.0 32.8 9.6 .. 12.3 20.9 19.4 ..
15.7 12.8 8.0 22.5 10.6 19.2 7.3 .. 20.7 7.2 13.0 9.2 9.5 .. 9.9 7.9 11.5 40.0 11.1 .. 12.4 .. 15.0 2.6 15.0 15.0 4.8 5.8 5.0 9.4 56.5 15.0 17.1 .. 10.4 .. 6.7 5.9 .. 12.1 .. 11.0 .. .. 7.8 8.0 .. .. 15.0 27.0 24.2 .. .. .. 13.3 .. ..
Real 2000
2010
.. 17.0 –11.7 –60.8 9.9 33.4 6.6 5.3 6.4 –2.4 13.4 –41.2 5.9 .. 27.9 1.3 15.4 47.7 4.4 .. 2.3 .. 18.6 3.0 18.3 15.9 9.8 3.7 13.6 –10.3 .. –17.0 16.7 .. 7.1 .. 4.0 5.6 4.9 18.6 26.0 7.9 10.5 .. 2.4 3.8 2.9 5.0 –4.8 19.6 26.8 10.4 .. 8.6 13.2 7.4 ..
11.6 9.0 –7.1 –3.2 –4.2 9.2 7.2 .. 8.5 –3.0 6.1 –0.9 8.2 .. 1.0 6.6 –2.8 30.4 8.0 .. 4.3 .. 12.7 –0.3 13.3 9.4 –8.4 –0.7 4.5 6.1 27.9 14.4 8.6 .. 9.3 .. 2.0 7.1 .. 6.7 .. 0.8 .. .. 6.2 –17.1 .. .. 9.3 17.2 14.3 .. .. .. 8.0 .. ..
Broad money
annual % growth 2000 2010
Haiti Honduras Hungary Indiaa Indonesia Iran, Islamic Rep.a Iraq Irelandb Israela Italy b Jamaica Japan Jordana Kazakhstan Kenya Korea, Dem. Rep. Korea, Rep.a Kosovo Kuwait Kyrgyz Republica Lao PDRa Latvia Lebanona Lesotho Liberiaa Libyaa Lithuania Macedonia, FYR Madagascara Malawia Malaysia Malia Mauritaniaa Mauritius Mexico Moldova Mongolia Morocco Mozambique Myanmar a Namibia Nepal Netherlandsb New Zealanda Nicaragua Niger a Nigeria Norwaya Oman Pakistan Panama Papua New Guinea Paraguay Perua Philippines Poland Portugalb
20.3 15.4 12.6 15.2 16.6 22.4 .. .. 8.0 .. –7.0 1.3 7.6 45.0 4.9 .. 25.4 –12.2 6.3 11.7 46.0 27.0 9.8 1.4 18.3 3.1 16.5 22.2 17.2 45.5 10.0 12.5 .. 9.2 –4.5 41.7 17.6 8.4 38.3 42.4 13.2 18.8 .. 1.5 9.4 12.8 48.1 8.7 6.0 12.1 9.3 5.0 2.8 –0.4 8.1 11.6 ..
26.1 9.8 4.2 17.8 15.4 27.7 31.2 .. 5.7 .. 5.8 2.0 9.2 13.3 22.4 .. 14.9 13.5 3.0 33.2 39.1 11.5 12.1 14.5 43.4 –0.6 8.4 12.1 9.7 17.2 7.3 12.2 15.5 7.6 12.8 13.4 62.5 7.9 22.8 42.5 9.6 9.6 .. 8.4 21.7 21.6 9.3 .. 11.3 15.1 11.1 10.2 19.0 21.5 10.9 8.7 ..
Claims on domestic economy
Claims on central government
Annual growth % of broad money 2000 2010
Annual growth % of broad money 2000 2010
Deposit 2000 2010
% Lending 2000 2010
13.8 –2.6 –2.0 4.7 17.2 –7.9 .. .. –4.8 .. –2.3 2.6 –1.2 –3.2 –2.1 .. –1.1 –37.7 –7.4 7.8 –17.6 7.8 10.5 14.9 197.0 –10.4 0.5 –15.9 0.1 7.7 2.1 –4.2 .. –4.7 3.5 –5.7 –7.1 3.6 6.9 24.9 –4.0 2.6 .. –1.0 10.0 –13.8 –43.0 –4.7 9.5 2.6 0.2 –4.6 4.7 2.3 1.5 –5.8 ..
12.1 15.9 9.5 .. 12.5 11.7 .. 0.1 8.6 1.8 11.6 0.1 7.0 .. 8.1 .. 7.9 .. 5.9 18.4 12.0 4.4 11.2 4.9 6.2 3.0 3.9 11.2 15.0 33.3 3.4 3.5 9.4 9.6 8.3 24.9 16.8 5.2 9.7 9.8 7.4 6.0 2.9 6.4 10.8 3.5 11.7 6.7 7.6 .. 7.1 8.5 15.7 9.8 8.3 14.2 2.4
31.4 26.8 12.6 12.3 18.5 .. .. 4.8 12.9 7.0 23.3 2.1 11.8 .. 22.3 .. 8.5 .. 8.9 51.9 32.0 11.9 18.2 17.1 20.5 7.0 12.1 18.9 26.5 53.1 7.7 .. 25.6 20.8 16.9 33.8 37.0 13.3 19.0 15.3 15.3 9.5 4.8 7.8 18.1 .. 21.3 8.9 10.1 .. 10.5 17.5 26.8 30.0 10.9 20.0 5.2
12.3 7.7 14.5 9.9 7.2 .. .. .. .. .. 9.1 –5.4 .. 32.2 4.7 .. .. 12.1 8.5 .. .. 31.2 .. 6.6 .. .. 14.4 2.7 .. .. 5.5 .. .. 5.8 10.1 24.4 29.6 4.0 11.9 .. 19.4 –10.4 .. .. 7.0 .. 5.8 .. 1.1 2.0 –8.4 1.2 1.7 .. 2.2 .. ..
–0.2 5.1 5.0 15.8 17.1 .. 8.1 .. .. .. –0.7 –1.4 .. 7.1 14.3 .. .. 10.9 2.0 .. .. –18.1 .. 5.9 .. .. –6.0 6.3 .. .. 8.1 .. .. 9.3 8.7 9.3 23.7 8.8 18.3 .. 10.1 8.9 .. .. 4.8 .. –11.2 .. 16.1 2.3 13.6 9.3 27.2 .. 7.5 9.0 ..
–9.0 1.4 1.3 5.3 –3.7 2.0 14.4 .. 1.3 .. –8.2 2.8 0.4 1.2 12.8 .. 0.4 1.4 –12.9 –8.8 1.5 2.4 1.1 13.9 47.7 –19.6 –0.6 6.6 –6.4 –13.1 –0.2 1.6 12.6 1.0 0.8 –4.0 –4.1 0.3 –1.3 32.6 7.1 6.2 .. 2.1 3.9 0.8 24.0 .. –4.1 10.9 1.6 –6.6 –6.3 –3.4 2.9 1.2 ..
4.15
Interest rate
0.7 9.8 4.9 .. 7.0 11.9 6.1 .. 1.6 .. 6.3 0.5 3.5 .. 4.6 .. 3.9 3.4 2.3 4.1 3.0 1.9 6.2 3.7 4.1 2.5 4.8 7.1 10.5 3.6 2.5 3.5 8.0 8.4 1.2 7.7 11.9 3.7 9.7 12.0 5.0 3.6 2.3 4.6 3.0 3.5 6.5 2.3 3.4 8.1 3.0 1.4 1.2 1.5 3.2 .. ..
17.5 18.9 7.6 12.2 13.3 12.0 14.3 .. 4.5 4.0 20.5 1.6 9.0 .. 14.4 .. 5.5 14.3 4.9 31.5 22.6 9.6 8.3 11.2 14.2 6.0 8.4 9.5 49.0 24.6 5.0 .. 17.0 8.9 5.3 16.4 20.1 .. 16.3 17.0 9.7 8.0 1.8 6.3 13.3 .. 17.6 4.3 6.8 14.0 7.7 10.4 26.0 19.0 7.7 .. ..
Real 2000
2010
18.3 –3.1 2.6 8.5 –1.7 .. .. –2.2 11.1 5.0 11.5 3.9 12.2 .. 15.3 .. 3.4 .. –9.7 19.4 5.5 7.4 20.7 14.4 22.1 –9.6 11.1 9.9 18.0 17.3 –1.1 .. 23.9 18.3 4.3 5.1 22.3 14.0 6.3 12.5 –9.0 4.8 0.6 4.7 8.8 .. –12.2 –5.8 –8.3 .. 11.9 3.9 13.1 25.4 4.9 11.9 1.8
11.5 12.4 4.4 12.6 4.8 11.3 –8.5 .. 3.4 3.6 8.9 3.8 2.6 .. 10.1 .. 1.7 10.9 2.5 23.1 12.0 12.2 3.8 7.3 5.9 57.8 12.6 7.2 37.9 15.7 –0.1 .. –2.0 7.2 0.9 4.7 0.0 .. 3.1 8.1 0.4 –4.8 0.4 4.9 10.1 .. 9.4 11.4 40.4 1.9 4.6 1.1 18.1 11.3 3.3 .. ..
2012 World Development Indicators
271
ECONOMY
Monetary indicators
4.15
Monetary indicators Broad money
annual % growth 2000 2010
Puerto Rico Qatar Romania Russian Federation Rwandaa Saudi Arabiaa Senegala Serbia Sierra Leonea Singaporea Slovak Republicb Sloveniab Somalia South Africa South Sudan Spainb Sri Lankaa Sudan Swaziland Sweden Switzerlanda Syrian Arab Republic Tajikistana Tanzania Thailand Timor-Leste Togoa Trinidad and Tobagoa Tunisiaa Turkey Turkmenistana Uganda Ukraine United Arab Emiratesa United Kingdoma United States Uruguay Uzbekistan Venezuela, RBa Vietnama West Bank and Gaza Yemen, Rep.a Zambia Zimbabwea
.. 10.7 40.8 58.5 15.6 4.5 10.8 160.8 12.1 –2.0 15.2 .. .. 7.2 .. .. 12.9 36.9 –6.6 1.9 –16.9 19.0 63.3 14.8 4.9 41.1 15.4 11.7 14.1 40.7 83.3 18.1 44.5 15.3 11.1 8.1 9.5 .. 33.7 35.4 .. 25.3 73.8 45.7
.. 23.1 6.8 24.6 .. 5.2 13.7 13.1 32.7 8.6 5.0 .. .. 6.9 .. .. 15.8 25.4 7.9 –8.5 5.5 13.5 –3.6 25.4 10.9 9.9 16.3 30.5 11.3 18.5 .. 37.8 22.7 6.2 4.0 –2.1 22.1 .. 28.0 29.7 .. 11.8 29.9 ..
Claims on domestic economy
Claims on central government
Annual growth % of broad money 2000 2010
Annual growth % of broad money 2000 2010
Deposit 2000 2010
% Lending 2000 2010
.. –23.1 –1.1 –18.0 –11.4 –3.5 –3.9 22.5 54.6 –1.6 4.1 .. .. 0.2 .. .. 12.5 33.9 1.7 2.4 2.1 –6.1 36.6 0.7 0.5 –36.8 –0.3 –13.2 5.6 26.8 –53.4 29.4 –1.7 –9.6 –2.4 0.5 –1.8 .. –6.4 –2.4 .. –45.6 162.0 29.5
.. 0.0 33.1 6.5 10.1 .. 3.5 78.7 9.2 1.7 8.5 10.0 .. 9.2 .. 3.0 9.2 .. 6.5 2.2 3.0 4.0 1.3 7.4 3.3 0.8 3.5 8.2 .. 47.2 .. 9.8 13.7 6.2 4.5 .. 18.3 .. 16.3 3.7 1.5 14.0 20.2 50.2
.. .. 53.9 24.4 17.0 .. .. 6.3 26.3 5.8 14.9 15.8 .. 14.5 .. 5.2 16.2 .. 14.0 5.8 4.3 9.0 25.6 21.6 7.8 16.7 .. 16.5 .. .. .. 22.9 41.5 9.7 6.0 9.2 46.1 .. 25.2 10.6 .. 19.5 38.8 68.2
.. –1.7 20.0 33.1 .. .. .. –71.0 .. .. 8.2 .. .. –11.8 .. .. .. 16.9 16.9 8.5 .. –4.1 .. 12.2 6.2 45.7 .. .. .. 16.2 .. 8.2 30.9 .. .. 5.0 45.1 .. .. .. .. .. –11.4 ..
.. 21.6 5.8 12.4 .. .. .. 29.5 .. .. 11.7 .. .. 6.8 .. .. .. 10.7 5.0 9.5 .. 10.3 .. 11.4 10.7 2.3 .. .. .. 27.6 .. 20.7 2.1 .. .. –1.5 11.3 .. .. .. .. .. 8.1 ..
.. 4.2 8.0 9.7 .. –5.4 3.8 5.9 30.0 –4.9 1.1 .. .. –0.2 .. .. –0.6 12.5 26.0 –0.9 0.2 1.9 –9.8 7.7 –1.2 –46.5 4.3 25.3 –1.2 4.0 .. 14.0 8.4 2.6 2.9 0.6 8.6 .. –1.5 2.3 .. 12.9 11.5 ..
Interest rate
.. 2.9 7.3 6.0 7.1 .. 3.5 11.3 8.9 0.2 3.8 1.4 .. 6.5 .. .. 6.9 .. 3.9 .. 0.1 6.2 6.1 6.6 1.0 0.8 3.5 1.5 .. 15.3 .. 7.7 10.6 .. .. .. 4.2 .. 14.8 11.2 0.3 18.7 7.4 121.5
a. Includes claims on the private sector only. b. As members of the European Monetary Union, these countries share a single currency, the euro.
272
2012 World Development Indicators
.. 7.3 14.1 10.8 16.7 .. .. 17.3 21.3 5.4 5.8 5.9 .. 9.8 .. .. 10.2 .. 9.8 .. 2.7 9.9 24.1 14.5 5.9 11.0 .. 9.3 .. .. .. 20.2 15.9 .. 0.5 3.3 10.3 .. 18.3 13.1 .. 23.8 20.9 579.0
Real 2000
2010
.. .. 6.7 –9.6 20.6 .. .. –40.1 19.0 2.1 5.0 10.0 .. 5.2 .. 1.7 8.3 .. 13.8 4.3 3.1 –0.6 2.4 13.0 6.4 11.4 .. 3.2 .. .. .. 10.6 15.0 –1.5 5.3 6.9 41.1 .. –3.3 6.9 .. –3.1 6.7 67.8
.. 31.0 10.1 –0.5 14.3 .. .. 7.6 6.0 5.9 2.8 2.9 .. 1.6 .. .. 2.7 .. 3.4 .. 2.7 3.4 7.6 6.4 2.2 1.7 .. 4.6 .. .. .. 10.2 0.7 .. –2.3 2.4 4.9 .. –19.3 1.1 .. –0.7 8.2 605.4
About the data
4.15
Definitions
Money and the financial accounts that record the
reporting period. The valuation of financial deriva-
• Broad money (IFS line 35L..ZK) is the sum of cur-
supply of money lie at the heart of a country’s
tives and the net liabilities of the banking system
rency outside banks; demand deposits other than
financial system. There are several commonly used
can also be difficult. The quality of commercial bank
those of the central government; the time, savings,
defi nitions of the money supply. The narrowest,
reporting also may be adversely affected by delays in
and foreign currency deposits of resident sectors
M1, encompasses currency held by the public and
reports from bank branches, especially in countries
other than the central government; bank and travel-
demand deposits with banks. M2 includes M1 plus
where branch accounts are not computerized. Thus
er’s checks; and other securities such as certificates
time and savings deposits with banks that require
the data in the balance sheets of commercial banks
of deposit and commercial paper. Change in broad
prior notice for withdrawal. M3 includes M2 as well
may be based on preliminary estimates subject to
money is measured as the difference in end-of-year
as various money market instruments, such as cer-
constant revision. This problem is likely to be even
totals relative to the preceding year. Data for 2010
tificates of deposit issued by banks, bank deposits
more serious for nonbank financial intermediaries.
for countries reporting under the old presentation
denominated in foreign currency, and deposits with
Many interest rates coexist in an economy, reflect-
of monetary statistics and data for 2000 for all
fi nancial institutions other than banks. However
ing competitive conditions, the terms governing
countries are based on money plus quasi money.
defined, money is a liability of the banking system,
loans and deposits, and differences in the position
• Claims on domestic economy (IFS line 32S..ZK)
distinguished from other bank liabilities by the spe-
and status of creditors and debtors. In some econo-
are gross credit from the financial system to house-
cial role it plays as a medium of exchange, a unit of
mies interest rates are set by regulation or adminis-
holds, nonprofit institutions serving households, non-
account, and a store of value.
trative fiat. In economies with imperfect markets, or
financial corporations, state and local governments,
The banking system’s assets include its net for-
where reported nominal rates are not indicative of
and social security funds. Data for countries where
eign assets and net domestic credit. Net domestic
effective rates, it may be difficult to obtain data on
claims on the domestic economy are not available
credit includes credit extended to the private sector
interest rates that reflect actual market transactions.
are claims on the private sector (IFS line 32D..ZK
and general government and credit extended to the
Deposit and lending rates are collected by the Inter-
or 32D.ZF) and are footnoted as such. • Claims on
nonfinancial public sector in the form of investments
national Monetary Fund (IMF) as representative inter-
central government (IFS line 32AN..ZK) are loans
in short- and long-term government securities and
est rates offered by banks to resident customers.
to central government institutions less deposits.
loans to state enterprises; liabilities to the public
The terms and conditions attached to these rates
• Deposit interest rate is the rate paid by commer-
and private sectors in the form of deposits with the
differ by country, however, limiting their comparabil-
cial or similar banks for demand, time, or savings
banking system are netted out. Net domestic credit
ity. Real interest rates are calculated by adjusting
deposits. • Lending interest rate is the rate charged
also includes credit to banking and nonbank financial
nominal rates by an estimate of the inflation rate in
by banks on loans to prime customers. • Real inter-
institutions.
the economy. A negative real interest rate indicates
est rate is the lending interest rate adjusted for infla-
Domestic credit is the main vehicle through which
a loss in the purchasing power of the principal. The
tion as measured by the GDP deflator.
changes in the money supply are regulated, with cen-
real interest rates in the table are calculated as
tral bank lending to the government often playing the
(i – P) / (1 + P), where i is the nominal lending inter-
most important role. The central bank can regulate
est rate and P is the inflation rate (as measured by
lending to the private sector in several ways—for
the GDP deflator).
example, by adjusting the cost of the refinancing
In 2009 the IMF began publishing a new presenta-
facilities it provides to banks, by changing market
tion of monetary statistics for countries that report
interest rates through open market operations, or by
data in accordance with its Monetary Financial Sta-
controlling the availability of credit through changes
tistical Manual 2000. The presentation for countries
in the reserve requirements imposed on banks and
that report data in accordance with its International
ceilings on the credit provided by banks to the pri-
Financial Statistics (IFS) remains the same.
vate sector.
Data sources Data on monetary and financial statistics are published by the IMF in its monthly International
Monetary accounts are derived from the balance
Financial Statistics and annual International Finan-
sheets of financial institutions—the central bank,
cial Statistics Yearbook. The IMF collects data on
commercial banks, and nonbank financial interme-
the financial systems of its member countries.
diaries. Although these balance sheets are usually
The World Bank receives data from the IMF in
reliable, they are subject to errors of classification,
electronic files that may contain more recent revi-
valuation, and timing and to differences in account-
sions than the published sources. The discussion
ing practices. For example, whether interest income
of monetary indicators draws from Caiola (1995).
is recorded on an accrual or a cash basis can make
Also see the IMF’s Monetary and Financial Statis-
a substantial difference, as can the treatment of non-
tics Manual (2000) for guidelines for the presenta-
performing assets. Valuation errors typically arise
tion of monetary and financial statistics. Data on
for foreign exchange transactions, particularly in
real interest rates are derived from World Bank
countries with flexible exchange rates or in countries
data on the GDP deflator.
that have undergone currency devaluation during the
2012 World Development Indicators
273
ECONOMY
Monetary indicators
4.16 Afghanistan Albania Algeria Angola Argentina Armenia Australia Austriab Azerbaijan Bahrain Bangladesh Belarus Belgiumb Benin Bolivia Bosnia and Herzegovina Botswana Brazil Bulgaria Burkina Faso Burundi Cambodia Cameroon Canada Central African Republic Chad Chile China Hong Kong SAR, China Colombia Congo, Dem. Rep. Congo, Rep. Costa Rica Côte d’Ivoire Croatia Cuba Cyprusb Czech Republic Denmark Dominican Republic Ecuador Egypt, Arab Rep. El Salvador Eritrea Estonia Ethiopia Finlandb Franceb Gabon Gambia, The Georgia Germany b Ghana Greeceb Guatemala Guinea Guinea-Bissau
274
Exchange rates and prices Official exchange rate
Purchasing power parity (PPP) conversion factor
local currency units to $ 2010 2011 a
local currency units to international $ 2000 2010
46.45 46.58 13.7 19.3 103.94 100.51 40.6 44.5 74.39 72.94 25.1 40.3 91.91 93.74 2.8 66.9 3.90 4.11 0.8 2.2 373.66 372.50 164.8 207.3 1.09 0.97 1.3 1.5 0.76 0.72 0.9 0.9 0.80 0.79 0.3 0.5 0.38 0.38 0.2 0.3 69.65 74.15 21.3 28.1 2,978.51 4,974.63 177.3 1,232.9 0.76 0.72 0.9 0.9 495.28 471.87 212.7 233.9 7.02 6.94 2.0 2.9 1.48 1.41 0.7 0.8 6.79 6.84 1.9 3.6 1.76 1.67 1.0 1.7 1.48 1.41 0.5 0.7 495.28 471.87 196.9 211.2 1,230.75 1,261.07 255.8 535.3 4,184.92 4,058.50 1,232.7 1,516.7 495.28 471.87 256.8 248.1 1.03 0.99 1.2 1.2 495.28 471.87 271.9 287.2 495.28 471.87 180.3 244.3 510.25 483.67 284.0 402.0 6.77 6.46 3.3 4.0 7.77 7.78 7.5 5.3 1,898.57 1,848.14 898.0 1,248.6 905.91 919.49 26.0 519.9 495.28 471.87 264.4 343.3 525.83 505.66 173.9 349.6 495.28 471.87 278.7 301.0 5.50 5.34 3.7 3.9 .. .. .. .. 0.76 0.72 0.7 0.7 19.10 17.70 14.2 14.2 5.62 5.37 8.4 7.9 36.88 38.11 8.8 20.5 .. .. 0.3 0.5 5.62 5.93 1.4 2.4 8.75 8.75 0.5 0.5 15.38 15.38 2.9 11.4 11.81 0.72 7.1 8.2 14.41 16.90 2.2 4.4 0.76 0.72 1.0 0.9 0.76 0.72 0.9 0.9 495.28 471.87 248.7 284.7 28.01 29.46 4.1 9.1 1.78 1.69 0.6 0.9 0.76 0.72 1.0 0.8 1.43 1.51 0.2 1.1 0.76 0.72 0.7 0.7 8.06 7.79 3.8 4.8 5,726.07 6,620.84 829.0 2,480.8 495.28 471.87 121.8 230.2
2012 World Development Indicators
Ratio of PPP conversion factor to market exchange rate
Real effective exchange rate
GDP implicit deflator
Consumer price index
Wholesale price index
2010
Index average annual average annual average annual 2005 = 100 % growth % growth % growth 2010 1990–2000 2000–10 1990–2000 2000–10 1990–2000 2000–10
0.4 0.4 0.5 0.7 0.6 0.6 1.3 1.1 0.6 0.8 0.4 0.4 1.1 0.5 0.4 0.5 0.5 1.0 0.5 0.4 0.5 0.4 0.5 1.2 0.6 0.5 0.8 0.6 0.7 0.7 0.6 0.7 0.7 0.6 0.7 .. 0.9 0.7 1.4 0.6 0.5 0.4 0.5 0.7 0.7 0.3 1.2 1.2 0.6 0.3 0.5 1.1 0.8 0.9 0.6 0.4 0.5
.. .. 102.4 .. .. 126.2 115.2 98.7 .. 89.5 .. .. 100.7 .. 122.1 .. .. .. 121.1 .. 113.3 .. 101.1 111.8 110.3 .. 108.4 118.7 .. 120.3 1,025.3 .. 121.8 99.6 105.9 .. 102.3 122.5 102.0 97.4 96.7 .. .. .. .. .. 98.3 98.8 101.3 101.0 118.5 97.3 97.6 106.7 .. .. ..
.. 37.7 18.5 739.4 5.2 212.5 1.4 1.5 203.0 0.5 4.1 355.1 1.8 8.7 8.6 4.1 9.7 211.8 102.1 3.7 13.4 4.4 6.3 1.5 4.5 7.1 7.9 7.9 4.5 22.6 964.9 9.0 15.9 9.2 90.0 6.4 4.3 12.8 1.6 9.8 4.4 8.7 6.2 7.9 53.7 6.5 1.9 1.3 7.0 4.2 356.7 1.8 26.7 9.2 10.4 5.5 32.5
7.8 3.3 8.3 36.1 13.0 4.6 3.9 1.7 9.8 7.4 5.4 21.5 2.1 3.3 6.9 3.8 9.0 8.1 5.9 2.7 10.7 4.9 2.1 2.5 2.8 5.4 6.4 4.4 –1.1 5.9 26.7 7.4 10.1 3.2 3.9 3.3 3.1 2.0 2.5 12.6 8.0 8.7 3.3 16.7 5.4 11.5 1.2 1.9 4.9 10.9 6.9 1.0 26.2 3.2 5.5 16.2 10.2
.. 27.8 17.3 711.0 8.9 70.5 2.1 2.2 179.7 0.8 5.5 271.3 1.9 8.7 8.7 .. 10.4 199.5 117.5 5.5 16.1 6.6 6.5 1.7 5.3 6.9 .. 8.6 5.9 20.2 930.2 9.3 15.6 7.2 86.3 .. 3.7 7.8 2.1 8.7 37.1 8.8 8.5 .. 21.6 5.5 1.5 1.6 4.6 4.0 24.7 2.1 28.4 9.0 10.1 .. 34.0
8.9 2.8 3.4 36.7 9.8 4.3 2.9 1.9 8.4 2.1 6.8 17.4 2.1 3.2 5.4 3.3 8.8 6.6 6.2 3.0 9.4 6.2 2.5 2.1 3.3 2.7 .. 2.4 0.6 5.6 26.9 3.4 10.8 2.9 2.9 .. 2.7 2.5 2.0 13.4 6.3 8.5 3.8 .. 4.3 12.8 1.5 1.8 2.1 7.2 7.0 1.6 15.9 3.3 7.1 20.5 2.5
.. .. .. .. 0.1 .. 1.1 0.3 .. .. .. 267.8 1.2 .. .. .. .. 204.9 85.7 .. .. .. .. 2.7 6.0 .. 7.0 .. 0.6 16.4 .. .. 14.1 .. 69.8 .. 2.8 8.2 1.1 .. .. 6.1 .. .. 8.1 .. 0.9 .. .. .. .. 0.4 .. 3.6 .. .. ..
.. 4.1 4.0 .. 15.1 2.4 3.4 2.3 .. .. .. 21.2 2.6 .. .. .. .. 9.3 6.1 .. .. .. .. 1.3 4.4 .. 6.1 .. 0.2 4.6 .. .. 12.5 .. 3.1 .. 3.8 2.2 2.4 .. 7.6 9.4 4.4 .. 3.4 .. 2.1 1.7 .. .. 6.6 2.3 .. 4.2 .. .. ..
Haiti Honduras Hungary India Indonesia Iran, Islamic Rep. Iraq Irelandb Israel Italy b Jamaica Japan Jordan Kazakhstan Kenya Korea, Dem. Rep. Korea, Rep. Kosovo Kuwait Kyrgyz Republic Lao PDR Latvia Lebanon Lesotho Liberia Libya Lithuania Macedonia, FYR Madagascar Malawi Malaysia Mali Mauritania Mauritius Mexico Moldova Mongolia Morocco Mozambique Myanmar Namibia Nepal Netherlandsb New Zealand Nicaragua Niger Nigeria Norway Oman Pakistan Panama Papua New Guinea Paraguay Peru Philippines Poland Portugalb
Official exchange rate
Purchasing power parity (PPP) conversion factor
local currency units to $ 2010 2011 a
local currency units to international $ 2000 2010
39.80 18.90 207.94 45.73 9,090.43 10,254.18 1,170.00 0.76 3.74 0.76 87.20 87.78 0.71 147.36 79.23 .. 1,156.06 .. 0.29 45.96 8,258.77 0.53 1,507.50 7.32 71.32 1.27 2.61 46.49 2,089.95 150.49 3.22 495.28 275.89 30.78 12.64 12.37 1,357.06 8.42 33.96 5.58 7.32 73.16 0.76 1.39 21.36 495.28 150.30 6.04 0.38 85.19 1.00 2.72 4,735.46 2.83 45.11 3.02 0.76
40.52 18.90 201.06 46.67 8,770.43 10,616.31 1,170.00 0.72 3.58 0.72 85.89 79.81 0.71 146.62 88.81 .. 1,108.29 .. 0.28 46.14 8,058.40 0.50 1,507.50 7.26 72.38 1.22 2.48 44.23 2,025.12 155.78 3.06 471.87 281.12 28.71 12.42 11.74 1,265.52 8.09 29.07 5.39 7.26 74.02 0.72 1.27 22.42 471.87 153.90 5.60 0.38 86.34 1.00 2.37 4,176.07 2.75 43.31 2.96 0.72
8.9 24.1 6.7 9.8 107.9 130.2 13.2 18.8 2,801.1 6,190.8 1,335.8 3,858.8 501.8 842.5 1.0 0.9 3.4 3.7 0.8 0.8 25.7 59.2 154.8 111.5 0.4 0.6 36.5 109.8 27.2 37.3 .. .. 746.2 824.6 .. .. 0.2 .. 10.0 17.4 2,155.5 3,777.2 0.3 0.4 909.2 988.7 2.7 4.7 19.1 42.2 0.3 0.7 1.4 1.6 20.0 18.6 426.5 908.3 15.6 58.6 1.7 1.8 227.0 280.0 71.1 148.0 12.5 17.1 6.1 7.9 3.0 6.5 259.3 756.5 5.2 5.0 8.0 14.8 107.2 433.1 3.6 6.0 19.4 32.6 0.9 0.8 1.4 1.5 5.2 8.3 221.5 243.4 29.0 77.2 9.1 9.0 0.2 0.3 16.2 31.8 0.6 0.6 1.1 1.5 1,372.0 2,595.9 1.5 1.6 19.4 24.3 1.8 1.9 0.7 0.6
Ratio of PPP conversion factor to market exchange rate
2010
0.6 0.5 0.6 0.4 0.7 0.4 0.7 1.1 1.0 1.1 0.7 1.3 0.8 0.8 0.5 .. 0.7 .. .. 0.4 0.5 0.7 0.7 0.6 0.6 0.6 0.6 0.4 0.4 0.4 0.6 0.6 0.5 0.6 0.6 0.5 0.6 0.6 0.4 .. 0.8 0.4 1.1 1.0 0.4 0.5 0.5 1.5 0.7 0.4 0.6 0.6 0.6 0.6 0.5 0.6 0.9
Real effective exchange rate
GDP implicit deflator
Consumer price index
Wholesale price index
Index average annual average annual average annual 2005 = 100 % growth % growth % growth 2010 1990–2000 2000–10 1990–2000 2000–10 1990–2000 2000–10
.. .. 106.1 .. .. 145.9 .. 100.2 115.4 99.4 .. 102.7 .. .. .. .. .. .. .. .. .. .. .. 106.1 .. .. .. 100.3 .. 101.1 108.8 .. .. .. 92.7 126.6 .. 98.0 .. .. .. .. 99.0 94.9 104.3 .. 117.9 102.8 .. 103.4 .. 113.0 139.6 .. 126.8 104.7 99.9
18.1 19.9 20.0 8.1 15.8 27.7 .. 3.7 11.0 3.8 24.8 0.0 3.2 204.7 16.6 .. 5.9 .. 1.5 110.6 27.2 48.0 19.0 9.7 51.8 .. 75.0 79.3 19.1 33.6 4.1 7.0 8.7 6.3 19.0 119.6 56.3 4.0 34.1 25.3 11.1 8.0 2.1 1.7 42.4 6.0 29.5 2.7 0.1 11.1 3.6 7.6 11.5 26.7 9.4 23.2 5.2
14.2 6.3 5.0 5.9 11.1 16.4 11.1 1.7 1.4 2.3 11.5 –1.1 6.5 15.1 6.1 .. 2.3 0.5 9.8 8.6 8.3 8.1 3.1 8.0 10.3 17.9 3.9 3.7 10.9 15.8 3.8 4.5 10.7 6.0 7.4 10.8 14.7 2.0 8.2 17.6 7.3 7.0 1.9 3.0 7.8 3.3 13.7 4.6 8.5 9.2 2.6 6.2 9.8 3.4 4.7 2.7 2.5
21.9 18.8 20.3 9.1 13.7 26.0 97.6 2.3 9.7 3.7 23.5 0.8 3.5 67.8 15.6 .. 5.1 .. 2.0 23.3 28.3 29.2 .. 5.9 .. 5.6 32.6 10.6 18.7 33.8 3.6 5.2 6.1 6.9 19.5 21.4 35.7 3.9 31.8 25.9 .. 8.7 2.4 1.8 .. 6.1 32.5 2.2 .. 9.7 1.1 9.3 13.1 27.3 7.7 25.3 4.5
15.2 7.7 5.5 6.0 8.7 15.3 21.0 2.8 2.0 2.2 11.9 –0.1 4.6 8.8 11.2 .. 3.1 1.7 3.7 7.4 7.8 6.3 .. 7.6 10.6 0.4 3.3 2.4 10.6 11.7 2.4 2.5 7.1 6.2 4.5 10.4 9.1 1.9 10.6 21.1 6.1 6.7 1.9 2.7 8.8 2.9 12.4 1.9 3.2 8.8 2.7 5.9 8.1 2.5 5.4 2.6 2.5
.. .. 16.8 7.4 15.4 28.4 .. 1.6 8.1 2.9 .. –1.0 .. 16.3 .. .. 3.7 .. 1.4 35.6 .. 12.0 .. .. .. .. 24.8 8.5 .. .. 3.4 .. .. .. 18.4 .. .. 2.9 .. .. .. .. 1.3 1.5 .. .. .. 1.6 .. 10.4 1.0 .. .. 23.7 6.3 19.8 ..
2012 World Development Indicators
.. .. 3.7 5.3 10.9 10.8 .. –0.1 4.2 2.6 .. 0.6 8.7 12.6 .. .. 2.6 .. 2.9 11.2 .. 7.0 .. .. .. .. 4.7 2.7 .. .. 4.6 .. .. .. 5.8 .. .. .. .. .. .. .. 2.6 3.4 .. .. .. 8.4 .. 9.8 3.8 .. 9.6 2.8 5.9 2.7 2.5
275
ECONOMY
4.16
Exchange rates and prices
4.16 Puerto Rico Qatar Romania Russian Federation Rwanda Saudi Arabia Senegal Serbia Sierra Leone Singapore Slovak Republicb Sloveniab Somalia South Africa South Sudan Spainb Sri Lanka Sudan Swaziland Sweden Switzerland Syrian Arab Republic Tajikistan Tanzania Thailand Timor-Leste Togo Trinidad and Tobago Tunisia Turkey Turkmenistan Uganda Ukraine United Arab Emirates United Kingdom United States Uruguay Uzbekistan Venezuela, RB Vietnam West Bank and Gaza Yemen, Rep. Zambia Zimbabwe
Exchange rates and prices Official exchange rate
Purchasing power parity (PPP) conversion factor
local currency units to $ 2010 2011 a
local currency units to international $ 2000 2010
.. 3.64 3.18 30.37 583.13 3.75 495.28 77.73 3,978.09 1.36 0.76 0.76 .. 7.32 .. 0.76 113.06 2.31 7.32 7.21 1.04 11.23 4.38 1,409.27 31.69 1.00 495.28 6.38 1.43 1.50 .. 2,177.56 7.94 3.67 0.65 1.00 20.06 .. 2.58 18,612.92 .. 219.59 4,797.14 ..
.. .. .. 3.64 1.9 2.8 3.05 0.6 1.7 29.38 7.3 15.9 600.31 142.5 265.5 3.75 2.0 2.6 471.87 259.3 264.9 73.33 8.9 36.1 4,230.53 853.8 1,566.0 1.26 1.2 1.0 0.72 0.5 0.5 0.72 0.5 0.6 .. .. .. 7.26 3.1 5.0 .. .. .. 0.72 0.7 0.7 110.57 24.7 52.9 2.67 0.8 1.6 7.26 2.7 4.4 6.49 9.1 9.0 0.89 1.9 1.5 11.23 17.4 25.6 4.61 0.3 1.7 1,572.12 320.4 518.2 30.49 16.0 17.1 1.00 0.5 0.7 471.87 255.9 259.6 6.41 3.2 3.8 1.41 0.6 0.6 1.67 0.3 1.0 .. 0.6 1.4 2,522.75 564.1 811.9 7.97 1.1 3.6 3.67 2.1 3.1 0.62 0.6 0.7 1.00 1.0 1.0 19.31 9.8 16.5 .. 91.3 705.2 4.29 0.4 2.9 20,450.10 4,018.5 7,109.8 .. 2.0 .. 213.80 46.8 107.6 4,860.67 1,082.7 3,847.2 .. .. ..
Ratio of PPP conversion factor to market exchange rate
2010
.. 0.8 0.5 0.5 0.5 0.7 0.5 0.5 0.4 0.7 0.7 0.9 .. 0.7 .. 1.0 0.5 0.6 0.6 1.3 1.5 0.6 0.4 0.4 0.5 0.7 0.5 0.6 0.4 0.6 0.5 0.4 0.5 0.8 1.0 1.0 0.8 0.4 1.1 0.4 .. 0.5 0.8 ..
Real effective exchange rate
GDP implicit deflator
Consumer price index
Wholesale price index
Index average annual average annual average annual 2005 = 100 % growth % growth % growth 2010 1990–2000 2000–10 1990–2000 2000–10 1990–2000 2000–10
.. .. 104.2 125.9 .. 104.8 .. .. 99.7 111.3 132.2 .. .. 101.2 .. 103.7 .. .. .. 95.5 107.5 .. .. .. .. .. 98.1 130.7 93.5 .. .. 111.4 99.1 .. 83.7 91.4 137.2 .. 195.3 .. .. .. 126.0 ..
3.0 .. 98.0 161.5 14.3 1.6 6.0 .. 31.9 1.4 11.4 29.4 .. 9.9 .. 3.9 9.1 65.5 10.5 2.2 1.1 7.9 235.0 23.0 4.2 .. 7.0 5.4 5.7 81.7 408.2 12.1 271.0 2.2 2.4 2.0 32.6 245.8 45.3 15.2 5.7 20.9 52.1 –3.9
4.3 10.6 14.7 15.1 10.4 7.0 2.7 15.3 9.6 1.4 3.0 3.7 .. 7.2 .. 3.4 10.6 10.6 7.8 1.7 1.2 7.3 19.7 7.4 3.2 5.3 2.4 5.6 3.3 14.1 12.6 5.9 16.6 8.2 2.5 2.5 7.9 24.0 25.1 8.7 3.4 11.9 15.9 5.1
.. 2.8 100.5 99.1 16.2 1.0 5.4 50.2 .. 1.7 8.4 12.0 .. 8.7 .. 3.8 9.9 72.0 9.5 1.9 1.6 6.4 .. 20.9 4.9 .. 8.5 5.7 4.4 79.9 .. 8.3 155.7 .. 2.5 2.7 33.9 .. 49.0 4.1 .. 26.3 57.0 29.0
.. 6.7 10.7 12.1 8.9 2.7 2.2 14.3 .. 1.6 4.5 3.9 .. 5.8 .. 2.9 10.9 9.0 7.0 1.5 0.9 6.3 12.3 6.8 2.9 5.2 2.9 6.9 3.4 15.6 .. 6.9 11.4 .. 2.1 2.6 8.8 .. 21.9 8.2 4.0 11.4 15.2 497.7
.. .. 93.8 99.8 .. 1.3 .. .. .. –1.0 9.5 9.1 .. 7.7 .. 2.4 8.1 .. .. 2.3 –0.4 4.7 .. .. 3.8 .. .. 2.8 3.6 75.2 .. .. 161.6 .. 2.4 1.2 27.2 .. 44.1 .. .. .. 101.4 25.9
.. .. 14.1 14.7 .. 2.6 .. .. .. 2.4 4.1 3.7 .. 6.7 .. 3.1 12.2 .. .. 3.2 1.0 4.5 .. .. 5.6 .. .. 4.0 4.6 15.3 .. .. 15.2 .. 2.1 4.0 12.7 .. 25.7 .. .. .. .. ..
Note: The differences in the growth rates of the GDP deflator and consumer and wholesale price indexes are due mainly to differences in data availability for each of the indexes during the period. a. Average for December or latest monthly data available. b. As members of the euro area, these countries share a single currency, the euro.
276
2012 World Development Indicators
About the data
4.16
Definitions
In a market-based economy, household, producer,
effective exchange rate index and a cost indicator
• Official exchange rate is the exchange rate deter-
and government choices about resource allocation
of relative normalized unit labor costs in manufactur-
mined by national authorities or the rate determined
are influenced by relative prices, including the real
ing. For selected other countries the nominal effec-
in the legally sanctioned exchange market. It is cal-
exchange rate, real wages, real interest rates, and
tive exchange rate index is based on manufactured
culated as an annual average based on monthly
other prices in the economy. Relative prices also
goods and primary products trade with partner or
averages (local currency units relative to the U.S.
largely reflect these agents’ choices. Thus relative
competitor countries. For these countries the real
dollar). • Purchasing power parity (PPP) conversion
prices convey vital information about the interaction
effective exchange rate index is the nominal index
factor is the number of units of a country’s currency
of economic agents in an economy and with the rest
adjusted for relative changes in consumer prices; an
required to buy the same amount of goods and ser-
of the world.
increase represents an appreciation of the local cur-
vices in the domestic market that a U.S. dollar would
The exchange rate is the price of one currency
rency. Because of conceptual and data limitations,
buy in the United States. • Ratio of PPP conver-
in terms of another. Offi cial exchange rates and
changes in real effective exchange rates should be
sion factor to market exchange rate is the result
exchange rate arrangements are established by
interpreted with caution.
obtained by dividing the PPP conversion factor by the
governments. Other exchange rates recognized by
Inflation is measured by the rate of increase in a
market exchange rate. • Real effective exchange
governments include market rates, which are deter-
price index, but actual price change can be nega-
rate is the nominal effective exchange rate (a mea-
mined largely by legal market forces, and for coun-
tive. The index used depends on the prices being
sure of the value of a currency against a weighted
tries with multiple exchange arrangements, principal
examined. The GDP deflator reflects price changes
average of several foreign currencies) divided by
rates, secondary rates, and tertiary rates.
for total GDP. The most general measure of the over-
a price deflator or index of costs. • GDP implicit
Official or market exchange rates are often used
all price level, it accounts for changes in government
deflator measures the average annual rate of price
to convert economic statistics in local currencies to
consumption, capital formation (including inventory
change in the economy as a whole for the periods
a common currency in order to make comparisons
appreciation), international trade, and the main com-
shown. • Consumer price index reflects changes
across countries. Since market rates reflect at best
ponent, household final consumption expenditure.
in the cost to the average consumer of acquiring a
the relative prices of tradable goods, the volume of
The GDP deflator is usually derived implicitly as the
basket of goods and services that may be fixed or
goods and services that a U.S. dollar buys in the
ratio of current to constant price GDP—or a Paasche
may change at specified intervals, such as yearly.
United States may not correspond to what a U.S.
index. It is defective as a general measure of inflation
The Laspeyres formula is generally used. • Whole-
dollar converted to another country’s currency at
for policy use because of long lags in deriving esti-
sale price index refers to a mix of agricultural and
the official exchange rate would buy in that country,
mates and because it is often an annual measure.
industrial goods at various stages of production and
particularly when nontradable goods and services
Consumer price indexes are produced more fre-
account for a significant share of a country’s output.
quently and so are more current. They are also con-
An alternative exchange rate—the purchasing power
structed explicitly, based on surveys of the cost of
parity (PPP) conversion factor—is preferred because
a defined basket of consumer goods and services.
it reflects differences in price levels for both tradable
Nevertheless, consumer price indexes should be
and nontradable goods and services and therefore
interpreted with caution. The definition of a house-
provides a more meaningful comparison of real out-
hold, the basket of goods, and the geographic (urban
put. See table 1.1 for further discussion.
or rural) and income group coverage of consumer
The ratio of the PPP conversion factor to the official
price surveys can vary widely by country. In addi-
exchange rate—the national price level or compara-
tion, weights are derived from household expendi-
tive price level—measures differences in the price
ture surveys, which, for budgetary reasons, tend to
level at the gross domestic product (GDP) level. The
be conducted infrequently in developing countries,
price level index tends to be lower in poorer countries
impairing comparability over time. Although useful for
and to rise with income.
measuring consumer price inflation within a country,
The real effective exchange rate is a nominal effective exchange rate index adjusted for relative
distribution, including import duties. The Laspeyres formula is generally used.
consumer price indexes are of less value in comparing countries.
movements in national price or cost indicators of
Wholesale price indexes are based on the prices at
the home country, selected countries, and the euro
the first commercial transaction of commodities that
area. A nominal effective exchange rate index is the
are important in a country’s output or consumption.
ratio (expressed on the base 2005 = 100) of an
Prices are farm-gate for agricultural commodities and
Data on official and real effective exchange rates
index of a currency’s period-average exchange rate
ex-factory for industrial goods. Preference is given to
and consumer and wholesale price indexes are
to a weighted geometric average of exchange rates
indexes with the broadest coverage of the economy.
from the International Monetary Fund’s Interna-
for currencies of selected countries and the euro
The least squares method is used to calculate
tional Financial Statistics. PPP conversion factors
area. For most high-income countries weights are
growth rates of the GDP implicit deflator, consumer
and GDP deflators are from the World Bank’s data
derived from industrial country trade in manufac-
price index, and wholesale price index.
files.
Data sources
tured goods. Data are compiled from the nominal
2012 World Development Indicators
277
ECONOMY
Exchange rates and prices
4.17
Balance of payments current account Goods and services
Net income
Net current transfers
Current account balance
$ millions 2000 2010
$ millions 2000 2010
$ millions 2000 2010
Total reservesa
$ millions Exports
Afghanistan Albania Algeria Angola Argentina Armenia Australia Austria Azerbaijan Bahrain Bangladesh Belarus Belgium Benin Bolivia Bosnia and Herzegovina Botswana Brazil Bulgaria Burkina Faso Burundi Cambodia Cameroon Canada Central African Republic Chad Chile China† Hong Kong SAR. China Colombia Congo, Dem. Rep. Congo, Rep. Costa Rica Côte d’Ivoire Croatia Cuba Cyprus Czech Republic Denmark Dominican Republic Ecuador Egypt, Arab Rep. El Salvador Eritrea Estonia Ethiopia Finland France Gabon Gambia, The Georgia Germany Ghana Greece Guatemala Guinea Guinea-Bissau Haiti †Data for Taiwan, China
278
Imports
2000
2010
2000
2010
.. 704 .. 8,188 31,277 447 83,898 87,777 2,118 7,176 7,214 7,641 206,988 528 1,470 1,580 3,000 64,584 7,000 237 53 1,826 2,668 329,252 .. .. 23,293 279,561 243,127 15,808 .. 2,628 7,750 4,370 8,645 .. 5,019 35,858 73,805 8,964 5,906 16,864 3,662 98 4,784 992 53,431 380,260 3,498 .. 859 627,879 2,441 29,440 3,862 734 67 504 171,909
.. 3,791 48,171 51,452 81,251 1,937 261,340 202,416 28,590 17,880 21,661 29,909 366,855 1,446 6,840 6,221 5,028 233,736 27,326 1,053 181 6,887 5,645 462,349 .. .. 81,826 1,752,621 500,447 45,224 .. .. 13,662 11,478 23,105 .. 9,745 137,665 156,176 11,697 19,610 48,831 5,553 .. 16,169 4,644 97,979 662,123 .. 256 4,061 1,541,139 9,437 60,094 10,827 1,534 155 799 314,339
.. 1,499 .. 5,739 33,108 966 87,799 85,125 2,024 5,132 9,673 8,087 195,511 708 2,078 4,157 2,321 72,444 7,670 658 150 2,263 2,501 288,093 .. .. 21,893 250,688 235,589 14,397 .. 1,194 7,297 3,629 9,592 .. 5,142 37,550 64,506 10,852 4,927 22,895 5,636 500 4,965 1,621 40,459 366,239 1,656 .. 1,323 626,519 3,350 41,727 5,567 872 92 1,369 164,874
.. 6,316 49,082 35,421 67,992 4,212 246,140 189,033 10,592 13,095 29,477 37,367 363,040 2,234 6,159 9,819 5,718 244,360 28,421 1,942 607 7,879 6,408 493,120 .. .. 66,990 1,520,559 487,850 46,608 .. .. 14,723 8,803 23,409 .. 11,235 130,932 139,022 17,462 22,651 59,862 9,259 .. 14,771 9,911 93,836 720,573 .. 308 6,134 1,362,052 13,925 80,353 15,188 1,800 289 4,084 285,177
2012 World Development Indicators
.. 107 .. –1,681 –7,548 53 –10,814 –2,259 –335 –224 –266 –47 4,475 –12 –225 590 –351 –17,886 –323 –20 –12 –123 –493 –22,291 .. .. –2,856 –14,666 1,125 –2,289 .. –805 –1,252 –653 –466 .. –542 –1,371 –4,023 –1,041 –1,405 888 –253 –1 –203 –36 –1,724 19,425 –779 .. 37 –7,662 –108 –885 –210 –78 –12 –9 4,468
.. –101 –1,319 –8,172 –9,938 339 –45,803 737 –3,467 –2,373 –1,454 –1,163 10,958 –33 –889 331 –243 –39,486 –1,680 –5 –11 –533 –239 –15,968 .. .. –15,424 30,380 3,644 –11,945 .. .. –748 –890 –2,080 .. –1,267 –13,198 5,771 –1,788 –1,054 –5,912 –381 .. –1,067 –64 2,523 48,892 .. –8 –363 59,648 –535 –10,756 –1,211 –77 –11 22 13,447
.. 533 .. 28 399 188 –47 –1,732 73 –990 2,420 155 –4,341 111 387 1,591 217 1,521 290 122 59 425 109 754 .. .. 558 6,311 –1,670 1,673 .. 19 93 –330 880 .. 177 373 –3,014 1,902 1,352 4,172 1,797 299 86 678 –723 –13,771 –63 .. 250 –25,976 631 3,352 865 75 26 760 –2,604
.. 1,223 2,632 –438 –388 563 –1,388 –2,658 509 –1,642 11,379 304 –8,423 172 1,081 2,258 979 2,788 2,038 514 136 646 146 –2,569 .. .. 4,390 42,932 –3,443 4,475 .. .. 370 –115 1,436 .. –45 472 –5,792 3,118 2,310 12,439 3,599 .. 341 4,905 –2,207 –34,941 .. 113 1,098 –50,792 2,322 118 4,946 17 98 3,097 –2,710
.. –156 .. 796 –8,981 –278 –14,763 –1,339 –168 830 –306 –338 11,611 –81 –446 –396 545 –24,225 –703 –319 –50 –136 –218 19,622 .. .. –898 20,518 6,993 795 .. 648 –707 –241 –533 .. –488 –2,690 2,262 –1,027 926 –971 –431 –105 –299 13 10,526 19,674 1,001 .. –177 –32,279 –387 –9,820 –1,050 –140 –11 –114 8,899
$ millions 2000 2010
.. .. .. –1,403 646 2,541 402 13,556 170,461 7,421 1,198 19,749 2,932 25,152 52,208 –1,373 314 1,866 –31,990 18,822 42,268 11,461 17,650 22,242 15,040 680 6,409 770 1,605 .. 2,109 1,516 11,175 –8,317 350 5,025 6,349 12,272 26,779 –649 459 1,200 874 1,184 9,731 –1,008 497 4,411 46 6,318 7,885 –47,323 33,015 288,575 –736 3,507 17,223 –380 243 1,068 –301 38 332 –879 611 3,817 –856 220 3,643 –49,307 32,427 57,151 .. 136 181 .. 114 632 3,802 15,055 27,827 305,374 171,763 2,913,712 12,798 107,560 268,743 –8,855 9,006 28,076 .. 83 1,300 .. 225 4,447 –1,439 1,318 4,630 1,670 674 3,624 –947 3,524 14,133 .. .. .. –2,803 1,869 1,142 –5,992 13,142 42,483 17,134 15,696 76,510 –4,435 632 3,501 –1,785 1,179 2,622 –4,504 13,785 37,029 –488 1,901 2,897 .. 36 114 673 923 2,567 –425 363 1,781 4,459 8,410 9,547 –44,499 63,728 165,852 .. 194 1,736 52 109 202 –1,337 116 2,264 187,943 87,497 215,978 –2,700 309 .. –30,897 14,594 6,352 –626 1,806 5,949 –327 168 .. –48 67 156 –166 183 1,337 39,899 110,464 401,148
Goods and services
4.17
Net income
Net current transfers
Current account balance
$ millions 2000 2010
$ millions 2000 2010
$ millions 2000 2010
ECONOMY
Balance of payments current account
Total reservesa
$ millions Exports 2000
Honduras Hungary India Indonesia Iran, Islamic Rep. Iraq Ireland Israel Italy Jamaica Japan Jordan Kazakhstan Kenya Korea, Dem. Rep. Korea, Rep. Kosovo Kuwait Kyrgyz Republic Lao PDR Latvia Lebanon Lesotho Liberia Libya Lithuania Macedonia, FYR Madagascar Malawi Malaysia Mali Mauritania Mauritius Mexico Moldova Mongolia Morocco Mozambique Myanmar Namibia Nepal Netherlands New Zealand Nicaragua Niger Nigeria Norway Oman Pakistan Panama Papua New Guinea Paraguay Peru Philippines Poland Portugal Puerto Rico Qatar
3,850 34,662 59,932 70,622 29,727 .. 92,068 46,591 297,030 3,589 528,751 3,539 10,341 2,776 .. 209,589 .. 21,301 573 506 3,229 5,849 269 .. 12,210 5,109 1,637 1,188 437 112,370 644 393 2,622 179,876 641 614 10,453 689 2,139 1,483 1,282 254,590 17,864 1,102 321 20,965 78,111 11,770 10,119 7,833 2,337 2,924 8,510 40,724 46,300 34,102 .. ..
Imports 2010
6,764 110,843 349,264 174,840 .. 65,695 207,689 80,323 546,949 4,004 871,533 12,189 65,086 8,900 .. 547,006 .. 74,689 2,472 2,257 12,800 21,240 900 400 49,345 24,849 4,213 .. 1,399 231,714 2,128 .. 4,957 313,797 2,292 3,394 30,129 2,980 8,198 4,982 1,574 576,108 40,916 3,628 1,097 76,774 172,425 38,362 28,062 18,402 6,055 9,989 39,521 65,106 198,427 72,125 .. ..
2000
4,681 36,449 73,075 56,002 17,503 .. 79,792 46,794 286,526 4,427 459,660 5,796 8,970 3,763 .. 192,970 .. 11,372 654 578 3,813 9,600 1,044 .. 5,024 5,833 2,280 1,520 629 94,350 927 471 2,707 191,818 972 771 12,546 1,492 2,493 1,630 1,790 238,810 17,306 2,152 456 12,017 49,476 6,351 12,148 8,122 1,771 3,286 9,648 48,565 57,204 47,262 .. ..
2010
9,881 –215 –598 102,710 –2,575 –7,207 440,277 –4,892 –12,926 153,537 –8,443 –20,291 .. –200 .. 37,731 .. 2,106 168,853 –13,547 –36,293 76,094 –8,323 –6,312 586,410 –12,010 –11,410 6,454 –350 –495 796,674 60,401 133,291 17,949 100 507 43,268 –1,254 –18,325 13,543 –133 –155 .. .. .. 516,332 –2,383 768 .. .. .. 32,682 6,699 7,818 3,905 –82 –343 2,324 –52 –83 13,025 17 85 31,010 –868 76 2,515 467 532 1,800 .. 24 30,686 –429 –30 25,245 –194 –828 6,094 –70 –124 .. –42 .. 2,105 –17 –110 189,499 –7,608 –8,142 2,812 –98 –457 .. –32 .. 6,141 –16 202 327,077 –13,795 –13,948 4,581 22 487 3,869 –7 –599 40,083 –864 –1,242 4,666 –192 –85 5,173 –133 –1,740 5,620 –98 –564 5,887 37 94 513,539 –2,297 3,570 38,882 –3,202 –6,999 5,486 –296 –278 2,529 –16 –39 75,768 –3,148 –18,623 117,143 –2,305 872 24,400 –838 –3,162 40,021 –2,218 –3,187 19,882 –560 –1,859 6,286 –210 –592 10,671 22 –502 34,809 –1,410 –10,053 73,133 –30 347 207,139 –731 –16,923 87,411 –2,371 –10,423 .. .. .. .. .. ..
538 357 13,434 1,816 457 .. 915 6,470 –4,276 821 –9,831 2,184 249 921 .. 566 .. –1,956 87 116 195 78 237 .. –487 243 609 113 135 –1,924 126 187 64 6,994 211 94 2,483 231 276 436 340 –6,219 237 410 47 1,627 –1,250 –1,451 4,162 177 –5 177 1,001 5,643 1,292 3,342 .. ..
2,760 490 52,158 4,630 .. –2,936 –1,589 8,426 –21,144 2,010 –12,395 3,941 –481 2,286 .. –3,229 .. –13,003 1,391 179 871 785 661 960 –1,828 1,758 1,805 .. 253 –6,783 486 .. 183 21,504 1,319 187 7,270 657 241 1,232 4,092 –14,504 –29 1,173 151 20,093 –4,711 –5,704 13,778 477 190 542 3,026 16,604 3,762 2,858 .. ..
$ millions 2000 2010
–508 –955 1,319 2,701 –4,004 1,417 11,217 44,988 –4,601 –51,781 41,059 300,480 7,992 5,643 29,353 96,211 12,481 .. .. .. .. 27,133 7,882 50,642 –356 954 5,408 2,114 –2,056 6,342 23,281 70,907 –5,781 –72,015 47,201 158,478 –367 –934 1,054 2,501 119,660 195,755 361,639 1,096,069 27 –1,312 3,441 13,633 366 3,013 2,099 28,265 –199 –2,512 898 4,321 .. .. .. .. 14,803 28,214 96,251 292,143 .. .. .. 846 14,672 36,822 7,779 24,805 –76 –385 262 1,720 –8 29 144 1,105 –371 731 919 7,606 –4,541 –8,909 8,475 44,476 –71 –421 418 .. .. –416 0 372 6,270 16,801 13,730 106,144 –675 534 1,363 6,598 –103 –200 460 2,277 –260 .. 285 1,172 –73 –563 247 325 8,488 27,290 28,651 106,528 –255 –655 382 1,344 77 .. 49 288 –37 –800 914 2,619 –18,743 –5,724 35,577 120,584 –98 –484 222 1,718 –70 –887 202 2,288 –475 –3,925 5,017 23,609 –764 –1,113 742 2,265 –212 1,527 286 .. 192 30 260 1,696 –131 –128 987 2,925 7,264 51,635 17,688 46,147 –2,407 –4,994 3,952 16,723 –936 –963 492 1,799 –104 –1,320 81 760 7,427 2,476 10,099 35,885 25,079 51,444 27,922 52,798 3,129 5,096 2,460 13,025 –85 –1,368 2,087 17,256 –673 –2,862 723 2,714 351 –633 304 3,122 –163 –641 772 4,167 –1,546 –2,315 8,676 44,215 –2,228 8,924 15,074 62,326 –10,343 –21,873 27,469 93,472 –12,189 –22,850 14,262 20,937 .. .. .. .. .. .. 1,163 31,182
2012 World Development Indicators
279
4.17
Balance of payments current account Goods and services
Net income
Net current transfers
Current account balance
$ millions 2000 2010
$ millions 2000 2010
$ millions 2000 2010
Total reservesa
$ millions Exports 2000
Romania Russian Federation Rwanda Saudi Arabia Senegal Serbia Sierra Leone Singapore Slovak Republic Slovenia Somalia South Africa South Sudan Spain Sri Lanka Sudan Swaziland Sweden Switzerland Syrian Arab Republic Tajikistan Tanzania Thailand Timor-Leste Togo Trinidad and Tobago Tunisia Turkey Turkmenistan Uganda Ukraine United Arab Emirates United Kingdom United States Uruguay Uzbekistan Venezuela, RB Vietnam West Bank and Gaza Yemen, Rep. Zambia Zimbabwe World Low income Middle income Lower middle income Upper middle income Low & middle income East Asia & Pacific Europe & Central Asia Latin America & Carib. Middle East & N. Africa South Asia Sub-Saharan Africa High income Euro area
Imports 2010
12,113 58,268 114,598 445,539 128 608 82,259 261,832 1,307 3,119 .. 13,351 55 423 181,346 470,793 14,137 70,494 10,696 30,489 .. .. 36,995 99,703 .. .. 168,221 376,601 6,378 10,776 1,834 11,658 1,240 2,063 111,275 225,662 125,517 342,153 6,845 19,606 768 1,512 1,361 6,388 81,762 227,908 .. .. 424 1,197 4,844 9,940 8,607 22,236 50,353 155,632 .. .. 663 3,474 19,522 69,255 .. .. 404,775 667,596 1,072,780 1,837,576 3,660 10,555 .. .. 34,711 67,603 17,150 79,652 1,012 1,224 4,008 9,329 872 7,725 .. .. 7,977,777 t 18,856,365 t 32,005 105,896 1,592,025 5,719,672 369,018 1,288,993 1,224,283 4,435,558 1,623,276 5,824,525 612,105 2,566,113 251,713 993,266 418,299 996,199 .. .. 85,777 414,188 111,058 374,373 6,357,262 13,051,331 2,298,517 4,964,584
2000
14,043 66,807 61,091 323,070 423 1,641 52,932 174,204 1,742 5,276 .. 19,689 250 879 169,223 408,190 14,596 71,300 11,385 30,360 .. .. 33,075 100,318 .. .. 186,027 402,450 8,105 15,282 2,014 11,161 1,438 2,625 97,042 196,830 107,391 285,841 5,390 19,409 928 3,329 2,050 8,975 71,653 206,780 .. .. 602 1,690 3,709 7,356 9,311 24,351 61,035 197,042 .. .. 1,409 6,099 17,947 73,239 .. .. 433,976 731,828 1,449,535 2,337,607 4,193 9,743 .. .. 21,300 49,661 17,325 87,260 3,270 5,008 3,294 11,017 1,312 5,650 .. .. 7,950,280 t 18,328,230 t 45,127 156,378 1,495,508 5,389,112 375,652 1,374,029 1,121,580 4,021,226 1,540,600 5,547,163 550,904 2,271,625 223,344 944,420 438,522 1,009,026 122,648 384,516 106,235 537,246 102,161 394,903 6,409,545 12,801,007 2,262,865 4,779,448
a. International reserves including gold valued at London gold price.
280
2012 World Development Indicators
2010
–285 –6,736 –15 479 –111 .. –5 –784 –355 26 .. –3,184 .. –6,849 –300 –575 51 –1,231 19,187 –879 –41 –130 –1,381 .. –29 –629 –942 –4,002 .. –112 –942 .. 5,156 19,179 –61 .. –1,388 –451 628 –777 –164 .. .. .. .. .. .. .. .. .. .. .. .. .. .. ..
–2,532 –48,615 –46 7,044 –181 –899 –49 –8,230 –1,658 –662 .. –7,224 .. –28,985 –572 –2,472 –226 7,781 29,967 –1,514 –79 –216 –14,061 .. –19 –997 –1,925 –7,137 .. –305 –2,009 .. 20,679 165,224 –1,093 .. –5,302 –4,564 808 –1,812 –1,893 .. .. .. .. .. .. .. .. .. .. .. .. .. .. ..
$ millions 2000 2010
860 4,557 –1,355 –6,514 3,396 69 –3,600 46,839 70,255 27,656 216 657 –94 –421 191 –15,490 –27,921 14,317 66,751 20,847 214 1,473 –332 –865 388 .. 4,417 .. –2,819 517 88 185 –112 –320 49 –1,095 –4,815 10,244 49,558 80,170 120 –544 –694 –3,009 4,376 116 146 –548 –388 3,196 .. .. .. .. .. –926 –2,278 –191 –10,117 7,702 .. .. .. .. .. 1,469 –9,508 –23,185 –64,342 35,608 983 3,660 –1,044 –1,418 1,131 237 2,131 –518 157 138 101 400 –46 –388 352 –3,142 –6,204 9,860 30,408 16,499 –4,483 –12,263 32,830 74,015 53,620 485 949 1,061 –367 355 186 1,513 –15 –383 94 391 824 –428 –1,978 974 586 6,031 9,313 13,099 32,665 .. .. .. .. 43 68 336 –140 –177 141 38 27 544 1,614 1,403 825 1,935 –821 –2,104 1,871 4,764 1,448 –9,920 –47,099 23,515 .. .. .. .. 1,513 499 1,190 –359 –1,740 808 848 2,975 1,481 –3,018 1,477 .. .. .. .. 13,632 –14,756 –31,676 –38,800 –75,229 43,075 –58,767 –136,095 –416,343 –470,902 128,400 27 122 –566 –160 2,776 .. .. .. .. 1,242 –170 –568 11,853 12,072 15,899 1,732 7,885 1,106 –4,287 3,417 639 2,239 –990 –737 .. 1,399 2,291 1,337 –1,209 2,914 14 432 –591 615 245 .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. ..
48,048 479,222 813 459,313 2,047 13,308 409 225,715 2,156 1,071 .. 43,820 .. 31,872 7,195 1,036 756 48,246 270,480 20,632 .. 3,905 172,028 406 715 9,692 9,764 85,959 .. 2,706 34,571 42,785 82,365 488,928 7,656 .. 29,665 12,467 .. 5,939 2,094 .. .. .. .. .. .. .. .. .. .. .. .. .. .. ..
About the data
4.17
Definitions
The balance of payments records an economy’s
collection systems according to the fourth edition
• Exports and imports of goods and services are
transactions with the rest of the world. Balance of
of the Balance of Payments Manual (1977). Where
all transactions between residents of an economy
payments accounts are divided into two groups:
necessary, the IMF converts such reported data to
and the rest of the world involving a change in
the current account, which records transactions
conform to the fifth edition (see Primary data docu-
ownership of general merchandise, goods sent for
in goods, services, income, and current transfers,
mentation). Values are in U.S. dollars converted at
processing and repairs, nonmonetary gold, and ser-
and the capital and financial account, which records
market exchange rates.
vices. • Net income is receipts and payments of
capital transfers, acquisition or disposal of non-
employee compensation for nonresident workers,
produced, nonfinancial assets, and transactions in
and investment income (receipts and payments on
financial assets and liabilities. The table presents
direct investment, portfolio investment, and other
data from the current account plus gross interna-
investments and receipts on reserve assets). Income
tional reserves.
derived from the use of intangible assets is recorded
The balance of payments is a double-entry
under business services. • Net current transfers
accounting system that shows all flows of goods and
are recorded in the balance of payments whenever
services into and out of an economy; all transfers
an economy provides or receives goods, services,
that are the counterpart of real resources or financial
income, or financial items without a quid pro quo.
claims provided to or by the rest of the world without
All transfers not considered to be capital are cur-
a quid pro quo, such as donations and grants; and
rent. • Current account balance is the sum of net
all changes in residents’ claims on and liabilities to
exports of goods and services, net income, and net
nonresidents that arise from economic transactions.
current transfers. • Total reserves are holdings of
All transactions are recorded twice—once as a credit
monetary gold, special drawing rights, reserves of
and once as a debit. In principle the net balance
IMF members held by the IMF, and holdings of foreign
should be zero, but in practice the accounts often do
exchange under the control of monetary authorities.
not balance, requiring inclusion of a balancing item,
The gold component of these reserves is valued at
net errors and omissions.
year-end (December 31) London prices ($386.75 an
Discrepancies may arise in the balance of pay-
ounce in 1995 and $1,087.50 an ounce in 2009).
ments because there is no single source for balance of payments data and therefore no way to ensure that the data are fully consistent. Sources include customs data, monetary accounts of the banking system, external debt records, information provided by enterprises, surveys to estimate service transactions, and foreign exchange records. Differences in collection methods—such as in timing, definitions of residence and ownership, and the exchange rate used to value transactions—contribute to net errors and omissions. In addition, smuggling and other illegal or quasi-legal transactions may be unrecorded or misrecorded. For further discussion of issues relating to the recording of data on trade in goods and services, see About the data for tables 4.4–4.7.
Data sources
The concepts and definitions underlying the data
Data on the balance of payments are published in
in the table are based on the fi fth edition of the
the IMF’s Balance of Payments Statistics Yearbook
International Monetary Fund’s (IMF) Balance of Pay-
and International Financial Statistics. The World
ments Manual (1993). That edition redefined as capi-
Bank exchanges data with the IMF through elec-
tal transfers some transactions previously included
tronic files that in most cases are more timely and
in the current account, such as debt forgiveness,
cover a longer period than the published sources.
migrants’ capital transfers, and foreign aid to acquire
More information about the design and compila-
capital goods. Thus the current account balance now
tion of the balance of payments can be found in
reflects more accurately net current transfer receipts
the IMF’s Balance of Payments Manual, fifth edition
in addition to transactions in goods, services (pre-
(1993), Balance of Payments Textbook (1996), and
viously nonfactor services), and income (previously
Balance of Payments Compilation Guide (1995).
factor income). Many countries maintain their data
2012 World Development Indicators
281
ECONOMY
Balance of payments current account
STATES AND MARKETS
S
tates and markets includes indicators of private sector investment and performance, the role of the public sector in nurturing investment and growth, and the quality and availability of infrastructure essential for growth and development. When private firms make investments, create jobs, and improve productivity, they promote growth and expand opportunities for people. Tables 5.1–5.6 cover private investment in infrastructure, the business environment, and the development of financial systems. Just as a vibrant private sector is essential for job creation and growth, so are capable governments and high-quality institutions essential for promoting growth, raising incomes, and reducing poverty. Tables 5.7–5.9 cover these functions of governments, from tax policies and public institutions to crime statistics, and military expenditures. Tables 5.10–5.12 cover infrastructure —the systems for delivering energy, transport, water and sanitation, and information and communication technology services to people. Table 5.13 covers innovation in science and technology. World Development Indicators 2010 introduced a new table, continued this year as table 5.8, Fragile situations, with indicators for countries that have been identified as fragile or conflict- affected. World Development Report 2011: Conflict, Security, and Development (World Bank 2011d, p. xvi) defined fragility or fragile situations as “periods when states or institutions lack the capacity, accountability, or legitimacy to mediate relations between citizen groups and between citizens and the state, making them vulnerable to violence.” The report provided a framework and practical recommendations on how to move beyond fragility and conflict to secure development. Its central message was that strengthening legitimate institutions and governance to provide citizens security, justice,
and jobs is crucial to breaking cycles of violence and underdevelopment. Although civil wars and wars between states are less common than in the past, insecurity remains a primary development challenge. Some 1.5 billion people live in areas affected by fragility, conflict, or large-scale organized criminal violence, and no fragile or conflict-affected country has achieved a single Millennium Development Goal. People in these countries are more than twice as likely to be undernourished as people in other developing countries, more than three times as likely to be unable to send their children to school, twice as likely to see their children die before age 5, and more than twice as likely to lack access to clean water. A major episode of violence can wipe out an entire generation of economic progress. The average cost of a civil war is equivalent to more than 30 years of GDP growth for a medium-size developing country. World Development Report 2011 found that countries and areas with the weakest institutional legitimacy (both formal and informal) and poor governance are the most vulnerable to violence and instability and the least able to respond to internal and external stresses. The World Bank manages the State and Peace Building Fund, a multidonor trust fund that provides grants to scale up Bank engagement in fragile and conflict-affected countries; to promote cross-cutting, innovative approaches; and to foster strategic partnerships. As statebuilding and peace, security, and institutionbuilding become central objectives for developing countries and the development community, new indicators are being developed to measure progress. The International Dialogue on Peacebuilding and Statebuilding, with more than 40 participating countries and eight international organizations (including the World Bank) is contributing to this work.
5
2012 World Development Indicators
283
Tables
5.1
Private sector in the economy Domestic credit to private sector
Investment commitments in infrastructure projects with private participationa
$ millions Telecommunications
Afghanistan Albania Algeria Angola Argentina Armenia Australia Austria Azerbaijan Bahrain Bangladesh Belarus Belgium Benin Bolivia Bosnia and Herzegovina Botswana Brazil Bulgaria Burkina Faso Burundi Cambodia Cameroon Canada Central African Republic Chad Chile China Hong Kong SAR, China Colombia Congo, Dem. Rep. Congo, Rep. Costa Rica Côte d’Ivoire Croatia Cuba Cyprus Czech Republic Denmark Dominican Republic Ecuador Egypt, Arab Rep. El Salvador Eritrea Estonia Ethiopia Finland France Gabon Gambia, The Georgia Germany Ghana Greece Guatemala Guinea Guinea-Bissau Haiti
284
Energy
Transport
Water and sanitation
Businesses registered
% of GDP
New
Entry density
2000–05
2006–10
2000–05
2006–10
2000–05
2006–10
2000–05
2006–10
2010
2009
2009
466.1 569.2 3,422.5 278.7 5,836.8 317.1 .. .. 355.6 .. 1,294.3 735.4 .. 116.9 520.5 0.0 104.0 41,053.8 2,179.1 41.9 53.6 136.1 394.4 .. 0.0 11.0 1,260.7 8,548.0 .. 1,570.9 473.4 61.8 .. 134.9 1,205.7 60.0 .. .. .. 393.0 357.8 3,471.9 1,110.6 40.0 .. .. .. .. 26.6 6.6 201.3 .. 156.5 .. 560.1 50.6 9.7 18.0
1,040.4 778.8 2,162.0 1,663.0 5,993.9 586.7 .. .. 1,407.8 .. 4,250.3 2,638.7 .. 793.7 284.7 1,102.5 242.9 40,063.0 2,211.3 979.6 0.0 446.7 934.4 .. 30.8 591.4 1,326.7 0.0 .. 6,347.9 1,054.0 407.7 .. 1,204.4 3,035.0 0.0 .. .. .. 220.1 2,003.3 10,977.0 1,037.6 0.0 .. 0.0 .. .. 403.8 35.0 722.0 .. 3,206.0 .. 1,724.4 313.2 107.2 306.0
1.6 790.6 962.0 45.0 3,826.9 74.0 .. .. 375.2 .. 501.5 .. .. 590.0 884.4 .. .. 26,034.6 3,253.5 .. .. 82.1 91.8 .. .. 0.0 1,393.2 10,970.9 .. 351.6 .. .. 80.0 0.0 7.1 116.0 .. .. .. 1,306.6 302.0 678.0 85.0 .. .. .. .. .. 0.0 .. 40.0 .. 590.0 .. 110.0 .. .. 5.5
.. 692.0 2,320.0 9.4 3,801.9 127.0 .. .. .. .. 340.4 2,500.0 .. .. 137.3 908.6 .. 52,825.6 2,454.1 .. .. 2,452.3 908.0 .. .. .. 1,567.2 7,477.5 .. 1,080.9 .. .. 190.0 0.0 85.0 60.0 .. .. .. 0.0 129.0 469.0 16.0 .. .. 4.0 .. .. 0.0 0.0 634.2 .. 100.0 .. 1,021.8 .. .. 0.0
.. 308.0 120.9 .. 203.6 63.0 .. .. .. .. 0.0 .. .. .. 16.6 .. .. 3,156.5 2.1 .. .. 125.3 0.0 .. .. .. 4,830.7 15,454.0 .. 1,497.4 .. .. 465.2 176.4 451.0 0.0 .. .. .. 898.9 695.0 821.5 .. .. .. .. .. .. 177.4 .. .. .. 10.0 .. .. .. .. ..
.. .. 269.0 53.0 1,402.6 715.0 .. .. .. .. 0.0 4.0 .. .. .. .. .. 23,527.7 536.2 .. .. 40.1 .. .. .. .. 1,943.1 15,795.0 .. 4,461.4 .. 735.0 407.0 .. 492.0 .. .. .. .. 948.9 766.0 1,370.0 .. .. .. .. .. .. 3.9 .. 573.0 .. .. .. .. 159.0 .. ..
.. 8.0 510.0 .. 791.6 0.0 .. .. 0.0 .. .. .. .. .. .. .. .. 1,234.4 152.0 .. .. .. .. .. .. .. 1,495.2 3,505.2 .. 314.3 .. 0.0 .. .. 298.7 600.0 .. .. .. .. 510.0 .. .. .. .. .. .. .. .. .. .. .. 0.0 .. .. .. .. ..
.. 0.0 1,572.0 .. .. 0.0 .. .. .. .. .. .. .. .. .. .. .. 1,581.0 .. .. .. .. 0.0 .. .. .. 3.1 4,626.9 .. 305.0 .. .. .. 0.0 .. .. .. .. .. .. .. 475.0 .. .. .. .. .. .. .. .. 435.0 .. .. .. 6.7 .. .. 1.0
10.5 38.0 15.6 20.3 14.6 26.5 131.1 122.4 18.3 79.6 47.1 44.8 94.9 23.1 40.3 66.0 23.4 57.0 74.6 17.6 25.5 27.6 11.9 128.2 8.7 5.7 86.3 130.0 189.0 43.5 6.6 5.5 45.9 18.1 70.1 .. 283.6 56.2 223.5 22.7 30.8 33.1 41.0 16.0 97.2 17.8 95.2 114.4 8.2 19.1 32.4 107.8 15.2 115.9 23.4 .. 6.2 14.0
.. 2,045 10,544 .. 11,924 2,698 89,960 3,228 5,314 .. .. 5,508 29,548 .. 2,504 1,896 .. 315,645 35,545 610 .. 2,003 .. 174,000 .. .. 23,541 .. 101,023 31,132 .. .. 26,765 .. 7,800 .. 16,101 21,717 16,519 12,881 .. 6,291 4,400 .. 7,199 1,327 11,820 128,906 3,490 .. 7,226 64,840 9,606 8,426 5,133 .. .. ..
.. 0.84 0.44 .. 0.46 1.28 6.38 0.58 0.93 .. .. 0.80 4.28 .. 0.43 0.58 .. 2.38 7.20 0.08 .. 0.22 .. 7.56 .. .. 2.12 .. 19.19 1.07 .. .. 8.78 .. 2.57 .. 20.30 3.00 4.57 2.13 .. 0.13 1.19 .. 8.10 0.03 3.37 3.08 4.27 .. 2.32 1.19 0.72 1.18 0.68 .. .. ..
2012 World Development Indicators
Domestic credit to private sector
Investment commitments in infrastructure projects with private participationa
$ millions Telecommunications
Honduras Hungary India Indonesia Iran, Islamic Rep. Iraq Ireland Israel Italy Jamaica Japan Jordan Kazakhstan Kenya Korea, Dem. Rep. Korea, Rep. Kosovo Kuwait Kyrgyz Republic Lao PDR Latvia Lebanon Lesotho Liberia Libya Lithuania Macedonia, FYR Madagascar Malawi Malaysia Mali Mauritania Mauritius Mexico Moldova Mongolia Morocco Mozambique Myanmar Namibia Nepal Netherlands New Zealand Nicaragua Niger Nigeria Norway Oman Pakistan Panama Papua New Guinea Paraguay Peru Philippines Poland Portugal Puerto Rico Qatar
Energy
Transport
Water and sanitation
5.1
STATES AND MARKETS
Private sector in the economy
Businesses registered
% of GDP
New
Entry density
2000–05
2006–10
2000–05
2006–10
2000–05
2006–10
2000–05
2006–10
2010
2009
2009
135.0 5,172.8 20,551.8 6,557.2 695.0 984.0 .. .. .. 612.0 .. 1,589.0 1,153.7 1,434.0 .. .. .. .. 11.5 87.7 700.0 138.1 88.4 70.3 .. 993.0 706.6 12.6 36.3 3,294.9 82.6 92.1 393.0 18,758.0 46.1 22.1 6,139.5 123.0 .. 35.0 109.3 .. .. 218.5 85.5 6,949.7 .. .. 6,594.9 211.4 .. 199.0 2,241.4 4,616.4 16,800.1 .. .. ..
1,001.0 1,523.3 53,090.4 11,009.5 1,992.0 4,977.0 .. .. .. 157.9 .. 949.6 3,766.5 3,465.8 474.0 .. 385.1 .. 130.2 135.0 468.1 0.0 41.6 88.8 .. 548.2 575.4 436.8 313.7 3,689.9 837.0 266.1 63.1 16,290.6 426.8 0.0 3,673.6 236.2 .. 8.5 26.0 .. .. 512.2 358.7 14,384.1 .. .. 9,006.5 1,262.2 150.0 636.4 3,191.1 5,172.0 7,750.0 .. .. ..
358.8 851.6 8,663.6 1,280.5 650.0 .. .. .. .. 201.0 .. .. 300.0 .. .. .. .. .. .. 1,250.0 158.1 .. 0.0 .. .. 514.3 .. 0.0 0.0 6,637.6 365.9 .. 0.0 6,749.3 227.2 .. 1,049.0 1,205.8 .. 1.0 15.1 .. .. 126.3 .. 1,920.0 .. .. 375.4 449.3 .. .. 2,498.9 3,428.4 2,620.5 .. .. ..
250.0 1,707.0 90,973.0 6,079.3 .. 590.0 .. .. .. 210.0 .. 989.0 0.0 437.7 .. .. .. .. .. 5,285.0 184.0 .. .. 170.0 .. 464.1 655.0 17.8 .. 384.5 .. .. .. 2,282.7 68.0 .. .. .. 556.1 .. 34.1 .. .. 510.0 .. 280.0 .. .. 4,120.5 877.0 .. .. 2,191.0 10,942.5 2,475.4 .. .. ..
120.0 3,297.5 4,413.4 159.2 .. .. .. .. .. 565.0 .. 0.0 231.0 .. .. .. .. .. .. 1.5 .. 153.0 .. .. .. .. .. 61.0 .. 4,263.0 55.4 .. .. 2,970.4 0.0 .. 200.0 334.6 .. .. .. .. .. 104.0 .. 2,355.4 .. .. 112.8 51.4 .. .. 522.5 943.5 1,672.0 .. .. ..
.. 1,588.0 38,036.2 1,731.5 .. 500.0 .. .. .. .. .. 1,380.0 31.0 404.0 .. .. .. .. .. 1.5 135.0 .. .. 120.0 .. .. 295.0 17.5 .. 1,632.3 .. .. .. 12,651.5 60.0 .. 200.0 0.0 .. .. .. .. .. .. .. 644.1 .. .. 923.7 0.0 .. .. 3,289.6 968.9 3,642.3 .. .. ..
207.9 0.0 112.9 44.8 .. .. .. .. .. .. .. 169.0 .. .. .. .. 0.0 .. 0.0 .. .. 0.0 .. .. .. .. .. .. .. 6,502.2 .. .. .. 523.7 .. .. .. .. .. 0.0 .. .. .. .. 3.4 .. .. .. .. .. .. .. 152.0 0.0 64.3 .. .. ..
.. 0.0 241.7 20.2 .. .. .. .. .. .. .. 951.0 .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. 0.0 .. .. 0.0 1,096.8 .. .. .. 0.0 .. .. 0.0 .. .. .. .. .. .. .. .. .. .. .. 119.8 530.5 0.8 .. .. ..
50.2 72.6 49.0 29.1 36.7 9.1 215.0 95.7 122.0 24.8 169.3 70.3 39.3 33.8 .. 100.8 37.3 82.4 .. 20.4 103.7 81.3 13.6 16.0 10.9 66.4 45.3 11.7 16.0 114.9 18.4 30.4 87.8 24.6 33.3 39.6 68.7 25.8 4.7 45.6 55.6 199.3 149.0 32.5 12.6 29.4 .. 48.2 21.5 91.5 31.8 37.8 24.3 29.6 54.8 191.0 .. 51.5
.. 42,951 84,800 28,998 .. .. 13,188 19,758 68,508 2,003 105,698 2,737 27,978 17,896 .. 60,039 141 .. 4,412 .. 7,175 .. .. .. .. 5,399 8,074 724 619 41,638 .. .. 6,626 44,084 4,180 .. 26,166 .. .. .. .. 35,100 47,897 .. 24 65,089 13,805 3,165 2,759 548 .. .. 51,151 11,435 14,434 27,759 .. ..
.. 6.26 0.12 0.18 .. .. 4.67 4.46 1.78 1.16 1.28 0.74 2.59 0.85 .. 1.72 0.12 .. 1.26 .. 4.62 .. .. .. .. 2.18 5.63 0.07 0.08 2.55 .. .. 7.33 0.61 1.32 .. 1.28 .. .. .. .. 3.10 17.08 .. 0.00 0.79 4.49 1.67 0.03 0.26 .. .. 2.65 0.19 0.52 3.92 .. ..
2012 World Development Indicators
285
5.1
Private sector in the economy Investment commitments in infrastructure projects with private participationa
$ millions Telecommunications 2000–05
Romania Russian Federation Rwanda Saudi Arabia Senegal Serbia Sierra Leone Singapore Slovak Republic Slovenia Somalia South Africa South Sudan Spain Sri Lanka Sudan Swaziland Sweden Switzerland Syrian Arab Republic Tajikistan Tanzania Thailand Timor-Leste Togo Trinidad and Tobago Tunisia Turkey Turkmenistan Uganda Ukraine United Arab Emirates United Kingdom United States Uruguay Uzbekistan Venezuela, RB Vietnam West Bank and Gaza Yemen, Rep. Zambia Zimbabwe World Low income Middle income Lower middle income Upper middle income Low & middle income East Asia & Pacific Europe & Central Asia Latin America & Carib. Middle East & N. Africa South Asia Sub-Saharan Africa High income Euro area
2006–10
3,906.9 4,996.4 22,049.4 30,982.6 72.3 414.0 .. .. 593.1 1,569.0 563.5 3,024.1 48.8 149.2 .. .. .. .. .. .. 13.4 0.0 10,519.5 9,815.0 .. .. .. .. 714.6 1,283.5 747.7 2,226.3 27.7 63.3 .. .. .. .. 583.0 372.7 8.5 196.4 515.3 2,109.5 5,602.7 3,526.0 0.0 0.0 0.0 67.0 .. .. 751.0 3,771.0 12,788.6 13,751.7 20.0 202.5 387.6 1,720.0 3,162.9 4,921.4 .. .. .. .. .. .. 114.2 200.2 285.6 2,046.8 3,337.0 3,042.8 430.0 1,593.7 279.8 644.0 376.8 451.2 208.3 1,077.0 72.0 644.0 .. s .. s 4,646.3 21,615.6 226,353.6 317,609.7 68,257.9 144,334.1 158,095.7 34,212.5 232,154.6 339,225.4 29,380.1 4,662.0 51,002.1 76,087.4 78,935.4 85,698.9 13,435.4 29,970.1 29,784.3 68,726.2 24,622.2 52,365.4 .. .. .. ..
Energy 2000–05
2006–10
1,240.8 1,726.0 1.6 .. 93.3 .. .. .. .. .. .. 1,251.3 .. .. 270.8 .. .. .. .. .. 16.0 348.0 4,693.3 .. 657.7 .. 30.0 5,854.8 .. 113.9 160.0 .. .. .. 330.0 .. 39.5 2,360.6 150.0 .. 3.0 .. .. s .. 107,559.1 19,986.8 83,156.9 85,938.8 30,710.4 4,439.1 45,347.6 .. 9,828.0 .. .. ..
6,288.7 32,401.2 .. .. 22.0 .. 1.2 .. .. .. .. 15.9 .. .. .. .. .. .. .. .. .. 28.4 6,641.4 .. 190.0 .. .. 13,504.2 .. 1,000.6 64.9 .. .. .. .. .. .. 297.0 .. 15.8 .. .. .. s .. 266,780.1 123,341.9 143,438.2 272,012.7 40,115.6 58,924.3 67,445.3 .. 95,669.0 .. .. ..
Transport 2000–05
2006–10
Domestic credit to private sector
Water and sanitation 2000–05
2006–10
.. 116.8 116.0 41.0 109.4 4,786.9 904.7 1,241.7 .. .. .. .. .. .. .. .. 55.4 398.0 0.0 0.0 .. .. 0.0 .. .. 130.0 .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. 504.7 3,483.0 31.3 0.0 .. .. .. .. .. .. .. .. .. .. .. .. .. 30.0 .. 120.7 .. .. .. .. .. .. .. .. .. .. .. .. .. 82.0 .. .. .. .. .. .. 27.7 134.0 8.5 .. 939.0 .. 524.7 18.8 .. .. .. .. .. .. .. .. .. .. .. .. .. 840.0 .. 95.0 3,203.6 4,471.2 .. .. .. .. .. .. .. 404.0 0.0 .. .. 130.0 100.0 102.0 .. .. .. .. .. .. .. .. .. .. .. .. 251.1 .. 368.0 .. .. 25.0 0.0 .. 34.0 .. 15.0 .. 20.0 1,120.0 266.0 .. .. .. .. .. .. 220.0 .. .. 15.6 .. 0.0 .. .. .. .. .. .. s .. s .. s .. s .. .. .. .. 51,413.6 106,204.4 16,177.1 6,654.9 1,781.0 34,127.5 192.0 .. 41,607.7 72,076.9 18,427.2 9,705.8 5,437.5 86,673.9 2,481.0 .. 21,905.4 20,538.0 10,842.9 5,196.4 .. .. .. .. 16,419.9 49,397.7 2,516.1 .. .. .. .. .. 4,526.2 39,437.9 112.9 241.7 .. .. .. .. .. .. .. .. .. .. .. ..
% of GDP 2010
46.1 45.1 .. 47.6 25.9 51.5 10.4 102.1 44.9 94.4 .. 145.5 .. 211.6 26.6 11.6 23.0 140.2 174.6 22.5 .. 16.2 116.6 15.6 23.0 39.2 68.8 44.0 .. 15.8 61.7 72.5 202.9 202.2 23.0 .. 18.9 125.0 .. 6.2 11.5 .. 134.8 w 28.3 73.7 40.2 83.3 73.0 116.3 46.2 41.9 35.0 45.7 65.4 164.6 133.6
a. Data refer to total for the period shown. Includes infrastructure projects with private sector participation that reached financial closure in 1990–2010.
286
2012 World Development Indicators
Businesses registered
New
Entry density
2009
2009
56,698 261,633 3,028 .. 1,636 9,715 .. 26,416 15,825 5,836 .. 24,700 .. 79,757 4,223 .. .. 24,228 25,250 .. 2,171 .. 27,520 .. 125 .. 9,079 44,472 .. 11,152 19,300 .. 330,100 .. 4,664 14,428 .. .. .. .. 5,509 ..
3.66 2.61 0.51 .. 0.22 1.94 .. 7.40 4.04 4.16 .. 0.77 .. 2.92 0.29 .. .. 4.09 4.88 .. 0.48 .. 0.59 .. 0.04 .. 1.23 0.87 .. 0.72 0.60 .. 8.05 .. 2.08 0.78 .. .. .. .. 0.88 ..
About the data
5.1
STATES AND MARKETS
Private sector in the economy Definitions
Private sector development and investment—tapping
involving local and small-scale operators—may be
• Investment commitments in infrastructure
private sector initiative and investment for socially
omitted because they are not publicly reported.
projects with private participation refers to infra-
useful purposes—are critical for poverty reduction.
The database is a joint product of the World Bank’s
structure projects in telecommunications, energy
In parallel with public sector efforts, private invest-
Finance, Economics, and Urban Development
(electricity and natural gas transmission and dis-
ment, especially in competitive markets, has tre-
Department and the Public-Private Infrastructure
tribution), transport, and water and sanitation that
mendous potential to contribute to growth. Private
Advisory Facility. Geographic and income aggregates
have reached financial closure and directly or indi-
markets are the engine of productivity growth, creat-
are calculated by the World Bank’s Development
rectly serve the public. Incinerators, movable assets,
ing productive jobs and higher incomes. And with gov-
Data Group. For more information, see http://ppi.
standalone solid waste projects, and small projects
ernment playing a complementary role of regulation,
worldbank.org/.
such as windmills are excluded. Included are opera-
funding, and service provision, private initiative and
Credit is an important link in money transmission;
tion and management contracts, concessions (oper-
investment can help provide the basic services and
it finances production, consumption, and capital for-
ation and management contracts with major capital
conditions that empower poor people—by improving
mation, which in turn affect economic activity. The
expenditure), greenfield projects (new facilities built
health, education, and infrastructure.
data on domestic credit to the private sector are
and operated by a private entity or a public-private
Investment in infrastructure projects with private
taken from the banking survey of the International
joint venture), and divestitures. Investment commit-
participation has made important contributions to
Monetary Fund’s (IMF) International Financial Statis-
ments are the sum of investments in physical assets
easing fiscal constraints, improving the efficiency
tics or, when unavailable, from its monetary survey.
and payments to the government. Investments in
of infrastructure services, and extending delivery
The monetary survey includes monetary authorities
physical assets are resources the project company
to poor people. Developing countries have been in
(the central bank), deposit money banks, and other
commits to invest during the contract period in new
the forefront, pioneering better approaches to infra-
banking institutions, such as finance companies,
facilities or in expansion and modernization of exist-
structure services and reaping the benefits of greater
development banks, and savings and loan institu-
ing facilities. Payments to the government are the
competition and customer focus.
tions. Credit to the private sector may sometimes
resources the project company spends on acquir-
include credit to state-owned or partially state-owned
ing government assets such as state-owned enter-
enterprises.
prises, rights to provide services in a specific area, or
The data on investment in infrastructure projects with private participation refer to all investment (public and private) in projects in which a private com-
Entrepreneurship is essential to the dynamism of
use of specific radio spectrums. • Domestic credit
pany assumes operating risk during the operating
the modern market economy, and a greater entry
to private sector is financial resources provided
period or development and operating risk during the
density of new businesses can foster competition
to the private sector—such as through loans, pur-
contract period. Investment refers to commitments
and economic growth. The table includes data on
chases of nonequity securities, and trade credits and
not disbursements. Foreign state-owned companies
business registrations from the 2008 and 2010
other accounts receivable—that establish a claim for
are considered private entities for the purposes of
World Bank Group Entrepreneurship Survey, which
repayment. For some countries these claims include
this measure.
includes entrepreneurial activity in more than 100
credit to public enterprises. • New businesses regis-
Investments are classified into two types: invest-
countries for 2000–09. Survey data are used to
tered are the number of limited liability corporations
ments in physical assets—the resources a com-
analyze firm creation, its relationship to economic
registered in the calendar year. • Entry density is the
pany commits to invest in expanding and modern-
growth and poverty reduction, and the impact of
number of newly registered limited liability corpora-
izing facilities—and payments to the government to
regulatory and institutional reforms. The 2010 sur-
tions per 1,000 people ages 15–64.
acquire state-owned enterprises or rights to provide
vey improves on earlier surveys’ methodology and
services in a specific area or to use part of the radio
country coverage for better cross-country compara-
spectrum.
bility; the database will be updated in 2012. Data on
The data are from the World Bank’s Private Par-
total registered businesses were collected directly
ticipation in Infrastructure (PPI) Project database,
from national registrars of companies. For cross-
which tracks infrastructure projects with private par-
country comparability, only limited liability corpora-
ticipation in developing countries. It provides infor-
tions that operate in the formal sector are included.
mation on more than 4,800 infrastructure projects
For additional information on sources, methodol-
in 139 developing economies from 1984 to 2010.
ogy, calculation of entrepreneurship rates, and data
Data on investment commitments in infra-
The database contains more than 30 fields per proj-
limitations see http://econ.worldbank.org/research/
structure projects with private participation are
ect record, including country, financial closure year,
entrepreneurship.
from the World Bank’s PPI Project database
Data sources
infrastructure services provided, type of private par-
(http://ppi.worldbank.org). Data on domestic
ticipation, investment, technology, capacity, project
credit are from the IMF’s International Finan-
location, contract duration, private sponsors, bidding
cial Statistics. Data on business registration
process, and development bank support. Data on the
are from the World Bank’s Entrepreneurship
projects are compiled from publicly available infor-
Snapshots (http://econ.worldbank.org/research/
mation. The database aims to be as comprehensive
entrepreneurship).
as possible, but some projects—particularly those
2012 World Development Indicators
287
5.2
Business environment: enterprise surveys Survey year
Regulations and tax
Time dealing with officials
Average number of times % of management meeting with tax officials time
Afghanistan Albania Algeria Angola Argentina Armenia Australia Austria Azerbaijan Bahrain Bangladesh Belarus Belgium Benin Bolivia Bosnia and Herzegovina Botswana Brazil Bulgaria Burkina Faso Burundi Cambodia Cameroon Canada Central African Republic Chad Chile China Hong Kong SAR, China Colombia Congo, Dem. Rep. Congo, Rep. Costa Rica Côte d’Ivoire Croatia Cuba Cyprus Czech Republic Denmark Dominican Republic Ecuador Egypt, Arab Rep. El Salvador Eritrea Estonia Ethiopia Finland France Gabon Gambia, The Georgia Germany Ghana Greece Guatemala Guinea Guinea-Bissau Haiti
288
2008 2007 2007 2010 2010 2009 2009 2007 2008 2009 2010 2009 2010 2009 2009 2009 2006 2007 2009 2009 2010 2003 2010 2010 2009 2010 2009 2007 2009 2005 2010 2008 2010 2009 2009 2006 2009 2006 2008 2005 2007 2005 2010 2006 2006
6.8 18.7 25.1 12.2 20.8 10.3 .. .. 3.0 .. 3.2 13.6 .. 20.7 28.5 11.2 10.2 18.7 10.6 22.2 5.7 5.6 7.0 .. .. 20.8 9.9 18.3 .. 12.9 29.4 6.0 8.4 1.6 10.9 .. .. 10.4 .. 8.8 22.5 8.8 19.7 0.5 5.5 3.8 .. .. 2.8 7.3 2.1 1.2 3.2 1.8 10.2 2.7 2.9 ..
2012 World Development Indicators
1.2 3.9 2.3 2.5 2.7 2.1 .. .. 2.1 .. 1.3 1.1 .. 1.2 1.6 1.0 1.0 1.2 2.2 1.5 1.8 1.0 4.4 .. .. 3.4 2.9 14.4 .. 0.9 8.0 2.7 0.8 3.7 0.7 .. .. 1.5 .. 0.5 0.8 3.4 3.0 0.2 0.4 1.1 .. .. 15.2 2.5 0.6 1.3 4.3 1.7 2.7 2.8 3.4 ..
Permits Corruption and licenses
Crime
Informality
Firms formally registered when operations started
Gender
Finance
Infrastructure Innovation
Trade
Average Intertime to nationally recognized clear direct exports quality Value lost due to electrical certification through customs ownership outages
Workforce
Time required to obtain operating license
Informal payments to public officials
Losses due to theft, robbery, vandalism, and arson
days
% of firms
% of sales
% of firms
% of firms
% of firms
% of sales
% of firms
days
% of firms
13.8 21.2 19.3 34.7 176.1 20.0 .. .. 15.8 .. 6.0 38.2 .. 64.3 37.3 21.4 27.2 83.5 20.8 35.8 27.3 .. 30.0 .. .. 24.3 68.5 11.6 .. 25.6 40.0 .. 35.6 44.1 26.5 .. .. 19.9 .. .. 30.0 42.7 44.4 .. 8.3 11.4 .. .. 12.1 8.4 11.8 .. 6.4 .. 41.1 13.0 30.4 ..
41.5 57.7 66.6 48.9 18.2 16.0 .. .. 52.2 .. 85.1 26.1 .. 54.5 17.6 10.3 7.3 11.9 22.4 8.5 56.5 61.2 51.2 .. .. 41.8 0.7 72.6 .. 2.8 65.7 81.8 3.7 38.5 14.5 .. .. 12.8 .. 26.3 11.8 15.2 12.7 0.0 3.7 12.4 .. .. 41.8 52.4 14.7 .. 38.8 21.6 6.3 84.8 63.1 ..
1.5 0.5 0.9 1.5 0.6 0.6 .. .. 0.3 .. 0.1 0.4 .. 1.9 0.8 0.4 1.5 1.7 0.5 0.3 1.1 0.4 1.6 .. .. 2.5 0.8 0.1 .. 0.3 1.8 3.3 0.4 3.4 0.2 .. .. 0.4 .. 0.7 1.0 3.0 1.6 0.0 0.9 1.4 .. .. 0.4 2.7 0.7 0.5 0.9 0.0 1.3 2.0 1.1 ..
88.0 89.4 98.3 62.7 92.4 96.2 .. .. 85.1 .. .. 98.5 .. 87.9 72.4 98.6 93.9 95.8 98.5 77.7 .. 87.5 82.1 .. .. 77.1 96.1 .. .. 94.3 61.9 84.3 80.8 56.4 98.1 .. .. 98.0 .. .. 85.1 17.9 75.7 100.0 97.4 .. .. .. 63.7 .. 99.6 .. 66.4 .. 90.0 .. .. ..
2.8 10.8 15.0 56.6 38.0 31.8 .. .. 10.8 .. 16.1 52.9 .. 43.9 41.3 32.8 55.3 59.3 33.9 19.2 34.8 .. 15.7 .. .. 40.1 29.6 .. .. 35.3 38.9 31.8 43.5 61.9 33.5 .. .. 25.0 .. .. 24.1 34.0 40.2 4.2 36.3 30.9 .. .. 33.1 21.3 40.8 20.3 44.0 24.4 44.2 25.4 19.9 ..
1.4 12.4 8.9 13.1 30.3 31.9 .. .. 19.0 .. 24.7 35.8 .. 4.2 27.8 59.7 32.8 48.4 34.7 25.6 12.3 11.3 31.4 .. .. 4.2 44.8 28.8 .. 35.0 6.7 7.7 22.2 13.9 60.0 .. .. 33.4 .. 12.5 17.0 5.6 31.7 11.9 41.5 11.0 .. .. 6.3 7.6 38.2 45.0 16.0 25.9 26.6 0.9 0.7 ..
6.5 13.7 4.0 12.6 3.5 1.8 .. .. 1.8 .. 10.6 0.8 .. 7.5 2.5 1.9 3.7 3.0 1.6 5.8 10.7 2.4 4.9 .. .. 3.3 1.3 1.3 .. 1.8 22.7 16.4 1.7 5.0 0.8 .. .. 0.6 .. 15.2 3.9 3.2 7.0 0.2 0.5 0.9 .. .. 1.7 11.8 1.4 .. 5.6 .. 2.8 14.0 5.3 ..
8.5 24.6 5.0 21.7 18.2 26.9 .. .. 18.2 .. 7.8 13.9 .. 7.3 22.4 30.1 21.3 25.7 19.9 14.4 7.1 2.8 20.4 .. .. 43.3 22.1 35.9 .. 20.8 8.5 19.6 13.3 4.3 16.5 .. .. 43.5 .. 9.6 9.7 21.1 14.5 15.1 21.2 4.2 .. .. 18.6 22.2 16.0 .. 6.8 11.7 11.9 5.2 8.4 ..
14.6 1.9 14.1 6.7 7.3 3.3 .. .. 1.9 .. 8.4 2.6 .. 9.6 12.4 1.3 6.2 15.9 4.2 7.4 .. 1.5 15.1 .. .. 11.9 10.8 6.6 .. 8.6 18.0 .. 10.0 16.6 1.3 .. .. 5.7 .. 11.4 18.2 6.2 3.7 9.6 1.8 4.3 .. .. 3.8 5.0 3.8 4.7 7.8 5.5 4.7 4.3 5.6 ..
14.6 19.9a 17.3a 23.5 63.6 30.4 .. .. 10.5 .. 27.2a 44.4 .. 32.4 57.1 66.5 51.9 52.9 30.7 24.8 22.1a 48.4a 25.5 .. .. 43.4 57.5 84.8 .. 65.2 24.1 37.5 54.7 19.1 28.0a .. .. 70.7 .. 53.3 65.9 21.7 61.0 26.1 69.3 38.2a .. .. 30.9 25.6a 14.5 35.4 33.0a 20.0 51.9 21.1a 12.4a ..
Firms with Firms using banks to female finance participation in ownership investment
Firms offering formal training
Survey year
Regulations and tax
Time dealing with officials
Average number of times % of management meeting with tax officials time
Honduras Hungary India Indonesia Iran, Islamic Rep. Iraq Ireland Israel Italy Jamaica Japan Jordan Kazakhstan Kenya Korea, Dem. Rep. Korea, Rep. Kosovo Kuwait Kyrgyz Republic Lao PDR Latvia Lebanon Lesotho Liberia Libya Lithuania Macedonia, FYR Madagascar Malawi Malaysia Mali Mauritania Mauritius Mexico Moldova Mongolia Morocco Mozambique Myanmar Namibia Nepal Netherlands New Zealand Nicaragua Niger Nigeria Norway Oman Pakistan Panama Papua New Guinea Paraguay Peru Philippines Poland Portugal Puerto Rico Qatar
2010 2009 2006 2009 2005 2010 2006 2009 2007 2005 2009 2009 2009 2009 2009 2009 2009 2009 2009 2009 2009 2007 2010 2006 2009 2010 2009 2009 2007 2007 2006 2009 2010 2009 2007 2007 2010 2010 2010 2009 2009 2005
17.0 13.5 6.7 1.6 .. .. 2.3 .. .. 1.7 .. 6.7 4.7 5.1 .. 0.1 9.8 .. 4.9 1.2 9.7 8.9 5.6 7.5 .. 9.3 14.5 17.1 3.5 7.8 2.0 5.8 9.4 13.6 7.0 12.1 11.4 3.3 .. 2.9 6.5 .. .. 20.2 22.9 6.1 .. 4.4 1.9 33.3 .. 20.7 14.1 9.1 12.8 1.1 .. ..
2.2 0.8 2.6 0.2 .. .. 1.3 .. .. 0.6 .. 1.7 2.6 6.7 .. 2.2 4.5 .. 2.1 4.4 1.5 2.2 1.8 6.5 .. 0.8 3.0 0.9 2.6 2.1 1.2 1.8 0.5 1.1 1.9 2.0 0.9 1.9 .. 0.3 1.3 .. .. 1.5 1.2 3.0 .. 11.8 1.5 0.8 .. 1.0 1.7 1.5 0.6 1.6 .. ..
Permits Corruption and licenses
Crime
Informality
Firms formally registered when operations started
Gender
Finance
Infrastructure Innovation
Trade
Average Intertime to nationally recognized clear direct exports quality Value lost due to electrical certification through customs ownership outages
STATES AND MARKETS
5.2
Business environment: enterprise surveys
Workforce
Time required to obtain operating license
Informal payments to public officials
Losses due to theft, robbery, vandalism, and arson
days
% of firms
% of sales
% of firms
% of firms
% of firms
% of sales
% of firms
days
% of firms
28.8 35.6 .. 21.1 .. .. .. .. .. 9.3 .. 6.4 30.8 23.4 .. .. 18.8 .. 18.0 13.6 11.5 81.0 16.4 16.0 .. 65.5 33.8 41.3 15.0 22.4 41.0 10.7 19.1 54.0 13.9 43.5 3.4 35.2 .. 9.6 14.5 .. .. 17.6 39.7 12.1 .. 33.2 10.2 66.3 .. 81.3 46.5 10.6 14.6 .. .. ..
6.1 5.4 47.5 14.9 .. .. 8.3 .. .. 10.6 .. 18.1 34.1 79.2 .. 14.1 7.5 .. 47.8 39.8 13.4 23.0 28.1 55.4 .. 10.7 16.9 21.8 10.8 .. 19.4 82.1 5.9 11.6 33.5 33.4 13.4 14.8 .. 11.4 15.2 .. .. 8.3 35.2 40.9 .. .. 48.0 30.5 .. 17.5 21.4 18.6 14.7 14.5 .. ..
2.2 0.1 0.1 0.4 .. .. 0.3 .. .. 0.4 .. 0.1 1.0 3.9 .. 0.0 0.3 .. 0.3 0.3 0.3 0.0 2.9 2.8 .. 0.4 0.7 1.2 5.7 1.0 0.5 0.6 1.4 1.4 0.4 0.6 0.0 1.8 .. 1.3 0.9 .. .. 2.2 0.9 4.1 .. .. 0.4 0.3 .. 1.3 0.6 1.1 0.5 0.2 .. ..
81.3 100.0 .. 29.1 .. .. .. .. .. 90.0 .. .. 97.4 .. .. .. 89.2 .. 95.9 93.5 98.5 97.6 86.8 73.8 .. 97.1 99.2 97.5 78.6 53.0 79.2 .. 84.2 84.7 97.9 90.1 86.0 85.9 .. .. 94.0 .. .. 74.0 90.5 .. .. .. .. 99.7 .. 98.7 82.6 97.5 99.3 .. .. ..
43.3 42.4 9.1 42.8 .. .. 41.6 .. .. 38.2 .. 13.1 34.4 37.1 .. 19.1 10.9 .. 60.4 39.4 46.3 33.5 18.4 53.0 .. 38.7 36.4 50.0 23.9 13.1 58.3 17.3 16.9 25.7 53.1 52.0 13.1 24.4 .. 33.4 27.4 .. .. 61.9 17.6 20.0 .. 31.0 6.7 24.7 .. 51.6 28.7 69.4 47.9 50.8 .. ..
17.0 48.7 46.6 11.7 .. .. 37.4 .. .. 44.4 .. 8.6 31.0 22.9 .. 39.9 25.3 .. 17.9 0.0 37.3 23.8 32.7 10.1 .. 47.4 47.0 12.2 20.6 48.6 29.3 3.2 37.5 16.2 30.8 26.5 12.3 10.5 .. 8.1 17.5 .. .. 21.9 9.3 2.7 .. 4.2 9.7 1.2 .. 30.1 45.9 22.0 40.7 24.4 .. ..
9.2 0.9 6.6 2.2 .. .. 1.5 .. .. 0.2 .. 1.7 3.7 6.4 .. .. 17.1 .. 10.5 4.3 1.1 9.4 6.7 2.9 .. 0.7 5.9 7.7 13.3 3.0 4.1 1.6 2.2 3.4 2.0 0.8 1.3 2.4 .. 0.7 27.0 .. .. 18.2 1.9 8.9 .. 10.8 9.2 2.1 .. 1.4 3.2 3.9 1.9 .. .. ..
16.3 39.4 22.5 2.9 .. .. 17.2 .. .. 16.5 .. 15.5 10.8 9.8 .. 17.6 7.9 .. 16.2 7.2 18.2 17.9 24.7 2.4 .. 15.6 21.5 8.7 17.9 54.1 24.8 5.9 11.1 24.0 9.1 16.7 17.3 18.7 .. 17.6 3.1 .. .. 15.5 4.6 8.5 .. 3.4 9.6 22.5 .. 15.0 14.2 15.7 17.3 12.7 .. ..
10.1 4.3 15.1 2.3 .. .. 2.6 .. .. 13.1 .. 3.8 8.5 5.6 .. 7.2 1.7 .. 15.8 7.5 1.9 7.6 5.4 .. .. 2.4 2.5 14.2 9.9 2.7 12.9 3.9 10.3 7.1 2.4 18.6 1.8 10.1 .. 1.4 5.6 .. .. 4.7 2.6 7.5 .. 20.9 2.6 7.6 .. 21.7 16.1 10.0 6.0 7.2 .. ..
35.8 14.8 15.9a 4.7 .. .. 73.2 .. .. 25.9 .. 23.9a 40.9 48.5a .. 39.5 24.6 .. 29.7 11.1 43.4 52.4 42.5 17.0 .. 46.0 19.0 27.0 48.4 50.1a 32.1 25.5a 25.6 50.8 33.1 61.2 24.7a 22.1a .. 44.5a 8.8 .. .. 47.2 32.1 25.7a .. .. 6.7a 11.0 .. 54.9 60.1 31.1 60.9 31.9 .. ..
Firms with Firms using banks to female finance participation in ownership investment
2012 World Development Indicators
Firms offering formal training
289
5.2
Business environment: enterprise surveys Survey year
Regulations and tax
Time dealing with officials
Average number of times % of management meeting with tax officials time
Romania Russian Federation Rwanda Saudi Arabia Senegal Serbia Sierra Leone Singapore Slovak Republic Slovenia Somalia South Africa South Sudan Spain Sri Lanka Sudan Swaziland Sweden Switzerland Syrian Arab Republic Tajikistan Tanzania Thailand Timor-Leste Togo Trinidad and Tobago Tunisia Turkey Turkmenistan Uganda Ukraine United Arab Emirates United Kingdom United States Uruguay Uzbekistan Venezuela, RB Vietnam West Bank and Gaza Yemen, Rep. Zambia Zimbabwe
2009 2009 2006 2007 2009 2009 2009 2009 2007 2005 2004 2006 2009 2008 2006 2006 2009 2009 2008 2006 2008 2010 2008 2010 2009 2006 2010 2007
9.2 19.9 5.9 .. 2.9 12.2 7.4 .. 6.7 7.3 .. 6.0 .. 0.8 3.5 .. 4.4 .. .. 12.2 11.7 4.0 0.4 3.8 2.7 .. .. 27.1 .. 5.2 11.3 .. .. .. 11.6 11.1 27.6 4.6 5.7 11.8 4.6 ..
2.3 1.6 3.3 .. 1.3 1.4 1.9 .. 0.9 0.3 .. 0.8 .. 1.5 4.9 .. 1.4 .. .. 2.3 1.4 2.7 1.0 0.9 1.2 .. .. 1.3 .. 2.4 2.1 .. .. .. 1.0 0.7 3.0 0.9 1.7 7.3 1.9 ..
Permits Corruption and licenses
Crime
Informality
Firms formally registered when operations started
Gender
2012 World Development Indicators
Infrastructure Innovation
Trade
Average Intertime to nationally recognized clear direct exports quality Value lost due to electrical certification through customs ownership outages
Workforce
Time required to obtain operating license
Informal payments to public officials
Losses due to theft, robbery, vandalism, and arson
days
% of firms
% of sales
% of firms
% of firms
% of firms
% of sales
% of firms
days
% of firms
23.7 57.4 6.5 .. 21.4 28.0 12.6 .. 32.1 56.1 .. 36.2 .. .. 49.5 .. 24.0 .. .. 169.2 22.6 15.9 32.1 16.6 56.4 .. .. 36.0 .. 9.3 31.0 .. .. .. 108.0 9.1 117.2 15.9 21.3 6.5 48.3 ..
22.2 39.6 20.0 .. 18.1 21.4 20.4 .. 15.7 5.8 .. 15.1 .. 4.4 16.3 .. 40.6 .. .. 83.8 44.6 49.5 .. 19.4 16.7 .. .. 18.0 .. 51.7 31.8 .. .. .. 8.1 59.5 23.7 52.5 13.3 68.2 14.3 ..
0.3 0.8 1.3 .. 0.5 0.6 0.8 .. 0.7 0.4 .. 1.0 .. 0.2 0.5 .. 1.3 .. .. 0.8 0.3 1.2 0.1 2.7 2.4 .. .. 0.4 .. 1.0 0.6 .. .. .. 0.3 0.7 1.4 0.3 1.2 0.6 1.0 ..
98.7 94.7 .. .. 78.9 95.0 89.2 .. 100.0 99.9 .. 91.0 .. .. .. .. .. .. .. .. 92.7 .. .. 91.8 75.8 .. .. 94.1 .. .. 95.8 .. .. .. 94.6 100.0 95.6 87.5 .. 81.7 96.2 ..
47.9 33.1 41.0 .. 26.3 28.8 7.9 .. 29.6 42.2 .. 22.6 .. 34.1 .. .. 28.6 .. .. 14.4 34.4 30.9 .. 42.9 31.8 .. .. 40.7 .. 34.7 47.1 .. .. .. 23.1 39.8 30.7 59.2 18.0 6.4 37.2 ..
37.3 30.6 15.9 .. 19.8 42.8 6.9 .. 33.5 52.2 .. 34.8 .. 32.6 26.2 .. 7.7 .. .. 20.7 21.4 6.8 74.4 1.6 16.9 .. .. 51.9 .. 7.7 32.1 .. .. .. 13.7 8.2 35.3 21.5 4.2 4.2 10.2 ..
2.2 1.2 8.7 .. 5.0 1.3 6.6 .. 0.3 0.5 .. 1.6 .. 3.0 .. .. 2.5 .. .. 8.6 15.1 9.6 1.5 7.6 10.5 .. .. 2.8 .. 10.2 4.4 .. .. .. 0.3 5.4 8.3 3.6 4.6 13.2 3.7 ..
26.1 11.7 10.8 .. 6.1 21.8 13.8 .. 28.6 28.0 .. 26.4 .. 21.3 .. .. 22.1 .. .. 7.4 16.7 14.7 39.0 2.2 6.6 .. .. 30.0 .. 15.5 13.0 .. .. .. 10.8 1.3 25.9 16.7 18.2 4.4 17.2 ..
2.0 4.6 6.7 .. 7.4 1.6 .. .. 2.4 2.2 .. 4.5 .. 4.9 7.6 .. 2.1 .. .. 5.1 20.4 5.7 1.3 .. 6.7 .. .. 5.2 .. 3.2 3.4 .. .. .. 6.2 5.1 18.4 4.2 6.0 6.2 2.3 ..
24.9 52.2 27.6a .. 16.3a 36.5 18.6 .. 33.1 47.5 .. 36.8a .. 51.3 32.6 .. 51.0a .. .. 38.3 21.1 36.5a 75.3a 49.7 31.0 .. .. 28.8
Firms with Firms using banks to female finance participation in ownership investment
Note: Enterprise surveys are updated several times a year; see www.enterprisesurveys.org for the most recent updates. a. The sample was drawn from the manufacturing sector only.
290
Finance
Firms offering formal training
35.0a 24.8 .. .. .. 48.6 9.6 56.0 43.6 26.5a 12.9 26.0a
About the data
5.2
STATES AND MARKETS
Business environment: enterprise surveys Definitions
The World Bank Enterprise Surveys gather firm-level
distortions limit access to credit and thus restrain
• Survey year is the year in which the underlying
data to benchmark the business environment of
growth.
data were collected. • Time dealing with tax offi -
economies and assess how business environment
The reliability and availability of infrastructure ben-
cials is the average percentage of senior manage-
constraints affect productivity and job creation. Stan-
efit households and support development. Firms with
ment’s time that is spent in a typical week dealing
dardized surveys are conducted all over the world,
access to modern and efficient infrastructure—tele-
with requirements imposed by government regula-
and data are available on more than 130,000 firms
communications, electricity, and transport—can be
tions. • Average number of times meeting with tax
in 128 countries. The survey covers 11 dimensions
more productive. Firm-level innovation and use of
officials is the average number of visits or required
of the business environment, including regulation,
modern technology may help firms compete.
meetings with tax officials. • Time required to obtain
corruption, crime, informality, finance, infrastructure,
Delays in clearing customs can be costly, deterring
operating license is the average wait to obtain an
and trade. For several countries firm-level panel
firms from engaging in trade or making them uncom-
operating license from the day applied for to the day
data are also available, making it possible to track
petitive globally. Ill-considered labor regulations dis-
granted. • Informal payments to public officials are
changes in the business environment over time.
courage firms from creating jobs, and while employed
the percentage of firms that answered positively
Firms evaluating investment options, governments
workers may benefit, unemployed, low-skilled, and
to the question “Was a gift or informal payment
interested in improving business conditions, and
informally employed workers will not. A trained labor
expected or requested during a meeting with tax
economists seeking to explain economic perfor-
force enables firms to thrive, compete, innovate, and
officials?” • Losses due to theft, robbery, vandal-
mance have all grappled with defining and measur-
adopt new technology.
ism, and arson are the estimated losses from those
ing the business environment. The firm-level data
The data in the table are from Enterprise Surveys
from Enterprise Surveys provide a useful tool for
implemented by the World Bank’s Financial and Pri-
as a percentage of annual sales. • Firms formally
benchmarking economies across a large number of
vate Sector Development Enterprise Analysis Unit. All
registered when operations started are firms for-
indicators measured at the firm level.
causes that occurred on establishments’ premises
economies in East Asia and Pacific, Europe and Cen-
mally registered when they started operations in the
Most countries can improve regulation and taxa-
tral Asia, Latin America and the Caribbean, Middle
country. Firms not formally registered (the residual)
tion without compromising broader social interests.
East and North Africa, and Sub-Saharan Africa (for
are in the informal sector of the economy. • Firms
Excessive regulation may harm business perfor-
2009) and Afghanistan, Bangladesh, and India draw
with female participation in ownership are firms with
mance and growth. For example, time spent with
a sample of registered nonagricultural businesses,
a woman among the owners. • Firms using banks to
tax officials is a burden firms may face in paying
excluding those in the financial and public sectors.
finance investment are firms that invested in fixed
taxes. The business environment suffers when gov-
Samples for other economies are drawn only from
assets during the last fiscal year that used banks to
ernments increase uncertainty and risks or impose
the manufacturing sector and are footnoted in the
finance fixed assets. • Value lost due to electrical
unnecessary costs and unsound regulation and taxa-
table. Typical Enterprise Survey sample sizes range
outages is losses that resulted from power outages
tion. Time to obtain licenses and permits and the
from 150 to 1,800, depending on the size of the
as a percentage of annual sales. • Internationally
associated red tape constrain firm operations.
economy. In each country samples are selected by
recognized quality certifi cation ownership is the
In some countries doing business requires informal
stratified random sampling, unless otherwise noted.
percentage of firms that have an internationally rec-
payments to “get things done” in customs, taxes,
Stratified random sampling allows indicators to be
ognized quality certification, such as International
licenses, regulations, services, and the like. Such
computed by sector, firm size, and location and
Organization for Standardization 9000, 9001, 9002,
corruption can harm the business environment by
increases the precision of economywide indicators
or 14000 or Hazard Analysis and Critical Control
distorting policymaking, undermining government
compared with alternative simple random sampling.
Points. • Average time to clear direct exports
credibility, and diverting public resources. Crime,
Stratification by sector of activity divides the econ-
through customs is the average number of days to
theft, and disorder also impose costs on businesses
omy into manufacturing and retail and other services
clear direct exports through customs. • Firms offer-
and society.
sectors. For medium-size and large economies the
ing formal training are firms offering formal training
In many developing countries informal businesses
manufacturing sector is further stratified by industry.
programs for their permanent, full-time employees.
operate without formal registration. These firms have
Firm size is stratified into small (5–19 employees),
less access to financial and public services and can
medium (20–99 employees), and large (more than
engage in fewer types of contracts and investments,
99 employees). Geographic stratification divides the
constraining growth.
national economy into the main centers of economic
Equal opportunities for men and women contribute
activity.
to development. Female participation in firm ownership and in management measures women’s integration as decisionmakers. Financial markets connect firms to lenders and
Data sources
investors, allowing firms to grow their businesses:
Data on the business environment are from the
creditworthy firms can obtain credit from financial
World Bank Enterprise Surveys website (www.
intermediaries at competitive prices. But too often
enterprisesurveys.org).
market imperfections and government-induced
2012 World Development Indicators
291
5.3
Business environment: Doing Business indicators Starting a business
Afghanistan Albania Algeria Angola Argentina Armenia Australia Austria Azerbaijan Bahrain Bangladesh Belarus Belgium Benin Bolivia Bosnia and Herzegovina Botswana Brazil Bulgaria Burkina Faso Burundi Cambodia Cameroon Canada Central African Republic Chad Chile China Hong Kong SAR, China Colombia Congo, Dem. Rep. Congo, Rep. Costa Rica Côte d’Ivoire Croatia Cuba Cyprus Czech Republic Denmark Dominican Republic Ecuador Egypt, Arab Rep. El Salvador Eritrea Estonia Ethiopia Finland France Gabon Gambia, The Georgia Germany Ghana Greece Guatemala Guinea Guinea-Bissau Haiti
292
Registering property
Number of procedures June 2011
Time required days June 2011
Cost % of per capita income June 2011
Number of procedures June 2011
Time required days June 2011
4 5 14 8 14 3 2 8 6 7 7 5 3 6 15 12 10 13 4 3 9 9 5 1 7 11 7 14 3 9 10 10 12 10 6 .. 6 9 4 7 13 6 8 13 5 5 3 5 9 8 2 9 7 10 12 12 9 12
7 5 25 68 26 8 2 28 8 9 19 5 4 29 50 40 61 119 18 13 14 85 15 5 21 66 7 38 3 14 65 160 60 32 7 .. 8 20 6 19 56 7 17 84 7 9 14 7 58 27 2 15 12 10 37 40 9 105
25.8 29.0 12.1 118.9 11.9 2.9 0.7 5.2 2.7 0.7 30.6 1.3 5.2 149.9 90.4 17.0 1.8 5.4 1.5 47.7 116.8 109.7 45.5 0.4 175.5 208.5 5.1 3.5 1.9 8.0 551.4 85.2 11.1 132.6 8.6 .. 13.1 8.4 0.0 18.2 28.8 5.6 45.1 62.6 1.8 12.8 1.0 0.9 17.3 206.1 4.3 4.6 17.3 20.1 52.5 118.0 49.8 314.2
9 6 10 7 7 3 5 3 4 2 8 2 8 4 7 7 5 13 8 4 5 7 5 6 5 6 6 4 5 7 6 6 5 6 5 .. 6 4 3 7 9 7 5 11 3 10 3 8 7 5 1 5 5 11 4 6 8 5
250 33 48 184 53 7 5 21 11 31 245 10 64 120 92 33 16 39 15 59 94 56 93 17 75 44 31 29 36 15 54 55 20 62 104 .. 42 25 16 60 16 72 31 78 18 41 14 59 39 66 2 40 34 18 23 59 210 301
2012 World Development Indicators
Dealing with construction permits Time Number of required procedures to build a to build a warehouse warehouse days June June 2011 2011
12 .. 19 11 25 18 15 13 30 12 11 13 12 12 14 18 22 17 23 12 22 21 11 12 18 13 17 33 6 8 11 14 20 18 12 .. 9 33 5 14 16 22 33 .. 13 9 16 10 13 14 9 9 16 14 19 29 12 9
334 .. 281 321 365 79 147 194 212 43 201 140 169 372 249 181 145 469 120 98 135 652 147 73 203 154 155 311 67 46 117 186 188 583 317 .. 677 120 67 216 128 218 157 .. 148 128 66 184 201 143 74 97 218 169 165 287 170 1,129
Getting electricity
Time required days June 2011
109 177 159 48 67 242 81 23 241 90 372 254 88 158 42 125 121 34 130 158 188 183 67 168 102 67 31 145 43 165 58 129 62 33 70 .. 247 279 38 87 89 54 78 59 111 95 53 123 160 78 97 17 78 77 39 69 455 66
Enforcing contracts
Protecting Resolving investors insolvency
Number of procedures June 2011
Time required days June 2011
Disclosure index 0–10 (least to most disclosure) June 2011
Time required years June 2011
47 39 45 46 36 49 28 25 39 48 41 29 26 42 40 37 28 45 39 37 44 44 43 36 43 41 36 34 26 34 43 44 40 33 38 .. 43 27 35 34 39 41 34 39 35 37 33 29 38 33 36 30 36 39 31 49 40 35
1,642 390 630 1,011 590 440 395 397 237 635 1,442 275 505 795 591 595 625 731 564 446 832 401 800 570 660 743 480 406 280 1,346 610 560 852 770 561 .. 735 611 410 460 588 1,010 786 405 425 620 375 331 1,070 434 285 394 487 819 1,459 276 1,715 530
1 8 6 5 6 5 8 3 7 8 6 7 8 6 1 3 7 6 10 6 8 5 6 8 6 6 8 10 10 8 3 6 2 6 1 .. 8 2 7 5 1 8 3 4 8 4 6 10 6 2 9 5 7 1 3 6 6 2
2.0 2.0 2.5 6.2 2.8 1.9 1.0 1.1 2.7 2.5 4.0 5.8 0.9 4.0 1.8 3.3 1.7 4.0 3.3 4.0 .. 6.0 3.2 0.8 4.8 4.0 4.5 1.7 1.1 1.3 5.2 3.3 3.5 2.2 3.1 .. 1.5 3.2 1.0 3.5 5.3 4.2 4.0 .. 3.0 3.0 0.9 1.9 5.0 3.0 3.3 1.2 1.9 2.0 3.0 3.8 .. 5.7
Starting a business
Honduras Hungary India Indonesia Iran, Islamic Rep. Iraq Ireland Israel Italy Jamaica Japan Jordan Kazakhstan Kenya Korea, Dem. Rep. Korea, Rep. Kosovo Kuwait Kyrgyz Republic Lao PDR Latvia Lebanon Lesotho Liberia Libya Lithuania Macedonia, FYR Madagascar Malawi Malaysia Mali Mauritania Mauritius Mexico Moldova Mongolia Morocco Mozambique Myanmar Namibia Nepal Netherlands New Zealand Nicaragua Niger Nigeria Norway Oman Pakistan Panama Papua New Guinea Paraguay Peru Philippines Poland Portugal Puerto Rico Qatar
Registering property
Number of procedures June 2011
Time required days June 2011
Cost % of per capita income June 2011
Number of procedures June 2011
Time required days June 2011
13 4 12 8 6 11 4 5 6 6 8 7 6 11 .. 5 10 12 2 7 4 5 7 4 .. 6 3 3 10 4 4 9 5 6 7 7 6 9 .. 10 7 6 1 8 9 8 5 5 10 6 6 7 5 15 6 5 6 8
14 4 29 45 8 77 13 34 6 7 23 12 19 33 .. 7 58 32 10 93 16 9 40 6 .. 22 3 8 39 6 8 19 6 9 9 13 12 13 .. 66 29 8 1 39 17 34 7 8 21 8 51 35 26 35 32 5 6 12
46.7 7.6 46.8 17.9 3.8 115.7 0.4 4.4 18.2 7.2 7.5 13.9 0.8 37.8 .. 14.6 26.7 1.2 3.5 7.6 2.6 67.1 24.9 68.4 .. 2.8 2.4 12.1 90.9 16.4 90.5 48.3 3.6 11.2 9.1 2.9 15.7 11.7 .. 17.2 37.4 5.5 0.4 107.9 114.4 70.6 1.8 3.1 11.2 9.9 15.6 47.2 11.9 19.1 17.3 2.3 0.6 8.3
7 4 5 6 9 5 5 7 7 6 6 7 4 8 .. 7 8 8 4 5 5 8 6 10 .. 3 4 6 6 5 5 4 4 7 5 5 8 8 .. 7 3 5 2 8 4 13 1 2 6 8 4 6 4 8 6 1 8 7
23 17 44 22 36 51 38 144 27 37 14 21 40 64 .. 11 33 47 5 98 18 25 101 50 .. 3 40 74 69 48 29 49 22 74 5 11 75 42 .. 39 5 7 2 49 35 82 3 16 50 32 72 46 7 39 152 1 194 13
Dealing with construction permits Time Number of required procedures to build a to build a warehouse warehouse days June June 2011 2011
14 29 34 13 16 13 10 19 11 8 14 17 32 8 .. 12 17 24 12 23 23 19 12 23 .. 15 10 16 18 22 11 18 16 10 27 19 15 13 .. 12 13 15 6 16 12 15 11 14 11 17 21 12 16 30 30 14 18 17
94 102 227 158 320 187 141 212 258 145 193 70 189 125 .. 30 301 130 142 108 205 219 510 75 .. 142 117 172 200 260 179 119 136 81 291 208 97 370 .. 139 222 176 64 218 326 85 250 174 222 113 219 137 188 85 301 255 189 70
Getting electricity
Time required days June 2011
33 252 67 108 140 47 205 132 192 96 117 43 88 163 .. 49 60 42 337 134 108 75 140 586 .. 148 151 450 244 51 120 75 91 114 140 156 71 117 .. 55 70 143 50 70 120 260 66 62 206 35 66 53 100 50 143 64 32 90
Enforcing contracts
5.3
STATES AND MARKETS
Business environment: Doing Business indicators
Protecting Resolving investors insolvency
Number of procedures June 2011
Time required days June 2011
Disclosure index 0â&#x20AC;&#x201C;10 (least to most disclosure) June 2011
Time required years June 2011
47 35 46 40 39 51 21 35 41 35 30 38 36 40 .. 33 53 50 38 42 27 37 40 41 .. 30 37 38 42 29 36 46 36 38 30 32 40 30 .. 33 39 26 30 37 39 40 34 51 46 31 42 38 41 37 37 31 39 43
920 395 1,420 570 505 520 650 890 1,210 655 360 689 390 465 .. 230 420 566 260 443 369 721 785 1,280 .. 275 370 871 312 425 620 370 645 415 352 314 510 730 .. 270 910 514 216 409 545 457 280 598 976 686 591 591 428 842 830 547 620 570
0 2 7 10 5 4 10 7 7 4 7 5 9 3 .. 7 3 7 8 2 5 9 2 4 .. 7 9 5 4 10 6 5 6 8 7 5 7 5 .. 5 6 4 10 4 6 5 7 8 6 1 5 6 8 2 7 6 7 5
3.8 2.0 7.0 5.5 4.5 .. 0.4 4.0 1.8 1.1 0.6 4.3 1.5 4.5 .. 1.5 2.0 4.2 4.0 .. 3.0 4.0 2.6 3.0 .. 1.5 2.0 2.0 2.6 1.5 3.6 8.0 1.7 1.8 2.8 4.0 1.8 5.0 .. 1.5 5.0 1.1 1.3 2.2 5.0 2.0 0.9 4.0 2.8 2.5 3.0 3.9 3.1 5.7 3.0 2.0 3.8 2.8
2012 World Development Indicators
293
5.3
Business environment: Doing Business indicators Starting a business
Number of procedures June 2011
Romania Russian Federation Rwanda Saudi Arabia Senegal Serbia Sierra Leone Singapore Slovak Republic Slovenia Somalia South Africa South Sudan Spain Sri Lanka Sudan Swaziland Sweden Switzerland Syrian Arab Republic Tajikistan Tanzania Thailand Timor-Leste Togo Trinidad and Tobago Tunisia Turkey Turkmenistan Uganda Ukraine United Arab Emirates United Kingdom United States Uruguay Uzbekistan Venezuela, RB Vietnam West Bank and Gaza Yemen, Rep. Zambia Zimbabwe World Low income Middle income Lower middle income Upper middle income Low & middle income East Asia & Pacific Europe & Central Asia Latin America & Carib. Middle East & N. Africa South Asia Sub-Saharan Africa High income Euro area
6 9 2 3 3 7 6 3 6 2 .. 5 .. 10 4 10 12 3 6 7 5 12 5 10 7 9 10 6 .. 16 9 7 6 6 5 6 17 9 11 6 6 9 7u 8 8 8 8 8 8 6 9 8 7 8 6 6
Time required days June 2011
14 30 3 5 5 13 12 3 18 6 .. 19 .. 28 35 36 56 15 18 13 24 29 29 103 84 43 11 6 .. 34 24 13 13 6 7 14 141 44 49 12 18 90 31 u 33 36 34 38 35 39 16 57 23 23 34 17 12
Registering property
Cost % of per capita income June 2011
3.0 2.0 4.7 5.9 68.0 7.8 93.3 0.7 1.8 0.0 .. 0.3 .. 4.7 4.7 31.4 29.2 0.6 2.1 17.1 33.3 28.8 6.2 4.5 177.2 0.9 4.2 11.2 .. 84.5 4.4 5.6 0.7 1.4 24.9 6.4 26.1 10.6 96.0 83.8 27.4 148.9 36.2 u 106.0 28.0 40.9 13.9 46.3 26.4 8.0 40.8 50.4 21.6 80.8 6.8 5.4
Number of procedures June 2011
8 5 5 2 6 6 7 3 3 5 .. 6 .. 5 8 6 9 1 4 4 6 9 2 .. 5 8 4 6 .. 13 10 1 6 4 8 12 8 4 7 6 5 5 6u 6 6 6 6 6 5 6 7 7 6 6 5 5
Time required days June 2011
26 43 25 2 122 11 86 5 17 110 .. 23 .. 13 83 9 21 7 16 19 37 73 2 .. 295 162 39 6 .. 48 117 2 29 12 66 78 38 57 47 19 40 31 55 u 86 51 66 36 60 80 29 56 41 103 66 44 32
Dealing with construction permits Time Number of required procedures to build a to build a warehouse warehouse days June June 2011 2011
16 51 12 9 13 19 20 11 11 13 .. 13 .. 8 18 16 13 7 13 23 26 19 8 19 12 17 17 24 .. 15 21 14 9 15 27 25 10 10 18 12 14 12 16 u 15 17 16 17 16 17 21 14 17 16 15 14 12
287 423 164 75 210 279 238 26 286 199 .. 127 .. 182 217 270 95 116 154 104 228 303 157 238 309 297 88 189 .. 125 375 46 99 26 234 243 381 200 119 116 196 614 193 u 260 188 181 195 205 172 214 221 166 222 212 159 210
Getting electricity
Time required days June 2011
223 281 30 71 125 131 137 36 177 38 .. 226 .. 101 132 70 137 52 39 71 238 109 35 63 74 61 65 70 .. 91 274 55 109 68 48 117 125 142 63 35 117 125 111 u 167 102 98 106 117 98 168 66 84 145 138 93 111
Enforcing contracts
Number of procedures June 2011
31 36 24 43 43 36 39 21 32 32 .. 29 .. 39 40 53 40 30 32 55 35 38 36 51 41 42 39 36 .. 38 30 49 28 32 41 42 30 34 44 36 35 38 38 u 39 39 40 38 39 37 37 40 42 43 39 35 32
Time required days June 2011
512 281 230 635 780 635 515 150 565 1,290 .. 600 .. 515 1,318 810 972 508 390 872 430 462 479 1,285 588 1,340 565 420 .. 490 343 537 399 300 720 195 510 295 540 520 471 410 610 u 662 626 662 588 634 548 390 699 692 1,075 657 539 600
Protecting Resolving investors insolvency
Disclosure index 0â&#x20AC;&#x201C;10 (least to most disclosure) June 2011
9 6 7 9 6 7 6 10 3 3 .. 8 .. 5 6 0 2 8 0 7 8 3 10 3 6 4 5 9 .. 2 5 4 10 7 3 4 3 6 6 6 3 8 5u 5 5 4 6 5 5 7 4 6 5 5 6 6
Time required years June 2011
3.3 2.0 3.0 1.5 3.0 2.7 2.6 0.8 4.0 2.0 .. 2.0 .. 1.5 1.7 2.0 2.0 2.0 3.0 4.1 1.7 3.0 2.7 .. 3.0 4.0 1.3 3.3 .. 2.2 2.9 5.1 1.0 1.5 2.1 4.0 4.0 5.0 .. 3.0 2.7 3.3 2.9 u 3.7 3.1 3.3 2.8 3.2 3.2 2.8 3.1 3.5 3.4 3.4 2.1 1.7
Note: Regional aggregates differ from those reported on the Doing Business website because the regional aggregates reported on the Doing Business website include developed countries.
294
2012 World Development Indicators
About the data
5.3
STATES AND MARKETS
Business environment: Doing Business indicators Definitions
The economic health of a country is measured not only
The Doing Business project encompasses two
• Number of procedures for starting a business is
in macroeconomic terms but also by other factors that
types of data: data from readings of laws and regu-
the number of procedures required to start a busi-
shape daily economic activity such as laws, regula-
lations and data on time and motion indicators that
ness, including interactions to obtain necessary per-
tions, and institutional arrangements. The Doing Busi-
measure efficiency in achieving a regulatory goal.
mits and licenses and to complete all inscriptions,
ness indicators measure business regulation, gauge
Within the time and motion indicators cost estimates
verifications, and notifications to start operations
regulatory outcomes, and measure the extent of legal
are recorded from official fee schedules where appli-
for businesses with specific characteristics of own-
protection of property, the flexibility of employment
cable. The data from surveys are subjected to numer-
ership, size, and type of production. • Time required
regulation, and the tax burden on businesses.
ous tests for robustness, which lead to revision or
for starting a business is the number of calendar
expansion of the information collected.
days to complete the procedures for legally operating
The table presents a subset of Doing Business indicators covering 7 of the 11 sets of indicators:
The Doing Business methodology has limitations
a business using the fastest procedure, independent
starting a business, registering property, dealing with
that should be considered when interpreting the
of cost. • Cost for starting a business is normalized
construction permits, getting electricity, enforcing
data. First, the data collected refer to businesses
as a percentage of gross national income (GNI) per
contracts, protecting investors, and resolving insol-
in the economy’s largest city and may not represent
capita. It includes all official fees and fees for legal
vency. Table 5.5 includes Doing Business measures
regulations in other locations of the economy. To
or professional services if they are required by law.
of getting credit, and table 5.6 presents data on pay-
address this limitation, subnational indicators are
• Number of procedures for registering property is
ing business taxes.
being collected for selected economies. These sub-
the number of procedures required for a business to
The fundamental premise of the Doing Business
national studies point to significant differences in
legally transfer property. • Time required for register-
project is that economic activity requires good rules
the speed of reform and the ease of doing business
ing property is the number of calendar days for a busi-
and regulations that are efficient, accessible to all
across cities in the same economy. Second, the data
ness to legally transfer property. • Number of proce-
who need to use them, and simple to implement.
often focus on a specific business form—generally
dures for dealing with construction permits to build
Thus some Doing Business indicators give a higher
a limited liability company of a specified size—and
a warehouse is the number of interactions of a com-
score for more regulation, such as stricter disclosure
may not represent regulation for other types of busi-
pany’s employees or managers with external parties,
requirements in related-party transactions, and oth-
nesses such as sole proprietorships. Third, transac-
including government staff, public inspectors, nota-
ers give a higher score for simplified regulations,
tions described in a standardized business case refer
ries, land registry and cadastre staff, and technical
such as a one-stop shop for completing business
to a specific set of issues and may not represent the
experts apart from architects and engineers. • Time
startup formalities.
full set of issues a business encounters. Fourth, the
required for dealing with construction permits to
In constructing the indicators, it is assumed that
time measures involve an element of judgment by the
build a warehouse is the number of calendar days
entrepreneurs know about all regulations and comply
expert respondents. When sources indicate different
to complete the required procedures for building a
with them; in practice, entrepreneurs may not be
estimates, the Doing Business time indicators repre-
warehouse using the fastest procedure, independent
aware of all required procedures or may avoid legally
sent the median values of several responses given
of cost. • Time required for getting electricity is the
required procedures altogether. But where regula-
under the assumptions of the standardized case.
number of calendar days required for a business to
tion is particularly onerous, levels of informality are
Fifth, the methodology assumes that a business has
obtain a permanent electricity connection and supply.
higher, which comes at a cost: firms in the informal
full information on what is required and does not
• Number of procedures for enforcing contracts is
sector usually grow more slowly, have less access
waste time when completing procedures.
the number of independent actions, mandated by law
to credit, and employ fewer workers—and those
or court regulation, that demand interaction between
workers remain outside the protections of labor law.
the parties to a contract or between them and the
The indicators in the table can help policymakers
judge or court officer. • Time required for enforcing
understand the business environment in a country
contracts is the number of calendar days from the
and—along with information from other sources such
time of the filing of a lawsuit in court to the final deter-
as the World Bank’s Enterprise Surveys—provide
mination and payment. • Disclosure index measures
insights into potential areas of reform.
the degree to which investors are protected through
Doing Business data are collected with a stan-
disclosure of ownership and financial information.
dardized survey that uses a simple business case
Higher values indicate more disclosure. • Time
to ensure comparability across economies and over
required to resolve insolvency is the number of years
time—with assumptions about the legal form of the
from time of filing for insolvency in court until resolu-
business, its size, its location, and nature of its oper-
tion of distressed assets and payment of creditors.
ation. Surveys in 183 countries are administered through more than 9,000 local experts, including
Data sources
lawyers, business consultants, accountants, freight
Data on the business environment are from
forwarders, government officials, and other profes-
the World Bank’s Doing Business project (www.
sionals who routinely administer or advise on legal
doingbusiness.org).
and regulatory requirements.
2012 World Development Indicators
295
5.4
Stock markets Market capitalization
$ millions
Afghanistan Albania Algeria Angola Argentina Armenia Australia Austria Azerbaijan Bahrain Bangladesh Belarus Belgium Benin Bolivia Bosnia and Herzegovina Botswana Brazil Bulgaria Burkina Faso Burundi Cambodia Cameroon Canada Central African Republic Chad Chile China Hong Kong SAR, China Colombia Congo, Dem. Rep. Congo, Rep. Costa Rica Côte d’Ivoire Croatia Cuba Cyprus Czech Republic Denmark Dominican Republic Ecuador Egypt, Arab Rep. El Salvador Eritrea Estonia Ethiopia Finland France Gabon Gambia, The Georgia Germany Ghana Greece Guatemala Guinea Guinea-Bissau Haiti
296
% of GDP
Market liquidity
Turnover ratio
Value of shares traded % of GDP
Value of shares traded % of market capitalization
Listed domestic companies
number
S&P/Global Equity Indices
% change
2005
2011
2005
2010
2005
2010
2005
2011
2005
2011
2010
2011
.. .. .. .. 61,478 43 804,074 124,390 .. 17,364 3,035 .. 288,515 .. 2,200 .. 2,437 474,647 5,086 .. .. .. .. 1,480,891 .. .. 136,446 780,763 693,486 46,016 .. .. 1,478 2,327 12,918 .. 6,583 38,345 178,038 .. 3,214 79,672 3,623 .. 3,495 .. 209,504 1,758,721 .. .. 355 1,221,250 1,661 145,013 .. .. .. ..
.. .. .. .. 43,580 44 1,198,164 82,374 .. 17,152 23,546 .. 229,896 .. 4,125 .. 4,107 1,228,969 8,253 .. .. .. .. 1,906,589 .. .. 270,289 3,389,098 889,597 201,296 .. .. 1,443 6,288 21,796 .. 2,853 38,352 179,529 .. 5,779 48,683 5,474 .. 1,611 .. 143,081 1,568,730 .. .. 796 1,184,459 3,097 33,648 .. .. .. ..
.. .. .. .. 33.6 0.9 115.4 40.8 .. 129.0 5.0 .. 76.5 .. 23.0 .. 23.8 53.8 17.6 .. .. .. .. 130.6 .. .. 115.4 34.6 390.1 31.4 .. .. 7.4 14.2 28.8 .. 38.7 30.8 69.1 .. 8.7 88.8 21.2 .. 25.1 .. 107.0 82.3 .. .. 5.5 44.1 15.5 60.4 .. .. .. ..
.. .. .. .. 17.3 0.3 128.5 17.9 .. 82.2 15.6 .. 57.4 .. 17.2 .. 27.4 74.0 15.2 .. .. .. .. 137.0 .. .. 160.6 80.4 481.0 72.3 .. .. 4.0 31.2 40.9 .. 29.5 22.4 74.3 .. 9.1 37.7 19.9 .. 11.8 .. 49.6 75.3 .. .. 9.1 43.6 11.3 24.1 .. .. .. ..
.. .. .. .. 9.0 0.0 88.5 15.1 .. 5.3 1.7 .. 33.3 .. 0.0 .. 0.4 17.5 4.8 .. .. .. .. 74.5 .. .. 16.0 26.0 165.4 4.3 .. .. 0.1 0.2 1.8 .. 2.4 33.0 59.0 .. 0.4 28.3 0.4 .. 17.8 .. 139.7 71.4 .. .. 0.6 63.7 0.6 27.2 .. .. .. ..
.. .. .. .. 0.7 0.0 108.0 12.7 .. 4.2 14.6 .. 23.7 .. 0.1 .. 0.9 43.2 0.4 .. .. .. .. 86.6 .. .. 25.5 135.5 711.7 8.0 .. .. 0.1 0.6 1.7 .. 2.7 7.3 46.3 .. 0.2 17.0 0.2 .. 1.7 .. 42.8 57.3 .. .. 0.0 42.8 0.3 14.3 .. .. .. ..
.. .. .. .. 30.4 3.7 78.0 43.6 .. 4.6 31.5 .. 44.8 .. 0.1 .. 1.8 38.3 35.2 .. .. .. .. 63.6 .. .. 14.9 82.5 43.3 17.9 .. .. 1.7 1.4 6.7 .. 7.1 118.6 92.3 .. 5.0 43.0 2.3 .. 51.1 .. 139.1 92.0 .. .. 13.6 146.0 3.2 48.3 .. .. .. ..
.. .. .. .. 4.8 1.5 94.0 51.6 .. 1.5 92.6 .. 43.0 .. 0.4 .. 3.6 69.3 3.4 .. .. .. .. 74.8 .. .. 18.6 188.2 157.6 13.3 .. .. 2.6 1.8 4.1 .. 10.0 38.0 73.2 .. 1.9 33.5 1.6 .. 12.6 .. 133.5 84.4 .. .. 0.2 134.5 4.1 46.5 .. .. .. ..
.. .. .. .. 101 198 1,643 92 .. 47 262 .. 222 .. 36 .. 18 381 331 .. .. .. .. 3,721 .. .. 245 1,387 1,020 114 .. .. 19 39 145 .. 144 36 179 .. 32 744 35 .. 15 .. 134 885 .. .. 257 648 30 307 .. .. .. ..
.. .. .. .. 99 12 1,922 73 .. 44 216 .. 158 .. 40 .. 23 366 393 .. .. .. .. 3,932 .. .. 229 2,342 1,472 79 .. .. 9 33 209 .. 117 15 186 .. 41 231 65 .. 15 .. 121 893 .. .. 135 670 36 275 .. .. .. ..
.. .. .. .. 55.3a .. 12.5 10.9 .. 10.0a 37.6a .. 0.5 .. .. .. –6.8a 6.5 –15.2a .. .. .. .. 22.0 .. .. 47.2 6.9 21.3 44.1 .. .. .. 19.3a –0.4 a .. .. 0.2 25.1 .. 9.7a 11.5 .. .. 56.0a .. 10.7 –9.9b .. .. .. 7.4 c 94.1a –43.8 .. .. .. ..
.. .. .. .. –30.2a .. –15.7 –35.8 .. –14.4 a –42.3a .. –15.1 .. .. .. –7.5a –24.4 –22.1a .. .. .. .. –14.7 .. .. –24.1 –21.7 –20.2 –12.0 .. .. .. –15.2a –30.3a .. –71.9a –15.0 –17.3 .. –9.6a –49.1 .. .. –23.3a .. –33.1 –19.5b .. .. .. –16.6c –22.8a –58.3 .. .. .. ..
2012 World Development Indicators
Market capitalization
$ millions
Honduras Hungary India Indonesia Iran, Islamic Rep. Iraq Ireland Israel Italy Jamaica Japan Jordan Kazakhstan Kenya Korea, Dem. Rep. Korea, Rep. Kosovo Kuwait Kyrgyz Republic Lao PDR Latvia Lebanon Lesotho Liberia Libya Lithuania Macedonia, FYR Madagascar Malawi Malaysia Mali Mauritania Mauritius Mexico Moldova Mongolia Morocco Mozambique Myanmar Namibia Nepal Netherlands New Zealand Nicaragua Niger Nigeria Norway Oman Pakistan Panama Papua New Guinea Paraguay Peru Philippines Poland Portugal Puerto Rico Qatar
% of GDP
Market liquidity
Turnover ratio
Listed domestic companies
Value of shares traded % of GDP
Value of shares traded % of market capitalization
number
5.4
STATES AND MARKETS
Stock markets
S&P/Global Equity Indices
% change
2005
2011
2005
2010
2005
2010
2005
2011
2005
2011
2010
2011
.. 32,576 553,074 81,428 38,724 .. 114,134 120,114 798,167 13,028 4,736,513 37,639 10,521 6,384 .. 718,180 .. 130,080 42 .. 2,527 4,929 .. .. .. 8,183 646 .. 230 181,236 .. .. 2,617 239,128 .. 46 27,220 .. .. 415 1,344 592,906 43,409 .. .. 19,356 190,952 15,269 45,937 5,074 3,166 257 35,995 40,153 93,873 66,981 .. 87,316
.. 18,773 1,015,370 390,107 107,249 .. 35,363 144,970 431,471 7,223 3,540,685 27,183 43,301 10,203 .. 994,302 .. 100,869 165 .. 1,076 10,164 .. .. .. 4,075 2,504 .. 1,384 395,083 .. .. 6,540 408,691 .. 1,579 60,088 .. .. 1,152 4,529 594,732 71,657 .. .. 39,270 219,245 19,719 32,764 10,682 8,999 958 79,329 165,380 138,246 61,688 .. 125,413
.. 29.5 66.3 28.5 20.2 .. 56.1 89.5 44.7 116.8 104.0 299.0 18.4 34.1 .. 85.0 .. 161.0 1.7 .. 15.8 22.5 .. .. .. 31.5 10.8 .. 8.4 131.4 .. .. 41.7 28.2 .. 1.8 45.7 .. .. 5.7 16.5 92.9 39.1 .. .. 17.2 62.8 49.4 41.9 32.8 64.6 3.4 45.3 39.0 30.9 34.9 .. 202.9
.. 21.5 93.6 51.0 19.1 .. 16.3 100.3 15.4 46.5 75.1 111.9 40.8 46.0 .. 107.4 .. 87.6 1.7 .. 5.2 32.3 .. .. .. 15.6 28.8 .. 26.7 172.6 .. .. 66.9 43.9 .. 17.6 76.2 .. .. 9.7 30.8 84.8 52.9 .. .. 26.3 60.1 36.9 21.6 40.9 102.8 0.2 63.6 78.8 40.5 35.9 .. 89.4
.. 21.7 52.0 14.7 4.3 .. 31.8 44.6 62.4 3.9 109.8 189.1 1.9 2.7 .. 142.4 .. 116.4 0.5 .. 0.6 4.2 .. .. .. 2.9 1.6 .. 0.3 36.2 .. .. 2.4 6.2 0.6 0.1 7.0 .. .. 0.1 0.6 130.9 15.7 .. .. 1.7 64.1 10.4 128.6 0.5 0.4 0.0 2.5 6.7 9.9 21.7 .. 65.2
.. 20.6 61.2 18.3 5.2 .. 8.1 61.4 26.2 1.5 78.4 34.3 1.5 3.5 .. 160.3 .. 63.9 0.2 .. 0.1 4.8 .. .. .. 0.8 0.4 .. 0.4 37.9 .. .. 3.7 10.5 0.2 0.8 11.8 .. .. 0.2 0.6 76.0 14.2 .. .. 2.7 52.0 12.4 7.3 0.7 0.2 0.1 2.5 13.4 16.5 13.7 .. 25.9
.. 78.0 92.2 54.2 19.1 .. 56.7 55.5 140.5 3.1 118.8 85.0 14.9 9.8 .. 209.8 .. 94.3 34.1 .. 4.6 25.5 .. .. .. 10.1 18.3 .. 4.1 26.9 .. .. 6.0 25.7 .. 6.1 15.9 .. .. 1.5 4.4 147.7 40.0 .. .. 11.5 117.2 29.8 376.3 1.8 0.6 0.6 7.2 20.1 36.3 60.7 .. 40.0
.. 83.9 56.3 37.2 20.7 .. 45.3 64.7 236.8 3.1 108.9 13.9 2.1 7.1 .. 195.1 .. 19.4 2.7 .. 4.4 4.5 .. .. .. 5.0 2.0 .. 3.9 32.0 .. .. 8.0 26.0 .. 3.4 9.8 .. .. 1.2 1.7 88.3 39.6 .. .. 9.2 88.6 12.9 28.6 0.6 0.6 3.0 5.5 20.4 58.4 50.3 .. 18.6
.. 44 4,763 335 420 .. 53 572 275 39 3,279 201 62 47 .. 1,620 .. 143 8 .. 45 11 .. .. .. 43 57 .. 9 1,020 .. .. 42 151 .. 392 56 .. .. 13 125 237 154 .. .. 214 191 96 661 24 9 54 196 235 248 48 .. 31
.. 52 5,112 440 347 .. 48 576 287 37 3,961 247 63 58 .. 1,792 .. 206 34 .. 32 10 .. .. .. 33 32 .. 13 941 .. .. 86 128 .. 332 75 .. .. 7 181 108 144 .. .. 196 192 136 638 21 11 66 202 251 757 46 .. 42
.. –10.8 18.7 37.9 .. .. –7.7 7.4 –17.4 22.4a 9.6d –8.6a –1.0a 33.8a .. 25.3 .. 29.1a .. .. 39.4a –8.7a .. .. .. 44.0a .. .. .. 35.1 .. .. 8.2a 26.6 .. .. 13.1 .. .. 24.2a .. 1.2 5.2 .. .. 20.3a 13.7 12.2a 15.3a 12.8 a .. .. 51.3 56.7 11.3 –16.6 .. 27.7a
.. –35.3 –38.0 1.1 .. .. –1.5 –29.7 –27.6 28.4 a –12.2d –16.2a –36.4 a –31.6a .. –10.9 .. –21.4 a .. .. –17.3a –22.2a .. .. .. –16.1a .. .. .. –1.1 .. .. –2.5a –14.8 .. .. –17.7 .. .. 6.2a .. –16.4 –3.8 .. .. –29.5a –18.1 –14.1a –18.8a 18.0a .. .. –21.3 0.2 –33.4 –31.0 .. 3.3a
2012 World Development Indicators
297
5.4
Stock markets Market capitalization
$ millions 2005
Romania Russian Federation Rwanda Saudi Arabia Senegal Serbia Sierra Leone Singapore Slovak Republic Slovenia Somalia South Africa South Sudan Spain Sri Lanka Sudan Swaziland Sweden Switzerland Syrian Arab Republic Tajikistan Tanzania Thailand Timor-Leste Togo Trinidad and Tobago Tunisia Turkey Turkmenistan Uganda Ukraine United Arab Emirates United Kingdom United States Uruguay Uzbekistan Venezuela, RB Vietnam West Bank and Gaza Yemen, Rep. Zambia Zimbabwe World Low income Middle income Lower middle income Upper middle income Low & middle income East Asia & Pacific Europe & Central Asia Latin America & Carib. Middle East & N. Africa South Asia Sub-Saharan Africa High income Euro area
20,588 548,579 .. 646,104 .. 5,409 .. 316,658 4,393 7,899 .. 565,408 .. 960,024 5,720 .. 197 403,948 938,624 .. .. 588 124,864 .. .. 16,972 2,876 161,537 .. 103 24,976 225,568 3,058,182 16,970,865 96 37 5,017 461 4,461 .. 989 2,402 43,319,352 s .. 4,426,053 898,133 3,527,921 4,440,182 1,212,704 789,576 1,028,157 135,018 609,110 605,113 38,879,170 6,357,326
% of GDP 2011
21,197 796,376 .. 338,873 .. 8,365 .. 308,320 4,736 6,326 .. 855,711 .. 1,030,951 19,437 .. .. 470,122 932,207 .. .. 1,539 268,489 .. .. 14,725 9,662 201,817 .. 7,727 25,558 93,767 1,202,031 15,640,707 175 .. 5,143 18,316 2,532 .. 4,009 10,903 45,082,821s 59,995 10,282,607 1,854,685 8,427,921 10,342,602 4,638,422 1,116,849 2,274,194 265,561 1,095,645 951,930 34,740,219 5,482,967
2005
20.8 71.8 .. 204.7 .. 21.4 .. 256.4 7.2 22.1 .. 228.9 .. 84.9 23.4 .. 7.8 109.0 252.0 .. .. 4.2 70.8 .. .. 106.2 8.9 33.4 .. 1.1 29.0 124.9 134.1 134.9 0.6 0.3 3.4 0.9 111.1 .. 13.8 43.0 96.6 w .. 49.3 48.0 49.6 48.8 40.1 48.7 40.5 36.8 58.8 128.6 108.8 62.7
2010
20.0 67.9 .. 81.3 .. 25.2 .. 177.3 4.8 20.1 .. 278.4 .. 83.2 40.2 .. .. 126.7 232.9 .. .. 5.5 87.2 .. .. 59.0 24.1 41.8 .. 10.5 28.6 35.2 137.4 117.5 0.4 .. 1.0 19.2 .. .. 17.4 153.6 88.7 w 26.6 72.6 65.9 74.4 72.1 79.9 51.8 57.6 34.6 81.9 149.5 95.9 51.7
Market liquidity
Turnover ratio
Value of shares traded % of GDP
Value of shares traded % of market capitalization
2005
3.4 20.9 .. 349.7 .. 2.6 .. 97.0 0.1 2.2 .. 81.2 .. 137.8 4.7 .. 0.0 125.2 237.1 .. .. 0.1 50.6 .. .. 3.9 1.4 41.7 .. 0.0 0.8 79.2 182.7 171.0 0.0 0.3 0.2 0.2 52.2 .. 0.2 5.9 105.7 w .. 24.9 35.2 22.2 24.6 25.6 22.7 9.9 7.2 55.7 43.3 126.3 73.1
2010
2005
2011
1.1 21.0 12.0 54.0 39.0 127.3 .. .. .. 46.7 231.7 84.6 .. .. .. 0.6 15.3 3.7 .. .. .. 135.1 40.4 74.8 0.2 1.6 10.2 0.6 9.0 6.5 .. .. .. 93.5 39.3 39.8 .. .. .. 96.7 163.9 128.9 6.7 24.3 25.1 .. .. .. .. 0.0 .. 95.9 118.9 96.2 164.7 100.1 85.9 .. .. .. .. .. .. 0.1 2.3 2.5 68.4 73.9 85.1 .. .. .. .. .. .. 0.7 3.7 1.2 3.8 16.5 11.0 57.4 154.9 162.7 .. .. .. 0.1 3.1 .. 1.5 3.6 14.1 9.2 89.6 15.9 132.9 141.9 137.9 208.8 129.2 187.6 0.0 1.1 0.4 0.1 184.7 .. 0.0 4.5 0.9 16.2 24.8 29.5 .. 75.4 14.7 .. .. .. 1.6 2.0 .. 15.3 15.3 .. 106.4 w 116.3 w 133.4 w .. 15.3 48.6 67.7 58.4 103.0 34.2 86.4 44.8 76.5 51.4 116.1 67.0 58.3 102.8 113.3 68.4 154.3 42.7 61.6 121.1 22.9 28.4 46.4 7.5 39.3 19.4 52.6 111.6 55.4 46.6 37.3 37.2 123.5 122.4 143.0 47.1 120.5 110.4
Listed domestic companies
number 2005
3,747 296 .. 77 .. 864 .. 685 209 116 .. 388 .. 3,300 239 .. 6 252 263 .. .. 6 504 .. .. 37 46 302 .. 5 221 79 2,759 5,143 9 114 50 33 28 .. 15 79 50,936 s .. 19,925 8,741 11,184 20,466 3,931 6,564 1,504 1,531 6,050 911 30,470 6,737
2011
S&P/Global Equity Indices
% change 2010
1,267 –6.6a 327 21.7 .. .. 150 9.0 e .. .. 1,322 .. .. .. 462 18.4 81 5.4 a 66 –20.3a .. .. 355 32.1 .. .. 3,241 –24.5 253 84.6a .. .. 5 .. 340 32.6 246 11.0 .. .. .. .. 17 .. 545 52.1 .. .. .. .. 37 0.8a 57 11.7a 362 21.4 .. .. 8 .. 195 53.8a 104 –6.8 a 2,001 5.2 f 4,171 12.8g 6 .. 132 .. 36 .. 301 0.5a 45 .. .. .. 20 17.4 a 75 .. 49,553 s 602 18,737 8,653 10,084 19,339 5,181 4,368 1,446 1,012 6,400 932 30,214 6,250
2011
–18.2a –23.4 .. –3.9e .. .. .. –21.2 3.0a –30.7a .. –17.4 .. –16.8 –23.0a .. .. –18.5 –9.4 .. .. .. –4.7 .. .. 26.7a –13.4 a –37.0 .. .. –36.3a –16.5a –6.1f 0.0 g .. .. .. –26.8a .. .. –1.3a ..
a. Refers to the S&P Frontier BMI index. b. Refers to the CAC 40 index. c. Refers to the DAX index. d. Refers to the Nikkei 225 index. e. Refers to the Saudi Arabia country index. f. Refers to the FTSE 100 index. g. Refers to the S&P 500 index.
298
2012 World Development Indicators
About the data
5.4
STATES AND MARKETS
Stock markets Definitions
The development of an economy’s financial markets is
size of the stock market in U.S. dollars and as a
• Market capitalization (also known as market
closely related to its overall development. Well function-
percentage of GDP. The number of listed domestic
value) is the share price times the number of shares
ing financial systems provide good and easily acces-
companies is another measure of market size. Mar-
outstanding. • Market liquidity is the total value
sible information. That lowers transaction costs, which
ket size is positively correlated with the ability to
of shares traded during the period divided by gross
in turn improves resource allocation and boosts eco-
mobilize capital and diversify risk.
domestic product (GDP). This indicator complements
nomic growth. Both banking systems and stock mar-
Market liquidity, the ability to easily buy and sell secu-
the market capitalization ratio by showing whether
kets enhance growth, the main factor in poverty reduc-
rities, is measured by dividing the total value of shares
market size is matched by trading. • Turnover ratio
tion. At low levels of economic development commercial
traded by GDP. The turnover ratio—the value of shares
is the total value of shares traded during the period
banks tend to dominate the financial system, while at
traded as a percentage of market capitalization—is
divided by the average market capitalization for the
higher levels domestic stock markets tend to become
also a measure of liquidity as well as of transaction
period. Average market capitalization is calculated as
more active and efficient relative to domestic banks.
costs. (High turnover indicates low transaction costs.)
the average of the end-of-period values for the cur-
Open economies with sound macroeconomic poli-
The turnover ratio complements the ratio of value
rent period and the previous period. • Listed domes-
cies, good legal systems, and shareholder protection
traded to GDP, because the turnover ratio is related to
tic companies are the domestically incorporated
attract capital and therefore have larger financial mar-
the size of the market and the value traded ratio to the
companies listed on the country’s stock exchanges
kets. Recent research on stock market development
size of the economy. A small, liquid market will have
at the end of the year. This indicator does not include
shows that modern communications technology and
a high turnover ratio but a low value of shares traded
investment companies, mutual funds, or other col-
increased financial integration have resulted in more
ratio. Liquidity is an important attribute of stock mar-
lective investment vehicles. • S&P/Global Equity
cross-border capital flows, a stronger presence of
kets because, in theory, liquid markets improve the allo-
Indices measure the U.S. dollar price change in the
financial firms around the world, and the migration of
cation of capital and enhance prospects for long-term
stock markets.
stock exchange activities to international exchanges.
economic growth. A more comprehensive measure of
Many firms in emerging markets now cross-list on inter-
liquidity would include trading costs and the time and
national exchanges, which provides them with lower
uncertainty in finding a counterpart in settling trades.
cost capital and more liquidity-traded shares. However,
S&P Indices, the source for all the data in the
this also means that exchanges in emerging markets
table, provides regular updates on 21 emerging stock
may not have enough financial activity to sustain them,
markets and 36 frontier markets. S&P Indices main-
putting pressure on them to rethink their operations.
tains a series of indexes for investors interested in
The indicators in the table are from Standard &
investing in stock markets in developing countries.
Poor’s Emerging Markets Data Base. They include
The S&P/IFCI index, S&P Indices’s leading emerging
measures of size (market capitalization, number
markets index, is designed to be sufficiently invest-
of listed domestic companies) and liquidity (value
able to support index tracking portfolios in emerging
of shares traded as a percentage of gross domes-
market stocks that are legally and practically open to
tic product, value of shares traded as a percentage
foreign portfolio investment. The S&P Frontier BMI
of market capitalization). The comparability of such
measures the performance of 36 smaller and less
indicators across countries may be limited by concep-
liquid markets. The S&P Frontier BMI country indexes
tual and statistical weaknesses, such as inaccurate
aim to include all publicly listed equities represent-
reporting and differences in accounting standards.
ing an aggregate of at least 80 percent of the total
The percentage change in stock market prices in U.S.
market capitalization in each market, subject to
dollars for developing economies is from Standard &
the securities meeting size and liquidity thresholds
Poor’s Global Equity Indices (S&P IFCI) and Standard &
defined by three market size tiers. These indexes are
Poor’s Frontier Broad Market Index (BMI). The percent-
widely used benchmarks for international portfolio
age change for France, Germany, Japan, the United
management. See www.standardandpoors.com for
Kingdom, and the United States is from local stock
further information on the indexes.
Data sources
market prices. The indicator is an important measure
Because markets included in Standard & Poor’s
Data on stock markets are from Standard & Poor’s
of overall performance. Regulatory and institutional
emerging markets category vary widely in level of
Global Stock Markets Factbook 2011, which draws
factors that can affect investor confidence, such as
development, it is best to look at the entire category
on the Emerging Markets Data Base, supple-
entry and exit restrictions, the existence of a securi-
to identify the most significant market trends. And it
mented by other data from Standard & Poor’s.
ties and exchange commission, and the quality of laws
is useful to remember that stock market trends may
The firm collects data through an annual survey
to protect investors, may influence the functioning of
be distorted by currency conversions, especially when
of the world’s stock exchanges, supplemented by
stock markets but are not included in the table.
a currency has registered a significant devaluation.
information provided by its network of correspon-
Stock market size can be measured in various
About the data is based on Demirgüç-Kunt and
ways, and each may produce a different ranking of
Levine (1996), Beck and Levine (2001), and Claes-
countries. Market capitalization shows the overall
sens, Klingebiel, and Schmukler (2002).
dents and by Reuters. Data on GDP are from the World Bank’s national accounts data files.
2012 World Development Indicators
299
5.5
Financial access, stability, and efficiency Getting credit
Afghanistan Albania Algeria Angola Argentina Armenia Australia Austria Azerbaijan Bahrain Bangladesh Belarus Belgium Benin Bolivia Bosnia and Herzegovina Botswana Brazil Bulgaria Burkina Faso Burundi Cambodia Cameroon Canada Central African Republic Chad Chile China Hong Kong SAR, China Colombia Congo, Dem. Rep. Congo, Rep. Costa Rica Côte d’Ivoire Croatia Cuba Cyprus Czech Republic Denmark Dominican Republic Ecuador Egypt, Arab Rep. El Salvador Eritrea Estonia Ethiopia Finland France Gabon Gambia, The Georgia Germany Ghana Greece Guatemala Guinea Guinea-Bissau Haiti
300
Interest Domestic Ratio of bank Bank rate spread credit capital to nonperforming asset ratio loans to total provided by gross loans banking sector
Financial access and outreach
Strength of legal rights index 0–10 (weak to strong)
Depth of per 1,000 adults credit per 100,000 adults information Depositors Borrowers index with from Commercial Automated 0–6 (low commercial commercial bank teller to high) banks banks branches machines
June 2011
June 2011
6 9 3 3 4 6 9 7 6 4 7 3 7 6 1 5 7 3 8 6 3 8 6 7 6 6 6 6 10 5 3 6 3 6 6 .. 9 6 9 3 3 3 5 2 7 4 8 7 6 5 8 7 8 4 8 6 6 3
2012 World Development Indicators
0 4 3 4 6 6 5 6 5 3 2 5 4 1 6 5 4 5 6 1 1 0 2 6 2 2 5 4 5 5 0 2 5 1 5 .. 0 5 4 6 6 6 6 0 5 2 4 4 2 0 6 6 3 5 6 0 1 2
2010
2010
2010
100 .. 346 97 702 589 .. 1,376 41 .. 418 .. .. .. .. 914 496 .. 1,958 .. .. 108 72 .. 3 24 2,134 .. .. .. .. 20 .. .. .. .. .. .. .. .. .. .. .. .. 1,993 107 .. .. 95 .. 697 .. 324 .. .. .. .. 339
4 118 23 86 285 202 .. .. 256 .. 81 .. .. .. .. 251 238 194 421 .. .. 29 17 .. 1 3 310 .. .. .. .. 3 .. .. .. .. .. .. .. .. .. .. .. .. 542 2 .. .. 46 .. 377 .. 33 .. .. .. .. 12
2.2 22.6 5.2 1.3 13.3 17.5 31.6 11.4 10.4 .. 6.9 3.1 48.0 .. .. 30.9 8.6 13.8 91.2 .. .. 4.0 1.4 24.3 0.6 0.6 17.6 .. 23.6 .. .. 2.4 19.2 .. .. .. 151.5 22.5 41.1 .. .. .. .. .. 19.3 1.8 15.6 43.1 4.7 .. 18.6 17.6 5.0 41.2 36.5 .. .. 2.8
Risk premium on lending
Lending Prime lending rate minus rate minus deposit rate treasury bill rate percentage percentage points points
%
%
% of GDP
2010
2010
2010
2010
2010
2010
0.50 31.96 6.07 12.66 42.45 32.11 162.38 48.16 28.43 .. 1.93 37.17 86.37 .. .. 34.47 .. 120.62 80.53 .. .. 5.07 1.40 220.02 .. 0.36 62.51 .. .. .. .. 1.23 .. .. 100.86 .. 87.58 41.65 63.55 .. .. .. .. .. 88.09 0.31 91.72 110.07 8.62 .. 42.34 116.80 .. 78.24 .. .. .. ..
.. 8.5 .. .. 11.9 20.4 5.7 7.5 .. .. 6.5 13.7 5.0 .. 8.4 17.6 .. 11.1 10.5 .. .. .. .. 4.7 .. .. 8.3 6.1 12.3 14.3 .. .. 14.4 .. 13.9 .. 5.9 6.5 5.5 9.3 8.9 6.2 13.9 .. 9.3 .. 5.5 4.4 11.3 .. 16.9 4.3 7.9 6.9 10.3 .. .. ..
.. 13.9 .. .. 1.8 3.0 2.2 2.8 .. .. 11.2 3.5 2.8 .. 2.2 11.4 .. 3.1 11.9 .. .. .. .. 1.2 .. .. 2.7 1.1 0.8 2.9 .. .. 1.9 .. 11.2 .. 5.6 6.2 4.1 2.9 3.5 11.0 3.9 .. 5.4 .. 0.6 4.2 9.9 .. 12.5 3.3 17.6 10.4 2.1 .. .. ..
2.1 67.5 –7.4 22.9 29.2 25.8 147.6 137.5 23.5 79.3 65.9 45.7 117.1 18.1 49.4 67.2 9.4 97.8 71.0 16.4 40.1 22.7 9.0 177.6 18.8 9.3 90.3 146.4 199.0 65.7 1.0 –16.4 51.3 25.1 82.2 .. 315.8 64.7 215.3 39.6 26.4 69.4 45.1 114.4 97.6 37.1 100.8 134.4 10.2 45.4 33.8 132.0 28.3 145.5 38.5 .. 7.7 19.9
.. 6.4 6.3 9.8 1.4 10.3 3.1 .. 9.1 6.0 5.9 0.1 .. .. 8.9 4.7 5.9 31.1 7.1 .. .. .. .. 2.6 .. .. 3.0 3.1 5.0 5.7 39.7 .. 11.8 .. 8.6 .. .. 4.8 .. 7.3 .. 4.8 .. .. 6.7 3.3 .. .. .. 12.4 15.0 .. .. .. 7.9 .. .. 16.7
.. 7.0 7.7 .. .. 8.6 2.8 .. 18.9 6.4 .. .. 8.9 .. 9.8 .. .. 29.1 8.6 .. .. .. .. 2.0 .. .. .. .. 4.7 .. .. .. .. .. .. .. .. 5.0 .. .. .. 1.7 .. .. .. 7.3 .. .. .. .. 14.7 .. .. .. .. .. .. ..
Getting credit
Honduras Hungary India Indonesia Iran, Islamic Rep. Iraq Ireland Israel Italy Jamaica Japan Jordan Kazakhstan Kenya Korea, Dem. Rep. Korea, Rep. Kosovo Kuwait Kyrgyz Republic Lao PDR Latvia Lebanon Lesotho Liberia Libya Lithuania Macedonia, FYR Madagascar Malawi Malaysia Mali Mauritania Mauritius Mexico Moldova Mongolia Morocco Mozambique Myanmar Namibia Nepal Netherlands New Zealand Nicaragua Niger Nigeria Norway Oman Pakistan Panama Papua New Guinea Paraguay Peru Philippines Poland Portugal Puerto Rico Qatar
Interest Domestic Ratio of bank Bank rate spread credit capital to nonperforming asset ratio loans to total provided by gross loans banking sector
Financial access and outreach
Strength of legal rights index 0–10 (weak to strong)
Depth of per 1,000 adults credit per 100,000 adults information Depositors Borrowers index with from Commercial Automated 0–6 (low commercial commercial bank teller to high) banks banks branches machines
June 2011
June 2011
8 7 8 3 4 3 9 9 3 8 7 4 4 10 .. 8 8 4 10 4 10 4 6 7 .. 5 7 2 7 10 6 3 6 6 8 6 3 2 .. 8 7 6 10 3 6 9 7 4 6 5 5 3 7 4 9 3 8 4
6 4 4 4 4 0 5 5 5 0 6 2 5 4 .. 6 5 4 4 0 5 5 0 1 .. 6 6 0 0 6 1 1 3 6 4 4 5 4 .. 5 3 5 5 5 1 0 4 4 4 6 3 6 6 3 5 4 5 4
2010
2010
2010
.. 1,072 747 .. .. .. .. .. 1,307 .. 7,169 .. 874 370 .. 4,522 770 .. 181 44 1,286 873 291 .. .. .. .. 45 .. 1,458 .. .. 465 1,205 1,197 1,339 694 .. .. 624 .. 1,769 .. .. .. .. 529 1,012 249 .. 178 .. 436 488 .. 2,806 .. 770
.. .. 137 275 .. .. .. 927 480 .. 171 .. .. 73 .. .. 54 .. 26 4 273 276 39 .. .. .. 291 18 .. 280 .. 32 2,168 344 38 706 .. .. .. 213 .. 832 .. .. .. .. .. 401 28 .. 23 .. 139 .. .. 413 .. 315
.. 16.6 10.9 8.3 26.6 4.2 28.6 19.9 66.9 6.7 34.0 18.1 3.3 4.4 .. 18.6 17.5 .. 6.2 2.6 31.7 29.7 3.5 .. 10.5 27.3 26.6 1.6 .. 10.4 .. 4.3 23.0 15.2 10.3 53.6 21.0 3.4 .. 7.6 .. 23.2 34.7 .. .. .. 7.6 22.9 8.8 .. 1.8 .. 49.5 7.7 45.8 75.9 .. 23.4
5.5
2010
.. 56.73 .. 13.37 30.95 1.09 92.47 102.35 98.56 25.98 132.96 .. 62.75 7.27 .. 250.29 22.86 .. 7.45 4.33 69.98 38.25 7.28 .. 3.61 55.29 51.88 1.49 .. 46.38 .. .. 1,009.32 47.28 143.38 .. .. 5.70 .. 30.49 .. 58.27 72.75 .. .. .. 56.07 .. 4.40 .. 5.25 .. 25.21 14.88 52.10 197.05 .. 67.00
STATES AND MARKETS
Financial access, stability, and efficiency
Risk premium on lending
Lending Prime lending rate minus rate minus deposit rate treasury bill rate percentage percentage points points
%
%
% of GDP
2010
2010
2010
2010
2010
.. 9.8 7.1 11.4 .. .. 4.4 6.8 9.3 .. 4.8 10.5 10.9 13.2 .. 7.6 .. 12.6 .. .. 7.3 7.3 8.4 .. .. 8.9 10.6 .. .. 9.1 .. .. 7.3 10.4 16.0 .. 8.4 8.0 .. 8.4 .. 4.4 .. .. .. 3.2 6.4 13.5 9.8 12.5 .. 9.4 9.5 11.7 9.1 6.4 .. ..
.. 9.7 2.4 2.6 .. .. 8.6 1.4 7.8 .. 1.8 7.9 23.8 6.3 .. 1.9 4.4 8.9 .. .. 19.0 4.3 3.7 .. .. 19.7 9.0 .. .. 3.4 .. .. 2.8 2.0 13.3 .. 4.4 1.9 .. 2.0 .. 2.8 .. .. .. 17.2 1.5 3.3 14.7 1.1 .. 1.3 2.6 3.8 8.8 3.3 .. ..
52.3 81.7 71.1 36.5 37.2 –2.1 233.2 85.7 154.6 48.1 326.6 96.0 45.4 51.0 .. 103.2 17.8 84.0 .. 26.1 89.6 164.4 –6.2 148.9 –65.9 64.6 48.2 10.5 28.4 132.2 12.5 52.9 110.8 45.0 37.2 29.9 106.0 24.1 25.1 48.4 68.6 212.1 157.8 63.4 12.8 36.3 .. 41.3 46.3 88.0 35.7 32.5 18.0 49.2 63.6 209.2 .. 75.7
9.0 2.7 .. 6.2 0.1 8.3 .. 2.9 .. 14.1 1.1 5.5 .. 9.8 .. 1.7 10.9 2.6 27.4 19.6 7.7 2.1 7.5 10.1 3.5 3.6 2.4 38.5 21.0 2.5 .. 9.0 0.5 4.1 8.7 8.2 .. 6.6 5.0 4.7 4.4 –0.6 1.7 10.3 .. 11.1 2.0 3.5 5.9 4.7 9.1 24.8 17.4 4.5 .. .. .. 4.4
.. 2.2 .. .. .. 8.0 .. 2.3 2.9 11.2 1.5 .. .. 10.8 .. .. .. 4.3 27.0 14.6 7.1 4.2 5.0 .. .. –0.1 .. 39.7 17.5 2.4 .. 8.5 .. 0.9 9.2 .. .. 4.3 .. 3.3 1.2 .. 3.5 .. .. 13.7 .. .. 1.5 .. 5.8 .. .. 4.2 .. .. .. ..
2012 World Development Indicators
301
5.5
Financial access, stability, and efficiency Getting credit
Romania Russian Federation Rwanda Saudi Arabia Senegal Serbia Sierra Leone Singapore Slovak Republic Slovenia Somalia South Africa South Sudan Spain Sri Lanka Sudan Swaziland Sweden Switzerland Syrian Arab Republic Tajikistan Tanzania Thailand Timor-Leste Togo Trinidad and Tobago Tunisia Turkey Turkmenistan Uganda Ukraine United Arab Emirates United Kingdom United States Uruguay Uzbekistan Venezuela, RB Vietnam West Bank and Gaza Yemen, Rep. Zambia Zimbabwe World Low income Middle income Lower middle income Upper middle income Low & middle income East Asia & Pacific Europe & Central Asia Latin America & Carib. Middle East & N. Africa South Asia Sub-Saharan Africa High income Euro area
302
Interest Domestic Ratio of bank Bank rate spread credit capital to nonperforming asset ratio loans to total provided by gross loans banking sector
Financial access and outreach
Strength of legal rights index 0–10 (weak to strong)
Depth of per 1,000 adults credit per 100,000 adults information Depositors Borrowers index with from Commercial Automated 0–6 (low commercial commercial bank teller to high) banks banks branches machines
June 2011
June 2011
2010
9 3 8 5 6 8 7 10 9 4 .. 10 .. 6 4 4 6 7 8 1 2 8 5 2 6 8 3 4 .. 7 9 4 10 9 4 2 1 8 1 3 9 7 5.9 u 5.8 5.5 5.3 5.7 5.6 6.2 6.5 5.3 2.8 5.6 5.8 6.9 6.4
5 5 6 6 1 5 0 4 4 4 .. 6 .. 5 5 0 5 4 5 2 0 0 5 3 1 4 5 5 .. 4 4 5 6 6 6 3 0 5 3 2 5 0 3.2 u 1.3 3.3 2.8 3.7 2.8 2.0 4.7 3.4 3.2 2.8 1.8 4.3 4.1
.. .. 218 780 .. .. 190 2,134 .. .. .. 978 .. 775 .. .. 455 .. .. 220 .. 131 1,120 .. 181 .. .. 1,265 .. 192 3,220 .. .. .. 538 957 .. .. 543 101 .. .. .. w .. .. .. .. .. .. 894 .. 443 249 167 .. ..
2012 World Development Indicators
2010
222 .. 29 181 .. 186 12 968 .. .. .. 415 .. 337 .. .. 185 .. .. 67 .. 30 237 .. 0 .. 148 719 .. 18 .. .. .. .. 426 37 .. .. 68 2 .. .. .. w .. 167 .. 238 .. 221 212 .. 66 28 27 .. ..
2010
33.2 .. 2.2 9.0 .. 10.2 2.9 10.3 26.5 39.5 .. 10.1 .. 38.3 .. .. 5.7 .. 52.8 3.8 .. 1.8 11.2 .. 3.7 .. 16.6 17.4 .. 2.5 2.3 21.7 25.5 35.7 13.5 47.5 .. 3.3 10.3 2.0 .. .. 13.7 w .. 12.2 5.6 16.6 8.1 7.4 18.1 .. 10.9 7.8 2.7 23.9 24.9
2010
55.48 76.58 0.81 57.97 .. 47.43 0.43 58.57 50.38 103.04 .. 59.58 .. 153.63 .. .. 21.43 .. 97.47 7.35 .. 3.30 77.69 .. .. .. 20.84 43.74 .. 3.58 76.13 96.81 64.58 173.75 30.38 2.52 .. 17.64 13.68 2.75 .. .. 44.00 w .. 29.02 9.77 47.36 18.23 14.09 44.89 .. 6.84 1.93 .. 90.08 91.72
%
%
% of GDP
2010
2010
2010
8.9 14.0 11.4 12.6 10.0 19.7 17.5 9.6 9.7 8.2 .. 7.0 .. 6.2 .. .. 12.2 5.0 5.4 .. .. .. 11.3 .. .. .. .. 13.4 .. 13.9 14.6 17.7 5.4 11.1 9.5 .. 9.9 .. .. .. .. .. 9.4 m .. 10.2 .. 10.4 .. .. 13.6 10.1 .. 7.3 .. 6.8 6.2
11.9 8.2 10.8 3.0 20.2 16.9 15.6 1.8 5.8 3.6 .. 5.8 .. 4.6 .. .. 8.0 2.0 0.4 .. .. .. 3.9 .. .. .. 12.1 3.8 .. 2.1 15.3 5.6 4.0 4.9 1.0 .. 3.4 .. .. .. .. .. 4.0 m .. 3.9 .. 3.7 3.9 .. 12.2 2.4 .. 10.5 .. 4.1 4.4
54.9 38.6 .. –0.2 29.0 57.8 18.4 85.7 54.0 97.4 .. 182.2 .. 231.4 40.5 20.5 17.2 143.5 191.1 47.7 .. 21.1 135.5 –31.6 31.9 32.4 73.7 69.3 .. 17.1 78.6 92.3 222.6 231.4 32.4 .. 22.6 135.8 .. 19.3 18.9 .. 167.9 w 37.2 90.8 57.1 100.5 90.0 131.8 50.8 66.7 51.9 67.4 81.6 205.6 156.1
Risk premium on lending
Lending Prime lending rate minus rate minus deposit rate treasury bill rate percentage percentage points points 2010
2010
6.8 4.8 9.6 .. .. 6.0 12.3 5.2 2.0 4.5 .. 3.4 .. .. 3.3 .. 5.9 .. 2.7 3.7 18.0 8.0 4.9 10.2 .. 7.8 .. .. .. 12.5 5.3 .. .. .. 6.2 .. 3.5 1.9 .. 5.2 13.5 .. 6.2 m 11.4 6.3 8.7 5.8 7.3 6.9 6.9 7.3 5.0 5.9 9.7 .. ..
6.9 .. 9.2 .. .. 3.1 4.0 5.0 .. 4.8 .. 3.4 .. .. 1.7 .. 3.1 .. 2.7 .. .. 10.7 4.5 .. .. 8.4 .. .. .. 15.2 .. .. 0.0 3.1 1.3 .. .. 2.0 .. 2.9 14.6 ..
5.5
STATES AND MARKETS
Financial access, stability, and efficiency About the data
Access to finance can expand opportunities for all
identifying problems with asset quality in the loan
nonfinancial corporations (public and private) and
with higher levels of access and use of banking ser-
portfolio. A high ratio may signal deterioration of the
households that obtained loans from commercial
vices associated with lower financing obstacles for
credit portfolio. International guidelines recommend
banks and other banks functioning as commercial
people and businesses. A stable financial system
that loans be classified as nonperforming when pay-
banks. For many countries data cover the total num-
that promotes efficient savings and investment is
ments of principal and interest are 90 days or more
ber of loan accounts due to lack of information on
also crucial for a thriving democracy and market
past due or when future payments are not expected
loan account holders. • Commercial bank branches
economy.
to be received in full.
are retail locations of resident commercial banks and
There are several aspects of access to financial
Domestic credit provided by the banking sector as
other resident banks that function as commercial
services: availability, cost, and quality of services.
a share of GDP measures banking sector depth and
banks that provide financial services to customers
The development and growth of credit markets
financial sector development in terms of size. In a
and are physically separated from the main office
depend on access to timely, reliable, and accurate
few countries governments may hold international
but not legally separated subsidiaries. • Automated
data on borrowers’ credit experiences. Access to
reserves as deposits in the banking system rather
teller machines are computerized telecommunica-
credit can be improved by making it easy to create
than in the central bank. Since claims on the central
tions devices that provide clients of a financial insti-
and enforce collateral agreements and by increasing
government are a net item (claims on the central
tution access to financial transactions in a public
information about potential borrowers’ creditworthi-
government minus central government deposits), the
place. • Bank capital to asset ratio is the ratio of
ness. Lenders look at a borrower’s credit history
figure may be negative, resulting in a negative figure
bank capital and reserves to total assets. Capital
and collateral. Where credit registries and effective
for domestic credit provided by the banking sector.
and reserves include funds contributed by owners,
collateral laws are absent—as in many developing
The interest rate spread—the margin between
retained earnings, general and special reserves, pro-
countries —banks make fewer loans. Indicators that
the cost of mobilizing liabilities and the earnings on
visions, and valuation adjustments. • Ratio of bank
cover getting credit include the strength of legal
assets—measures financial sector efficiency in inter-
nonperforming loans to total gross loans is the value
rights index and the depth of credit information index.
mediation. A narrow spread means low transaction
of nonperforming loans (gross value of the loan as
The “unbanked” have to resort to informal services
costs, which reduces the cost of funds for invest-
recorded on the balance sheet) divided by the total
ment, crucial to economic growth.
value of the loan portfolio (including nonperforming
to manage their money—saving under the mattress, borrowing from family and friends, or using money
The risk premium on lending is the spread between
loans before the deduction of loan loss provisions).
lenders—that are usually less reliable and more
the lending rate to the private sector and the “risk-
• Domestic credit provided by banking sector is all
costly than formal banking institutions. The table
free” government rate. Spreads are expressed as an
credit to various sectors on a gross basis, except to
presents data on financial access covering deposi-
annual average. A small spread indicates that the
the central government, which is net. The banking
tors and borrowers and on outreach indicators such
market considers its best corporate customers to be
sector includes monetary authorities, deposit money
as the number of branches and automated teller
low risk; a negative value indicates that the market
banks, and other banking institutions for which data
machines. Data for these financial access and out-
considers its best corporate clients to be lower risk
are available. • Interest rate spread is the interest
reach indicators are from the International Monetary
than the government.
rate charged by banks on loans to prime customers
Fund’s (IMF) Financial Access Survey database; this sources differs from that in the 2011 edition.
minus the interest rate paid by commercial or similar Definitions
banks for demand, time, or savings deposits. • Risk
The size and mobility of international capital
• Strength of legal rights index measures the degree
premium on lending is the interest rate charged by
flows make it increasingly important to monitor the
to which collateral and bankruptcy laws protect the
banks on loans to prime private sector customers
strength of financial systems. Robust financial sys-
rights of borrowers and lenders and thus facilitate
minus the “risk-free” treasury bill interest rate at
tems can increase economic activity and welfare, but
lending. Higher values indicate that the laws are bet-
which short-term government securities are issued
instability can disrupt financial activity and impose
ter designed to expand access to credit. • Depth of
or traded in the market.
widespread costs on the economy. The ratio of bank
credit information index measures rules affecting
capital to assets, a measure of bank solvency and
the scope, accessibility, and quality of information
resiliency, shows the extent to which banks can deal
available through public or private credit registries.
with unexpected losses. Capital includes tier 1 capi-
Higher values indicate the availability of more credit
tal (paid-up shares and common stock), a common
information. • Depositors with commercial banks
Data on getting credit are from the World Bank’s
feature in all countries’ banking systems, and total
are deposit account holders at commercial banks
Doing Business project (www.doingbusiness.
regulatory capital, which includes several types of
and other resident banks functioning as commercial
org). Data on financial access and outreach are
subordinated debt instruments that need not be
banks that are resident nonfinancial corporations
from the IMF’s Financial Access Survey database
repaid if the funds are required to maintain minimum
(public and private) and households. For many coun-
(http://fas.imf.org). Data on bank capital and
capital levels (tier 2 and tier 3 capital). Total assets
tries data cover the total number of deposit accounts
nonperforming loans are from the IMF’s Financial
include all nonfinancial and financial assets. Data
due to lack of information on account holders. The
Soundness Indicators database (http://fsi.imf.
are from internally consistent financial statements.
major types of deposits are checking accounts, sav-
org). Data on credit and interest rates are from
The ratio of bank nonperforming loans to total
ings accounts, and time deposits. • Borrowers from
the IMF’s International Financial Statistics.
gross loans measures bank health and efficiency by
commercial banks are resident customers that are
Data sources
2012 World Development Indicators
303
5.6
Tax policies Tax revenue collected by central government
% of GDP
Afghanistan Albania Algeria Angola Argentina Armenia Australia Austria Azerbaijan Bahrain Bangladesh Belarus Belgium Benin Bolivia Bosnia and Herzegovina Botswana Brazil Bulgaria Burkina Faso Burundi Cambodia Cameroon Canada Central African Republic Chad Chile China Hong Kong SAR, China Colombia Congo, Dem. Rep. Congo, Rep. Costa Rica Côte d’Ivoire Croatia Cuba Cyprus Czech Republic Denmark Dominican Republic Ecuador Egypt, Arab Rep. El Salvador Eritrea Estonia Ethiopia Finland France Gabon Gambia, The Georgia Germany Ghana Greece Guatemala Guinea Guinea-Bissau Haiti
304
2005
2010
6.3 17.3 40.7a .. 14.2a 14.3 24.7a 20.1a .. 1.4 a 8.2 20.1 26.0a 15.5a 16.2a 20.7a .. 16.7 21.6 11.8a .. 7.9a .. 13.7a 6.2 .. 18.7a 8.7 12.7a 12.8a 10.0 6.2 .. 9.8 a 20.0 .. 45.4 a 15.6 32.6a 14.6a .. 14.1 12.5a .. 16.1a 8.8 22.6a 22.4 a .. .. 12.1 11.1a 21.3 20.3a 11.2 .. .. ..
8.3 .. 34.9a .. .. 16.9 22.1a 18.7a 15.9 .. 8.6 17.1 24.6a 16.2a .. 20.4 a .. 15.6 21.0 13.0a .. 10.1a .. 11.9a .. .. 17.8a 10.5 12.8a 11.5a 13.7 .. 13.9a 16.6a 18.8 .. 25.8a 13.9 34.3a 13.1a .. 14.1 13.7a .. 17.4a .. 21.2a 19.8a .. .. 22.1 12.2a 12.6 19.6a 10.3 .. .. ..
2012 World Development Indicators
Taxes payable by businesses
% of commercial profi ts
Number of payments
Time to prepare, file, and pay taxes hours
Profi t tax
Labor tax and contributions
Other taxes
Total tax rate
June 2011
June 2011
June 2011
June 2011
June 2011
June 2011
275 371 451 282 415 500 109 170 225 36 302 654 156 270 1,080 422 152 2,600 500 270 274 173 654 131 504 732 316 398 80 193 336 606 246 270 196 .. 149 557 135 324 654 433 320 216 85 198 93 132 488 376 387 221 224 224 344 416 208 184
0.0 8.7 6.6 24.6 2.8 16.8 26.0 15.0 12.9 0.0 25.7 20.2 5.2 14.8 0.0 7.1 15.9 22.4 4.9 14.8 37.4 18.9 29.9 9.3 0.0 31.3 18.0 5.9 17.6 18.9 58.9 18.1 18.9 8.8 11.5 .. 9.1 7.5 20.1 21.3 18.4 13.0 16.5 8.8 8.0 26.8 13.7 8.2 18.4 6.1 14.3 19.0 18.4 13.4 25.9 20.9 14.9 24.1
0.0 25.0 29.7 9.0 29.4 23.0 20.4 34.8 24.8 14.7 0.0 39.0 50.4 27.3 15.5 12.6 0.0 40.9 19.2 22.6 7.8 0.1 18.3 12.6 19.8 28.4 3.8 49.6 5.3 28.8 7.9 32.5 29.5 20.1 19.4 .. 11.8 38.4 3.6 18.6 14.2 27.1 17.2 0.0 39.4 0.0 24.2 51.7 22.7 12.8 0.0 21.8 14.7 31.7 14.3 22.8 24.8 12.4
36.4 4.8 35.7 19.5 76.1 1.1 1.3 3.4 2.2 0.4 9.2 3.5 1.7 23.9 64.6 5.3 3.6 3.8 4.1 6.2 1.0 3.5 0.9 6.8 34.8 5.7 3.2 7.9 0.1 27.1 272.8 15.4 6.6 15.4 1.5 .. 2.2 3.2 3.8 1.8 2.7 3.6 1.3 75.8 11.2 4.3 1.2 5.7 2.3 264.6 2.2 5.9 0.5 1.4 0.7 10.6 6.1 4.3
36.4 38.5 72.0 53.2 108.2 40.9 47.7 53.1 40.0 15.0 35.0 62.8 57.3 66.0 80.0 25.0 19.4 67.1 28.1 43.6 46.2 22.5 49.1 28.8 54.6 65.4 25.0 63.5 23.0 74.8 339.7 65.9 55.0 44.3 32.3 .. 23.1 49.1 27.5 41.7 35.3 43.6 35.0 84.5 58.6 31.1 39.0 65.7 43.5 283.5 16.5 46.7 33.6 46.4 40.9 54.3 45.9 40.8
8 44 29 31 9 34 11 14 18 25 21 18 11 55 42 40 19 9 17 46 24 39 44 8 54 54 9 7 3 9 32 61 31 62 17 .. 27 8 10 9 8 29 53 18 8 19 8 7 26 50 4 12 33 10 24 56 46 46
Tax revenue collected by central government
% of GDP
Honduras Hungary India Indonesia Iran, Islamic Rep. Iraq Ireland Israel Italy Jamaica Japan Jordan Kazakhstan Kenya Korea, Dem. Rep. Korea, Rep. Kosovo Kuwait Kyrgyz Republic Lao PDR Latvia Lebanon Lesotho Liberia Libya Lithuania Macedonia, FYR Madagascar Malawi Malaysia Mali Mauritania Mauritius Mexico Moldova Mongolia Morocco Mozambique Myanmar Namibia Nepal Netherlands New Zealand Nicaragua Niger Nigeria Norway Oman Pakistan Panama Papua New Guinea Paraguay Peru Philippines Poland Portugal Puerto Rico Qatar
2005
2010
14.5a 20.3a 9.9 12.5 7.9 .. 24.9a 26.9a 21.1a 25.5a 10.9a 24.4 17.1 18.7 .. 14.7 .. 1.0 14.2 10.4 15.1 14.9a 46.6 0.2 .. 17.3a 19.3 10.1a .. 15.4 15.7a .. .. .. 18.5 28.7 22.0a .. 3.9 25.8 9.2 22.6a 30.8b 16.7 10.1b 0.2 28.7a .. 9.6 .. .. 11.8 13.5 12.4 16.7a 20.6a .. 21.0
14.8a 23.9a 9.5 10.9 9.3 .. 21.2a 24.3a 22.9a 21.0a 8.7a 15.3 8.9 19.5 .. 15.2 .. 0.9 15.6 12.7 12.8 17.1a 59.8 0.3 .. 13.4 a 19.1 13.0a .. 14.3 14.7a .. 18.5a .. 18.2 22.7 23.4 a .. .. .. 13.3 22.6a .. 18.3 .. 0.3 26.9a .. 10.0 .. .. 13.1 14.5 12.1 16.3a 19.6a .. 19.8
STATES AND MARKETS
5.6
Tax policies Taxes payable by businesses
% of commercial profi ts
Number of payments
Time to prepare, file, and pay taxes hours
Profi t tax
Labor tax and contributions
Other taxes
Total tax rate
June 2011
June 2011
June 2011
June 2011
June 2011
June 2011
224 277 254 266 344 312 76 235 285 414 330 116 188 393 .. 225 164 118 210 362 290 180 324 158 .. 175 119 201 157 133 270 696 161 347 228 192 238 230 .. 375 326 127 172 207 270 938 87 62 560 482 194 387 309 195 296 275 218 36
24.7 14.8 24.7 23.7 17.8 14.9 11.9 22.8 22.8 25.6 27.0 13.0 15.9 33.1 .. 15.1 9.1 4.7 6.2 24.8 6.1 6.1 13.1 0.0 .. 5.7 6.3 14.7 23.6 17.0 10.8 0.0 11.6 24.5 0.0 10.2 25.2 27.7 .. 4.0 17.2 20.9 29.9 24.5 17.3 22.3 24.4 10.0 17.9 13.7 22.0 9.6 26.6 21.0 17.4 15.1 28.3 0.0
10.7 34.1 18.2 10.6 25.9 13.5 11.6 5.3 43.4 13.0 16.5 12.4 11.2 6.8 .. 13.0 5.6 10.7 19.5 5.6 27.2 24.1 0.0 5.4 .. 35.1 0.0 20.3 1.1 15.6 34.3 17.6 6.1 26.8 30.6 12.4 22.7 4.5 .. 1.0 11.3 18.1 2.9 20.3 20.1 9.7 15.9 11.8 15.1 21.7 11.7 18.6 11.0 11.3 23.6 26.8 14.4 11.3
8.6 3.5 19.0 0.1 0.4 0.0 2.7 3.1 2.2 7.0 5.7 2.3 1.6 9.7 .. 1.5 0.6 0.0 43.4 2.9 4.7 0.0 3.0 38.3 .. 3.1 3.4 1.6 3.5 1.4 6.6 50.7 7.3 1.4 0.7 2.0 1.8 2.1 .. 4.8 3.0 1.5 1.7 22.0 6.3 0.7 1.3 0.1 2.3 9.7 8.6 6.7 3.1 14.2 2.6 1.5 20.5 0.0
44.0 52.4 61.8 34.5 44.1 28.4 26.3 31.2 68.5 45.6 49.1 27.7 28.6 49.6 .. 29.7 15.4 15.5 69.0 33.3 37.9 30.2 16.0 43.7 .. 43.9 9.7 36.6 28.2 34.0 51.8 68.3 25.0 52.7 31.3 24.6 49.6 34.3 .. 9.8 31.5 40.5 34.4 66.8 43.8 32.7 41.6 22.0 35.3 45.2 42.3 35.0 40.7 46.5 43.6 43.3 63.1 11.3
47 13 33 51 20 13 8 33 15 72 14 25 7 41 .. 12 33 15 52 34 7 19 21 33 .. 11 28 23 19 13 59 37 7 6 48 41 17 37 .. 37 34 9 8 42 41 35 4 14 47 53 33 35 9 47 29 8 16 3
2012 World Development Indicators
305
5.6
Tax policies Tax revenue collected by central government
% of GDP
Romania Russian Federation Rwanda Saudi Arabia Senegal Serbia Sierra Leone Singapore Slovak Republic Slovenia Somalia South Africa South Sudan Spain Sri Lanka Sudan Swaziland Sweden Switzerland Syrian Arab Republic Tajikistan Tanzania Thailand Timor-Leste Togo Trinidad and Tobago Tunisia Turkey Turkmenistan Uganda Ukraine United Arab Emirates United Kingdom United States Uruguay Uzbekistan Venezuela, RB Vietnam West Bank and Gaza Yemen, Rep. Zambia Zimbabwe World Low income Middle income Lower middle income Upper middle income Low & middle income East Asia & Pacific Europe & Central Asia Latin America & Carib. Middle East & N. Africa South Asia Sub-Saharan Africa High income Euro area
2005
2010
12.2a 16.6a .. .. .. 23.6 10.8 11.8 14.9a 20.5 .. 26.9a .. 12.9a 13.7 .. 26.1 22.6a 10.3 .. 9.8 .. 17.2a .. 13.9a 26.4 18.9 19.7a .. 11.8 17.1 .. 27.2a 11.2a 17.9 .. 15.5 .. .. .. 17.2 .. 15.4 w 10.7 12.9 11.1 13.5 12.8 10.0 16.5 .. 18.7 9.9 17.5 16.0 18.1
17.9a 13.4 a .. .. .. 22.0 11.0 13.8 12.4a 17.1 .. 25.5a .. 11.2a 13.3 .. .. 21.6a 10.9 .. .. .. 16.0a .. 15.8a 26.2 20.1 20.5a .. 12.2 16.4 .. 26.0a 9.3a 19.5 .. .. .. .. .. 16.6 .. 13.5 w 11.8 13.0 10.9 13.4 13.0 11.0 15.5 .. 17.5 9.5 .. 13.6 17.2
Taxes payable by businesses
% of commercial profi ts
Number of payments
Time to prepare, file, and pay taxes hours
Profi t tax
Labor tax and contributions
Other taxes
Total tax rate
June 2011
June 2011
June 2011
June 2011
June 2011
June 2011
222 290 148 79 666 279 357 84 231 260 .. 200 .. 187 256 180 104 122 63 336 224 172 264 276 270 210 144 223 .. 213 657 12 110 187 336 205 864 941 154 248 132 242 277 u 271 327 324 330 314 232 314 404 253 281 314 169 171
10.4 8.9 21.2 2.1 14.8 11.6 17.6 6.5 7.2 14.1 .. 24.4 .. 1.2 26.7 13.8 28.1 15.7 8.9 20.0 0.0 20.1 28.8 0.0 9.3 21.6 15.2 17.9 .. 23.3 12.2 0.0 23.1 27.6 23.6 1.1 6.9 17.2 16.2 20.0 1.5 20.3 16.0 u 18.7 16.5 16.0 17.0 17.0 17.9 9.2 20.2 15.5 18.6 18.5 13.0 11.8
31.8 32.1 5.7 12.4 24.1 20.2 11.3 15.9 39.6 18.2 .. 4.1 .. 36.7 16.9 19.2 4.0 35.5 17.5 19.3 28.5 18.0 5.7 0.0 26.5 5.8 25.2 18.8 .. 11.3 43.3 14.1 11.0 10.0 15.6 28.2 18.0 22.6 0.0 11.3 10.4 5.1 16.2 u 13.0 15.5 13.7 17.3 14.9 10.5 22.2 15.2 19.1 7.7 13.2 20.2 29.7
2.2 5.8 4.4 0.0 7.0 2.2 3.3 4.7 2.0 2.4 .. 4.6 .. 0.7 61.6 3.1 4.7 1.6 3.6 0.5 56.0 7.3 3.0 0.2 13.7 1.8 22.5 4.4 .. 1.1 1.6 0.0 3.2 9.1 2.9 68.1 38.5 0.3 0.6 1.5 2.5 10.1 12.6 u 36.1 9.2 10.7 7.5 15.5 7.9 10.2 12.4 6.0 18.2 25.6 4.2 3.0
44.4 46.9 31.3 14.5 46.0 34.0 32.1 27.1 48.8 34.7 .. 33.1 .. 38.7 105.2 36.1 36.8 52.8 30.1 39.7 84.5 45.5 37.5 0.2 49.5 29.1 62.9 41.1 .. 35.7 57.1 14.1 37.3 46.7 42.0 97.5 63.5 40.1 16.8 32.9 14.5 35.6 44.8 u 67.8 41.1 40.4 41.9 47.4 36.2 41.6 47.8 40.6 44.4 57.3 37.4 44.5
113 9 18 14 59 66 29 5 31 22 .. 9 .. 8 71 42 33 4 19 19 69 48 23 6 53 39 8 15 .. 32 135 14 8 11 53 41 70 32 27 44 37 49 29 u 38 32 36 26 33 27 39 33 24 28 37 15 14
Note: Regional aggregates differ from those reported on the Doing Business website because the regional aggregates reported on the Doing Business website include developed countries. a. Data were reported on a cash basis and have been adjusted to the accrual framework of the International Monetary Fundâ&#x20AC;&#x2122;s Government Finance Statistics Manual 2001.
306
2012 World Development Indicators
About the data
5.6
STATES AND MARKETS
Tax policies Definitions
Taxes are the main source of revenue for most
To make the data comparable across countries,
• Tax revenue collected by central government
governments. The sources of tax revenue and their
several assumptions are made about businesses.
is compulsory transfers to the central government
relative contributions are determined by government
The main assumptions are that they are limited liabil-
for public purposes. Certain compulsory transfers
policy choices about where and how to impose taxes
ity companies, they operate in the country’s most
such as fines, penalties, and most social security
and by changes in the structure of the economy. Tax
populous city, they are domestically owned, they per-
contributions are excluded. Refunds and corrections
policy may refl ect concerns about distributional
form general industrial or commercial activities, and
of erroneously collected tax revenue are treated as
effects, economic efficiency (including corrections
they have certain levels of start-up capital, employ-
negative revenue. The analytic framework of the
for externalities), and the practical problems of
ees, and turnover. For details about the assump-
International Monetary Fund’s (IMF) Government
administering a tax system. There is no ideal level
tions, see the World Bank’s Doing Business 2012.
Finance Statistics Manual 2001 (GFSM 2001) is
of taxation. But taxes influence incentives and thus
The Doing Business methodology on business
based on accrual accounting and balance sheets.
the behavior of economic actors and the economy’s
taxes is consistent with the Total Tax Contribution
For countries still reporting government finance data
competitiveness.
framework developed by PwC, which measures the
on a cash basis, the IMF adjusts reported data to the
The level of taxation is typically measured by tax
taxes that are borne by companies and that affect
GFSM 2001 accrual framework. These countries are
revenue as a share of gross domestic product (GDP).
their income statements. However, PwC bases its
footnoted in the table. • Number of tax payments
Comparing levels of taxation across countries pro-
calculation on data from the largest companies in
by businesses is the total number of taxes paid by
vides a quick overview of the fiscal obligations and
the economy, while Doing Business focuses on a
businesses during one year. When electronic filing is
incentives facing the private sector. The table shows
standardized medium-size company.
available, the tax is counted as paid once a year even
only central government data, which may significantly
if payments are more frequent. • Time to prepare,
understate the total tax burden, particularly in coun-
file, and pay taxes is the time, in hours per year, it
tries where provincial and municipal governments are
takes to prepare, file, and pay (or withhold) three
large or have considerable tax authority.
major types of taxes: the corporate income tax, the
Low ratios of tax revenue to GDP may reflect weak
value-added or sales tax, and labor taxes, includ-
administration and large-scale tax avoidance or eva-
ing payroll taxes and social security contributions.
sion. Low ratios may also reflect a sizable parallel
• Profit tax is the amount of taxes on profits paid
economy with unrecorded and undisclosed incomes.
by businesses. • Labor tax and contributions are
Tax revenue ratios tend to rise with income, with
the amount of taxes and mandatory contributions
higher income countries relying on taxes to finance
on labor paid by businesses. • Other taxes are the
a much broader range of social services and social
amounts paid by businesses for property taxes, turn-
security than lower income countries are able to.
over taxes, and other small taxes such as municipal
The total tax rate payable by businesses provides
fees and vehicle and fuel taxes. • Total tax rate is
a comprehensive measure of the cost of all the taxes
the amount of taxes and mandatory contributions
a business bears. It differs from the statutory tax
payable by businesses in the second year of opera-
rate, which is the factor applied to the tax base. In
tion, expressed as a share of commercial profi ts.
computing business tax rates, actual tax payable is
Taxes withheld (such as sales or value added tax
divided by commercial profit.
or personal income tax) but not paid by businesses
The indicators covering taxes payable by busi-
are excluded. For further details on the method used
nesses measure all taxes and contributions that
for assessing the total tax payable, see the World
are government mandated (at any level—federal,
Bank’s Doing Business 2012.
state, or local), apply to standardized businesses, and have an impact in their income statements. The taxes covered go beyond the definition of a tax for government national accounts (compulsory, unrequited payments to general government) and also measure any imposts that affect business accounts. The main differences are in labor contributions and value added taxes. The indicators account for
Data sources
government-mandated contributions paid by the
Data on central government tax revenue are from
employer to a requited private pension fund or work-
print and electronic editions of the IMF’s Govern-
ers insurance fund but exclude value added taxes
ment Finance Statistics Yearbook. Data on taxes
because they do not affect the accounting profits of
payable by businesses are from Doing Business
the business —that is, they are not reflected in the
2012 (www.doingbusiness.org).
income statement.
2012 World Development Indicators
307
5.7
Military expenditures and arms transfers Military expenditures
% of GDP
Afghanistan Albania Algeria Angola Argentina Armenia Australia Austria Azerbaijan Bahrain Bangladesh Belarus Belgium Benin Bolivia Bosnia and Herzegovina Botswana Brazil Bulgaria Burkina Faso Burundi Cambodia Cameroon Canada Central African Republic Chad Chile China Hong Kong SAR, China Colombia Congo, Dem. Rep. Congo, Rep. Costa Rica Côte d’Ivoire Croatia Cuba Cyprus Czech Republic Denmark Dominican Republic Ecuador Egypt, Arab Rep. El Salvador Eritrea Estonia Ethiopia Finland France Gabon Gambia, The Georgia Germany Ghana Greece Guatemala Guinea Guinea-Bissau Haiti
308
Armed forces personnel
% of central government expenditure
thousands
Arms transfers
% of labor force
Trend indicator values 1990 $ millions Exports Imports
2005
2010
2005
2010
2005
2010
2005
2010
2005
2010
2005
2010
1.6 1.3 2.9 4.5 0.9 2.9 1.9 0.9 2.3 3.6 1.2 1.5 1.1 1.1 1.9 1.6 2.9 1.5 2.4 1.2 6.2 1.1 1.3 1.1 1.1 1.0 3.6 2.1a .. 3.4 2.3 1.3 .. 1.5 1.6 3.8 2.2 2.0 1.3 0.8 2.6 2.9 0.6 20.9 1.9 2.8 1.4 2.5 1.3 0.6 3.3 1.4 0.6 2.9 0.4 2.2 2.1 ..
1.8 1.6 3.5 4.4 0.9 4.5 2.0 0.9 2.9 3.7 1.2 1.3 1.1 1.0 1.6 1.4 2.7 1.6 1.4 1.5 3.8 1.8 1.6 1.4 2.6 3.0 3.2 2.0a .. 3.7 1.4 1.1 .. 1.6 1.7 3.2 2.2 1.3 1.4 0.6 3.8 2.0 0.6 .. 1.7 1.1 1.5 2.3 1.0 .. 3.9 1.4 0.4 3.1 0.4 .. .. ..
8.7 6.2 13.8 .. 5.2 15.8 7.3 2.2 .. 17.6 12.4 5.0 2.5 8.1 7.2 4.7 .. 6.0 7.6 10.2 .. 14.8 .. 6.3 12.3 .. 20.0 15.3a .. 12.7 10.5 8.8 .. 9.0 4.7 .. 3.1 5.4 4.1 5.6 .. 10.4 3.6 .. 7.2 18.6 3.9 5.4 .. .. 19.3 4.4 2.6 6.9 2.9 .. .. ..
4.6 .. 15.0 .. .. 19.8 7.3 2.2 22.5 .. 10.2 4.2 2.5 6.8 .. 3.4 .. 6.3 6.3 12.4 .. 16.6 .. 7.5 .. .. 15.5 16.1a .. 20.3 10.2 .. .. 8.8 4.6 .. 4.7 3.6 3.3 4.4 .. 6.9 3.0 .. 6.2 .. 3.8 5.3 .. .. 14.8 4.5 2.4 6.3 3.1 .. .. ..
27 23 319 118 102 49 53 40 82 21 252 183 37 8 70 12 11 673 85 11 82 191 23 71 3 35 116 3,755 .. 336 65 12 10 19 31 76 11 28 21 40 47 799 16 202 8 183 31 359 7 1 23 285 7 168 48 13 9 5
307 15 317 117 104 56 57 26 82 19 221 183 36 7 83 11 11 713 65 11 51 191 23 66 3 35 104 2,945 .. 442 159 12 10 19 22 76 13 29 19 40 59 836 33 202 6 138 25 342 7 1 32 251 16 150 34 19 6 0
0.4 1.6 3.2 1.9 0.6 3.4 0.5 1.0 2.0 6.2 0.4 3.9 0.8 0.3 1.7 0.9 1.2 0.7 2.5 0.2 2.3 2.8 0.3 0.4 0.2 0.9 1.7 0.5 .. 1.7 0.3 0.8 0.5 0.3 1.6 1.6 2.1 0.5 0.7 1.0 0.7 3.3 0.7 9.3 1.2 0.5 1.2 1.2 1.4 0.1 1.0 0.7 0.1 3.3 1.0 0.4 1.6 0.1
3.4 1.0 2.8 1.6 0.6 3.9 0.5 0.6 1.8 2.7 0.3 4.1 0.7 0.2 1.8 0.7 1.0 0.7 1.9 0.2 1.2 2.4 0.3 0.3 0.2 0.8 1.3 0.4 .. 2.0 0.6 0.7 0.4 0.2 1.1 1.4 2.2 0.5 0.6 0.9 0.9 3.1 1.3 7.8 0.8 0.3 0.9 1.1 1.1 0.1 1.4 0.6 0.1 2.8 0.6 0.5 1.0 0.0
.. .. .. .. 2 .. 50 3 .. .. .. 24 161 .. .. .. .. 1 66 .. .. .. .. 226 .. .. .. 303 .. .. .. .. .. .. .. .. .. 66 9 .. .. .. .. .. .. .. 27 1,724 .. .. 17 2,080 .. 13 .. .. .. ..
.. .. .. .. .. .. 119 33 .. .. .. 42 7 .. .. .. .. 179 14 .. .. .. .. 258 .. .. 133 1,423 .. .. .. .. 0 .. .. .. .. 3 11 .. .. .. .. .. .. .. 34 834 .. .. .. 2,340 .. .. .. .. .. ..
42 42 155 40 3 120 467 22 45 63 9 6 0 2 1 .. 8 223 149 19 .. 12 5 116 9 9 460 3,536 .. 15 17 4 .. 10 8 .. 20 621 124 2 48 641 9 69 17 240 99 60 17 5 74 195 0 389 .. 1 .. ..
407 13 791 20 17 36 1,677 5 62 71 45 3 30 0 1 .. 10 314 17 2 2 28 9 373 0 17 434 559 .. 172 25 1 .. .. 10 .. .. 73 16 33 116 681 4 .. 1 .. 72 120 17 .. 34 101 6 703 0 .. .. ..
2012 World Development Indicators
Military expenditures
% of GDP 2005
Honduras Hungary India Indonesia Iran, Islamic Rep. Iraq Ireland Israel Italy Jamaica Japan Jordan Kazakhstan Kenya Korea, Dem. Rep. Korea, Rep. Kosovo Kuwait Kyrgyz Republic Lao PDR Latvia Lebanon Lesotho Liberia Libya Lithuania Macedonia, FYR Madagascar Malawi Malaysia Mali Mauritania Mauritius Mexico Moldova Mongolia Morocco Mozambique Myanmar Namibia Nepal Netherlands New Zealand Nicaragua Niger Nigeria Norway Oman Pakistan Panama Papua New Guinea Paraguay Peru Philippines Poland Portugal Puerto Rico Qatar
0.8 1.4 2.7 1.1 3.5 3.6 0.6 7.6 1.9 0.5 1.0 4.8 1.0 1.7 .. 2.6 .. 4.3 3.1 0.4 1.7 4.4 2.5 1.5 1.5 1.6 2.1 1.1 1.2 2.3 2.3 3.1 0.2 0.4 0.4 1.2 3.4 0.9 .. 2.6 1.9 1.5 1.0 0.7 1.0 0.6 1.6 11.8 4.0 .. 0.6 0.8 1.5 0.8 1.9 2.1 .. 2.5
Armed forces personnel
% of central government expenditure
thousands
Arms transfers
% of labor force
2010
2005
2010
2005
2010
2005
1.6 1.0 2.4 1.0 1.9 6.0 0.6 6.5 1.8 0.8 1.0 5.2 0.9 1.9 .. 2.7 .. 4.0 3.6 0.3 1.1 4.2 2.7 0.8 1.2 1.3 1.5 0.7 .. 1.5 1.9 3.8 0.2 0.5 0.3 0.9 3.5 0.9 .. 3.3 1.5 1.4 1.1 0.7 0.9 1.0 1.6 9.6 3.2 .. 0.5 0.9 1.4 0.8 1.9 2.2 .. 2.3
3.9 3.4 18.3 6.9 15.3 .. 1.8 16.7 4.8 1.7 .. 13.5 5.7 9.3 .. 13.3 .. 16.7 20.0 3.8 5.9 16.7 5.9 .. .. 5.7 7.0 9.7 .. 12.3 15.0 .. .. .. 1.4 4.9 11.4 .. .. 10.3 12.8 3.8 3.1 4.0 10.6 12.1 4.9 .. 27.6 .. .. 4.8 8.5 4.9 5.3 5.1 .. 12.1
6.8 2.5 16.0 7.1 8.6 .. 1.4 15.7 4.1 2.3 .. 20.1 5.5 8.3 .. 13.7 .. 10.2 18.9 2.7 3.0 14.0 3.1 .. .. 3.4 5.8 9.3 .. 7.8 13.3 .. .. .. 0.9 3.3 11.4 .. .. .. .. 3.4 .. 3.4 .. 10.8 4.5 .. 18.5 .. .. 5.8 8.3 4.8 5.1 4.8 .. 15.4
20 44 3,047 582 585 227 10 176 445 3 272 111 101 29 1,295 693 .. 23 18 129 5 85 2 15 76 29 19 22 7 135 12 21 2 204 10 16 251 11 483 15 131 60 9 14 10 161 47 46 921 12 3 25 157 147 162 93 .. 12
20 35 2,626 582 563 802 10 185 359 3 260 111 81 29 1,379 660 .. 23 20 129 5 79 2 2 76 25 8 22 5 134 12 21 2 332 8 17 246 11 513 15 158 43 10 12 11 162 24 47 946 12 3 25 192 166 121 90 .. 12
0.8 1.0 0.7 0.5 2.4 3.6 0.5 6.4 1.8 0.2 0.4 8.0 1.3 0.2 9.2 2.9 .. 2.0 0.8 4.7 0.4 6.5 0.2 1.4 3.6 1.8 2.2 0.3 0.1 1.2 0.3 2.2 0.4 0.5 0.7 1.5 2.3 0.1 1.9 1.9 0.9 0.7 0.4 0.7 0.2 0.4 1.9 5.1 1.8 0.8 0.1 0.9 1.2 0.4 0.9 1.7 .. 2.3
5.7
STATES AND MARKETS
Military expenditures and arms transfers
2010
0.7 0.8 0.6 0.5 2.2 10.6 0.5 5.8 1.4 0.2 0.4 7.1 0.9 0.2 9.5 2.7 .. 1.7 0.8 4.1 0.4 5.4 0.2 0.1 3.2 1.5 0.8 0.2 0.1 1.1 0.3 1.9 0.3 0.7 0.6 1.5 2.2 0.1 1.8 1.6 1.0 0.5 0.4 0.5 0.2 0.3 0.9 3.9 1.6 0.7 0.1 0.8 1.2 0.4 0.7 1.6 .. 0.9
Trend indicator values 1990 $ millions Exports Imports 2005
2010
.. 82 19 8 2 .. .. 368 774 .. .. 17 12 .. 40 48 .. .. 92 .. .. .. .. .. 113 .. .. .. .. .. .. .. .. .. 18 .. .. .. .. .. .. 583 0 .. .. .. 12 1 22 .. .. .. 5 4 17 .. .. 6
.. .. 4 .. 5 .. 4 472 627 .. .. 88 .. .. .. 95 .. .. 14 .. .. .. .. .. 28 .. .. .. .. 0 .. .. .. .. 20 .. .. .. .. .. .. 503 .. .. .. .. 141 .. .. .. .. .. .. .. 8 0 .. ..
2005
2010
.. 13 1,059 32 66 165 4 1,133 150 13 375 35 42 8 5 796 .. 16 3 4 7 1 1 .. 2 15 0 .. .. 49 13 7 6 36 .. .. 87 1 65 72 5 96 8 .. 14 14 14 164 406 .. .. 1 368 14 95 172 .. 11
0 18 3,337 198 88 464 1 43 85 2 369 114 57 73 1 1,131 .. 17 .. 26 15 60 .. .. 7 81 .. .. 2 411 9 4 .. 188 .. 13 138 0 76 14 1 162 71 .. 1 189 205 36 2,580 5 .. 3 60 8 142 941 .. 20
2012 World Development Indicators
309
5.7
Military expenditures and arms transfers Military expenditures
% of GDP 2005
Romania Russian Federation Rwanda Saudi Arabia Senegal Serbia Sierra Leone Singapore Slovak Republic Slovenia Somalia South Africa South Sudan Spain Sri Lanka Sudan Swaziland Sweden Switzerland Syrian Arab Republic Tajikistan Tanzania Thailand Timor-Leste Togo Trinidad and Tobago Tunisia Turkey Turkmenistan Uganda Ukraine United Arab Emirates United Kingdom United States Uruguay Uzbekistan Venezuela, RB Vietnam West Bank and Gaza Yemen, Rep. Zambia Zimbabwe World Low income Middle income Lower middle income Upper middle income Low & middle income East Asia & Pacific Europe & Central Asia Latin America & Carib. Middle East & N. Africa South Asia Sub-Saharan Africa High income Euro area
2.0 3.7 1.7 8.0 1.4 2.5 1.9 4.4 1.7 1.4 .. 1.5 .. 1.0 2.6 4.3 2.3 1.5 0.9 5.0 2.2 1.0 1.1 2.5 1.6 .. 1.5 2.5 .. 2.4 2.8 4.2 2.4 4.0 1.3 0.5 1.4 1.9 .. 4.9 2.0 2.3 2.5 w 1.6 2.1 2.4 2.0 2.1 1.9 2.9 1.3 3.2 2.8 1.7 2.6 1.7
2010
1.4 4.0 1.4 10.4 1.6 2.2 2.3 3.8 1.1 1.6 .. 1.2 .. 1.1 2.8 .. 3.4 1.2 0.9 3.9 .. 1.0 1.5 2.8 1.8 .. 1.2 2.4 .. 1.7 2.7 5.4 2.6 4.8 1.5 .. 0.9 2.2 .. 4.4 1.7 1.3 2.6 w 1.4 2.0 2.1 2.0 2.0 1.9 3.0 1.4 3.3 2.4 1.6 2.9 1.6
Armed forces personnel
% of central government expenditure
thousands
2005
2010
2005
8.3 18.7 .. .. .. 6.5 8.3 33.9 4.9 3.6 .. 5.1 .. 4.2 13.1 .. 7.0 .. 4.9 .. 15.8 .. 6.7 .. 9.8 .. 5.6 11.5 .. 14.4 7.7 .. 5.9 18.9 5.0 .. 5.6 .. .. .. 8.5 .. 10.8 w 12.0 12.5 13.8 12.3 12.5 13.9 15.2 .. 12.5 18.7 .. 10.5 4.5
4.4 177 154 14.1 1,452 1,430 .. 53 35 .. 216 249 .. 19 19 5.5 110 29 11.2 13 11 28.2 167 148 3.7 20 16 3.8 12 12 .. .. 2 4.1 56 77 .. .. 140 3.6 220 223 19.3 200 223 .. 123 127 .. .. .. .. 29 21 4.8 109 25 .. 416 403 .. 13 16 .. 28 28 8.2 421 420 .. 1 1 13.0 10 9 .. 3 4 4.5 47 48 9.4 617 613 .. 26 22 15.3 47 47 7.0 273 215 .. 51 51 5.7 217 174 17.9 1,546 1,569 5.0 25 25 .. 91 87 .. 82 115 .. 495 522 .. 56 56 .. 138 138 10.0 16 17 .. 51 51 10.0 w 28,539 s 27,994 s .. 3,330 3,714 12.3 19,030 18,527 12.7 8,635 8,858 12.4 10,395 9,669 12.2 22,360 22,241 14.6 7,656 7,005 11.9 3,398 3,169 .. 2,072 2,439 .. 3,123 3,611 16.2 4,578 4,480 .. 1,533 1,536 10.0 6,179 5,753 4.3 1,802 1,606
2010
Arms transfers
% of labor force 2005
2010
1.8 2.0 1.2 2.7 0.4 2.8 0.7 7.4 0.8 1.2 .. 0.3 .. 1.0 2.5 1.0 .. 0.6 2.6 8.0 0.5 0.1 1.1 0.3 0.4 0.4 1.4 2.7 1.3 0.4 1.2 2.0 0.7 1.0 1.6 0.9 0.7 1.1 7.1 2.6 0.3 0.8 0.9 w 1.1 0.9 0.9 0.8 0.9 0.7 1.9 0.8 3.3 0.7 0.5 1.1 1.2
1.5 1.9 0.7 2.6 0.3 0.8 0.5 5.3 0.6 1.2 0.1 0.4 .. 1.0 2.6 0.9 .. 0.4 0.6 7.4 0.6 0.1 1.1 0.4 0.3 0.6 1.2 2.3 1.0 0.3 0.9 1.0 0.5 1.0 1.5 0.7 0.9 1.0 5.7 2.1 0.3 0.8 0.9 w 1.0 0.8 0.9 0.7 0.8 0.6 1.6 0.9 3.5 0.7 0.5 1.0 1.0
Trend indicator values 1990 $ millions Exports Imports 2005
2010
2005
2010
2 5,134 .. .. .. 4 .. 3 7 .. .. 26 .. 108 .. .. .. 538 246 3 .. .. 8 .. .. .. .. 47 .. .. 290 11 1,039 6,700 .. 4 7 12 .. .. .. .. .. s .. .. .. 6,188 .. 311 5,585 .. .. 41 .. 14,880 5,473
4 6,039 .. 58 .. 5 .. 27 8 .. .. 80 .. 513 .. .. .. 806 137 25 .. .. .. .. .. .. .. 31 .. .. 201 37 1,054 8,641 .. 90 40 .. .. .. .. .. .. s .. .. .. 7,020 .. 1,423 6,380 .. .. 4 .. 16,758 4,891
491 5 3 150 15 .. 10 537 4 2 .. 262 37 339 33 104 .. 82 164 7 13 9 63 .. .. 6 168 1,065 22 9 .. 2,199 27 520 20 .. 23 328 2 306 0 25 20,973 s .. 10,757 3,334 7,423 11,031 4,096 1,152 1,213 1,639 1,554 986 9,942 1,527
109 19 13 787 4 14 .. 1,078 8 73 .. 183 1 313 5 14 .. 35 34 167 .. 9 83 20 1 45 2 468 29 1 .. 493 518 893 36 .. 365 515 4 7 .. .. 24,960 s .. 13,328 8,426 4,902 14,002 1,912 920 1,739 2,523 6,378 530 10,958 2,620
Note: For some countries data are partial or uncertain or based on rough estimates; see SIPRI (2011). a. Estimates differ from official statistics of the government of China, which has published the following estimates: military expenditure as 1.4 percent of GDP in 2005 and 1.5 percent in 2009 and 7.3 percent of national government expenditure in 2005 and 6.5 percent in 2009 (see National Bureau of Statistics of China, www.stats.gov.cn).
310
2012 World Development Indicators
About the data
5.7
STATES AND MARKETS
Military expenditures and arms transfers Definitions
Although national defense is an important function
provided. Because of the differences in definitions
• Military expenditures are SIPRI data derived from
of government and security from external threats
and the difficulty in verifying the accuracy and com-
NATO’s former definition, (in use until 2002) which
that contributes to economic development, high
pleteness of data, data on military expenditures are
includes all current and capital expenditures on
military expenditures for defense or civil conflicts
not always comparable across countries. However,
the armed forces, including peacekeeping forces;
burden the economy and may impede growth. Data
SIPRI puts a high priority on ensuring that the data
defense ministries and other government agencies
on military expenditures as a share of gross domes-
series for each country is comparable over time.
engaged in defense projects; paramilitary forces, if
tic product (GDP) are a rough indicator of the portion
More information on SIPRI’s military expenditure proj-
judged to be trained and equipped for military opera-
of national resources used for military activities and
ect can be found at www.sipri.org/contents/milap/.
tions; and military space activities. Such expendi-
of the burden on the economy. As an “input” mea-
Data on armed forces refer to military personnel on
tures include military and civil personnel, including
sure military expenditures are not directly related
active duty, including paramilitary forces. Because
retirement pensions and social services for military
to the “output” of military activities, capabilities, or
data exclude personnel not on active duty, they
personnel; operation and maintenance; procurement;
security. Comparisons of military spending among
underestimate the share of the labor force working
military research and development; and military aid
countries should take into account the many fac-
for the defense establishment. Governments rarely
(in the military expenditures of the donor country).
tors that influence perceptions of vulnerability and
report the size of their armed forces, so such data
Excluded are civil defense and current expenditures
risk, including historical and cultural traditions, the
typically come from intelligence sources.
for previous military activities, such as for veterans
length of borders that need defending, the quality of
SIPRI’s Arms Transfers Programme collects data
benefits, demobilization, and weapons conversion
relations with neighbors, and the role of the armed
on arms transfers from open sources. Since publicly
and destruction. This definition cannot be applied
forces in the body politic.
available information is inadequate for tracking all
for all countries, however, since that would require
Data on military expenditures are reported to
weapons and other military equipment, SIPRI covers
more detailed information than is available about mili-
the United Nations Office for Disarmament Affairs
only what it terms major conventional weapons. Data
tary budgets and off-budget military expenditures (for
(UNODA) by about 60 governments. Data on military
cover the supply of weapons through sales, aid, gifts,
example, whether military budgets cover civil defense,
expenditures are not compiled using standard defini-
and manufacturing licenses; therefore the term arms
reserves and auxiliary forces, police and paramilitary
tions and are often incomplete and unreliable due
transfers rather than arms trade is used. SIPRI data
forces, and military pensions). • Armed forces per-
to countries’ reluctance to disclose military informa-
also cover weapons supplied to or from rebel forces
sonnel are active duty military personnel, including
tion. Even in countries where the parliament vigilantly
in an armed conflict as well as arms deliveries for
paramilitary forces if the training, organization, equip-
reviews budgets and spending, military expenditures
which neither the supplier nor the recipient can be
ment, and control suggest they may be used to sup-
and arms transfers rarely receive close scrutiny or
identified with acceptable certainty; these data are
port or replace regular military forces. Reserve forces,
full, public disclosure (see Ball 1984 and Happe
available in SIPRI’s database.
which are not fully staffed or operational in peace
and Wakeman-Linn 1994). However, the Stockholm
SIPRI’s estimates of arms transfers are designed
time, are excluded. The data also exclude civilians in
International Peace Research Institute (SIPRI) has
as a trend-measuring device in which similar weap-
the defense establishment and so are not consistent
adopted a definition of military expenditure derived
ons have similar values, reflecting both the quantity
with the data on military expenditures on personnel.
from the North Atlantic Treaty Organization’s (NATO)
and quality of weapons transferred. The estimated
• Arms transfers cover the supply of military weapons
former definition (in use until 2002; see Definitions).
values do not reflect financial value (payments for
through sales, aid, gifts, and manufacturing licenses
The data on military expenditures as a share of GDP
weapons transferred) because reliable data on the
and are based on actual deliveries only. Weapons
are SIPRI estimates. Data on military expenditures
value of the transfer are not available, and even
must be transferred voluntarily by the supplier, have
as a share of central government expenditures use
when values are known, the transfer usually includes
a military purpose, and be destined for the armed
data on central government expenditures from the
more than the actual conventional weapons, such as
forces, paramilitary forces, or intelligence agencies
International Monetary Fund (IMF). Therefore the
spares, support systems, and training, and details of
of another country. Data cover major conventional
data in the table may differ from comparable data
the financial arrangements (such as credit and loan
weapons such as aircraft, armored vehicles, artil-
published by national governments.
conditions and discounts) are usually not known.
lery, radar systems and other sensors, missiles, and
SIPRI’s primary source of military expenditure data
Given these measurement issues, SIPRI’s method
ships designed for military use as well as some major
is official data provided by national governments.
of estimating the transfer of military resources
components such as turrets for armored vehicles and
These data are derived from budget documents,
includes an evaluation of the technical parameters
engines. Excluded are other military equipment such
defense white papers, and other public documents
of the weapons. Weapons for which a price is not
as most small arms and light weapons, trucks, small
from official government agencies, including govern-
known are compared with the same weapons for
artillery, ammunition, support equipment, technology
ment responses to questionnaires sent by SIPRI,
which actual acquisition prices are available (core
transfers, and other services.
the UNODA, or the Organization for Security and
weapons) or for the closest match. These weapons
Co-operation in Europe. Secondary sources include
are assigned a value in an index that reflects their
international statistics, such as those of NATO and
military resource value in relation to the core weap-
the IMF’s Government Finance Statistics Yearbook.
ons. These matches are based on such characteris-
Other secondary sources include country reports of
tics as size, performance, and type of electronics,
the Economist Intelligence Unit, country reports by
and adjustments are made for secondhand weap-
IMF staff, and specialist journals and newspapers.
ons. More information on SIPRI’s Arms Transfers
In the many cases where SIPRI cannot make independent estimates, it uses the national data
Programme is available at www.sipri.org/research/ armaments/transfers.
Data sources Data on military expenditures are from SIPRI’s Military Expenditure Database (www.sipri.org/databases/milex). Data on armed forces personnel are from the International Institute for Strategic Studies’ The Military Balance 2012. Data on arms transfers are from SIPRI’s Arms Transfers Database (www.sipri.org/databases/armstransfers).
2012 World Development Indicators
311
5.8
Fragile situations International Development Association Resource Allocation Index 1–6 (low to high) 2010
Afghanistan Angola Bosnia and Herzegovina Burundi Central African Republic Chad Comoros Congo, Dem. Rep. Congo, Rep. Côte d’Ivoire Eritrea Georgia Guinea Guinea-Bissau Haiti Iraq Kiribati Kosovo Liberia Marshall Islands Micronesia, Fed. Sts. Myanmar Nepal Sierra Leone Solomon Islands Somalia Sudan Timor-Leste Togo West Bank and Gaza Western Saharam Yemen, Rep. Zimbabwe Fragile situations Low income
2.6 2.8 3.7 3.1 2.8 2.4 2.5 2.7 2.9 2.7 2.2 4.4 2.8 2.7 2.9 .. 3.0 3.4 2.9 .. .. .. 3.3 3.3 2.8 .. 2.4 3.0 2.9 .. .. 3.2 2.0
Peacebuilding and peacekeeping
Battlerelated deaths
Intentional homicides
number
December 2011
December 2011
2000–10c
2008
UNAMA
15 .. .. 1 4 .. .. 18,928 .. 10,999 .. .. .. 17 11,611 361 .. 16 9,206 .. .. .. .. .. 468 6 .. 1,216 .. .. 228 .. .. .. ..
35,460 2,620 .. 6,413 544 4,238 .. 6,348 167 809 25,057 648 647 0 244 24,088 .. .. 2,427 .. .. 1,590 9,418 156 .. 7,574 17,658 .. .. .. .. 259 .. 82,763 s ..
2.4 19.0 1.7 21.7 29.3 15.8 12.2 21.7 30.8 56.9 17.8 4.1h 22.5 20.2 6.9h 2.0 7.3 .. 10.1 .. 0.9 10.2 2.8i 14.9 3.7 1.5 24.2 6.9 10.9 .. .. 4.2i 14.3 15.8 w 17.5
BNUB BINUCA
MONUSCO UNOCI
UNIOGBIS MINUSTAH UNAMI UNMIK UNMIL
RAMSI UNPOS UNMIT
MINURSO
number
Business environment
per 100,000 people
Troops, police, and military observers
Combined source estimatesb
Operation namea
Military expenditures
% of GDP 2010
1.8 4.4 1.4 3.8 2.6 3.0 .. 1.4 1.1 1.6 .. 3.9 .. .. .. 6.0 .. .. 0.8 .. .. .. 1.5 2.3 .. .. .. 2.8 1.8 .. .. 4.4 1.3 3.8 w 1.4
Survey year
2008 2010 2009 2006 2009 2010 2009 2009 2009 2008 2006 2006 2009 2009 2009 2009 2009 2009 2009 2006 2010
Losses due to Firms formally theft, robbery, registered when operations vandalism, started and arson % of sales
% of firms
1.5 1.5 0.4 1.1 .. 2.5 .. 1.8 3.3 3.4 0.0 0.7 2.0 1.1 .. .. .. 0.3 2.8 .. 2.1 .. 0.9 0.8 .. .. .. 2.7 2.4 1.2 .. 0.6 ..
88.0 62.7 98.6 .. .. 77.1 .. 61.9 84.3 56.4 100.0 99.6 .. .. .. .. .. 89.2 73.8 .. 96.9 .. 94.0 89.2 .. .. .. 91.8 75.8 .. .. 81.7 ..
Note: The countries and territories with fragile situations in the table are primarily International Development Association-eligible countries and nonmember or inactive countries and territories that have a 3.2 or lower harmonized average of the World Bank’s Country Policy and Institutional Assessment rating and the corresponding rating by a regional development bank or that have had a UN or regional peacebuilding and political mission (for example by the African Union, European Union, or Organization of American States) or peacekeeping mission (for example, by the African Union, European Union, North Atlantic Treaty Organization, or Organization of American States) during the last three years. This definition is pursuant to an agreement among the World Bank and other multilateral development banks at the start of the International Development Association 15 round in 2007. The list of countries and territories with fragile situations is imperfect and is used here to reflect a complex concept. The World Bank continues to work with partners and client countries to refine the concept. a. UNAMA is United Nations Assistance Mission in Afghanistan, BNUB is United Nations Office in Burundi, BINUCA is United Nations Integrated Peacebuilding Office in the Central African Republic, MONUSCO is United Nations Organization Stabilization Mission in the Democratic Republic of the Congo, UNOCI is United Nations Operation in Côte d’Ivoire, UNIOGBIS is United Nations Integrated Peacebuilding Office in Guinea-Bissau, MINUSTAH is United Nations Stabilization Mission in Haiti, UNAMI is United Nations Assistance Mission for Iraq, UNMIK is Interim Administration Mission in Kosovo, UNMIL is United Nations Mission in Liberia, RAMSI is Regional Assistance Mission to Solomon Islands, UNPOS is United Nations Political Office for Somalia, UNMIT is United Nations Integrated Mission in Timor-Leste, and MINURSO is United Nations Mission for the Referendum in Western Sahara. b. Best estimate from the UN Office on Drug and Crime’s Intentional Homicide Statistics database. The combined source estimate uses both public health and law enforcement and criminal justice sources. c. Total over the period. d. Data are for the most recent year available. e. Average over the period. f. Covers only Angola-secured territory. g. Covers children ages 10–14. h. Data are for 2010. i. Data are for 2009. j. Northern Sudan only. k. Figure represents the Internal Displacement Monitoring Centre’s (IDMC) high estimate; the low estimates is 4,500,000. l. Includes Palestinian refugees under the mandate of the United Nations Relief and Works Agency for Palestine Refugees in the Near East, who are not included in data from the Office of the United Nations High Commissioner for Refugees. m. The designation Western Sahara is used instead of Former Spanish Sahara (the designation used on the maps on the inside front and back cover flaps) because it is the designation used by the UN operation established there by Security Council resolution 690/1991. Neither designation expresses any World Bank view on the status of the territory so-identified. n. Figure represents IDMC’s high estimate; the low estimate is 570,000.
312
2012 World Development Indicators
Children in employment
Refugees
Internally displaced persons
Access to an improved water source
Access to improved sanitation facilities
% of population 2010
% of population 2010
Maternal mortality Under-five Depth of Primary ratio mortality hunger gross rate enrollment ratio per 100,000 live births
% of Survey children year ages 7–14
Afghanistan Angola Bosnia and Herzegovina Burundi Central African Republic Chad Comoros Congo, Dem. Rep. Congo, Rep. Côte d’Ivoire Eritrea Georgia Guinea Guinea-Bissau Haiti Iraq Kiribati Kosovo Liberia Marshall Islands Micronesia, Fed. Sts. Myanmar Nepal Sierra Leone Solomon Islands Somalia Sudan Timor-Leste Togo West Bank and Gaza Western Saharam Yemen, Rep. Zimbabwe Fragile situations Low income
2001 2006 2005 2000 2004 2000 2005 2006 2006 1994 2006 2005 2006
2007
1999 2007 2006 2000 2006
2006 1999
.. 30.1f 10.6 11.7 67.0 60.4 .. 39.8g 30.1 45.7 .. 31.8 48.3 50.5 33.4 14.7 .. .. 37.4 .. .. .. 47.2 14.9 .. 43.5 19.1j .. 38.7 .. .. 18.3 14.3
By country of origin 2010
By country of asylum 2010
3,054,709 6,434 134,858 15,155 63,004 7,016 84,064 29,365 164,905 21,574 53,733 347,939 368 .. 476,693 166,336 20,679 133,112 41,758 26,218 222,460 4,809 10,640 639 11,985 14,113 1,127 7,679 25,892 3 1,683,579 34,655 33 .. .. .. 70,129 24,743 .. .. .. 1 415,670 .. 5,889 89,808 11,275 8,363 75 .. 770,154 1,937 387,288 178,308 8 1 18,330 14,051 93,323 1,910,677l .. .. 2,076 190,092 24,089 4,435 7,848,793 s 3,237,459 s 5,650,811 1,873,979
number 2010
352,000 .. 113,400 100,000 192,000 171,000 .. 1,700,000 7,800 621,000 10,000 258,000 .. .. .. 2,800,000 .. 18,300 .. .. .. 446,000 50,000 .. .. 1,500,000 5,200,000k 400 .. 160,000 .. 250,000 1,000,000n 14,328,500 s 7,113,000
50 51 99 72 67 51 95 45 71 80 61 98 74 64 69 79 .. .. 73 94 .. 83 89 55 .. 29 58 69 61 85 .. 55 80 65 w 65
37 58 95 46 34 13 36 24 18 24 14 95 18 20 17 73 .. .. 18 75 .. 76 31 13 .. 23 26 47 13 92 .. 53 40 41 w 37
5.8
STATES AND MARKETS
Fragile situations
National estimates 2005–10 d
.. .. 3 620 540 .. .. 550 780 540 .. 52 980 410 630 84 .. .. 990 .. .. 320 280 860 .. 1,000 1,100 560 .. .. .. .. 730 .. ..
Modeled estimates 2008
per 1,000 live births 2010
1,400 610 9 970 850 1,200 340 670 580 470 280 48 680 1,000 300 75 .. .. 990 .. .. 240 380 970 100 1,200 750 370 350 .. .. 210 790 650 w 590
149 161 8 142 159 173 86 170 93 123 61 22 130 150 165 39 49 .. 103 26 42 66 50 174 27 180 103 81 103 22 .. 77 80 120 w 108
kilocalories per person % of relevant per day age group 2006–08e 2010
.. 320 140 390 300 320 300 .. 230 230 350 160 260 250 420 .. 180 .. 330 .. .. .. 220 340 190 .. 240 260 280 190 .. 260 300 271 w 281
97 124 88 156 93 90 104 94 115 88 45 109 94 123 .. .. 113 .. 96 102 .. 126 .. 125 .. .. 73 117 140 91 .. 87 .. 102 w 104
About the data
The table focuses on countries and territories with
According to the Geneva Declaration on Armed Vio-
fragile situations have to build their own institutions
fragile situations and highlights the links among weak
lence and Development, more than 526,000 people
tailored to their own needs. Peacekeeping opera-
institutions, poor development outcomes, fragility,
die each year because of the violence associated with
tions in post-conflict situations have been effective
and risk of conflict. Many of these countries and ter-
armed conflict and large- and small-scale criminal-
in reducing the risks of reversion to conflict.
ritories have weak institutions that are ill-equipped
ity. Recovery and rebuilding can take years, and the
The countries and territories with fragile situations
to handle economic shocks, natural disasters, and
challenges are numerous: infrastructure to be rebuilt,
in the table are primarily International Development
illegal trade or to resist conflict, which increasingly
persistently high crime, widespread health problems,
Association– eligible countries and nonmember or
spills across borders. Organized violence, including
education systems in disrepair, and unexploded ord-
inactive countries or territories of the World Bank that
violent crime, interrupts economic and social devel-
nance to be cleared. Most countries emerging from
have a 3.2 or lower harmonized average of the World
opment through lost human and social capital, dis-
conflict lack the capacity to rebuild the economy.
Bank’s Country Policy and Institutional Assessment
rupted services, displaced populations, and reduced
Thus, capacity building is one of the first tasks for
rating and the corresponding rating by a regional
confidence for future investment. As a result, coun-
restoring growth and is linked to building peace and
development bank or that have had a UN or regional
tries with fragile situations achieve lower develop-
creating the conditions that lead to sustained poverty
peacebuilding mission (for example, by the African
ment outcomes and make slower progress toward
reduction. The World Bank and other international
Union, European Union, or Organization of American
the Millennium Development Goals.
development agencies can help, but countries with
States) or peacekeeping mission (for example, by the
2012 World Development Indicators
313
5.8
Fragile situations
About the data (continued)
African Union, European Union, North Atlantic Treaty
governments. For a more detailed discussion of mili-
peace and development) or peacekeeping (providing
Organization [NATO], or Organization of American
tary expenditures, see About the data for table 5.7.
essential security to preserve the peace where fight-
States) during the last three years. Peacebuilding
Along with public sector efforts, private sector devel-
ing has been halted and to assist in implementing
and peacekeeping involve many elements—military,
opment and investment, especially in competitive mar-
agreements achieved by the peacemakers). UN
police, and civilian—working together to lay the foun-
kets, has tremendous potential to contribute to growth
peacekeeping operations are authorized by the UN
dations for sustainable peace. The list of countries
and poverty reduction. The World Bank’s Enterprise
Secretary-General and planned, managed, directed,
and territories with fragile situations is imperfect and
Surveys review the business environment, assessing
and supported by the United Nations Department of
is used here to reflect a complex concept. The World
constraints to private sector growth and enterprise
Peacekeeping Operations and the Department of
Bank continues to work with partners and client coun-
performance. Crime, theft, and disorder impose
Field Support. The UN Charter gives the Security
tries to refine the concept.
costs on businesses and society. And in many devel-
Council primary responsibility for maintaining interna-
An armed conflict is a contested incompatibility
oping countries informal businesses operate without
tional peace and security, including the establishment
that concerns a government or territory where the
licenses. These firms have less access to financial
of a UN peacekeeping operation. • Troops, police,
use of armed force between two parties (one of
and public services and can engage in fewer types of
and military observers in peacebuilding and peace-
them the government) results in at least 25 battle-
contracts and investments, constraining growth. The
keeping are people active in peacebuilding and
related deaths in a calendar year. There were 30
table presents data on the loss of sales due to theft,
peacekeeping as part of an official operation. Peace-
active armed conflicts in 2010. This table presents
robbery, vandalism, and arson and on the percentage
keepers deploy to war-torn regions where no one else
data from the Uppsala Conflict Data Program (UCDP)
of firms operating informally. For further information on
is willing or able to go to prevent conflict from return-
Battle-Related Deaths Dataset (v.5 2011), which
enterprise surveys, see About the data for table 5.2.
ing or escalating. • Battle-related deaths are deaths
focuses on the incompatibility and lists the country,
As the table shows, the human toll of armed vio-
of members of warring parties in battle-related con-
as well as the battle location and territory where
lence across various contexts is severe. Additionally,
flicts. Typically, battle-related deaths occur in warfare
battle-related deaths are reported. When more than
in countries with fragile situations weak institutional
involving the armed forces of the warring parties
one country is listed in the dataset, the assignment
capacity often results in poor performance and fail-
(battlefield fighting, guerrilla activities, and all kinds
of battle-related deaths in the table is determined by
ure to meet expectations of effective service deliv-
of bombardments of military units, cities, and vil-
the battle location. See country footnotes for addi-
ery. Failure to deliver water, health, and education
lages). The targets are usually the military and its
tional details.
services can weaken struggling governments. The
installations or state institutions and state represen-
Data on intentional homicides are from the United
table includes several indicators related to living con-
tatives, but there is often substantial collateral dam-
Nations Office on Drugs and Crime (UNODC), which
ditions in fragile situations: children in employment,
age of civilians killed in crossfire, indiscriminate
uses a variety of national and international sources
refugees, internally displaced persons, access to
bombings, and other military activities. All deaths—
on homicides—primarily criminal justice sources
water and sanitation, maternal and under-five mortal-
civilian as well as military— incurred in such situa-
as well as public health data from the World Health
ity, depth of hunger, and primary school enrollment.
tions are counted as battle-related deaths. • Inten-
Organization (WHO) and the Pan American Health
For more detailed information on these indicators,
tional homicides are estimates of unlawful homicides
Organization—and the United Nations Survey of
see About the data for table 2.6 (children in employ-
purposely inflicted as a result of domestic disputes,
Crime Trends and Operations of Criminal Justice
ment), table 6.14 (refugees), table 2.18 (access to
interpersonal violence, violent conflicts over land
Systems to present accurate and comparable sta-
improved water and sanitation), table 2.19 (maternal
resources, intergang violence over turf or control, and
tistics. The UNODC defines homicide as “unlawful
mortality), table 2.23 (under-five mortality), and table
predatory violence and killing by armed groups. Inten-
death purposefully inflicted on a person by another
2.12 (primary school enrollment).
tional homicide does not include all intentional killing;
person.” This definition excludes deaths arising from armed conflict. For additional information, see the
the difference is usually in the organization of the Definitions
killing. Individuals or small groups usually commit
UNODC Homicide Statistics database at www.unodc.
• International Development Association Resource
homicide, whereas killing in armed conflict is usually
org/unodc/en/data-and-analysis/homicide.html.
Allocation Index is from the Country Policy and Insti-
committed by fairly cohesive groups of up to several
Data on military expenditures reported by govern-
tutional Assessment rating, which is the average
hundred members and is thus usually excluded. • Mil-
ments are not compiled using standard definitions
score of four clusters of indicators designed to mea-
itary expenditures are SIPRI data derived from NATO’s
and are often inconsistent across countries, incom-
sure macroeconomic, governance, social, and struc-
former definition (in use until 2002), which includes
plete, and unreliable. Even in countries where the
tural dimensions of development: economic manage-
all current and capital expenditures on the armed
parliament vigilantly reviews budgets and spend-
ment, structural policies, policies for social inclusion
forces, including peacekeeping forces; defense min-
ing, military expenditures and arms transfers rarely
and equity, and public sector management and insti-
istries and other government agencies engaged in
receive close scrutiny or full public disclosure.
tutions (see table 5.9). Countries are rated on a scale
defense projects; paramilitary forces, if judged to be
Data are from the Stockholm International Peace
of 1 (low) to 6 (high). • Peacebuilding and peacekeep-
trained and equipped for military operations; and mili-
Research Institute (SIPRI), which uses NATO’s former
ing refer to operations that engage in peacebuilding
tary space activities. Such expenditures include mili-
definition of military expenditure (in use until 2002;
(reducing the risk of lapsing or relapsing into conflict
tary and civil personnel, including retirement pensions
see Definitions). Therefore, the data in the table may
by strengthening national capacities for conflict man-
and social services for military personnel; operation
differ from comparable data published by national
agement and laying the foundation for sustainable
and maintenance; procurement; military research and
314
2012 World Development Indicators
5.8
STATES AND MARKETS
Fragile situations development; and military aid (in the military expen-
Reasonable access is the availability of at least 20
from the UNODC’s International Homicide Statis-
ditures of the donor country). Excluded are civil
liters a person a day from a source within 1 kilometer
tics database (www.unodc.org/unodc/en/data-
defense and current expenditures for previous military
of the dwelling. • Access to improved sanitation
and-analysis/homicide.html). Data on military
activities, such as for veterans benefits, demobiliza-
facilities refers to people with at least adequate
expenditures are from SIPRI’s Military Expendi-
tion, and weapons conversion and destruction. This
access to excreta disposal facilities that can effec-
ture database (www.sipri.org/databases/milex).
definition cannot be applied to all countries, however,
tively prevent human, animal, and insect contact with
Data on the business environment are from the
since the necessary detailed information is missing
excreta. Improved facilities range from protected pit
World Bank’s Enterprise Surveys (www.enterprise-
in some cases for military budgets and off-budget
latrines to flush toilets. • Maternal mortality ratio is
surveys.org). Data on children in employment are
military expenditures (for example, whether military
the number of women who die from pregnancy-related
estimates produced by the Understanding Chil-
budgets cover civil defense, reserves and auxiliary
causes during pregnancy and childbirth per 100,000
dren’s Work project based on household survey
forces, police and paramilitary forces, and military
live births. National estimates are based on national
data sets made available by the International
pensions). • Survey year is the year in which the
surveys, vital registration records, and surveillance
Labour Organization’s International Programme
underlying data were collected. • Losses due to theft,
data or are derived from community and hospital
on the Elimination of Child Labour under its Sta-
robbery, vandalism, and arson are the estimated
records. Modeled estimates are based on an exercise
tistical Monitoring Programme on Child Labour,
losses from those causes that occurred on business
by the WHO, United Nations Children’s Fund (UNICEF),
UNICEF under its Multiple Indicator Cluster Survey
establishment premises calculated as a percentage
United Nations Population Fund (UNFPA), and the
program, the World Bank under its Living Stan-
of annual sales. • Firms formally registered when
World Bank. See About the data for table 2.19 for
dards Measurement Study program, and national
operations started are firms formally registered when
further details. • Under-five mortality rate is the prob-
statistical offices (see table 2.6). Data on refu-
they started operations in the country. • Children in
ability of a child born in a specific year dying before
gees are from the UNHCR’s Statistical Yearbook
employment are children involved in any economic
reaching age 5, if subject to the age-specific mortality
2010, complemented by statistics on Palestinian
activity for at least one hour in the reference week of
rate of that year. The probability is derived from life
refugees under the mandate of the United Nations
the survey. • Refugees are people who are recognized
tables and expressed as rate per 1,000 live births.
Relief and Works Agency for Palestine Refugees in
as refugees under the 1951 Convention Relating to
• Depth of hunger, or the intensity of food depriva-
the Near East as published on its website (www.
the Status of Refugees or its 1967 Protocol, the 1969
tion, indicates how much people who are food-
unrwa.org). Data on internally displaced persons
Organization of African Unity Convention Governing
deprived fall short of minimum food needs in terms of
are from the Internal Displacement Monitoring
the Specific Aspects of Refugee Problems in Africa,
dietary energy. It is measured by comparing the aver-
Centre. Data on access to water and sanitation
people recognized as refugees in accordance with the
age amount of dietary energy that undernourished
are from the WHO and UNICEF’s Progress on Drink-
UN Refugee Agency (UNHCR) statute, people granted
people get from the foods they eat with the minimum
ing Water and Sanitation (2012). National esti-
refugee-like humanitarian status, and people pro-
amount of dietary energy they need to maintain body
mates of maternal mortality are from UNICEF’s
vided temporary protection. Asylum seekers—people
weight and undertake light activity. Depth of hunger
The State of the World’s Children 2009 and Child-
who have applied for asylum or refugee status and
is low when it is less than 200 kilocalories per person
info and MEASURE DHS Demographic and Health
who have not yet received a decision, or who are reg-
per day and high when it is above 300. • Primary
Surveys by ICF International. Modeled estimates
istered as asylum seekers—are excluded. Palestinian
gross enrollment ratio is the ratio of total enrollment,
for maternal mortality are from WHO, UNICEF,
refugees are people (and their descendants) whose
regardless of age, to the population of the age group
UNFPA, and the World Bank’s Trends in Maternal
residence was Palestine between June 1946 and May
that officially corresponds to the primary level of edu-
Mortality: 1990–2008 (2010). Data on under-fi ve
1948 and who lost their homes and means of liveli-
cation. Primary education provides children with basic
mortality estimates by the UN Inter-agency Group
hood as a result of the 1948 Arab-Israeli conflict.
reading, writing, and mathematics skills along with an
for Child Mortality Estimation (which comprises
• Country of origin refers to the nationality or country
elementary understanding of such subjects as his-
UNICEF, WHO, the World Bank, United Nations
of citizenship of a claimant. • Country of asylum is
tory, geography, natural science, social science, art,
Population Division, and other universities and
the country where an asylum claim was filed and
and music.
research institutes) and are based mainly on
granted. • Internally displaced persons are people or groups of people who have been forced or obliged to
household surveys, censuses, and vital regisData sources
tration data, supplemented by the World Bank’s
flee or to leave their homes or places of habitual resi-
Data on the International Development Associa-
Human Development Network estimates based
dence, in particular as a result of armed conflict, or to
tion Resource Allocation Index are from the World
on vital registration and sample registration data
avoid the effects of armed conflict, situations of gen-
Bank Group’s International Development Associa-
(see table 2.23). Data on depth of hunger are
eralized violence, violations of human rights, or natu-
tion database (www.worldbank.org/ida). Data on
from the Food and Agriculture Organization’s Food
ral or human-made disasters and who have not
peacebuilding and peacekeeping operations are
Security Statistics database (www.fao.org/eco-
crossed an international border. • Access to an
from the UN Department of Peacekeeping Opera-
nomic/ess/ess-fs/fs-data/ess-fadata/en/). Data
improved water source refers to people with reason-
tions. Data on battle-related deaths are from the
on primary gross enrollment are from the United
able access to water from an improved source, such
UCDP Battle-Related Deaths Dataset (v.5-2011)
Nations Educational, Scientific, and Cultural Orga-
as piped water into a dwelling, public tap, tubewell,
1989-2010. Data on intentional homicides are
nization’s Institute for Statistics.
protected dug well, and rainwater collection.
2012 World Development Indicators
315
5.9
Public policies and institutions International Development Association Resource Allocation Index 1–6 (low to high)
Afghanistan Angola Armenia Azerbaijan Bangladesh Benin Bhutan Bolivia Bosnia and Herzegovina Burkina Faso Burundi Cambodia Cameroon Cape Verde Central African Republic Chad Comoros Congo, Dem. Rep. Congo, Rep. Côte d’Ivoire Djibouti Dominica Eritrea Ethiopia Gambia, The Georgia Ghana Grenada Guinea Guinea-Bissau Guyana Haiti Honduras India Kenya Kiribati Kosovo Kyrgyz Republic
Economic management
Structural policies
1–6 (low to high)
1–6 (low to high)
Macroeconomic management
Fiscal policy
Debt policy
Average
2010
2010
2010
2010
2010
2.6 2.8 4.1 3.7 3.5 3.5 3.9 3.7 3.7 3.8 3.1 3.4 3.2 4.1 2.8 2.4 2.5 2.7 2.9 2.7 3.2 3.8 2.2 3.4 3.4 4.4 3.9 3.8 2.8 2.7 3.4 2.9 3.6 3.7 3.8 3.0 3.4 3.7
3.5 3.0 4.5 4.0 4.0 4.0 4.5 4.0 4.0 4.5 3.5 4.5 4.0 4.5 3.5 2.5 3.0 3.5 3.5 3.5 3.5 4.0 2.0 3.5 4.0 4.5 3.5 3.5 2.5 3.0 3.5 4.0 3.5 4.5 4.5 2.5 3.5 4.5
3.0 3.0 5.0 4.5 4.0 3.0 4.5 4.0 3.5 4.5 3.5 4.0 3.5 4.5 3.5 2.5 2.0 3.5 3.0 2.5 3.0 4.5 2.0 4.0 3.5 4.5 3.5 3.0 2.5 2.5 3.5 3.5 3.5 3.5 4.0 3.0 3.0 4.0
2.5 3.0 4.5 4.5 4.0 3.5 4.5 4.5 4.0 4.0 3.0 3.5 3.0 4.0 3.0 2.5 2.0 3.0 3.0 2.0 2.5 3.0 1.5 3.5 3.0 5.0 4.0 3.0 2.0 2.0 4.0 2.5 4.0 4.0 4.0 3.5 3.5 4.0
3.0 3.0 4.7 4.3 4.0 3.5 4.5 4.2 3.8 4.3 3.3 4.0 3.5 4.3 3.3 2.5 2.3 3.3 3.2 2.7 3.0 3.8 1.8 3.7 3.5 4.7 3.7 3.2 2.3 2.5 3.7 3.3 3.7 4.0 4.2 3.0 3.3 4.2
Trade
Financial sector
Business regulatory environment
Average
2010
2010
2010
2010
3.0 4.0 4.5 3.5 3.0 4.0 3.0 5.0 4.0 4.0 4.0 4.0 3.5 4.0 3.5 3.0 3.5 3.0 3.5 4.0 4.0 4.0 1.5 3.0 3.5 6.0 4.0 4.5 4.0 4.0 4.0 4.0 4.5 3.5 4.0 3.0 5.0 5.0
2.0 2.5 3.5 3.0 3.5 3.5 3.0 3.5 4.0 3.0 2.5 2.5 3.0 4.0 2.5 2.5 2.5 2.0 3.0 3.0 3.0 3.5 1.0 3.0 3.5 3.5 4.0 4.0 3.0 2.5 3.5 3.0 3.0 4.0 4.0 3.0 3.5 3.0
2.5 2.0 4.0 4.0 3.5 3.5 3.5 2.5 4.0 3.5 2.5 3.5 3.0 3.5 2.0 2.0 2.5 2.0 2.5 3.0 3.5 4.5 2.0 3.5 3.5 5.5 4.5 4.5 2.5 2.5 3.0 2.5 3.5 3.5 4.0 3.0 3.5 3.5
2.5 2.8 4.0 3.5 3.3 3.7 3.2 3.7 4.0 3.5 3.0 3.3 3.2 3.8 2.7 2.5 2.8 2.3 3.0 3.3 3.5 4.0 1.5 3.2 3.5 5.0 4.2 4.3 3.2 3.0 3.5 3.2 3.7 3.7 4.0 3.0 4.0 3.8
About the data The International Development Association (IDA) is the
assessments have been carried out annually since
a higher IDA allocation in per capita terms. The IRAI
part of the World Bank Group that helps the poorest
the mid-1970s by World Bank staff. Over time the
is a key element in the country performance rating.
countries reduce poverty by providing concessional loans
criteria have been revised from a largely macro-
The CPIA exercise is intended to capture the quality
and grants for programs aimed at boosting economic
economic focus to include governance aspects and
of a country’s policies and institutional arrangements,
growth and improving living conditions. IDA funding helps
a broader coverage of social and structural dimen-
focusing on key elements that are within the country’s
these countries deal with the complex challenges they
sions. Country performance is assessed against a
control, rather than on outcomes (such as economic
face in meeting the Millennium Development Goals.
set of 16 criteria grouped into four clusters: eco-
growth rates) that are influenced by events beyond
The World Bank’s IDA Resource Allocation Index
nomic management, structural policies, policies
the country’s control. More specifically, the CPIA
(IRAI), presented in the table, is based on the results
for social inclusion and equity, and public sector
measures the extent to which a country’s policy and
of the annual Country Policy and Institutional Assess-
management and institutions. IDA resources are
institutional framework supports sustainable growth
ment (CPIA) exercise, which covers the IDA-eligible
allocated to a country on per capita terms based
and poverty reduction and, consequently, the effective
countries. The table does not include Myanmar and
on its IDA country performance rating and, to a lim-
use of development assistance.
Somalia because they were not rated in the 2010
ited extent, based on its per capita gross national
All criteria within each cluster receive equal weight,
exercise even though they are IDA eligible. Country
income. This ensures that good performers receive
and each cluster has a 25 percent weight in the
316
2012 World Development Indicators
International Development Association Resource Allocation Index 1–6 (low to high)
Lao PDR Lesotho Liberia Madagascar Malawi Maldives Mali Mauritania Moldova Mongolia Mozambique Nepal Nicaragua Niger Nigeria Pakistan Papua New Guinea Rwanda Samoa São Tomé and Príncipe Senegal Sierra Leone Solomon Islands Sri Lanka St. Lucia St. Vincent & Grenadines Sudan Tajikistan Tanzania Timor-Leste Togo Tonga Uganda Uzbekistan Vanuatu Vietnam Yemen, Rep. Zambia Zimbabwe
Economic management
Structural policies
1–6 (low to high)
1–6 (low to high)
Macroeconomic management
Fiscal policy
Debt policy
Average
2010
2010
2010
2010
2010
3.3 3.5 2.9 3.4 3.3 3.4 3.6 3.2 3.7 3.4 3.7 3.3 3.7 3.4 3.4 3.1 3.3 3.8 4.1 3.0 3.7 3.3 2.8 3.5 3.8 3.8 2.4 3.3 3.8 3.0 2.9 3.5 3.8 3.4 3.4 3.8 3.2 3.4 2.0
4.0 4.0 3.5 3.5 3.0 2.5 4.5 3.5 3.5 3.5 4.5 3.5 4.0 4.0 4.0 2.5 4.0 4.0 4.0 3.0 4.0 4.0 3.5 3.0 4.0 4.0 3.5 4.0 4.5 3.0 3.0 3.0 4.5 4.0 4.0 4.0 3.5 4.0 2.0
4.0 3.5 3.5 3.0 3.5 2.0 4.0 3.0 3.5 3.0 4.5 4.0 4.0 3.5 4.0 2.5 3.5 4.0 4.0 3.0 4.0 3.5 2.5 3.0 3.5 3.5 3.0 3.5 4.0 3.5 3.0 3.0 4.0 4.0 3.5 4.5 3.0 3.0 2.0
3.0 4.0 3.0 4.0 3.0 2.5 4.0 3.5 4.0 3.5 4.5 3.0 4.5 4.0 4.5 3.5 4.5 3.5 5.0 2.5 4.0 3.5 3.0 3.5 3.5 3.5 1.5 3.5 4.0 4.0 3.0 2.5 4.5 4.0 4.5 4.0 3.0 3.5 1.0
3.7 3.8 3.3 3.5 3.2 2.3 4.2 3.3 3.7 3.3 4.5 3.5 4.2 3.8 4.2 2.8 4.0 3.8 4.3 2.8 4.0 3.7 3.0 3.2 3.7 3.7 2.7 3.7 4.2 3.5 3.0 2.8 4.3 4.0 4.0 4.2 3.2 3.5 1.7
STATES AND MARKETS
5.9
Public policies and institutions
Trade
Financial sector
Business regulatory environment
Average
2010
2010
2010
2010
3.5 3.5 3.0 4.0 3.5 4.0 4.0 4.0 4.5 4.5 4.5 3.5 4.5 4.0 3.5 3.5 4.5 4.0 5.0 4.0 4.0 3.5 3.0 3.5 4.0 4.0 2.5 4.0 4.0 4.5 4.0 5.0 4.0 2.5 3.0 3.5 4.5 4.0 3.0
2.0 3.0 2.5 3.0 3.0 3.0 3.0 2.5 3.5 2.5 3.5 3.0 3.0 3.0 3.5 3.5 3.0 3.5 4.0 2.5 3.5 3.0 3.0 4.0 3.5 3.5 2.5 2.5 4.0 2.5 2.5 3.5 3.5 3.0 3.0 3.0 2.5 3.5 2.0
3.0 3.0 3.0 3.0 3.0 4.0 3.5 3.0 3.5 3.5 3.0 3.0 3.5 3.0 3.5 3.5 3.0 4.0 3.5 2.5 4.0 3.0 2.5 4.0 4.5 4.5 2.5 3.0 3.5 1.5 3.0 3.0 4.0 3.0 3.5 3.5 3.5 3.5 2.0
2.8 3.2 2.8 3.3 3.2 3.7 3.5 3.2 3.8 3.5 3.7 3.2 3.7 3.3 3.5 3.5 3.5 3.8 4.2 3.0 3.8 3.2 2.8 3.8 4.0 4.0 2.5 3.2 3.8 2.8 3.2 3.8 3.8 2.8 3.2 3.3 3.5 3.7 2.3
over all score, which is obtained by averaging the
should be noted that the criteria are designed in a
consistent across countries, the process involves
average scores of the four clusters. For each of the
developmentally neutral manner. Accordingly, higher
two key phases. In the benchmarking phase a small
16 criteria countries are rated on a scale of 1 (low)
scores can be attained by a country that, given its
representative sample of countries drawn from all
to 6 (high). The scores depend on the level of perfor-
stage of develop ment, has a policy and institutional
regions is rated. Country teams prepare proposals
mance in a given year assessed against the criteria,
framework that more strongly fosters growth and
that are reviewed first at the regional level and then
rather than on changes in performance compared
poverty reduction.
in a Bankwide review process. A similar process is
with the previous year. All 16 CPIA criteria contain a
The country teams that prepare the ratings are very
followed to assess the performance of the remaining
detailed description of each rating level. In assess-
familiar with the country, and their assessments are
countries, using the benchmark countries’ scores as
ing country performance, World Bank staff evaluate
based on country diagnostic studies prepared by the
guideposts. The final ratings are determined following
the country’s performance on each of the criteria
World Bank or other development organizations and
a Bankwide review. The overall numerical IRAI score
and assign a rating. The ratings reflect a variety of
on their own professional judgment. An early con-
and the separate criteria scores were first publicly
indicators, observations, and judgments based on
sultation is conducted with country authorities to
disclosed in June 2006.
country knowledge and on relevant publicly available
make sure that the assessments are informed by
indicators. In interpreting the assessment scores, it
up-to-date information. To ensure that scores are
See IDA’s website at www.worldbank.org/ida for more information.
2012 World Development Indicators
317
5.9 Afghanistan Angola Armenia Azerbaijan Bangladesh Benin Bhutan Bolivia Bosnia and Herzegovina Burkina Faso Burundi Cambodia Cameroon Cape Verde Central African Republic Chad Comoros Congo, Dem. Rep. Congo, Rep. Côte d’Ivoire Djibouti Dominica Eritrea Ethiopia Gambia, The Georgia Ghana Grenada Guinea Guinea-Bissau Guyana Haiti Honduras India Kenya Kiribati Kosovo Kyrgyz Republic
Public policies and institutions Policies for social inclusion and equity
Public sector management and institutions
1–6 (low to high)
1–6 (low to high)
Policies and institutions for Social protection environmental and labor sustainability Average
Quality of budgetary and Property financial rights and rule-based management governance
Transparency, accountability, and corruption Quality Efficiency in the public of public of revenue sector Average mobilization administration
Gender equality
Equity of public resource use
Building human resources
2010
2010
2010
2010
2010
2010
2010
2010
2010
2010
2010
2010
2.0 3.5 4.5 4.0 4.0 3.5 4.0 4.0 4.5 3.5 4.0 4.0 3.0 4.5 2.5 2.5 3.0 2.5 3.0 2.5 3.0 3.0 3.5 3.0 3.5 4.5 4.0 4.5 3.5 2.5 3.5 3.0 4.0 3.5 3.5 2.5 3.5 4.5
3.0 2.5 4.5 4.0 3.5 3.5 4.0 4.0 3.5 4.0 3.5 3.5 3.0 4.5 2.5 2.0 2.5 3.0 2.5 2.0 3.0 3.5 2.5 4.5 4.0 4.5 4.0 3.5 3.0 3.0 3.5 3.0 4.0 4.0 4.0 4.0 3.5 3.5
3.0 2.5 4.0 4.0 4.0 3.5 4.5 4.0 3.5 3.5 3.5 3.5 3.5 4.5 2.5 2.5 3.0 3.5 3.5 2.5 3.5 4.0 3.5 4.0 3.5 4.5 4.5 4.0 3.0 2.5 4.0 2.5 4.0 4.0 4.0 2.5 2.5 3.5
2.5 2.5 4.5 4.0 3.5 3.0 3.5 3.5 3.5 3.5 3.0 3.0 3.0 4.5 2.0 2.5 2.5 2.5 2.5 2.5 3.5 3.5 2.5 3.5 2.5 4.5 4.0 3.5 3.0 2.5 3.0 2.5 3.0 3.5 3.5 3.0 3.5 3.5
2.5 3.0 3.0 3.0 3.0 3.5 4.5 3.5 3.5 3.5 3.0 3.0 3.0 3.5 3.0 2.0 2.0 2.5 2.5 2.5 3.5 3.5 2.0 3.0 3.5 3.0 3.5 4.0 2.5 3.0 3.0 2.5 3.5 3.5 3.5 3.0 3.0 3.0
2.6 2.8 4.1 3.8 3.6 3.4 4.1 3.8 3.7 3.6 3.4 3.4 3.1 4.3 2.5 2.3 2.6 2.8 2.8 2.4 3.3 3.5 2.8 3.6 3.4 4.2 4.0 3.9 3.0 2.7 3.4 2.7 3.7 3.7 3.7 3.0 3.2 3.6
1.5 2.0 3.5 3.0 3.0 3.0 3.5 2.5 3.0 3.5 2.5 2.5 2.5 4.0 2.0 2.0 2.5 2.0 2.5 2.0 2.5 4.0 2.5 3.0 3.0 3.5 3.5 3.5 2.0 2.5 3.0 2.0 3.0 3.5 2.5 3.5 3.0 2.5
3.5 2.5 4.5 3.5 3.0 3.5 3.5 3.5 3.5 4.5 3.0 3.5 3.0 4.0 3.0 2.0 2.0 2.5 2.5 2.5 3.0 4.0 2.5 3.5 3.5 4.0 3.5 4.0 3.0 2.5 3.5 3.0 3.5 3.5 3.5 3.0 4.0 3.5
3.0 2.5 3.5 3.5 3.0 3.5 4.0 4.0 4.0 3.5 3.0 3.0 3.5 3.5 2.5 2.5 2.5 2.5 3.0 3.5 3.5 4.0 3.5 3.5 3.5 4.5 4.0 3.5 3.0 3.0 3.5 2.5 4.0 4.0 4.0 3.0 3.5 3.5
2.0 2.5 4.0 3.0 3.0 3.0 4.0 3.0 3.0 3.5 2.5 2.5 3.0 4.0 2.5 2.5 2.5 2.0 2.5 2.0 2.5 3.5 3.0 3.5 3.0 4.0 3.5 3.5 3.0 2.5 2.5 2.5 3.0 3.5 3.5 3.0 2.5 3.0
2.0 2.5 3.0 2.5 3.0 3.5 4.5 3.5 3.0 3.5 2.0 2.0 2.5 4.5 2.5 2.0 2.5 2.0 2.5 2.0 2.5 4.0 2.0 2.5 2.0 3.5 4.0 4.0 2.0 2.5 2.5 2.5 3.0 3.5 3.0 3.0 3.0 2.5
2.4 2.4 3.7 3.1 3.0 3.3 3.9 3.3 3.3 3.7 2.6 2.7 2.9 4.0 2.5 2.2 2.4 2.2 2.6 2.4 2.8 3.9 2.7 3.2 3.0 3.9 3.7 3.7 2.6 2.6 3.0 2.5 3.3 3.6 3.3 3.1 3.2 3.0
Definitions • International Development Association Resource
long-term debt sustainability. • Structural policies
protection under law. • Equity of public resource use
Allocation Index is obtained by calculating the aver-
cluster: Trade assesses how the policy framework
assesses the extent to which the pattern of public
age score for each cluster and then by averaging
fosters trade in goods. • Financial sector assesses
expenditures and revenue collection affects the poor
those scores. For each of 16 criteria countries are
the structure of the financial sector and the poli-
and is consistent with national poverty reduction
rated on a scale of 1 (low) to 6 (high) • Economic
cies and regulations that affect it. • Business regu-
priorities. • Building human resources assesses
management cluster: Macro economic manage-
latory environment assesses the extent to which
the national policies and public and private sec-
ment assesses the monetary, exchange rate, and
the legal, regulatory, and policy environments help
tor service delivery that affect the access to and
aggregate demand policy framework. • Fiscal policy
or hinder private businesses in investing, creating
quality of health and education services, including
assesses the short- and medium-term sustainability
jobs, and becoming more productive. • Policies for
prevention and treatment of HIV/AIDS, tuberculosis,
of fiscal policy (taking into account monetary and
social inclusion and equity cluster: Gender equal-
and malaria. • Social protection and labor assess
exchange rate policy and the sustainability of the
ity assesses the extent to which the country has
government policies in social protection and labor
public debt) and its impact on growth. • Debt policy
installed institutions and programs to enforce laws
market regulations that reduce the risk of becoming
assesses whether the debt management strategy is
and policies that promote equal access for men
poor, assist those who are poor to better manage
conducive to minimizing budgetary risks and ensuring
and women in education, health, the economy, and
further risks, and ensure a minimal level of welfare
318
2012 World Development Indicators
Lao PDR Lesotho Liberia Madagascar Malawi Maldives Mali Mauritania Moldova Mongolia Mozambique Nepal Nicaragua Niger Nigeria Pakistan Papua New Guinea Rwanda Samoa São Tomé and Príncipe Senegal Sierra Leone Solomon Islands Sri Lanka St. Lucia St. Vincent & Grenadines Sudan Tajikistan Tanzania Timor-Leste Togo Tonga Uganda Uzbekistan Vanuatu Vietnam Yemen, Rep. Zambia Zimbabwe
5.9
Policies for social inclusion and equity
Public sector management and institutions
1–6 (low to high)
1–6 (low to high)
Policies and institutions for Social protection environmental and labor sustainability Average
Quality of budgetary and Property financial rights and rule-based management governance
STATES AND MARKETS
Public policies and institutions
Transparency, accountability, and corruption Quality Efficiency in the public of public of revenue sector Average mobilization administration
Gender equality
Equity of public resource use
Building human resources
2010
2010
2010
2010
2010
2010
2010
2010
2010
2010
2010
2010
3.5 4.0 2.5 3.5 3.5 4.0 3.5 4.0 5.0 3.5 3.5 4.0 3.5 2.5 3.0 2.0 2.5 4.0 3.5 3.0 3.5 3.0 3.0 4.0 3.5 4.0 2.5 4.0 3.5 3.5 3.0 3.0 3.5 4.0 3.5 4.5 2.0 3.5 2.5
4.0 3.0 3.5 4.0 3.5 4.0 4.0 3.5 3.5 3.5 3.5 4.0 4.0 3.5 3.5 3.5 4.0 4.5 4.5 3.0 3.5 3.5 2.5 3.5 4.0 3.5 2.5 3.5 4.0 3.0 2.5 4.0 4.0 4.0 3.5 4.5 3.5 3.5 2.0
3.5 3.5 2.5 3.5 3.5 4.0 3.5 3.0 4.0 3.5 3.5 4.0 3.5 3.5 3.0 3.0 2.5 4.5 4.0 3.5 3.5 3.5 3.0 4.5 4.0 4.0 2.5 3.0 3.5 3.0 3.0 4.0 3.5 4.0 2.5 4.0 3.0 4.0 1.5
2.5 3.0 2.5 3.5 3.5 3.5 3.5 2.5 3.5 3.5 3.0 3.0 3.5 3.0 3.5 3.5 3.0 3.5 3.5 2.5 3.0 3.5 2.5 3.5 3.5 3.5 2.5 3.5 4.0 2.5 3.0 3.0 3.5 3.5 2.5 3.5 3.5 3.0 1.0
4.0 3.0 2.5 3.5 3.5 4.0 3.0 3.0 4.0 3.0 3.5 3.5 3.5 3.5 3.0 2.5 2.0 3.5 4.0 3.0 3.5 2.5 2.0 3.0 3.5 3.5 2.0 3.0 3.5 2.5 2.5 3.0 4.0 3.5 3.0 3.5 3.5 3.5 2.0
3.5 3.3 2.7 3.6 3.5 3.9 3.5 3.2 4.0 3.4 3.4 3.7 3.6 3.2 3.2 2.9 2.8 4.0 3.9 3.0 3.4 3.2 2.6 3.7 3.7 3.7 2.4 3.4 3.7 2.9 2.8 3.4 3.7 3.8 3.0 4.0 3.1 3.5 1.8
3.0 3.5 2.5 3.0 3.5 3.5 3.5 3.0 3.5 3.0 3.0 2.5 2.5 3.0 2.5 3.0 2.0 3.5 4.0 2.5 3.5 2.5 3.0 3.5 4.0 4.0 2.0 2.5 3.5 2.0 2.5 4.0 3.5 2.5 3.5 3.5 2.5 3.0 1.5
3.5 3.5 2.5 2.5 3.0 3.0 3.5 3.0 4.0 4.0 4.0 2.5 4.0 3.5 3.0 3.5 3.0 4.0 3.5 3.0 3.5 3.5 2.5 4.0 3.5 3.5 2.0 3.5 3.5 3.0 3.0 3.5 3.5 3.5 4.0 4.0 3.5 3.5 2.0
3.5 4.0 3.5 3.5 4.0 4.5 3.5 3.5 3.5 3.5 4.0 3.5 4.0 3.5 3.0 3.0 3.5 3.5 4.5 3.5 4.0 3.0 3.0 3.5 4.5 4.0 3.0 3.0 4.0 3.0 3.0 4.5 3.5 3.5 3.5 4.0 3.0 3.5 3.5
3.0 3.0 2.5 3.5 3.5 3.5 3.0 3.0 3.0 3.5 3.0 3.0 3.0 3.0 3.0 3.5 3.0 4.0 4.0 3.0 3.5 3.0 2.0 3.0 3.5 3.5 2.5 3.0 3.0 2.5 2.0 3.5 3.0 3.0 3.0 3.5 3.0 3.0 2.0
2.5 3.5 3.0 2.5 3.0 3.0 3.5 2.5 3.0 3.0 3.0 2.5 2.5 3.0 3.0 2.5 3.0 3.5 4.0 3.5 3.0 3.0 3.0 3.0 4.5 4.0 1.5 2.0 2.5 3.0 2.5 3.5 2.5 1.5 3.0 3.0 2.5 2.5 1.5
3.1 3.5 2.8 3.0 3.4 3.5 3.4 3.0 3.4 3.4 3.4 2.8 3.2 3.2 2.9 3.1 2.9 3.7 4.0 3.1 3.5 3.0 2.7 3.4 4.0 3.8 2.2 2.8 3.3 2.7 2.6 3.8 3.2 2.8 3.4 3.6 2.9 3.1 2.1
to all people. • Policies and institutions for envi-
and timely and accurate accounting and fiscal report-
extent to which public employees within the executive
ronmental sustainability assess the extent to which
ing, including timely and audited public accounts.
are required to account for administrative decisions,
environmental policies foster the protection and sus-
• Efficiency of revenue mobilization assesses the
use of resources, and results obtained. The three
tainable use of natural resources and the manage-
overall pat tern of revenue mobilization—not only
main dimensions assessed are the accountability
ment of pollution. • Public sector management and
the de facto tax structure, but also revenue from
of the executive to oversight institutions and of pub-
institutions cluster: Property rights and rule-based
all sources as actually collected. • Quality of public
lic employees for their performance, access of civil
governance assess the extent to which private eco-
administration assesses the extent to which civilian
society to information on public affairs, and state
nomic activity is facilitated by an effective legal sys-
central government staff is structured to design and
capture by narrow vested interests.
tem and rule-based governance structure in which
implement government policy and deliver services
property and contract rights are reliably respected
effectively. • Transparency, accountability, and cor-
and enforced. • Quality of budgetary and financial
ruption in the public sector assess the extent to
management assesses the extent to which there is
which the executive can be held accountable for its
a comprehensive and credible budget linked to policy
use of funds and for the results of its actions by the
priorities, effective financial management systems,
elector ate, the legislature, and the judiciary and the
Data sources Data on public policies and institutions are from the World Bank Group’s CPIA database (www. worldbank.org/ida).
2012 World Development Indicators
319
5.10
Transport services Roads
Afghanistan Albania Algeria Angola Argentina Armenia Australia Austria Azerbaijan Bahrain Bangladesh Belarus Belgium Benin Bolivia Bosnia and Herzegovina Botswana Brazil Bulgaria Burkina Faso Burundi Cambodia Cameroon Canada Central African Republic Chad Chile China Hong Kong SAR, China Colombia Congo, Dem. Rep. Congo, Rep. Costa Rica Côte d’Ivoire Croatia Cuba Cyprus Czech Republic Denmark Dominican Republic Ecuador Egypt, Arab Rep. El Salvador Eritrea Estonia Ethiopia Finland France Gabon Gambia, The Georgia Germany Ghana Greece Guatemala Guinea Guinea-Bissau Haiti
320
Railways
Total road network km
Paved roads %
Passengers carried million passengerkm
2000–09a
2000–09a
2000–09a
2000–09a
232 197 .. 166,045 .. 2,356 301,524 69,000 15,291 .. .. 8,184 131,470 .. .. 1,959 .. .. 13,839 .. .. 201 .. 493,814 .. .. .. 1,351,144 .. 157 .. .. 27 .. 3,438 6,634 .. 88,352 68,907 .. 11,819 12,793 .. .. 2,453 219,113 72,700 773,000 .. 16 5,724 949,306 .. .. .. .. .. ..
6,575 2,200 .. 4,709 .. 182 189,847 16 10,634 .. .. 22,767 36,174 .. .. 1,711 .. .. 17,742 .. .. .. .. 129,600 .. .. .. 3,718,882 .. 39,726 .. .. 1 .. 9,429 2,315 944 44,955 10,003 .. 1,193 .. .. .. 5,249 2,456 25,200 265,000 .. .. 611 427,300 .. 28,585 .. .. .. ..
42,150 18,000 112,039 51,429 231,374 7,705 817,089 106,840 52,942 4,083 239,226 94,797 153,872 19,000 80,294 21,846 25,798 1,751,868 40,231 92,495 12,322 38,257 28,857 1,409,000 24,307 40,000 78,425 3,860,823 2,050 129,485 153,497 17,000 39,039 81,996 29,343 .. 12,380 130,573 73,330 12,600 43,670 100,472 10,029 4,010 58,382 44,359 78,925 951,260 9,170 3,742 20,329 643,969 109,515 116,929 14,095 44,348 3,455 4,160
29.3 39.0 74.0 10.4 30.0 93.6 43.5 100.0 50.6 82.1 9.5 88.6 78.2 9.5 7.9 52.3 32.6 5.5 98.4 4.2 10.4 6.3 17.0 39.9 .. 0.8 22.5 53.5 100.0 .. 1.8 7.1 26.0 7.9 90.5 49.0 64.9 100.0 100.0 49.4 14.8 89.4 19.8 21.8 28.6 13.7 65.5 100.0 12.0 19.3 94.1 100.0 12.6 91.8 34.5 9.8 27.9 24.3
2012 World Development Indicators
Goods hauled million ton-km
Passengers carried million Rail lines total route- passengerkm km 2000–10a
.. 423 3,512 .. 25,023 826 8,615 5,066 2,079 .. 2,835 5,503 3,578 758 2,866 1,026 888 29,817 4,098 622 .. 650 977 58,345 .. .. 5,352 66,239 .. 1,672 3,641 795 .. 639 2,722 5,076 .. 9,569 2,131 .. .. 5,195 .. .. 787 .. 5,919 33,608 810 .. 1,566 33,708 953 2,552 .. .. .. ..
2000–10a
.. 32 1,045 .. 6,979 50 1,500 10,306 917 .. 7,305 7,578 10,493 .. 313 59 94 .. 2,100 .. .. 45 377 2,875 .. .. 840 791,158 .. .. 37 211 .. 10 1,742 1,285 .. 6,553 7,405 .. .. 40,837 .. .. 248 .. 3,959 86,853 111 .. 655 78,582 85 1,413 .. .. .. ..
Ports
Air
Goods hauled million ton-km
Port container traffic thousand TEU
Registered carrier departures worldwide thousands
Passengers carried thousands
Air freight million ton-km
2000–10a
2010
2010
2010
2010
.. 46 1,281 .. 12,025 346 64,172 23,104 8,250 .. 710 46,224 5,439 36 1,060 1,227 674 267,700 3,061 .. .. 92 978 322,741 .. .. 4,032 2,451,185 .. 9,049 193 234 .. 675 2,618 1,351 .. 13,592 2,030 .. .. 3,840 .. .. 6,261 .. 9,760 22,840 2,238 .. 6,228 105,794 181 538 .. .. .. ..
.. 87 266 .. 2,018 .. 6,536 350 .. 290 1,356 .. 10,985 278 .. .. .. 8,121 143 .. .. 224 250 4,813 .. .. 3,172 129,611 23,699 2,444 .. 297 1,013 608 137 228 349 .. 709 1,383 1,222 6,709 146 .. 152 .. 1,247 5,323 136 .. 226 14,625 514 1,165 1,012 .. .. ..
.. 10 44 14 100 9 418 184 10 58 12 13 143 .. 36 2 8 885 31 4 .. 5 10 1,235 .. .. 109 2,391 151 180 .. .. 49 .. 26 10 22 94 4b .. 50 113 20 .. 11 48 183 751 1 .. 6 1,086 .. 148 .. .. .. ..
.. 920 3,686 1,283 10,030 757 45,268 13,869 801 5,339 2,177 888 7,263 .. 1,916 133 290 77,255 2,243 254 .. 312 466 67,325 .. .. 10,234 267,691 25,383 15,111 .. .. 1,747 .. 1,641 862 2,363 7,103 519b .. 4,461 9,637 2,150 .. 809 3,141 11,578 65,401 96 .. 328 112,399 .. 15,588 .. .. .. ..
.. 0 6 91 178 6 2,380 430 8 843 85 2 1,274 .. 14 0 0 976 2 0 .. 18 23 2,011 .. .. 1,400 17,441 7,970 1,576 .. .. 20 .. 2 16 38 23 0b .. 115 188 17 .. 1 672 736 5,081 2 .. 15 9,245 .. 37 .. .. .. ..
Roads
Honduras Hungary India Indonesia Iran, Islamic Rep. Iraq Ireland Israel Italy Jamaica Japan Jordan Kazakhstan Kenya Korea, Dem. Rep. Korea, Rep. Kosovo Kuwait Kyrgyz Republic Lao PDR Latvia Lebanon Lesotho Liberia Libya Lithuania Macedonia, FYR Madagascar Malawi Malaysia Mali Mauritania Mauritius Mexico Moldova Mongolia Morocco Mozambique Myanmar Namibia Nepal Netherlands New Zealand Nicaragua Niger Nigeria Norway Oman Pakistan Panama Papua New Guinea Paraguay Peru Philippines Poland Portugal Puerto Rico Qatar
Railways
Passengers carried million Rail lines total route- passengerkm km
Total road network km
Paved roads %
Passengers carried million passengerkm
2000–09a
2000–09a
2000–09a
2000–09a
2000–10a
20.4 38.0 49.5 56.9 73.3 84.3 100.0 100.0 100.0 73.3 80.1 100.0 88.5 14.3 2.8 79.3 .. 85.0 91.1 13.7 20.9 .. 18.3 6.2 57.2 29.4 57.6 11.6 45.0 81.3 24.6 26.8 98.0 35.3 85.8 3.5 70.3 20.8 11.9 14.7 53.9 90.0 66.2 11.6 20.7 15.0 80.7 46.0 65.4 42.0 3.5 50.8 13.9 9.9 69.9 86.0 95.0 90.0
.. 20,449 .. .. .. .. .. .. 97,560 .. 905,907 .. 110,475 .. .. 100,617 .. .. 6,745 2,113 14,625 .. .. .. .. 20,376 1,239 .. .. .. .. .. .. 436,900 2,268 1,215 .. .. .. 47 .. .. .. 133 .. .. 64,014 .. 263,788 .. .. .. .. .. 24,386 .. .. ..
.. 35,373 .. .. .. .. 12,787 .. 192,700 .. 334,667 .. 66,254 22 .. 12,545 .. .. 912 287 8,115 .. .. .. .. 17,757 3,978 .. .. .. .. .. .. 211,600 2,714 782 800 .. .. 591 .. 72,675 .. .. .. .. 16,109 .. 129,249 .. .. .. .. .. 191,484 35,808 10 ..
.. 7,893 63,974 3,370 6,073 2,025 1,919 1,034 18,011 .. 20,035 294 14,202 1,917 .. 3,379 .. .. 417 .. 1,897 .. .. .. .. 1,767 699 854 797 1,665 733 728 .. 26,704 1,157 1,814 2,109 3,116 .. .. .. 3,016 .. .. .. 3,528 4,114 .. 7,791 .. .. .. 2,020 479 19,702 2,843 .. ..
13,600 197,519 4,109,592 476,337 192,685 40,988 96,424 18,318 487,700 22,121 1,207,867 7,878 96,846 61,945 25,554 104,983 .. 6,524 34,000 39,568 69,148 6,970 5,940 10,600 83,200 81,331 13,940 49,827 15,451 98,722 22,474 11,066 2,066 366,807 12,779 49,250 58,216 30,331 27,000 42,100 19,875 136,827 94,301 21,975 18,948 193,200 93,853 56,361 258,350 13,974 19,600 31,531 126,500 200,037 384,104 82,900 26,677 7,790
Goods hauled million ton-km
2000–10a
.. 5,398 903,465 14,344 16,814 54 1,678 1,986 44,535 .. 244,235 .. 15,448 226 .. 33,027 .. .. 99 .. 79 .. .. .. .. 373 155 10 44 1,527 196 47 .. 178 399 1,220 4,398 114 4,163 .. .. 15,400 .. .. .. 174 2,674 .. 24,731 .. .. .. 76 83 15,715 3,718 .. ..
Ports
5.10
STATES AND MARKETS
Transport services
Air
Goods hauled million ton-km
Port container traffic thousand TEU
Registered carrier departures worldwide thousands
Passengers carried thousands
Air freight million ton-km
2000–10a
2010
2010
2010
2010
.. 1,000 600,548 4,390 20,247 121 92 1,062 12,037 .. 20,432 353 213,174 1,399 .. 9,452 .. .. 738 .. 17,164 .. .. .. .. 13,431 497 12 33 1,384 189 7,566 .. 71,136 927 10,287 5,572 695 885 .. .. 4,331 4,078 .. .. 77 2,092 .. 6,187 .. .. .. 900 1 34,266 1,932 .. ..
620 .. 9,753 8,371 2,593 .. 773 2,282 9,787 1,892 18,060 619 .. 696 .. 18,538 .. 888 .. .. 257 949 .. .. 162 295 .. 141 .. 18,247 .. 66 445 3,694 .. .. 2,058 223 167 256 .. 11,331 2,427 68 .. 90 331 3,893 2,149 5,906 269 7 1,534 4,947 1,045 1,610 1,526 346
.. 118 630 405 152 .. 683 54 364 8 650 39 27 36 1 483 .. 22 5 11 66 17 .. .. 25 5 2 12 3 240 .. 1 12 186 6 8 92 14 8 9 2 243 186 .. .. 17 3b 35 50 81 20 6 68 169 91 171 .. 91
.. 11,829 64,144 35,321 17,585 .. 76,488 6,141 48,748 974 94,212 2,972 2,426 3,284 84 42,763 .. 2,741 270 645 4,466 1,600 .. .. 2,345 167 137 417 172 26,255 .. 154 1,268 13,608 455 417 8,971 553 396 488 288 27,554 10,128 .. .. 1,555 399b 4,068 6,012 7,366 1,158 606 6,130 18,933 5,466 11,124 .. 12,391
2012 World Development Indicators
.. 8 1,720 660 131 .. 350 865 1,164 6 8,380 219 48 258 7 12,802 .. 274 1 0 23 43 .. .. 17 120 0 22 4 2,451 .. 0 179 1,380 1 3 11 8 2 0 4 5,028 668 .. .. 1 0b 27 310 2 26 0 248 472 91 375 .. 2,946
321
5.10
Transport services Roads
Romania Russian Federation Rwanda Saudi Arabia Senegal Serbia Sierra Leone Singapore Slovak Republic Slovenia Somalia South Africa South Sudan Spain Sri Lanka Sudan Swaziland Sweden Switzerland Syrian Arab Republic Tajikistan Tanzania Thailand Timor-Leste Togo Trinidad and Tobago Tunisia Turkey Turkmenistan Uganda Ukraine United Arab Emirates United Kingdom United States Uruguay Uzbekistan Venezuela, RB Vietnam West Bank and Gaza Yemen, Rep. Zambia Zimbabwe World Low income Middle income Lower middle income Upper middle income Low & middle income East Asia & Pacific Europe & Central Asia Latin America & Carib. Middle East & N. Africa South Asia Sub-Saharan Africa High income Euro area
Total road network km
Paved roads %
Passengers carried million passengerkm
2000–09a
2000–09a
2000–09a
198,817 982,000 14,008 221,372 14,825 44,334 11,300 3,356 43,879 38,927 22,100 362,099 .. 667,064 97,286 11,900 3,594 582,950 71,371 68,157 27,767 103,706 180,053 .. 11,652 8,320 19,371 362,660 24,000 70,746 169,495 4,080 419,665 6,545,839 77,732 81,600 96,155 160,089 5,588 71,300 66,781 97,267
Railways
Goods hauled million ton-km 2000–09a
30.2 12,805 20,878 80.1 139,034 180,135 19.0 .. .. 21.5 .. .. 32.0 .. .. 63.2 4,169 1,184 8.0 .. .. 100.0 5,762 .. 87.1 31,093 27,484 100.0 777 14,762 11.8 .. .. 17.3 .. 434 .. .. .. 99.0 410,192 211,891 81.0 21,067 .. 36.3 .. .. 30.0 .. .. 24.4 109,100 35,000 100.0 95,090 16,734 90.3 589 .. .. 8,591 5,013 6.7 8 7 98.5 .. .. .. .. .. 21.0 .. .. 51.1 .. .. 75.2 .. 16,611 88.7 212,464 176,455 81.2 .. .. 23.0 .. .. 97.8 54,631 33,193 100.0 .. .. 100.0 736,000 143,453 67.4 7,874,329 1,889,923 10.0 2,588 .. 87.3 56,674 21,038 33.6 .. .. 47.6 59,735 30,261 91.7 .. .. 8.7 .. .. 22.0 .. .. 19.0 .. .. 64.9 m .. m .. m 20.7 .. .. 53.5 .. .. 48.6 .. .. 57.6 .. .. 44.8 .. .. 30.7 .. .. 85.8 14,625 9,375 22.5 .. .. 75.2 .. .. 53.9 .. .. 18.8 .. .. 81.1 .. 28,585 87.3 69,000 28,035
Passengers carried million Rail lines total route- passengerkm km 2000–10a
13,620 85,292 .. 1,020 906 4,058 .. .. 3,587 1,228 .. 22,051 .. 15,317 1,463 4,508 300 9,957 3,543 2,139 621 2,600 c 4,429 .. .. .. 1,119 9,594 3,115 259 21,705 .. 31,471 228,513 2,993 4,227 336 2,347 .. .. 1,273 2,583 .. s .. .. .. .. .. .. 177,892 .. .. .. .. .. 131,414
2000–10a
Ports
Air
Goods hauled million ton-km
Port container traffic thousand TEU
Registered carrier departures worldwide thousands
Passengers carried thousands
Air freight million ton-km
2000–10a
2010
2010
2010
2010
5,248 9,134 139,028 2,011,308 .. .. 337 1,748 129 384 658 3,868 .. .. .. .. 2,291 7,669 813 3,283 .. .. 18,865 113,342 .. .. 22,304 7,844 4,767 135 34 766 0 776 6,774 11,500 17,609 8,725 1,120 2,370 33 808 475c 728c 8,037 3,161 .. .. .. .. .. .. 1,493 2,073 5,491 11,030 1,811 11,992 .. 218 50,240 218,091 .. .. 55,019 12,512 9,518 2,468,738d 15 284 2,905 22,282 .. 81 4,378 3,901 .. .. .. .. 183 .. .. 1,580 2,134 m 4,532 m .. .. 1,220 4,802 1,120 3,910 1,269 4,032 .. 3,127 4,248 3,384 658 8,250 .. .. 1,493 2,222 24,731 6,187 .. .. 7,090 8,285 10,306 7,669
557 3,130 .. 5,313 349 .. .. 29,179 .. 477 .. 3,806 .. 12,608 4,080 439 .. 1,391 99 649 .. 427 6,649 .. .. 573 466 5,547 .. .. 660 15,174 7,389 42,190 672 .. 1,216 5,984 .. 370 .. .. 538,284 s .. 260,211 51,323 208,887 263,722 174,467 10,901 36,495 15,441 17,396 .. 274,561 73,233
66 3,996 673 56,848 .. .. 163 18,998 0 573 18 1,059 0 24 81 25,319 14 991 25 1,170 .. .. 210 16,779 .. .. 606 58,563 17 2,800 6 602 .. .. 363b 3b 220 21,477 11 1,018 5 951 21 749 122 20,303 .. .. .. .. 25 1,721 44 5,464 400 51,590 3 399 0 70 84 5,679 261 42,555 1,173 123,972 8,934 e 707,426e 19 744 24 2,167 125 5,881 103 14,099 .. .. 11 1,134 4 62 5 302 28,078 s 2,595,449 s 182 13,444 8,539 834,810 1,973 185,587 6,566 649,224 8,721 848,254 3,572 388,023 1,464 136,681 1,974 160,869 547 54,412 719 75,735 445 32,534 19,357 1,747,195 4,711 457,356
5 4,614 .. 1,336 0 2 9 4,004 0 2 .. 1,107 .. 1,684 329 13 .. 0b 1,275 3 5 2 3,133 .. .. 13 21 1,101 6 32 598 10,126 8,555 50,743e 0 166 5 428 .. 28 .. 12 189,325 s 1,142 41,820 5,190 36,630 42,962 24,716 6,725 5,967 668 2,453 2,432 146,364 30,754
a. Data are for the most recent year available in the period shown. b. Covers international nonscheduled carriers only. Total for scheduled international and domestic carriers for Scandinavian countries are 621,715 registered carrier departures worldwide, 48,916,632 passengers carried, and 583,140,000 ton-kilometers of air freight. c. Includes Tazara railway. d. Refers to class 1 railways only. e. Covers only carriers designated by the U.S. Department of Transportation as major and national air carriers.
322
2012 World Development Indicators
About the data
5.10
Definitions
Transport infrastructure—highways, railways, ports
Measures of port container traffi c, much of it
• Total road network covers motorways, highways,
and waterways, and airports and air traffic control
commodities of medium to high value added, give
main or national roads, secondary or regional roads,
systems—and the services that fl ow from it are
some indication of economic growth in a country.
and all other roads in a country. • Paved roads are
crucial to the activities of households, producers,
But when traffic is merely transshipment, much of
roads surfaced with crushed stone (macadam) and
and governments. Because performance indicators
the economic benefit goes to the terminal operator
hydrocarbon binder or bituminized agents, with con-
vary widely by transport mode and focus (whether
and ancillary services for ships and containers rather
crete, or with cobblestones. • Passengers carried
physical infrastructure or the services flowing from
than to the country more broadly. In transshipment
by road are the number of passengers transported
that infrastructure), highly specialized and carefully
centers empty containers may account for as much
by road times kilometers traveled. • Goods hauled
specified indicators are required. The table provides
as 40 percent of traffic.
by road are the volume of goods transported by road
selected indicators of the size, extent, and produc-
The air transport data represent the total (interna-
vehicles, measured in millions of metric tons times
tivity of roads, railways, and air transport systems
tional and domestic) scheduled traffic carried by the
kilometers traveled. • Rail lines are the length of rail-
and of the volume of traffic in these modes as well
air carriers registered in a country. Countries submit
way route available for train service, irrespective of
as in ports. Indicators on traffic and congestion are
air transport data to ICAO on the basis of standard
the number of parallel tracks. • Passengers carried
presented in table 3.15, and indicators on logistics
instructions and definitions issued by ICAO. In many
by railway are the number of passengers transported
performance are presented in table 6.8.
cases, however, the data include estimates by ICAO
by rail times kilometers traveled. • Goods hauled
Data for transport sectors are not always inter-
for nonreporting carriers. Where possible, these esti-
by railway are the volume of goods transported by
nationally comparable. Unlike for demographic sta-
mates are based on previous submissions supple-
railway, measured in metric tons times kilometers
tistics, national income accounts, and international
mented by information published by the air carriers,
traveled. • Port container traffic measures the flow
trade data, the collection of infrastructure data has
such as flight schedules.
of containers from land to sea transport modes and
not been “internationalized.” But data on roads are
The data cover the air traffic carried on scheduled
vice versa in twenty-foot-equivalent units (TEUs), a
collected by the International Road Federation (IRF),
services, but changes in air transport regulations
standard-size container. Data cover coastal shipping
and data on air transport by the International Civil
in Europe have made it more diffi cult to classify
as well as international journeys. Transshipment traf-
Aviation Organization (ICAO).
traffic as scheduled or nonscheduled. Thus recent
fic is counted as two lifts at the intermediate port
National road associations are the primary source
increases shown for some European countries may
(once to off-load and again as an outbound lift) and
of IRF data. In countries where a national road asso-
be due to changes in the classification of air traffic
includes empty units. • Registered carrier depar-
ciation is lacking or does not respond, other agencies
rather than actual growth. For countries with few air
tures worldwide are domestic takeoffs and takeoffs
are contacted, such as road directorates, ministries
carriers or only one, the addition or discontinuation
abroad of air carriers registered in the country. • Pas-
of transport or public works, or central statistical
of a home-based air carrier may cause significant
sengers carried by air include both domestic and
offices. As a result, definitions and data collection
changes in air traffic.
international passengers of air carriers registered
methods and quality differ, and the compiled data
in the country. • Air freight is the volume of freight,
are of uneven quality. Moreover, the quality of trans-
express, and diplomatic bags carried on each flight
port service (reliability, transit time, and condition of
stage (operation of an aircraft from takeoff to its next
goods delivered) is rarely measured, though it may be
landing), measured in metric tons times kilometers
as important as quantity in assessing an economy’s
traveled.
transport system. Unlike the road sector, where numerous qualified motor vehicle operators can operate anywhere on
Data sources
the road network, railways are a restricted transport
Data on roads are from the IRF’s World Road
system with vehicles confined to a fixed guideway.
Statistics, supplemented by World Bank staff
Considering the cost and service characteristics,
estimates. Data on railways are from a database
railways generally are best suited to carry—and can
maintained by the World Bank’s Transport, Water,
effectively compete for—bulk commodities and con-
and Information and Communication Technologies
tainerized freight for distances of 500–5,000 kilo-
Department, Transport Division, based on data
meters, and passengers for distances of 50–1,000
from the International Union of Railways. Data
kilometers. Below these limits road transport
on port container traffic are from Containerisa-
tends to be more competitive, while above these
tion International’s Containerisation International
limits air transport for passengers and freight and
Yearbook and the United Nations Conference on
sea transport for freight tend to be more competi-
Trade and Development’s UNCTADstat database
tive. The railways indicators in the table focus on
(http://unctadstat.unctad.org). Data on air trans-
scale and output measures: total route-kilometers,
port are from the ICAO’s Civil Aviation Statistics of
passenger- kilometers, and goods (freight) hauled in
the World and ICAO staff estimates.
ton-kilometers.
2012 World Development Indicators
323
STATES AND MARKETS
Transport services
5.11
Power and communications Telephonesa
Electric power Access and use
Consumption per capita kWh
Transmission Subscriptions and per 100 people distribution Fixed Mobile losses cellular % of output telephone
2009
Afghanistan Albania Algeria Angola Argentina Armenia Australia Austria Azerbaijan Bahrain Bangladesh Belarus Belgium Benin Bolivia Bosnia and Herzegovina Botswana Brazil Bulgaria Burkina Faso Burundi Cambodia Cameroon Canada Central African Republic Chad Chile China Hong Kong SAR, China Colombia Congo, Dem. Rep. Congo, Rep. Costa Rica Côte d’Ivoire Croatia Cuba Cyprus Czech Republic Denmark Dominican Republic Ecuador Egypt, Arab Rep. El Salvador Eritrea Estonia Ethiopia Finland France Gabon Gambia, The Georgia Germany Ghana Greece Guatemala Guinea Guinea-Bissau Haiti
324
.. 1,747 971 202 2,759 1,550 11,113 7,944 1,620 9,214 252 3,299 7,903 91 558 2,867 1,503 2,206 4,401 .. .. 131 271 15,471 .. .. 3,283 2,631 5,925 1,047 104 146 1,813 203 3,712 1,348 4,620 6,114 6,246 1,358 1,115 1,549 845 51 5,950 46 15,242 7,488 922 .. 1,585 6,779 265 5,540 548 .. .. 36
2012 World Development Indicators
2009
2010
2010
.. 21 21 10 15 15 7 5 22 12 2 11 5 .. 11 12 79 17 11 .. .. 18 9 8 .. .. 11 5 13 15 5 73 11 25 16 14 4 5 6 11 14 11 12 12 10 10 4 6 18 .. 13 4 23 5 14 .. .. 51
0 10 8 2 25 19 39 39 17 18 1 44 43 2 9 27 7 22 30 1 0 3 3 50 0 0 20 22 62 16 0 0 32 1 42 10 37 23 47 10 14 12 16 1 36 1 23 54 2 3 25 56 1 46 10 0 0 1
38 142 92 47 142 125 101 146 101 124 46 109 112 80 72 83 118 104 135 35 14 58 44 70 22 24 116 64 195 96 18 94 65 76 144 9 94 137 125 90 102 87 124 4 123 8 156 97 107 86 89 128 71 109 126 40 39 40
Quality
International voice traffic minutes per person
Affordability and efficiency $ per month
Mobile cellular and fi xedTelecomtelephone munications subscriptions revenue per employee % of GDP
Fixed telephone
Mobile cellular network
Population covered by mobile cellular network %
2010
2010
2010
2010
2010
2010
2010
.. 201 36 .. .. 233 .. .. 74 .. .. 25 .. 52 .. 73 .. .. 84 .. .. .. 26 .. 12 .. 9 .. 504 .. 7 .. 77 47 66 16 504 80 186 .. 20 22 .. 18 99 4 .. 74 23 .. 64 43 60 114 .. .. .. ..
75 98 .. .. .. 99 99 99 100 100 .. 100 100 90 .. 100 99 100 100 .. 83 99 .. 99 55 .. 100 99 100 .. 50 .. 70 92 100 78 100 100 .. 81 93 100 .. 90 100 .. 100 99 .. .. 99 99 77 100 .. 80 .. ..
.. 6.4 5.4 16.5 3.8 4.2 27.6 25.3 2.5 4.8 1.3 1.3 31.4 9.0 23.7 9.3 18.7 23.0 12.5 10.9 .. 7.4 15.0 19.4 10.1 16.7 25.0 4.7 8.5 5.9 .. .. 6.9 19.6 17.0 0.3 23.8 26.8 27.8 15.5 14.1 2.9 10.2 .. 11.5 1.0 16.8 26.6 .. 2.4 2.3 27.0 7.3 23.8 5.6 1.6 .. ..
.. 25.8 12.5 19.2 30.7 8.8 27.7 13.9 6.9 15.0 2.0 7.9 40.2 13.0 11.3 15.4 13.2 57.2 30.6 21.2 .. 6.7 20.1 34.3 12.9 15.4 23.7 6.0 1.4 16.9 .. .. 3.4 13.6 17.1 33.9 7.7 28.1 9.8 15.6 13.8 7.0 9.5 .. 22.3 4.1 13.1 48.6 .. 6.3 11.6 13.1 7.4 38.5 7.8 4.1 .. ..
.. 4.7 3.6 .. 3.2 4.4 .. 1.8 2.7 4.4 .. 3.1 2.6 5.4 7.3 5.3 3.5 4.9 3.2 .. 3.1 5.5 3.8 2.7 2.9 .. .. 2.0 3.5 4.2 4.9 .. 2.1 6.7 4.6 .. 3.6 3.4 2.3 2.2 .. 3.2 3.5 3.2 4.9 1.5 2.7 2.3 .. .. 3.7 2.4 3.7 3.0 .. .. .. ..
.. 1,151 .. .. .. 969 315 961 645 698 .. .. 907 3,146 .. 499 4,031 .. 543 2,306 838 1,333 2,251 .. .. .. 646 1,254 1,066 466 5,538 .. 498 3,912 945 129 458 778 616 1,394 .. 1,217 2,748 149 631 604 708 789 .. .. 697 851 3,764 804 .. .. .. ..
.. 46 17 .. 44 185 .. .. 20 .. .. 98 .. 12 84 190 113 .. 70 .. .. .. 3 .. 0 .. 30 9 1,463 83 0 .. 141 30 248 19 367 90 189 322 55 11 .. 6 85 6 .. 288 26 .. 56 .. 10 185 .. .. .. ..
Residential fi xedtelephone tariff
Mobile cellular prepaid tariff
Telephonesa
Electric power Access and use
Consumption per capita kWh 2009
Honduras Hungary India Indonesia Iran, Islamic Rep. Iraq Ireland Israel Italy Jamaica Japan Jordan Kazakhstan Kenya Korea, Dem. Rep. Korea, Rep. Kosovo Kuwait Kyrgyz Republic Lao PDR Latvia Lebanon Lesotho Liberia Libya Lithuania Macedonia, FYR Madagascar Malawi Malaysia Mali Mauritania Mauritius Mexico Moldova Mongolia Morocco Mozambique Myanmar Namibia Nepal Netherlands New Zealand Nicaragua Niger Nigeria Norway Oman Pakistan Panama Papua New Guinea Paraguay Peru Philippines Poland Portugal Puerto Rico Qatar
678 3,773 571 590 2,238 1,069 6,034 6,608 5,271 1,902 7,819 2,112 4,448 147 733 8,980 .. 17,610 1,386 .. 2,875 3,130 .. .. 4,170 3,431 3,442 .. .. 3,614 .. .. .. 1,943 1,018 1,411 756 453 104 1,576 91 6,896 9,346 460 .. 121 23,550 5,724 449 1,735 .. 1,056 1,136 593 3,591 4,815 .. 14,421
5.11
Transmission Subscriptions and per 100 people distribution Fixed Mobile losses cellular % of output telephone
Quality
International voice traffic minutes per person Fixed telephone
Mobile cellular network
Population covered by mobile cellular network %
Affordability and efficiency $ per month Residential fi xedtelephone tariff
Mobile cellular prepaid tariff
Mobile cellular and fi xedTelecomtelephone munications subscriptions revenue per employee % of GDP
2009
2010
2010
2010
2010
2010
2010
2010
2010
2010
22 10 24 10 17 40 8 3 7 7 5 14 8 16 16 4 .. 12 30 .. 13 13 .. .. 14 7 17 .. .. 4 .. .. .. 16 40 12 12 9 16 15 31 4 7 24 .. 6 8 13 20 13 .. 6 8 12 8 8 .. 7
9 30 3 16 36 5 46 43 36 10 32 8 25 1 5 58 .. 21 9 2 24 21 2 0 19 22 20 1 1 16 1 2 30 18 33 7 12 0 1 7 3 44 43 4 1 1 34 10 2 16 2 6 11 7 20 42 22 17
125 120 61 92 91 75 105 130 150 118 95 109 119 62 2 104 .. 161 97 65 103 68 45 39 172 149 105 37 20 119 48 79 93 81 89 91 100 31 1 67 31 115 115 65 25 55 116 166 57 185 28 92 100 86 123 143 74 132
39 .. .. .. .. 0 .. 413 .. 119 .. 23 23 1 .. 24 .. .. 40 .. .. .. .. .. .. 49 165 1 5 85 2 4 105 159 126 4 66 .. .. .. .. .. .. 39 .. 1 .. 24 5 53 .. 41 .. .. .. .. .. ..
197 .. .. .. .. .. .. .. .. 978 .. 217 54 23 .. 45 .. .. 19 .. .. .. 26 52 .. 61 86 6 1 .. 21 .. 148 .. 133 48 63 9 .. .. .. 84 .. .. .. 30 .. 406 33 77 .. 21 .. .. .. 121 .. ..
.. 99 83 .. .. .. 99 100 99 .. 100 99 95 89 .. 100 .. 100 96 80 .. 95 .. .. 98 100 100 .. 85 95 .. 62 99 100 .. 85 98 32 .. .. 35 98 97 100 .. 90 .. 98 92 91 .. 94 97 99 99 99 68 100
6.3 21.2 3.3 5.0 0.2 .. 26.3 15.7 25.6 11.6 26.4 9.5 2.4 14.2 .. 5.3 .. 8.6 1.2 4.0 10.1 10.3 13.0 .. .. 13.1 11.9 18.2 4.3 5.1 8.5 18.0 5.1 18.9 1.9 0.7 21.3 12.4 0.9 15.1 3.1 22.4 34.1 4.5 11.7 14.0 32.7 13.1 3.3 12.0 4.5 6.8 14.6 15.3 20.2 25.3 .. 9.1
8.9 25.7 3.4 7.8 3.6 .. 37.3 34.1 29.8 12.0 55.9 10.5 14.4 10.8 .. 14.4 .. 7.8 3.6 6.3 10.1 27.3 24.1 .. .. 9.6 23.4 15.4 21.2 7.5 14.4 14.6 6.8 17.4 12.6 3.6 33.1 17.0 12.8 17.0 2.7 33.2 47.2 13.2 20.8 13.7 20.5 9.1 2.5 8.5 23.3 8.6 43.2 10.1 15.8 22.9 .. 18.4
6.2 3.8 2.3 .. .. .. 2.5 4.0 2.1 5.4 3.0 5.9 2.2 6.3 .. 4.6 .. .. 7.9 3.8 3.6 .. 3.1 .. .. 2.7 5.7 2.8 3.6 .. 6.1 6.9 2.4 3.0 8.5 5.6 4.7 3.9 .. .. .. 2.3 3.2 .. .. 3.4 1.3 3.5 2.4 3.3 .. 4.0 2.9 .. 3.0 4.5 .. 1.6
.. 1,127 .. .. .. 1,466 534 .. 1,341 .. 1,304 1,499 463 2,546 .. 703 .. .. 442 1,283 661 .. 6,933 .. .. .. 329 3,203 .. .. 4,091 2,842 .. 918 461 372 2,770 2,430 83 .. 981 .. 600 .. .. .. .. 1,275 1,888 712 .. .. 2,512 4,504 945 1,507 411 927
2012 World Development Indicators
325
STATES AND MARKETS
Power and communications
5.11
Power and communications Telephonesa
Electric power Access and use
Consumption per capita kWh
Fixed telephone
Mobile cellular network
Population covered by mobile cellular network %
2010
2010
2010
2010
115 168 33 188 67 136 34 145 109 104 7 101 .. 112 83 41 69 116 122 58 86 47 104 53 41 141 105 85 63 38 118 145 130 90 132 74 97 177 45 46 42 61 78 w 33 78 72 84 72 73 124 98 86 59 45 111 122
49 .. 0 .. 12 125 .. .. 170 81 .. .. .. 163 .. 6 .. .. .. 35 .. 0 .. 98 4 .. 21 73 .. .. .. .. .. .. .. .. 46 .. .. 20 .. 15 .. w .. .. .. 30 .. 11 .. .. 17 .. .. .. ..
97 .. 15 .. 101 59 .. .. 109 117 .. .. .. .. .. .. .. .. .. 74 .. 7 .. 22 42 .. 52 29 .. .. .. .. .. .. .. .. .. .. .. 61 .. 23 .. w .. .. .. .. .. .. .. .. .. .. 21 .. ..
100 .. 96 99 90 97 .. 100 100 100 .. .. .. 100 98 66 91 99 100 98 .. 85 .. 69 75 100 100 100 .. 100 100 100 100 100 100 93 .. .. .. 84 90 80 93 w .. 92 86 99 91 99 .. 98 .. 84 .. 100 99
Transmission Subscriptions and per 100 people distribution Fixed Mobile losses cellular % of output telephone
2009
Romania Russian Federation Rwanda Saudi Arabia Senegal Serbia Sierra Leone Singapore Slovak Republic Slovenia Somalia South Africa South Sudan Spain Sri Lanka Sudan Swaziland Sweden Switzerland Syrian Arab Republic Tajikistan Tanzania Thailand Timor-Leste Togo Trinidad and Tobago Tunisia Turkey Turkmenistan Uganda Ukraine United Arab Emirates United Kingdom United States Uruguay Uzbekistan Venezuela, RB Vietnam West Bank and Gaza Yemen, Rep. Zambia Zimbabwe World Low income Middle income Lower middle income Upper middle income Low & middle income East Asia & Pacific Europe & Central Asia Latin America & Carib. Middle East & N. Africa South Asia Sub-Saharan Africa High income Euro area
2,267 6,136 .. 7,427 196 4,224 .. 7,949 4,925 6,103 .. 4,532 .. 6,006 408 114 .. 14,142 8,021 1,563 1,985 86 2,045 .. 111 5,662 1,311 2,298 2,446 .. 3,200 11,464 5,692 12,914 2,671 1,636 3,152 918 .. 219 635 1,026 2,803 w 229 1,675 644 2,714 1,525 2,095 3,859 1,892 1,497 517 511 9,064 6,592
2009
12 11 .. 8 17 16 .. 5 3 5 .. 10 .. 3 15 28 .. 7 6 28 17 19 6 .. 53 2 13 15 14 .. 12 12 7 6 13 9 27 10 .. 24 23 7 8w 12 11 19 9 11 5 12 16 18 23 11 6 5
2010
21 32 0 15 3 43 0 39 20 44 1 8 .. 44 17 1 4 53 56 20 5 0 10 0 4 22 12 22 10 1 28 20 54 49 29 7 25 19 9 4 1 3 17 w 1 14 6 22 12 19 26 18 16 3 1 44 46
Quality
International voice traffic minutes per person
Affordability and efficiency $ per month Residential fi xedtelephone tariff 2010
13.3 6.2 13.2 9.2 10.3 5.4 .. 8.2 20.4 17.9 .. 25.0 .. 27.0 4.9 3.9 5.0 25.5 29.4 1.3 0.9 8.8 8.8 17.3 11.0 19.5 2.5 16.8 .. 8.8 3.0 4.1 21.0 12.8 13.3 1.1 1.7 2.4 .. 1.1 24.1 9.1 11.3 m 8.8 7.1 5.3 9.8 8.8 5.1 4.8 10.9 4.1 3.3 12.0 21.2 25.3
Mobile cellular prepaid tariff
Mobile cellular and fi xedTelecomtelephone munications subscriptions revenue per employee % of GDP
2010
2010
21.8 9.2 13.9 14.1 12.7 11.6 .. 8.1 38.2 20.8 .. 23.3 .. 53.2 1.9 3.4 24.2 17.2 57.0 19.9 1.8 9.7 8.7 16.1 19.9 12.1 10.0 43.9 .. 12.2 7.5 8.6 31.0 32.7 17.8 3.0 22.3 5.4 .. 8.1 16.9 20.5 14.4 m 13.0 12.6 10.7 14.9 12.7 7.7 10.8 14.7 11.5 2.5 15.0 22.3 29.8
3.0 2.8 3.0 3.8 9.9 5.1 .. 4.0 3.0 3.6 .. .. .. 2.7 .. 3.2 4.5 1.6 3.2 11.7 .. .. .. 8.1 9.5 2.8 3.9 2.0 .. 4.3 4.0 3.6 4.5 2.0 3.1 .. 3.3 7.1 .. 3.3 .. .. 2.7 w .. 3.2 3.4 2.4 3.2 2.1 2.7 4.2 .. 2.3 .. 2.6 2.4
a. Data are from the International Telecommunication Unionâ&#x20AC;&#x2122;s (ITU) World Telecommunication/ICT Indicators database. Please cite the ITU for third-party use of these data.
326
2012 World Development Indicators
2010
883 820 3,892 2,381 3,710 865 .. .. 688 552 .. .. .. 923 1,524 2,168 2,000 813 599 .. .. .. .. 1,751 1,896 1,862 1,130 2,069 .. 2,046 .. 1,094 .. 478 765 739 1,490 .. .. 1,273 .. 2,369 887 m 2,306 1,044 .. 765 1,168 1,283 697 .. 1,131 1,363 1,840 784 789
About the data
5.11
STATES AND MARKETS
Power and communications Definitions
The quality of an economy’s infrastructure, includ-
growth was driven primarily by wireless technologies
• Electric power consumption per capita is the pro-
ing power and communications, is an important ele-
and liberalization of telecommunications markets,
duction of power plants and combined heat and power
ment in investment decisions for both domestic and
which have enabled faster and less costly network
plants less transmission, distribution, and transfor-
foreign investors. Government effort alone is not
rollout. Developing countries’ share of world mobile
mation losses and own use by heat and power plants,
enough to meet the need for investments in modern
subscriptions rose from 53 percent in 2005 to 73
divided by midyear population. • Electric power
infrastructure; public-private partnerships, especially
percent in 2010. And the number of short message
transmission and distribution losses are losses in
those involving local providers and financiers, are
service texts sent globally tripled between 2007 and
transmission between sources of supply and points
critical for lowering costs and delivering value for
2010, from 1.8 trillion to 6.1 trillion. The Interna-
of distribution and in distribution to consumers,
money. In telecommunications, competition in the
tional Telecommunication Union (ITU) estimates that
including pilferage. • Fixed-telephone subscriptions
marketplace, along with sound regulation, is lower-
there were 5.9 billion mobile subscriptions globally in
are the sum of the active number of analog fixed-
ing costs, improving quality, and easing access to
2011. No technology has ever spread faster around
telephone lines, voice-over-IP subscriptions, fixed
services around the globe.
the world. Mobile communications have a particu-
wireless local loop subscriptions, Integrated Ser-
An economy’s production and consumption of elec-
larly important impact in rural areas. The mobility,
vices Digital Network voice-channel equivalents, and
tricity are basic indicators of its size and level of
ease of use, flexible deployment, and relatively low
fixed public payphones. • Mobile cellular telephone
development. Although a few countries export elec-
and declining rollout costs of wireless technologies
subscriptions are subscriptions to a public mobile
tric power, most production is for domestic consump-
enable them to reach rural populations with low lev-
telephone service using cellular technology, which
tion. Expanding the supply of electricity to meet the
els of income and literacy. The next billion mobile
provide access to the public switched telephone
growing demand of increasingly urbanized and indus-
subscribers will consist mainly of the rural poor.
network. Post-paid and prepaid subscriptions are
trialized economies without incurring unacceptable
Access is the key to delivering telecommunications
included. • International voice traffic is the sum of
social, economic, and environmental costs is one
services to people. If the service is not affordable
international incoming and outgoing telephone traffic
of the great challenges facing developing countries.
to most people, goals of universal usage will not be
(in minutes) divided by total population. • Population
Data on electric power production and consumption
met. Two indicators of telecommunications afford-
covered by mobile cellular network is the percentage
are collected from national energy agencies by the
ability are presented in the table: fi xed- telephone
of people that live in areas served by a mobile cellular
International Energy Agency (IEA) and adjusted by the
service tariff and prepaid mobile cellular service
signal regardless of whether they use it. • Residen-
IEA to meet international definitions (for data on elec-
tariff. Telecommunications efficiency is measured
tial fixed-telephone tariff is the monthly subscription
tricity production, see table 3.8). Electricity consump-
by total telecommunications revenue divided by GDP
charge plus the cost of 30 three-minute local calls
tion is equivalent to production less power plants’ own
and by mobile cellular and fixed- telephone subscrip-
(15 peak and 15 off-peak). • Mobile cellular prepaid
use and transmission, distribution, and transformation
tions per employee.
tariff is based on the Organisation for Economic Co-
losses less exports plus imports. It includes consump-
Operators have traditionally been the main source
operation and Development’s low-user definition,
tion by auxiliary stations, losses in transformers that
of telecommunications data, so information on
which includes the cost of monthly mobile use for
are considered integral parts of those stations, and
subscriptions has been widely available for most
25 outgoing calls per month spread over the same
electricity produced by pumping installations. Where
countries. This gives a general idea of access, but a
mobile network, other mobile networks, and mobile
data are available, it covers electricity generated by
more precise measure is the penetration rate—the
to fixed- telephone calls and during peak, off-peak,
primary sources of energy—coal, oil, gas, nuclear,
share of households with access to telecommunica-
and weekend times as well as 30 text messages per
hydro, geothermal, wind, tide and wave, and combus-
tions. During the past few years more information
month. • Telecommunications revenue is the rev-
tible renewables. Neither production nor consumption
on information and communication technology use
enue from the provision of telecommunications ser-
data capture the reliability of supplies, including break-
has become available from household and business
vices such as fixed telephone, mobile, and Internet
downs, load factors, and frequency of outages.
surveys. Also important are data on actual use of
divided by GDP. • Mobile cellular and fixed-telephone
Over the past decade new financing and technol-
telecommunications services. Ideally, statistics on
subscriptions per employee are telephone subscrip-
ogy, along with privatization and market liberalization,
telecommunications (and other information and com-
tions (fixed telephone plus mobile) divided by the total
have spurred dramatic growth in telecommunications
munications technologies) should be compiled for all
number of telecommunications employees.
in many countries. With the rapid development of
three measures: subscriptions, access, and use. The
mobile telephony and the global expansion of the
quality of data varies among reporting countries as
Internet, information and communication technolo-
a result of differences in regulations covering data
Data on electricity consumption and losses are
gies are increasingly recognized as essential tools of
provision and availability.
from the IEA’s Energy Statistics of Non-OECD
Data sources
development, contributing to global integration and
Countries 2011, Energy Balances of Non-OECD
enhancing public sector effectiveness, efficiency,
Countries 2011, and Energy Statistics of OECD
and transparency. The table presents telecommuni-
Countries 2011 and from the United Nations Sta-
cations indicators covering access and use, quality,
tistics Division’s Energy Statistics Yearbook. Data
and affordability and efficiency.
on telecommunications are from the ITU’s World
Access to telecommunication services rose on an unprecedented scale over the past 15 years. This
Telecommunication/ICT Indicators database and TeleGeography.
2012 World Development Indicators
327
5.12
The information society Daily Households newspapers with televisiona
Personal computers and the Internet Use
per 100 people
Afghanistan Albania Algeria Angola Argentina Armenia Australia Austria Azerbaijan Bahrain Bangladesh Belarus Belgium Benin Bolivia Bosnia and Herzegovina Botswana Brazil Bulgaria Burkina Faso Burundi Cambodia Cameroon Canada Central African Republic Chad Chile China Hong Kong SAR, China Colombia Congo, Dem. Rep. Congo, Rep. Costa Rica Côte d’Ivoire Croatia Cuba Cyprus Czech Republic Denmark Dominican Republic Ecuador Egypt, Arab Rep. El Salvador Eritrea Estonia Ethiopia Finland France Gabon Gambia, The Georgia Germany Ghana Greece Guatemala Guinea Guinea-Bissau Haiti
328
Quality Affordability International Fixed Internet Fixed broadband bandwidtha broadband Internet a Internet subscriptions bits per second per access tariffa per 100 capita people $ per month
per 1,000 people
%
Computers usersa
Internet usersa
2000–05b
2010
2010
2010
2010
2010
.. 24 .. 2 36 8 155 311 16 .. .. 81 165 0 .. .. 41 36 79 .. .. .. .. 175 .. .. 51 74 222 23 .. .. 65 .. .. 65 .. 183 353 39 99 .. 38 .. 191 5 431 164 .. .. 4 267 .. .. .. .. .. ..
.. .. 98 38 .. 97 .. .. 100 .. 36 98 .. 25 69 97 .. 98 99 18 .. 60 33 99 .. .. .. .. .. 91 .. 38 96 .. 97 .. 100 .. 98 86 85 94 83 .. 99 .. .. 99 .. 76 94 95 47 100 71 .. .. ..
.. .. .. .. .. .. .. 77.8 38.6 72.0 .. .. 78.8 .. .. .. .. 44.1 48.0 .. .. .. .. .. .. .. 42.8 .. 70.0 43.1 .. .. 42.6 .. 58.0 .. 57.2 70.7 89.9 .. 37.5 21.6 21.0 .. 75.6 .. 88.7 78.0 .. .. .. 85.4 .. 49.5 .. .. .. ..
3.7 45.0 12.5 10.0 36.0 44.0 75.9 72.7 46.7 55.0 3.7 32.1 73.7 3.1 20.0 52.0 6.0 40.7 46.0 1.4 2.1 1.3 4.0 81.3 2.3 1.7 45.0 34.4 71.8 36.5 0.7 5.0 36.5 2.6 60.1 15.9 53.0 68.6 88.8 39.5 29.0 26.7 15.9 5.4 74.2 0.7 86.9 77.5 7.2 9.2 26.3 82.5 9.5 44.6 10.5 1.0 2.5 8.4
0.00 3.29 2.54 0.10 9.56 2.75 24.15 23.86 5.08 5.36 0.04 17.55 30.96 0.04 0.97 8.18 0.60 6.81 14.44 0.09 0.00 0.25 0.01 29.71 0.00 0.00 10.45 9.44 29.87 5.60 0.01 0.00 6.19 0.04 18.19 0.03 17.63 14.46 37.72 3.63 1.37 1.79 2.83 0.00 25.10 0.00 28.57 32.89 0.27 0.02 5.70 31.90 0.21 19.95 1.80 0.01 0.00 0.00
58 5,304 1,015 63 9,898 3,411 31,392 53,635 4,524 7,924 103 7,060 82,599 70 854 8,138 386 5,130 29,520 49 2 354 16 43,955 4 2 8,613 821 557,998 3,739 2 2 4,630 203 25,804 35 27,380 47,529 126,194 4,029 2,394 1,762 242 6 17,164 40 93,214 53,933 3,767 98 5,615 61,142 139 13,816 417 15 1 9
2012 World Development Indicators
2010
.. 11 15 133 26 32 37 26 12 27 15 18 25 50 35 14 30 17 13 83 .. 47 80 26 1,329 12 39 18 19 35 .. .. 7 40 18 1,753 21 31 44 19 20 8 25 .. 21 294 35 30 .. 307 39 39 32 19 33 800 .. ..
Information and communications technology trade Application Secure Internet servers per million people
Goods Imports Exports % of total % of total goods goods imports exports
Services Exports % of total services exports
December 2011
2010
2010
2010
1 14 1 3 34 28 2,006 996 5 118 1 12 604 1 10 20 9 54 139 1 0 3 1 1,369 0 .. 67 2 568 21 0 1 111 1 225 0 1,121 387 2,185 20 20 3 17 .. 534 0 1,489 356 8 3 19 1,026 2 154 14 0 1 1
.. 0.8 0.0 .. 0.1 0.8 1.0 3.9 0.0 0.9 .. 0.5 2.3 .. 0.0 0.1 0.3 1.0 2.5 0.0 0.4 0.1 0.0 2.8 0.0 .. 0.4 29.1 44.2 0.1 .. .. 19.9 0.2 2.1 .. 9.1 15.0 3.6 2.3 0.1 0.1 0.3 .. 7.8 0.2 6.4 4.4 0.0 0.4 0.2 5.1 0.0 2.5 0.9 0.0 .. ..
0.4 4.1 3.0 .. 9.1 4.7 10.6 5.8 3.5 4.4 .. 2.4 3.5 .. 3.4 2.9 3.1 9.5 5.6 2.5 5.8 2.5 2.6 8.4 6.8 .. 8.2 20.4 42.8 9.6 .. .. 17.7 3.3 5.5 .. 4.8 17.8 7.9 4.8 6.3 3.7 5.6 .. 9.5 8.4 8.2 7.3 3.5 1.5 5.3 9.2 7.1 5.0 6.8 5.2 .. ..
.. 4.8 3.5 5.4 11.7 16.8 4.9 6.2 4.0 .. 13.0 8.8 9.2 .. 11.8 .. 5.5 2.0 8.9 10.5 .. 5.5 3.6 11.4 .. .. 2.2 6.1 1.8 6.2 .. .. 26.6 11.0 3.6 .. 2.3 8.6 .. 4.5 .. 4.2 17.7 .. 8.9 4.5 24.5 4.2 .. 17.8 2.2 9.1 .. 2.4 14.3 21.6 .. ..
Daily Households newspapers with televisiona
Personal computers and the Internet Use
per 100 people
Honduras Hungary India Indonesia Iran, Islamic Rep. Iraq Ireland Israel Italy Jamaica Japan Jordan Kazakhstan Kenya Korea, Dem. Rep. Korea, Rep. Kosovo Kuwait Kyrgyz Republic Lao PDR Latvia Lebanon Lesotho Liberia Libya Lithuania Macedonia, FYR Madagascar Malawi Malaysia Mali Mauritania Mauritius Mexico Moldova Mongolia Morocco Mozambique Myanmar Namibia Nepal Netherlands New Zealand Nicaragua Niger Nigeria Norway Oman Pakistan Panama Papua New Guinea Paraguay Peru Philippines Poland Portugal Puerto Rico Qatar
Quality Affordability International Fixed Internet Fixed broadband bandwidtha broadband Internet a Internet subscriptions bits per second per access tariffa per 100 capita people $ per month
5.12
Information and communications technology trade Application Secure Internet servers per million people
Goods Imports Exports % of total % of total goods goods imports exports
Services Exports % of total services exports
per 1,000 people
%
Computers usersa
2000â&#x20AC;&#x201C;05b
2010
2010
2010
2010
2010
2010
December 2011
2010
2010
2010
.. 217 71 .. .. .. 182 .. 137 .. 551 .. .. .. .. .. .. .. 1 3 154 54 .. .. .. 108 89 .. .. 109 .. .. 77 93 .. 20 12 3 .. 28 .. 307 182 .. 0 .. 516 .. 50 65 9 .. .. 79 114 .. .. ..
69 99 60 72 97 98 .. 90 .. 88 99 98 87 28 .. .. .. .. .. .. .. .. .. 9 .. 99 .. 16 .. .. 31 25 97 95 93 89 93 .. .. 42 36 99 97 66 10 41 .. .. 68 92 .. 88 77 74 .. .. .. 95
.. 67.1 .. .. .. 13.5 72.0 65.6 55.7 .. 66.9 56.0 .. .. .. 81.5 .. .. .. .. 69.7 .. .. .. .. 64.2 57.5 .. .. .. .. .. 35.8 40.1 42.4 .. 50.9 .. .. .. .. 91.7 82.7 .. 0.9 .. 93.8 .. .. .. .. .. .. .. 65.4 58.2 61.3 84.6
11.1 65.2 7.5 9.9 13.0 2.5 69.8 65.4 53.7 26.5 77.6 38.9 33.4 25.9 0.0 82.5 .. 38.3 19.6 7.0 71.5 31.0 3.9 7.0 14.0 62.8 51.9 1.7 2.3 56.3 2.7 3.0 28.7 31.1 40.1 12.9 49.0 4.2 0.2 6.5 7.9 90.7 83.0 10.0 0.8 28.4 93.3 62.0 16.8 42.7 1.3 19.8 34.3 25.0 62.5 51.3 42.7 81.6
1.00 19.56 0.90 0.79 0.68 0.00 21.04 24.46 21.92 4.32 26.71 3.24 8.74 0.01 0.00 35.18 .. 1.68 0.28 0.19 19.42 4.73 0.02 0.00 1.15 20.81 12.47 0.03 0.03 7.32 0.02 0.19 6.18 9.98 7.55 2.60 1.56 0.06 0.03 0.42 0.20 38.10 24.93 0.82 0.02 0.06 35.26 1.63 0.31 7.84 0.09 0.44 3.14 1.85 12.99 19.30 13.86 8.19
658 6,500 437 292 406 2 44,693 5,247 33,067 5,551 12,293 2,481 2,868 499 .. 9,802 .. 3,655 55 161 21,438 591 12 14 1,574 28,533 8,738 94 1 6,443 50 81 2,646 2,272 26,390 6,237 2,347 55 21 287 134 139,986 16,026 864 19 32 102,270 3,786 435 9,096 41 1,644 2,911 2,681 23,570 75,202 57,818 13,930
22 21 5 22 30 .. 33 8 26 25 23 19 13 38 .. 24 .. 19 55 140 13 23 51 .. .. 10 13 91 562 20 50 24 16 17 6 8 12 22 28 95 23 33 29 34 60 53 49 31 14 17 140 19 42 22 18 26 .. 55
8 220 3 3 1 0 1,145 471 192 48 743 25 6 3 0 2,536 .. 180 3 1 205 41 0 1 1 237 29 1 0 54 1 2 117 27 20 14 4 1 0 20 2 2,757 1,597 10 0 2 1,822 53 1 143 7 10 19 8 270 224 98 126
0.1 25.6 2.0 5.1 0.0 .. 7.5 12.3 2.1 0.4 10.7 1.3 0.1 1.4 .. 21.4 .. 0.3 0.6 .. 5.8 7.1 20.9 .. .. 2.7 0.4 0.2 0.4 34.0 0.1 .. 1.1 20.2 0.7 .. 3.8 0.1 .. 0.5 0.4 12.5 1.2 0.1 0.3 0.0 1.4 0.1 0.2 9.6 .. 0.1 0.1 35.6 9.5 4.0 .. 0.0
6.3 21.3 6.6 8.6 3.6 .. 10.2 9.2 7.4 3.9 12.0 4.3 2.9 7.2 .. 11.9 .. 6.4 2.7 .. 6.4 2.8 2.7 .. .. 4.1 4.7 3.1 5.2 29.8 2.6 0.9 5.1 19.2 3.9 .. 5.9 1.8 .. 4.0 7.2 14.5 8.3 4.8 1.9 6.6 7.5 3.1 3.3 9.6 .. 27.0 7.4 31.6 9.7 5.7 .. 4.3
25.2 8.5 47.0 7.4 .. 0.6 39.0 33.0 8.9 6.4 1.3 .. 2.4 9.8 .. 1.2 .. .. 3.3 .. 6.2 3.0 4.5 .. .. 3.7 14.1 .. .. 7.0 23.2 .. 3.8 .. 22.7 2.0 8.0 7.0 .. 1.7 .. 11.7 4.6 .. 12.8 .. 9.5 .. 6.6 4.8 0.7 1.4 3.1 12.6 6.4 4.2 .. ..
Internet usersa
2012 World Development Indicators
329
STATES AND MARKETS
The information society
5.12
The information society Daily Households newspapers with televisiona
Personal computers and the Internet Use
per 100 people
Romania Russian Federation Rwanda Saudi Arabia Senegal Serbia Sierra Leone Singapore Slovak Republic Slovenia Somalia South Africa South Sudan Spain Sri Lanka Sudan Swaziland Sweden Switzerland Syrian Arab Republic Tajikistan Tanzania Thailand Timor-Leste Togo Trinidad and Tobago Tunisia Turkey Turkmenistan Uganda Ukraine United Arab Emirates United Kingdom United States Uruguay Uzbekistan Venezuela, RB Vietnam West Bank and Gaza Yemen, Rep. Zambia Zimbabwe World Low income Middle income Lower middle income Upper middle income Low & middle income East Asia & Pacific Europe & Central Asia Latin America & Carib. Middle East & N. Africa South Asia Sub-Saharan Africa High income Euro area
per 1,000 people
%
2000–05b
2010
70 92 .. .. 9 .. .. 361 126 173 .. 30 .. 144 26 .. 24 481 420 .. .. 2 .. .. 2 149 23 .. 9 .. 131 .. 290 193 .. .. 93 .. 10 4 5 .. 104 w .. 68 .. 69 65 74 .. 64 .. 68 .. 255 201
.. 99 3 .. 57 .. 10 .. .. .. .. 75 .. .. .. .. .. .. .. .. .. 10 97 .. .. .. .. .. .. 6 95 96 .. .. 94 .. 96 87 96 .. 27 .. .. m .. 87 72 96 84 .. .. 87 97 48 26 .. ..
Computers usersa
Internet usersa
2010
2010
45.9 40.3 .. .. 29.9 54.5 .. 69.1 82.4 71.0 .. .. .. 69.7 .. .. .. 92.8 .. .. .. .. 30.9 .. .. .. .. 41.0 .. .. .. 74.0 86.7 .. 49.1 .. .. .. 55.6 .. .. .. .. w .. .. .. .. .. .. 40.9 .. .. .. .. .. 73.7
40.0 43.4 13.0 41.0 16.0 43.1 0.3 71.1 79.9 69.3 1.2 12.3 .. 65.8 12.0 10.2 9.0 90.0 82.2 20.7 11.5 11.0 21.2 0.2 5.4 48.5 36.6 39.8 2.2 12.5 44.6 78.0 84.7 74.2 47.9 19.4 35.9 27.9 36.4 12.3 10.1 11.5 30.2 w 5.6 23.8 13.5 34.1 21.5 29.8 39.3 34.0 20.9 8.1 11.3 73.4 71.2
Quality Affordability International Fixed Internet Fixed broadband bandwidtha broadband Internet a Internet subscriptions bits per second per access tariffa per 100 capita people $ per month 2010
2010
13.90 20,571 11.08 13,346 0.02 155 5.45 11,584 0.63 386 11.77 20,241 .. 29 24.99 122,454 12.79 9,208 24.02 48,804 0.00 .. 1.49 211 .. .. 22.87 36,900 1.09 398 0.38 305 0.15 52 31.85 213,265 37.16 127,779 0.33 280 0.07 .. 0.01 77 4.61 2,296 0.04 28 0.06 230 10.81 8,656 4.57 4,854 9.73 7,601 0.01 77 0.16 108 6.44 2,616 10.47 13,991 31.46 112,482 27.71 29,093 10.95 11,004 0.32 53 5.40 2,428 4.18 1,546 .. .. 0.35 133 0.08 39 0.26 35 7.75 w 8,662 w 0.05 93 4.65 1,856 1.04 656 8.36 3,084 4.05 1,628 7.18 992 9.12 9,777 6.59 4,062 1.36 1,139 0.73 394 0.18 116 26.46 44,235 27.65 53,594
2010
5 10 86 27 36 15 .. 27 26 34 .. 27 .. 26 5 23 875 35 33 22 362 21 19 99 166 13 11 19 .. 14 8 41 25 20 19 200 16 10 .. 119 59 406 26 m 55 20 32 18 23 22 13 23 17 14 52 26 26
Information and communications technology trade Application Secure Internet servers per million people December 2011
54 27 1 22 1 29 1 607 164 433 0 74 .. 284 6 0 15 1,455 2,153 0 1 0 17 3 2 85 19 143 0 2 18 180 1,594 1,562 70 0 8 5 4 0 2 1 184 w 1 12 3 20 10 4 48 36 3 2 6 1,068 647
Goods Imports Exports % of total % of total goods goods imports exports
Services Exports % of total services exports
2010
2010
2010
8.4 0.2 0.6 0.1 0.4 1.6 .. 34.3 19.3 2.2 .. 1.0 .. 2.2 0.5 0.0 .. 9.8 1.6 0.0 .. 0.4 18.9 .. 0.2 0.2 6.5 1.8 .. 5.7 1.1 2.0 5.9 10.5 0.1 .. 0.0 5.8 0.9 0.0 0.0 0.0 11.1 w .. 14.2 8.3 15.5 14.1 26.2 1.2 10.3 .. 1.6 0.5 10.4 5.2
9.3 7.9 11.7 7.2 3.3 4.2 .. 27.9 15.4 4.6 .. 9.4 .. 6.7 2.9 3.3 .. 11.3 5.9 1.1 .. 3.8 14.2 .. 5.1 3.0 6.3 4.5 .. 7.4 3.2 4.5 9.3 14.2 6.4 .. 7.6 8.6 3.2 2.0 2.3 3.6 12.7 w .. 14.2 10.4 15.0 14.0 21.3 5.5 14.0 4.2 5.7 7.0 12.5 8.2
18.0 6.0 4.4 .. 12.9 9.2 .. 2.8 9.2 7.3 .. 3.7 .. 6.9 14.1 25.8 9.0 13.9 .. 1.9 37.0 2.1 .. .. 18.6 .. 5.9 1.6 .. 5.2 5.5 .. 8.8 4.6 8.2 .. 8.2 .. 6.0 .. 8.0 .. 9.3 w .. 13.6 27.9 5.8 13.5 7.1 5.9 .. .. 43.8 .. 8.1 10.7
a. Data are from the International Telecommunication Union’s (ITU) World Telecommunication/ICT Indicators database. Please cite the ITU for third-party use of these data. b. Data are for the most recent year available.
330
2012 World Development Indicators
5.12
About the data
The digital and information revolution has changed
of upstream and downstream capacity and includes
high-speed technology (excluding wireless). • Inter-
the way the world learns, communicates, does
digital subscriber lines, cable modems, satellite
national Internet bandwidth is the contracted capac-
business, and treats illnesses. New information
broadband Internet, fiber-to-home Internet access,
ity of international connections between countries
and communications technologies (ICT) offer vast
Ethernet local access networks, and wireless area
for transmitting Internet traffic. • Fixed broadband
opportunities for progress in all walks of life in all
networks. Bandwidth refers to the range of frequen-
Internet access tariff is the lowest sampled cost per
countries —opportunities for economic growth,
cies available for signals. The higher the bandwidth,
100 kilobits a second per month and are calculated
improved health, better service delivery, learning
the more information that can be transmitted at
from low- and high-speed monthly service charges.
through distance education, and social and cultural
one time. Reporting countries may have different
Monthly charges do not include installation fees or
advances.
definitions of broadband, so data are not strictly
modem rentals. • Secure Internet servers are serv-
comparable.
ers using encryption technology in Internet transac-
Comparable statistics on access, use, quality,
tions. • Information and communication technology
and affordability of ICT are needed to formulate
The number of secure Internet servers, from the
growth-enabling policies for the sector and to moni-
Netcraft Secure Server Survey, indicates how many
goods exports and imports include telecommunica-
tor and evaluate the sector’s impact on develop-
companies conduct encrypted transactions over the
tions, audio and video, computer and related equip-
ment. Although basic access data are available for
Internet. The survey examines the use of encrypted
ment; electronic components; and other information
many countries, in most developing countries little
transactions through extensive automated explora-
and communication technology goods. Software is
is known about who uses ICT; what they are used for
tion, tallying the number of Web sites using a secure
excluded. • Information and communication tech-
(school, work, business, research, government); and
socket layer (SSL). The country of origin of more than
nology services exports include computer and
how they affect people and businesses. The global
a third of the 1.5 million distinct valid third-party
communications services (telecommunications and
Partnership on Measuring ICT for Development is
certificates is unknown. Some countries, such as the
postal and courier services) and information services
helping to set standards, harmonize information and
Republic of Korea, use application layers to estab-
(databases, data processes, software design and
communications technology statistics, and build sta-
lish the encryption channel, which is SSL equivalent;
development, maintenance and repair, and news-
tistical capacity in developing countries. For more
these data are reported in the table.
related service transactions).
information see www.itu.int/ITU-D/ict/partnership/.
Information and communication technology goods
Data on daily newspaper circulation are from
exports and imports are defined by the Working
United Nations Educational, Scientific, and Cultural
Party on Indicators for the Information Society and
Organization (UNESCO) Institute for Statistics sur-
are reported in the Organisation for Economic Co-
veys on newspaper statistics.
operation and Development’s Guide to Measuring the
Estimates of households with television are derived
Information Society (2005). Information and commu-
from household surveys. Some countries report only
nication technology service exports data are based
the number of households with a color television set,
on the International Monetary Fund’s (IMF) Balance
and so the true number may be higher than reported.
of Payments Statistics Yearbook classification.
Data on computer users, Internet users, and related indicators (broadband and bandwidth) are col-
Definitions
lected by national statistics offices through house-
• Daily newspapers are newspapers published at
hold surveys. Since survey questions and definitions
least four times a week that report mainly on events
differ, the estimates may not be strictly comparable
since the previous issue. The indicator is average
across countries. In particular, in the “post-PC age”
circulation per 1,000 people. • Households with
Data on newspapers are compiled by the UNESCO
what constitutes a computer is becoming harder
television are the percentage of households with a
Institute for Statistics. Data on televisions, com-
to define. Today’s smartphones and tablets have
television set. • Computer users are individuals who
puter users, Internet users, Internet broadband
computer power equivalent to that of yesterday’s
have used a computer (in any location) in the last
users and cost, and Internet bandwidth are from
computers and provide a similar range of functions.
12 months. Computers include desktop, portable,
the ITU’s World Telecommunication/ICT Indicators
Device convergence is thus rendering the conven-
or handheld computers (such as a personal digital
database and TeleGeography. Data on secure
tional definition obsolete. Countries without surveys
assistant) and exclude equipment with some embed-
Internet servers are from Netcraft (www.netcraft.
generally derive their estimates by multiplying sub-
ded computing abilities (such as mobile phones or
com) and official government sources. Data on
scriber counts reported by Internet service providers
television sets). • Internet users are individuals who
information and communication technology goods
by a multiplier. This method may undercount actual
have used the Internet (in any location) with a device
trade are from the United Nations Conference on
users, particularly in developing countries, where
such as a computer, smartphone, or digital television
Trade and Development’s UNCTADstat database
many commercial subscribers rent out computers
in the last 12 months via a fixed or mobile network.
(http://unctadstat.unctad.org). Data on informa-
connected to the Internet or prepaid cards are used
The Internet provides access to the worldwide net-
tion and communication technology services
to access the Internet.
work. • Fixed broadband Internet subscriptions are
exports are from the IMF’s Balance of Payments
Broadband refers to technologies that provide
the number of fixed broadband subscriptions with
Statistics database.
Internet speeds of at least 256 kilobits a second
a digital subscriber line, cable modem, or other
Data sources
2012 World Development Indicators
331
STATES AND MARKETS
The information society
5.13
Science and technology Research and development (R&D)
Scientific Expenditures and for R&D technical journal articles
High-technology exports
full-time equivalent per million people
Afghanistan Albania Algeria Angola Argentina Armenia Australia Austria Azerbaijan Bahrain Bangladesh Belarus Belgium Benin Bolivia Bosnia and Herzegovina Botswana Brazil Bulgaria Burkina Faso Burundi Cambodia Cameroon Canada Central African Republic Chad Chile China Hong Kong SAR, China Colombia Congo, Dem. Rep. Congo, Rep. Costa Rica Côte d’Ivoire Croatia Cuba Cyprus Czech Republic Denmark Dominican Republic Ecuador Egypt, Arab Rep. El Salvador Eritrea Estonia Ethiopia Finland France Gabon Gambia, The Georgia Germany Ghana Greece Guatemala Guinea Guinea-Bissau Haiti
332
Researchers
Technicians
2005–09d
2005–09d
.. 147 170 .. 1,046 .. 4,259 4,122 .. .. .. .. 3,491 .. .. 197 .. 696 1,587 .. .. .. .. 4,335 .. .. 355 1,199 2,759 157 .. .. 257 70 1,571 .. 752 2,755 6,390 .. 106 420 .. .. 3,210 21 7,647 3,690 .. .. .. 3,780 17 1,849 39 .. .. ..
.. 38 34 .. 207 .. 1,144 1,959 .. .. .. .. 1,406 .. .. 71 .. 560 492 .. .. .. .. 1,740 .. .. 293 .. 352 .. .. .. .. .. 636 .. 216 1,533 2,628 .. 31 394 .. .. 627 13 .. 1,872 .. .. .. 1,329 15 756 35 .. .. ..
2012 World Development Indicators
Royalty and license fees
Patent applications fileda,b
Trademark applications fileda,c
% of GDP
$ millions
% of manufactured exports
2009
2005–09d
2010
2010
2010
2010
2010
2010
2010
12 8 607 6 3,655 164 18,923 4,832 151 36 260 380 7,218 48 45 64 45 12,306 735 50 3 27 145 29,017 4 2 1,868 74,019 .. 608 19 18 98 56 1,164 222 195 3,946 5,306 6 68 2,247 6 4 518 175 4,949 31,748 18 20 129 45,003 102 4,881 22 3 6 7
.. 0.15 0.07 .. 0.52 0.27 2.35 2.75 0.26 .. .. 0.64 1.96 .. .. 0.02 0.52 1.08 0.53 0.21 .. .. .. 1.95 .. .. 0.39 1.47 0.79 0.16 0.48 .. 0.40 .. 0.83 0.49 0.46 1.53 3.02 .. 0.26 0.21 0.11 .. 1.44 0.17 3.96 2.23 0.64 0.02 0.18 2.82 0.23 0.58 0.06 .. .. ..
.. 9 5 .. 1,635 4 3,826 13,721 6 1 134 408 32,227 .. 37 71 15 8,122 802 3 1 5 14 23,966 .. .. 483 406,090 1,106 425 .. .. 2,193 126 732 .. 140 17,469 8,291 102 145 96 186 .. 721 6 5,776 99,736 7 0 11 158,507 8 1,090 205 0 .. ..
.. 0.9 0.5 .. 7.5 1.8 11.9 11.9 1.1 0.1 1.2 3.0 10.5 .. 8.6 2.6 0.4 11.2 7.9 7.8 8.5 0.1 4.9 14.1 .. .. 5.5 27.5 16.1 5.1 .. .. 40.0 8.2 9.2 .. 37.1 15.3 14.2 2.9 8.4 0.9 5.8 .. 9.1 3.1 10.8 24.9 3.0 1.1 1.8 15.3 2.0 10.2 5.7 0.1 .. ..
.. 1 2 12 119 .. 703 646 0 .. 1 9 2,138 0 3 15 0 397 34 0 .. 0 0 3,813 .. .. 64 830 383 56 .. .. 8 0 32 .. 8 105 .. .. .. 122 0 .. 20 0 2,340 10,407 .. .. 5 14,384 .. 69 13 0 .. ..
.. 12 17 6 1,541 .. 3,026 1,403 17 .. 18 101 1,904 3 20 5 11 2,850 115 1 .. 6 12 8,665 .. .. 496 13,040 1,700 362 .. .. 64 21 225 .. 31 771 .. 63 54 226 31 .. 60 1 1,236 5,559 .. .. 7 13,051 .. 627 94 1 0 0
.. .. 76 .. 801 136 2,409 2,424 222 .. 66 1,759 620 .. .. 56 .. 2,705 243 2 .. .. .. 4,550 .. .. 328 293,066 133 133 .. .. 8 .. 257 59 4 868 1,626 .. 4 605 .. .. 84 12 1,731 14,748 .. .. 179 47,047 .. 728 7 .. .. ..
.. 361 730 .. 4,781 6 22,478 249 5 .. 276 174 140 .. .. 9 .. 19,981 17 .. .. .. .. 30,899 .. .. 748 98,111 11,569 1,739 .. .. 1,212 .. 21 172 4 114 142 .. 690 1,625 .. .. 13 25 102 1,832 .. .. 180 12,198 .. 16 374 .. .. ..
.. 2,920 5,632 .. 69,565 4,620 59,459 10,375 3,310 2,044 10,231 10,695 25,799e .. 6,081 4,730 674 125,654 7,140 34 .. 2,866 .. 45,220 .. .. 45,104 1,057,480 28,872 25,990 .. .. 11,265 .. 7,950 1,397 2,381 11,048 5,788 6,453 16,195 3,955 .. .. 3,140 719 5,504 93,187 .. 327 4,301 74,339 884 6,559 9,175 .. 6 ..
$ millions Receipts
Payments
Residents
Nonresidents
Total
Research and development (R&D)
Scientific Expenditures and for R&D technical journal articles
High-technology exports
full-time equivalent per million people
Honduras Hungary India Indonesia Iran, Islamic Rep. Iraq Ireland Israel Italy Jamaica Japan Jordan Kazakhstan Kenya Korea, Dem. Rep. Korea, Rep. Kosovo Kuwait Kyrgyz Republic Lao PDR Latvia Lebanon Lesotho Liberia Libya Lithuania Macedonia, FYR Madagascar Malawi Malaysia Mali Mauritania Mauritius Mexico Moldova Mongolia Morocco Mozambique Myanmar Namibia Nepal Netherlands New Zealand Nicaragua Niger Nigeria Norway Oman Pakistan Panama Papua New Guinea Paraguay Peru Philippines Poland Portugal Puerto Rico Qatar
Researchers
Technicians
2005–09d
2005–09d
.. 2,006 136 90 751 49 3,373 .. 1,690 .. 5,189 .. .. 56 .. 4,947 .. 152 .. .. 1,601 .. 21 .. .. 2,541 472 46 30 365 38 .. .. 347 794 .. 661 16 .. .. .. 2,818 4,324 .. 8 39 5,504 .. 162 111 .. 75 .. 78 1,598 4,308 668 ..
.. 553 93 .. .. 26 742 .. .. .. 597 .. .. 63 .. 825 .. 35 .. .. 561 .. 22 .. .. 445 82 25 58 43 12 .. .. 183 78 .. 61 35 .. .. .. 1,131 886 .. 11 13 .. .. 64 .. .. .. .. 11 189 383 .. ..
Royalty and license fees
5.13
Patent applications fileda,b
Trademark applications fileda,c
% of GDP
$ millions
% of manufactured exports
2009
2005–09d
2010
2010
2010
2010
2010
2010
2010
6 2,397 19,917 262 6,313 70 2,799 6,304 26,755 51 49,627 383 99 291 8 22,271 .. 214 15 12 162 256 4 0 34 388 57 35 53 1,351 25 3 22 4,128 80 42 391 29 10 14 56 14,866 3,188 12 16 462 4,440 114 1,043 73 17 11 159 223 7,355 4,157 .. 64
.. 1.15 0.76 0.08 0.79 .. 1.77 4.27 1.27 .. 3.45 0.42 0.23 0.42 .. 3.36 .. 0.11 0.16 .. 0.46 .. 0.03 .. .. 0.84 0.23 0.15 .. 0.63 0.25 .. 0.37 0.37 0.53 0.24 0.64 0.21 .. .. .. 1.84 1.17 .. .. 0.22 1.80 .. 0.46 0.21 .. 0.06 .. 0.11 0.68 1.66 0.49 ..
12 18,771 10,087 6,673 584 0 21,232 7,979 26,366 3 122,047 122 2,110 100 .. 92,856 .. 16 2 .. 396 279 0 .. .. 1,190 40 5 1 59,332 2 .. 6 37,657 17 7 897 1 .. 19 4 59,510 548 6 4 63 3,830 18 262 1 .. 32 252 29,792 8,378 1,213 .. 1
1.3 24.2 7.2 11.4 4.5 0.1 21.2 14.7 7.2 0.6 18.0 2.9 29.9 5.7 .. 28.7 .. 0.5 1.0 .. 7.6 12.8 0.2 .. .. 10.6 2.9 1.0 1.3 44.5 2.4 .. 0.7 16.9 8.3 7.4 7.7 1.3 .. 0.9 0.6 21.3 9.0 4.8 6.6 1.1 16.1 0.6 1.7 0.8 .. 6.6 6.6 67.8 6.6 3.4 .. 0.0
.. 1,051 129 60 .. 1,312 2,252 849 3,603 5 26,680 .. .. 54 .. 3,146 .. .. 1 .. 12 7 .. .. .. 1 7 .. .. 266 0 .. 1 .. 5 0 4 0 .. 0 .. 5,491 183 .. 0 .. 498 .. 4 .. .. 254 3 4 237 41 .. ..
30 1,386 2,438 1,616 .. 396 37,823 860 6,986 36 18,769 .. 86 18 .. 8,965 .. .. 3 .. 33 13 3 .. .. 35 18 .. 0 1,133 3 .. 12 .. 13 3 30 4 .. 8 .. 3,707 669 .. 2 224 536 .. 123 46 .. 3 197 445 2,248 548 .. ..
.. 649 7,262 .. .. .. 733 1,450 8,814 .. 290,081 45 11 77 8,018 131,805 .. .. 134 .. 178 .. .. .. .. 108 34 9 .. 1,233 .. .. 2 951 134 110 152 18 .. .. .. 2,575 1,585 .. .. .. 1,117 .. 170 .. 1 18 39 166 3,203 499 .. ..
.. 47 27,025 .. .. .. 59 5,856 903 .. 54,517 429 162 120 39 38,296 .. .. 6 .. 7 .. .. .. .. 6 406 34 .. 5,230 .. .. 22 13,625 5 69 882 22 .. .. .. 279 5,051 .. .. .. 696 .. 1,375 468 45 347 261 3,223 227 46 .. ..
7,403 6,298 141,943 47,794 3,096 .. 3,769 8,614 4,387 1,708 124,726 5,971 3,615 4,321 1,231 129,486 .. .. 2,535 .. 3,589 .. 566 612 .. 4,351 3,436 1,773 .. 26,370 .. .. 2,032 94,457 5,459 2,403 11,030 891 .. 804 1,132 .. 17,124 5,975 .. .. 13,835 1,913 15,734 9,629 612 22,102 23,120 16,838 18,251 19,636 .. ..
$ millions Receipts
Payments
Residents
Nonresidents
Total
2012 World Development Indicators
333
STATES AND MARKETS
Science and technology
5.13
Science and technology Research and development (R&D)
Scientific Expenditures and for R&D technical journal articles
High-technology exports
full-time equivalent per million people
Romania Russian Federation Rwanda Saudi Arabia Senegal Serbia Sierra Leone Singapore Slovak Republic Slovenia Somalia South Africa South Sudan Spain Sri Lanka Sudan Swaziland Sweden Switzerland Syrian Arab Republic Tajikistan Tanzania Thailand Timor-Leste Togo Trinidad and Tobago Tunisia Turkey Turkmenistan Uganda Ukraine United Arab Emirates United Kingdom United States Uruguay Uzbekistan Venezuela, RB Vietnam West Bank and Gaza Yemen, Rep. Zambia Zimbabwe World Low income Middle income Lower middle income Upper middle income Low & middle income East Asia & Pacific Europe & Central Asia Latin America & Carib. Middle East & N. Africa South Asia Sub-Saharan Africa High income Euro area
Researchers
Technicians
2005–09d
2005–09d
2009
895 3,091 12 .. 384 1,060 .. 5,834 2,438 3,679 .. 396 .. 2,932 96 .. .. 5,018 3,320 .. .. .. 316 .. 38 .. 1,863 804 .. .. 1,353 .. 3,947 4,673 346 .. 183 .. 144 .. 43 .. 1,269 w .. 591 .. 1,197 574 1,199 2,006 482 .. 129 .. 3,982 3,119
185 475 .. .. 53 224 .. 597 345 1,863 .. 124 .. 1,148 77 .. .. 2,006 2,874 .. .. .. 140 .. 18 .. 43 122 .. .. 288 .. 871 .. .. .. .. .. 27 .. 67 .. .. w .. .. .. .. .. .. 329 346 .. 86 .. .. 1,355
1,367 14,016 12 710 56 1,173 3 4,187 1,000 1,234 1 2,864 .. 21,543 135 63 8 9,478 9,469 72 12 152 2,033 .. 7 48 1,022 8,301 1 143 1,639 265 45,649 208,601 246 139 354 326 .. 25 35 56 788,333 s 1,561 167,803 28,049 139,753 169,364 78,373 29,089 23,970 11,421 21,432 5,079 618,970 171,873
% of GDP
$ millions
% of manufactured exports
2005–09d
2010
2010
0.48 4,249 1.25 5,193 .. 1 0.08 201 0.37 9 0.89 .. .. .. 2.66 126,982 0.48 3,921 1.86 1,131 .. .. 0.93 1,420 .. .. 1.38 11,290 0.11 57 0.29 10 .. 1 3.62 16,133 3.00 42,820 .. 86 0.09 .. 0.43 25 0.21 34,156 .. .. .. 0 0.04 3 1.10 611 0.85 1,714 .. .. 0.41 6 0.86 1,441 .. 50 1.87 59,447 2.79 145,498 0.66 79 .. .. .. 145 .. 2,101 .. .. .. 0 0.34 4 .. 9 2.15 w 1,572,076 s .. .. 1.07 490,375 0.61 46,413 1.10 569,935 1.07 527,339 1.47 .. 0.96 17,622 0.65 51,633 .. 1,572 0.75 .. 0.58 2,570 2.44 1,081,514 2.09 439,190
Royalty and license fees
Patent applications fileda,b
$ millions
Trademark applications fileda,c
Receipts
Payments
Residents
Nonresidents
Total
2010
2010
2010
2010
2010
10.9 466 453 1,382 36 12,063 8.8 625 5,066 28,722 13,778 56,856 5.9 0 0 .. .. 238 0.7 .. .. 288 643 .. 1.2 1 12 .. .. .. .. 39 156 290 39 7,005 .. 1 0 .. .. 676 49.9 1,867 15,857 895 8,878 17,504 7.0 45 147 234 48 5,027 5.5 69 369 442 11 3,894 .. .. .. .. .. .. 4.3 59 1,941 821 5,562 30,549 .. .. .. .. .. .. 6.4 877 2,649 3,566 213 47,120 1.0 .. .. 225 235 6,244 29.4 3 11 3 13 1,026 0.1 0 16 .. .. 659 13.9 6,133 1,383 2,196 353 12,662 24.8 .. .. 1,622 533 27,972 1.8 1 37 .. .. 2,362 .. 1 0 7 3 2,293 3.5 0 0 .. .. 556 24.0 153 3,084 1,214 723 37,656 .. .. .. .. .. .. 0.1 .. 5 .. .. .. 0.2 .. .. 1 280 .. 4.9 25 15 .. .. .. 1.9 .. 816 2,555 177 8,241 .. .. .. .. .. 2,245 2.4 4 4 6 1 .. 4.3 132 744 2,556 2,756 28,915 3.2 .. .. .. .. .. 20.9 13,822 8,499 15,490 6,439 36,484 19.9 105,583 33,450 241,977 248,249 281,867 5.8 0 17 23 761 5,730 .. .. .. 370 262 4,863 5.1 .. 340 .. .. .. 6.2 .. .. 306 3,276 32,289 .. 0 0 .. .. .. 0.4 33 5 20 55 4,165 1.0 .. 1 .. .. 765 0.8 .. .. .. .. .. 17.5 w 210,577 s 216,352 s 1,060,313 s 621,207 s 3,023,628 s 3.1 62 56 .. .. .. 17.9 3,612 37,290 281,357 190,556 1,826,974 11.0 665 6,170 11,816 44,547 352,071 19.5 2,947 31,120 333,407 164,472 1,737,397 17.8 3,674 37,346 289,428 191,053 1,850,304 28.7 1,049 18,196 304,113 110,671 1,222,061 6.7 1,352 7,678 36,143 17,415 187,119 10.9 970 6,291 4,216 40,206 466,522 3.2 37 326 .. .. 36,211 6.7 142 2,578 7,519 27,500 172,509 2.8 124 2,277 .. .. .. 17.4 206,903 179,006 775,219 450,049 1,183,907 14.9 42,900 76,676 72,951 14,959 305,982
a. Original information was provided by the World Intellectual Property Organization (WIPO). The International Bureau of WIPO assumes no responsibility with respect to the transformation of these data. b. Excludes applications filed under the auspices of the European Patent Office (150,961 by nonresidents) and the Eurasian Patent Organization (3,329 by nonresidents). c. Excludes applications filed under the auspices of the Office for Harmonization in the Internal Market (98,616). d. Data are for the most recent year available. e. Includes Luxembourg and the Netherlands.
334
2012 World Development Indicators
About the data
5.13
Definitions
of high technology are also important, such as know-
• Researchers in research and development (R&D)
Cultural Organization (UNESCO) Institute for Statis-
how, scientific personnel, and technology embodied
are professionals engaged in conceiving of or creating
tics collects data on researchers, technicians, and
in patents. Considering these characteristics would
new knowledge, products, processes, methods, and
expenditure on research and development (R&D)
yield a different list (see Hatzichronoglou 1997).
systems and in managing the projects concerned.
The United Nations Educational, Scientifi c, and
through its biennial R&D survey and from other
A patent is an exclusive right granted for a specified
Postgraduate doctoral students (International Stan-
international partners such as the Organisation for
period (generally 20 years) for a new way of doing
dard Classification of Education 1997 level 6) engaged
Economic Co-operation and Development (OECD),
something or a new technical solution to a problem—
in R&D are considered researchers. • Technicians in
Eurostat, and the Network for Science and Technol-
an invention. The invention must be of practical use
R&D and equivalent staff are people whose main tasks
ogy Indicators—Ibero-American and Inter-American.
and display a characteristic unknown in the existing
require technical knowledge and experience in engi-
The OECD’s Frascati Manual 2002 (OECD 2002)
body of knowledge in its field. Most countries have
neering, physical and life sciences (technicians), and
defines research and experimental development as
systems to protect patentable inventions. The Patent
social sciences and humanities (equivalent staff). They
“creative work undertaken on a systemic basis in
Cooperation Treaty (www.wipo.int/pct) provides a two-
engage in R&D by performing scientific and technical
order to increase the stock of knowledge, including
phase system for filing patent applications in 144 eli-
tasks involving the application of concepts and opera-
knowledge of man, culture and society, and the use
gible countries (as of November 2011). International
tional methods, normally under researcher supervision.
of this stock of knowledge to devise new applica-
applications under the treaty provide for a national
• Scientific and technical journal articles are published
tions.” R&D covers basic research, applied research,
patent grant only—there is no international patent.
articles in physics, biology, chemistry, mathematics,
and experimental development. Data on research-
The national filing represents the applicant’s seek-
clinical medicine, biomedical research, engineering and
ers and technicians in R&D are measured in both
ing of patent protection for a given territory, whereas
technology, and earth and space sciences. • Expen-
full-time equivalent and headcount but are shown in
international filings, while representing a legal right,
ditures for R&D are current and capital expenditures
full-time equivalent only.
do not accurately reflect where patent protection is
on creative work undertaken on a systematic basis to
Scientific and technical article counts are from jour-
sought. Resident filings are those from residents of
increase the stock of knowledge, including knowledge
nals classified by the Institute for Scientific Informa-
the country concerned. Nonresident filings are from
on humanity, culture, and society, and the use of knowl-
tion’s Science Citation Index (SCI) and Social Sciences
applicants abroad. For regional offices applications
edge to devise new applications. • High-technology
Citation Index (SSCI). Counts are based on fractional
from residents of any member state of the regional
exports are products with high R&D intensity, such
assignments; articles with authors from different
patent convention are considered nonresident filings.
as in aerospace, computers, pharmaceuticals, scien-
countries are allocated proportionately to each country
Some offices (notably the U.S. Patent and Trademark
tific instruments, and electrical machinery. • Royalty
(see Definitions for fields covered). The SCI and SSCI
Office) use the residence of the inventor rather than
and license fees are payments and receipts between
databases cover the core set of scientific journals but
the applicant to classify filings.
residents and nonresidents for authorized use of intan-
may exclude some of local importance and may reflect
A trademark is a distinctive sign identifying goods
gible, nonproduced, nonfinancial assets and proprietary
or services as produced or provided by a specific per-
rights (such as patents, copyrights, trademarks, and
R&D expenditures include expenditures from all
son or enterprise. A trademark protects the owner of
industrial processes) and for the use, through licens-
sources for R&D performed within a country, includ-
the mark by ensuring exclusive right to use it to iden-
ing, of produced originals of prototypes (such as films
ing capital expenditures and current costs (wages
tify goods or services or to authorize another to use
and manuscripts). • Patent applications filed are pat-
and associated costs of researchers, technicians,
it. The period of protection varies, but a trademark
ent applications at a national or regional patent office;
and other supporting staff and other current costs,
can be renewed indefinitely for an additional fee.
an international patent application (Patent Coopera-
including noncapital purchases of materials, supplies,
Detailed components of trademark filings, available
tion Treaty filing) provides a national patent grant only.
and minor equipment to support R&D such as utilities,
on the World Development Indicators CD-ROM and
• Trademark applications filed are applications to reg-
reference materials, subscriptions to libraries and
at http://data.worldbank.org, include applications
ister a trademark with a national or regional IP office.
scientific societies, and materials for laboratories).
filed by direct residents (domestic applicants filing
some bias toward English-language journals.
Data sources
The method for determining high- technology
directly at a given national or regional intellectual
exports was developed by the Organisation for Eco-
property [IP] office); direct nonresident (foreign appli-
Data on R&D are provided by the UNESCO Insti-
nomic Co-operation and Development in collabora-
cants filing directly at a given national or regional IP
tute for Statistics. Data on scientific and technical
tion with Eurostat. It takes a “product approach”
office); aggregate direct (applicants not identified as
journal articles are from the U.S. National Science
(rather than a “sectoral approach”) based on R&D
direct resident or direct nonresident by the national
Board’s Science and Engineering Indicators 2012.
intensity (expenditure divided by total sales) for
or regional office); and Madrid (designations received
Data on high-technology exports are from the United
groups of products from Germany, Italy, Japan,
by the national or regional IP office based on inter-
Nations Statistics Division’s Commodity Trade
the Netherlands, Sweden, and the United States.
national applications filed via the World Intellectual
(Comtrade) database. Data on royalty and license
Because industrial sectors specializing in a few high-
Property Organization–administered Madrid System).
fees are from the International Monetary Fund’s
technology products may also produce low-technol-
Data are based on information supplied to WIPO by
Balance of Payments Statistics Yearbook. Data on
ogy products, the product approach is more appro-
IP offices in annual surveys, supplemented by data
patents and trademarks are from the World Intellec-
priate for international trade. The method takes only
in national IP office reports. Data may be missing for
tual Property Organization (www.wipo.int/ipstats).
R&D intensity into account, but other characteristics
some offices or periods.
2012 World Development Indicators
335
STATES AND MARKETS
Science and technology
GLOBAL LINKS
T
he world economy is bound together by trade in goods and services, financial flows, and movements of people. As economies develop, their links expand and grow more complex. The indicators in Global links measure the size and direction of these flows and document policy interventions, such as tariffs, trade facilitation, and aid flows. During economic crises, declining trade and financing and changes in migration patterns in one country or region can transmit shocks to others. An example is the financial crisis that began in the United States in 2007 and spread rapidly to Europe. But the same links that transfer shocks also allow expansion into new markets and access to new sources of finance, reducing the impacts of shocks. In an increasingly multipolar world, developing countries are trading and investing more with each other, reducing their dependence on high-income economies. Evidence of this shift can be found in the tables of Global links. Capital inflows rebounded strongly in 2010, especially in low- and middle-income economies, where combined net debt and equity inflows rose 37 percent, to $799 billion. Foreign direct investment (FDI) inflows rose 3 percent—with wide disparity across income groups. In high-income economies FDI inflows fell 7 percent, dragged down by a 13 percent drop in net inflows to the euro area. But inflows to the United States—the world’s largest FDI recipient—surged. And low- and middle-income economies saw net FDI inflows jump 27 percent, to $514 billion, and their share of global FDI inflows increase to almost 36 percent, up from 29 percent in 2009. A better investment environment, corporate earnings revival, and increased developing country investment in
extractive industries and infrastructure development in other developing countries fueled the rebound. China was a key driver, with net FDI inflows up 62 percent in 2010 (to $185 billion). Portfolio equity flows recovered rapidly as the global financial crisis ebbed, particularly in emerging markets with good growth prospects. Low- and middle-income economies recorded a 19 percent increase (to $130 billion), underpinned by a 89 percent rise in net inflows to India. For high-income economies the story was mixed—particularly in the euro area, where net inflows rose 13 percent. Debt-related flows to low- and middleincome economies from private creditors rose in 2010 to $111 billion, more than twice their 2009 level, underpinned by a resurgence of bond issuance by public and private sector borrowers. Similarly, net inflows from banks and other private creditors showed signs of recovery, rising 123 percent, to $44 billion, albeit from a low base of $20 billion in 2009. Net inflows from bilateral and multilateral creditors (loans and grants) fell 11 percent, due largely to the redirection of International Monetary Fund flows to countries in the euro zone. Low- and middle-income economies’ combined external debt rose $437 billion in 2010, to $4 trillion, but it remained within sustainable bounds, an average of 21 percent of gross national income and 69 percent of export earnings. Short-term external debt was the fastest growing component, rising 34 percent in 2010 compared with 6 percent for outstanding long-term external debt. Risks associated with a high proportion of short-term debt—25 percent of total debt stock at end-2010—were mitigated by international reserves equivalent to 137 percent of total outstanding external debt.
6
2012 World Development Indicators
337
6.1 Afghanistan Albania Algeria Angola Argentina Armenia Australia Austria Azerbaijan Bahrain Bangladesh Belarus Belgium Benin Bolivia Bosnia and Herzegovina Botswana Brazil Bulgaria Burkina Faso Burundi Cambodia Cameroon Canada Central African Republic Chad Chile China† Hong Kong SAR, China Colombia Congo, Dem. Rep. Congo, Rep. Costa Rica Côte d’Ivoire Croatia Cuba Cyprus Czech Republic Denmark Dominican Republic Ecuador Egypt, Arab Rep. El Salvador Eritrea Estonia Ethiopia Finland France Gabon Gambia, The Georgia Germany Ghana Greece Guatemala Guinea Guinea-Bissau Haiti †Data for Taiwan, China
338
Growth of merchandise trade Export volume
Import volume
Export value
Import value
average annual % growth
average annual % growth
average annual % growth
average annual % growth
2000–10
2000–10
2000–10
2000–10
2010
9.9 15.0 0.0 11.4 5.9 3.6 2.3 5.9 22.3 0.6 11.4 6.8 2.5 6.6 9.4 13.4 3.5 6.6 7.9 11.8 –3.1 12.7 –2.5 –0.9 –4.3 23.9 4.3 20.5 6.7 6.2 10.1 1.4 7.3 –0.1 6.3 0.5 0.3 9.9 2.1 –0.8 7.5 9.0 2.6 –10.9 3.3 9.1 2.6 1.5 0.1 –3.7 8.9 5.2 5.2 0.8 7.9 –0.2 1.8 4.9 7.5
4.4 11.6 12.2 22.0 11.7 12.6 8.3 4.5 14.1 3.5 5.0 8.7 3.0 7.1 8.2 5.1 5.7 8.1 9.3 7.9 9.7 9.3 5.2 2.5 6.6 11.6 11.2 15.0 6.6 11.0 14.9 16.6 6.8 5.1 6.0 8.9 5.5 8.0 2.9 3.3 12.2 10.2 3.4 –3.8 4.1 16.5 2.9 2.9 7.2 0.9 19.5 4.9 9.2 1.2 5.1 2.8 7.9 4.0 2.4
20.1 20.2 14.4 27.6 11.9 12.3 14.0 9.9 38.6 11.7 13.0 16.4 9.7 15.5 21.2 20.4 7.1 15.5 18.0 19.3 7.9 15.0 9.8 4.9 –0.4 42.2 17.1 22.4 7.8 14.7 20.9 15.9 7.0 11.6 12.5 11.2 5.7 18.5 8.3 2.1 16.4 23.5 4.8 –6.7 13.3 19.2 6.7 6.2 14.4 2.7 19.8 10.3 17.2 9.1 12.4 8.5 9.0 8.2 8.0
11.0 17.5 17.9 26.4 14.6 19.4 13.1 9.7 20.8 11.9 13.3 18.3 10.1 16.2 13.7 11.5 11.6 14.9 18.4 14.5 15.8 15.3 13.9 6.4 13.1 17.4 14.9 20.9 8.3 15.3 21.8 22.4 9.2 13.7 12.2 12.4 10.9 15.7 8.7 6.7 17.5 17.9 6.6 3.6 11.4 23.6 10.0 8.0 11.9 8.4 27.5 9.9 16.3 10.3 10.5 9.6 17.5 11.2 8.0
146.5 95.3 177.5 210.9 126.6 126.2 178.9 91.1 160.3 114.5 59.0 102.8 100.8 103.5 152.4 101.1 84.7 125.1 108.8 120.9 153.1 75.9 143.8 119.9 85.9 180.0 204.0 77.4 96.0 133.9 137.9 182.3 78.2 161.6 100.7 .. 103.5 106.4 106.6 98.4 121.9 152.4 91.3 77.2 144.4 127.5 77.1 98.2 195.8 93.3 132.2 103.3 175.4 92.7 92.6 110.2 83.5 77.9 66.0
2012 World Development Indicators
Net barter terms of trade index
2000 = 100
Honduras Hungary India Indonesia Iran, Islamic Rep. Iraq Ireland Israel Italy Jamaica Japan Jordan Kazakhstan Kenya Korea, Dem. Rep. Korea, Rep. Kosovo Kuwait Kyrgyz Republic Lao PDR Latvia Lebanon Lesotho Liberia Libya Lithuania Macedonia, FYR Madagascar Malawi Malaysia Mali Mauritania Mauritius Mexico Moldova Mongolia Morocco Mozambique Myanmar Namibia Nepal Netherlands New Zealand Nicaragua Niger Nigeria Norway Oman Pakistan Panama Papua New Guinea Paraguay Peru Philippines Poland Portugal Puerto Rico Qatar
6.1
Export volume
Import volume
Export value
Import value
average annual % growth
average annual % growth
average annual % growth
average annual % growth
2000 = 100
2000–10
2000–10
2000–10
2000–10
2010
3.5 10.5 11.4 0.8 2.9 1.7 1.7 3.3 –0.1 0.0 5.2 3.9 8.6 5.1 7.1 12.1 .. 4.1 7.7 11.1 9.6 13.4 12.8 –7.4 4.2 12.7 6.2 1.5 6.9 5.5 2.9 11.2 3.2 2.9 11.3 3.9 0.6 10.0 7.4 6.2 –2.9 5.2 3.3 9.5 4.0 3.0 –0.1 –0.2 5.8 0.0 –1.1 14.8 7.2 2.8 10.0 5.1 .. 8.1
4.7 7.6 19.5 6.5 7.6 9.2 0.0 1.5 0.4 0.0 1.8 5.1 14.2 8.9 –1.7 7.1 .. 9.2 16.4 9.3 7.3 4.4 7.3 4.5 13.5 10.7 6.0 8.6 9.9 5.1 7.7 11.3 6.0 3.4 18.8 12.4 8.1 7.3 0.3 10.6 4.5 5.7 5.5 5.6 14.4 13.9 5.0 11.1 4.6 9.7 6.0 16.1 9.8 –0.1 7.4 3.4 .. 23.0
6.2 15.1 19.9 11.0 17.5 16.8 4.7 8.1 8.1 4.0 6.3 15.0 25.3 12.2 13.5 12.4 .. 18.7 16.8 19.6 20.5 20.6 13.5 0.5 19.4 21.0 13.4 4.8 12.2 9.2 13.7 23.6 2.9 7.0 12.6 21.2 10.9 19.1 17.3 14.5 2.5 11.5 9.5 12.9 16.4 18.4 11.3 14.8 9.6 1.7 14.0 19.1 20.6 3.4 19.8 9.0 .. 23.3
9.7 12.8 24.2 14.4 14.8 15.1 3.8 6.4 9.1 7.7 8.0 15.6 20.7 17.1 7.5 12.6 .. 13.8 26.0 16.0 17.1 12.1 12.0 10.4 20.8 18.3 14.3 15.2 16.6 8.6 14.6 18.0 9.1 6.8 20.2 20.5 15.4 14.6 7.6 15.4 14.0 11.3 9.5 10.7 21.2 20.3 11.1 17.1 16.8 13.7 15.1 19.9 17.4 4.9 16.5 9.0 .. 27.9
83.4 95.4 127.2 127.3 157.9 184.3 95.4 98.1 99.0 70.7 67.8 85.4 192.6 91.7 77.0 68.0 .. 187.3 107.6 119.5 105.2 95.5 66.2 146.7 162.5 103.8 89.1 76.3 87.7 100.2 158.7 132.7 73.2 104.5 104.8 215.6 134.2 108.9 110.2 120.3 78.3 101.8 124.4 81.7 150.1 186.9 140.4 193.1 51.9 87.5 150.4 102.9 152.5 68.6 101.9 88.0 .. 187.8
GLOBAL LINKS
Growth of merchandise trade
Net barter terms of trade index
2012 World Development Indicators
339
6.1 Romania Russian Federation Rwanda Saudi Arabia Senegal Serbia Sierra Leone Singapore Slovak Republic Slovenia Somalia South Africa South Sudan Spain Sri Lanka Sudan Swaziland Sweden Switzerland Syrian Arab Republic Tajikistan Tanzania Thailand Timor-Leste Togo Trinidad and Tobago Tunisia Turkey Turkmenistan Uganda Ukraine United Arab Emirates United Kingdom United States Uruguay Uzbekistan Venezuela, RB Vietnam West Bank and Gaza Yemen, Rep. Zambia Zimbabwe
340
Growth of merchandise trade Export volume
Import volume
Export value
Import value
average annual % growth
average annual % growth
average annual % growth
average annual % growth
2000 = 100
2000–10
2000–10
2000–10
2000–10
2010
12.4 3.6 1.0 0.3 1.1 .. 25.9 10.2 17.1 11.2 –0.2 1.5 .. 3.1 2.5 8.0 –2.2 2.8 6.8 0.7 –1.0 7.0 7.2 .. 2.0 0.7 6.6 10.4 –0.6 13.0 4.7 6.5 0.4 3.9 8.5 7.6 –2.5 11.4 .. –4.1 9.4 –4.9
13.7 16.8 17.0 10.8 5.7 .. 4.5 7.5 13.8 8.6 4.0 7.4 .. 3.9 2.1 17.0 –0.9 3.0 3.3 11.7 9.3 12.3 7.5 .. –2.2 1.4 4.8 9.2 8.4 8.9 9.9 14.6 2.0 2.5 7.6 10.6 10.8 12.6 .. 9.7 14.1 –1.3
18.5 17.7 18.3 16.1 9.3 .. 32.7 12.0 21.7 15.5 7.7 12.2 .. 9.5 5.9 23.5 5.0 8.3 10.4 12.5 7.0 18.2 12.6 .. 10.1 14.4 12.4 17.4 14.0 21.7 15.1 19.3 4.9 6.6 14.3 18.9 12.2 19.4 .. 9.7 25.9 4.5
19.9 21.1 23.4 16.3 14.4 .. 15.2 11.4 19.6 14.5 11.6 14.6 .. 9.9 9.2 22.1 5.8 9.9 9.1 19.2 18.9 19.9 12.7 .. 12.4 10.1 11.2 17.0 13.1 15.7 18.7 20.2 6.1 5.9 14.3 16.0 14.3 20.6 .. 18.2 20.8 7.5
99.9 197.3 234.4 222.6 98.9 .. 70.2 83.2 90.0 89.3 106.2 139.4 .. 104.5 75.7 196.8 110.1 88.6 81.0 139.5 96.8 139.2 98.0 .. 30.7 133.5 95.2 91.8 195.6 111.1 118.9 163.3 103.3 97.1 99.6 151.6 216.3 100.6 .. 149.6 189.0 106.7
2012 World Development Indicators
Net barter terms of trade index
About the data
6.1
GLOBAL LINKS
Growth of merchandise trade Definitions
Data on international trade in goods are available
from national and international sources such as
• Export and import volumes are indexes of the
from each country’s balance of payments and
the IMF’s International Financial Statistics data-
quantity of goods traded. They are derived from UNC-
customs records. While the balance of payments
base, the United Nations Economic Commission for
TAD’s volume index series and are the ratio of the
focuses on the financial transactions that accom-
Latin America and the Caribbean, the U.S. Bureau
export or import value indexes to the corresponding
pany trade, customs data record the direction of
of Labor Statistics, Japan Customs, Bank of Japan,
unit value indexes. Unit value indexes are based on
trade and the physical quantities and value of goods
and UNCTAD’s Commodity Price Statistics and Mer-
data reported by countries that demonstrate consis-
entering or leaving the customs area. Customs data
chandise Trade Matrix. The IMF also compiles data
tency under UNCTAD quality controls, supplemented
may differ from data recorded in the balance of pay-
on trade prices and volumes in its International
by UNCTAD’s estimates using the previous year’s
ments because of differences in valuation and time
Financial Statistics (IFS) database.
trade values at the Standard International Trade
of recording. The 2008 United Nations System of
The growth rates and terms of trade in the table
Classification three-digit level as weights. To improve
National Accounts and the fifth edition of the Inter-
were calculated from index numbers compiled by
data coverage, especially for the most recent peri-
national Monetary Fund’s (IMF) Balance of Payments
UNCTAD.
ods, UNCTAD constructs a set of average price
Manual (1993) attempted to reconcile definitions and
The terms of trade index measures the relative
indexes at the three-digit product classification of the
reporting standards for international trade statistics,
prices of a country’s exports and imports. There are
Standard International Trade Classification revision
but differences in sources, timing, and national prac-
several ways to calculate it. The most common is the
3 using its Commodity Price Statistics database,
tices limit comparability. Real growth rates derived
net barter (or commodity) terms of trade index, or
international and national sources, and estimates
from trade volume indexes and terms of trade based
the ratio of the export price index to the import price
by the UNCTAD secretariat and calculates unit value
on unit price indexes may therefore differ from those
index. When a country’s net barter terms of trade
indexes at the country level using the current year’s
derived from national accounts aggregates.
index increases, its exports become more expensive
trade values as weights. • Export and import values
or its imports become cheaper.
are the current value of exports (free on board, f.o.b.)
Trade in goods, or merchandise trade, includes all goods that add to or subtract from an economy’s
or imports (cost, insurance, and freight, c.i.f.), con-
material resources. Trade data are collected on the
verted to U.S. dollars and expressed as a percentage
basis of a country’s customs area, which in most
of the average for the base period (2000). UNCTAD’s
cases is the same as its geographic area. Goods
export or import value indexes are reported for most
provided as part of foreign aid are included, but
economies. • Net barter terms of trade index is
goods destined for extraterritorial agencies (such
calculated as the percentage ratio of the export unit
as embassies) are not.
value indexes to the import unit value indexes, mea-
Collecting and tabulating trade statistics are dif-
sured relative to the base year 2000.
ficult. Some developing countries lack the capacity to report timely data, especially landlocked countries and countries whose territorial boundaries are porous. Their trade has to be estimated from the data reported by their partners. (For further discussion of the use of partner country reports, see About the data for table 6.2.) Countries that belong to common customs unions may need to collect data through direct inquiry of companies. Economic or political concerns may lead some national authorities to suppress or misrepresent data on certain trade flows, such as oil, military equipment, or the exports of a dominant producer. In other cases reported trade data may be distorted by deliberate under- or overinvoicing to affect capital transfers or avoid taxes. And in some regions smuggling and black market trading result in unreported trade flows. By international agreement customs data are reported to the United Nations Statistics Division, which maintains the Commodity Trade (Comtrade) and Monthly Bulletin of Statistics databases. The
Data sources
United Nations Conference on Trade and Develop-
Data on trade indexes are from UNCTAD’s annual
ment (UNCTAD) compiles international trade sta-
Handbook of Statistics.
tistics, including price, value, and volume indexes,
2012 World Development Indicators
341
6.2
Direction and growth of merchandise trade
Direction of trade Low- and middle-income importers
High-income importers
% of world trade, 2010
Source of exports High-income economies European Union Japan United States Other high-income economies Low- and middle-income economies East Asia & Pacific China Europe & Central Asia Russian Federation Latin America & Caribbean Brazil Middle East & N. Africa Algeria South Asia India Sub-Saharan Africa South Africa World
% of world trade, 2010
East Asia & Pacific
Europe & Central Asia
Latin America & Caribbean
Middle East & N. Africa
South Asia
Sub-Saharan Africa
Total
Total
9.2 1.2 1.6 0.9 5.6 3.3 1.9 0.7 0.3 0.2 0.6 0.2 0.2 0.0 0.2 0.2 0.1 0.1 12.5
2.6 1.9 0.1 0.1 0.5 1.9 0.5 0.5 1.1 0.4 0.1 0.0 0.1 0.0 0.1 0.0 0.0 0.0 4.5
3.5 0.7 0.3 1.9 0.6 2.0 0.7 0.6 0.0 0.0 1.1 0.3 0.0 0.0 0.1 0.0 0.1 0.0 5.5
1.4 0.7 0.0 0.1 0.5 0.9 0.3 0.3 0.3 0.1 0.1 0.1 0.2 0.0 0.1 0.1 0.0 0.0 2.3
1.6 0.3 0.1 0.2 1.0 1.2 0.6 0.4 0.1 0.0 0.1 0.0 0.2 0.0 0.1 0.1 0.2 0.0 2.9
1.0 0.5 0.1 0.1 0.3 0.9 0.4 0.3 0.0 0.0 0.1 0.0 0.1 0.0 0.1 0.1 0.2 0.1 1.8
19.3 5.3 2.1 3.4 8.5 10.5 4.4 2.8 1.8 0.7 2.0 0.7 0.8 0.1 0.6 0.5 0.9 0.2 29.7
47.6 26.1 3.0 5.2 13.3 20.9 10.8 7.8 2.9 1.5 3.6 0.6 1.4 0.3 1.2 0.9 1.1 0.3 68.5
Nominal growth of trade
Source of exports High-income economies European Union Japan United States Other high-income economies Low- and middle-income economies East Asia & Pacific China Europe & Central Asia Russian Federation Latin America & Caribbean Brazil Middle East & N. Africa Algeria South Asia India Sub-Saharan Africa South Africa World
342
Low- and middle-income importers
High-income importers
average annual % growth, 2000–10
average annual % growth, 2000–10
East Asia & Pacific
Europe & Central Asia
Latin America & Caribbean
Middle East & N. Africa
South Asia
Sub-Saharan Africa
Total
Total
15.1 15.0 12.5 12.1 16.5 22.3 20.9 26.1 19.4 17.4 30.5 31.8 22.0 32.5 25.8 27.2 22.6 31.2 16.5
17.3 16.7 24.8 14.0 21.0 20.8 34.8 37.2 17.8 15.8 19.5 20.4 16.4 11.2 17.5 16.0 24.5 23.9 18.6
8.8 9.5 11.1 7.2 13.4 18.1 27.7 31.5 20.5 21.0 14.5 16.6 14.4 8.6 23.3 25.2 20.4 12.3 11.4
13.7 11.4 12.5 12.9 20.1 22.7 25.9 30.3 22.4 19.8 18.0 21.8 22.5 19.0 21.5 23.8 13.8 19.5 16.6
21.4 15.5 14.1 21.1 25.7 27.0 28.1 35.0 24.3 20.5 29.4 25.6 37.9 64.1 19.2 19.8 21.7 29.0 23.5
12.5 11.3 11.2 13.2 15.0 21.5 27.1 31.0 21.9 14.0 20.9 22.4 18.5 15.9 23.7 24.7 16.1 13.8 15.8
14.1 13.8 12.7 9.4 17.4 21.7 24.9 31.0 19.0 16.9 18.2 20.9 23.4 16.0 22.5 23.8 21.8 19.8 16.3
7.5 8.2 3.4 5.2 8.2 13.9 15.5 20.4 16.7 16.4 8.6 12.1 13.9 14.3 15.4 18.2 13.5 12.4 9.1
2012 World Development Indicators
About the data
6.2
GLOBAL LINKS
Direction and growth of merchandise trade Definitions
The table provides estimates of the flow of trade in
• Merchandise trade includes all trade in goods;
goods between groups of economies. The data are
trade in services is excluded. • High-income econo-
from the International Monetary Fund’s (IMF) Direc-
mies are those classified as such by the World Bank
tion of Trade database. All high-income economies
(see front cover flap). • European Union is defined
and major developing economies report trade on
as all high-income EU members: Austria, Belgium,
a timely basis, covering about 85 percent of trade
Cyprus, Czech Republic, Denmark, Estonia, Finland,
for recent years. Trade by less timely reporters and
France, Germany, Greece, Hungary, Ireland, Italy, Lux-
by countries that do not report is estimated using
embourg, Malta, the Netherlands, Portugal, Slovak
reports of trading partner countries and extrapola-
Republic, Slovenia, Spain, Sweden, and the United
tion. Because the largest exporting and importing
Kingdom. • Other high-income economies include
countries are reliable reporters, a large portion of
all high-income economies (both Organisation for
the missing trade flows can be estimated from part-
Economic Co-operation and Development members
ner reports. Partner country data may introduce dis-
and others) except the high-income European Union,
crepancies due to confidentiality, different exchange
Japan, and the United States. • Low- and middle-
rates, overreporting of transit trade, inclusion or
income regional groupings are based on World Bank
exclusion of freight rates and insurance, and differ-
classifications (see back cover flap) and may differ
ent points of valuation and times of recording.
from those used by other organizations.
Most countries report their trade data in national currencies, which are converted into U.S. dollars using the IMF’s published period average exchange rate (series rf or rh, monthly averages of the market or official rates) for the reporting country. Because imports are reported at cost, insurance, and freight (c.i.f.) valuations, and exports at free on board (f.o.b.) valuations, the IMF adjusts country reports of import values by dividing them by 1.10 to estimate equivalent export values. The accuracy of this approximation depends on the set of partners and the items traded. Other factors affecting the accuracy of trade data include lags in reporting, recording differences across countries, and whether the country reports trade according to the general or special system of trade. (For further discussion of the measurement of exports and imports, see About the data for tables 4.4 and 4.5.) The regional trade flows in the table are calculated from current price values. The growth rates are in nominal terms; that is, they include the effects of changes in both volumes and prices.
Data sources Data on the direction and growth of merchandise trade were calculated using the IMF’s Direction of Trade database. Regional and income group classifications are according to the World Bank classification of economies as of July 1, 2011, and are as shown on the cover flaps of this report.
2012 World Development Indicators
343
6.3
High-income economy trade with low- and middle-income economies High-income economies
European Union
Japan
United States
2000
2010
2000
2010
2000
2010
2000
2010
19.5
53.9
8.8
19.3
2.3
5.3
1.9
7.3
11.8 2.5 1.0 5.6 74.1 5.1
9.8 2.1 0.9 8.7 64.8 13.7
13.8 2.1 0.6 2.9 77.7 2.9
12.0 2.1 0.7 10.2 71.0 4.0
0.5 1.3 0.5 0.4 94.7 2.6
0.3 1.4 1.1 0.2 95.2 1.8
27.5 7.2 0.4 1.3 54.6 9.0
15.1 4.5 0.5 1.7 56.7 21.5
19.6
45.9
10.5
23.9
1.1
1.5
5.6
11.7
20.9 6.1 5.7 2.9 62.8 1.6
14.0 3.6 11.5 7.6 58.1 5.2
24.2 8.6 5.3 0.4 59.4 2.2
17.5 4.9 11.7 2.5 61.8 1.6
42.7 3.3 21.6 1.1 30.8 0.5
26.3 2.7 12.2 0.0 58.5 0.2
8.0 1.4 2.8 3.3 83.6 0.9
5.0 1.4 1.7 22.8 67.5 1.6
Exports to low-income economies Total ($ billions) % of total exports Food Agricultural raw materials Ores and nonferrous metals Fuels Manufactured goods Miscellaneous goods Imports from low-income economies Total ($ billions) % of total imports Food Agricultural raw materials Ores and nonferrous metals Fuels Manufactured goods Miscellaneous goods
Simple applied tariff rates on imports from low-income economies (%)a 4.2 5.9 5.2 1.2 3.2 4.0 0.6
2.5 4.4 1.5 0.7 1.5 2.3 0.8
0.8 3.2 0.0 0.3 0.0 0.5 0.1
0.9 0.3 0.2 0.2 1.2 1.1 0.2
3.1 10.9 0.4 1.4 0.0 1.9 0.0
0.5 1.2 0.1 0.2 0.0 0.4 0.0
5.2 2.8 0.2 0.0 0.0 5.8 0.0
3.3 1.9 0.1 0.1 0.2 3.8 0.1
737.3
2,225.7
252.8
846.5
107.1
314.4
216.6
436.2
6.1 1.9 2.1 3.9 81.5 4.3
6.3 2.2 5.1 6.9 74.3 5.3
7.0 1.4 1.8 2.2 83.4 4.3
6.1 1.5 3.0 3.8 82.0 3.5
0.3 1.0 1.9 0.5 93.5 2.8
0.4 1.1 3.4 1.5 89.5 4.1
7.8 2.4 1.6 3.1 80.3 4.9
12.0 4.1 4.2 9.6 61.3 8.8
1,231.2
3,446.5
362.3
1,229.0
136.2
318.9
453.1
1,004.2
8.0 2.1 4.7 17.0 65.9 2.3
6.8 1.3 4.2 20.5 64.4 2.9
10.3 3.2 5.9 24.2 53.1 3.3
7.9 1.7 4.5 26.4 56.8 2.7
12.9 3.6 8.8 16.4 56.9 1.5
8.0 2.1 10.5 18.3 59.5 1.6
5.4 1.0 2.7 15.5 72.8 2.6
5.3 0.9 1.9 20.3 69.2 2.4
1.6 3.2 0.4 0.8 0.3 1.5 0.5
2.7 12.9 1.1 0.1 0.8 1.4 0.0
2.3 7.1 0.5 0.1 0.2 1.9 0.0
3.4 3.7 0.4 0.3 0.6 3.6 0.5
2.5 2.9 0.4 0.4 1.3 2.6 0.3
Average Food Agricultural raw materials Ores and nonferrous metals Fuels Manufactured goods Miscellaneous goods Exports to middle-income economies Total ($ billions) % of total exports Food Agricultural raw materials Ores and nonferrous metals Fuels Manufactured goods Miscellaneous goods Imports from middle-income economies Total ($ billions) % of total imports Food Agricultural raw materials Ores and nonferrous metals Fuels Manufactured goods Miscellaneous goods
Simple applied tariff rates on imports from middle-income economies (%)a Average Food Agricultural raw materials Ores and nonferrous metals Fuels Manufactured goods Miscellaneous goods
5.1 8.8 2.7 1.8 3.2 4.8 1.9
a. Includes ad valorem equivalents of specific rates.
344
2012 World Development Indicators
3.7 6.0 2.1 1.4 1.7 3.5 1.0
2.5 10.4 0.5 0.9 0.0 1.7 0.9
About the data
6.3
GLOBAL LINKS
High-income economy trade with low- and middle-income economies Definitions
Developing economies are becoming increasingly
table may not be fully comparable with those used
The product groups in the table are defined in accor-
important in the global trading system. Since the
to calculate the direction of trade statistics in tables
dance with SITC revision 2: food (0, 1, 22, and 4),
early 1990s trade between high-income economies
6.2 and 6.4 or the aggregate flows in tables 4.4,
agricultural raw materials (2 excluding 22, 27, and
and low- and middle-income economies has grown
4.5, and 6.1. Tariff data are from the United Nations
28), ores and nonferrous metals (27, 28, and 68),
faster than trade among high-income economies. The
Conference on Trade and Development’s (UNCTAD)
fuels (3), manufactured goods (5–8 excluding 68),
increased trade benefits consumers and producers.
Trade Analysis and Information System database. For
and miscellaneous goods (9). • Exports are all mer-
But as was apparent at the World Trade Organiza-
further discussion of merchandise trade statistics,
chandise exports by high-income economies to low-
tion’s (WTO) Ministerial Conferences in Doha, Qatar,
see About the data for tables 4.4, 4.5, 6.1, 6.2, and
income and middle-income economies as recorded
in October 2001; Cancun, Mexico, in September
6.4, and for information about tariff barriers, see
in the United Nations Statistics Division’s Comtrade
2003; Hong Kong SAR, China, in December 2005;
table 6.7.
database. Exports are recorded free on board
and Geneva, Switzerland, in December 2009 and
(f.o.b.). • Imports are all merchandise imports by
December 2011, achieving a more pro-development
high-income economies from low-income and middle-
outcome from trade remains a challenge. Doing so
income economies as recorded in the United Nations
will require strengthening international consultation.
Statistics Division’s Comtrade database. Imports
After the Doha meetings, negotiations were launched
include insurance and freight charges (c.i.f.). • High-,
on services, agriculture, manufactures, WTO rules,
middle-, and low-income economies are those
the environment, dispute settlement, intellec-
classified as such by the World Bank as of July 1,
tual property rights protection, and disciplines on
2011 (see front cover flap). • European Union is
regional integration. At the most recent negotiations
defined as all high-income EU members: Austria, Bel-
in Geneva, Switzerland, trade ministers reaffirmed
gium, Cyprus, Czech Republic, Denmark, Estonia,
that development is a core element of the WTO’s
Finland, France, Germany, Greece, Hungary, Ireland,
work and that the WTO needs to assist in further
Italy, Luxembourg, Malta, the Netherlands, Poland,
integrating developing countries into the multilateral
Portugal, Slovak Republic, Slovenia, Spain, Sweden,
trading system.
and the United Kingdom.
Trade flows between high-income and low- and middle-income economies reflect the changing mix of exports to and imports from developing economies. While food and primary commodities have continued to fall as a share of high-income economies’ imports, manufactures as a share of goods imports from both low- and middle-income economies have grown. And trade between developing economies has grown substantially over the past decade, a result of their increasing share of world output and liberalization of trade, among other influences. Yet trade barriers remain high. The table includes information about tariff rates by selected product groups. Applied tariff rates are the tariffs in effect for partners in preferential trade agreements such as the North American Free Trade Agreement. When these rates are unavailable, most favored nation rates are used. The difference between most favored nation and applied rates can be substantial. Simple averages of applied rates are shown because they are generally a better indicator of tariff protection than weighted average rates are.
Data sources Data on trade fl ows are from United Nations
The data on trade flows are from the United Nations
Statistics Division’s Comtrade database. Data
Statistics Division’s Commodity Trade (Comtrade)
on tariffs are from UNCTAD’s Trade Analysis and
database. Partner country reports by high-income
Information System database and are calculated
economies were used for both exports and imports.
by World Bank staff using the World Integrated
Because of differences in sources of data, timing,
Trade Solution system.
and treatment of missing data, the numbers in the
2012 World Development Indicators
345
6.4
Direction of trade of developing economies Exports
Imports
% of total merchandise exports To developing economies
East Asia & PaciďŹ c Cambodia China Fiji Indonesia Korea, Dem. Rep. Lao PDR Malaysia Mongolia Myanmar Papua New Guinea Philippines Thailand Vietnam Europe & Central Asia Albania Armenia Azerbaijan Belarus Bosnia and Herzegovina Bulgaria Cyprus Georgia Kazakhstan Kyrgyz Republic Lithuania Macedonia, FYR Moldova Romania Russian Federation Serbia Tajikistan Turkey Turkmenistan Ukraine Uzbekistan Latin America & Carib. Argentina Bolivia Brazil Chile Colombia Costa Rica Cuba Dominican Republic Ecuador El Salvador Guatemala Haiti Honduras Jamaica Mexico Nicaragua Panama Paraguay Peru Uruguay Venezuela, RB
346
% of total merchandise imports
Within region 2000 2010
Outside region 2000 2010
To high-income economies 2000 2010
8.9 w 7.4 4.9 10.9 11.5 7.5 .. 11.1 .. 22.6 9.0 9.2 15.0 22.6 24.7 w 3.9 25.0 21.0 72.2 6.6 29.0 .. 64.7 25.2 45.3 33.2 31.7 69.4 14.3 21.6 .. 56.2 9.2 60.5 42.6 57.0 15.4 w 48.1 44.5 22.9 21.1 27.2 17.9 8.8 3.9 31.4 27.7 40.4 4.4 24.3 3.5 3.1 30.5 22.3 74.5 18.0 53.9 19.7
7.3 w 0.2 8.9 2.0 7.6 43.9 0.5 5.9 9.5 10.6 0.1 1.6 6.2 6.1 9.6 w 0.0 10.5 2.3 7.5 .. 5.3 35.1 5.9 11.3 .. 1.2 1.0 1.8 7.6 10.1 .. .. 9.8 .. 18.5 .. 3.8 w 14.5 1.0 8.6 8.6 1.2 2.7 31.9 0.7 3.0 0.9 2.7 0.8 0.8 2.5 0.3 0.0 0.4 1.9 11.6 9.7 0.5
83.3 w 91.6 86.2 74.5 80.8 48.6 34.6 83.1 40.6 56.6 58.5 88.8 78.3 70.6 63.0 w 96.0 54.2 76.2 20.0 90.3 56.6 52.9 29.2 42.3 43.1 65.5 67.1 28.8 77.6 68.1 .. 41.0 75.4 26.2 37.8 36.4 77.6 w 35.6 53.4 57.3 65.3 70.7 79.4 59.4 95.3 62.1 70.8 56.4 94.7 69.7 93.5 96.1 64.0 70.3 20.7 68.8 35.4 74.2
12.4 w 6.1 7.0 21.1 22.6 50.8 .. 24.6 .. 59.4 12.1 19.3 29.1 22.3 20.7 w 10.7 38.7 13.2 60.3 6.5 29.9 .. 61.3 18.8 80.9 37.6 27.7 64.3 18.3 14.1 32.5 46.9 14.5 26.9 47.2 56.5 18.9 w 41.9 64.4 22.7 18.2 25.0 28.5 21.6 24.9 39.8 42.8 43.3 5.1 29.5 10.1 6.9 20.3 20.3 67.8 18.4 43.5 12.0
2012 World Development Indicators
16.6 w 2.3 19.0 2.2 14.3 36.7 1.1 11.6 3.4 20.2 1.5 2.9 11.8 8.3 14.0 w 7.4 13.1 13.9 11.9 .. 7.1 21.1 6.5 22.8 .. 1.6 3.2 2.7 6.1 12.0 2.3 .. 24.5 .. 22.2 .. 15.6 w 26.0 3.7 29.8 30.4 8.3 6.3 35.2 3.0 7.4 1.1 3.7 3.3 3.4 6.8 2.1 1.4 7.5 13.7 21.9 24.6 16.0
70.3 w 91.6 73.7 48.2 63.1 12.5 15.4 63.8 14.7 14.5 48.0 77.0 58.5 69.4 56.6 w 81.9 47.1 73.0 26.8 90.3 61.2 66.9 32.2 47.1 7.0 60.8 55.9 32.2 75.5 60.3 56.5 4.7 57.9 24.5 29.7 10.2 63.0 w 30.3 31.5 45.5 51.3 66.1 65.2 43.2 65.3 52.4 56.1 52.7 91.3 67.1 81.8 90.2 77.9 70.0 17.3 59.7 29.0 52.7
From developing economies Within region 2000 2010
Outside region 2000 2010
From high-income economies 2000 2010
11.7 w 39.7 7.8 8.3 14.1 34.9 78.4 13.7 19.1 48.0 11.2 11.2 15.9 20.2 31.3 w 14.0 25.2 44.4 71.8 5.4 36.1 .. 52.6 57.6 59.1 34.1 36.5 54.0 16.4 36.8 .. 91.2 12.8 54.3 60.8 45.3 15.2 w 34.3 51.2 20.8 32.2 28.0 22.3 40.3 20.7 41.4 30.8 36.2 21.8 31.9 15.3 2.5 45.8 30.6 49.5 38.0 51.7 21.1
8.5 w 1.0 10.4 2.6 7.5 32.9 1.3 2.8 37.2 2.7 1.8 4.2 7.2 4.6 11.8 w 2.7 13.6 11.4 3.2 .. 6.0 20.3 3.7 7.0 10.1 3.9 4.1 1.5 5.5 10.5 .. .. 13.7 .. 4.7 .. 3.4 w 9.9 4.5 12.5 10.9 3.9 3.9 12.7 3.0 3.7 2.0 2.6 4.5 3.5 3.4 4.3 0.0 1.4 12.5 8.1 12.3 2.4
78.1 w 58.5 78.4 88.1 77.4 32.3 18.7 81.6 43.7 49.3 85.8 83.9 74.8 74.4 57.9 w 83.3 55.8 44.1 23.8 94.4 56.0 79.7 42.0 35.2 30.8 60.5 59.4 44.4 74.4 52.5 .. 5.8 70.4 32.2 34.2 51.2 75.0 w 53.5 44.1 66.4 46.4 66.8 38.9 47.0 76.2 51.5 64.2 60.2 72.1 62.8 78.3 91.1 42.6 55.2 38.0 53.1 35.6 57.1
16.3 w 55.4 9.6 16.7 28.4 62.3 87.1 28.3 42.7 70.0 19.8 27.6 26.5 38.8 25.6 w 16.1 43.0 44.6 61.0 12.6 36.4 .. 54.1 33.2 29.2 43.2 34.1 50.1 16.6 13.3 21.2 65.3 19.7 51.0 48.7 40.2 19.4 w 39.9 73.3 16.6 29.3 27.4 22.8 47.4 25.2 37.6 40.3 33.8 38.7 40.6 24.8 4.2 52.7 21.3 46.1 32.3 48.1 34.8
18.5 w 2.1 18.0 3.2 9.7 30.3 0.5 7.1 30.3 4.6 1.1 4.4 7.3 6.7 14.7 w 10.6 22.6 16.4 11.1 .. 7.3 14.9 11.9 36.3 63.5 4.4 11.5 12.3 8.9 26.6 5.3 .. 25.4 .. 13.6 .. 14.8 w 19.3 4.4 28.5 22.1 18.4 10.1 21.8 8.5 11.9 7.1 10.7 10.8 6.6 7.4 20.2 9.5 7.5 37.2 21.1 23.7 11.5
62.8 w 42.5 63.4 78.1 61.8 7.4 11.0 64.0 27.0 25.4 77.9 68.1 64.8 54.5 52.5 w 73.3 34.3 39.0 24.4 86.4 55.7 83.3 34.0 30.5 7.2 52.4 54.0 37.5 74.4 58.1 61.0 14.5 53.9 32.8 36.7 42.6 58.4 w 36.5 22.2 54.5 47.9 51.0 65.5 30.8 63.8 49.6 52.6 54.5 50.4 52.8 65.3 74.2 37.5 42.9 16.5 46.5 28.0 52.2
Exports
Imports
% of total merchandise exports To developing economies
Middle East & N. Africa Algeria Bahrain Egypt, Arab Rep. Iran, Islamic Rep. Iraq Jordan Lebanon Libya Morocco Syrian Arab Republic Tunisia Yemen, Rep. South Asia Afghanistan Bangladesh India Nepal Pakistan Sri Lanka Sub-Saharan Africa Angola Benin Burkina Faso Burundi Cameroon Central African Republic Chad Comoros Congo, Dem. Rep. Congo, Rep. Côte d’Ivoire Ethiopia Gabon Gambia, The Ghana Guinea Guinea-Bissau Kenya Liberia Madagascar Malawi Mali Mauritania Mauritius Mozambique Niger Nigeria Rwanda Senegal Sierra Leone Somalia South Africa Sudan Tanzania Togo Uganda Zambia Zimbabwe
% of total merchandise imports
Within region 2000 2010
Outside region 2000 2010
To high-income economies 2000 2010
3.9 w 1.4 .. 6.9 1.4 7.3 22.8 17.8 3.5 2.8 8.7 7.5 0.5 4.6 w 42.4 1.7 4.3 42.9 4.6 3.5 10.5 w 0.2 10.7 22.0 17.0 7.0 1.5 6.0 .. 2.2 3.0 27.5 0.6 1.1 18.0 5.3 6.4 1.2 40.0 1.2 3.0 15.6 6.4 20.4 7.6 36.0 48.5 7.0 7.3 27.7 4.2 4.0 12.9 0.2 18.2 40.4 30.9 41.8 17.4
15.6 w 15.3 13.6 9.8 22.8 7.4 35.9 13.0 7.5 10.9 12.5 5.6 56.3 16.0 w 18.2 4.1 18.8 .. 14.3 10.5 14.7 w 25.0 65.9 0.0 0.0 9.4 7.9 .. .. 0.4 17.4 10.4 18.3 9.3 10.7 11.6 13.0 .. 18.7 14.6 3.5 14.7 40.5 8.3 0.9 7.0 0.5 22.3 16.1 15.5 2.4 21.2 7.2 50.6 17.8 30.2 2.2 1.8 8.9
76.0 w 83.4 24.4 73.5 63.3 85.3 38.9 68.2 89.1 83.1 75.9 83.2 40.4 75.3 w 39.4 79.7 73.4 55.3 80.6 79.9 63.2 w 74.7 23.4 76.5 61.9 77.9 90.7 82.3 96.2 97.2 79.5 50.2 81.1 83.7 71.3 74.8 80.6 6.9 39.6 84.2 88.4 69.2 50.7 70.2 91.5 36.7 51.0 70.4 42.6 48.7 91.2 74.9 52.9 49.2 63.1 25.1 63.8 49.8 29.4
7.4 w 3.2 .. 19.2 1.3 3.7 30.1 39.6 3.3 3.8 49.5 11.3 2.2 5.8 w 52.6 2.9 5.0 65.9 12.4 7.4 12.4 w 3.7 29.9 13.7 12.0 13.6 10.2 0.4 .. 25.7 1.1 30.6 5.3 2.4 7.0 10.7 2.3 30.5 37.4 32.2 4.9 22.3 6.6 14.4 14.6 25.9 77.6 9.6 58.4 50.2 8.0 4.1 14.9 1.6 19.6 63.2 50.9 19.1 47.3
27.0 w 14.7 11.4 24.0 42.3 31.3 25.6 14.4 13.7 22.4 6.6 9.5 70.2 28.4 w 16.7 9.6 30.4 .. 27.2 16.4 32.2 w 54.1 57.1 43.0 20.4 20.5 40.7 .. .. 48.3 38.7 13.0 26.0 28.1 48.4 24.4 46.9 .. 15.6 9.4 13.1 28.1 41.5 44.4 4.6 8.3 5.6 25.1 21.2 14.9 25.8 23.9 28.9 78.6 33.3 25.2 6.8 21.5 23.1
6.4
GLOBAL LINKS
Direction of trade of developing economies
60.4 w 82.1 17.1 53.1 40.9 65.0 42.9 45.1 83.0 70.2 43.9 79.2 26.8 63.9 w 30.6 83.5 63.2 27.8 58.5 65.5 53.5 w 42.1 13.0 39.2 51.3 62.8 49.1 82.2 59.3 25.8 60.0 51.6 55.2 55.8 44.7 52.8 35.1 4.0 38.2 58.4 72.1 49.1 43.9 40.0 80.8 64.0 16.7 64.3 19.7 26.9 63.1 71.9 56.1 19.7 38.4 11.2 39.3 59.4 29.4
From developing economies Within region 2000 2010
Outside region 2000 2010
From high-income economies 2000 2010
5.1 w 1.4 .. 1.1 0.9 10.1 18.0 7.9 12.1 10.2 3.6 6.6 4.4 4.3 w 27.3 11.8 0.9 37.4 2.7 10.6 12.0 w 19.8 22.2 31.0 23.8 27.3 12.2 13.7 .. 43.2 13.2 28.1 1.8 4.8 16.0 22.1 24.4 13.6 8.4 1.3 6.2 71.8 26.0 4.4 18.3 51.8 13.2 4.1 33.9 19.2 6.4 13.4 2.0 3.5 20.2 22.1 39.9 69.0 49.6
18.2 w 16.7 12.8 19.6 30.1 38.5 18.1 20.1 10.0 13.0 23.6 9.4 22.9 12.8 w 35.5 18.3 20.0 .. 22.3 16.7 18.2 w 13.8 15.8 11.7 8.1 8.8 7.0 .. .. 6.8 11.2 13.1 39.6 3.4 37.2 15.7 13.8 .. 16.0 5.7 33.0 6.9 7.5 21.0 26.8 7.4 5.8 20.1 5.4 17.4 8.8 50.9 15.5 36.6 21.1 12.1 15.6 3.7 6.3
71.6 w 81.8 86.7 72.7 68.3 51.4 61.0 70.6 77.7 75.2 54.5 82.4 70.1 58.2 w 37.2 53.1 55.6 40.5 74.7 64.2 70.0 w 66.2 61.5 52.6 46.7 54.7 57.0 81.7 52.4 49.4 68.5 46.2 48.9 90.6 46.8 61.3 61.5 48.5 75.0 93.0 52.6 20.0 32.7 65.8 54.9 28.6 80.5 75.2 37.6 58.8 81.3 24.7 82.1 59.9 58.6 62.6 44.4 26.4 33.0
7.9 w 2.9 .. 3.2 0.5 24.1 10.0 15.5 13.4 7.1 18.4 7.4 4.0 4.0 w 28.4 15.8 0.6 57.5 6.3 25.7 13.0 w 7.3 7.7 43.4 28.6 21.2 12.3 19.2 .. 50.3 6.3 29.1 2.8 10.9 16.9 24.9 6.4 22.1 12.3 0.9 12.9 61.8 30.9 4.7 11.5 38.1 21.1 4.9 50.1 15.2 24.8 8.8 7.9 7.4 16.6 13.6 27.1 63.6 69.2
26.5 w 28.2 26.4 35.2 24.7 48.2 28.0 26.6 29.5 23.5 35.7 16.6 39.6 17.0 w 26.9 37.0 39.2 .. 36.0 32.3 31.0 w 31.8 57.5 10.8 17.4 29.5 11.2 .. .. 18.8 31.4 28.0 28.6 18.8 56.1 33.5 21.1 .. 38.4 29.6 27.3 16.0 12.5 33.5 48.6 15.9 29.8 28.5 12.7 32.8 34.9 64.8 32.3 49.6 42.1 34.1 23.3 9.6 13.0
61.3 w 68.2 73.0 61.2 73.2 27.7 61.9 56.0 57.0 69.2 45.9 74.7 55.4 56.9 w 44.7 40.4 59.7 15.1 57.0 39.8 50.0 w 60.7 34.8 37.3 44.4 48.7 55.3 45.4 46.4 30.8 60.4 34.7 35.1 68.3 27.0 40.7 26.2 38.8 48.3 69.6 48.2 21.5 28.1 51.9 39.8 39.6 49.1 49.6 36.1 51.7 35.5 13.7 59.8 39.2 37.1 50.9 49.5 26.8 13.6
Note: Bilateral trade data are not available for Timor-Leste, Kosovo, West Bank and Gaza, Botswana, Eritrea, Lesotho, Namibia, South Sudan, and Swaziland. Components may not sum to 100 percent because of trade with unspecified partners or with economies not covered by World Bank classification. 2012 World Development Indicators
347
6.4
Direction of trade of developing economies
About the data
Definitions
Developing economies are an increasingly impor-
2010. This is due partly to trade-related advantages,
• Exports to developing economies within region
tant part of the global trading system. From 2009 to
such as proximity, lower transport costs, increased
are the sum of merchandise exports from the report-
2010 the volume of merchandise exports increased
knowledge from repeated interaction, and cultural
ing economy to other developing economies in the
14.5 percent globally and 12.9 percent in develop-
and historical affinity. The direction of trade is also
same World Bank region as a percentage of total
ing countries; the volume of merchandise imports
influenced by preferential trade agreements that a
merchandise exports by the economy. • Exports to
increased 13.5 percent globally and 10.7 percent
country has made with other economies. Though
developing economies outside region are the sum
in developing countries. Trade between high-income
formal agreements on trade liberalization do not
of merchandise exports from the reporting econ-
economies and low- and middle-income economies
automatically increase trade, they nevertheless
omy to other developing economies in other World
has grown faster than trade between high-income
affect the direction of trade between the participat-
Bank regions as a percentage of total merchandise
economies. This increased trade benefi ts both
ing economies. Table 6.6 illustrates the size of exist-
exports by the economy. • Exports to high-income
producers and consumers in developing and high-
ing regional trade blocs that have formal preferential
economies are the sum of merchandise exports from
income economies.
trade agreements.
the reporting economy to high-income economies as
The table shows trade in goods between develop-
Although global integration has increased, develop-
a percentage of total merchandise exports by the
ing economies in the same region and other regions
ing economies still face trade barriers when access-
economy. • Imports from developing economies
and between developing economies and high-income
ing other markets (see table 6.7).
within region are the sum of merchandise imports
economies. Data on exports and imports are from
by the reporting economy from other developing
the International Monetary Fund’s (IMF) Direction of
economies in the same World Bank region as a per-
Trade database and should be broadly consistent
centage of total merchandise imports by the econ-
with data from other sources, such as the United
omy. • Imports from developing economies outside
Nations Statistics Division’s Commodity Trade (Com-
region are the sum of merchandise imports by the
trade) database. All high-income economies and
reporting economy from other developing economies
major developing economies report trade data to the
in other World Bank regions as a percentage of total
IMF on a timely basis, covering about 85 percent of
merchandise imports by the economy. • Imports
trade for recent years. Trade data for less timely
from high-income economies are the sum of mer-
reporters and for countries that do not report are
chandise imports by the reporting economy from
estimated using reports of trading partner countries.
high-income economies as a percentage of total
Therefore, data on trade between developing and
merchandise imports by the economy.
high-income economies shown in the table should be generally complete. But trade fl ows between many developing economies—particularly those in Sub-Saharan Africa—are not well recorded, and the value of trade among developing economies may be understated. The table does not include some developing economies because data on their bilateral trade flows are not available. Data on the direction of trade between selected high-income economies are presented and discussed in tables 6.2 and 6.3. At the regional level most exports from developing economies are to high-income economies, but the share of intraregional trade is increasing. Geographic patterns of trade vary widely by country and commodity. Larger shares of exports from oil- and resourcerich economies are to high-income economies.
Data sources
The relative importance of intraregional trade
Data on merchandise trade flows are published in
is higher for both landlocked countries and small
the IMF’s Direction of Trade Statistics Yearbook and
countries with close trade links to the largest
Direction of Trade Statistics Quarterly; the data in
regional economy. For most developing economies—
the table were calculated using the IMF’s Direction
especially smaller ones—there is a “geographic
of Trade database. Regional and income group
bias” favoring intraregional trade. Despite the broad
classifications are according to the World Bank
trend toward globalization and the reduction of trade
classification of economies as of July 1, 2011,
barriers, the relative share of intraregional trade
and are as shown on the cover flaps of this report.
increased for most economies between 1999 and
348
2012 World Development Indicators
1970
World Bank commodity price index (2005 = 100) Energy Nonenergy commodities Agriculture Beverages Food Fats and oils Grains Other food Raw materials Timber Other raw materials Fertilizers Metals and minerals Base metals Steel productsa Commodity prices (2005 prices) Energy Coal, Australian ($/mt) Natural gas, Europe ($/mmBtu) Natural gas, U.S. ($/mmBtu) Natural gas, liquefied, Japan ($/mmBtu) Petroleum, avg, spot ($/bbl) Beverages (cents/kg) Cocoa Coffee, Arabica Coffee, robusta Tea, avg., 3 auctions Tea, Colombo auctions Tea, Kolkata auctions Tea, Mombasa auctions Food Fats and oils ($/mt) Coconut oil Copraa Groundnut oil Palm oil Palm kernel oila Soybeans Soybean meal Soybean oil Grains ($/mt) Barley Maize Rice, Thailand, 5% Rice, Thailand, 25% a Rice, Thailand, A1a Rice, Vietnam, 5% a Sorghuma Wheat, Canadaa Wheat, U.S., soft red winter a Wheat, U.S., hard red winter
1980
1990
1995
2000
2005
2006
2007
2008
2009
2010
GLOBAL LINKS
6.5
Primary commodity prices
2011
11 138 155 182 165 190 176 124 115 94 138 61 112 125 ..
87 134 157 207 163 158 166 168 116 89 144 117 89 97 77
45 87 94 94 94 85 103 97 93 85 102 68 75 81 76
32 92 104 113 100 105 110 83 109 107 111 75 69 76 71
60 81 88 86 86 86 89 83 95 102 87 75 67 71 61
100 100 100 100 100 100 100 100 100 100 100 100 100 100 100
115 122 110 105 108 102 116 110 115 112 119 102 151 162 95
120 139 124 114 128 145 139 96 119 115 123 137 171 171 90
156 156 146 129 159 178 190 106 122 117 128 341 154 142 133
105 130 136 144 142 151 155 120 118 116 121 186 110 109 112
128 154 151 161 150 163 152 131 147 116 182 166 159 150 110
153 171 170 169 171 181 194 136 168 125 215 217 167 157 117
29 2 1 .. 4
53 6 2 7 48
41 3 2 4 24
37 3 2 3 16
29 4 5 5 32
48 6 9 6 53
48 8 7 7 63
61 8 6 7 66
109 11 8 11 83
66 8 4 8 56
88 7 4 10 70
98 9 3 12 85
249 423 337 308 231 365 327
342 455 426 218 146 269 238
131 204 122 213 194 290 154
133 309 257 138 132 162 121
101 215 102 210 201 202 227
154 253 111 165 184 162 148
156 247 146 183 187 172 191
180 251 176 188 232 177 153
220 263 198 207 238 193 189
264 290 150 249 287 230 230
277 383 154 255 291 248 227
242 486 196 238 266 226 221
1,464 829 1,395 959 .. 431 378 1,056
884 594 1,127 766 .. 389 344 784
348 239 997 300 .. 255 207 463
621 407 920 583 .. 241 183 580
504 341 799 347 496 237 212 378
617 414 1,060 422 627 275 214 545
594 394 950 468 569 263 205 586
846 559 1,245 719 818 354 284 812
1,046 697 1,821 810 965 447 363 1,075
664 439 1,083 625 640 400 373 776
995 664 1,243 798 1,049 398 335 890
1,407 941 1,615 915 1,341 440 324 1,057
.. 215 466 .. .. .. 191 231 210 202
103 164 539 .. .. .. 169 250 221 227
83 113 280 270 209 .. 107 162 133 140
97 115 298 276 269 .. 110 192 155 164
86 99 227 193 187 .. 99 165 111 128
95 99 286 265 218 258 96 198 136 152
114 119 298 271 215 255 120 212 156 188
159 151 301 282 251 284 150 277 220 235
171 191 555 289 412 485 178 388 232 279
117 151 508 419 299 .. 138 275 170 205
140 165 433 391 340 380 146 277 203 198
169 237 442 412 373 418 219 358 233 257
2012 World Development Indicators
349
6.5
Primary commodity prices
1970
Commodity prices (continued) (2005 prices) Food (continued) Other food Bananas, U.S. ($/mt) Beef (cents/kg) Chicken meat (cents/kg) Fishmeal ($/mt)a Oranges ($/mt) Shrimp, Mexico (cents/kg)a Sugar, EU domestic (cents/kg) Sugar, U.S. domestic (cents/kg) Sugar, world (cents/kg) Agricultural raw materials Cotton A Index (cents/kg) Cotton, Memphis (cents/kg) Logs, Cameroon ($/cu. m)a Logs, Malaysian ($/cu. m) Rubber, Singapore (cents/kg) Rubber, TSR 20 (cents/kg)a Rubber, US (cents/kg)a Plywood (cents/sheet)a Sawnwood, Malaysian ($/cu. m) Sawnwood, Cameroon ($/cu. m)a Tobacco ($/mt)a Woodpulp ($/mt)a Fertilizers ($/mt) Diammonium phosphate Phosphate rock Potassium chloride Triple superphosphate Urea Metals and minerals Aluminum ($/mt) Copper ($/mt) Gold ($/toz)a Iron ore, spot, cfr China ($/dmt)a Lead (cents/kg) Nickel ($/mt) Silver (cents/toz)a Tin (cents/kg) Zinc (cents/kg) MUV G-15 index (2005 = 100) MUV G-5 index (2005 = 100)
1980
1990
1995
2000
2005
2006
2007
612 481 .. 726 619 .. 41 61 30
495 362 99 662 513 1,511 64 87 83
559 265 112 426 549 1,106 60 53 29
413 177 114 459 493 1,401 64 47 27
475 216 147 462 407 1,696 62 48 20
603 262 163 731 875 1,034 67 47 22
663 249 149 1,142 812 1,002 63 48 32
622 240 159 1,084 881 930 63 42 20
249 .. 159 159 150 0 170 380 647 .. 3,967 654
271 .. 330 257 187 0 213 359 520 .. 2,986 704
188 191 355 183 89 0 106 367 551 .. 3,508 842
197 200 315 237 147 0 168 542 687 553 2,453 792
146 164 308 213 75 70 93 502 666 547 3,332 744
122 130 334 203 149 139 166 509 659 559 2,790 635
124 131 312 234 203 190 226 583 734 610 2,906 684
199 41 116 157 67
292 61 152 237 252
177 42 101 136 123
201 32 109 139 174
173 49 137 154 113
247 42 158 201 219
2,050 5,219 132 .. 112 10,492 653 1,354 109
1,910 2,863 798 .. 119 8,553 2,708 2,201 100
1,695 2,752 397 .. 84 9,167 498 629 157
1,676 2,724 357 .. 59 7,636 482 577 96
1,734 2,030 312 .. 51 9,669 560 608 126
27 26
76 74
97 93
108 109
89 91
2008
2009
2010
2011
721 268 159 968 946 913 60 40 24
775 241 173 1,125 832 865 48 50 37
769 297 168 1,494 915 890 39 70 42
787 329 157 1,251 725 971 37 68 47
129 132 351 247 208 199 228 590 743 700 3,054 706
134 138 450 250 221 216 243 551 760 819 3,066 701
126 133 386 263 176 165 196 517 737 685 3,880 562
202 207 380 246 324 299 342 504 751 720 3,812 768
271 59 394 318 392 368 421 494 764 672 3,641 732
255 43 171 197 218
398 65 184 312 285
826 295 487 751 421
296 111 577 235 228
443 109 294 338 256
503 150 354 438 342
1,898 3,679 445 .. 98 14,744 734 738 138
2,516 6,580 592 68 126 23,742 1,132 860 321
2,430 6,557 642 113 238 34,293 1,235 1,339 299
2,198 5,943 745 133 179 18,035 1,281 1,581 160
1,523 4,711 890 73 157 13,406 1,344 1,242 151
1,924 6,672 1,084 129 190 19,313 1,789 1,807 191
1,953 7,181 1,276 136 195 18,637 2,868 2,119 178
100 100
102 102
109 106
117 114
109 107
113 110
123 115
Note: bbl = barrel, cu. m = cubic meter, dmtu = dry metric ton unit, kg = kilogram, mmBtu = million British thermal units, mt = metric ton, toz = troy ounce. a. Series not included in the nonenergy index.
350
2012 World Development Indicators
About the data
6.5
GLOBAL LINKS
Primary commodity prices Definitions
Primary commodities—raw or partially processed
economies (France, Germany, Japan, the United
• Energy price index is the composite price index for
materials that will be transformed into fi nished
Kingdom, and the United States) and is included in
coal, petroleum, and natural gas, weighted by exports
goods—are often developing countries’ most impor-
the table for comparison purposes.
of each commodity from low- and middle-income
tant exports, and commodity revenues can affect liv-
countries. • Nonenergy commodity price index cov-
ing standards. Price data are collected from various
ers the 38 nonenergy primary commodities that
sources, including international commodity study
make up the agriculture, fertilizer, and metals and
groups, government agencies, industry trade jour-
minerals indexes. • Agriculture includes beverages,
nals, and Bloomberg and Datastream. Prices are
food, and agricultural raw materials. • Beverages
compiled in U.S. dollars or converted to U.S. dollars
include cocoa, coffee, and tea. • Food includes fats
when quoted in local currencies.
and oils, grains, and other food items. Fats and oils
The table is based on frequently updated price
include coconut oil, copra, groundnut oil, palm oil,
reports. Prices are those received by exporters when
palm kernel oil, soybeans, soybean meal, and soy-
available, or the prices paid by importers or trade
bean oil. Grains include barley, maize, rice, sorghum,
unit values. Annual price series are generally simple
and wheat. Other food items include bananas, beef,
averages based on higher frequency data. The con-
chicken meat, fishmeal, oranges, shrimp, and sugar.
stant price series in the table are deflated by the
• Agricultural raw materials include timber and
manufactures unit value (MUV) index for the Group
other raw materials. Timber includes tropical hard
of Fifteen (G-15) countries (see below).
logs and sawnwood. Other raw materials include cot-
Commodity price indexes are calculated as
ton, natural rubber, and tobacco. • Fertilizers include
Laspeyres index numbers; the fixed weights are the
phosphate, phosphate rock, potassium, and nitrog-
2002–04 average export values for low- and middle-
enous products. • Metals and minerals include base
income economies (based on 2001 gross national
metals (aluminum, copper, lead, nickel, tin, and zinc)
income) rebased to 2005. Data for exports are from
and iron ore. • Steel products price index is the
the United Nations Statistics Division’s Commod-
composite price index for eight steel products based
ity Trade Statistics (Comtrade) database Standard
on quotations free on board (f.o.b.) Japan excluding
International Trade Classification (SITC) revision 3,
shipments to the United States for all years and to
the Food and Agriculture Organization’s FAOSTAT
China prior to 2002, weighted by product shares of
database, the International Energy Agency data-
apparent combined consumption (volume of deliv-
base, BP’s Statistical Review of World Energy, the
eries) for Germany, Japan, and the United States.
World Bureau of Metal Statistics, and World Bank
• Commodity prices—for definitions and sources,
staff estimates.
see “Commodity price data” (also known as the “Pink
Each index in the table represents a fixed basket of
Sheet”) at the World Bank Prospects for Develop-
primary commodity exports over time. The nonenergy
ment website (www.worldbank.org/prospects, click
commodity price index contains 39 price series for
on Products). • MUV G-15 index is the manufactures
38 nonenergy commodities. Separate indexes are
unit value index for G-15 country exports to low- and
compiled for energy and steel products, which are
middle-income economies.
not included in the nonenergy commodity price index. The MUV G-15 index is a composite index of prices for manufactured exports from the 15 major (G-15) developed and emerging economies (Brazil, Canada, China, France, Germany, India, Italy, Japan, the Republic of Korea, Mexico, South Africa, Spain, Thailand, the United Kingdom, and the United States) to low- and middle-income economies, valued in U.S. dollars. For the MUV G-15 index, unit value indexes in local currency for each country are converted to U.S. dollars using market exchange rates and are
Data sources
combined using weights determined by the share of
Data on commodity prices and the MUV G-15 index
each country’s exports to low- and middle-income
are compiled by the World Bank’s Development
countries in the base year (2005). The MUV G-5
Prospects Group. Monthly updates of commodity
index is a composite index of prices for manufac-
prices are available at http://data.worldbank.org.
tured exports from the fi ve major (G-5) industrial
2012 World Development Indicators
351
6.6
Regional trade blocs Year of creation
High-income and low- and middle-income economies APECb EEA EFTA European Union NAFTA SPARTECA Trans-Pacific SEP East Asia and Pacific and South Asia APTA ASEAN MSG PICTA SAARC Europe, Central Asia, and Middle East Agadir agreement CEFTA CEZ CIS Customs Union of Belarus, Kazakhstan, and Russian Federation EAEC ECO GCC PAFTA UMA Latin America and the Caribbean Andean Community CACM CARICOM LAIA MERCOSUR OECS Sub-Saharan Africa CEMAC CEPGL COMESA EAC ECCAS ECOWAS Indian Ocean Commission SADC UEMOA
Year of entry into force of the most recent agreement
Type of most recent agreementa
Merchandise exports within bloc
Merchandise exports by bloc
$ millions 2010
% of total bloc exports 2010
% of world exports 2010
4,868,838 3,519,827 2,096 3,356,310 955,598 20,717 3,969
67.5 68.7 0.6 67.3 48.7 8.1 0.9
47.3 33.6 2.2 32.7 12.9 1.7 3.0
1989 1994 1960 1957 1994 1981 2006
1994 2002 1958 1994 1981 2006
None EIA EIA EIA, CU FTA PTA EIA, FTA
1975 1967 1993 2001 1985
1976 1992 1994 2003 2006
PTA FTA PTA FTA FTA
278,451 262,270 99 308 15,702
12.1 25.0 0.8 2.6 5.8
15.1 6.9 0.1 0.1 1.8
2004 1992 2003 1991
1994 2004 1994
NNA FTA FTA FTA
2,068 5,229 18,065 68,596
3.2 17.5 4.0 12.9
0.4 0.2 2.9 3.5
2010 1997 1985 1981 1997 1989
2000 2003 2003c 1998 1994 c
CU CU PTA CU FTA NNA
18,065 21,200 27,654 28,623 81,816 3,926
4.0 4.7 8.8 4.8 9.7 2.9
2.9 2.9 2.1 3.9 5.5 0.9
1969 1961 1973 1980 1991 1981
1988 1961 1997 1981 2005 1981c
CU CU EIA PTA EIA NNA
7,825 6,330 3,356 128,829 44,239 132
8.5 22.5 15.2 15.9 15.7 17.5
0.6 0.2 0.1 5.3 1.9 0.0
1994 1976 1994 1996 1983 1975 1984 1992 1994
1999
CU NNA CU CU NNA PTA NNA FTA CU
383 81 8,158 1,997 483 8,911 184 14,576 2,250
1.2 1.5 7.7 20.3 0.6 8.8 5.3 9.8 14.6
0.2 0.0 0.7 0.1 0.6 0.7 0.0 1.0 0.1
1994 2000 2004 c 1993 2005c 2000 2000
Note: APEC is Asia Pacific Economic Cooperation, EEA is European Economic Area, EFTA is European Free Trade Association, NAFTA is North American Free Trade Agreement, SPARTECA is South Pacific Regional Trade and Economic Cooperation Agreement, Trans-Pacific SEP is Trans-Pacific Strategic Economic Partnership, APTA is Asia-Pacific Trade Agreement, ASEAN is Association of South East Asian Nations, MSG is Melanesian Spearhead Group, PICTA is Pacific Island Countries Trade Agreement, SAARC is South Asian Association for Regional Cooperation, CEFTA is Central European Free Trade Area, CEZ is Common Economic Zone, CIS is Commonwealth of Independent States, EAEC is Eurasian Economic Community, ECO is Economic Cooperation Organization, GCC is Gulf Cooperation Council, PAFTA is Pan-Arab Free Trade Area, UMA is Arab Maghreb Union, CACM is Central American Common Market, CARICOM is Caribbean Community and Common Market, LAIA is Latin American Integration Association, MERCOSUR is Southern Common Market, OECS is Organization of Eastern Caribbean States, CEMAC is Economic and Monetary Community of Central Africa, CEPGL is Economic Community of the Great Lakes Countries, COMESA is Common Market for Eastern and Southern Africa, EAC is East African Community, ECCAS is Economic Community of Central African States, ECOWAS is Economic Community of West African States, SADC is Southern African Development Community, UEMOA is West African Economic and Monetary Union. For regional bloc memberships, see the World Trade Organization’s Regional Trade Agreements Information System (http://rtais.wto.org/UI/PublicMaintainRTAHome.aspx). a. CU is customs union; EIA is economic integration agreement; FTA is free trade agreement; PTA is preferential trade agreement; and NNA is not notified agreement, which refers to preferential trade arrangements established among member countries that are not notified to the World Trade Organization (these agreements may be functionally equivalent to any of the other agreements). b. No preferential trade agreement c. Collected from the official website of the trade bloc.
352
2012 World Development Indicators
About the data
6.6
GLOBAL LINKS
Regional trade blocs Definitions
Trade blocs are groups of countries that have estab-
important regional trade blocs and the size of intrare-
• Type of most recent agreement includes customs
lished preferential arrangements governing trade
gional trade relative to each bloc’s exports of goods
union, under which members substantially eliminate
between members. Although in some cases the pref-
and the share of the bloc’s exports in world exports.
all tariff and nontariff barriers among themselves
erences—such as lower tariff duties or exemptions
Although the Asia Pacific Economic Cooperation has
and establish a common external tariff for nonmem-
from quantitative restrictions—may be no greater
no preferential arrangements, it is included because
bers; economic integration agreement, which liberal-
than those available to other trading partners, such
of the volume of trade between its members.
izes trade in services among members and covers a
arrangements are intended to encourage exports by
The data on country exports are from the Interna-
substantial number of sectors, affects a sufficient
bloc members to one another—sometimes called
tional Monetary Fund’s (IMF) Direction of Trade data-
volume of trade, includes substantial modes of
intraregional trade. Most countries are members
base and should be broadly consistent with those
supply, and is nondiscriminatory (in the sense that
of a regional trade bloc, and the surge of countries
from sources such as the United Nations Statistics
similarly situated service suppliers are treated the
participating in such arrangements has continued
Division’s Commodity Trade Statistics (Comtrade) data-
same); free trade agreement, under which members
unabated since the early 1990s. While trade blocs
base. All high-income economies and major developing
substantially eliminate all tariff and nontariff barriers
vary in structure, they all have the same objective: to
economies report trade to the IMF on a timely basis,
but set tariffs on imports from nonmembers; pref-
reduce trade barriers among member countries. But
covering about 85 percent of trade for recent years.
erential trade agreement, which is an agreement
effective integration requires more than reducing tar-
Trade by less timely reporters and by countries that do
notified to the WTO that is not a free trade agree-
iffs and quotas. Economic gains from competition and
not report is estimated using reports of trading partner
ment, a customs union, or an economic integration
scale may not be achieved unless other barriers that
countries. Therefore, data on trade between developing
agreement; and not notified agreement, which is a
divide markets and impede the free flow of goods, ser-
and high-income economies shown in the table should
preferential trade arrangement established among
vices, and investments are lifted. For example, many
be generally complete. However, trade flows between
member countries that is not notified to the World
regional trade blocs retain contingent protections on
many developing countries—particularly those in Sub-
Trade Organization (the agreement may be func-
intrabloc trade, including antidumping, countervailing
Saharan Africa—are not well recorded, and the value of
tionally equivalent to any of the other agreements).
duties, and “emergency protection” to address bal-
trade among developing countries may be understated.
• Merchandise exports within bloc are the sum of
ance of payments problems or protect an industry from
Membership in the trade blocs shown is based
merchandise exports by members of a trade bloc to
import surges. Other barriers include differing product
on the most recent information available (see Data
other members of the bloc. They are shown both in
standards, discrimination in public procurement, and
sources). Other types of preferential trade agree-
U.S. dollars and as a percentage of total merchan-
cumbersome border formalities. In addition, becoming
ments may have entered into force earlier than
dise exports by the bloc. • Merchandise exports by
a member of a trade bloc can result in trade diversion,
those shown in the table and may still be effective.
bloc as a share of world exports are the bloc’s total
where a member switches from being a relatively effi -
Unless otherwise footnoted, information on the type
merchandise exports (within the bloc and to the rest
cient, low-cost producer outside a trade bloc to a less
of agreement and date of enforcement are based on
of the world) as a share of total merchandise exports
efficient, higher cost producer within a trade bloc. On a
the World Trade Organization’s (WTO) list of regional
by all economies in the world.
global scale this could lead to misallocated resources.
trade agreements. Information on trade agreements
Membership in a regional trade bloc may reduce
not notified to the WTO was collected from the Global
the frictional costs of trade, increase the credibility
Preferential Trade Agreements database (box 6.6a)
of reform initiatives, and strengthen security among
and from official websites of the trade blocs.
partners. But making it work effectively is challeng-
Some countries belong to more than one trade bloc,
ing. All economic sectors may be affected, and some
so shares of world exports exceed 100 percent. Exports
may expand while others contract, so it is important
include all commodity trade, which may include items
to weigh the potential costs and benefits of mem-
not specified in trade bloc agreements. Differences
bership. The table shows the value of intraregional
from previously published estimates may be due to
merchandise trade (service exports are excluded) for
changes in membership or revisions in underlying data.
Global Preferential Trade Agreement Database
6.6a
The Global Preferential Trade Agreement Database provides information on preferential trade agreements around the world, including agreements that have not been notified to the World Trade Organization (WTO). It is designed to help trade policymakers, scholars, and business operators better understand and navigate the world of preferential trade agreements. Updated regularly, the database currently covers more than 330 preferential trade agreements in their original language, which have been indexed by WTO
Data sources Data on merchandise trade flows are published in the IMF’s Direction of Trade Statistics Yearbook and Direction of Trade Statistics Quarterly; the data in the table were calculated using the IMF’s Direction of Trade database. Data on trade bloc membership are from World Bank (2000b), UNC-
criteria and can be downloaded as PDFs. Users can search by provision or keyword; compare provisions
TAD’s Trade and Development Report 2007, WTO’s
across multiple agreements; and sort agreements by membership, date of signature, in-force status,
Regional Trade Agreements Information System
and other criteria. The database was developed jointly by the World Bank and the Center for International
(http://rtais.wto.org/UI/PublicMaintainRTAHome.
Business, Tuck School of Business at Dartmouth College. It was supported by the Multidonor Trust Fund
aspx), and the World Bank and the Center for Inter-
for Trade and Development, with financing from the governments of Finland, Norway, Sweden, and the
national Business at the Tuck School of Business
United Kingdom. The database is integrated with the World Integrated Trade Solution database and is
at Dartmouth College’s Global Preferential Trade
part of the World Bank’s Open Data initiative (http://wits.worldbank.org/gptad/).
Agreements Database.
2012 World Development Indicators
353
6.7
Tariff barriers All products
Primary products
Manufactured products
%
Afghanistan Albania Algeria Angola Antigua and Barbuda Argentina Armenia Australia Azerbaijan Bahamas, The Bahrain Bangladesh Barbados Belarus Belize Benin Bermuda Bhutan Bolivia Bosnia and Herzegovina Botswana Brazil Brunei Darussalam Burkina Faso Burundi Cambodia Cameroon Canada Cape Verde Central African Republic Chad Chile China† Hong Kong SAR, China Macao SAR, China Colombia Comoros Congo, Dem. Rep. Congo, Rep. Costa Rica Côte d’Ivoire Croatia Cuba Cyprus Djibouti Dominica Dominican Republic Ecuador Egypt, Arab Rep. El Salvador Equatorial Guinea Eritrea Ethiopia European Union Fiji French Polynesia Gabon Gambia, The Georgia Ghana †Data for Taiwan, China
354
Most recent year
2008 2009 2009 2009 2009 2010 2008 2010 2009 2010 2009 2008 2007 2010 2010 2010 2010 2007 2010 2010 2010 2010 2010 2010 2010 2008 2009 2010 2010 2007 2009 2010 2010 2010 2010 2010 2010 2009 2007 2009 2010 2010 2010 2010 2009 2007 2010 2010 2009 2010 2007 2006 2010 2010 2010 2009 2009 2009 2010 2009 2010
Binding coverage
Simple mean bound rate
Simple mean tariff
.. 100.0 .. 100.0 97.9 100.0 100.0 97.0 .. .. 73.6 15.9 97.8 .. 97.9 39.5 .. .. 100.0 .. 96.1 100.0 95.3 39.4 22.3 100.0 13.7 99.7 100.0 62.5 13.9 100.0 100.0 45.8 28.2 100.0 .. 100.0 16.5 100.0 33.8 100.0 31.7 100.0 100.0 94.7 100.0 100.0 99.3 100.0 .. .. .. 100.0 51.4 .. 100.0 13.7 100.0 14.4 100.0
.. 7.1 .. 59.2 58.7 31.9 8.5 10.0 .. .. 34.8 169.9 78.1 .. 58.4 28.7 .. .. 40.0 .. 19.0 31.4 24.1 42.5 67.8 19.1 79.9 5.2 15.8 36.0 79.9 25.1 10.0 0.0 0.0 43.1 .. 96.2 27.4 43.2 11.2 6.0 21.4 4.2 41.2 58.7 34.9 21.7 37.3 36.9 .. .. .. 4.2 40.1 .. 21.4 101.8 7.2 92.5 6.0
6.2 5.7 14.2 7.4 13.8 11.4 3.7 2.8 8.3 33.2 4.4 13.9 15.1 6.7 11.5 13.3 18.0 18.2 9.6 3.3 8.8 13.4 3.8 12.4 9.8 12.4 18.4 2.9 14.7 17.5 17.6 4.9 7.7 0.0 0.0 11.2 7.6 12.9 18.6 4.8 13.1 2.4 10.5 1.9 20.6 11.9 8.3 9.3 12.6 5.1 18.3 9.6 18.1 1.9 11.9 6.8 18.7 18.7 0.5 13.0 5.3
2012 World Development Indicators
Weighted mean tariff
6.4 5.1 8.6 7.4 14.6 6.2 2.3 1.9 3.9 21.5 3.6 13.0 14.8 2.1 6.4 15.4 26.1 17.8 5.4 1.8 5.2 7.6 4.1 8.8 5.5 9.9 15.0 0.9 11.6 13.6 14.7 4.0 4.0 0.0 0.0 8.9 8.1 11.0 14.7 2.4 7.3 1.2 8.7 1.6 15.2 7.9 6.1 6.0 8.1 5.5 15.6 5.4 10.5 1.6 11.0 4.2 14.5 14.8 0.4 8.6 2.5
Share of tariff Share of tariff lines lines with international with specific rates peaks
4.4 0.0 53.2 23.4 49.4 24.3 0.0 0.0 46.5 68.8 0.3 38.0 44.9 4.5 31.9 50.2 64.3 50.7 11.9 0.0 20.2 26.4 20.8 44.5 29.8 19.7 52.5 6.5 44.3 47.4 47.4 0.0 11.2 0.0 0.0 19.8 1.5 42.5 52.6 0.7 47.9 4.1 11.6 1.9 69.4 43.3 30.1 20.2 18.4 1.9 52.3 22.4 55.6 1.9 23.3 28.0 53.1 91.2 0.0 40.5 6.0
0.7 0.0 0.0 0.6 0.1 0.0 0.4 0.2 2.3 0.5 0.2 0.9 1.0 14.0 1.9 0.0 2.8 0.0 0.0 4.2 1.0 0.0 1.2 0.0 0.1 0.0 0.0 3.4 0.0 0.1 0.3 0.0 0.2 0.0 0.0 0.0 0.3 0.2 0.2 0.0 0.0 5.2 0.0 7.8 0.5 0.0 0.0 0.0 0.2 0.0 0.2 0.0 0.0 7.8 1.9 0.0 0.1 0.1 1.0 1.2 1.7
%
%
Simple mean tariff
Weighted mean tariff
Simple mean tariff
Weighted mean tariff
7.0 6.8 14.5 11.6 17.2 7.5 5.6 1.3 9.5 21.9 7.0 16.3 26.3 6.3 17.5 15.5 10.0 43.5 8.4 1.4 6.1 8.1 0.2 11.4 15.4 13.8 20.5 1.7 16.2 18.9 22.5 4.4 8.1 0.0 0.0 10.9 4.5 14.2 21.9 6.3 15.1 4.5 11.1 2.5 15.9 19.2 11.8 9.0 37.6 8.4 21.5 9.2 19.7 2.5 13.5 4.1 21.2 16.9 4.0 16.6 8.4
6.7 5.4 7.8 13.9 14.8 1.6 2.2 0.4 3.8 7.6 6.9 8.8 21.9 0.9 4.0 12.4 14.3 44.9 5.8 1.1 0.5 1.5 0.1 8.1 9.4 11.8 12.9 0.3 12.2 13.8 17.2 2.7 1.8 0.0 0.0 8.8 3.9 10.8 18.6 3.3 5.4 1.9 6.2 0.5 8.7 5.7 4.6 4.3 6.4 7.4 21.4 3.5 5.1 0.5 9.8 2.7 15.1 12.8 1.0 8.9 2.0
6.1 5.6 14.2 6.8 13.1 11.9 3.5 3.0 8.1 36.1 4.0 13.6 13.4 6.8 10.6 12.9 19.4 15.5 9.7 3.6 9.0 14.0 4.4 12.5 9.1 12.2 18.1 3.2 14.4 17.3 16.8 4.9 7.7 0.0 0.0 11.3 8.4 12.6 18.2 4.6 12.8 2.1 10.4 1.9 21.3 10.6 7.8 9.4 9.4 4.7 17.8 9.6 18.0 1.9 11.6 7.3 18.4 19.2 0.1 12.5 4.8
6.3 5.0 8.9 5.9 14.5 7.0 2.4 2.5 4.0 32.6 3.1 14.0 12.3 3.4 10.1 17.0 27.7 16.0 5.2 2.3 6.6 9.8 5.0 9.2 4.6 9.6 15.9 1.1 11.0 13.3 13.8 4.8 5.6 0.0 0.0 8.9 11.2 11.1 14.1 2.1 9.3 1.0 9.8 2.3 18.6 9.3 6.9 6.7 9.5 4.3 14.3 7.2 13.2 2.3 12.2 5.2 14.3 17.0 0.0 8.4 2.9
All products
Primary products
6.7
GLOBAL LINKS
Tariff barriers
Manufactured products
% Most recent year
Grenada Guatemala Guinea Guinea-Bissau Guyana Haiti Honduras Iceland India Indonesia Iran, Islamic Rep. Iraq Israel Jamaica Japan Jordan Kazakhstan Kenya Korea, Dem. Rep. Korea, Rep. Kosovo Kuwait Kyrgyz Republic Lao PDR Lebanon Lesotho Liberia Libya Macedonia, FYR Madagascar Malawi Malaysia Maldives Mali Mauritania Mauritius Mayotte Mexico Moldova Mongolia Montenegro Morocco Mozambique Myanmar Namibia Nepal New Zealand Nicaragua Niger Nigeria Norway Oman Pakistan Palau Panama Papua New Guinea Paraguay Peru Philippines Puerto Rico
2010 2010 2010 2010 2010 2009 2009 2010 2009 2010 2008 2009 2010 2010 2009 2010 2010 2010 2009 2010 2008 2007 2010 2006 2010 2010 2010 2009 2009 2010 2007 2010 2010 2010 2010 2009 2010 2009 2010 2008 2010 2010 2010 2010 2010 2010 2010 2009 2009 2010 2009 2010 2010 2010 2010
Binding coverage
Simple mean bound rate
Simple mean tariff
Weighted mean tariff
100.0 100.0 38.6 97.6 100.0 89.8 100.0 95.0 74.5 96.6 .. .. 75.2 100.0 99.7 100.0 .. 15.2 .. 95.1 .. 99.9 99.9 .. .. 100.0 .. .. 100.0 30.5 32.0 83.9 97.0 40.5 39.4 17.7 .. 100.0 99.9 100.0 .. 100.0 14.0 17.6 96.1 99.4 100.0 100.0 96.6 19.5 100.0 100.0 98.6 .. 99.9 100.0 100.0 100.0 67.2 ..
56.8 42.3 20.3 48.6 56.8 17.6 32.5 13.5 50.2 37.5 .. .. 22.0 49.7 3.0 16.3 .. 95.3 .. 16.1 .. 100.0 7.5 .. .. 78.9 .. .. 6.9 27.3 75.9 14.6 37.2 28.9 19.6 98.3 .. 35.1 6.7 17.5 .. 41.3 97.4 83.8 19.4 26.2 10.0 41.7 44.9 119.4 3.0 13.9 60.0 .. 23.5 31.5 33.5 30.1 25.8 ..
8.9 4.1 13.5 13.3 10.1 3.0 6.4 1.8 11.5 4.8 24.8 .. 6.0 8.4 2.6 9.7 6.4 12.1 .. 10.3 .. 4.1 3.3 9.3 5.6 9.5 .. 0.0 3.6 10.6 11.7 6.8 21.7 12.8 12.6 2.0 5.1 7.4 4.6 4.9 3.0 9.1 7.7 4.0 6.3 12.6 2.5 4.2 13.0 10.9 0.5 3.7 14.8 2.7 7.6 4.4 8.1 4.8 5.3 ..
7.7 2.4 11.9 9.9 6.9 5.1 6.5 1.1 8.2 2.5 19.6 .. 3.5 7.5 1.6 5.2 3.4 9.2 .. 8.7 .. 4.1 2.3 13.2 4.8 10.5 .. 0.0 2.7 7.7 6.6 4.0 20.6 8.4 10.1 1.1 1.8 2.2 2.5 5.1 3.5 7.1 4.8 3.2 1.8 12.1 1.6 2.3 9.1 10.6 0.4 3.2 9.5 1.0 7.6 2.5 3.7 2.5 4.8 ..
Share of tariff Share of tariff lines lines with international with specific rates peaks
36.7 16.0 56.1 51.8 39.2 5.1 0.5 5.2 10.4 7.0 56.5 .. 2.9 42.6 8.6 29.5 19.6 36.6 .. 7.0 .. 0.0 1.8 20.4 11.6 21.6 .. 0.0 14.5 38.3 42.7 16.9 88.1 47.9 49.0 10.4 1.4 6.1 8.4 0.1 4.5 23.6 23.9 4.1 16.7 46.3 0.0 16.2 48.9 34.9 0.5 0.3 45.3 1.3 2.8 23.3 18.3 10.0 5.4 ..
0.0 0.0 0.0 0.0 0.0 0.3 0.1 3.4 4.7 0.5 0.3 .. 4.1 0.0 3.3 0.6 16.4 0.1 .. 0.6 .. 0.3 1.7 0.1 10.9 1.4 .. 0.0 2.5 0.2 0.3 0.8 0.0 0.0 0.0 8.7 0.0 0.1 2.8 0.0 2.9 0.0 0.1 0.0 2.0 1.2 2.9 0.0 0.0 0.0 6.9 0.2 0.3 6.6 0.0 1.0 0.0 0.0 0.0 ..
%
%
Simple mean tariff
Weighted mean tariff
Simple mean tariff
Weighted mean tariff
10.1 4.8 15.6 14.6 15.5 5.8 9.9 2.3 20.1 3.2 21.7 .. 8.8 13.9 5.1 14.2 6.1 16.0 .. 26.3 .. 3.4 4.5 16.0 8.2 9.2 .. 0.0 7.4 12.6 13.4 10.1 17.5 12.8 11.1 1.2 3.7 9.3 7.0 5.1 5.8 18.0 8.7 5.1 4.1 12.9 1.4 5.6 14.0 11.8 2.0 5.0 14.5 0.7 11.5 11.8 5.8 3.8 6.8 ..
7.0 1.7 13.9 10.0 5.9 4.1 8.1 1.1 7.5 1.6 12.5 .. 3.3 6.1 1.6 3.9 0.9 12.6 .. 12.7 .. 3.0 0.9 14.2 5.0 1.6 .. 0.0 5.7 3.3 6.0 5.0 18.4 7.9 9.2 0.4 1.5 1.5 2.3 5.4 4.8 8.9 4.7 2.7 2.1 10.3 0.4 2.8 10.7 9.1 1.2 3.3 6.5 0.1 8.4 1.8 0.8 1.3 5.1 ..
8.6 4.0 13.2 12.9 9.4 2.5 6.0 1.7 10.3 5.0 25.1 .. 5.6 7.7 2.1 9.0 6.4 11.7 .. 7.4 .. 4.2 3.1 8.4 5.2 9.5 .. 0.0 3.2 10.3 11.5 6.1 22.8 12.8 12.9 2.1 5.3 7.2 4.3 4.9 2.7 8.2 7.5 3.9 6.7 12.6 2.6 4.0 12.8 10.7 0.3 3.5 14.8 3.3 7.1 3.4 8.2 4.9 5.1 ..
8.3 2.8 10.2 9.7 7.7 5.9 5.5 1.1 8.3 2.9 21.2 .. 3.7 8.9 1.7 6.1 4.0 6.7 .. 5.1 .. 4.4 3.6 12.6 5.1 10.9 .. 0.0 1.8 8.9 6.8 3.7 22.6 8.7 11.0 1.6 1.9 2.4 2.6 4.9 2.8 5.8 4.7 3.4 1.6 14.4 2.1 1.9 7.6 10.8 0.2 3.2 12.3 3.1 7.3 2.8 4.8 3.0 4.7 ..
2012 World Development Indicators
355
6.7
Tariff barriers All products
Primary products
Manufactured products
% Most recent year
Qatar Russian Federation Rwanda Saudi Arabia Senegal Serbia Seychelles Sierra Leone Singapore Solomon Islands Somalia South Africa South Sudan Sri Lanka St. Kitts and Nevis St. Lucia St. Vincent & Grenadines Sudan Suriname Swaziland Switzerland Syrian Arab Republic Tajikistan Tanzania Thailand Timor-Leste Togo Tonga Trinidad and Tobago Tunisia Turkey Turkmenistan Uganda Ukraine United Arab Emirates United States Uruguay Uzbekistan Vanuatu Venezuela, RB Vietnam West Bank and Gaza Yemen, Rep. Zambia Zimbabwe World Low income Middle income Lower middle income Upper middle income Low & middle income East Asia & Pacific Europe & Central Asia Latin America & Carib. Middle East & N. Africa South Asia Sub-Saharan Africa High income OECD Non-OECD
2009 2010 2010 2009 2010 2005a 2007 2004 2010 2010 2010 2010 2010 2007 2007 2010 2010 2010 2010 2010 2010 2010 2009 2010 2010 2008 2008 2010 2002 2010 2010 2009 2010 2010 2009 2009 2010 2010 2009 2009 2007b
Binding coverage
100.0 .. 100.0 100.0 100.0 .. .. 100.0 69.6 100.0 .. 96.1 .. 38.1 97.8 99.6 99.7 .. 27.6 96.1 99.8 .. .. 13.8 74.7 .. 14.3 100.0 100.0 58.3 50.3 .. 16.1 100.0 100.0 100.0 100.0 .. .. 100.0 100.0 .. .. 17.1 22.2 78.3 w 48.3 87.7 80.6 90.3 76.5 84.2 91.7 96.1 99.9 81.5 53.4 90.3 89.6 73.1
Simple mean bound rate
Simple mean tariff
Weighted mean tariff
16.0 .. 89.3 10.8 30.0 .. .. 47.4 7.0 78.8 .. 19.4 .. 30.1 76.1 61.9 62.5 .. 18.1 19.4 0.0 .. .. 120.0 26.1 .. 80.0 17.6 55.8 58.0 29.2 .. 73.5 5.8 14.8 3.7 31.6 .. .. 36.5 11.5 .. .. 106.9 91.4 31.2 52.3 32.9 37.3 29.3 36.5 22.3 8.9 36.6 30.4 41.6 48.4 17.3 7.4 9.1
4.2 6.0 9.9 4.1 13.4 8.1 6.5 .. 0.0 9.2 .. 7.6 .. 9.4 9.6 9.6 11.3 13.3 11.6 10.9 0.0 6.7 4.4 12.9 11.2 .. 12.8 10.9 8.7 21.9 2.5 5.4 12.1 4.5 4.5 2.9 9.6 11.8 18.2 13.1 7.1 .. 5.5 10.8 16.7 6.9 12.5 8.5 9.1 8.3 9.0 8.3 4.9 8.1 10.6 13.7 11.4 3.6 3.3 2.9
3.8 3.8 6.0 3.9 8.9 6.0 28.3 .. 0.0 8.7 .. 4.4 .. 6.9 10.5 9.0 8.4 14.8 11.9 10.2 0.0 6.1 5.9 8.2 4.9 .. 14.2 7.2 10.0 16.0 2.4 2.9 8.2 2.8 3.7 1.8 3.6 6.9 18.5 10.6 5.7 .. 4.2 3.8 17.3 3.1 9.2 5.0 6.3 4.6 5.1 4.2 4.1 4.9 7.4 8.5 7.2 2.2 2.2 0.8
a. Includes Montenegro. b. Rates are most favored nation rates.
356
2012 World Development Indicators
Share of tariff Share of tariff lines lines with international with specific rates peaks
0.2 17.4 31.4 0.0 50.5 17.8 12.8 .. 0.0 0.0 .. 17.9 .. 23.8 36.1 39.9 44.4 28.3 36.2 26.2 0.0 27.6 8.2 39.9 20.5 .. 47.3 65.6 43.6 57.8 5.4 14.8 37.5 1.1 0.3 3.4 29.3 20.1 66.6 21.9 23.6 .. 1.4 51.2 38.8 15.0 41.4 20.1 20.0 20.7 21.5 15.8 13.1 18.1 25.0 39.6 36.7 5.0 5.1 4.9
0.2 15.4 0.1 0.3 0.0 0.0 2.0 .. 0.1 4.4 .. 1.8 .. 1.0 0.5 0.0 0.5 0.1 0.1 3.2 26.9 0.0 0.8 0.1 5.2 .. 0.0 0.3 0.5 0.0 0.2 3.2 0.2 0.4 0.3 6.9 0.0 10.9 3.4 0.0 0.5 .. 0.8 0.0 6.5 2.0 0.2 0.8 0.8 0.8 0.8 0.9 0.9 0.4 0.0 2.7 1.0 4.2 5.4 0.0
% Simple mean tariff
5.1 5.8 11.5 3.5 14.1 10.9 14.0 .. 0.0 9.3 .. 5.4 .. 15.3 9.9 12.7 15.1 16.6 18.3 9.7 0.0 6.5 3.5 17.5 15.9 .. 14.4 11.9 16.6 26.8 14.4 14.7 15.7 5.9 6.0 2.6 5.6 12.6 26.7 12.2 8.6 .. 7.1 9.2 19.6 9.0 14.3 10.8 13.2 9.3 11.3 10.6 7.7 7.9 17.2 17.8 12.8 6.4 7.1 3.2
% Weighted mean tariff
4.0 3.4 6.4 2.8 7.7 4.5 50.5 .. 0.0 8.5 .. 1.9 .. 9.6 7.0 4.9 7.8 10.9 15.0 1.3 0.0 6.1 1.1 8.7 2.9 .. 12.4 5.1 3.1 12.0 4.8 12.6 8.8 2.5 2.8 1.2 1.1 3.9 27.8 10.0 6.0 .. 3.8 3.1 20.4 2.7 7.9 3.4 5.6 2.7 3.5 2.3 3.1 3.1 6.8 7.6 6.1 2.4 2.6 0.8
Simple mean tariff
4.1 6.0 9.7 4.1 13.3 7.8 4.8 .. 0.0 9.2 .. 7.9 .. 8.8 9.6 9.1 10.6 12.9 10.4 11.1 0.0 6.7 4.5 12.4 10.5 .. 12.6 10.6 7.6 21.4 1.2 3.8 11.6 4.3 4.2 3.0 10.0 11.7 16.5 13.2 6.9 .. 5.2 11.0 16.3 6.6 12.3 8.2 8.6 8.1 8.7 7.9 4.6 8.1 9.7 13.1 11.3 3.1 2.6 2.8
Weighted mean tariff
3.8 3.9 5.9 4.2 10.2 6.8 6.4 .. 0.0 8.8 .. 5.7 .. 5.7 11.8 12.2 8.6 15.8 10.4 16.2 0.0 6.1 7.8 8.0 6.1 .. 14.9 9.0 17.4 18.2 1.3 1.1 7.9 3.1 4.4 2.0 5.3 7.4 14.6 10.8 5.6 .. 4.6 4.2 15.1 3.3 w 9.8 5.7 6.6 5.5 5.8 5.4 4.5 5.5 7.8 8.7 7.7 2.1 2.0 0.8
About the data
6.7
GLOBAL LINKS
Tariff barriers Definitions
Poor people in developing countries work primarily
trade and reduce the weights applied to these tariffs.
in agriculture and labor-intensive manufactures, sec-
Bound rates result from trade negotiations incorpo-
• Binding coverage is the percentage of product lines with an agreed bound rate. • Simple mean
tors that confront the greatest trade barriers. Remov-
rated into a country’s schedule of concessions and
bound rate is the unweighted average of all the lines
ing barriers to merchandise trade could increase
are thus enforceable.
in the tariff schedule in which bound rates have been
Some countries set fairly uniform tariff rates
set. • Simple mean tariff is the unweighted average
across all imports. Others are selective, setting high
of effectively applied rates or most favored nation
In general, tariffs in high-income countries on
tariffs to protect favored domestic industries. The
rates for all products subject to tariffs calculated
imports from developing countries, though low, are
share of tariff lines with international peaks provides
for all traded goods. • Weighted mean tariff is the
twice those collected from other high-income coun-
an indication of how selectively tariffs are applied.
average of effectively applied rates or most favored
tries. But protection is also an issue for developing
The effective rate of protection—the degree to which
nation rates weighted by the product import shares
countries, which maintain high tariffs on agricultural
the value added in an industry is protected—may
corresponding to each partner country. • Share of
commodities, labor-intensive manufactures, and
exceed the nominal rate if the tariff system system-
tariff lines with international peaks is the share
other products and services.
atically differentiates among imports of raw materi-
of lines in the tariff schedule with tariff rates that
als, intermediate products, and finished goods.
exceed 15 percent. • Share of tariff lines with spe-
growth in these countries—even more if trade in services were also liberalized.
Countries use a combination of tariff and nontariff measures to regulate imports. The most common
The share of tariff lines with specific rates shows
cific rates is the share of lines in the tariff schedule
form of tariff is an ad valorem duty, based on the
the extent to which countries use tariffs based on
that are set on a per unit basis or that combine ad
value of the import, but tariffs may also be levied
physical quantities or other, non–ad valorem mea-
valorem and per unit rates. • Primary products are
on a specific, or per unit, basis or may combine ad
sures. Some countries such as Switzerland apply
commodities classified in SITC revision 2 sections
valorem and specific rates. Tariffs may be used to
mainly specific duties. To the extent possible, these
0–4 plus division 68 (nonferrous metals). • Manu-
raise fiscal revenues or to protect domestic indus-
specifi c rates have been converted to their ad
factured products are commodities classified in
tries from foreign competition—or both. Nontariff
valorem equivalent rates and have been included in
SITC revision 2 sections 5–8 excluding division 68.
barriers, which limit the quantity of imports of a par-
the calculation of simple and weighted tariffs.
ticular good, include quotas, prohibitions, licensing
Data are classified using the Harmonized System
schemes, export restraint arrangements, and health
at the six- or eight-digit level. Tariff line data were
and quarantine measures. Because of the difficulty
matched to Standard International Trade Classifi -
of combining nontariff barriers into an aggregate indi-
cation (SITC) revision 2 codes to define commodity
cator, they are not included in the table.
groups and import weights. Import weights were cal-
Unless specified as most favored nation rates, the
culated using the United Nations Statistics Division’s
tariff rates used in calculating the indicators in the
Commodity Trade Statistics (Comtrade) database.
table are effectively applied rates. Effectively applied
The table shows tariff rates for three commodity
rates are those in effect for partners in preferen-
groups: all products, primary products, and manu-
tial trade arrangements such as the North Ameri-
factured products. Effectively applied rates at the
can Free Trade Agreement. The difference between
six- and eight-digit product level are averaged for
most favored nation and applied rates can be sub-
products in each commodity group. When an effec-
stantial. Because more countries now report their
tively applied rate is not available, the most favored
free trade agreements, suspensions of tariffs, and
nation rate is used instead.
other special preferences, this year’s World Develop-
Data are shown only for the last year for which
ment Indicators includes effectively applied rates for
complete data are available. EU member countries
most countries. All estimates are calculated using
apply a common tariff schedule that is listed under
the most recent information, which is not necessarily
European Union and are thus not listed separately.
revised every year. As a result, data for the same year may differ from data in last year’s edition.
Data sources All indicators in the table were calculated by World
Three measures of average tariffs are shown: sim-
Bank staff using the World Integrated Trade Solu-
ple bound rates and the simple and the weighted
tion system (http://wits.worldbank.org). Data on
tariffs. Bound rates are based on all products in a
tariffs are from the United Nations Conference
country’s tariff schedule, while the most favored
on Trade and Development’s Trade Analysis and
nation or applied rates are calculated using all traded
Information System database and the World Trade
items. Weighted mean tariffs are weighted by the
Organization’s Integrated Data Base and Consoli-
value of the country’s trade with each trading part-
dated Tariff Schedules database. Data on global
ner. Simple averages are often a better indicator of
imports are from the United Nations Statistics
tariff protection than weighted averages, which are
Division’s Comtrade database.
biased downward because higher tariffs discourage
2012 World Development Indicators
357
6.8
Trade facilitation Logistics Performance Index
1–5 (worst to best) 2010
Afghanistan Albania Algeria Angola Argentina Armenia Australia Austria Azerbaijan Bahrain Bangladesh Belarus Belgium Benin Bolivia Bosnia and Herzegovina Botswana Brazil Bulgaria Burkina Faso Burundi Cambodia Cameroon Canada Central African Republic Chad Chile China Hong Kong SAR, China Colombia Congo, Dem. Rep. Congo, Rep. Costa Rica Côte d’Ivoire Croatia Cuba Cyprus Czech Republic Denmark Dominican Republic Ecuador Egypt, Arab Rep. El Salvador Eritrea Estonia Ethiopia Finland France Gabon Gambia, The Georgia Germany Ghana Greece Guatemala Guinea Guinea-Bissau Haiti
358
Burden of customs procedures
1–7 (worst to best) 2010–11b
2.24 2.46 2.36 2.25 3.10 2.52 3.84 3.76 2.64 3.37 2.74 2.53 3.94 2.79 2.51 2.66 2.32 3.20 2.83 2.23 2.29 2.37 2.55 3.87 .. 2.49 3.09 3.49 3.88 2.77 2.68 2.48 2.91 2.53 2.77 2.07 3.13 3.51 3.85 2.82 2.77 2.61 2.67 1.70 3.16 2.41 3.89 3.84 2.41 2.49 2.61 4.11 2.47 2.96 2.63 2.60 2.10 2.59
2012 World Development Indicators
.. 4.2 2.8 2.7 2.7 2.9 5.1 5.0 3.5 5.5 3.4 .. 4.6 3.7 3.0 3.4 4.7 3.1 3.5 4.0 2.9 3.7 4.0 4.9 .. 2.8 5.5 4.4 6.2 4.0 .. .. 3.9 3.9 4.1 .. 4.9 4.4 5.7 4.4 3.5 4.1 3.9 .. 5.2 3.5 6.0 4.9 .. 5.2 4.9 4.7 3.6 4.0 4.2 .. .. 2.5
Lead time
Documents
days
number
Liner Quality of Freight Shipping port costs to the Connectivity infrastructure United Index States
To export 2010
To import 2010
To export June 2011
To import June 2011
0–100 (low to high) 2011
1–7 (worst to best) 2010–11
1 kilogram DHL nondocument air packagea $ 2012
2.0 1.7 4.6 6.0 3.7 .. 2.6 2.0 7.0 1.0 1.4 .. 1.7 3.0 15.0 2.0 .. 2.8 2.0 4.0 .. 1.3 3.4 2.8 .. 74.0 3.5 2.8 1.7 7.0 2.0 .. 2.0 1.0 1.0 .. 1.0 2.5 1.0 2.2 2.1 1.3 2.0 3.0 4.0 5.0 1.6 3.2 4.3 4.6 .. 3.6 2.9 3.0 2.6 3.5 .. 4.2
4.0 2.0 7.1 8.0 3.8 .. 2.8 3.7 3.0 2.0 1.4 .. 1.6 7.0 28.3 2.0 .. 3.9 3.9 14.0 .. 4.0 8.9 3.7 .. 35.0 3.0 2.6 1.6 7.0 3.0 .. 2.0 1.0 1.0 .. 2.0 3.5 1.0 3.5 3.4 3.1 2.0 3.0 4.0 6.0 1.8 4.5 13.0 3.5 .. 2.4 6.8 3.5 3.4 3.9 .. 5.3
10 7 8 11 7 5 6 4 8 6 6 9 4 7 8 8 6 7 5 10 9 9 11 3 9 8 6 8 4 5 8 11 6 10 7 .. 5 4 4 6 8 8 8 10 3 7 4 2 7 6 4 4 6 5 10 7 6 8
10 8 9 8 7 8 5 5 10 7 8 10 5 8 7 9 8 8 6 10 10 10 12 4 17 11 6 5 4 6 9 10 7 9 8 .. 7 7 3 7 7 9 8 12 4 9 5 2 8 7 4 5 7 6 9 9 6 10
.. 4.5 31.1 11.3 30.6 .. 28.3 .. .. 9.8 8.2 .. 88.5 12.7 .. .. .. 34.6 5.4 .. .. 5.4 11.4 38.4 .. .. 22.8 152.1 115.3 27.3 3.7 10.8 10.7 17.4 21.8 6.6 17.1 0.4 26.4 22.9 22.5 51.2 12.0 4.0 5.8 .. 11.3 71.8 8.0 5.4 3.8 93.3 18.0 32.2 20.9 6.2 4.1 4.8
.. 3.9 3.0 2.3 3.7 2.7c 5.1 4.7c 4.1c 6.0 3.4 .. 6.5 3.9 3.1c 1.7 3.9c 2.7 3.8 3.7c 3.0 c 4.0 3.5 5.8 .. 2.7c 5.2 4.5 6.6 3.4 .. .. 2.3 4.9 4.0 .. 5.1 4.7c 6.2 4.4 3.8 4.0 3.8 .. 5.6 3.9c 6.2 5.6 .. 4.9 4.2 6.1 4.2 4.1 4.3 .. .. 1.8
148.25 163.10 162.00 162.00 92.30 148.25 102.10 142.10 163.10 148.25 102.10 163.10 116.75 162.00 92.30 163.10 162.00 92.30 142.10 162.00 162.00 111.40 162.00 76.15 162.00 162.00 92.30 86.75 92.75 92.30 162.00 162.00 92.30 162.00 163.10 80.30 142.10 142.10 142.10 80.30 92.30 148.25 92.30 162.00 142.10 162.00 142.10 116.75 162.00 162.00 163.10 116.75 162.00 142.10 92.30 162.00 162.00 80.30
Logistics Performance Index
1–5 (worst to best) 2010
Honduras Hungary India Indonesia Iran, Islamic Rep. Iraq Ireland Israel Italy Jamaica Japan Jordan Kazakhstan Kenya Korea, Dem. Rep. Korea, Rep. Kosovo Kuwait Kyrgyz Republic Lao PDR Latvia Lebanon Lesotho Liberia Libya Lithuania Macedonia, FYR Madagascar Malawi Malaysia Mali Mauritania Mauritius Mexico Moldova Mongolia Morocco Mozambique Myanmar Namibia Nepal Netherlands New Zealand Nicaragua Niger Nigeria Norway Oman Pakistan Panama Papua New Guinea Paraguay Peru Philippines Poland Portugal Puerto Rico Qatar
2.78 2.99 3.12 2.76 2.57 2.11 3.89 3.41 3.64 2.53 3.97 2.74 2.83 2.59 .. 3.64 .. 3.28 2.62 2.46 3.25 3.34 2.30 2.38 2.33 3.13 2.77 2.66 2.42 3.44 2.27 2.63 2.72 3.05 2.57 2.25 2.38 2.29 2.33 2.02 2.20 4.07 3.65 2.54 2.54 2.59 3.93 2.84 2.53 3.02 2.41 2.75 2.80 3.14 3.44 3.34 .. 2.95
Burden of customs procedures
1–7 (worst to best) 2010–11b
4.0 4.5 3.8 3.9 3.5 .. 5.2 4.7 4.0 3.7 4.7 4.4 3.5 3.3 .. 4.4 .. 4.1 2.8 .. 4.1 3.5 3.7 .. 3.5 4.5 4.2 3.4 3.8 5.0 4.1 4.1 4.6 4.1 3.5 3.3 4.4 3.7 .. 4.1 3.4 5.2 5.8 3.0 .. 3.5 5.2 5.0 3.6 4.2 .. 3.9 4.4 3.0 4.4 4.7 4.6 4.8
Lead time
Documents
days
number
6.8
GLOBAL LINKS
Trade facilitation
Liner Quality of Freight Shipping port costs to the Connectivity infrastructure United Index States
To export 2010
To import 2010
To export June 2011
To import June 2011
0–100 (low to high) 2011
1–7 (worst to best) 2010–11
1 kilogram DHL nondocument air packagea $ 2012
2.4 3.5 2.3 2.1 2.6 .. 1.0 2.0 2.6 10.0 1.0 3.2 2.8 3.0 .. 1.6 .. 2.0 2.0 .. 1.3 3.4 .. 4.0 3.2 2.0 .. .. 4.2 2.6 5.0 2.0 3.0 2.1 .. 14.0 2.0 .. 4.6 3.0 1.8 1.8 1.3 3.2 .. 2.5 1.0 .. 2.3 1.4 .. 1.0 2.0 1.8 3.0 2.5 .. 3.8
3.2 5.0 5.3 5.4 28.3 .. 1.0 2.0 3.0 10.0 1.0 4.6 11.5 5.9 .. 2.0 .. 3.0 .. .. 1.6 2.2 .. 5.0 10.0 2.3 .. .. 3.7 2.8 4.0 3.0 2.4 2.5 .. 12.0 3.2 .. 8.4 3.0 6.3 1.9 1.6 3.2 .. 4.1 2.0 .. 1.6 1.4 .. 4.0 3.8 5.0 3.6 5.0 .. 2.3
6 6 8 4 7 10 4 5 4 6 3 6 9 8 .. 3 8 7 8 9 5 5 8 10 .. 6 6 4 10 6 6 8 5 5 6 8 6 7 .. 9 9 4 7 5 8 10 4 8 7 3 7 8 6 7 5 4 6 5
8 7 9 7 8 10 4 4 4 6 5 7 12 7 .. 3 8 10 9 10 6 7 8 9 .. 6 6 9 9 7 9 8 6 4 7 8 8 10 .. 7 9 5 5 5 10 9 4 8 8 4 9 10 8 8 5 5 10 7
9.4 .. 41.5 25.9 30.3 4.2 5.9 28.5 70.2 28.2 67.8 16.7 .. 12.0 .. 92.0 .. 5.6 .. .. 5.5 35.1 .. 6.2 6.6 9.8 .. 7.7 .. 91.0 .. 5.6 15.4 36.1 .. .. 55.1 10.1 3.2 12.0 .. 92.1 18.5 8.4 .. 19.9 7.3 49.3 30.5 37.5 8.8 0.0 21.2 18.6 26.5 21.1 .. 3.6
5.1 4.0 c 3.9 3.6 3.9 .. 5.2 4.2 3.9 5.3 5.2 4.3 3.6c 3.8 .. 5.5 .. 4.2 1.5c .. 4.7 4.3 3.4 c .. 3.2 4.9 4.1c 3.3 3.6c 5.7 3.7c 3.3 4.7 4.0 2.9 2.8 c 4.5 3.4 .. 5.5 2.6c 6.6 5.5 2.7 .. 3.3 5.5 5.4 4.1 6.4 .. 3.4 c 3.5 3.0 3.4 4.9 5.3 5.4
92.30 142.10 102.10 102.10 148.25 148.25 116.75 148.25 116.75 80.30 124.90 148.25 163.10 162.00 111.40 102.10 163.10 148.25 163.10 111.40 142.10 148.25 162.00 162.00 162.00 142.10 163.10 162.00 162.00 102.10 162.00 162.00 162.00 51.15 163.10 111.40 162.00 162.00 111.40 162.00 111.40 116.75 102.10 92.30 162.00 162.00 142.10 148.25 148.25 92.30 111.40 92.30 92.30 102.10 142.10 116.75 .. 148.25
2012 World Development Indicators
359
6.8
Trade facilitation Logistics Performance Index
1–5 (worst to best) 2010
Romania Russian Federation Rwanda Saudi Arabia Senegal Serbia Sierra Leone Singapore Slovak Republic Slovenia Somalia South Africa South Sudan Spain Sri Lanka Sudan Swaziland Sweden Switzerland Syrian Arab Republic Tajikistan Tanzania Thailand Timor-Leste Togo Trinidad and Tobago Tunisia Turkey Turkmenistan Uganda Ukraine United Arab Emirates United Kingdom United States Uruguay Uzbekistan Venezuela, RB Vietnam West Bank and Gaza Yemen, Rep. Zambia Zimbabwe World Low income Middle income Lower middle income Upper middle income Low & middle income East Asia & Pacific Europe & Central Asia Latin America & Carib. Middle East & N. Africa South Asia Sub-Saharan Africa High income Euro area
Burden of customs procedures
1–7 (worst to best) 2010–11b
2.84 2.61 2.04 3.22 2.86 2.69d 1.97 4.09 3.24 2.87 1.34 3.46 .. 3.63 2.29 2.21 .. 4.08 3.97 2.74 2.35 2.60 3.29 1.71 2.60 .. 2.84 3.22 2.49 2.82 2.57 3.63 3.95 3.86 2.75 2.79 2.68 2.96 .. 2.58 2.28 2.29 2.87e u 2.38 2.68 2.54 2.80 2.60 2.73 2.71 2.74 2.60 2.49 2.42 3.54 3.55
3.3 2.8 5.3 5.0 4.7 3.7 .. 6.2 4.3 5.0 .. 4.2 .. 4.5 4.4 .. 3.4 5.8 5.1 2.9 3.6 3.6 3.9 3.4 .. 3.0 4.6 3.7 .. 4.4 2.8 5.6 4.9 4.3 4.2 .. 2.3 3.4 .. 2.9 4.1 3.8 4.1 u 3.7 3.8 3.6 3.9 3.7 3.8 3.7 3.7 3.7 3.7 3.9 4.9 4.9
Lead time
Documents
days
number
To export 2010
2.0 4.0 .. 2.3 1.4 2.0 d 2.0 2.2 3.0 1.0 .. 2.3 .. 4.0 1.3 39.0 .. 1.0 2.6 2.5 7.0 3.2 1.6 .. .. .. 1.7 2.2 3.0 5.5 1.7 2.5 3.3 2.8 3.0 1.4 9.4 1.4 .. 3.1 9.2 25.0 3.8e u 7.3 3.7 4.8 2.9 4.5 3.6 2.8 3.9 2.7 1.9 8.1 2.1 2.3
To import 2010
2.0 2.9 .. 6.3 2.7 3.0 d 32.0 1.8 5.0 2.0 .. 3.3 .. 7.1 2.5 5.0 .. 2.6 2.6 3.2 .. 7.1 2.6 .. .. .. 7.0 3.8 .. 14.0 7.0 2.0 1.9 4.0 3.0 2.0 12.1 1.7 .. 3.6 4.0 18.0 4.6e u 7.8 4.9 5.3 4.6 5.5 4.9 3.0 5.5 7.2 3.3 7.0 2.7 3.0
Liner Quality of Freight Shipping port costs to the Connectivity infrastructure United Index States
To export June 2011
To import June 2011
0–100 (low to high) 2011
5 8 8 5 6 6 7 4 6 6 .. 8 .. 6 6 7 9 3 4 8 11 6 5 6 6 5 4 7 .. 7 6 4 4 4 9 10 8 6 6 6 6 8 7u 8 7 7 6 7 7 7 6 7 8 8 5 4
6 10 8 5 5 6 7 4 7 8 .. 8 .. 7 6 7 9 3 5 9 9 6 5 7 8 6 7 8 .. 9 8 5 4 5 9 11 9 8 6 9 8 9 7u 9 7 8 7 8 7 8 7 8 9 9 5 5
21.4 20.6 .. 60.0 12.3 .. 5.4 105.0 .. 21.9 4.2 35.7 .. 76.6 41.1 9.3 .. 30.0 1.9 16.8 .. 11.5 36.7 .. 14.1 17.9 6.3 39.4 .. .. 21.4 62.5 87.5 81.6 24.4 .. 20.0 49.7 .. 11.9 .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. ..
1–7 (worst to best) 2010–11
2.8 3.7 3.2 5.4 4.5 2.7 .. 6.8 3.9c 5.2 .. 4.7 .. 5.8 4.9 .. 4.2 6.0 5.2c 3.4 1.8 c 3.3 4.7 2.6 .. 3.9 4.6 4.2 .. 3.7c 3.7 6.2 5.6 5.5 5.1 .. 2.5 3.4 .. 2.9 4.0 c 4.4 c 4.3 u 3.3 3.9 3.6 4.1 3.7 3.8 3.4 3.8 3.9 3.8 3.8 5.3 5.3
1 kilogram DHL nondocument air packagea $ 2012
142.10 163.10 162.00 148.25 162.00 163.10 162.00 92.75 142.10 142.10 162.00 162.00 .. 116.75 102.10 162.00 162.00 142.10 142.10 148.25 163.10 162.00 102.10 111.40 162.00 80.30 162.00 148.25 163.10 162.00 163.10 148.25 116.75 .. 92.30 163.10 92.30 102.10 .. 148.25 162.00 162.00
a. Transportation charges only; excludes fuel, surcharges, duties, and taxes. b. Average of the 2010 and 2011 survey ratings. c. Landlocked country. d. Includes Montenegro. e. Aggregates are computed according to the World Bank classification of economies as of July 1, 2011, and may differ from data published in the original source.
360
2012 World Development Indicators
About the data
6.8
GLOBAL LINKS
Trade facilitation Definitions
Trade facilitation encompasses customs efficiency
include the value of time to import or export and the
• Logistics Performance Index refl ects percep-
and other physical and regulatory environments
risk of delay or loss of shipments. Long lead times
tions of a country’s logistics based on efficiency of
where trade takes place, harmonization of stan-
and burdensome regulatory procedures may lower
customs clearance process, quality of trade- and
dards and conformance to international regulations,
competitiveness. Data on lead time are from the
transport-related infrastructure, ease of arranging
and the logistics of moving goods and associated
Logistics Performance Index survey. Respondents
competitively priced shipments, quality of logistics
documentation through countries and ports. Though
provided separate values for the best case (10 per-
services, ability to track and trace consignments, and
collection of trade facilitation data has improved
cent of shipments) and the median case (50 percent
frequency with which shipments reach the consignee
over the last decade, data that allow meaningful
of shipments). The data are exponentiated averages
within the scheduled time. The index ranges from 1
evaluation, especially for developing economies, are
of the logarithm of single value responses and of mid-
to 5, with a higher score representing better perfor-
lacking. Data on trade facilitation are drawn from
point values of range responses for the median case.
mance. • Burden of customs procedure measures
research by private and international agencies. Most
Data on the number of documents needed to
business executives’ perceptions of their country’s
data are perception-based evaluations by business
export or import are from the World Bank’s Doing
efficiency of customs procedures. Values range from
executives and professionals. Because of different
Business surveys, which compile procedural require-
1 to 7, with a higher rating indicating greater effi -
backgrounds, values, and personalities, those sur-
ments for exporting and importing a standardized
ciency. • Lead time to export is the median time (the
veyed may evaluate the same situation differently.
cargo of goods by ocean transport from local freight
value for 50 percent of shipments) from shipment
Caution should thus be used when interpreting per-
forwarders, shipping lines, customs brokers, port
point to port of loading. • Lead time to import is the
ception-based indicators. Nevertheless, they convey
officials, and banks. To make the data comparable
median time (the value for 50 percent of shipments)
much needed information on trade facilitation.
across economies, several assumptions about the
from port of discharge to arrival at the consignee.
business and the traded goods are used (see www.
• Documents to export and documents to import are
doingbusiness.org).
all documents required per shipment by government
The table presents data from Logistics Performance Surveys conducted by the World Bank in partnership with academic and international institutions
Access to global shipping and air freight networks
ministries, customs authorities, port and container
and private companies and individuals engaged in
and the quality and accessibility of ports and roads
terminals, health and technical control agencies, and
international logistics. The Logistics Performance
affect logistics performance. The table shows two
banks to export or import goods. Documents renewed
Index assesses performance across six aspects of
indicators related to trade and transport service
annually and not requiring renewal per shipment are
the logistics environment (see Definitions), based
infrastructure: the Liner Shipping Connectivity Index
excluded. • Liner Shipping Connectivity Index indi-
on more than 5,000 country assessments by nearly
and the quality of port infrastructure rating. The Liner
cates how well countries are connected to global ship-
1,000 international freight forwarders. Respondents
Shipping Connectivity Index captures how well coun-
ping networks based on the status of their maritime
evaluate eight markets on six core dimensions. The
tries are connected to global shipping networks. It
transport sector. The highest value in 2004 is 100.
markets are chosen based on the most important
is computed by the United Nations Conference on
• Quality of port infrastructure measures business
export and import markets of the respondent’s coun-
Trade and Development (UNCTAD) based on five com-
executives’ perceptions of their country’s port facili-
try, random selection, and, for landlocked countries,
ponents of the maritime transport sector: number of
ties. Values range from 1 to 7, with a higher rating
neighboring countries that connect them with inter-
ships, their container-carrying capacity, maximum
indicating better development of port infrastructure.
national markets. Scores for the six dimensions are
vessel size, number of services, and number of
• Freight costs to the United States is the DHL inter-
averaged across all respondents and aggregated to
companies that deploy container ships in a coun-
national U.S. inbound worldwide priority express rate
a single score. Details of the survey methodology
try’s ports. For each component a country’s value is
for a 1 kilogram nondocument air package.
and index construction methodology are in Arvis and
divided by the maximum value of each component
others (2010).
in 2004, the five components are averaged for each
Data sources
Data on the burden of customs procedures are
country, and the average is divided by the maximum
Data on the Logistics Performance Index and
from the World Economic Forum’s Executive Opinion
average for 2004 and multiplied by 100. The index
lead time to export and import are from Arvis and
Survey. The 2011 round included more than 15,000
generates a value of 100 for the country with the
others (2010). Data on the burden of customs
respondents from 142 countries. Sampling follows a
highest average index in 2004.
procedure and quality of port infrastructure rat-
dual stratification based on company size and sec-
The quality of port infrastructure measures busi-
ings are from the World Economic Forum’s Global
tor of activity. Data are collected online, through in-
ness executives’ perception of their country’s port
Competitiveness Report 2011–2012. Data on num-
person interviews, and through mail and telephone
facilities. Values range from 1 (extremely underde-
ber of documents to export and import are from
interviews. Responses are aggregated using sector-
veloped) to 7 (efficient). Respondents in landlocked
the World Bank’s Doing Business project (www.
weighted averaging. Data are a two-year moving aver-
countries were asked: “How accessible are port
doingbusiness.org). Data on the Liner Shipping
age. Respondents evaluated the efficiency of cus-
facilities (1 = extremely inaccessible; 7 = extremely
Connectivity Index are from UNCTAD’s Review of
toms procedures in their country. The lowest value (1)
accessible.)”
Maritime Transport (2011). Freight costs to the
rates the customs procedure as extremely inefficient, and the highest score (7) as extremely efficient. The direct costs of cross-border trade include freight, customs, and storage fees. Indirect costs
The costs of transport services are a crucial determinant of export competitiveness. The proxy indica-
United States are based on DHL’s “DHL Rate and Transit Guide 2012” (2012).
tor in the table is the shipping rates to the United States of an international freight moving business.
2012 World Development Indicators
361
6.9
External debt Total external debt
Long-term debt
Short-term debt
Total debt service
% of total debt 2010
% of total reserves 2010
% of exports of goods and services and incomea 2010
4.4 12.1 33.7 12.1 27.4 10.1 .. .. 12.6 .. 11.9 46.6 .. 2.7 2.0 12.3 20.9 18.9 32.0 0.0 2.9 5.6 1.0 .. 19.9 0.5 30.0 63.4 .. 13.0 8.6 5.9 27.5 3.1 .. .. .. .. .. 14.9 2.5 9.0 10.0 0.7 .. 4.4 .. .. 7.5 9.4 10.4 .. 26.9 .. 11.1 4.2 11.7 0.0
.. 22.6 1.0 11.3 67.1 33.1 .. .. 13.7 .. 26.6 238.4 .. 2.7 1.1 23.7 4.5 22.7 89.3 0.0 4.7 6.9 0.8 .. 42.3 1.3 93.0 11.9 .. 29.2 38.0 5.0 52.5 9.7 .. .. .. .. .. 55.6 14.1 8.5 38.1 6.3 .. .. .. .. 10.0 21.9 42.6 .. .. .. 26.8 .. 81.6 0.0
.. 11.1 1.0 4.5 16.7 33.4 .. .. 1.4 .. 4.7 4.6 .. .. 9.3 19.9 1.5 19.0 14.2 .. .. .. 3.6 .. .. .. 15.2 3.3 .. 21.0 3.8 .. 7.7 .. .. .. .. .. .. 11.0 9.4 6.0 19.0 .. .. .. .. .. .. 7.2 18.1 .. 3.4 .. 14.3 5.6 .. 15.7
$ millions
Afghanistan Albania Algeria Angola Argentina Armenia Australia Austria Azerbaijan Bahrain Bangladesh Belarus Belgium Benin Bolivia Bosnia and Herzegovina Botswana Brazil Bulgaria Burkina Faso Burundi Cambodia Cameroon Canada Central African Republic Chad Chile China Hong Kong SAR, China Colombia Congo, Dem. Rep. Congo, Rep. Costa Rica Côte d’Ivoire Croatia Cuba Cyprus Czech Republic Denmark Dominican Republic Ecuador Egypt, Arab Rep. El Salvador Eritrea Estonia Ethiopia Finland France Gabon Gambia, The Georgia Germany Ghana Greece Guatemala Guinea Guinea-Bissau Haiti
362
$ millions 2010
% of GNI 2010
2,297 4,736 5,276 18,562 127,849 6,103 .. .. 6,974 .. 24,963 25,726 .. 1,221 5,267 8,457 1,709 346,978 48,077 2,053 537 4,676 2,964 .. 385 1,733 86,349 548,551 .. 63,064 5,774 3,781 8,849 11,430 .. .. .. .. .. 13,045 14,815 34,844 11,069 1,010 .. 7,147 .. .. 2,331 470 9,238 .. 8,368 .. 14,340 2,923 1,095 492
.. 40.5 3.4 24.6 36.1 64.8 .. .. 14.9 .. 22.8 46.8 .. 18.4 27.8 48.8 11.6 16.9 104.8 23.3 33.8 43.4 13.5 .. 19.2 25.7 45.9 9.3 .. 22.8 47.1 43.9 26.8 52.6 .. .. .. .. .. 26.2 23.1 16.2 53.2 48.2 .. 24.1 .. .. 20.3 63.3 80.4 .. 27.2 .. 35.9 69.1 124.8 7.3
2012 World Development Indicators
Public and publicly guaranteed 2010
2,076 2,972 2,530 15,440 67,331 2,557 .. .. 3,892 .. 21,371 7,850 .. 1,134 2,806 3,751 1,352 96,542 4,466 1,925 412 4,414 2,185 .. 218 1,708 12,929 90,180 .. 36,777 4,957 3,531 3,725 10,416 .. .. .. .. .. 9,115 8,598 31,641 6,394 1,003 .. 6,545 .. .. 2,158 395 4,081 .. 5,727 .. 5,527 2,752 963 479
Private nonguaranteed 2010
.. 1,133 968 .. 25,514 2,187 .. .. 2,158 .. .. 2,401 .. .. 2,358 3,149 .. 184,940 28,238 .. .. .. 577 .. .. .. 47,541 110,847 .. 18,078 .. .. 2,693 280 .. .. .. .. .. 843 5,848 54 3,569 .. .. .. .. .. .. .. 3,143 .. 0 .. 7,218 .. .. ..
$ millions 2010
102 573 1,778 2,241 35,005 618 .. .. 878 .. 2,974 11,980 .. 32 103 1,037 357 65,496 15,373 0 16 262 31 .. 77 8 25,879 347,524 .. 8,209 494 222 2,431 351 .. .. .. .. .. 1,948 369 3,149 1,105 7 .. 314 .. .. 174 44 963 .. 2,249 .. 1,595 123 128 0
Present value of debt
% of GNIa 2010
6.5 29.5 3.0 22.1 37.5 46.5 .. .. 9.1 .. 16.2 41.8 .. 12.8b 17.4b 37.3 8.7 18.8 94.9 18.6b 14.3b 35.8 5.3b .. 12.4b 28.3b 47.7 10.1 .. 37.5 27.0 b 19.5b 26.8 48.0 b .. .. .. .. .. 23.7 22.1 15.2 46.5 33.8b .. 13.3b .. .. 18.7 29.5b 65.0 .. 17.5b .. 31.7 53.6b 14.6b 4.3b
% of exports of goods and services and incomea 2010
21.1 87.6 5.4 22.3 150.3 182.6 .. .. 13.7 .. 84.3 74.8 .. 70.1b 40.9b 95.0 18.9 146.0 159.1 168.7b 151.5b 58.6 19.1b .. 75.9b 57.3b 98.9 31.2 .. 211.9 77.8b 19.2b 59.1 88.2b .. .. .. .. .. 89.1 64.3 46.0 177.1 731.6b .. 106.7b .. .. 16.2 72.6b 183.9 .. 61.0 b .. 117.6 145.2b 86.6b 31.7b
Total external debt
Long-term debt
Short-term debt
Total debt service
% of total debt 2010
% of total reserves 2010
% of exports of goods and services and incomea 2010
% of GNIa 2010
9.6 .. 19.4 17.5 49.0 .. .. .. .. 8.5 .. 16.7 7.6 12.0 .. .. 0.0 .. 4.9 0.0 32.2 14.3 0.0 0.0 .. 18.5 35.4 9.3 6.6 43.0 0.3 9.6 0.3 19.5 33.9 9.4 7.1 23.6 30.8 .. 1.6 .. .. 14.6 8.4 40.6 .. .. 4.0 0.0 6.7 23.2 16.7 8.7 .. .. .. ..
14.7 .. 18.8 32.5 .. .. .. .. .. 47.2 .. 9.6 32.0 23.3 .. .. 0.0 .. 11.3 0.0 167.3 7.8 .. .. .. 80.0 90.2 18.3 18.7 32.9 0.5 82.4 0.1 32.4 91.1 10.1 7.6 43.0 .. .. 2.1 .. .. 38.7 12.4 8.9 .. .. 13.3 0.0 12.6 27.5 13.7 10.1 .. .. .. ..
7.6 .. 5.6 16.6 2.0 .. .. .. .. 27.9 .. 4.9 71.4 4.4 .. .. 1.6 .. 21.9 .. 76.4 19.1 1.9 1.3 .. 34.3 15.2 2.6 .. 5.2 2.5 .. 2.4 9.8 12.8 5.0 10.7 2.9 .. .. 10.5 .. .. 14.3 .. 0.4 .. .. 15.2 5.7 12.9 4.6 16.7 18.4 .. .. .. ..
14.0b .. 17.7 28.2 3.3 .. .. .. .. 102.2 .. 25.9 89.1 19.6 .. .. 4.2 .. 37.8 b 65.4 129.1 67.1 17.1 11.7b .. 68.7 57.0 20.8 b 15.7b 35.7 16.5b 68.4b 8.5 18.0 65.5 33.1 23.4 20.9b .. .. 19.9b .. .. 36.8b 10.5b 3.2 .. .. 24.1 43.7 54.9 25.7 24.9 34.6 .. .. .. ..
$ millions
Honduras Hungary India Indonesia Iran, Islamic Rep. Iraq Ireland Israel Italy Jamaica Japan Jordan Kazakhstan Kenya Korea, Dem. Rep. Korea, Rep. Kosovo Kuwait Kyrgyz Republic Lao PDR Latvia Lebanon Lesotho Liberia Libya Lithuania Macedonia, FYR Madagascar Malawi Malaysia Mali Mauritania Mauritius Mexico Moldova Mongolia Morocco Mozambique Myanmar Namibia Nepal Netherlands New Zealand Nicaragua Niger Nigeria Norway Oman Pakistan Panama Papua New Guinea Paraguay Peru Philippines Poland Portugal Puerto Rico Qatar
$ millions 2010
% of GNI 2010
4,168 .. 290,282 179,064 12,570 .. .. .. .. 13,865 .. 7,822 118,723 8,400 .. .. 342 .. 3,984 5,559 39,555 24,293 726 228 .. 29,602 5,804 2,295 922 81,497 2,326 2,461 1,076 200,081 4,615 2,444 25,403 4,124 6,352 .. 3,702 .. .. 4,786 1,127 7,883 .. .. 56,773 11,412 5,822 4,938 36,271 72,337 .. .. .. ..
28.2 .. 16.9 26.1 .. .. .. .. .. 104.2 .. 27.9 94.3 26.9 .. .. 6.0 .. 89.2 79.0 164.3 60.7 28.4 28.3 .. 83.0 65.1 26.6 18.5 35.4 26.1 67.0 11.0 19.5 73.5 44.3 28.1 43.8 .. .. 23.4 .. .. 76.9 20.5 4.5 .. .. 31.3 45.8 62.9 25.3 24.6 36.2 .. .. .. ..
Public and publicly guaranteed 2010
2,798 .. 106,205 91,024 6,411 .. .. .. .. 7,593 .. 6,504 3,842 6,978 .. .. 342 .. 2,442 2,939 6,891 20,213 698 184 .. 11,664 1,865 1,981 715 25,795 2,271 2,174 972 111,467 817 1,795 21,015 2,960 4,395 .. 3,527 .. .. 2,668 972 4,686 .. .. 43,202 10,421 1,030 2,369 20,027 44,641 .. .. .. ..
Private nonguaranteed 2010
942 .. 127,629 56,785 0 .. .. .. .. 4,307 .. .. 105,844 0 .. .. .. .. 1,171 2,610 18,428 500 .. .. .. 12,468 1,885 3 .. 20,626 .. .. 100 49,600 1,906 221 2,589 .. .. .. .. .. .. 1,255 0 .. .. .. 2,544 991 4,400 1,421 10,189 21,402 .. .. .. ..
$ millions 2010
398 .. 56,448 31,255 6,159 .. .. .. .. 1,180 .. 1,310 9,037 1,005 .. .. 0 .. 195 0 12,723 3,482 0 0 .. 5,469 2,054 214 61 35,076 6 237 3 39,013 1,564 230 1,800 975 1,956 .. 61 .. .. 697 94 3,197 .. .. 2,291 0 392 1,147 6,055 6,295 .. .. .. ..
6.9
GLOBAL LINKS
External debt
Present value of debt
2012 World Development Indicators
% of exports of goods and services and incomea 2010
26.9b .. 79.4 102.0 .. .. .. .. .. 221.3 .. 51.2 154.7 71.8 .. .. 24.8 .. 66.0 b 210.8 260.0 98.4 23.6 13.8b .. 107.1 118.4 60.2b 63.4b 33.1 57.6b 126.1b 15.2 60.5 135.9 57.9 67.1 65.6b .. .. 150.6b .. .. 70.7b 53.2b 7.4 .. .. 158.8 54.7 79.1 47.7 88.9 99.6 .. .. .. ..
363
6.9
External debt Total external debt
Long-term debt
Short-term debt
Total debt service
% of GNIa 2010
58.1 24.7 11.5b .. 20.0 b 67.1 22.8b .. .. .. .. 14.7 .. .. 36.6 70.2b 19.5 .. .. 7.9 42.4 22.8 b 24.2 .. 13.9b .. 47.5 39.4 2.2 7.1b 75.0 .. .. .. 30.6 18.1 .. 28.9 .. 15.2 12.0 b 110.3 .. .. .. .. .. .. .. .. .. .. .. .. .. ..
$ millions
$ millions 2010
Romania Russian Federation Rwanda Saudi Arabia Senegal Serbia Sierra Leone Singapore Slovak Republic Slovenia Somalia South Africa South Sudan Spain Sri Lanka Sudan Swaziland Sweden Switzerland Syrian Arab Republic Tajikistan Tanzania Thailand Timor-Leste Togo Trinidad and Tobago Tunisia Turkey Turkmenistan Uganda Ukraine United Arab Emirates United Kingdom United States Uruguay Uzbekistan Venezuela, RB Vietnam West Bank and Gaza Yemen, Rep. Zambia Zimbabwe World Low income Middle income Lower middle income Upper middle income Low & middle income East Asia & Pacific Europe & Central Asia Latin America & Carib. Middle East & N. Africa South Asia Sub-Saharan Africa High income Euro area
% of GNI 2010
Public and publicly guaranteed 2010
Private nonguaranteed 2010
$ millions 2010
% of total debt 2010
% of total reserves 2010
% of exports of goods and services and incomea 2010
76.4 26.9 14.2 .. 28.5 84.3 40.8 .. .. .. .. 12.7 .. .. 41.8 39.1 17.2 .. .. 8.2 53.1 37.7 23.4 .. 61.1 .. 51.1 40.4 2.1 17.9 85.9 .. .. .. 29.0 19.0 14.3 36.5 .. .. 25.8 71.8 .. 28.5 20.8 24.7 19.7 21.0 13.5 43.0 21.7 14.1 19.2 20.0 .. ..
20,557 162,924 766 .. 3,155 9,477 661 .. .. .. 1,990 17,753 .. .. 16,449 14,444 385 .. .. 4,171 1,806 5,572 11,357 .. 1,534 .. 14,609 93,088 359 2,671 16,246 .. .. .. 9,704 3,426 37,086 28,145 .. 5,933 1,309 3,686 .. 95,929 1,486,657 518,320 968,337 1,582,586 306,774 366,663 457,714 113,766 194,376 143,293 .. ..
60,826 183,059 .. .. 308 17,912 .. .. .. .. .. 15,107 .. .. 919 0 .. .. .. 0 927 1,224 21,434 .. .. .. 1,996 117,035 7 .. 59,858 .. .. .. 93 3,741 3,060 .. .. .. 794 378 .. 3,703 1,388,971 308,804 1,080,167 1,392,674 238,402 627,496 370,470 6,106 131,428 18,772 .. ..
25,029 38,756 14 .. 0 2,798 4 .. .. .. 780 12,305 .. .. 1,773 7,012 231 .. .. 558 122 1,515 38,471 .. 61 .. 4,979 78,123 55 314 26,459 .. .. .. 1,550 238 15,426 6,949 .. 313 1,191 843 .. 12,806 1,023,572 163,917 859,655 1,036,378 468,525 234,232 208,277 23,528 63,879 37,936 .. ..
20.6 10.1 1.8 .. 0.0 8.7 0.5 .. .. .. 26.5 27.2 .. .. 8.7 32.1 37.5 .. .. 11.8 4.1 17.5 54.0 .. 3.5 .. 23.1 26.6 13.0 10.5 22.7 .. .. .. 13.7 3.2 27.8 19.8 .. 5.0 32.3 16.8 .. 11.0 25.8 16.1 29.3 25.4 46.2 18.4 20.1 16.4 15.9 18.4 .. ..
52.1 8.1 1.7 .. 0.0 21.0 1.0 .. .. .. .. 28.1 .. .. 24.6 676.7 30.5 .. .. 2.7 .. 38.8 22.4 .. 8.5 .. 51.0 90.9 .. 11.1 76.5 .. .. .. 20.2 .. 52.0 55.7 .. 5.3 56.9 .. .. 19.0 18.3 21.9 17.7 18.3 13.8 31.1 32.3 5.3 18.7 21.8 .. ..
31.2 12.8 2.3 .. .. 30.9 2.6 .. .. .. .. 4.9 .. .. 13.0 4.2 .. .. .. .. 44.8 3.0 4.8 .. .. .. 10.4 36.7 .. 1.8 40.7 .. .. .. 12.4 .. .. 1.7 .. 2.8 1.9 .. .. .. .. .. .. .. .. .. .. .. .. .. .. ..
121,505 384,740 795 .. 3,677 32,222 778 .. .. .. 2,942 45,165 .. .. 20,452 21,846 616 .. .. 4,729 2,955 8,664 71,263 .. 1,728 .. 21,584 293,872 422 2,994 116,808 .. .. .. 11,347 7,404 55,572 35,139 .. 6,324 3,689 5,016 .. 116,593 3,959,705 1,021,016 2,938,688 4,076,298 1,013,971 1,273,418 1,038,725 143,595 400,596 205,992 .. ..
Present value of debt
% of exports of goods and services and incomea 2010
170.9 72.1 102.7b .. 67.3b 201.2 115.1b .. .. .. .. 45.4 .. .. 156.1 339.2b 26.3 .. .. 23.1 145.9 84.7b 31.2 .. 34.9b .. 77.1 165.0 2.5 33.1b 144.0 .. .. .. 101.6 45.3 .. 37.1 .. 44.4 26.6b 275.3 .. .. .. .. .. .. .. .. .. .. .. .. .. ..
a. The numerator refers to 2010, whereas the denominator is a three-year average of 2008–10 data. b. Data are from debt sustainability analyses for low-income countries and include the effects of traditional relief, debt relief under the Heavily Indebted Poor Country Initiative, and relief under the Multilateral Debt Relief Initiative. Present value estimates for these countries are for public and publicly guaranteed debt only.
364
2012 World Development Indicators
About the data
6.9
GLOBAL LINKS
External debt Definitions
External indebtedness affects a country’s credit-
Where such information is not available from the
• Total external debt is debt owed to nonresident
worthiness and investor perceptions. Data on exter-
debtor country, data are derived from BIS data on
creditors and repayable in foreign currencies, goods, or
nal debt are gathered through the World Bank’s
international bank lending based on time remaining
services by public and private entities in the country. It
Debtor Reporting System (DRS). Indebtedness is
to original maturity. The data are reported based
is the sum of long-term external debt, short-term debt,
calculated using loan-by-loan reports submitted by
on residual maturity, but an estimate of short-term
and use of IMF credit. Debt repayable in domestic cur-
countries on long-term public and publicly guaran-
external liabilities by original maturity can be derived
rency is excluded. • Long-term debt is debt that has
teed borrowing and information on short-term debt
by deducting from claims due in one year those that
an original or extended maturity of more than one year.
collected by the countries or from creditors through
have a maturity of between one and two years.
It has three components: public, publicly guaranteed,
the reporting systems of the Bank for International
However, BIS data include liabilities reported only
and private nonguaranteed debt. • Public and publicly
Settlements (BIS). These data are supplemented by
by banks within the BIS reporting area. The results
guaranteed debt is the long-term external obligations
information from major multilateral banks and official
should thus be interpreted with caution. Because
of public debtors, including the national government
lending agencies in major creditor countries and by
short-term debt poses an immediate burden and is
and political subdivisions (or an agency of either) and
estimates by World Bank and International Monetary
particularly important for monitoring vulnerability, it
autonomous public bodies, and the external obliga-
Fund (IMF) staff.
is compared with total debt and foreign exchange
tions of private debtors that are guaranteed for repay-
reserves, which are instrumental in providing cover-
ment by a public entity. • Private nonguaranteed debt
age for such obligations.
is the long-term external obligations of private debt-
Currently, 129 developing countries report to the DRS. Nonreporting countries might have outstanding debt with the World Bank, other international finan-
Total debt service is contrasted with countries’
ors that are not guaranteed for repayment by a public
ability to obtain foreign exchange through exports of
entity. • Short-term debt is debt owed to nonresidents
Debt data, normally reported in the currency of
goods, services, income, and workers’ remittances.
having an original maturity of one year or less and inter-
repayment, are converted into U.S. dollars to pro-
The present value of external debt provides a mea-
est in arrears on long-term debt and on the use of IMF
duce summary tables. Stock fi gures (amount of
sure of future debt service obligations. It is calcu-
credit. • Total reserves are holdings of monetary gold,
debt outstanding) are converted using end-of-period
lated by discounting the debt service (interest plus
special drawing rights, reserves of IMF members held
exchange rates, as published in the IMF’s Interna-
amortization) due on long-term external debt over
by the IMF, and holdings of foreign exchange under
tional Financial Statistics. Flow figures are converted
the life of existing loans. Short-term debt is included
the control of monetary authorities. • Total debt ser-
at annual average exchange rates. Projected debt
at face value. The discount rate on long-term debt
vice is the sum of principal repayments and interest
service is converted using end-of-period exchange
depends on the currency of repayment and is based
actually paid in foreign currency, goods, or services
rates. Debt repayable in multiple currencies, goods,
on commercial interest reference rates established
on long-term debt, interest paid on short-term debt,
or services and debt with a provision for maintenance
by the Organisation for Economic Co-operation and
and repayments (repurchases and charges) to the
of the value of the currency of repayment are shown
Development. Loans from the International Bank
IMF. • Exports of goods and services and income
at book value.
for Reconstruction and Development (IBRD), cred-
are the total value of exports of goods and services,
A country’s external debt burden, both debt out-
its from the International Development Association
receipts of compensation of nonresident workers, and
standing and debt service, affects its creditworthi-
(IDA), and obligations to the IMF are discounted using
investment income from abroad. • Present value of
ness and vulnerability. The table shows total exter-
a special drawing rights reference rate. When the
debt is the sum of short-term external debt plus the
nal debt relative to a country’s size—gross national
discount rate is greater than the loan interest rate,
discounted sum of total debt service payments due on
income (GNI). While data related to public and pub-
the present value is less than the nominal sum of
public, publicly guaranteed, and private nonguaranteed
licly guaranteed debt are reported to the DRS on a
future debt service obligations.
long-term external debt over the life of existing loans.
cial institutions, or private creditors.
loan-by-loan basis. Aggregate data on long-term pri-
Debt ratios are used to assess the sustainability of
vate nonguaranteed debt are reported annually and
a country’s debt service obligations, but no absolute
are reported by the country or estimated by World
rules determine what values are too high. Empirical
Data on external debt are mainly from reports
Bank staff for countries where this type of external
analysis of developing countries’ experience and
to the World Bank through its DRS from member
debt is known to be significant. Estimates are based
debt service performance shows that debt service
countries that have received IBRD loans or IDA
on national data from the World Bank’s Quarterly
difficulties become increasingly likely when the pres-
credits, with additional information from the files
External Debt Statistics.
ent value of debt reaches 200 percent of exports.
of the World Bank, the IMF, the African Develop-
The DRS encourages debtor countries to volun-
Still, what constitutes a sustainable debt burden var-
ment Bank and African Development Fund, the
tarily provide information on their short-term external
ies by country. Countries with fast-growing econo-
Asian Development Bank and Asian Development
obligations. By its nature, short-term external debt
mies and exports are likely to be able to sustain
Fund, and the Inter-American Development Bank.
is diffi cult to monitor: loan-by-loan registration is
higher debt levels.
Summary tables of the external debt of develop-
Data sources
normally impractical, and monitoring systems typi-
ing countries are published annually in the World
cally rely on information requested periodically by
Bank’s Global Development Finance, on its Global
the central bank from the banking sector. The World
Development Finance CD-ROM, and in its Global
Bank regards the debtor country as the authorita-
Development Finance database.
tive source of information on its short-term debt.
2012 World Development Indicators
365
6.10
Global private financial flows Equity net flows
Foreign direct investment Net inflow $ millions % of GDP
Afghanistan Albania Algeria Angola Argentina Armenia Australia Austria Azerbaijan Bahrain Bangladesh Belarus Belgium Benin Bolivia Bosnia and Herzegovina Botswana Brazil Bulgaria Burkina Faso Burundi Cambodia Cameroon Canada Central African Republic Chad Chile China Hong Kong SAR, China Colombia Congo, Dem. Rep. Congo, Rep. Costa Rica Côte d’Ivoire Croatia Cuba Cyprus Czech Republic Denmark Dominican Republic Ecuador Egypt, Arab Rep. El Salvador Eritrea Estonia Ethiopia Finland France Gabon Gambia, The Georgia Germany Ghana Greece Guatemala Guinea Guinea-Bissau Haiti
366
Debt flows
Portfolio equity $ millions
Bonds
$ millions Commercial bank and other lending
2010
2010
2010
2010
2010
76 1,110 2,291 –3,227 7,055 570 30,576 –25,636 563 156 917 1,403 72,914 111 622 232 265 48,506 2,355 37 1 783 –1 23,587 72 781 15,095 185,081 71,066 6,914 2,939 2,816 1,466 418 408 86 1,886 6,720 –7,697 1,626 167 6,386 –6 56 1,539 288 7,072 33,672 170 37 817 46,127 2,527 430 881 101 9 150
0.4 9.4 1.4 –3.8 1.9 6.1 2.7 –6.8 1.1 .. 0.9 2.6 15.5 1.7 3.2 1.4 1.8 2.3 4.9 0.4 0.1 7.0 0.0 1.5 3.6 10.3 7.1 3.1 31.7 2.4 22.4 23.7 4.1 1.8 0.7 .. 8.2 3.5 –2.5 3.1 0.3 2.9 0.0 2.6 8.0 1.0 3.0 1.3 1.3 4.6 7.0 1.4 8.1 0.1 2.1 2.3 1.0 2.2
.. 8 .. 0 –208 0 9,974 –385 1 1,653 0 1 –1,837 .. 0 0 18 37,684 9 .. 0 0 0 17,775 .. .. 1,748 31,357 18,534 1,351 .. .. 0 .. 112 .. 440 287 7,262 0 0 1,724 0 .. 15 0 1,980 –8,442 .. 0 –20 –1,991 0 –1,459 0 0 .. 0
0 398 0 0 –1,660 0 .. .. 0 .. 0 1,777 .. 0 0 –25 0 24,086 0 0 0 0 0 .. 0 0 4,867 11,112 .. 972 0 0 0 0 .. .. .. .. .. 645 –6 1,500 0 0 .. 0 .. .. –23 0 250 .. 0 .. 0 0 0 0
0 –35 –398 –1,553 –2,477 703 .. .. 2,021 .. –11 702 .. 0 –359 –913 –1 19,981 –1,820 –2 0 0 –32 .. 0 –2 3,813 2,066 .. 4,302 –4 –24 313 –58 .. .. .. .. .. –85 –3 19 131 0 .. 647 .. .. 189 6 100 .. 251 .. –88 4 0 0
2012 World Development Indicators
Equity net flows
Foreign direct investment Net inflow $ millions % of GDP 2010
Honduras Hungary India Indonesia Iran, Islamic Rep. Iraq Ireland Israel Italy Jamaica Japan Jordan Kazakhstan Kenya Korea, Dem. Rep. Korea, Rep. Kosovo Kuwait Kyrgyz Republic Lao PDR Latvia Lebanon Lesotho Liberia Libya Lithuania Macedonia, FYR Madagascar Malawi Malaysia Mali Mauritania Mauritius Mexico Moldova Mongolia Morocco Mozambique Myanmar Namibia Nepal Netherlands New Zealand Nicaragua Niger Nigeria Norway Oman Pakistan Panama Papua New Guinea Paraguay Peru Philippines Poland Portugal Puerto Rico Qatar
797 –42,283 24,159 13,371 3,617 1,426 27,085 5,152 9,594 228 –1,359 1,701 10,677 186 38 –150 481 81 438 279 369 4,280 117 453 1,784 748 207 860 140 9,167 148 14 431 19,792 193 1,455 1,241 789 910 796 88 –15,597 701 508 947 6,049 11,747 2,333 2,022 2,350 29 345 7,328 1,713 9,104 1,476 .. 5,534
2010
5.2 –32.9 1.4 1.9 .. 1.7 13.1 2.4 0.5 1.6 0.0 6.2 7.2 0.6 .. 0.0 8.7 .. 9.5 3.8 1.5 11.0 5.5 45.9 .. 2.1 2.3 9.9 2.7 3.9 1.6 0.4 4.4 1.9 3.3 23.5 1.4 8.2 .. 6.5 .. –2.0 .. 7.8 17.1 3.1 2.8 .. 1.1 8.8 0.3 1.9 4.7 0.9 1.9 0.7 .. ..
6.10
Debt flows
$ millions Commercial bank and other lending
Portfolio equity $ millions
Bonds
2010
2010
2010
0 .. 10,339 2,329 0 .. .. .. .. 1,007 .. 733 –1,053 0 .. .. 0 .. 0 0 0 –396 0 0 .. 2,458 0 0 0 2,024 0 0 0 13,338 0 –75 1,327 0 0 .. 0 .. .. 0 0 0 .. .. –1,200 –150 0 0 4,635 2,712 .. .. .. ..
30 .. 12,971 3,563 –1,084 .. .. .. .. 146 .. 2 7,156 8 .. .. 0 .. 97 –14 –4,045 5 0 0 .. –3,897 36 –1 0 2,017 –1 –11 27 643 119 151 77 71 –546 .. 0 .. .. –63 –7 –33 .. .. –227 0 2,418 426 971 3,767 .. .. .. ..
0 –143 39,972 2,132 .. .. 152,236 –612 3,826 0 40,328 –20 131 33 .. 23,026 0 –815 –18 54 9 163 0 0 0 37 –4 .. .. .. .. .. –40 641 6 680 132 0 .. 4 .. 11,327 –298 0 .. 2,161 1,993 703 524 0 .. 0 87 503 7,875 –1,628 .. ..
2012 World Development Indicators
367
GLOBAL LINKS
Global private financial flows
6.10
Global private financial flows Equity net flows
Foreign direct investment Net inflow $ millions % of GDP
Romania Russian Federation Rwanda Saudi Arabia Senegal Serbia Sierra Leone Singapore Slovak Republic Slovenia Somalia South Africa South Sudan Spain Sri Lanka Sudan Swaziland Sweden Switzerland Syrian Arab Republic Tajikistan Tanzania Thailand Timor-Leste Togo Trinidad and Tobago Tunisia Turkey Turkmenistan Uganda Ukraine United Arab Emirates United Kingdom United States Uruguay Uzbekistan Venezuela, RB Vietnam West Bank and Gaza Yemen, Rep. Zambia Zimbabwe World Low income Middle income Lower middle income Upper middle income Low & middle income East Asia & Pacific Europe & Central Asia Latin America & Carib. Middle East & N. Africa South Asia Sub-Saharan Africa High income Euro area
Portfolio equity $ millions
Bonds
2010
2010
2010
2010
2,941 43,288 42 21,560 237 1,340a 87 38,638 553 366 112 1,224 .. 24,658 478 2,064 136 5,847 21,707 1,469 16 433 9,679 280 41 549 1,401 9,084 2,083 817 6,495 3,948 52,968 236,226 1,627 822 1,209 8,000 .. 56 1,041 105 1,430,438 s 13,017 501,236 90,233 411,003 514,253 231,299 86,991 117,368 25,688 27,923 24,984 916,185 395,004
1.8 2.9 0.8 5.0 1.8 3.5 4.6 18.5 0.6 0.8 .. 0.3 .. 1.8 1.0 3.3 3.7 1.3 4.1 2.5 0.3 1.9 3.0 39.9 1.3 2.7 3.2 1.2 10.4 4.8 4.7 1.3 2.3 1.6 4.2 2.1 0.3 7.5 .. 0.2 6.4 1.4 2.3 s 3.4 2.6 2.1 2.8 2.6 3.1 2.8 2.4 2.7 1.3 2.3 2.1 3.3
–25 –4,808 21 .. .. 84 0 3,559 25 169 .. 5,826 .. –4,790 –1,049 1 5 5,474 –7,210 .. 0 3 2,606 .. .. .. –26 3,468 .. –70 290 .. –11,488 172,376 –12 .. 10 2,383 .. 0 101 .. 779,547 s –31 129,690 49,598 80,092 129,660 39,715 –840 41,302 1,973 39,447 8,063 649,887 359,369
–929 14,900 0 .. 0 0 0 .. .. .. 0 1,422 .. .. 1,000 0 0 .. .. 0 0 0 1,730 .. 0 .. 0 5,961 0 0 3,089 .. .. .. –93 0 1,141 981 .. 0 0 0 .. s 0 111,383 22,252 89,130 111,383 20,813 27,091 48,776 3,164 10,139 1,400 .. ..
a. Includes Montenegro.
368
Debt flows
2012 World Development Indicators
$ millions Commercial bank and other lending 2010
–858 –6,734 0 .. –63 312 0 .. .. .. 0 795 .. .. 72 0 0 .. .. 0 50 137 –452 .. 0 .. –550 –8,310 –39 0 6,892 .. .. .. 7 534 –264 129 .. –1 –21 289 .. s 733 43,393 29,824 13,569 44,126 13,127 –7,863 27,376 –1,933 12,751 669 .. ..
About the data
6.10
Definitions
Private financial flows—equity and debt—account for
from the International Development Association. The
• Foreign direct investment is net inflows of invest-
the bulk of development finance. Equity flows com-
reports are cross-checked with data from market
ment to acquire a lasting interest in or management
prise foreign direct investment (FDI) and portfolio
sources that include transactions data. Information
control over an enterprise operating in an economy
equity. Debt flows are financing raised through bond
on private nonguaranteed bonds and bank lending
other than that of the investor. It is the sum of equity
issuance, bank lending, and supplier credits. Data
is collected from market sources when data are not
capital, reinvested earnings, other long-term capital,
on equity flows are based on balance of payments
reported to the Debtor Reporting System.
and short-term capital, as shown in the balance of
data reported by the International Monetary Fund
Data on equity flows are shown for all countries
payments. Net inflows are new investments less dis-
(IMF). FDI data are supplemented by staff estimates
for which data are available. Debt flows are shown
investments. • Portfolio equity includes net inflows
using data from the United Nations Conference on
only for 129 developing countries that report to the
from equity securities other than those recorded
Trade and Development and official national sources.
Debtor Reporting System; nonreporting countries
as direct investment and including shares, stocks,
may also receive debt flows.
depository receipts, and direct purchases of shares
The internationally accepted definition of FDI (from the fifth edition of the IMF’s Balance of Payments
The volume of global private fi nancial fl ows
in local stock markets by foreign investors • Bonds
Manual [1993]), includes three components: equity
reported by the World Bank generally differs from
are securities issued with a fixed rate of interest for a
investment, reinvested earnings, and short- and
that reported by other sources because of differ-
period of more than one year. They include net flows
long-term loans between parent firms and foreign
ences in sources, classification of economies, and
through cross-border public and publicly guaranteed
affiliates. Distinguished from other kinds of interna-
method used to adjust and disaggregate reported
and private nonguaranteed bond issues. • Commer-
tional investment, FDI is made to establish a lasting
information. In addition, particularly for debt financ-
cial bank and other lending includes net commercial
interest in or effective management control over an
ing, differences may also reflect how some install-
bank lending (public and publicly guaranteed and pri-
enterprise in another country. A lasting interest in an
ments of the transactions and certain offshore issu-
vate nonguaranteed) and other private credits.
investment enterprise typically involves establish-
ances are treated.
ing warehouses, manufacturing facilities, and other permanent or long-term organizations abroad. Direct investments may take the form of greenfield investment, where the investor starts a new venture in a foreign country by constructing new operational facilities; joint venture, where the investor enters into a partnership agreement with a company abroad to establish a new enterprise; or merger and acquisition, where the investor acquires an existing enterprise abroad. The IMF suggests that investments should account for at least 10 percent of voting stock to be counted as FDI. In practice many countries set a higher threshold. Many countries fail to report reinvested earnings, and the definition of long-term loans differs among countries. FDI data do not give a complete picture of international investment in an economy. Balance of payments data on FDI do not include capital raised locally, an important source of investment financing in some developing countries. In addition, FDI data omit nonequity cross-border transactions such as intrafirm flows of goods and services. For a detailed
Data sources
discussion of the data issues, see the World Bank’s
Data on equity and debt flows are compiled from a
Global Development Finance.
variety of public and private sources, including the
Statistics on bonds, bank lending, and supplier
World Bank’s Debtor Reporting System, the IMF’s
credits are produced by aggregating transactions
International Financial Statistics and Balance of
of public and publicly guaranteed debt and private
Payments databases, and Dealogic. These data
nonguaranteed debt. Data on public and publicly
are also published annually in the World Bank’s
guaranteed debt are reported through the Debtor
Global Development Finance, on its Global Develop-
Reporting System by World Bank member econo-
ment Finance CD-ROM, and in the Global Develop-
mies that have received loans from the International
ment Finance database.
Bank for Reconstruction and Development or credits
2012 World Development Indicators
369
GLOBAL LINKS
Global private financial flows
6.11
Net official financial flows Total
International financial institutions
$ millions From From multilateral bilateral sourcesa sources 2010
Afghanistan Albania Algeria Angola Argentina Armenia Australia Austria Azerbaijan Bahrain Bangladesh Belarus Belgium Benin Bolivia Bosnia and Herzegovina Botswana Brazil Bulgaria Burkina Faso Burundi Cambodia Cameroon Canada Central African Republic Chad Chile China Hong Kong SAR, China Colombia Congo, Dem. Rep. Congo, Rep. Costa Rica Côte d’Ivoire Croatia Cuba Cyprus Czech Republic Denmark Dominican Republic Ecuador Egypt, Arab Rep. El Salvador Eritrea Estonia Ethiopia Finland France Gabon Gambia, The Georgia Germany Ghana Greece Guatemala Guinea Guinea-Bissau Haiti
370
0.0 –7.1 –142.4 3,372.3 –249.8 64.9 .. .. 147.7 .. –112.3 1,589.5 .. 19.2 45.8 65.5 –13.6 3,615.0 –52.6 16.5 .. 262.7 34.8 .. –0.1 –4.1 36.9 –123.3 .. –81.8 –23.4 –42.4 10.1 –36.3 .. .. .. .. .. 362.0 890.3 –1,015.2 –44.4 –4.1 .. 510.7 .. .. 15.9 6.5 36.1 .. 357.9 .. –14.6 –9.0 .. 121.4
2012 World Development Indicators
$ millions World Bank
IMF
2010
IDA 2010
IBRD 2010
Concessional 2010
Nonconcessional 2010
77.8 102.5 –2.3 1.5 788.3 109.5 .. .. 310.4 .. 352.1 33.6 .. 144.2 297.4 277.4 –24.8 5,069.6 –8.6 228.4 33.9 50.6 119.4 .. 5.1 17.9 –44.8 828.7 .. 1,094.1 –15.8 –11.2 527.6 –81.3 .. .. .. .. .. 470.1 826.1 772.7 365.8 0.1 .. 472.1 .. .. –7.4 18.5 294.0 .. 427.0 .. 631.0 –11.0 –2.5 42.3
8.4 15.1 .. –0.7 .. 20.3 .. .. 35.9 .. 83.5 .. .. 78.3 44.0 97.2 –0.5 .. .. 66.6 17.8 9.0 80.6 .. 5.0 –16.0 –0.7 –348.4 .. –0.7 17.5 1.5 –0.2 –0.9 .. .. .. .. .. –0.7 –1.1 –44.1 –0.8 –0.5 .. 384.7 .. .. .. 1.9 52.9 .. 304.8 .. .. 0.0 –1.0 –35.9
.. 3.1 –0.5 .. 46.2 52.4 .. .. 101.2 .. .. 35.7 .. .. 0.0 25.7 6.5 3,511.7 –69.9 .. .. .. –5.4 .. .. .. –25.8 195.6 .. 937.2 .. .. 512.3 –24.9 .. .. .. .. .. 116.2 –85.5 693.6 345.5 .. .. .. .. .. 5.6 .. 72.8 .. .. .. 260.0 .. .. ..
8.6 –12.1 .. .. .. 35.5 .. .. –14.7 .. –45.6 .. .. 16.2 .. .. .. .. .. 20.2 20.1 .. 0.0 .. 13.2 –11.1 .. .. .. .. 18.9 1.8 .. 44.4 .. .. .. .. .. .. .. .. .. .. .. 122.4 .. .. .. 3.0 –21.5 .. 124.4 .. .. –10.2 15.7 124.8
.. –0.2 .. 524.2 .. 127.4 .. .. .. .. 0.0 668.3 .. .. .. 237.4 .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. 382.9 .. .. .. .. .. .. .. .. .. .. 296.9 .. .. .. .. .. –8.1 ..
Regional development banksa NonConcessional concessional 2010 2010
64.9 .. .. 3.0 .. 22.3 .. .. 5.1 .. 75.0 .. .. 40.1 86.2 .. –1.8 .. .. 63.4 1.6 35.1 50.3 .. 0.0 3.8 .. .. .. –1.6 17.5 –0.2 –8.2 –2.2 .. .. .. .. .. –21.2 –26.7 –6.0 –23.2 3.7 .. 80.8 .. .. –0.2 2.3 38.3 .. 130.0 .. –18.3 6.3 –2.4 69.5
.. 1.9 .. –0.4 609.9 8.7 .. .. 33.6 .. 166.1 –2.1 .. .. 29.7 58.6 –3.0 951.3 –11.8 .. .. .. –9.7 .. .. .. –18.3 1,033.3 .. 188.0 –38.1 –8.3 18.6 –32.5 .. .. .. .. .. 359.5 64.0 140.5 26.0 .. .. –6.7 .. .. 2.6 .. 118.7 .. 0.0 .. 320.8 –4.6 .. ..
Other institutions 2010
4.5 82.3 –1.7 –0.5 136.7 5.9 .. .. 134.6 .. 27.6 .. .. 25.7 140.8 100.7 –26.0 589.8 73.0 98.5 14.4 6.6 3.6 .. 0.1 30.2 .. –51.8 .. –28.7 –12.7 0.2 5.0 –5.0 .. .. .. .. .. 16.2 455.4 –11.2 18.3 –3.1 .. 13.3 .. .. –15.4 14.3 11.2 .. –7.1 .. 68.5 –12.7 0.8 8.8
Total
International financial institutions
$ millions From From multilateral bilateral sourcesa sources 2010
Honduras Hungary India Indonesia Iran, Islamic Rep. Iraq Ireland Israel Italy Jamaica Japan Jordan Kazakhstan Kenya Korea, Dem. Rep. Korea, Rep. Kosovo Kuwait Kyrgyz Republic Lao PDR Latvia Lebanon Lesotho Liberia Libya Lithuania Macedonia, FYR Madagascar Malawi Malaysia Mali Mauritania Mauritius Mexico Moldova Mongolia Morocco Mozambique Myanmar Namibia Nepal Netherlands New Zealand Nicaragua Niger Nigeria Norway Oman Pakistan Panama Papua New Guinea Paraguay Peru Philippines Poland Portugal Puerto Rico Qatar
2.4 .. 826.1 –8.2 –289.5 .. .. .. .. 32.1 .. 13.1 –195.0 40.4 .. .. .. .. 101.3 57.3 –17.6 –71.8 0.6 3.7 .. .. 0.0 68.9 22.8 –599.4 42.9 97.6 110.7 464.4 –6.8 11.6 504.5 7.8 –103.6 .. –4.2 .. .. –3.1 14.7 –28.7 .. .. –8.4 87.2 –21.9 –62.9 –841.9 –292.0 .. .. .. ..
6.11
2010
388.9 .. 4,575.8 1,367.1 73.4 .. .. .. .. 835.2 .. 153.9 1,436.1 223.3 .. .. –16.3 .. 7.7 –31.2 1,086.1 –26.3 20.9 –3.7 .. 446.0 85.4 106.4 44.8 –69.9 230.6 249.4 136.8 3,580.4 48.8 20.2 625.7 237.6 –0.8 .. –11.1 .. .. 215.0 64.1 866.5 .. .. 439.4 188.1 –2.1 142.4 –527.3 –179.9 .. .. .. ..
$ millions World Bank IDA 2010
108.7 .. 231.5 110.0 .. .. .. .. .. .. .. –2.6 .. 132.4 .. .. .. .. 5.4 –12.6 .. .. 22.3 –1.7 .. .. –8.2 74.6 33.8 .. 151.3 38.7 –0.6 .. 59.4 17.3 –1.4 157.7 .. .. –32.6 .. .. 36.2 14.1 975.4 .. .. 204.9 .. 4.6 –1.5 .. –6.9 .. .. .. ..
IMF IBRD 2010
.. .. 2,795.2 1,177.4 64.2 .. .. .. .. 180.6 .. –68.5 1,283.0 .. .. .. –16.3 .. .. .. 117.6 3.0 –0.7 .. .. –3.1 61.6 .. .. –38.5 .. .. –3.0 2,255.6 –17.8 .. 69.2 .. .. .. .. .. .. .. .. –70.8 .. .. –127.1 –14.8 –10.6 –25.0 118.9 58.9 .. .. .. ..
Concessional 2010
–1.6 .. .. .. .. .. .. .. .. .. .. .. .. –25.5 .. .. .. .. 12.4 –5.5 .. .. 4.4 13.6 .. .. .. –1.7 21.2 .. 5.9 33.7 .. .. 113.6 –4.6 .. 21.4 .. .. 39.4 .. .. 19.5 4.9 .. .. .. –264.6 .. .. .. .. .. .. .. .. ..
Nonconcessional 2010
.. .. .. .. .. .. .. .. .. 778.1 .. –3.9 .. .. .. .. .. .. .. .. 409.6 –19.4 .. .. .. .. .. .. .. .. .. .. .. .. 61.0 23.4 .. .. .. .. .. .. .. .. .. .. .. .. 1,633.8 .. .. .. .. .. .. .. .. ..
Regional development banksa NonConcessional concessional 2010 2010
101.0 .. .. 66.1 .. .. .. .. .. –4.2 .. .. –0.2 83.7 .. .. .. .. –8.5 –11.1 .. .. –1.6 –2.1 .. .. .. 12.5 4.9 .. 65.5 9.9 –0.2 .. .. 0.5 –1.1 68.4 0.0 .. 15.8 .. .. 97.3 16.4 21.9 .. .. 87.5 –5.6 –5.5 –11.3 .. –41.1 .. .. .. ..
36.1 .. 1,440.1 13.6 .. .. .. .. .. 559.8 .. .. 101.9 –2.4 .. .. .. .. 2.3 0.2 –1.8 .. .. 0.0 .. –4.9 –5.8 .. –2.1 –21.5 .. –8.1 153.9 1,299.1 –3.1 .. 160.3 .. .. .. .. .. .. 43.7 .. –65.2 .. .. 148.2 214.9 13.1 140.4 –755.5 –203.2 .. .. .. ..
2012 World Development Indicators
Other institutions 2010
143.2 .. 97.2 0.0 .. .. .. .. .. 99.1 .. 225.0 51.3 9.6 .. .. .. .. 8.5 –7.6 970.3 –29.3 0.9 .. .. 454.0 37.9 19.3 8.2 –10.0 13.8 209.0 –13.3 25.7 10.2 2.3 398.7 11.4 –0.8 .. 5.7 .. .. 37.4 33.7 5.1 .. .. 126.0 –6.5 –3.8 43.3 109.3 12.5 .. .. .. ..
371
GLOBAL LINKS
Net official financial flows
6.11
Net official financial flows Total
International financial institutions
$ millions From From multilateral bilateral sourcesa sources
Romania Russian Federation Rwanda Saudi Arabia Senegal Serbia Sierra Leone Singapore Slovak Republic Slovenia Somalia South Africa South Sudan Spain Sri Lanka Sudan Swaziland Sweden Switzerland Syrian Arab Republic Tajikistan Tanzania Thailand Timor-Leste Togo Trinidad and Tobago Tunisia Turkey Turkmenistan Uganda Ukraine United Arab Emirates United Kingdom United States Uruguay Uzbekistan Venezuela, RB Vietnam West Bank and Gaza Yemen, Rep. Zambia Zimbabwe World Low income Middle income Lower middle income Upper middle income Low & middle income East Asia & Pacific Europe & Central Asia Latin America & Carib. Middle East & N. Africa South Asia Sub-Saharan Africa High income Euro area
$ millions World Bank
2010
2010
IDA 2010
84.4 –297.2 17.1 .. 68.0 142.5 6.1 .. .. .. 0.0 .. .. .. 1,002.8 471.9 0.9 .. .. –309.3 83.7 143.5 –352.5 .. 36.7 .. 83.8 174.9 –75.8 25.5 –148.3 .. .. .. –20.6 72.7 –78.7 588.6 .. –15.2 59.3 18.7 .. s 1,525.0 10,366.5 5,826.9 4,539.6 11,891.5 –498.0 1,760.8 4,312.3 –1,234.4 1,841.2 5,709.6 .. ..
3,374.6 –659.5 36.0 .. 204.8 708.5 55.5 .. .. .. 0.0 872.8 .. .. 293.8 65.9 –19.8 .. .. 98.3 129.8 817.7 –21.2 .. 20.9 .. 547.3 3,657.1 –2.9 421.2 –6.1 .. .. .. –175.0 106.3 895.0 1,924.7 .. 148.6 70.6 –0.2 .. s 3,790.5 41,745.3 14,764.8 26,980.5 45,535.8 3,926.3 11,516.2 15,755.4 2,401.1 5,802.0 6,134.8 .. ..
.. .. 9.4 .. 108.5 16.4 32.1 .. .. .. 0.0 .. .. .. 82.9 0.0 –0.3 .. .. –1.5 10.7 650.0 –3.4 .. –22.0 .. –1.9 –5.9 .. 323.0 .. .. .. .. .. 32.1 .. 800.9 .. 28.2 29.9 0.0 .. s 2,155.6 3,190.5 3,365.2 –174.8 5,346.1 589.6 334.3 163.8 –24.0 594.6 3,687.8 .. ..
a. Aggregates include amounts for economies not specified elsewhere.
372
2012 World Development Indicators
IMF IBRD 2010
–74.6 –596.4 .. .. .. 172.0 .. .. .. .. .. 363.1 .. .. .. .. –5.2 .. .. 0.0 .. .. –1.7 .. .. .. 76.4 2,094.3 –1.4 .. –84.7 .. .. .. –42.0 –27.6 .. 700.0 .. .. .. –0.1 .. s –0.1 17,081.8 5,803.1 11,278.7 17,081.7 2,081.2 3,121.8 8,090.7 837.3 2,668.1 282.7 .. ..
Concessional 2010
.. .. –0.1 .. 48.9 .. 40.7 .. .. .. .. .. .. .. –11.7 .. .. .. .. 0.0 59.8 29.9 .. .. 43.6 .. .. .. .. –0.3 .. .. .. .. .. .. .. –37.9 .. 26.0 55.3 –4.0 .. s 559.7 143.2 163.2 –20.0 702.8 –38.5 172.9 146.7 22.7 –272.4 671.5 .. ..
Nonconcessional 2010
5,664.3 .. .. .. .. 457.6 .. .. .. .. .. .. .. .. 608.5 –5.8 .. .. .. 0.0 .. .. .. .. .. .. .. –2,170.8 .. .. 3,433.3 .. .. .. .. .. .. .. .. .. .. .. .. s –8.1 13,125.0 6,702.9 6,422.2 13,116.9 23.4 9,184.9 1,161.0 –23.2 2,247.8 523.1 .. ..
Regional development banksa NonConcessional concessional 2010 2010
4.7 .. 14.8 .. 63.0 .. 14.9 .. .. .. 0.0 .. .. .. 18.0 0.0 –1.4 .. .. 0.0 33.0 138.6 .. .. –2.0 .. .. .. .. 92.0 .. .. .. .. –1.8 36.3 .. 171.4 .. .. 51.7 0.0 .. s 1,008.3 986.7 1,026.0 –39.2 1,995.0 230.9 130.9 276.8 –5.4 301.2 1,060.6 .. ..
–51.5 –61.1 .. .. –12.7 181.4 .. .. .. .. 0.0 509.8 .. .. 184.6 0.0 –7.8 .. .. 0.0 –0.9 –1.0 –4.8 .. .. .. 103.6 .. .. 0.0 –11.9 .. .. .. –408.4 55.0 425.5 170.3 .. .. –3.2 –0.1 .. s 112.5 8,277.1 2,747.0 5,530.1 8,389.7 1,012.0 408.2 4,120.5 404.3 1,960.1 484.6 .. ..
Other institutions 2010
3,496.0 –2.1 11.9 .. 46.2 326.2 8.6 .. .. .. 0.0 .. .. .. 8.3 65.9 –5.1 .. .. 99.8 17.1 30.2 –11.3 .. 34.7 .. 369.3 1,533.7 –1.5 6.3 90.5 .. .. .. 279.2 10.5 469.4 82.0 .. 120.4 –7.9 0.0 .. s 434.0 11,723.0 1,839.5 9,883.5 12,157.0 12.7 7,408.4 2,659.8 1,179.7 266.3 630.0 .. ..
About the data
6.11
Definitions
The table shows concessional and nonconcessional
and the Rapid Credit Facility. Eligibility is based prin-
• Total net official financial flows are disbursements
financial flows from official bilateral sources and the
cipally on a country’s per capita income and eligibility
of public or publicly guaranteed loans and credits,
major international financial institutions. The inter-
under IDA. Nonconcessional lending from the IMF
less repayments of principal. • IDA is the Interna-
national financial institutions fund nonconcessional
is provided mainly through Stand-by Arrangements,
tional Development Association, the concessional
lending operations primarily by selling low-interest,
the Flexible Credit Line, and the Extended Fund Facil-
arm of the World Bank Group. • IBRD is the Inter-
highly rated bonds backed by prudent lending and
ity. The IMF’s loan instruments have changed over
national Bank for Reconstruction and Development,
financial policies and the strong financial support of
time to address the specific circumstances of its
the founding and largest member of the World Bank
their members. Funds are then on-lent to developing
members.
Group. • IMF is the International Monetary Fund,
countries at slightly higher interest rates with 15- to
Regional development banks also maintain conces-
which provides concessional lending through its
20-year maturities. Lending terms vary with market
sional windows. Their loans are recorded in the table
Extended Credit Facility, Standby Credit Facility, and
conditions and institutional policies.
according to each institution’s classification and not
Rapid Credit Facility and nonconcessional lending
according to the DAC definition.
through credit to members, mainly for balance of
Concessional fl ows from international financial institutions are credits provided through conces-
Data for flows from international financial institu-
payments needs. • Regional development banks are
sional lending facilities. Subsidies from donors or
tions are available for 129 countries that report to
the African Development Bank, which serves Africa,
other resources reduce the cost of these loans.
the World Bank’s Debtor Reporting System. Non-
including North Africa; the Asian Development Bank,
Grants are not included in net flows. The Organisa-
reporting countries may have net flows from other
which serves South and Central Asia and East Asia
tion for Economic Co-operation and Development’s
international financial institutions.
and Pacific; the European Bank for Reconstruction
(OECD) Development Assistance Committee (DAC)
and Development, which serves Europe and Central
defines concessional flows from bilateral donors as
Asia; and the Inter-American Development Bank,
flows with a grant element of at least 25 percent;
which serves the Americas. • Concessional financial
they are evaluated assuming a 10 percent nominal
flows are disbursements through concessional lend-
discount rate.
ing facilities. • Nonconcessional financial flows are
World Bank concessional lending is done by the
all disbursements that are not concessional. • Other
International Development Association (IDA) based
institutions, a residual category, includes such insti-
on gross national income (GNI) per capita and per-
tutions as the Caribbean Development Fund, Coun-
formance standards assessed by World Bank staff.
cil of Europe, European Development Fund, Islamic
The cutoff for IDA eligibility, set at the beginning of
Development Bank, and Nordic Development Fund.
the World Bank’s fiscal year, has been $1,175 since July 1, 2011, measured in 2010 U.S. dollars using the World Bank Atlas method (see Users guide). In exceptional circumstances IDA extends temporary eligibility to countries above the cutoff that are undertaking major adjustments but are not creditworthy for International Bank for Reconstruction and Development (IBRD) lending. Exceptions are also made for small island economies. The IBRD lends to creditworthy countries at a variable base rate of six-month LIBOR plus a spread, either variable or fixed, for the life of the loan. The rate is reset every six months and applies to the interest period beginning on that date. Although some outstanding IBRD loans have a low enough interest rate to be classified as concessional under the DAC definition, all IBRD loans in the table are classified as nonconcessional. Lending by the International Finance Corporation, the
Data sources
Multilateral Investment Guarantee Agency, and the
Data on net financial flows from international finan-
International Centre for Settlement of Investment
cial institutions are from the World Bank’s Debtor
Disputes is excluded.
Reporting System and published in the World
The International Monetary Fund (IMF) makes con-
Bank’s Global Development Finance 2012, on its
cessional funds available through its Extended Credit
Global Development Finance CD-ROM, and in its
Facility (which replaced the Poverty Reduction and
Global Development Finance database.
Growth Facility in 2010), the Standby Credit Facility,
2012 World Development Indicators
373
GLOBAL LINKS
Net official financial flows
6.12
Aid dependency Net official development assistance
Aid dependency ratios
$ millions
Afghanistan Albania Algeria Angola Argentina Armenia Australia Austria Azerbaijan Bahrain Bangladesh Belarus Belgium Benin Bolivia Bosnia and Herzegovina Botswana Brazil Bulgaria Burkina Faso Burundi Cambodia Cameroon Canada Central African Republic Chad Chile China Hong Kong SAR, China Colombia Congo, Dem. Rep. Congo, Rep. Costa Rica Côte d’Ivoire Croatia Cuba Cyprus Czech Republic Denmark Dominican Republic Ecuador Egypt, Arab Rep. El Salvador Eritrea Estonia Ethiopia Finland France Gabon Gambia, The Georgia Germany Ghana Greece Guatemala Guinea Guinea-Bissau Haiti
374
Technical cooperation 2010
Net official development assistance % of % of imports of % of gross capital goods, services, central government expense and income formation 2010 2010 2010
Total $ millions 2010
Per capita $ 2010
Grants 2010
6,374 357 319 239 127 526
185 112 9 13 3 170
5,476 160 74 207 36 159
982 101 185 42 69 32
42.0 2.9 0.1 0.3 0.0 3.5
227.0 11.1 0.3 1.9 0.2 10.9
.. 5.0 .. 0.5 0.2 7.0
80.2 .. .. .. .. 15.9
232 .. 1,226 98
26 .. 8 10
84 .. 1,082 105
53 .. 218 37
0.3 .. 1.3 0.3
1.8 .. 5.8 0.6
1.1 .. 4.6 0.4
.. .. .. 0.8
682 725 414 279 337 .. 1,083 561 721 648
79 74 110 141 2 .. 68 69 52 34
462 527 214 141 413 .. 766 525 453 358
92 133 138 21 237 .. 125 74 186 155
10.4 3.6 2.9 1.1 0.0 .. 12.1 39.8 6.9 2.4
40.4 20.2 15.2 2.9 0.2 .. .. .. 37.7 ..
.. 9.5 4.9 2.5 0.2 .. .. 102.2 8.7 8.0
69.7 .. 7.3 .. .. .. 99.6 .. 58.1 ..
242 561 79 1,129 .. 1,059 2,357 283 109 2,402 169 115 .. ..
56 51 5 1 .. 23 37 72 24 124 38 10 .. ..
241 453 83 459 .. 669 5,480 1,574 75 646 106 96 .. ..
17 40 65 960 .. 141 234 24 29 54 39 33 .. ..
13.1 7.3 0.1 0.0 .. 0.3 27.8 15.2 0.3 3.9 0.3 .. .. ..
.. 17.6 0.4 0.0 .. 1.3 .. 44.3 1.3 26.9 1.0 .. .. ..
.. .. 0.2 0.0 .. 1.5 .. .. 0.6 .. 0.6 .. .. ..
.. .. 0.4 .. .. 1.7 189.1 .. .. .. 0.7 .. .. ..
119 208 999 276 144 .. 3,819
12 15 13 45 28 .. 47
197 166 655 331 147 .. 2,628
41 81 165 59 7 .. 264
0.4 0.3 0.3 1.4 7.7 .. 11.9
2.1 1.0 1.4 10.1 .. .. 55.3
0.9 0.6 0.9 2.9 .. .. 35.3
.. .. 0.9 6.4 .. .. ..
77 127 907
52 76 206
56 94 377
41 9 122
0.9 16.3 5.5
3.1 57.8 27.5
.. 36.6 9.1
.. .. 20.4
1,582
66
920
123
5.5
24.2
11.7
..
376 214 147 1,120
27 22 99 114
297 167 270 3,496
119 62 18 269
1.0 5.1 16.0 ..
6.6 23.6 .. 183.4
2.4 11.3 .. 75.1
.. .. .. ..
2012 World Development Indicators
% of GNI 2010
Net official development assistance
Aid dependency ratios
$ millions Total $ millions 2010
Honduras Hungary India Indonesia Iran, Islamic Rep. Iraq Ireland Israel Italy Jamaica Japan Jordan Kazakhstan Kenya Korea, Dem. Rep. Korea, Rep. Kosovo Kuwait Kyrgyz Republic Lao PDR Latvia Lebanon Lesotho Liberia Libya Lithuania Macedonia, FYR Madagascar Malawi Malaysia Mali Mauritania Mauritius Mexico Moldova Mongolia Morocco Mozambique Myanmar Namibia Nepal Netherlands New Zealand Nicaragua Niger Nigeria Norway Oman Pakistan Panama Papua New Guinea Paraguay Peru Philippines Poland Portugal Puerto Rico Qatar
Per capita $ 2010
Grants 2010
Technical cooperation 2010
% of GNI 2010
Net official development assistance % of % of imports of % of gross capital goods, services, central government expense and income formation 2010 2010 2010
456 .. 2,500 1,047 92 2,791
61 .. 2 4 1 90
279 .. 1,007 851 78 1,881
55 .. 283 520 73 126
3.9 .. 0.2 0.2 .. 2.8
16.3 .. 0.5 0.6 .. ..
5.5 .. 0.6 0.8 .. ..
16.0 .. 1.1 1.4 .. ..
..
..
..
..
..
..
..
..
149
55
152
15
1.0
4.8
2.0
..
740 298 1,776 65
125 18 45 3
762 186 1,228 72
73 50 153 5
3.4 0.2 5.2 ..
22.6 0.6 24.4 ..
5.2 0.3 11.8 ..
13.4 0.9 22.6 ..
781 .. 313 419 .. 580 122 513 41 .. 192 444 771 143 984 373 155 184 244 371 930 2,012 356 326 854
433 .. 58 69 .. 138 57 134 7 .. 94 22 53 5 66 111 122 2 68 137 29 88 7 145 29
318 .. 296 281 .. 343 219 1,698 12 .. 97 307 880 50 741 120 73 286 241 189 424 1,443 339 194 684
275 .. 58 110 .. 90 12 30 9 .. 68 67 85 49 128 53 20 112 54 96 291 171 57 44 125
10.3 .. 8.7 6.0 .. 1.2 10.1 176.8 .. .. 2.0 5.5 20.6 0.0 12.3 10.2 1.3 0.0 7.5 5.4 1.1 20.8 .. 2.1 ..
35.0 .. 28.4 21.9 .. 3.5 35.7 .. .. .. 7.6 .. 82.2 0.0 .. 36.6 5.7 0.2 34.1 12.0 3.1 86.2 .. 9.1 ..
.. .. 8.6 16.9 .. 1.4 9.0 78.7 0.0 .. 2.8 .. .. 0.0 .. .. 2.0 0.1 9.7 6.7 2.4 39.9 5.1 4.0 13.7
.. .. 36.8 52.8 .. .. .. .. .. .. .. .. .. 0.0 .. .. 5.7 .. 23.0 18.4 3.6 .. .. .. ..
773 469 1,657
135 31 11
316 589 827
98 105 198
10.0 13.6 1.2
34.9 .. ..
10.9 .. 2.2
47.8 .. ..
154 2,769 65 412 148 441 309 ..
57 16 19 61 23 15 3 ..
18 2,743 27 221 104 327 429 ..
3 232 18 313 53 173 212 ..
.. 1.6 0.5 5.5 0.6 –0.2 0.3 ..
.. 11.1 1.8 30.4 2.9 –0.7 1.3 ..
–0.1 6.9 0.6 7.4 0.9 –0.6 0.7 ..
.. 9.7 .. .. 3.8 –1.0 1.6 ..
.. ..
.. ..
.. ..
.. ..
.. ..
.. ..
.. ..
.. ..
2012 World Development Indicators
375
GLOBAL LINKS
6.12
Aid dependency
6.12
Aid dependency Net official development assistance
Aid dependency ratios
$ millions Total $ millions 2010
Romania Russian Federation Rwanda Saudi Arabia Senegal Serbia Sierra Leone Singapore Slovak Republic Slovenia Somalia South Africa South Sudan Spain Sri Lanka Sudan Swaziland Sweden Switzerland Syrian Arab Republic Tajikistan Tanzania Thailand Timor-Leste Togo Trinidad and Tobago Tunisia Turkey Turkmenistan Uganda Ukraine United Arab Emirates United Kingdom United States Uruguay Uzbekistan Venezuela, RB Vietnam West Bank and Gaza Yemen, Rep. Zambia Zimbabwe World Low income Middle income Lower middle income Upper middle income Low & middle income East Asia & Pacific Europe & Central Asia Latin America & Carib. Middle East & N. Africa South Asia Sub-Saharan Africa High income Euro area
Per capita $ 2010
Grants 2010
Technical cooperation 2010
% of GNI 2010
Net official development assistance % of % of imports of % of gross capital goods, services, central government expense and income formation 2010 2010 2010
.. .. 934 .. 1,016 614 448 .. .. .. 662 1,075 ..
.. .. 91 .. 84 84 78 .. .. .. 73 22 ..
.. .. 848 .. 449 444 301 .. .. .. 470 841 ..
.. .. 153 .. 195 127 75 .. .. .. 30 161 ..
.. .. 18.5 .. 7.2 1.7 24.9 .. .. .. .. 0.3 ..
.. .. .. .. 24.8 7.4 158.2 .. .. .. .. 1.1 ..
.. .. 60.9 .. .. 3.1 50.6 .. .. .. .. 0.9 ..
.. .. .. .. .. 4.3 .. .. .. .. .. .. ..
703 2,351 56
34 55 54
332 1,760 90
78 228 8
1.2 3.7 2.6
4.2 14.2 15.3
3.6 14.9 3.0
.. .. ..
208 408 2,933 –78 216 499 7 503 1,362 40 1,785 666 ..
10 60 67 –1 197 84 5 48 19 8 55 14 ..
203 267 1,814 167 144 455 3 142 340 24 1,189 338 ..
119 40 214 115 149 52 2 162 154 20 105 239 ..
0.2 7.7 12.9 0.0 10.8 14.9 0.0 1.3 0.1 0.2 10.3 0.5 ..
1.2 33.4 42.0 0.0 .. .. .. 4.7 0.7 0.4 42.8 2.3 ..
0.6 12.6 31.7 0.0 .. .. .. 2.1 0.5 .. 26.9 0.8 ..
.. .. .. 0.0 .. 92.1 .. 4.6 0.6 .. .. .. ..
30 70 36 656 2,345 523 706 669 .. .. .. .. .. 95,651 .. .. .. .. .. .. .. ..
24 53 19 378 169 63 72 78 .. .. .. .. .. 19,564 .. .. .. .. .. .. .. ..
0.4 .. 0.1 3.2 .. 5.2 12.1 .. 0.6 w 24.6 0.9 2.6 0.3 2.1 0.4 0.7 0.9 3.1 2.7 9.9 0.0 ..
0.4 .. .. .. .. .. 32.9 .. .. .. .. .. .. .. .. .. .. .. .. .. .. ..
50 190 66 3,732 2,817 558 1,267 736 126,968 s 36,252 54,621 40,510 13,151 126,593 10,165 8,087 9,036 13,383 14,591 44,582 374 ..
15 7 2 43 697 24 100 59 19 w 46 11 16 5 22 5 20 16 41 9 54 0 ..
0.1 0.6 0.0 2.9 .. 2.3 6.4 10.6 0.2 w 9.6 0.3 0.9 0.1 0.7 0.1 0.2 0.2 0.9 0.7 4.3 0.0 ..
0.7 2.2 0.1 7.1 .. 18.3 25.2 1,860.4 0.9 w 40.9 0.8 3.0 0.2 2.0 0.3 1.0 1.0 .. 2.3 18.8 0.0 ..
Note: Regional aggregates include data for economies not listed in the table. World and income group totals include aid not allocated by country or region—including administrative costs, research on development issues, and aid to nongovernmental organizations. Thus regional and income group totals do not sum to the world total.
376
2012 World Development Indicators
About the data
The flows of official and private financial resources from the members of the Development Assistance
6.12
Definitions
Saudi Arabia) are shown in the table as aid recipients
• Net official development assistance is flows (net
(see table 6.13a).
of repayment of principal) that meet the DAC defini-
Committee (DAC) of the Organisation for Economic
The table does not distinguish types of aid (pro-
tion of ODA and are made to countries and territo-
Co-operation and Development (OECD) to developing
gram, project, or food aid; emergency assistance; or
ries on the DAC list of aid recipients. • Net official
economies are compiled by DAC, based principally on
post-conflict peacekeeping assistance), which may
development assistance per capita is net ODA
reporting by DAC members using standard question-
have different effects on the economy.
divided by midyear population. • Grants are legally
naires issued by the DAC Secretariat.
Ratios of aid to gross national income (GNI), gross
binding commitments that obligate a specific value
DAC exists to help its members coordinate their
capital formation, imports, and government spending
of funds available for disbursement for which there
development assistance and to encourage the
provide measures of recipient country dependency
is no payment requirement. • Technical coopera-
expansion and improve the effectiveness of the
on aid. But care must be taken in drawing policy con-
tion is the provision of resources whose main aim is
aggregate resources flowing to recipient economies.
clusions. For foreign policy reasons some countries
to augment the stock of human intellectual capital,
In this capacity DAC monitors the flow of all financial
have traditionally received large amounts of aid. Thus
such as the level of knowledge, skills, and techni-
resources, but its main concern is official develop-
aid dependency ratios may reveal as much about
cal know-how in the recipient country (including the
ment assistance (ODA). Grants or loans to countries
a donor’s interests as about a recipient’s needs.
cost of associated equipment). Contributions take
and territories on the DAC list of aid recipients have
Ratios are generally much higher in Sub-Saharan
the form mainly of the supply of human resources
to meet three criteria to be counted as ODA. They
Africa than in other regions, and they increased in
from donors or action directed to human resources
are provided by official agencies, including state and
the 1980s. High ratios are due only in part to aid
(such as training or advice). Also included are aid for
local governments, or by their executive agencies.
flows. Many African countries saw severe erosion
promoting development awareness and aid provided
They promote economic development and welfare as
in their terms of trade in the 1980s, which, along
to refugees in the donor economy. Assistance spe-
the main objective. And they are provided on conces-
with weak policies, contributed to falling incomes,
cifically to facilitate a capital project is not included.
sional financial terms (loans must have a grant ele-
imports, and investment. Thus the increase in aid
• Aid dependency ratios are calculated using values
ment of at least 25 percent, calculated at a discount
dependency ratios reflects events affecting both the
in U.S. dollars converted at official exchange rates.
rate of 10 percent). The DAC Statistical Reporting
numerator (aid) and the denominator (GNI).
Imports of goods, services, and income refer to inter-
Directives provide the most detailed explanation of
Because the table relies on information from
national transactions involving a change in owner-
donors, it is not necessarily consistent with infor-
ship of general merchandise, goods sent for process-
This definition excludes nonconcessional fl ows
mation recorded by recipients in the balance of pay-
ing and repairs, nonmonetary gold, services, receipts
from official creditors, which are classified as “other
ments, which often excludes all or some technical
of employee compensation for nonresident workers,
official flows,” and aid for military and anti-terrorism
assistance—particularly payments to expatriates
and investment income. For definitions of GNI, gross
purposes. Transfer payments to private individuals,
made directly by the donor. Similarly, grant commod-
capital formation, and central government expense,
such as pensions, reparations, and insurance pay-
ity aid may not always be recorded in trade data or in
see Definitions for tables 1.1, 4.8, and 4.10.
outs, are in general not counted. In addition to finan-
the balance of payments. Moreover, DAC statistics
cial flows, ODA includes technical cooperation, most
exclude aid for military and antiterrorism purposes.
this definition and all ODA-related rules.
expenditures for peacekeeping under UN mandates
The nominal values used here may overstate the
and assistance to refugees, contributions to multi-
real value of aid to recipients. Changes in interna-
lateral institutions such as the United Nations and
tional prices and exchange rates can reduce the pur-
its specialized agencies, and concessional funding
chasing power of aid. Tying aid, still prevalent though
to multilateral development banks.
declining in importance, also tends to reduce its pur-
Flows are transfers of resources, either in cash or
chasing power. Tying requires recipients to purchase
in the form of commodities or services measured on
goods and services from the donor country or from
a cash basis. Short-term capital transactions (with
a specified group of countries. Such arrangements
Data on financial flows are compiled by OECD DAC
one year or less maturity) are not counted. Repay-
prevent a recipient from misappropriating or misman-
and published in its annual statistical report, Geo-
ments of the principal (but not interest) of ODA loans
aging aid receipts, but they may also be motivated by
graphical Distribution of Financial Flows to Develop-
are recorded as negative flows. Proceeds from offi -
a desire to benefit donor country suppliers.
ing Countries, and in its annual Development Co-
Data sources
cial equity investments in a developing country are
The aggregates refer to World Bank classifications
operation Report. Data are available electronically
reported as ODA, while proceeds from their later sale
of economies and therefore may differ from those
on the OECD DAC International Development Sta-
are recorded as negative flows.
of the OECD.
tistics CD-ROM and at www.oecd.org/dac/stats/
The table shows data on ODA for aid-receiving
idsonline. Data on population, GNI, gross capital
countries. The data cover loans and grants from
formation, imports of goods and services, and
DAC member countries, multilateral organizations,
central government expense used in computing
and non-DAC donors. They do not reflect aid given by
the ratios are from World Bank and International
recipient countries to other developing countries. As
Monetary Fund databases.
a result, some countries that are net donors (such as
2012 World Development Indicators
377
GLOBAL LINKS
Aid dependency
6.13
Distribution of net aid by Development Assistance Committee members Ten major DAC donors
$ millions Total $ millions 2010
Afghanistan Albania Algeria Angola Argentina Armenia Australia Austria Azerbaijan Bahrain Bangladesh Belarus Belgium Benin Bolivia Bosnia and Herzegovina Botswana Brazil Bulgaria Burkina Faso Burundi Cambodia Cameroon Canada Central African Republic Chad Chile China Hong Kong SAR, China Colombia Congo, Dem. Rep. Congo, Rep. Costa Rica Côte d’Ivoire Croatia Cuba Cyprus Czech Republic Denmark Dominican Republic Ecuador Egypt, Arab Rep. El Salvador Eritrea Estonia Ethiopia Finland France Gabon Gambia, The Georgia Germany Ghana Greece Guatemala Guinea Guinea-Bissau Haiti
378
United States 2010
EU institutions 2010
Germany 2010
United Kingdom 2010
France 2010
Japan 2010
Netherlands 2010
Spain 2010
Canada 2010
Norway 2010
Other DAC donors $ millions 2010
5,701.3 301.6 194.7 174.0 149.4 238.8
2,893.4 30.1 8.6 54.8 3.9 91.6
285.0 75.0 51.8 24.4 8.0 33.4
469.8 35.4 10.2 7.1 21.9 16.7
234.8 0.9 2.2 16.7 0.5 0.5
58.6 4.3 69.8 4.1 13.5 4.5
745.7 2.4 15.5 37.6 73.8 77.5
59.2 4.0 0.2 –2.7 0.5 1.2
60.8 4.8 9.5 5.5 23.0 0.3
267.1 0.0 –0.5 0.7 1.0 0.0
120.2 2.5 1.0 13.2 0.1 3.6
506.8 142.2 26.5 12.7 3.2 9.7
81.2 .. 1,071.6 96.0
35.9 .. 124.7 27.2
20.8 .. 188.7 15.2
15.9 .. 65.1 18.0
0.9 .. 228.3 0.4
5.1 .. –3.0 4.1
–11.0 .. 24.2 1.4
0.0 .. 78.0 0.0
0.1 .. 5.5 0.3
0.0 .. 86.1 0.0
3.4 .. 16.9 2.2
10.2 .. 257.2 27.2
461.8 521.4 348.2 145.4 632.3 .. 619.9 413.8 544.8 340.7
98.9 86.1 28.3 77.0 24.4 .. 62.1 43.5 84.7 18.0
122.8 64.7 105.1 39.3 21.3 .. 164.1 131.2 27.3 74.2
34.7 42.5 30.0 2.4 247.5 .. 52.5 29.5 41.3 90.5
0.0 0.1 9.7 1.1 40.7 .. 0.1 20.1 26.0 1.0
48.8 12.2 3.5 6.5 46.6 .. 63.8 15.2 26.6 82.1
29.1 54.2 2.2 10.7 –62.7 .. 41.6 39.1 147.5 42.0
31.3 46.9 14.3 0.0 0.4 .. 51.4 19.1 1.3 0.2
1.1 69.0 20.2 0.0 26.4 .. 12.3 1.2 23.1 9.0
6.4 19.0 0.4 1.5 7.1 .. 30.8 4.7 8.2 7.2
0.1 5.9 18.2 0.6 245.4 .. 0.8 19.5 4.7 0.4
88.7 120.8 116.4 6.3 35.2 .. 140.4 90.8 154.3 16.0
197.6 386.9 173.4 745.4 .. 840.7 2,750.9 1,247.5 94.2 504.5 142.2 112.4 .. ..
20.5 134.6 13.3 86.5 .. 424.0 277.9 21.4 0.7 76.3 0.2 16.4 .. ..
84.8 101.9 16.2 42.6 .. 53.7 364.3 32.2 4.2 66.9 105.4 24.9 .. ..
3.1 20.1 71.8 321.5 .. 45.3 77.1 9.4 21.9 92.6 22.5 2.5 .. ..
3.0 2.9 0.7 86.7 .. 2.6 250.8 78.8 0.8 26.0 1.1 0.4 .. ..
24.5 40.7 10.1 316.7 .. 160.3 13.5 909.4 4.8 138.5 3.8 2.7 .. ..
8.1 13.8 15.9 –192.7 .. –26.2 80.0 6.0 63.7 81.3 1.9 5.2 .. ..
2.0 4.9 0.3 4.0 .. 26.3 420.5 0.0 3.4 5.5 0.2 0.1 .. ..
3.3 7.8 11.3 1.4 .. 56.2 306.2 0.5 5.2 –7.6 0.4 42.8 .. ..
1.4 11.7 2.7 9.0 .. 22.6 26.5 21.9 0.7 6.9 0.0 5.7 .. ..
0.0 1.7 13.1 22.6 .. 14.2 28.3 0.6 0.5 1.7 3.5 1.1 .. ..
46.9 47.1 18.2 47.1 .. 61.8 905.9 167.4 –11.8 16.5 3.3 10.6 .. ..
172.5 160.8 502.8 291.1 70.8 .. 2,164.3
35.5 33.0 52.7 151.3 0.9 .. 875.3
80.7 24.7 136.9 52.5 37.1 .. 237.6
0.9 27.8 104.5 17.1 1.1 .. 96.5
0.1 0.0 9.0 –48.8 5.5 .. 407.0
1.0 0.5 140.1 3.3 0.7 .. 13.3
–1.9 –5.2 –17.7 8.8 9.9 .. 93.9
0.2 0.5 11.0 0.1 0.5 .. 53.2
49.9 55.3 7.2 85.5 0.2 .. 39.5
1.3 1.4 8.9 2.0 0.0 .. 140.4
0.3 3.1 0.7 1.1 9.6 .. 32.6
4.6 19.7 49.7 18.2 5.4 .. 175.1
96.9 56.0 504.7
1.5 6.5 202.2
13.1 22.7 154.7
–1.3 0.6 82.0
0.2 2.0 3.4
58.1 0.4 6.2
24.8 17.2 6.5
0.0 0.0 3.2
0.2 4.0 0.1
0.1 0.5 0.0
0.0 0.1 10.0
0.3 2.1 36.4
1,005.2
208.1
105.6
58.2
166.6
33.8
70.0
72.9
14.3
114.2
3.6
158.1
390.3 164.1 70.4 2,612.1
105.0 21.7 6.5 1,106.9
37.4 72.4 16.6 284.3
13.5 13.3 1.3 43.6
0.2 0.0 0.1 26.2
3.3 36.0 1.8 144.1
41.2 10.8 16.1 72.0
19.6 0.0 0.0 19.2
92.9 2.1 8.3 155.8
10.3 2.2 0.5 458.9
9.4 0.0 0.0 66.8
57.6 5.6 19.1 234.6
2012 World Development Indicators
GLOBAL LINKS
6.13
Distribution of net aid by Development Assistance Committee members Ten major DAC donors
$ millions
Honduras Hungary India Indonesia Iran, Islamic Rep. Iraq Ireland Israel Italy Jamaica Japan Jordan Kazakhstan Kenya Korea, Dem. Rep. Korea, Rep. Kosovo Kuwait Kyrgyz Republic Lao PDR Latvia Lebanon Lesotho Liberia Libya Lithuania Macedonia, FYR Madagascar Malawi Malaysia Mali Mauritania Mauritius Mexico Moldova Mongolia Morocco Mozambique Myanmar Namibia Nepal Netherlands New Zealand Nicaragua Niger Nigeria Norway Oman Pakistan Panama Papua New Guinea Paraguay Peru Philippines Poland Portugal Puerto Rico Qatar
Total $ millions 2010
United States 2010
339.0 .. 2,313.5 1,093.5 76.1 2,059.3
102.9 .. 57.4 180.3 1.6 1,622.9
..
EU institutions 2010
Germany 2010
United Kingdom 2010
58.5 .. 94.3 105.5 3.4 54.1
14.1 .. 396.9 –12.6 45.8 36.9
..
..
104.5
–3.6
544.4 112.7 1,260.5 42.6
France 2010
Japan 2010
Netherlands 2010
25.3 .. 650.3 26.9 0.0 31.0
1.4 .. 2.8 262.5 14.2 9.6
16.0 .. 981.1 61.1 –7.1 144.4
0.3 .. 2.5 34.1 3.4 2.6
..
..
..
..
106.4
–6.2
3.9
–0.4
371.6 68.1 565.9 5.4
129.9 17.4 101.6 14.8
39.4 13.6 79.8 2.5
2.6 0.3 105.2 0.4
558.2 .. 182.9 301.9 .. 316.9 168.4 793.4 18.4 .. 150.1 254.5 725.5 –13.7 782.4 130.9 126.0 428.6 228.4 232.1 822.3 1,549.7 304.1 222.1 521.9
101.0 .. 56.0 12.8 .. 84.1 57.5 131.4 6.6 .. 20.4 77.0 126.3 18.6 197.9 11.4 0.5 205.6 19.4 47.2 47.7 277.9 31.3 117.2 51.9
279.3 .. 24.4 16.0 .. 53.4 74.3 90.9 1.1 .. 55.1 40.1 208.3 1.2 98.5 25.3 67.9 7.5 138.0 13.4 223.4 192.3 55.9 10.8 46.2
30.6 .. 25.3 24.8 .. 28.1 5.0 50.1 3.5 .. 14.1 13.1 41.9 11.2 60.3 7.7 –0.2 35.5 8.7 29.1 38.9 76.9 18.3 24.0 42.0
416.2 531.6 909.5
54.5 102.6 445.9
21.9 150.8 60.3
7.6 2,586.5 125.3 490.8 97.4 –288.2 505.0 ..
2.9 1,196.8 11.9 2.3 28.0 130.6 114.8 ..
.. ..
.. ..
Spain 2010
Other DAC donors $ millions 2010
Canada 2010
Norway 2010
69.1 .. 11.4 –10.6 0.2 0.4
17.6 .. 7.9 10.9 0.1 6.3
1.5 .. 24.0 41.9 7.3 7.7
32.4 .. 84.8 393.6 7.3 143.5
..
..
..
..
..
–2.1
–2.9
0.5
4.1
0.1
4.7
6.2 4.1 123.4 0.7
–50.9 –1.8 36.7 0.0
1.0 0.4 17.6 0.1
9.2 0.1 8.3 0.0
7.1 0.1 25.9 0.0
0.4 4.3 13.4 2.5
27.9 6.2 182.6 16.2
9.5 .. 7.3 0.1 .. 4.0 4.8 25.6 1.6 .. 1.2 –0.3 148.0 –0.8 0.1 0.0 5.5 9.4 14.5 0.8 3.2 104.4 44.2 0.6 105.2
1.7 .. 1.5 15.0 .. 59.7 –1.4 232.0 3.8 .. 2.9 84.0 –1.0 1.0 77.6 32.2 54.0 205.8 6.4 5.0 254.4 38.1 2.0 0.4 –3.2
1.1 .. 23.2 121.5 .. 3.2 8.8 134.3 0.1 .. 23.1 9.6 69.5 –53.2 38.3 14.6 –2.9 –46.7 0.9 53.9 121.2 62.9 46.8 40.6 81.2
3.2 .. 0.6 0.1 .. 1.0 0.0 40.0 0.1 .. 2.3 0.0 0.0 0.2 56.1 0.0 0.0 –0.3 5.6 8.8 1.1 81.8 2.7 1.6 0.0
0.1 .. 0.9 0.0 .. 23.4 0.0 1.8 0.2 .. 0.4 0.3 0.6 0.1 28.4 34.7 0.0 5.3 0.1 –0.3 90.6 43.9 0.0 8.5 0.2
0.0 .. 2.0 0.6 .. 5.3 0.4 1.1 0.0 .. 0.0 1.9 16.5 0.0 96.0 0.5 0.0 1.4 0.2 8.3 3.1 82.0 0.6 0.5 11.8
24.2 .. 6.3 2.6 .. 9.3 1.1 22.8 0.0 .. 7.5 13.0 64.7 0.8 16.0 0.6 0.3 0.1 3.0 1.7 0.0 73.7 21.7 –3.0 47.2
107.5 .. 35.6 108.5 .. 45.7 17.9 63.3 1.5 .. 23.2 15.7 50.5 7.2 113.2 4.0 0.8 5.0 31.7 64.2 38.6 515.7 80.5 20.9 139.4
27.7 22.6 39.0
7.3 3.2 264.6
1.0 50.0 8.9
34.4 25.2 23.9
26.3 2.8 9.1
106.2 25.9 0.5
12.5 53.7 12.6
18.5 3.5 14.1
106.1 91.4 30.7
0.0 172.3 1.5 50.1 29.9 25.8 51.9 ..
1.0 142.1 1.4 0.8 5.4 51.9 26.3 ..
0.9 298.5 0.0 1.0 0.0 1.3 0.6 ..
0.8 14.4 0.1 0.2 0.1 10.1 189.4 ..
1.5 207.9 101.8 22.2 –3.7 –711.6 –87.7 ..
0.4 52.1 0.0 0.0 0.0 0.3 0.4 ..
0.0 22.7 5.9 0.2 21.8 118.1 27.0 ..
0.0 101.9 0.5 0.1 0.5 22.2 16.7 ..
0.0 83.1 2.4 1.8 1.0 3.8 17.6 ..
0.0 294.8 –0.3 412.2 14.4 59.5 148.1 ..
.. ..
.. ..
.. ..
.. ..
.. ..
.. ..
.. ..
.. ..
.. ..
.. ..
2012 World Development Indicators
379
6.13
Distribution of net aid by Development Assistance Committee members Ten major DAC donors
$ millions Total $ millions 2010
Romania Russian Federation Rwanda Saudi Arabia Senegal Serbia Sierra Leone Singapore Slovak Republic Slovenia Somalia South Africa South Sudan Spain Sri Lanka Sudan Swaziland Sweden Switzerland Syrian Arab Republic Tajikistan Tanzania Thailand Timor-Leste Togo Trinidad and Tobago Tunisia Turkey Turkmenistan Uganda Ukraine United Arab Emirates United Kingdom United States Uruguay Uzbekistan Venezuela, RB Vietnam West Bank and Gaza Yemen, Rep. Zambia Zimbabwe World Low income Middle income Lower middle income Upper middle income Low & middle income East Asia & Pacific Europe & Central Asia Latin America & Carib. Middle East & N. Africa South Asia Sub-Saharan Africa High income Euro area
United States 2010
EU institutions 2010
Germany 2010
United Kingdom 2010
France 2010
Japan 2010
Netherlands 2010
Spain 2010
Canada 2010
Norway 2010
.. .. 652.1 .. 618.4 603.2 279.9 .. .. .. 435.5 974.7 ..
.. .. 140.6 .. 101.4 57.9 29.8 .. .. .. 59.4 529.5 ..
.. .. 104.4 .. 84.1 290.1 80.3 .. .. .. 127.1 153.1 ..
.. .. 48.3 .. 23.1 126.3 13.3 .. .. .. 12.5 39.5 ..
.. .. 106.2 .. 0.9 5.4 84.8 .. .. .. 62.3 39.3 ..
.. .. 4.1 .. 157.2 14.0 0.3 .. .. .. 3.5 47.6 ..
.. .. 22.8 .. 55.2 5.2 12.2 .. .. .. 29.1 7.1 ..
.. .. 39.4 .. 30.0 3.8 1.1 .. .. .. 9.3 36.1 ..
.. .. 1.8 .. 45.6 0.4 1.9 .. .. .. 5.9 1.1 ..
.. .. 58.7 .. 56.7 0.8 33.0 .. .. .. 4.4 16.5 ..
.. .. 4.4 .. 0.3 20.2 3.0 .. .. .. 31.6 24.7 ..
.. .. 121.5 .. 64.0 79.2 20.1 .. .. .. 90.5 80.1 ..
433.8 1,793.4 52.6
26.1 726.4 23.6
48.1 284.2 21.5
–6.4 39.2 –0.6
–8.5 119.1 0.0
45.1 10.1 0.2
155.4 119.1 4.4
3.6 57.6 0.0
–0.7 23.0 0.0
11.5 108.3 0.8
29.0 116.7 1.6
130.6 189.9 1.3
95.7 201.2 1,848.1 –76.1 272.7 301.7 4.1 447.6 1,029.7 17.0 1,162.0 545.6 ..
6.9 45.9 457.4 47.2 27.3 4.0 1.3 –3.3 6.4 8.4 378.1 140.2 ..
51.6 36.6 192.6 24.1 14.6 48.9 0.6 92.3 295.2 5.7 128.9 153.0 ..
45.9 34.7 134.5 –23.2 9.4 8.8 0.4 23.9 –10.3 1.8 40.9 89.1 ..
2.0 12.5 240.9 7.2 0.0 –0.1 0.2 2.5 3.8 0.1 179.3 0.8 ..
23.1 0.2 21.3 –13.6 0.1 168.0 0.5 126.8 88.4 0.1 1.8 21.5 ..
–54.7 43.4 104.6 –143.5 27.7 7.5 0.1 35.9 543.5 –0.9 71.2 53.2 ..
0.1 0.6 59.2 0.3 0.0 17.6 0.0 –0.7 0.3 0.0 36.7 0.1 ..
6.1 0.0 3.2 0.6 6.0 1.6 0.1 158.0 56.0 0.0 4.3 0.3 ..
0.1 0.7 111.6 –0.6 1.1 23.1 0.3 –2.9 –2.6 0.0 5.7 20.2 ..
1.3 3.2 124.0 0.3 7.8 0.1 0.0 0.0 0.1 0.6 71.5 3.8 ..
13.4 23.3 398.8 25.3 178.7 22.1 0.6 15.1 49.0 1.4 243.6 63.4 ..
0.8 25.0 7.0 96.4 104.6 82.1 33.3 33.1 8,035.5 s 1,708.5 3,372.7 1,899.4 1,312.3 8,011.3 565.1 664.7 912.0 622.9 1,127.7 1,736.0 24.3 ..
0.1 1.2 1.1 82.2 97.6 63.9 79.3 108.0 8,016.8 s 2,544.1 2,351.5 2,088.0 254.1 8,014.4 281.2 72.5 179.2 229.7 1,510.9 3,003.3 2.4 ..
1.2 2.3 6.9 242.4 69.3 7.0 0.8 3.0 7,786.7 s 1,313.1 4,952.3 2,899.8 1,954.3 7,778.5 1,221.0 173.7 664.2 859.7 115.4 3,526.9 8.2 ..
11.4 7.0 3.1 807.8 78.6 26.7 46.1 18.9 7,331.1 s 2,186.9 3,460.9 3,710.9 –252.0 7,326.1 973.2 780.3 –311.4 335.8 2,277.8 1,694.5 5.0 ..
40.0 1.2 7.1 90.8 13.4 6.5 43.4 8.6 5.9 1,866.5 93.1 41.9 2,069.2 720.8 441.1 322.8 45.4 40.7 685.5 225.1 92.5 630.8 175.2 109.5 103,174.7 s 26,586.4 s 12,428.0 s 29,779.4 8,672.4 4,003.4 41,152.0 10,211.0 6,373.5 29,587.9 7,602.4 3,681.5 10,377.3 2,549.0 2,088.8 102,918.5 26,581.7 12,301.2 7,875.1 1,032.2 530.7 5,556.1 979.9 1,754.1 9,201.1 2,720.6 1,268.2 8,009.6 3,006.8 1,441.4 12,867.1 4,383.0 850.7 32,263.2 7,637.2 4,788.9 256.2 4.7 126.9 .. .. ..
0.1 0.0 0.1 21.2 33.7 26.5 36.1 11.1 4,644.2 s 1,117.1 724.8 519.5 178.7 4,643.7 73.1 41.4 223.2 80.1 200.0 1,313.7 0.5 ..
8.4 0.2 8.2 16.0 97.6 1.1 0.2 3.6 3,998.9 s 763.8 1,637.4 868.2 715.0 3,978.6 88.4 97.7 1,369.9 462.6 108.8 912.0 20.3 ..
0.5 0.1 9.4 0.0 0.3 34.9 0.4 0.1 2.0 25.0 20.2 420.3 65.1 109.5 251.4 2.6 0.4 26.4 8.7 54.1 109.3 9.3 24.5 134.6 3,919.6 s 3,560.9 s 16,866.6 s 1,583.6 848.0 5,038.5 844.5 1,103.9 6,119.4 696.2 671.5 4,950.4 107.2 410.5 1,059.4 3,918.5 3,557.5 16,807.2 116.2 151.5 2,842.4 37.0 120.8 834.1 807.1 434.2 933.9 123.5 150.0 697.3 498.1 324.5 1,470.1 1,512.0 945.2 5,193.7 1.0 3.5 59.4 .. ..
Note: Regional aggregates include data for economies not specified elsewhere. World and income group totals include aid not allocated by country or region.
380
Other DAC donors $ millions 2010
2012 World Development Indicators
About the data
6.13
Definitions
The table shows net bilateral aid to low- and middle-
controls the use of the funds) and are included in the
• Net aid refers to net bilateral official development
income economies from members of the Development
data reported in the table.
assistance that meets the DAC definition of official
Assistance Committee (DAC) of the Organisation for
The data include aid to some countries and terri-
development assistance and is made to countries
Economic Co-operation and Development (OECD).
tories not shown in the table and aid to unspecified
and territories on the DAC list of aid recipients.
DAC has 24 members—23 individual economies and
economies recorded only at the regional or global
• Other DAC donors are Australia, Austria, Belgium,
1 multilateral institution (European Union institutions).
level. Aid to countries and territories not shown in
Denmark, Finland, Greece, Ireland, Italy, the Republic
The table is based on donor country reports of
the table has been assigned to regional totals based
of Korea, Luxembourg, New Zealand, Portugal, Swe-
bilateral programs, which may differ from reports
on the World Bank’s regional classification system.
den, and Switzerland.
by recipient countries. Recipients may lack
Aid to unspecified economies is included in regional
access to information on such aid expenditures as
totals and, when possible, income group totals. Aid
development- oriented research, stipends and tuition
not allocated by country or region—including admin-
costs for aid-financed students in donor countries,
istrative costs, research on development, and aid to
and payment of experts hired by donor countries.
nongovernmental organizations—is included in the
Moreover, a full accounting would include donor
world total. Thus regional and income group totals
country contributions to multilateral institutions,
do not sum to the world total.
the flow of resources from multilateral institutions
Some of the aid recipients shown in the table are
to recipient countries, and flows from countries that
also aid donors. Development cooperation activities
are not members of DAC.
by non-DAC members have increased in recent years
Data in the table exclude DAC members’ multi-
and in some cases surpass those of individual DAC
lateral aid (contributions to the regular budgets of the
members. Some non-DAC donors report their devel-
multilateral institutions). However, projects executed
opment cooperation activities to DAC on a voluntary
by multilateral institutions or nongovernmental orga-
basis, but many do not yet report their aid flows to
nizations on behalf of DAC members are classified
DAC. See table 6.13a for a summary of ODA from
as bilateral aid (since the donor country effectively
non-DAC countries.
6.13a
Official development assistance from non-DAC donors, 2006–10 Net disbursements ($ millions) 2006
2007
2008
2009
2010
OECD members (non-DAC) Czech Republic
161
179
249
215
228
Hungary
149
103
107
117
114
Iceland
42
48
48
34
29
Poland
297
363
372
375
378
Slovak Republic Turkey
55
67
92
75
74
714
602
780
707
967
Arab countries Kuwait Saudi Arabia United Arab Emirates
158
110
283
221
211
2,025
1,551
4,979
3,134
3,480
783
2,426
1,266
834
412
90
111
138
124
145
513
514
435
411
381
Other donors Israela Taiwan, China Thailand Othersb Total
Data sources
74
67
178
40
10
Data on financial fl ows are compiled by OECD
121
188
343
385
808
DAC and published in its annual statistical report,
5,181
6,329
9,271
6,672
7,235
Geographical Distribution of Financial Flows to
Note: The above table does not reflect aid provided by several major emerging non–Organisation for Economic Co-operation and Development (OECD) donors, as information on their aid has not been disclosed. a. Data are supplied by and under the responsibility of the relevant Israeli authorities. The use of such data by the OECD is without prejudice to the status of the Golan Heights, East Jerusalem, and Israeli settlements in the West Bank under the terms of international law. b. Includes Cyprus, Estonia, Latvia, Liechtenstein, Lithuania, Malta, Romania, the Russian Federation, and Slovenia.
Aid Recipients, and its annual Development Co-operation Report. Data are available electronically on the DAC’s International Development Statistics CD-ROM and at www.oecd.org/dac/stats/ idsonline.
2012 World Development Indicators
381
GLOBAL LINKS
Distribution of net aid by Development Assistance Committee members
6.14
Movement of people across borders Net migration
thousands 2005–10
Afghanistan Albania Algeria Angola Argentina Armenia Australia Austria Azerbaijan Bahrain Bangladesh Belarus Belgium Benin Bolivia Bosnia and Herzegovina Botswana Brazil Bulgaria Burkina Faso Burundi Cambodia Cameroon Canada Central African Republic Chad Chile China Hong Kong SAR, China Colombia Congo, Dem. Rep. Congo, Rep. Costa Rica Côte d’Ivoire Croatia Cuba Cyprus Czech Republic Denmark Dominican Republic Ecuador Egypt, Arab Rep. El Salvador Eritrea Estonia Ethiopia Finland France Gabon Gambia, The Georgia Germany Ghana Greece Guatemala Guinea Guinea-Bissau Haiti
382
–381 –48 –140 82 –200 –75 1,125 160 53 448 –2,908 –50 200 50 –165 –10 19 –500 –50 –125 370 –255 –19 1,098 5 –75 30 –1,884b 176 –120 –24 50 76 –360 10 –190 44 240 90 –140 –120 –347 –292 55 0 –300 73 500 5 –14 –150 550 –51 154 –200 –300 –10 –240
2012 World Development Indicators
International migrant stock
thousands 2010
91 89 242 65 1,449 324 4,711 1,310 264 315 1,085 1,090 975 232 146 28 115 688 107 1,043 61 336 197 7,202 80 388 320 686b 2,742 110 445 143 489 2,407 700 15 154 453 484 434 394 245 40 16 182 548 226 6,685 284 290 167 10,758 1,852 1,133 59 395 19 35
Emigration of tertiary educated population to OECD countries % of tertiary educated population ages 25 and older 2000
22.6 17.5 9.5 3.7 2.8 8.9 2.7 13.5 1.8 5.1 4.4 3.2 5.5 8.7 5.8 20.3 5.1 2.0 9.6 2.6 9.3 21.5 17.3 4.7 7.3 9.1 6.0 3.8 29.6 10.4 14.9 28.2 7.1 6.2 24.6 28.8 34.2 8.5 7.8 22.4 9.5 4.7 31.7 35.2 9.9 9.8 7.2 3.5 14.6 67.8 2.8 5.8 44.7 12.2 23.9 4.7 27.7 83.4
Refugees
Workers’ remittances and compensation of employees
thousands By country of origin By country of asylum 2010 2010
3,054.7 14.8 6.7 134.9 0.6 17.5 0.0 0.0 16.8 0.1 10.0 5.7 0.1 0.4 0.6 63.0 0.1 1.0 2.6 1.1 84.1 16.3 15.0 0.1 164.9 53.7 1.2 199.7c 0.0 395.6 476.7 20.7 0.4 41.8 65.9 7.5 0.0 0.8 0.0 0.2 0.9 6.9 5.0 222.5 0.2 68.8 0.0 0.1 0.2 2.2 10.6 0.2 20.2 0.1 5.7 12.0 1.1 25.9
6.4 0.1 94.1 15.2 3.3 3.3 21.8 42.6 1.9 0.2 229.3 0.6 17.9 7.1 0.7 7.0 3.0 4.4 5.5 0.5 29.4 0.1 104.3 165.5 21.6 347.9 1.6 301.0 0.2 0.2 166.3 133.1 19.5 26.2 0.9 0.4 3.4 2.4 17.9 0.6 121.2 95.1 0.0 4.8 0.0 154.3 8.7 200.7 9.0 8.4 0.6 594.3 13.8 1.4 0.1 14.1 7.7 0.0
$ millions Received 2010
.. 1,156 2,044 a 82 641 996 4,840a 3,220 1,432 .. 10,852 376 10,178 248a 1,088 1,905 100 4,000 1,387 95a 28 369 195 .. .. .. 3 53,038a 347 4,058 .. 15a 552 179 1,315 .. 146 1,122 633 3,369 2,569 7,725 3,449 .. 322 225 826 15,629 .. 116 806 11,338 136 1,499 4,229 60 48 1,499
Paid 2010
.. 24 46 716 993 157 3,776a 3,453 961 1,642 9 105 4,040 88 104 54 102 1,198 25 100 1 215 54 .. .. .. 5 1,754 433 112 .. 102 271 754 164 .. 404 1,812 3,184 29 81 255 23 .. 94 27 437 5,264 .. 58 50 15,908 .. 1,932 21 43 17a 135
Net migration
Honduras Hungary India Indonesia Iran, Islamic Rep. Iraq Ireland Israel Italy Jamaica Japan Jordan Kazakhstan Kenya Korea, Dem. Rep. Korea, Rep. Kosovo Kuwait Kyrgyz Republic Lao PDR Latvia Lebanon Lesotho Liberia Libya Lithuania Macedonia, FYR Madagascar Malawi Malaysia Mali Mauritania Mauritius Mexico Moldova Mongolia Morocco Mozambique Myanmar Namibia Nepal Netherlands New Zealand Nicaragua Niger Nigeria Norway Oman Pakistan Panama Papua New Guinea Paraguay Peru Philippines Poland Portugal Puerto Rico Qatar
International migrant stock
thousands 2005–10
thousands 2010
–100 75 –3,000 –1,293 –186 –150 100 274 1,999 –100 270 203 7 –189 0 –30 .. 278 –132 –75 –10 –13 –20 300 –20 –35 2 –5 –20 84 –101 10 0 –1,805 –172 –15 –675 –20 –500 –1 –100 50 65 –200 –28 –300 171 153 –2,000 11 0 –40 –725 –1,233 56 150 –145 857
24 368 5,436 123 2,129 83 899 2,940 4,463 30 2,176 2,973 3,079 818 37 535 .. 2,098 223 19 335 758 6 96 682 129 130 38 276 2,358 163 99 43 726 408 10 49 450 89 139 946 1,753 962 40 202 1,128 485 826 4,234 121 25 161 38 435 827 919 324 1,305
Emigration of tertiary educated population to OECD countries % of tertiary educated population ages 25 and older 2000
24.8 12.8 4.3 2.9 14.3 10.9 33.7 7.8 9.7 84.7 1.2 7.4 1.2 38.5 .. 7.5 .. 7.1 0.9 37.2 8.5 43.9 4.1 44.3 4.3 8.4 29.4 7.7 20.9 10.5 14.8 8.6 56.0 15.5 4.1 7.4 18.6 22.6 3.9 3.4 4.0 9.6 21.8 30.2 5.5 10.5 6.2 0.4 12.7 16.7 27.8 3.8 5.8 13.6 14.3 19.0 .. 2.1
Refugees
Workers’ remittances and compensation of employees
thousands By country of origin By country of asylum 2010 2010
1.3 1.4 17.8 16.9 68.8 1,683.6 0.0 1.3 0.1 1.1 0.2 2.3 3.6 8.6 0.9 0.6 .. 1.0 2.7 8.4 0.7 15.9 0.0 70.1 2.3 0.5 7.9 0.3 0.2 0.6 3.7 37.7 0.0 6.8 6.2 1.7 2.3 0.1 415.7 1.0 5.9 0.1 0.0 1.4 0.8 15.6 0.0 0.1 40.0 0.1 0.1 0.1 5.8 1.0 1.8 0.0 .. 0.1
6.14
0.0 5.4 184.8 0.8 1,073.4 34.7 9.1 25.5 56.4 0.0 2.6 2,455.7d 4.4 402.9 .. 0.4 .. 0.2 2.5 .. 0.1 435.1d .. 24.7 7.9 0.8 1.4 .. 5.7 81.5 13.6 26.7 .. 1.4 0.1 0.0 0.8 4.1 .. 7.3 89.8 75.0 2.3 0.1 0.3 8.7 40.3 0.1 1,900.6 17.1 9.7 0.1 1.1 0.2 15.6 0.4 .. 0.1
$ millions Received 2010
2,649 2,265 54,035 6,916 1,181a 71 601 1,411 6,803 2,011 1,802 3,641 291 1,777a .. 8,708 932 .. 1,275a 41 614 7,558 746 27a 17a 1,575 388 .. .. 1,301 436a .. 226a 22,048 1,370 277 6,423 132 133a 15 3,468 3,834 843 823 88 10,045a 680 39 9,690 231 15 673 2,534 21,423 7,614 3,540 .. ..
2012 World Development Indicators
Paid 2010
12 1,265 3,888 2,840 .. 32a 1,751 3,739 12,201 314 4,474 495 3,021 61 .. 11,385 146 11,770 297 8 43 3,737 19 1 1,361 538 23 .. .. 6,528 167 .. 13 .. 117 169 62 80 .. 16 32 12,923 1,167 .. 22a 48 4,045 5,704 19 248 323 .. 122 62 1,575 1,406 .. ..
383
GLOBAL LINKS
Movement of people across borders
6.14
Movement of people across borders Net migration
Romania Russian Federation Rwanda Saudi Arabia Senegal Serbia Sierra Leone Singapore Slovak Republic Slovenia Somalia South Africa South Sudan Spain Sri Lanka Sudan Swaziland Sweden Switzerland Syrian Arab Republic Tajikistan Tanzania Thailand Timor-Leste Togo Trinidad and Tobago Tunisia Turkey Turkmenistan Uganda Ukraine United Arab Emirates United Kingdom United States Uruguay Uzbekistan Venezuela, RB Vietnam West Bank and Gaza Yemen, Rep. Zambia Zimbabwe World Low income Middle income Lower middle income Upper middle income Low & middle income East Asia & Pacific Europe & Central Asia Latin America & Carib. Middle East & N. Africa South Asia Sub-Saharan Africa High income Euro area
International migrant stock
thousands 2005–10
thousands 2010
–100 1,136 15 1,056 –133 0 60 722 37 22 –300 700 .. 2,250 –250 135 –6 266 183 –56 –296 –300 492 –50 –5 –20 –20 –50 –54 –135 –40 3,077 1,020 4,955 –50 –518 40 –431 –90 –135 –85 –900 ..e s –6,818 –16,342 –12,613 –3,729 –23,160 –5,221 –595 –5,088 –1,628 –8,622 –2,006 22,906 6,336
133 12,270 465 7,289 210 525 107 1,967 131 164 23 1,863 .. 6,378 340 753 40 1,306 1,763 2,206 284 659 1,157 14 185 34 34 1,411 208 647 5,258 3,293 6,452 42,813 80 1,176 1,007 69 1,924 518 233 372 213,397f s 11,158 70,369 31,148 39,220 81,527 5,434 27,681 6,569 11,957 12,175 17,710 131,871 36,317
Emigration of tertiary educated population to OECD countries % of tertiary educated population ages 25 and older 2000
11.3 1.4 31.7 0.9 17.2 .. 49.2 14.5 14.3 11.0 34.5 7.4 .. 4.2 28.2 6.8 5.4 4.5 9.6 6.2 0.6 12.1 2.2 16.5 16.5 78.9 12.6 5.8 0.4 36.0 4.3 0.7 17.1 0.5 9.0 0.8 3.8 27.0 12.0 6.0 16.4 13.1 5.4 w 11.8 6.8 8.0 6.1 7.1 7.0 3.5 10.6 10.5 5.3 12.6 4.1 7.1
Refugees
thousands By country of origin By country of asylum 2010 2010
3.9 111.9 114.8 0.7 16.3 183.3 11.3 0.1 0.2 0.0 770.2 0.4 .. 0.0 141.1 387.3 0.0 0.0 0.0 18.5 0.6 1.1 0.4 0.0 18.3 0.3 2.2 146.8 0.7 6.4 25.1 0.4 0.2 3.0 0.2 8.8 6.7 338.7 93.3 2.1 0.2 24.1 15,369.9d,g s 5,650.8 4,518.3 3,223.5 1,294.8 10,169.1 1,002.3 637.2 470.4 1,905.3 3,344.6 2,809.5 79.4 1.0
1.0 4.9 55.4 0.6 20.7 73.6 8.4 0.0 0.5 0.3 1.9 57.9 .. 3.8 0.2 178.3 0.8 82.6 48.8 1,483.2d 3.1 109.3 96.7 0.0 14.1 0.0 0.1 10.0 0.1 135.8 3.0 0.5 238.2 264.6 0.2 0.3 201.5 1.9 1,910.7d 190.1 47.9 4.4 15,369.9d s 1,874.0 11,535.2 6,412.2 5,123.0 13,409.2 492.0 140.8 373.8 7,795.9 2,411.2 2,195.6 1,960.7 1,023.9
Workers’ remittances and compensation of employees
$ millions Received 2010
Paid 2010
3,883 5,264 92 236 1,346 3,351a 58 .. 1,591 309 .. 1,119 .. 10,507 4,155 1,974a 109 688 2,619 1,646a 2,254 25 1,764 .. 333a 120a 1,970 874 .. 915 5,607 .. 7,532 5,277 103 .. 143 8,260a 1,151a 1,240 44 .. 449,197 s 24,553 300,725 161,464 139,262 325,278 93,957 36,037 57,275 34,700 82,209 21,101 123,919 71,976
355 18,796 71 27,069 144 a 70 6 .. 70 158 .. 1,372 .. 12,227 545 1a 11 695 21,668 214 a 856 127 .. .. 72a .. 13 175 .. 602 24 .. 3,528 51,597 7 .. 805 .. 9a 337 68 .. 303,799 s 3,088 55,580 11,579 44,001 58,668 11,945 25,865 4,603 6,566 4,687 5,002 245,131 82,745
a. World Bank estimates. b. Includes Taiwan, China. c. Includes Tibetans, who are listed separately by the UN Refugee Agency (UNHCR). d. Includes Palestinian refugees under the mandate of the United Nations Relief and Works Agency for Palestine Refugees in the Near East (UNRWA), who are not included in data from the UNHCR. e. World totals computed by the United Nations sum to zero, but because the aggregates refer to World Bank definitions, regional and income group totals do not. f. World totals are computed by the World Bank and include only economies covered by World Development Indicators, so data may differ from what is published by the United Nations Population Division. g. Includes refugees without specified country of origin and Palestinian refugees under the mandate of the UNRWA, so regional and income group totals do not sum to the world total.
384
2012 World Development Indicators
About the data
6.14
Definitions
Movement of people, most often through migration,
an international border to find sanctuary and have
• Net migration is the net total of migrants (immi-
is a significant part of global integration. Migrants
been granted refugee or refugee-like status or tem-
grants less emigrants, including both citizens and
contribute to the economies of both their host coun-
porary protection. Asylum seekers— people who
noncitizens) during the period. Data are five-year esti-
try and their country of origin. Yet reliable statistics
have applied for asylum or refugee status and who
mates. • International migrant stock is the number
on migration are difficult to collect and are often
have not yet received a decision or who are regis-
of people, including refugees, born in a country other
incomplete, making international comparisons a
tered as asylum seekers—and internally displaced
than that in which they live. • Emigration of tertiary
challenge.
people—who are often confused with refugees—are
educated population to OECD countries is the stock
The United Nations Population Division provides
not included. Unlike refugees, internally displaced
of emigrants ages 25 and older with at least one year
data on net migration and migrant stock. Because
people remain under the protection of their own gov-
of tertiary education who reside in an OECD country
data on migrant stock is difficult for countries to col-
ernment, even if their reason for fleeing was similar
other than that in which they were born. • Refugees
lect, the United Nations Population Division takes
to that of refugees. Palestinian refugees are people
are people recognized as refugees under the 1951
into account the past migration history of a country
(and their descendants) whose residence was Pal-
Convention Relating to the Status of Refugees or
or area, the migration policy of a country, and the
estine between June 1946 and May 1948 and who
its 1967 Protocol, the 1969 Organization of African
influx of refugees in recent periods when deriving
lost their homes and means of livelihood as a result
Unity Convention Governing the Specific Aspects of
estimates of net migration. The data to calculate
of the 1948 Arab-Israeli conflict.
Refugee Problems in Africa; recognized as refugees
these estimates come from a variety of sources,
Registrations, together with other sources—
in accordance with the UNHCR statute; granted refu-
including border statistics, administrative records,
including estimates and surveys—are the main
gee-like humanitarian status; or provided temporary
surveys, and censuses. When there is insufficient
sources of refugee data. There are diffi culties in
protection. Asylum seekers and internally displaced
data, net migration is derived through the difference
collecting accurate statistics. Many refugees may
people are excluded. • Country of origin refers to
between the overall population growth rate and the
not be aware of the need to register or may choose
the nationality or country of citizenship of a claimant.
rate of natural increase (the difference between the
not to do so, and administrative records tend to
• Country of asylum is the country where an asylum
birth rate and the death rate) during the same period.
overestimate the number of refugees because it is
claim was filed and granted. • Workers’ remittances
Such calculations are usually made for intercensal
easier to register than to de-register. The UN Refu-
and compensation of employees, received and paid,
periods. The estimates are also derived from the
gee Agency (UNHCR) collects and maintains data on
are current transfers by migrant workers and wages
data on foreign-born population —people who have
refugees, except for Palestinian refugees residing in
and salaries earned by nonresident workers. Remit-
residence in one country but were born in another
areas under the mandate of the United Nations Relief
tances are classified as current private transfers
country. When data on the foreign-born population
and Works Agency for Palestine Refugees in the Near
from migrant workers resident in the host country
are not available, data on foreign population —that
East (UNRWA). Registration is voluntary, and esti-
for more than a year, irrespective of their immigra-
is, people who are citizens of a country other than the
mates by the UNRWA are not an accurate count of the
tion status, to recipients in their country of origin.
country in which they reside—are used as estimates.
Palestinian refugee population. The table shows esti-
Migrants’ transfers are defined as the net worth of
For countries with information on the interna-
mates of refugees collected by the UNHCR, comple-
migrants who are expected to remain in the host
tional migrant stock for at least two points in time,
mented by estimates of Palestinian refugees under
country for more than one year that is transferred to
interpolation or extrapolation was used to estimate
the UNRWA mandate. Thus, the aggregates differ
another country at the time of migration. Compen-
the international migrant stock on July 1 of the refer-
from those published by the UNHCR.
sation of employees is the income of migrants who
ence years. For countries with only one observation,
Workers’ remittances and compensation of
estimates for the reference years were derived using
employees are from the International Monetary
rates of change in the migrant stock in the years pre-
Fund’s (IMF) Balance of Payments Statistics Yearbook.
ceding or following the single observation available.
The IMF data are supplemented by World Bank staff
Data on net migration are from the United Nations
A model was used to estimate migrants for countries
estimates for missing data for countries where work-
Population Division’s World Population Prospects:
that had no data.
ers’ remittances are important. The data reported
The 2010 Revision. Data on international migra-
One negative effect of migration is “brain drain”—
here are the sum of three items defined in the fifth
tion stock are from the United Nations Population
emigration of highly educated people. The table
edition of the IMF’s Balance of Payments Manual:
Division’s Trends in Total Migrant Stock: The 2008
shows data on emigration of people with tertiary
workers’ remittances, compensation of employees,
Revision. Data on migration of tertiary educated
education, drawn from Docquier, Marfouk, and Low-
and migrants’ transfers.
population are from Docquier, Lowell, and Marfouk
have lived in the host country for less than a year. Data sources
ell (2007), which analyzes skilled migration using
(2009). Data on refugees are from the UNHCR’s
data from censuses and registers of Organisation
Statistical Yearbook 2010, complemented by sta-
for Economic Development and Co-operation (OECD)
tistics on Palestinian refugees under the mandate
countries and provides data disaggregated by gender
of the UNRWA as published on its website. Data
for 1990 and 2000.
on remittances are from the IMF’s Balance of Pay-
The table also shows data on refugees because they are an important part of migrant stock. The
ments Statistics Yearbook supplemented by World Bank staff estimates.
refugee data refer to people who have crossed
2012 World Development Indicators
385
GLOBAL LINKS
Movement of people across borders
6.15
Travel and tourism International tourists
Inbound tourism expenditure
thousands Inbound Outbound 2000 2010 2000 2010
Afghanistan Albania Algeria Angola Argentina Armenia Australia Austria Azerbaijan Bahrain Bangladesh Belarus Belgium Benin Bolivia Bosnia and Herzegovina Botswana Brazil Bulgaria Burkina Faso Burundi Cambodia Cameroon Canada Central African Republic Chad Chile China Hong Kong SAR, China Colombia Congo, Dem. Rep. Congo, Rep. Costa Rica Côte d’Ivoire Croatia Cuba Cyprus Czech Republic Denmark Dominican Republic Ecuador Egypt, Arab Rep. El Salvador Eritrea Estonia Ethiopia Finland France Gabon Gambia, The Georgia Germany Ghana Greece Guatemala Guinea Guinea-Bissau Haiti
386
.. 317a,b 866a,c 51 2,909 45 4,931a,b 17,982f .. 2,420 199 60 6,457f 96 319 171f 1,104 5,313 2,785 126g 29c .. 277g 19,627 11h 43g 1,742 31,229 8,814 557 103 19g 1,088 .. 5,831f 1,741h 2,686 .. 3,535f 2,978 c,h 627a,b 5,116 795 70a,c 1,220 136c 2,714 77,190 155h 79 387a 18,983f 399c 13,096 826a 33h .. 140h
.. 2,417a,b .. 425 5,325 .. 5,885a,b,e 22,004f 1,280 .. .. 119 7,186f 199 807 365f 2,145 5,161 6,047 274g .. 2,399 .. 16,097 .. .. 2,766 55,664 20,085 .. .. .. 2,100 .. 9,111f 2,507h 2,173 8,185 8,744f 4,125c,h 1,047a,b 14,051 1,150 84a,c 2,120 .. 3,670 77,148 .. 91 2,033a 26,875f .. 15,007 1,876a .. .. ..
2012 World Development Indicators
.. .. 1,006 .. 4,953 111 3,498 7,528 1,326 .. 1,128 1,289 7,932 .. 201 .. .. 3,228 2,337 .. 28 41 .. 19,182 .. 27 1,830 10,473 .. 1,235 .. .. 381 .. .. 139 503 .. 5,011 360 520 2,964 923 .. 1,800 .. 5,914 19,886 168 .. 315 74,400 .. .. 488 .. .. ..
.. 3,443 .. .. 5,307 .. 7,111 9,882 3,176 .. .. 415 10,170 .. 708 .. .. 5,305 3,676 .. .. 505 .. 28,678 .. .. 3,348 57,386 84,442 .. .. .. 662 .. 1,873 251 1,067 6,429 .. 401 899 .. .. .. 955 .. 6,633 21,609 .. .. 2,089 .. .. .. 1,136 .. .. ..
$ millions 2000 2010
.. 398 102d 34 e 3,195 52 13,016 11,382 68 854 50d 188 6,592d 77 101 246 227 1,969 1,364 23 1 345 132 13,035 5e 14 e 1,179 17,318 8,198 e 1,313 .. 12 1,477 53 2,871 1,948e 2,137 2,973d 3,671d 2,860 d 451 4,657 437 36e 657 205 2,035 38,534 99 .. 107 24,943 357 9,262 498 8 .. 128d
.. 1,780 .. 726 5,629 456 .. 20,931 792 2,163 103d 662 11,431 134 339 668 222 6,181 4,035 .. 2 1,412 171 18,281 .. .. 2,413 50,154 27,028e 2,797 .. .. 2,189 .. 8,209 2,396e 2,416 8,017 5,704 e 4,209d 786 13,633 646 .. 1,412 1,434 4,362 56,654 .. 38 738 49,133 706 12,579 1,378 2 .. 167d
% of exports 2000 2010
.. 56.6 .. 0.4 10.2 11.6 15.5 13.0 3.2 11.9 0.7 2.5 .. 14.6 6.9 15.6 7.6 3.0 19.5 9.7 2.6 18.9 4.9 4.0 .. .. 5.1 6.2 3.4 8.3 .. 0.5 19.1 1.2 33.2 .. 42.6 8.3 5.0 31.9 7.6 27.6 11.9 36.8 13.7 20.7 3.8 10.1 2.8 .. 12.5 4.0 14.6 31.5 12.9 1.1 .. 25.4
.. 47.0 .. 1.4 6.9 23.5 .. 10.3 2.8 12.1 0.5 2.2 3.1 .. 5.0 10.7 4.4 2.6 14.8 .. 1.2 20.5 3.0 4.0 .. .. 2.9 2.9 5.4 6.2 .. .. 16.0 .. 35.5 .. 24.8 5.8 3.7 36.0 4.0 27.9 11.6 .. 8.7 30.9 4.5 8.6 .. 14.9 18.2 3.2 7.5 20.9 12.7 0.1 .. 20.9
Outbound tourism expenditure
$ millions 2000 2010
.. 290 193e 146 5,460 56 8,780 7,001 138 425 471 247 9,429d,e 50 116 92 209 4,548 764 30 14 d 52 241 15,125 33e 56e 904 14,169 12,502d,e 1,452 .. 59 551 291 634 .. 543 1,276d 4,669d 440 416 1,206 219 .. 253 80 2,293 26,703 183 .. 129 57,601 162 4,564 216 13 .. 173
.. 1,454 .. 275 6,375 466 .. 12,215 856 684 835 738 20,558 97 421 244 26 19,340 1,382 .. 35 268 265 36,677 .. .. 2,339 59,840 17,461d,e 2,368 .. .. 534 .. 853 .. 1,436 4,166 9,082d 542 862 2,696 280 .. 719 143d 5,202 46,227 .. 11d 328 91,208 882 2,874 1,033 17 .. 431
% of imports 2000 2010
.. 19.3 .. 2.5 16.5 5.8 10.0 8.2 6.8 8.3 4.9 3.1 .. 7.1 5.6 2.2 9.0 6.3 10.0 4.6 9.3 2.3 9.6 5.3 .. .. 4.1 5.7 5.3 10.1 .. 4.9 7.6 8.0 6.6 .. 10.6 3.4 7.2 4.1 8.4 5.3 3.9 .. 5.1 4.9 5.7 7.3 11.1 .. 9.8 9.2 4.8 10.9 3.9 1.5 .. 12.6
.. 23.0 .. 0.8 9.4 11.1 .. 6.5 8.1 5.2 2.8 2.0 5.7 .. 6.8 2.5 0.5 7.9 4.9 .. 5.8 3.4 4.1 7.4 .. .. 3.5 3.9 3.6 5.1 .. .. 3.6 .. 3.6 .. 12.8 3.2 6.5 3.1 3.8 4.5 3.0 .. 4.9 1.4 5.5 6.4 .. 3.6 5.3 6.7 6.3 3.6 6.8 0.9 .. 10.6
International tourists
Inbound tourism expenditure
thousands Inbound Outbound 2000 2010 2000 2010
Honduras Hungary India Indonesia Iran, Islamic Rep. Iraq Ireland Israel Italy Jamaica Japan Jordan Kazakhstan Kenya Korea, Dem. Rep. Korea, Rep. Kosovo Kuwait Kyrgyz Republic Lao PDR Latvia Lebanon Lesotho Liberia Libya Lithuania Macedonia, FYR Madagascar Malawi Malaysia Mali Mauritania Mauritius Mexico Moldova Mongolia Morocco Mozambique Myanmar Namibia Nepal Netherlands New Zealand Nicaragua Niger Nigeria Norway Oman Pakistan Panama Papua New Guinea Paraguay Peru Philippines Poland Portugal Puerto Rico Qatar
471 .. 2,649 b 5,064 1,342 78a 6,646 2,417b 41,181 1,323c,h 4,757a,b 1,580 c 1,471 899 .. 5,322a,c .. 78g 59 191 509 742 .. .. .. 1,083 224f 160h 228 10,222 86g,h 30 656 20,641c 18 137 4,278c .. 208 656 464 10,003f 1,780 486 50 813 3,104 571g 557 484 58 289b 800 1,992c 17,400 5,599 f 3,341h 378g,i
896 274 9,510 11,065 5,776b 4,416 7,003 2,205 .. 2,286 1,518a .. .. 3,783 2,803b 3,530 43,626 21,993 1,922c,h .. 8,611a,b 17,819 4,557c 1,625 3,393 1,247 1,469 .. .. .. 8,798a,c 5,508 .. .. 207g 1,236 1,316 47 1,670 .. 1,373 2,596 2,168 .. 414 .. .. .. .. .. 1,507 3,632 262f .. 196h .. 746 .. 24,577 30,532 169g .. .. .. 935 163 22,260c 11,079 8 32 457 .. 9,288c 1,508 .. .. 311 .. 984 .. 603 155 10,883f 13,896 2,492 1,283 486 1,011c .. .. .. .. 4,767 2,394 1,048g .. .. .. 1,324 216 .. 52 465 175 2,299 730 3,520c 1,670 12,470 56,677 6,756f .. 3,679h 1,259 1,866g,i ..
429 16,082 12,988 6,235 .. .. .. 4,269 29,823 .. 16,637 2,917 .. .. .. .. .. .. 1,296 .. 3,332 .. .. .. .. 1,411 .. .. .. .. .. .. 212 14,395 117 .. .. .. .. .. 765 18,430 2,026 908 .. .. .. .. .. 392 .. 313 2,058 .. 42,760 .. 1,357 ..
$ millions 2000 2010
263 3,809 3,598 4,975d 677 2d 3,517 4,611 28,706 1,577 5,970 935 403 500 .. 8,527 .. 394 20 114 d 172 742 18d .. 84 430 88 152 29 5,873 47 .. 732 9,133 57 43 2,280 74 d 195 193d 219 11,285 2,272d 129d 23d 186 2,521 377 551 628 7d 88 861 2,334 6,128 6,027 2,388e 128
652 6,346 14,673 7,618 .. .. 8,071 5,474 40,058 2,095 15,356 4,018 1,236 1,620 .. 13,805 .. 510 336 385d 963 8,174 34 d 12d 170 1,097 209 633 .. 18,315 296 .. 1,585 12,417 233 288 8,176 230 92 560 d 378 18,690 4,907d 309d .. 738 5,083 1,251 998 2,552 2d 243 2,741 3,228 9,986 12,969 3,598e ..e
% of exports 2000 2010
6.8 11.0 6.0 7.0 2.3 .. 3.8 9.9 9.7 43.9 1.1 26.4 3.9 18.0 .. 4.1 .. 1.8 3.5 22.5 5.3 .. 6.7 .. 0.7 8.4 5.4 12.8 6.6 5.2 7.3 .. 27.9 5.1 8.9 7.0 21.8 10.7 9.1 13.0 17.1 4.4 12.7 11.7 7.2 0.9 3.2 3.2 5.4 8.0 0.3 3.0 10.1 5.7 13.2 17.7 .. ..
9.6 5.7 4.2 4.4 .. .. 3.9 6.8 7.3 52.3 1.8 33.0 1.9 18.2 .. 2.5 .. 0.7 13.6 17.1 7.5 38.5 3.8 3.0 0.3 4.4 5.0 .. .. 7.9 .. .. 32.0 4.0 10.2 8.5 27.1 7.7 1.1 11.2 24.0 3.2 12.0 8.5 .. 1.0 2.9 3.3 3.6 13.9 0.0 2.4 6.9 5.0 5.0 18.0 .. ..
6.15
GLOBAL LINKS
Travel and tourism
Outbound tourism expenditure
$ millions 2000 2010
198 1,722 3,686 3,197d 671 9d 2,626 3,733 18,169 238 42,643 387 483 156 .. 7,945 .. 2,852 28 8 281 .. 12 .. 495 261 58 139 53 2,543 66 .. 203 6,365 86 54 506 122 30 d 86d 109 13,649 1,235d 126 32 610 4,893 629d 574 241 50 d 154 641 1,841 3,417 2,754 1,333e 307d,e
406 2,867 13,746 8,432 .. .. 7,798 4,433 33,053 235 39,306 1,605 1,437 212d .. 19,695 .. 7,419 398 215d 771 5,080 .. 134 2,184 786 141 110 d .. 7,943d 235 .. 423 9,075 329 319 1,879 285 54 145 528 19,772 3,038d 323 .. 8,379 13,971 1,768 1,370 575 138 269 1,646 4,253 9,100 4,691 1,723e ..
% of imports 2000 2010
4.2 4.7 5.0 5.7 3.8 .. 3.3 8.0 6.3 5.4 9.3 6.7 5.4 4.1 .. 4.1 .. 25.1 4.3 1.4 7.4 .. 1.1 .. 9.9 4.5 2.5 9.1 8.4 2.7 7.1 .. 7.5 3.3 8.8 7.0 4.0 8.2 1.2 5.3 6.1 5.7 7.1 5.9 7.0 5.1 9.9 9.9 4.7 3.0 2.8 4.7 6.6 3.8 6.0 5.8 .. ..
2012 World Development Indicators
4.1 2.8 3.1 5.5 .. .. 4.6 5.8 5.6 3.6 4.9 8.9 3.3 1.6 .. 3.8 .. 22.7 10.2 9.3 5.9 16.4 .. 7.4 7.1 3.1 2.3 .. .. 4.2 .. .. 6.9 2.8 7.2 8.2 4.7 6.1 1.0 2.6 9.0 3.9 7.8 5.9 .. 11.1 11.9 7.2 3.4 2.9 2.2 2.5 4.7 5.8 4.4 5.4 .. ..
387
6.15
Travel and tourism International tourists
thousands Inbound Outbound 2000 2010 2000 2010
Romania Russian Federation Rwanda Saudi Arabia Senegal Serbia Sierra Leone Singapore Slovak Republic Slovenia Somalia South Africa South Sudan Spain Sri Lanka Sudan Swaziland Sweden Switzerland Syrian Arab Republic Tajikistan Tanzania Thailand Timor-Leste Togo Trinidad and Tobago Tunisia Turkey Turkmenistan Uganda Ukraine United Arab Emirates United Kingdom United States Uruguay Uzbekistan Venezuela, RB Vietnam West Bank and Gaza Yemen, Rep. Zambia Zimbabwe World Low income Middle income Lower middle income Upper middle income Low & middle income East Asia & Pacific Europe & Central Asia Latin America & Carib. Middle East & N. Africa South Asia Sub-Saharan Africa High income Euro area
5,264 a 21,169a .. 6,585 .. .. 16h 6,062 1,053f 1,090 f .. 5,872 .. 46,403 400 b 38 281i 3,828f 7,821g 2,100 c,f .. 459 9,579c .. 60 g 399h 5,058b 9,586 3 193 6,431 3,907c,i 23,212 51,238 1,968 302 469 2,140a 310 g 73 457 1,967a 681,151 t 7,122 196,996 39,368 157,416 207,153 62,673 52,830 47,146 21,987 4,839 17,703 470,103 254,371
Inbound tourism expenditure
$ millions 2000 2010
.. 6,388 .. 394 1,653 22,281a 18,371 39,323 3,429d 13,379 666 .. .. 27 218d 10,850 .. 7,233 .. 7,655 .. .. .. 152 .. 683 .. .. .. 951 39f,h 13 76 10 d 26d 14,181d 9,161 4,444 7,342 5,142d .. .. .. 441 2,335 1,869f 1,965 2,874 1,016 2,735 .. .. .. .. .. 8,074 3,834 5,165 3,338 10,308 .. .. .. .. .. 52,677 4,100 .. 32,656 58,810 654b 524 1,122 388 1,044 94 d .. .. .. 5d 868g .. 1,141 24 51 4,951f 10,147 13,042 4,825 13,316 8,628g 12,240 .. 8,988 17,847 8,546 3,863 6,259 1,082d 6,308 .. 6 .. .. 32 783 .. .. 381 1,279 15,936 1,909 5,451 9,935 23,407 40 .. .. .. 21 .. .. .. 11 .. .. .. .. 371 .. 6,903b 1,632 2,250 1,977 3,477 24,784 27,000 5,284 11,002 7,636d .. 78 .. .. .. 762 946 153 324 165d 21,203 13,422 17,180 563 4,696 .. .. .. 1,063e 8,577e 28,295 56,837 55,562 29,978 39,945 59,791 61,327 .. 120,912 165,777 2,353 667 1,027 827 1,607 121d,e 975 217 1,610 63e .. 954 .. 469 672 .. .. .. .. .. 522g .. .. 283d .. 622d 536 .. .. 73d 125d 815 .. .. 67d 2,239a .. 650 125e 634 e 941,666 t 730,907 t .. t 572,408 t 1,111,919 t 16,234 .. .. 3,304 11,469 360,007 179,453 370,724 112,754 304,678 98,178 45,422 86,323 26,019 77,617 261,779 124,379 .. 86,796 227,263 380,843 196,477 409,004 115,911 315,502 117,829 51,581 .. 42,855 109,418 106,138 59,247 114,685 16,274 60,832 64,289 29,567 43,711 30,617 53,813 53,132 18,577 .. 12,902 48,730 9,330 7,137 20,157 5,152 18,026 31,646 .. .. 8,000 24,873 558,474 481,718 .. 456,489 796,198 282,908 179,060 .. 181,607 307,946
% of exports 2000 2010
3.3 3.0 21.1 .. 11.6 .. 18.2 2.8 3.1 9.5 .. 9.0 .. 19.4 6.1 0.3 1.9 4.3 7.2 15.8 .. 28.0 12.2 .. 2.6 7.7 23.0 15.2 .. 24.9 2.9 .. 7.4 11.3 22.6 .. 1.4 .. 28.0 1.8 7.7 .. 7.4 w 11.0 7.2 7.7 7.0 7.2 7.0 6.5 6.9 12.9 6.0 7.2 7.4 8.4
2.8 3.0 35.9 2.9 .. 7.1 6.1 3.0 3.3 9.0 .. 10.3 .. 15.6 9.7 0.8 2.5 5.9 5.2 32.2 2.1 20.0 10.3 .. .. .. 15.6 15.9 .. 21.9 6.8 .. 6.0 9.0 15.2 .. 1.0 .. .. 6.7 1.6 .. 5.9 w 11.2 5.5 6.8 5.2 5.6 4.3 6.1 5.2 20.9 4.3 7.0 6.0 6.2
Outbound tourism expenditure
$ millions 2000 2010
447 8,848d 35 .. 125 .. 35 4,535d 341 544 .. 2,684 .. 7,710 383 55d 32 8,959 7,360 669d .. 369 3,218 .. 15 190 310 1,713d .. .. 561 3,019e 47,009 91,473 381 .. 1,647 .. 316 127 102 .. 531,843 t 2,837 82,078 17,992 64,076 84,837 26,308 15,983 25,096 5,237 5,333 7,133 446,402 155,713
1,897 29,993 94 22,803 .. 1,106 22 16,770 d 2,146 1,377 .. 8,139 .. 22,800 828 1,116d 87 14,878 13,317 1,598 18d 861 6,582 72 .. .. 611 5,451 .. 389 4,134 11,818e 60,291 109,975 534 .. 2,196 .. .. 252 128 .. 996,273 t 7,086 261,770 65,963 196,511 269,032 91,275 56,988 50,734 23,279 17,625 32,518 730,544 275,992
% of imports 2000 2010
3.2 14.5 8.3 .. 7.2 .. 13.8 2.7 2.3 4.8 .. 8.1 .. 4.1 4.7 2.7 2.2 9.2 6.9 12.4 .. 18.0 4.5 .. 2.5 5.1 3.3 2.8 .. .. 3.1 .. 10.8 6.3 9.1 .. 7.7 .. 9.7 3.9 7.8 .. 6.8 w 6.0 5.6 5.0 5.8 5.6 4.8 7.2 5.7 5.5 5.0 6.9 7.1 7.1
2.8 9.3 5.7 13.1 .. 5.6 2.5 4.1 3.0 4.5 .. 8.1 .. 5.7 5.4 10.0 3.3 7.6 4.7 8.2 0.5 9.6 3.2 .. .. .. 2.5 2.8 .. 6.4 5.6 .. 8.2 4.7 5.5 .. 4.4 .. .. 2.3 2.3 .. 5.4 w 4.3 4.8 4.7 4.8 4.8 4.1 5.9 5.0 6.8 3.3 6.9 5.7 5.8
Note: Aggregates are based on World Bank country classifications and differ from those of the World Tourism Organization. Regional and income group totals include countries not shown in the table for which data are available. a. Arrivals of nonresident visitors at national borders. b. Excludes nationals residing abroad. c. Includes nationals residing abroad. d. Expenditure of travel-related items only; excludes passenger transport items. e. Data are from national sources. f. Arrivals in all types of accommodation establishments. g. Arrivals in hotels and similar establishments. h. Arrivals in hotels only. i. Arrivals by air only.
388
2012 World Development Indicators
About the data
6.15
Definitions
Tourism is defined as the activities of people trav-
residing abroad while others do not. Caution should
• International inbound tourists (overnight visitors)
eling to and staying in places outside their usual
thus be used in comparing arrivals across countries.
are tourists who travel to a country other than that
environment for no more than one year for leisure,
The World Tourism Organization is improving its
in which they usually reside, and outside their usual
business, and other purposes not related to an activ-
coverage of tourism expenditure data, using balance
environment, for a period not exceeding 12 months
ity remunerated from within the place visited. The
of payments data from the International Monetary
and whose main purpose in visiting is other than
social and economic phenomenon of tourism has
Fund (IMF) supplemented by data from individual
an activity remunerated in the country visited. When
grown substantially over the past quarter century.
countries. These data, shown in the table, include
number of tourists are not available, data on visitors,
Statistical information on tourism is based mainly
travel and passenger transport items as defined in
which include tourists, same-day visitors, cruise pas-
on data on arrivals and overnight stays along with
the IMF’s Balance of Payments Manual, 5th edition.
sengers, and crew members, are shown. • Interna-
balance of payments information. These data do not
When the IMF does not report data on passenger
tional outbound tourists are departures that people
completely capture the economic phenomenon of
transport items, expenditure data for travel items
make from their country of usual residence to any
tourism or provide the information needed for effec-
are shown.
other country for any purpose other than an activity
tive public policies and efficient business operations.
Tourism can be either domestic or international.
remunerated in the country visited. • Inbound tour-
Data are needed on the scale and significance of
The table shows data relevant to international tour-
ism expenditure is expenditures by international
tourism. Information on the role of tourism in national
ism, where the traveler’s country of residence differs
inbound visitors, including payments to national
economies is particularly defi cient. Although the
from the visiting country. International tourism con-
carriers for international transport and any other pre-
World Tourism Organization reports progress in har-
sists of inbound and outbound tourism.
payment made for goods or services received in the
monizing definitions and measurement, differences
The aggregates are calculated using the World
destination country. They may include receipts from
in national practices still prevent full comparability.
Bank’s weighted aggregation methodology (see Sta-
same-day visitors, except when these are important
The usual environment of an individual is a key
tistical methods) and differ from the World Tourism
enough to justify separate classification. For some
Organization’s aggregates.
countries they do not include receipts for passenger
concept in tourism statistics and is defined as the
GLOBAL LINKS
Travel and tourism
geographical area within which an individual con-
transport items. Their share in exports is calculated
ducts regular life routines. This concept excludes
as a ratio to exports of goods and services (all trans-
travelers who commute regularly between their place
actions between residents of a country and the rest
of usual residence and place of work or study or
of the world involving a change of ownership from
who frequently visit places within their current life
residents to nonresidents of general merchandise,
routine—for instance, homes of friends or relatives;
goods sent for processing and repairs, nonmonetary
shopping centers; and religious, health care, or other
gold, and services). • Outbound tourism expenditure
facilities a substantial distance away or in a different
is expenditures of international outbound visitors in
administrative area.
other countries, including payments to foreign carri-
The data in the table are from the World Tourism
ers for international transport. These expenditures
Organization, a United Nations agency. The data on
may include those by residents traveling abroad as
inbound and outbound tourists refer to the number of
same-day visitors, except when these are important
arrivals and departures, not to the number of people
enough to justify separate classification. For some
traveling. Thus a person who makes several trips to
countries they do not include expenditures for pas-
a country during a given period is counted each time
senger transport items. Their share in imports is cal-
as a new arrival. Unless otherwise indicated in the
culated as a ratio to imports of goods and services
footnotes, the data on inbound tourism show the
(all transactions between residents of a country and
arrivals of nonresident tourists (overnight visitors)
the rest of the world involving a change of ownership
at national borders. When data on international tour-
from nonresidents to residents of general merchan-
ists are unavailable or incomplete, the table shows
dise, goods sent for processing and repairs, nonmon-
the arrivals of international visitors, which include
etary gold, and services).
tourists, same-day visitors, cruise passengers, and crew members.
Data sources
Sources and collection methods for arrivals differ
Data on visitors and tourism expenditure are from
across countries. In some cases data are from bor-
the World Tourism Organization’s Yearbook of Tour-
der statistics (police, immigration, and the like) and
ism Statistics and Compendium of Tourism Statistics
supplemented by border surveys. In other cases data
2011. Data in the table are updated from electronic
are from tourism accommodation establishments.
files provided by the World Tourism Organization.
For some countries number of arrivals is limited to
Data on exports and imports are from the IMF’s Bal-
arrivals by air and for others to arrivals staying in
ance of Payments Statistics Yearbook and data files.
hotels. Some countries include arrivals of nationals
2012 World Development Indicators
389 Text figures, tables, and boxes
PRIMARY DATA DOCUMENTATION As a major user of socioeconomic data, the World Bank recognizes the importance of data documentation to inform users of differences in the methods and conventions used by primary data collectors—usually national statistical agencies, central banks, and customs services—and by international organizations, which compile the statistics that appear in the World Development Indicators database. These differences may give rise to significant discrepancies over time both within countries and across them. Delays in reporting data and the use of old surveys as the base for current estimates may further compromise the quality of data reported here. The tables in this section provide information on sources, methods, and reporting standards of the principal demographic, economic, and environmental indicators in World Development Indicators. Additional documentation is available from the World Bank’s Bulletin Board on Statistical Capacity at http://data. worldbank.org. The demand for good-quality statistical data is ever increasing. Statistics provide the evidence needed to improve decisionmaking, document results, and heighten public accountability. The need for improved statistics to monitor the Millennium Development Goals and the parallel effort to support a culture of results-based management has stimulated a decade-long effort to improve statistics. The results has been impressive, but more needs to done. Thus a new action plan for statistics, “Statistics for Transparency, Accountability, and Results: A Busan Action Plan for Statistics,” was endorsed by the Busan Partnership for Effective Development Cooperation at the Fourth High-level Forum for Aid Effectiveness held November 29–December 1, 2011, in Busan, Republic of Korea. This new action plan builds on the progress made under the first global plan to improve national and international statistics, the 2004 Marrakech Action Plan for Statistics from 2004, but goes beyond it in many ways. The main objectives of the new plan are to integrate statistics into decisionmaking, promote open access to statistics within government and for all other uses, and increase resources for statistical systems, both for investment in new capacity and for maintaining current operations.
2012 World Development Indicators
391
PRIMARY DATA DOCUMENTATION Currency
National accounts
SNA System of price Reference National Accounts valuation year
Base year
Afghanistan
Afghan afghani
Albania Algeria American Samoa Andorra Angola
Albanian lek Algerian dinar U.S. dollar Euro Angolan kwanza
Antigua and Barbuda Argentina Armenia Aruba Australia Austria Azerbaijan Bahamas, The Bahrain Bangladesh Barbados Belarus Belgium Belize Benin Bermuda Bhutan Bolivia Bosnia and Herzegovina Botswana Brazil Brunei Darussalam Bulgaria
East Caribbean dollar 2006 Argentine peso 1993 a Armenian dram Aruban florin 1995 a Australian dollar Euro 2005 a New Azeri manat Bahamian dollar 2006 Bahraini dinar 1985 Bangladeshi taka 1995/96 Barbados dollar 1974 a Belarusian rubel Euro 2005 Belize dollar 2000 CFA franc 1985 Bermuda dollar 1996 Bhutanese ngultrum 2000 Bolivian Boliviano 1990 a Bosnia and Herzegovina convertible mark Botswana pula 1993/94 Brazilian real 2000 Brunei dollar 2000 a Bulgarian lev
Burkina Faso Burundi Cambodia Cameroon Canada Cape Verde Cayman Islands Central African Republic Chad Channel Islands
CFA franc Burundi franc Cambodian riel CFA franc Canadian dollar Cape Verde escudo Cayman Islands dollar CFA franc CFA franc Pound sterling
Chile China Hong Kong SAR, China Macao SAR, China Colombia Comoros Congo, Dem. Rep. Congo, Rep. Costa Rica Côte d’Ivoire Croatia Cuba Curaçao
Chilean peso Chinese yuan Hong Kong dollar Macao pataca Colombian peso Comorian franc Congolese franc CFA franc Costa Rican colon CFA franc Croatian kuna Cuban peso Netherlands Antilles guilder
392
2012 World Development Indicators
2002/03 a
1996
b
VAB VAB
1997
VAP VAB VAB VAB
b
1996
b
2009
b b
2003
b
2000
b b b
b b
1996
b
b b
2002
b
b b
b
2007
b
b b
2009 2009
b
b
2005 1990 1987 1978 1991 1996 1990
b b
1999 1980 2000 2000 2005 1980
a
Alternative conversion factor
PPP survey year
Balance of Payments Manual in use
VAB
1980
2000 1995 2007, 2003 2003 2000
Balance of payments and trade
b
2000
b
VAB VAB VAB VAB VAP VAB VAB VAB VAB VAB VAP VAB VAB VAB VAB VAB VAB VAP VAB VAB VAB VAB VAB VAB VAP
System of trade
Accounting concept
Actual
G
C
G
G S S S S
C B
G G
BPM5 BPM5
Actual Actual
1991–96
2005
BPM5
Actual
1971–84 1990–95
2005 2005
1992–95
2005 2005 2005 2005 2005
1990–95
2005 2005
1992
2005
1960–85
2005 2005 2005
1978–89, 1991–92 1992–93
VAB VAB VAB VAB VAP VAB VAB VAB VAP VAB VAP VAB VAP VAB VAB
External debt
2005
1978–93
1992–94 1999–2001 1993
BPM5 BPM5 BPM5 BPM5 BPM5 BPM5 BPM5 BPM5 BPM5 BPM5 BPM5 BPM5 BPM5 BPM5 BPM5 BPM5 BPM5 BPM5 BPM5
Actual Actual
Actual
Actual Actual Actual Actual Actual Actual Actual
2005 2005 2005 2005
BPM5 BPM5
Actual Actual
BPM5
Actual
2005 2005 2005 2005 2005 2005
BPM5 BPM5 BPM5 BPM5 BPM5 BPM5
Actual Preliminary Actual Actual
2005 2005
BPM5 BPM5
Preliminary Actual
2005 2005 2005 2005 2005 2005 2005 2005
BPM5 BPM5 BPM5 BPM5 BPM5
Actual Preliminary
2005 2005
BPM4 BPM5 BPM5 BPM5 BPM5
Government IMF data finance dissemination standard
Actual
Actual Actual Estimate Estimate Actual Estimate
G S S S G S G G G G G G S G S G G G S
G
C C
G S S
C C B B B C B C C B B
S S G G G G G S S G G
C C C
G G
G G S S
B C C
G S G S
G S S S G G G S S
B C C B C C
G G G G S G
B
G G
S S G G G S S S S S G S
C B C C B
S G S G S
C C C C C
G G S G S
PRIMARY DATA DOCUMENTATION Latest population census
Afghanistan
1979
Albania Algeria American Samoa Andorra Angola
2001 2008 2010 1989 1970
Antigua and Barbuda Argentina Armenia Aruba Australia Austria Azerbaijan Bahamas, The Bahrain Bangladesh Barbados Belarus Belgium Belize Benin Bermuda Bhutan Bolivia Bosnia and Herzegovina
2011 2010 2001 2010 2006 2011 2009 2010 2010 2011 2010 2009 2011 2010 2002 2010 2005 2001 1991
Botswana Brazil Brunei Darussalam Bulgaria
Latest demographic, education, or health household survey
MICS, 2003; Special Survey, 2010 DHS, 2008/09 MICS, 2006
Source of most recent income and expenditure data
Vital Latest Latest registration agricultural industrial complete census data
Latest water withdrawal data
2010
2010
2000
1998 2001 2007
2010 2009
2000 2000
1964–65
2010
2010 2010 2009 2006 1991
2007 2007
2010 2010 2010
1990 2000 2006
2011 2010
2010 2010 2010 2006 1995 2010 2005 2010 2009 2008 2005
IHS, 2008 LSMS, 2008 IHS, 1995
Yes Yes Yes
MICS, 2001; MIS, 2006/07
Latest trade data
IHS, 2000
DHS, 2005
IHS, 2010 IHS, 2010
DHS, 2006
ES/BS, 1994 IS, 2000 ES/BS, 2008
DHS, 2007
IHS, 2010
MICS, 2005
ES/BS, 2009 IHS, 2000 ES/BS, 1999 CWIQ, 2003
Yes Yes Yes Yes Yes Yes Yes
2008 2008
2009 2010 2010
2010 2010 2010 2010 2010 2010 2010 2010 2010 2007 2010 2010 2010 2010 2006 2009 2010 2010 2010
1993 2006
2010 2010 2008 2010
2010 2011 2006 2010
2000 2006 1994 2009
2012 1984 2011 2004
2006 2005 2010 2007 2007 2009
2010 2010 2010 2010 2010 2010
2000 2000 2006 2000 2000
1985 2011
2006 2008
2009 1995
2000 2000
2007 2007
2010 2010 2010 2009 2010 2009 2009 2010 2010 2010 2010 2008
2010 2010 2010 2010 2010 2007 1986 2005 2010 2010 2011 2006
2000 2005
Yes
MICS, 2006 DHS, 2006
2008 Yes Yes Yes
1994 1999–2000 2011–12
Yes MICS, 2010 DHS, 2008 MICS, 2006
IHS, 2007 IHS, 2008 LSMS, 2007
2011 2010 2001 2011
MICS, 2000 DHS, 1996
ES/BS, 2003 LFS, 2009
Burkina Faso Burundi Cambodia Cameroon Canada Cape Verde Cayman Islands Central African Republic Chad Channel Islands
2006 2008 2008 2005 2011 2010 2010 2003 2009 2001
MICS, 2006 MICS, 2005 DHS, 2010 MICS, 2006
Chile China Hong Kong SAR, China Macao SAR, China Colombia Comoros Congo, Dem. Rep. Congo, Rep. Costa Rica Côte d’Ivoire Croatia Cuba Curaçao
2002 2010 2006 2011 2006 2003 1984 2007 2011 1998 2011 2002
ES/BS, 2007
DHS, 2005
CWIQ, 2009 CWIQ, 2006 IHS, 2008 PS, 2007 LFS, 2000 ES/BS, 2007
MICS, 2006 DHS, 2004
PS, 2008 PS, 2002/03
NSS, 2007
IHS, 2009 IHS, 2008
Yes
Yes Yes
2010 2010
Yes Yes Yes
Yes Yes Yes
DHS, 2010 MICS, 2000 MICS, 2010 AIS, 2009; DHS, 2005 RHS, 1993 MICS, 2006 MICS, 2006
IHS, 2010 IHS, 2004 1–2-3, 2005/06 CWIQ/PS, 2005 LFS, 2010 IHS, 2008 ES/BS, 2008
2001
Yes Yes Yes
2000
1990 1985–86 1973 2001 2003
2012 World Development Indicators
2000 2000 2005 2003 2008 2000 2000 2007 2000 2001 2008 2000 2009
2000 1999 2000 2002 2000 2000 2009 2000
393
PRIMARY DATA DOCUMENTATION Currency
National accounts
Base year
Cyprus Czech Republic Denmark Djibouti Dominica Dominican Republic Ecuador Egypt, Arab Rep. El Salvador Equatorial Guinea Eritrea Estonia Ethiopia Faeroe Islands Fiji Finland France French Polynesia Gabon Gambia, The Georgia Germany Ghana Gibraltar Greece Greenland Grenada Guam Guatemala Guinea Guinea-Bissau Guyana Haiti Honduras Hungary Iceland India Indonesia Iran, Islamic Rep. Iraq Ireland Isle of Man Israel Italy Jamaica Japan Jordan Kazakhstan Kenya Kiribati Korea, Dem. Rep. Korea, Rep. Kosovo Kuwait Kyrgyz Republic Lao PDR Latvia Lebanon
394
a Euro Czech koruna 2000 Danish krone 2005 Djibouti franc 1990 East Caribbean dollar 2006 Dominican peso 1991 U.S. dollar 2000 Egyptian pound 1991/92 U.S. dollar 1990 CFA franc 2000 Eritrean nakfa 1992 Estonian kroon 2000 Ethiopian birr 1999/2000 Danish krone Fijian dollar 2005 Euro 2005 a Euro CFP franc CFA franc 1991 Gambian dalasi 1987 a Georgian lari Euro 2005 New Ghanaian cedi 2006 Gibraltar pound a Euro Danish krone East Caribbean dollar 2006 U.S. dollar Guatemalan quetzal 2001 Guinean franc 1996 CFA franc 2005 Guyana dollar 2006 Haitian gourde 1986/87 Honduran lempira 2000 a Hungarian forint Iceland krona 2005 Indian rupee 2004/05 Indonesian rupiah 2000 Iranian rial 1997/98 Iraqi dinar 1997 Euro 2005 Pound sterling 2005 Israeli new shekel 2005 Euro 2005 Jamaican dollar 2003 Japanese yen 2005 Jordanian dinar 1994 a Kazakh tenge Kenyan shilling 2001 Australian dollar 2006 Democratic People’s Republic of Korean won Korean won 2005 Euro Kuwaiti dinar 1995 a Kyrgyz som Lao kip 1990 Latvian lats 2000 Lebanese pound 1997
2012 World Development Indicators
SNA System of price Reference National Accounts valuation year
2000 1995
b b
b
b
b b
b
2005
1996
b
b b
2005
VAB VAB VAB VAB VAB VAB VAB VAB VAB VAB VAB VAB VAB VAB VAB VAB VAB VAP VAB VAB VAB VAB
Balance of payments and trade
Alternative conversion factor
PPP survey year
2005 2005 2005 2005
2005 2005 1965–84
2005
1987–95
2005 2005
1993 1990–95 1973–87
VAB
b
2005
b
b
b
VAB VAB VAB VAB VAB VAB VAB VAB VAB VAP VAB VAB VAB
Actual Actual Actual Actual Actual Actual Actual
2005 2005 2005 2005 2005 2005 2005 2005
BPM5 BPM5 BPM5 BPM5 BPM5
Actual Actual Actual
2005
BPM5
2005 2005 1991 1988–89
1980–2002 1997, 2004
BPM5 BPM5 BPM5 BPM5 BPM5 BPM5 BPM5 BPM5 BPM5
External debt
BPM4 BPM5 BPM5 BPM5 BPM5 BPM5 BPM5
VAB b
Balance of Payments Manual in use
2005 2005 2005 2005 2005 2005 2005
Actual Actual
Actual
BPM5
Actual
BPM5 BPM5 BPM5 BPM5 BPM5 BPM5 BPM5 BPM5 BPM5 BPM5 BPM4 BPM5 BPM5
Actual Estimate Estimate Actual Actual Actual
Actual Actual Actual
Government IMF data finance dissemination standard
System of trade
Accounting concept
G S S G S G G G S G
C C C
C B C C
S S S G G G S S S
S G G G G S S S G G S G
C B
S G
B C C
G S S
C C C B
G G S S G
C
S
B
G
B B
G G G G G G S S S S
S G S G S S G S G S S G G S S
C C C C B C
G
C
S S G G G G G G
C C C C B C B
S S G S S S G G
G
C
S S S S G
B B B C B
S G G S
G S
2003 b b
2000
b b
b
VAP VAB VAB VAB VAB VAB VAB VAB
2005 2005
1987–95
VAB
2005 2005 2005 2005
2005
BPM5 BPM5 BPM5 BPM5 BPM5 BPM5 BPM5 BPM5 BPM4
Actual Actual Actual Actual
BPM5 Actual
1995
b
b
VAP VAB VAB VAB VAB
1990–95 1987–95
2005 2005 2005 2005 2005
BPM5 BPM5 BPM5 BPM5 BPM5
Actual Preliminary Actual Actual
S G
PRIMARY DATA DOCUMENTATION Latest population census
Cyprus Czech Republic Denmark Djibouti Dominica Dominican Republic Ecuador Egypt, Arab Rep. El Salvador Equatorial Guinea Eritrea Estonia Ethiopia Faeroe Islands Fiji Finland France French Polynesia Gabon Gambia, The Georgia Germany Ghana Gibraltar Greece Greenland Grenada Guam Guatemala Guinea Guinea-Bissau Guyana Haiti Honduras Hungary Iceland India Indonesia Iran, Islamic Rep. Iraq Ireland Isle of Man Israel Italy Jamaica Japan Jordan Kazakhstan Kenya Kiribati Korea, Dem. Rep.
2001 2011 2011 2009 2011 2010 2010 2006 2007 2002 1984 2000 2007 2011 2007 2010 2006c 2007 2003 2003 2002 2011 2010 2001 2011 2010 2011 2010 2002 1996 2009 2002 2003 2001 2001
Korea, Rep. Kosovo Kuwait Kyrgyz Republic Lao PDR Latvia Lebanon
2010 1981 2010 2009 2005 2011 1970
Latest demographic, education, or health household survey
RHS, 1993
Source of most recent income and expenditure data
MICS, 2006
IS, 1996 ITR, 1997 PS, 2002
DHS, 2007 RHS, 2004 DHS, 2008 RHS, 2008
IHS, 2010 LFS, 2010 ES/BS, 2008 IHS, 2009
Yes Yes Yes
2010 2001–02
2008 2010 2010 2007 2010 2010 2010 2010 2010 2009 2009 2009 2010
2009 2010 2010
2010 2010 2009
1974–75 2001–02 2004 2010 2011
2010 2010 2010 2010 2010
2009
2009
2011
2010
2008 2000–01 1988
2010 2010 2002 2010
2010 2010
Yes
Yes Yes
1971 2012 2010 2007–08
DHS, 2002 DHS, 2005
ES/BS, 2004 ES/BS, 2005 ES/BS, 2009 IS, 2000 ES/BS, 1994/95
DHS, 2000 MICS, 2005/06 MICS, 2005; RHS, 2005 DHS, 2008
CWIQ/IHS, 2005 IHS, 2003 IHS, 2009 IHS, 2000 LSMS, 2006 IHS, 2000
RHS, 2002 DHS, 2005 MICS, 2010 DHS, 2009 DHS, 2005/06 DHS, 2005/06
LSMS, 2006 CWIQ, 2007 CWIQ, 2002 IHS, 1998 IHS, 2001 IHS, 2009 ES/BS, 2007
d
2011 2010 2006 1997 2011 2006 2009 2012 2011 2010 2004 2009 2009 2005 2009
Vital Latest Latest registration agricultural industrial complete census data
DHS, 2005/06 DHS, 2007 DHS, 2000 MICS, 2006
MICS, 2005 DHS, 2009 MICS, 2006 DHS, 2008/09; MIS, 2010
IHS, 2010 IHS, 2011 ES/BS, 2005 IHS, 2007 IHS, 2000 ES/BS, 2001 ES/BS, 2000 LSMS, 2007 IS, 1993 ES/BS, 2010 ES/BS, 2009 IHS, 2005/06
Yes Yes Yes Yes Yes Yes
Yes Yes Yes Yes Yes Yes Yes Yes
Yes Yes
Yes Yes Yes Yes Yes Yes
2009 2013 2007 2010 2011 2003 2003 2011 2010 1981 2010 2007 2010 2007
Yes 1977–79
Latest trade data
Latest water withdrawal data
2010 2010 2010 2009 2010 2010 2010 2010 2010
2009 2007 2009 2000 2004 2000 2000 2000 2000 2000 2004 2007 2002
2005 2011 2011 2009 2010 2010 2010 2010 2010 2010 2010 2010 2010
2007 2005
2010 2010 2009 2010 2010 2007 2003 2009
2010 2008 2005 2010 1997 2009 2010 2010 2010 2010 2010 2009 2010
2000 2000 2000 2000 2000 2000 2007 2005 2010 2000 2004 2000 2000
2010 2010 2009 2010 2010 2010 2009
2010 2010 2010 2011 2010 2009 2010 2009
2004 2000 2000 2001 2005 2000 2003 2005
ES/BS, 1998 IHS, 2009
MICS, 2000
2000 2000 2005 2007 2000
2010 2007 2009
MICS, 2010
FHS, 1996 MICS, 2005/06 MICS, 2006
2000 2005 2007
ES/BS, 2010 ES/BS, 2008 IHS, 2008
Yes
2000
Yes Yes
1970 2002 2010–11 2010 2010
Yes Yes
2010 2010 2003 2010 2010 2010 2010
2010
2002
2009 2010 1975 2010 2010
2002 2000 2005 2000 2005
2012 World Development Indicators
395
PRIMARY DATA DOCUMENTATION Currency
National accounts
SNA System of price Reference National Accounts valuation year
Base year
Lesotho Liberia Libya Liechtenstein Lithuania Luxembourg Macedonia, FYR Madagascar Malawi Malaysia Maldives Mali Malta Marshall Islands Mauritania Mauritius Mayotte Mexico Micronesia, Fed. Sts. Moldova Monaco Mongolia Montenegro Morocco Mozambique Myanmar Namibia
Lesotho loti Liberian dollar Libyan dinar Swiss franc Lithuanian litas Euro Macedonian denar Malagasy ariary Malawi kwacha Malaysian ringgit Maldivian rufiyaa CFA franc Euro U.S. dollar Mauritanian ouguiya Mauritian rupee Euro Mexican peso U.S. dollar Moldovan leu Euro Mongolian tugrik Euro Moroccan dirham New Mozambican metical Myanmar kyat Namibian dollar
1997 1984 1994 2000 2003 1987 2005 2004 2004 2006
396
2012 World Development Indicators
2005 1995
a
1996
b
b
2005 2000 1998 2003
b
2005/06 2004/05
2000 1995 2002 1995 2001
b
b
2003 2004
Nepalese rupee 2000/01 a Euro CFP franc New Zealand dollar 2005/06 Nicaraguan gold 1994 cordoba Niger CFA franc 1987 Nigeria Nigerian naira 2002 Northern Mariana Islands U.S. dollar a Norway Norwegian krone Oman Rial Omani 1988 Pakistan Pakistani rupee 1999/2000 Palau U.S. dollar 1995 Panama Panamanian balboa 1996 Papua New Guinea Papua New Guinea kina 1998 Paraguay Paraguayan guarani 1994 Peru Peruvian new sol 1994 Philippines Philippine peso 2000 a Poland Polish zloty Portugal Euro 2005 Puerto Rico U.S. dollar 1954 Qatar Qatari riyal 2001 a Romania New Romanian leu Russian ruble Rwandan franc Samoan tala Euro São Tomé and Príncipe dobra
b
2000
Nepal Netherlands New Caledonia New Zealand Nicaragua
Russian Federation Rwanda Samoa San Marino São Tomé and Príncipe
b
1995 1992 1999
b
2005
b
b
2005
b
b
b
2005
b b
2005
b
b
2000
b
VAB VAP VAB VAB VAB VAB VAB VAB VAB VAP VAB VAB VAB VAB VAB VAB
Balance of payments and trade
Alternative conversion factor
PPP survey year
Balance of Payments Manual in use
External debt
System of trade
Accounting concept
G S G S G S S S G G G S G G S G G G
C B
G G G
C C
B C B C
S S S G G S G G S
C
G G
C
S
G S G S S S
C
S
C
G G S G
2005 2005
BPM5 BPM5 BPM5
Actual Actual
2005 2005 2005 2005 2005 2005 2005 2005 2005
BPM5 BPM5 BPM5 BPM5 BPM5 BPM5 BPM5 BPM5 BPM5
Actual
2005 2005
BPM4 BPM5
Actual Actual
2005
BPM5
Actual
1990–95
2005
BPM5
Actual
1992–95
2005 2005 2005 2005
BPM5 BPM5 BPM5 BPM5
Actual Actual Actual Actual
VAP VAB
BPM5 BPM5
Estimate
2005
VAB VAB
2005 2005
BPM5 BPM5
Actual
2005
BPM5 BPM5
2005 2005
BPM5 BPM5
2005 2005 2005
BPM5 BPM5 BPM5
2005 2005 2005 2005 2005
BPM5 BPM5 BPM5 BPM5 BPM5 BPM5 BPM5
Actual Actual Actual Actual Actual
2005 2005
BPM5
2005 2005
2005
VAB VAB VAB VAB VAB VAB VAB
1986 1990–95
VAB VAB
1965–95
VAP VAB
1993 1971–98
VAB VAP VAB VAB VAB VAB VAP VAB VAP VAB VAB VAP VAP VAB VAB VAP VAB VAB VAP
1989 1985–90
1987–89, 1992 1987–95 1994
Government IMF data finance dissemination standard
Actual Actual Actual Estimate Actual Actual
C
C
G G
C B
G
C C
G S
Actual
G S S G G
C B
G
Actual Actual
S G
B B
G G
C B B
S G G
C B B C B C C
G G G S S S S
Actual
G G G S S G S S G S S G S S
B C
G S
BPM5 BPM5 BPM5
Preliminary Actual Actual
G G S
C C
S G
C BPM4
Actual
S
G G
Actual
PRIMARY DATA DOCUMENTATION Latest population census
Lesotho Liberia Libya Liechtenstein Lithuania Luxembourg Macedonia, FYR Madagascar Malawi Malaysia Maldives Mali Malta Marshall Islands Mauritania Mauritius Mayotte Mexico Micronesia, Fed. Sts. Moldova Monaco Mongolia Montenegro Morocco Mozambique
2006 2008 2006 2010 2011 2011 2010 1993 2008 2010 2011 2009 2005 2011 2000 2011 2007 2010 2000 2004 2008 2010 2011 2004 2007
Myanmar Namibia
1983 2001
Nepal Netherlands New Caledonia New Zealand Nicaragua
2001 2011 2009 2006 2005
Niger Nigeria Northern Mariana Islands Norway Oman Pakistan Palau Panama Papua New Guinea Paraguay Peru Philippines Poland Portugal Puerto Rico Qatar Romania
2001 2006 2010 2001 2010 1998 2010 2010 2000 2002 2007 2010 2011 2011 2010 2010 2011
Russian Federation Rwanda Samoa San Marino São Tomé and Príncipe
2010 2002 2006 2010 2001
Latest demographic, education, or health household survey
DHS, 2009/10 DHS, 2007; MIS, 2009 MICS, 2000
Source of most recent income and expenditure data
ES/BS, 2002/03 CWIQ, 2007
DHS, 2009 DHS, 2006; Special, 2010
2010 2001
ES/BS, 2008 MICS, 2005 DHS, 2008/09 DHS, 2010
Vital Latest Latest registration agricultural industrial complete census data
ES/BS, 2009 PS, 2010 LSMS, 2004/05 ES/BS, 2009 IHS, 2004 IHS, 2010
Yes Yes Yes Yes
Yes Yes Yes
MICS, 2007
2010 2010 2007 2004 2006–07 2012
2010
IHS, 2008 Yes Yes
ENPF, 1995 DHS, 2005
LFS, 2010 IHS, 2000 ES/BS, 2010
MICS, 2010 MICS, 2005/06 MICS, 2006 DHS, 2003; AIS, 2009
LSMS, 2007/08 ES/BS, 2008 ES/BS, 2007 ES/BS, 2008
MICS, 2000 DHS, 2006/07; HIV/MCH SPA, 2009 MICS, 2010
Yes Yes Yes Yes
ES/BS, 2004 LSMS, 2010 IHS, 1999
RHS, 2006/07
IS, 1997 LSMS, 2005
DHS, 2006 DHS, 2008
CWIQ/PS, 2008 IHS, 2010 IS, 2000
FHS, 1995 MICS, 2010
2007
Yes Yes Yes
Yes
IHS, 2008 LFS, 2010 IHS, 1996 IHS, 2010 IHS, 2010 ES/BS, 2009 ES/BS, 2009 IS, 1997
RHS, 1995/96 RHS, 1999
LFS, 2009
RHS, 1996 DHS, 2007/08 DHS, 2009
IHS, 2009 IHS, 2011
DHS, 2008/09
PS, 2000/01
Yes
2010 2008 2008
2008 1985 2004
2000 2000 2000
2009 2010 2010 2009 2010 2010 2010 2007 2009
2010 2010 2009 2010 2010 2010 2010 2010 2010
2007 1999 2007 2000 2000 2005 2008 2000 2002
2010 2010
2010 2010 2009 2010
2000 2003
2010
2000
2011 2011 2010 2012 2009–10
2010 2010 2010 2010
2007
2005
2010 2010
2000 2000
2010–11 1996–97
2010 2010
2001 2008
2000 2000
2011–12 2010
2010 2010 2010 2010 2010
2005 2008
2012 2011
2010 2010 1997 2006 2010
2002 2000
2005–07 2007
2003 2006
2010 2010
2000 2000
2010 1978–79 2000
2010 2004 2010 2007 2010 2010 2010 2010 2010 2010 2010 2010
2010 2010 2010
2006 2003 2008
2010 2005 2011 2010 2010 2010 2010
2000 2005 2000 2000 2009 2009 2002 2005 2005 2009
2011
Yes Yes Yes Yes Yes Yes
Latest water withdrawal data
2010 1983 2010
Yes LSMS, 2003 DHS, 1996 RHS, 2004 DHS, 2007/08 DHS, 2008
Latest trade data
2008 2012 2012 2010 2009 2007 2000–01 2010
2008
2010
2009 2010
2006 2008 2009
2010 2009 2010
2010 2010 2010
2001 2000
2011
2005
2010
1993
Yes
2012 World Development Indicators
397
PRIMARY DATA DOCUMENTATION Currency
National accounts
Base year
Saudi Arabia Senegal Serbia Seychelles Sierra Leone Singapore Sint Maarten
Saudi Arabian riyal 1999 CFA franc 1999 a New Serbian dinar Seychelles rupee 1986 Sierra Leonean leone 1990 Singapore dollar 2005 Netherlands Antilles guilder Slovak Republic Euro 2005 a Slovenia Euro Solomon Islands Solomon Islands dollar 2004 Somalia Somali shilling 1985 South Africa South African rand 2005 South Sudan South Sudanese Pound Spain Euro 2005 Sri Lanka Sri Lankan rupee 2002 St. Kitts and Nevis East Caribbean dollar 2006 St. Lucia East Caribbean dollar 2006 St. Martin Euro St. Vincent & Grenadines East Caribbean dollar 2006 Sudan Sudanese pound 1981/82e Suriname Suriname dollar 1990 Swaziland Swaziland lilangeni 2000 a Sweden Swedish krona Switzerland Swiss franc 2005 Syrian Arab Republic Syrian pound 2000 a Tajikistan Tajik somoni a Tanzania Tanzanian shilling Thailand Thai baht 1988 Timor-Leste U.S. dollar 2000 Togo CFA franc 1978 Tonga Tongan pa’anga 2000/01 Trinidad and Tobago Trinidad and 2000 Tobago dollar Tunisia Tunisian dinar 1990 Turkey New Turkish lira 1998 a Turkmenistan New Turkmen manat Turks and Caicos Islands Tuvalu Uganda Ukraine United Arab Emirates United Kingdom United States Uruguay Uzbekistan Vanuatu Venezuela, R.B. Vietnam Virgin Islands (U.S.) West Bank and Gaza Yemen, Rep. Zambia Zimbabwe
398
U.S. dollar Australian dollar Ugandan shilling 2001/02 a Ukrainian hryvnia U.A.E. dirham 2007 Pound sterling 2005 a U.S. dollar Uruguayan peso 2005 a Uzbek sum Vanuatu vatu 2006 Venezuelan bolivar fuerte 1997 Vietnamese dong 1994 U.S. dollar 1982 Israeli new shekel 1997 Yemeni rial 1990 Zambian kwacha 1994 U.S. dollar 2009
2012 World Development Indicators
SNA System of price Reference National Accounts valuation year
1987 2002
b b
b b
b
2005
b
b
b
b
1996 b
2005
2000 2001
b
b
2007
2003
b
b
b
2005 1997
b
b
Balance of payments and trade
Alternative conversion factor
PPP survey year
VAP VAB VAB VAP VAB VAB
2005 2005 2005
VAB VAB VAB VAB VAB
2005 2005
2005 2005
VAB VAB VAB
VAP VAB VAB VAP VAB VAB VAB VAB VAB VAB VAP VAB VAP VAB VAB
BPM5 BPM5 BPM5 BPM5 BPM5 BPM5
BPM5 BPM5 BPM5
System of trade
Accounting concept
Actual Actual Actual Actual
S G S G S G
B C C B C
2005
BPM5
2005 2005
BPM5 BPM5 BPM5 BPM5
Actual Actual Actual
2005
1970–2010 1990–95
External debt
Actual Estimate Preliminary
1977–90
VAB VAP VAB VAB VAB VAB VAB VAB VAB VAB VAB VAB VAB VAP VAP VAP VAB VAB
Balance of Payments Manual in use
2005 2005 2005 2005 2005 2005 2005 2005
2005 2005 1987–95, 1997–2007
Actual Actual Actual
Actual Actual Actual Actual
BPM5 BPM5 BPM5
Actual Actual
BPM5 BPM5 BPM4
Actual Actual Estimate
1991
2005 2005
BPM5 BPM5 BPM5 BPM5 BPM5 BPM5 BPM4 BPM5 BPM5 BPM5
Actual Actual Estimate Actual Preliminary
1990–96 1990–92 1991, 1998
2005 2005 2005
BPM5 BPM5 BPM5 BPM4
Actual Preliminary Actual
1987–95
2005 2005
BPM5 BPM5 BPM5 BPM5 BPM5 BPM5 BPM5 BPM5 BPM5 BPM5
2005 2005 2005 1990–95
Government IMF data finance dissemination standard
Actual Actual
G G G G G S
S S S
C C
S S G
G
C
S
S G S S
C B C
S G G G
S G G G G S S G G S G S G S
B B
G G G G S S G G G S
G S G G G G G G G G G G G G G G S S S G
B C C C C C B C
G G G
C B
S S
B C B C C C
G S G S S S
C C
G G G
B B B C
G G G G
PRIMARY DATA DOCUMENTATION Latest population census
Latest demographic, education, or health household survey
Saudi Arabia Senegal Serbia Seychelles Sierra Leone Singapore Sint Maarten
2010 2002 2011 2010 2004 2010
Demographic survey, 2007 DHS, 2005; MIS, 2008/09 MICS, 2005/06
Slovak Republic Slovenia Solomon Islands Somalia South Africa South Sudan Spain Sri Lanka St. Kitts and Nevis St. Lucia St. Martin St. Vincent & Grenadines Sudan Suriname Swaziland Sweden Switzerland Syrian Arab Republic Tajikistan Tanzania Thailand Timor-Leste Togo Tonga Trinidad and Tobago
2011 2011 2009 1987 2001 2008 2001 2001 2011 2010
2010 2004 2010 2002 2010 2010 2010 2006 2011
Tunisia Turkey Turkmenistan
2004 2000 1995
Turks and Caicos Islands Tuvalu Uganda Ukraine United Arab Emirates United Kingdom United States Uruguay Uzbekistan Vanuatu Venezuela, R.B. Vietnam Virgin Islands (U.S.) West Bank and Gaza Yemen, Rep. Zambia Zimbabwe
2001 2002 2002 2001 2010 2011 2010 2004 1989 2009 2001 2009 2010 2007 2004 2010 2002
2011 2008 2004 2007
DHS, 2008 General household, 2005
Source of most recent income and expenditure data
PS, 2005 IHS, 2009 IHS, 2007 IHS, 2003
Vital Latest Latest registration agricultural industrial complete census data
Yes Yes
MICS, 2006 DHS, 2003
ES/BS, 2009
DHS, 2006/07
IHS, 2000 ES/BS, 2007 IHS, 1995
Yes Yes
2010 2010 2010 2009 2003 2010
2010 2011 2008 2008 2002 2010
2006 2002 2009 2005 2000
2001 2010 2012
2010 2010 2009 1990 2010
2010 2010 2007 1982 2010
2007 2009
2009 2010 2010 2010
2010 2010 2008 2008
2008 2005
2010 2010 2009 2010 2010 2010 2002 2010 2010 2010 2000 2005 2010 2010
2010 2009 2010 2007 2010 2010 2008 2000 2011 2010 2005 2010 2010 2010
1995 2000 f 2000 2000 2007 2000 2005 2000 2002 2007 2004 2002
2010 2010 2004
2010 2010 2000
2001 2003 2000
2012 Yes Yes Yes Yes
1999 2012
Yes MICS, 2010 MICS, 2006 MICS, 2010
MICS, 2006 MICS, 2005 DHS, 2010 MICS, 2005/06 DHS, 2009/10 MICS, 2010
ES/BS, 2009 ES/BS, 1999 ES/BS, 2010 IS, 2000 ES/BS, 2000 ES/BS, 2004 LSMS, 2009 ES/BS, 2007 IHS, 2009 LSMS, 2007 CWIQ, 2006
MICS, 2006
IHS, 1992
MICS, 2006 DHS, 2003 MICS, 2006
IHS, 2005/06 LFS, 2008 LSMS, 1998
d
Yes Yes Yes
Yes Yes
2008 2003 2010 2008 2014 2013 2007–08 2013 2012 2011–12 2011–12 2004 2004 2001
Yes Yes
DHS, 2006; MIS, 2009/10 DHS, 2007
CPS (monthly) MICS, 2006 MICS, 2007 MICS, 2000 MICS, 2006 PAPFAM, 2006 MICS, 2006 DHS, 2007 DHS, 2005/06
PS, 2009 ES/BS, 2009
Yes
IS, 1999 LFS, 2000 IHS, 2010 ES/BS, 2003
Yes Yes Yes Yes
IHS, 2009 IHS, 2008
Yes Yes Yes
IHS, 2009 ES/BS, 2005 IHS, 2006 IHS, 2003
Latest water withdrawal data
2010 2011–12 2012 2011 1984–85
Yes
IS, 2009 ES/BS, 2004
Latest trade data
2008 2011 2012 2010 2007 2011 2007 2007 2011 2007 1971 2002 1990 1960
2008 2010 2010 2010 2010 2010 2010 2010 2009 2007 2010
2010 2010 2010
2009 2008 2010 2010 2009 2010 2010 2009
2003 2000
2005
2000
2002 2000 2005 2006 2005 2000 2000
2007 2010 2009
2000 2005
2008 2009 2010 2010
2005 2000 2002
Note: For explanation of the abbreviations used in the table, see notes following the table. a. Original chained constant price data are rescaled. b. Country uses the 1993 System of National Accounts methodology. c. Register based. d. Rolling. e. Reporting period switch from fiscal year to calendar year from 1996. Pre-1996 data converted to calendar year.
2012 World Development Indicators
399
Primary data documentation notes • Base year is the base or pricing period used for
staff estimation, and estimate that data are World
from the very recent censuses could be reflected in
constant price calculations in the country’s national
Bank staff estimates. • System of trade refers to
timely revisions if basic data are available, such as
accounts. Price indexes derived from national
the United Nations general trade system (G) or spe-
population by age and sex, as well as the detailed
accounts aggregates, such as the implicit deflator
cial trade system (S). Under the general trade system
definition of counting, coverage, and completeness.
for gross domestic product (GDP), express the price
goods entering directly for domestic consumption
Countries that hold register-based censuses produce
level relative to base year prices. • Reference year
and goods entered into customs storage are
similar census tables every 5 or 10 years. Germany’s
is the year in which the local currency, constant price
recorded as imports at arrival. Under the special
2001 census is a register-based test census using
series of a country is valued. The reference year is
trade system goods are recorded as imports when
a sample of 1.2 percent of the population. A rare
usually the same as the base year used to report the
declared for domestic consumption whether at time
case, France has been conducting a rolling census
constant price series. However, when the constant
of entry or on withdrawal from customs storage.
every year since 2004; the 1999 general population
price data are chain linked, the base year is changed
Exports under the general system comprise outward-
census was the last to cover the entire population
annually, so the data are rescaled to a specific refer-
moving goods: (a) national goods wholly or partly
simultaneously (www.insee.fr/en/recensement/
ence year to provide a consistent time series. When
produced in the country; (b) foreign goods, neither
page_accueil_rp.htm). • Latest demographic, edu-
the country has not rescaled following a change in
transformed nor declared for domestic consumption
cation, or health household survey indicates the
base year, World Bank staff rescale the data to main-
in the country, that move outward from customs stor-
household surveys used to compile the demographic,
tain a longer historical series. To allow for cross-
age; and (c) nationalized goods that have been
education, and health data in section 2. AIS is HIV/
country comparison and data aggregation, constant
declared for domestic consumption and move out-
AIDS Indicator Survey, CPS is Current Population
price data reported in World Development Indicators
ward without being transformed. Under the special
Survey, DGHS is Demographic and General Health
are rescaled to a common reference year (2000) and
system of trade, exports are categories a and c. In
Survey, DHS is Demographic and Health Survey,
currency (U.S. dollars). • System of National
some compilations categories b and c are classified
ENPF is National Family Planning Survey (Encuesta
Accounts identifies countries that use the 1993
as re-exports. Direct transit trade—goods entering
Nacional de Planificacion Familiar), FHS is Family
System of National Accounts (1993 SNA), the termi-
or leaving for transport only—is excluded from both
Health Survey, LSMS is Living Standards Measure-
nology applied in World Development Indicators since
import and export statistics. See About the data for
ment Survey, MICS is Multiple Indicator Cluster Sur-
2001, to compile national accounts. Although more
tables 4.4, 4.5, and 6.2 for further discussion.
vey, MIS is Malaria Indicator Survey, NSS is National
countries are adopting the 1993 SNA, many still fol-
• Government finance accounting concept is the
Sample Survey on Population Change, PAPFAM is Pan
low the 1968 SNA, and some low-income countries
accounting basis for reporting central government
Arab Project for Family Health, RHS is Reproductive
use concepts from the 1953 SNA. • SNA price valu-
fi nancial data. For most countries government
Health Survey, and SPA is Service Provision Assess-
ation shows whether value added in the national
finance data have been consolidated (C) into one set
ments. Detailed information for AIS, DHS, MIS, and
accounts is reported at basic prices (VAB) or pro-
of accounts capturing all central government fiscal
SPA are available at www.measuredhs.com/
ducer prices (VAP). Producer prices include taxes
activities. Budgetary central government accounts
aboutsurveys; for MICS at www.childinfo.org; and for
paid by producers and thus tend to overstate the
(B) exclude some central government units. See
RHS at www.cdc.gov/reproductivehealth/surveys.
actual value added in production. However, VAB can
About the data for tables 4.12, 4.13, and 4.14 for
• Source of most recent income and expenditure
be higher than VAP in countries with high agricultural
further details. • IMF data dissemination standard
data shows household surveys that collect income
subsidies. See About the data for tables 4.1 and 4.2
shows the countries that subscribe to the IMF’s Spe-
and expenditure data. Names and detailed informa-
for further discussion of national accounts valuation.
cial Data Dissemination Standard (SDDS) or General
tion on household surveys can be found on the
• Alternative conversion factor identifies the coun-
Data Dissemination System (GDDS). S refers to coun-
website of the International Household Survey Net-
tries and years for which a World Bank–estimated
tries that subscribe to the SDDS and have posted
work (www.surveynetwork.org). Core Welfare Indica-
conversion factor has been used in place of the offi -
data on the Dissemination Standards Bulletin Board
tor Questionnaire Surveys (CWIQ), developed by the
cial exchange rate (line rf in the International Mon-
at http://dsbb.imf.org. G refers to countries that sub-
World Bank, measure changes in key social indica-
etary Fund’s [IMF] International Financial Statistics).
scribe to the GDDS. The SDDS was established for
tors for different population groups— specifi cally
See Statistical methods for further discussion of
member countries that have or might seek access
indicators of access, utilization, and satisfaction
alternative conversion factors. • Purchasing power
to international capital markets to guide them in pro-
with core social and economic services. Expenditure
parity (PPP) survey year is the latest available sur-
viding their economic and financial data to the public.
survey/budget surveys (ES/BS) collect detailed infor-
vey year for the International Comparison Program’s
The GDDS helps countries disseminate comprehen-
mation on household consumption as well as on
estimates of PPPs. See About the data for table 1.1
sive, timely, accessible, and reliable economic, finan-
general demographic, social, and economic charac-
for a more detailed description of PPPs. • Balance
cial, and sociodemographic statistics. IMF member
teristics. Integrated household surveys (IHS) collect
of Payments Manual in use refers to the classifica-
countries elect to participate in either the SDDS or
detailed information on a wide variety of topics,
tion system used to compile and report data on bal-
the GDDS. Both standards enhance the availability
including health, education, economic activities,
ance of payments items in table 4.17. BPM4 refers
of timely and comprehensive data and therefore con-
housing, and utilities. Income surveys (IS) collect
to the 4th edition of the IMF’s Balance of Payments
tribute to the pursuit of sound macroeconomic poli-
information on the income and wealth of households
Manual (1977), and BPM5 to the 5th edition (1993).
cies. The SDDS is also expected to improve the
as well as various social and economic characteris-
• External debt shows debt reporting status for
functioning of financial markets. • Latest population
tics. Labor force surveys (LFS) collect information on
2010 data. Actual indicates that data are as
census shows the most recent year in which a cen-
employment, unemployment, hours of work, income,
reported, preliminary that data are based on reported
sus was conducted and in which at least preliminary
and wages. Living Standards Measurement Studies
or collected information but include an element of
results have been released. The preliminary results
(LSMS), developed by the World Bank, provide a
400
2012 World Development Indicators
Primary data documentation notes comprehensive picture of household welfare and the
the fiscal period; if the fiscal year ends on or after
line with State Statistical Committee data that were
factors that affect it; they typically incorporate data
June 30, data are shown in the second year of the
not previously available. • Botswana. The Central
collection at the individual, household, and commu-
period. Balance of payments data are reported in
Statistical Offi ce has revised national accounts
nity levels. Priority surveys (PS) are a light monitoring
World Development Indicators by calendar year.
series for 2004 onward. • Dominica. Based on offi -
survey, designed by the World Bank, for collecting data from a large number of households cost-effec-
cial government statistics, national accounts data Economies with exceptional reporting periods
tively and quickly. Income tax registers (ITR) provide
have been revised for 2000 onward; the new base year is 2006. • Grenada. Based on official govern-
Economy
Fiscal year end
Reporting period for national accounts data
Afghanistan
Mar. 20
FY
Australia
Jun. 30
FY
Bangladesh
Jun. 30
FY
Botswana
Jun. 30
FY
Canada
Mar. 31
CY
Egypt, Arab Rep.
Jun. 30
FY
Jul. 7
FY
Gambia, The
Jun. 30
CY
and reported in Population and Vital Statistics
Haiti
Sep. 30
FY
Based on official government statistics, data have
Reports. Countries with complete vital statistics reg-
India
Mar. 31
FY
been revised for 1991 onward; the new base year
istries may have more accurate and more timely
Indonesia
Mar. 31
CY
for constant price series is 2004. • Philippines.
demographic indicators than other countries. • Lat-
Iran, Islamic Rep.
Mar. 20
FY
National accounts data have been revised for 1998
est agricultural census shows the most recent year
Japan
Mar. 31
CY
onward. Because intellectual property products are
in which an agricultural census was conducted and
Kenya
Jun. 30
CY
now reported as a part of gross fixed capital forma-
reported to the Food and Agriculture Organization of
Kuwait
Jun. 30
CY
tion, gross domestic product (GDP) in current prices
the United Nations. • Latest industrial data show
Lesotho
Mar. 31
CY
averages 4 percent higher than previous estimates.
the most recent year for which manufacturing value
Malawi
Mar. 31
CY
• Puerto Rico. Based on data from the Instituto de
added data at the three-digit level of the International
Myanmar
Mar. 31
FY
Estadísticas de Puerto Rico, national accounts data
Namibia
Mar. 31
CY
information on a population’s income and allowance, such as gross income, taxable income, and taxes by socioeconomic group. 1-2-3 surveys (1-2-3) are implemented in three phases and collect sociodemographic and employment data, data on the informal sector, and information on living conditions and household consumption. • Vital registration complete identifies countries that report at least 90 percent complete registries of vital (birth and death) statistics to the United Nations Statistics Division
Standard Industrial Classification (ISIC, revision 2 or 3) are available in the United Nations Industrial Development Organization database. • Latest trade data show the most recent year for which structure of merchandise trade data from the United Nations
Ethiopia
Nepal
Jul. 14
FY
New Zealand
Mar. 31
FY
Pakistan
Jun. 30
FY
ment statistics, national accounts data have been revised for 2000 onward; the new base year is 2006. • Hungary. Based on data from the Organisation for Economic Co-operation and Development, national accounts data have been revised for 1991 onward. • Macedonia. Based on official statistics, national accounts data have been revised for 2003 onward. • Maldives. The Department of National Planning has revised national accounts data for 2000 onward; the new base year is 2003. • Mauritania.
have been revised for 2001 onward. • Serbia. The Statistical Office has improved the methodology of national accounts data for 2003 onward. Specifically, the classification of sectors has been revised, and GDP is now calculated using chain linked volumes in
Puerto Rico
Jun. 30
FY
Sierra Leone
Jun. 30
CY
Singapore
Mar. 31
CY
South Africa
Mar. 31
CY
variety of sources. See About the data for table 3.5
Swaziland
Mar. 31
CY
government statistics, national accounts data have
for more information.
Sweden
Jun. 30
CY
been revised for 2000 onward; the new base year
Thailand
Sep. 30
CY
is 2006. • Swaziland. The Central Statistical Office
Exceptional reporting periods
Uganda
Jun. 30
FY
has revised national accounts data for 1990 onward.
In most economies the fiscal year is concurrent with
United States
Sep. 30
CY
• Syria. The Central Bureau of Statistics has revised
the calendar year. Exceptions are shown in the table
Zimbabwe
Jun. 30
CY
national accounts data for 2003 onward. • Tunisia.
Statistics Division’s Commodity Trade (Comtrade) database are available. • Latest water withdrawal data show the most recent year for which data on freshwater withdrawals have been compiled from a
2005 prices. • Singapore. National accounts time series have been replaced with official government statistics. • St. Kitts and Nevis, St. Lucia, and St. Vincent and the Grenadines. Based on official
Based on data from the Central Bank and its Sta-
at right. The ending date reported here is for the fiscal year of the central government. Fiscal years for
Revisions to national accounts data
tistical Bulletin, national accounts data have been
other levels of government and reporting years for
National accounts data are revised by national statisti-
revised for 1997 onward. • Uganda. The Bureau of
statistical surveys may differ.
cal offices when methodologies change or data sources
Statistics has revised national accounts series for
The reporting period for national accounts data
improve. National accounts data in World Develop-
1998 onward; the new base year for constant price
is designated as either calendar year basis (CY) or
ment Indicators are also revised when data sources
series is 2001/02. • United Arab Emirates. The
fiscal year basis (FY). Most economies report their
change. The following notes, while not comprehensive,
National Bureau of Statistics has revised national
national accounts and balance of payments data
provide information on revisions from previous data.
accounts data for 2001 onward; the new base year
using calendar years, but some use fiscal years. In
• Antigua and Barbuda. Based on official govern-
is 2007. • Uruguay. The Central Bank has revised
World Development Indicators fiscal year data are
ment statistics, national accounts data have been
national accounts data for 2006 onward. • Yemen.
assigned to the calendar year that contains the larger
revised for 2000 onward; the new base year is 2006.
Based on official government statistics and Interna-
share of the fiscal year. If a country’s fiscal year ends
• Azerbaijan. National accounts historical expendi-
tional Monetary Fund data, national accounts data
before June 30, data are shown in the first year of
ture series in constant prices have been revised in
have been revised for 1990 onward.
2012 World Development Indicators
401
STATISTICAL METHODS This section describes some of the statistical procedures used in preparing World
indicator as a weight) and denoted by a u when calculated as unweighted
Development Indicators. It covers the methods employed for calculating regional
averages. The aggregate ratios are based on available data, including data
and income group aggregates and for calculating growth rates, and it describes the
for economies not shown in the main tables. Missing values are assumed
World Bank Atlas method for deriving the conversion factor used to estimate gross
to have the same average value as the available data. No aggregate is cal-
national income (GNI) and GNI per capita in U.S. dollars. Other statistical procedures
culated if missing data account for more than a third of the value of weights
and calculations are described in the About the data sections following each table.
in the benchmark year. In a few cases the aggregate ratio may be computed as the ratio of group totals after imputing values for missing data according
Aggregation rules Aggregates based on the World Bank’s regional and income classifications of econo-
to the above rules for computing totals. •
Aggregate growth rates are denoted by a w when calculated as a weighted
mies appear at the end of most tables. The countries included in these classifications
average of growth rates. In a few cases growth rates may be computed from
are shown on the flaps on the front and back covers of the book. Most tables also
time series of group totals. Growth rates are not calculated if more than half
include the aggregate euro area. This aggregate includes the member states of the
the observations in a period are missing. For further discussion of methods
Economic and Monetary Union (EMU) of the European Union that have adopted the euro as their currency: Austria, Belgium, Cyprus, Estonia, Finland, France, Germany, Greece, Ireland, Italy, Luxembourg, Malta, Netherlands, Portugal, Slovak Republic,
of computing growth rates see below. •
Aggregates denoted by an m are medians of the values shown in the table. No value is shown if more than half the observations for countries with a
Slovenia, and Spain. Other classifications, such as the European Union and regional
population of more than 1 million are missing.
trade blocs, are documented in About the data for the tables in which they appear.
Exceptions to the rules occur throughout the book. Depending on the judgment
Because of missing data, aggregates for groups of economies should be
of World Bank analysts, the aggregates may be based on as little as 50 percent of
treated as approximations of unknown totals or average values. Regional and
the available data. In other cases, where missing or excluded values are judged to
income group aggregates are based on the largest available set of data, includ-
be small or irrelevant, aggregates are based only on the data shown in the tables.
ing values for the 158 economies shown in the main tables, other economies shown in table 1.6, and Taiwan, China. The aggregation rules are intended to
Growth rates
yield estimates for a consistent set of economies from one period to the next and
Growth rates are calculated as annual averages and represented as percentages.
for all indicators. Small differences between sums of subgroup aggregates and
Except where noted, growth rates of values are computed from constant price
overall totals and averages may occur because of the approximations used. In
series. Three principal methods are used to calculate growth rates: least squares,
addition, compilation errors and data reporting practices may cause discrepan-
exponential endpoint, and geometric endpoint. Rates of change from one period
cies in theoretically identical aggregates such as world exports and world imports.
to the next are calculated as proportional changes from the earlier period.
Five methods of aggregation are used in World Development Indicators: •
For group and world totals denoted in the tables by a t, missing data are
Least squares growth rate. Least squares growth rates are used wherever
imputed based on the relationship of the sum of available data to the total
there is a sufficiently long time series to permit a reliable calculation. No growth
in the year of the previous estimate. The imputation process works forward
rate is calculated if more than half the observations in a period are missing.
and backward from 2000. Missing values in 2000 are imputed using one of
The least squares growth rate, r, is estimated by fitting a linear regression trend
several proxy variables for which complete data are available in that year. The
line to the logarithmic annual values of the variable in the relevant period. The
imputed value is calculated so that it (or its proxy) bears the same relation-
regression equation takes the form
ship to the total of available data. Imputed values are usually not calculated if missing data account for more than a third of the total in the benchmark
ln Xt = a + bt
year. The variables used as proxies are GNI in U.S. dollars, total population, exports and imports of goods and services in U.S. dollars, and value added in agriculture, industry, manufacturing, and services in U.S. dollars. •
•
which is the logarithmic transformation of the compound growth equation, Xt = Xo (1 + r ) t.
Aggregates marked by an s are sums of available data. Missing values are not imputed. Sums are not computed if more than a third of the observations
In this equation X is the variable, t is time, and a = ln Xo and b = ln (1 + r) are
in the series or a proxy for the series are missing in a given year.
parameters to be estimated. If b* is the least-squares estimate of b, then the
Aggregates of ratios are denoted by a w when calculated as weighted averages
average annual growth rate, r, is obtained as [exp(b*) – 1] and is multiplied by
of the ratios (using the value of the denominator or, in some cases, another
100 for expression as a percentage. The calculated growth rate is an average
402
2012 World Development Indicators
rate that is representative of the available observations over the entire period.
“SDR deflator.” (Special drawing rights, or SDRs, are the International Monetary
It does not necessarily match the actual growth rate between any two periods.
Fund’s unit of account.) The SDR deflator is calculated as a weighted average of these countries’ GDP deflators in SDR terms, the weights being the amount of
Exponential growth rate. The growth rate between two points in time for cer-
each country’s currency in one SDR unit. Weights vary over time because both
tain demographic indicators, notably labor force and population, is calculated
the composition of the SDR and the relative exchange rates for each currency
from the equation
change. The SDR deflator is calculated in SDR terms first and then converted to U.S. dollars using the SDR to dollar Atlas conversion factor. The Atlas converr = ln(pn/p 0)/n
sion factor is then applied to a country’s GNI. The resulting GNI in U.S. dollars is divided by the midyear population to derive GNI per capita.
where pn and p 0 are the last and first observations in the period, n is the number
When official exchange rates are deemed to be unreliable or unrepresenta-
of years in the period, and ln is the natural logarithm operator. This growth rate is
tive of the effective exchange rate during a period, an alternative estimate of the
based on a model of continuous, exponential growth between two points in time.
exchange rate is used in the Atlas formula (see below).
It does not take into account the intermediate values of the series. Nor does it correspond to the annual rate of change measured at a one-year interval, which
The following formulas describe the calculation of the Atlas conversion factor for year t:
is given by (pn – pn–1)/pn–1. Geometric growth rate. The geometric growth rate is applicable to compound growth over discrete periods, such as the payment and reinvestment of interest or dividends. Although continuous growth, as modeled by the exponential growth rate, may be more realistic, most economic phenomena are measured only at
and the calculation of GNI per capita in U.S. dollars for year t:
intervals, in which case the compound growth model is appropriate. The average Yt$ = (Yt/Nt)/et*
growth rate over n periods is calculated as r = exp[ln(pn/p 0)/n] – 1.
where et* is the Atlas conversion factor (national currency to the U.S. dollar) for year t, et is the average annual exchange rate (national currency to the U.S. dollar) for year t, pt is the GDP deflator for year t, ptS$ is the SDR deflator in U.S.
World Bank Atlas method
dollar terms for year t, Yt$ is the Atlas GNI per capita in U.S. dollars in year t, Yt is
In calculating GNI and GNI per capita in U.S. dollars for certain operational
current GNI (local currency) for year t, and Nt is the midyear population for year t.
purposes, the World Bank uses the Atlas conversion factor. The purpose of the Atlas conversion factor is to reduce the impact of exchange rate fluctuations in
Alternative conversion factors
the cross-country comparison of national incomes.
The World Bank systematically assesses the appropriateness of official exchange
The Atlas conversion factor for any year is the average of a country’s exchange
rates as conversion factors. An alternative conversion factor is used when the
rate (or alternative conversion factor) for that year and its exchange rates for the
official exchange rate is judged to diverge by an exceptionally large margin from
two preceding years, adjusted for the difference between the rate of inflation in
the rate effectively applied to domestic transactions of foreign currencies and
the country and that in Japan, the United Kingdom, the United States, and the
traded products. This applies to only a small number of countries, as shown
euro area. A country’s inflation rate is measured by the change in its GDP deflator.
in Primary data documentation. Alternative conversion factors are used in the
The inflation rate for Japan, the United Kingdom, the United States, and the
Atlas methodology and elsewhere in World Development Indicators as single-year
euro area, representing international inflation, is measured by the change in the
conversion factors.
2012 World Development Indicators
403
CREDITS 1. World view
and Edward Gillin of the Food and Agriculture Organization of the United Nations;
Section 1 was prepared by a team led by Eric Swanson. Eric Swanson wrote the
Ricardo Quercioli and Karen Treanton of the International Energy Agency; Laura
introduction with input from Neil Fantom, Shota Hatakeyama, Masako Hiraga,
Battlebury of the World Conservation Monitoring Centre; Gerhard Metchies and
Wendy Huang, Hiroko Maeda, Johan Mistiaen, Sulekha Patel, William Prince, Evis
Armin Wagner of German International Cooperation; and Craig Hilton-Taylor and
Rucaj, and Emi Suzuki and with valuable suggestions from Jose Alejandro Quijada,
Caroline Pollock of the International Union for Conservation of Nature. The World
Sachin Shahria, Jos Verbeek, and participants of the Global Monitoring Report
Bank’s Environment Department devoted substantial staff resources.
2012 seminar series. Bala Bhaskar Naidu Kalimili coordinated tables 1.1 and 1.6. Shota Hatakeyama, Masako Hiraga, Hiroko Maeda, and Johan Mistiaen prepared
4. Economy
tables 1.2 and 1.5, and Mahyar Eshragh-Tabary, Shota Hatakeyama, Masako
Section 4 was prepared by Bala Bhaskar Naidu Kalimili in close collaboration
Hiraga, Buyant Erdene Khaltarkhuu, and Hiroko Maeda prepared table 1.3. Wendy
with the Sustainable Development and Economic Data Team of the World Bank’s
Huang prepared table 1.4 with input from Azita Amjadi. Signe Zeikate of the
Development Data Group and with valuable suggestions from Liu Cui and William
World Bank’s Economic Policy and Debt Department provided the estimates of
Prince. Bala Bhaskar Naidu Kalimili wrote the introduction with valuable sugges-
debt relief for the Heavily Indebted Poor Countries Debt Initiative and Multilateral
tions from Eric Swanson. Azita Amjadi and Esther G. Naikal also contributed to
Debt Relief Initiative.
the section. The national accounts data for low- and middle-income economies were gathered by the World Bank’s regional staff through the annual Unified
2. People
Survey. Maja Bresslauer, Mahyar Eshragh-Tabary, Bala Bhaskar Naidu Kalimili,
Section 2 was prepared by Shota Hatakeyama, Masako Hiraga, Hiroko Maeda, and
Buyant Erdene Khaltarkhuu, and Maurice Nsabimana updated, estimated, and
Sulekha Patel in partnership with the World Bank’s Human Development Network
validated the databases for national accounts. The team is grateful to Eurostat,
and the Development Research Group in the Development Economics Vice Presi-
the International Monetary Fund, Organisation for Economic Co-operation and
dency. Masako Hiraga, Sulekha Patel, and Johan Mistiaen wrote the introduction
Development, United Nations Industrial Development Organization, and World
with valuable input and comments from Eric Swanson. Emi Suzuki prepared the
Trade Organization for access to their databases.
demographic estimates and projections. The poverty estimates at national poverty lines were compiled by the Global Poverty Working Group, a team of poverty experts
5. States and markets
from the Poverty Reduction and Equality Network, the Development Research
Section 5 was prepared by Buyant Erdene Khaltarkhuu in partnership with the
Group, and the Development Data Group. Shaohua Chen and Prem Sangraula of
World Bank’s Financial and Private Sector Development Network, Poverty Reduc-
the World Bank’s Development Research Group prepared the poverty estimates at
tion and Economic Management Network, and Sustainable Development Net-
international poverty lines. Lorenzo Guarcello and Furio Rosati of the Understanding
work; the International Finance Corporation; and external partners. David Cies-
Children’s Work project prepared the data on children at work. Other contributions
likowski and Buyant Erdene Khaltarkhuu wrote the introduction with input from
were provided by Emi Suzuki (health and nutrition); Montserrat Pallares-Miralles and
Eric Swanson. Other contributors include Ada Karina Izaguirre (privatization and
Carolina Romero Robayo (vulnerability and security); Theo door Sparreboom and
infrastructure projects); Leora Klapper (business registration); Federica Saliola
Alan Wittrup of the International Labour Organization (labor force); Amelie Gagnon,
and Joshua Wimpey (Enterprise Surveys); Carolin Geginat and Frederic Meunier
Said Ould Voffal, and Weixin Lu of the United Nations Educational, Scientific, and
(Doing Business); Alka Banerjee, Trisha Malinky, and Michael Orzano (Standard
Cultural Organization Institute for Statistics (education and literacy); the World
& Poor’s global stock market indexes); Oya Pinar Ardic Alper (financial access);
Health Organization’s Chandika Indikadahena (health expenditure), Charu Garg
Gary Milante, Holly Benner, and Kenneth Anya (fragile situations); Satish Mannan
(national health account), Monika Bloessner and Mercedes de Onis (malnutrition
(public policies and institutions); James Hackett of the International Institute
and overweight), Teena Kunjument (health workers), Jessica Ho (hospital beds),
for Strategic Studies (military personnel); Sam Perlo-Freeman of the Stockholm
Rifat Hossain (water and sanitation), and Hazim Timimi (tuberculosis); Leonor Guar-
International Peace Research Institute (military expenditures and arms transfers);
iguata of the International Diabetes Federation (diabetes); and Colleen Murray
Christian Gonzalez of the International Road Federation, Narjess Teyssier and
of the United Nations Children’s Fund (health). Eric Swanson provided valuable
Zubair Anwar of the International Civil Aviation Organization, and Hélène Stephan
comments and suggestions on the introduction and at all stages of production.
and Marc Juhel (transport); Jane Degerlund of Containerisation International and Vincent Valentine of the United Nations Conference on Trade and Development
3. Environment
(ports); Azita Amjadi (high-tech exports); Vanessa Grey, Esperanza Magpantay, and
Section 3 was prepared by Mahyar Eshragh-Tabary in partnership with the Environ-
Susan Teltscher of the International Telecommunication Union; Torbjörn Fredriks-
ment Department of the Sustainable Development Vice Presidency of the World
son and Rémi Lang of the United Nations Conference on Trade and Development
Bank. Mahyar Eshragh-Tabary wrote the introduction with input from Neil Fantom
(information and communication technology goods trade); Martin Schaaper of
and Eric Swanson and with valuable suggestions from Jane Ebinger, Tim Herzog,
the United Nations Educational, Scientific, and Cultural Organization Institute for
Glenn-Marie Lange, and Soong Sup Lee. Esther G. Naikal and William Prince also
Statistics (research and development, researchers, and technicians); and Ryan
contributed to the section. Other contributors include Kirk Hamilton; Carola Fabi
Lamb of the World Intellectual Property Organization (patents and trademarks).
404
2012 World Development Indicators
6. Global links Section 6 was prepared by Wendy Huang and Evis Rucaj with valuable input
Administrative assistance, office technology, and systems development support
from Uranbileg Batjargal and in partnership with the Financial Data Team of
Awatif Abuzeid, Elysee Kiti, Premi Ratham Raj, and Estela Zamora provided admin-
the World Bank’s Development Data Group, Development Research Group
istrative assistance. Jean-Pierre Djomalieu, Gytis Kanchas, and Nacer Megherbi
(trade), Development Prospects Group (commodity prices and remittances),
provided information technology support. Ramvel Chandrasekaran, Ugendran
International Trade Department (trade facilitation), and external partners.
Machakkalai, Shanmugam Natarajan, Atsushi Shimo, and Malarvizhi Veerappan
Malvina Pollock and Evis Rucaj wrote the introduction with substantial input
provided software support on the Development Data Platform application.
from Eric Swanson. Azita Amjadi (trade and tariffs) and Rubena Sukaj (external debt and fi nancial data) provided substantial input on the data and tables.
Publishing and dissemination
Other contributors include Frédéric Docquier (emigration rates); Flavine Creppy
The Office of the Publisher, under the direction of Carlos Rossel, provided valu-
and Yumiko Mochizuki of the United Nations Conference on Trade and Devel-
able assistance throughout the production process. Denise Bergeron, Dina Tow-
opment and Mondher Mimouni of the International Trade Centre (trade); Cris-
bin, Stephen McGroarty, and Nora Ridolfi coordinated printing and supervised
tina Savescu (commodity prices); Jeff Reynolds and Joseph Siegel of DHL
marketing and distribution. Merrell Tuck-Primdahl of the Development Economics
(freight costs); Yasmin Ahmad and Elena Bernaldo of the Organisation for
Vice President’s Office managed the communications strategy.
Economic Co-operation and Development (aid); Ibrahim Levent, Hiroko Maeda, and Maryna Taran (external debt); Sanket Mohapatra and Ani Rudra Silwal
World Development Indicators CD-ROM
(remittances); and Teresa Ciller of the World Tourism Organization (tourism).
Software preparation and testing was managed by Vilas Mandlekar with the
Ramgopal Erabelly, Shelley Lai Fu, and William Prince provided valuable tech-
assistance of Ramgopal Erabelly, Parastoo Oloumi, and William Prince. Systems
nical assistance.
development was undertaken by the Data and Information Systems Team led by Reza Farivari. William Prince coordinated user interface design and overall
Other parts of the book
production and provided quality assurance, with assistance from Jomo Tariku.
Jeff Lecksell of the World Bank’s Map Design Unit coordinated preparation of the maps on the inside covers. William Prince prepared Users guide. Eric Swanson
World Development Indicators mobile applications
wrote Statistical methods. Maja Bresslauer, Federico Escaler, and Buyant Erdene
Software preparation and testing was managed by Vilas Mandlekar and Shelley
Khaltarkhuu, prepared Primary data documentation. Alison Kwong prepared Part-
Fu with assistance from Azita Amjadi, Prashant Chaudhari, Ying Chi, Liu Cui,
ners and Index of indicators.
Ghislaine Delaine, Ramgopal Erabelly, Federico Escaler, Buyant Erdene Khaltarkhuu, Maurice Nsabimana Parastoo Oloumi, Beatriz Prieto Oramas, William
Database management
Prince, Virginia Romand, Jomo Tariku, and Vera Wen. Systems development was
William Prince coordinated management of the World Development Indicators
undertaken in the Data and Information Systems Team led by Reza Farivari. Wil-
database, with assistance from Liu Cui. Operation of the database management
liam Prince provided data quality assurance.
system was made possible by Ramgopal Erabelly and Shelley Fu in the Data and Information Systems Team under the leadership of Reza Farivari.
Open Data and online access Coordination of the Open Data website (http://data.worldbank.org) was provided
Design, production, and editing
by Neil Fantom. Design, programming, and testing were carried out by Reza
Azita Amjadi and Alison Kwong coordinated all stages of production with Com-
Farivari and his team: Azita Amjadi, Ramvel Chandrasekaran, Ying Chi, Federico
munications Development Incorporated, which provided overall design direc-
Escaler, Shelley Fu, Buyant Erdene Khaltarkhuu, Ugendran Machakkalai, Shan-
tion, editing, and layout, led by Meta de Coquereaumont, Bruce Ross-Larson,
mugam Natarajan, Atsushi Shimo, Sun Hwa Song, Lakshmikanthan Subrama-
and Christopher Trott and assisted by Rob Elson. Elaine Wilson created the
nian, Maryna Taran, Jomo Tariku, Malarvizhi Veerappan, and Vera Wen. William
cover and graphics and typeset the book. Joseph Caponio provided produc-
Prince coordinated production and provided quality assurance. Support from
tion assistance. Peter Grundy, of Peter Grundy Art & Design, designed the
the Corporate Communications Unit in External Affairs was provided by a team
report.
including George Gongadze and Jeffrey McCoy. The multilingual web team was led by James Edward Rosenberg.
Client services The Development Data Group’s Client Services and Communications Team (Fed-
Client feedback
erico Escaler, Alison Kwong, Beatriz Prieto-Oramas, Maryna Taran, Jomo Tariku,
The team is grateful to the many people who have taken the time to provide
and Vera Wen) contributed to the design and planning and helped coordinate work
feedback and suggestions, which have helped improve this year’s edition. Please
with the Office of the Publisher under the leadership of Azita Amjadi.
contact us at data@worldbank.org.
2012 World Development Indicators
405
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INDEX OF INDICATORS References are to table numbers. Aid
A
by recipient aid dependency ratios
Agriculture agricultural raw materials commodity prices
6.5
exports as share of total exports
4.4
from high-income economies as share of total exports
6.3
6.12
grants
6.12
per capita
6.12
technical cooperation
6.12
total
6.12
net disbursements as share of donor GNI
imports
1.4
from major donors, by recipient
6.13
as share of total imports
4.5
by high-income economies as share of total imports
6.3
bilateral aid
6.3
from bilateral sources
6.11
from international financial institutions
6.11
from multilateral sources
6.11
tariff rates applied by high-income countries
net flows
cereal area under production
3.2
yield
3.3
employment, as share of total
6.13
for basic social services, as share of sector-allocable bilateral
3.2
ODA commitments
1.4
fertilizer commodity prices
6.5
consumption, per hectare of arable land
3.2
food
AIDS—see HIV, prevalence Air pollution—see Pollution
commodity prices exports from high-income economies as share of total exports imports by high-income economies as share of total imports tariff rates applied by high-income countries freshwater withdrawals for, as share of total
6.5 4.4
Air transport
6.3
air freight
5.10
4.5
passengers carried
5.10
6.3
registered carrier departures worldwide
5.10
6.3 3.5
Antiretroviral therapy coverage
3.2
Asylum seekers—see Migration; Refugees
2.22
land agricultural, as share of land area arable as share of land area per 100 people
3.1 3.1
Balance of payments
3.2
Irrigated
3.2
current account balance
permanent cropland, as share of land area
3.1
as share of GDP
machinery tractors per 100 square kilometers of arable land
3.2
4.17 4.a
exports and imports of goods and services
4.17
net current transfers
4.17
net income
production indexes
4.17
crop
3.3
total reserves
food
3.3
See also Exports; Imports; Investment; Private financial flows; Trade
livestock
3.3
annual growth
4.1
as share of GDP
4.2
2012 World Development Indicators
4.a, 4.17
Base metals
value added
414
B
area under cereal production
commodity prices and price index
6.5
Battle-related deaths
5.8
time required
5.3
finance Beverages commodity prices
firms using banks to finance investment 6.5
5.2
gender female participation in ownership
Biodiversityâ&#x20AC;&#x201D;see Biological diversity
getting electricity
Biological diversity
informality
5.2
time required assessment, date prepared, by country GEF benefits index threatened species birds
3.17 3.4 3.4 3.4
treaty
5.2, 5.8
infrastructure value lost due to electrical outages
3.4
higher plants mammals
firms formally registered when operations started
3.4
fish
5.3
internationally recognized quality certification ownership
3.4
time required to obtain operating license
3.17
protecting investors
2.1
registering property
See also Fertility rate Births attended by skilled health staff
2.19
Birthweight, low
2.21
5.2
permits and licenses
disclosure, index Birth rate, crude
5.2
innovation
5.2 5.3
number of procedures
5.3
time to register
5.3
regulation and tax average number of times firms spend meeting with tax officials
5.2
time dealing with officials
5.2
resolving insolvency Bondsâ&#x20AC;&#x201D;see Debt flows; Private financial flows
time required
5.3
starting a business Brain drainâ&#x20AC;&#x201D;see Emigration of people with tertiary education to OECD countries
cost to start a business
5.3
number of start-up procedures
5.3
time to start a business
5.3
Breastfeeding, exclusive
2.21
trade
Broad money
4.15
workforce
average time to clear direct exports firms offering formal training
5.2 5.2
Business environment businesses registered entry density
5.1
new
5.1 5.2 5.2, 5.8
3.11
from electricity and heat production, as share of total fuel combustion
dealing with construction permits to build a warehouse number of procedures
5.3
time required
5.3
3.11
from manufacturing industries and construction, as share of total fuel combustion
3.11
from other sectors, as share of total fuel combustion
enforcing contracts number of procedures
4.11
emissions by sector
crime losses due to theft, robbery, vandalism, and arson
Carbon dioxide damage, as share of GNI
corruption informal payments to public officials
C
3.11
5.3
2012 World Development Indicators
415
INDEX OF INDICATORS from residential buildings and commercial and public services, as share of total fuel combustion from transport as share of total fuel combustion per capita per unit of GDP total intensity
Computer users per 100 people 3.11 1.3, 3.9 3.9
Consumption as share of GDP
4.8
average annual growth
4.9
1.6, 3.9 3.9
per capita
4.9
distribution—see Income distribution fixed capital
Cause of death
4.10, 4.11
government, general final expenditure
communicable diseases and maternal, prenatal, and nutritional conditions
5.12
3.11
annual growth 2.22
injuries
2.22
noncommunicable disease
2.22
as share of GDP
4.9 4.8
See also Purchasing power parity (PPP) Contraceptives
Children at work by economic activity
2.6
male and female
2.6
status in employment
2.6
study and work
2.6
total
2.6, 5.8
work only
prevalence rate
1.3, 2.19
unmet need for
2.19
Contract enforcement number of procedures
5.3
time required for
5.3
2.6 Corruption, informal payments to public officials
5.2
Cities—see Urban environment; Country Policy and Institutional Assessment (CPIA)—see Economic Climate variability
management; Social inclusion and equity policies; Public sector management
average daily minimum/maximum temperature
3.12
projected annual precipitation change
3.12
projected annual temperature change
3.12
projected change in annual cool days/cold nights
3.12
projected change in annual hot days/warm nights
3.12
Closing a business—see Business environment Commercial bank and other lending
6.5
households with
416
2012 World Development Indicators
depth of credit information index
5.5
strength of legal rights index
5.5
provided by banking sector
5.5
to private sector
5.1
business losses due to
5.2
intentional homicide rate
5.8
Current account balance
4.17
See also Balance of payments
Compensation See also Remittances
getting credit
Crime
Communications—see Internet; Newspapers, daily; Telephones; Television,
of central government employees
Credit
6.10
See also Debt flows; Private financial flows Commodity prices and price indexes
and institutions; Structural policies
Customs 4.13
average time to clear exports
5.2
burden of procedures
6.8
D DAC (Development Assistance Committee)—see Aid Death rate, crude
2.1
See also Mortality rate
6.9
debt service total, as share of exports of goods and services and income
5.9
fiscal policy
5.9
macroeconomic management
5.9
Education children out of school
Debt, external as share of GNI
economic management cluster average
6.9
long-term
male and female
2.12
poorest and richest wealth quintile
2.15
cohort survival rate to grade 5, male and female
2.13
to last grade of primary education, male and female
2.13
completion rate, primary
private nonguaranteed
6.9
male and female
public and publicly guaranteed
6.9
poorest and richest wealth quintiles
present value
2.14, 2.15
total
as share of GNI
6.9
as share of exports of goods and services and income
6.9
short-term
2.15 1.2, 2.14
enrollment ratio girls to boys enrollment in primary and secondary education
1.2
gross
as share of total debt
6.9
by level
2.12
as share of total reserves
6.9
primary
5.8
total
6.9
secondary
2.4
net Debt flows bonds
6.10
commercial banks and other lending
6.10
See also Private financial flows Deforestation, average annual
3.4
by level
2.12
primary, adjusted
2.12
intake ratio, gross first grade of primary education
2.13
grade 1
2.15
primary participation rate, gross
2.15
public expenditure on Demand—see Consumption; Imports; Exports; Savings
as share of GDP
Density—see Population, density
2.11
as share of GNI
4.11
as share of total government expenditure
2.11
per student, as share of GDP per capita, by level Dependency ratio—See Population, age dependency ratio Development assistance—see Aid Disaster risk reduction progress score—see Resilience
2.11
pupil–teacher ratio, primary
2.11
repeaters, primary, male and female
2.13
teachers, primary, trained
2.11
transition to secondary school, male and female
2.13
unemployment by level of educational attainment years of schooling, average, poorest and richest wealth quintiles
2.5 2.15
Disease—see Health risks Electricity Distribution of income or consumption—see Income distribution
access to
3.8
business lost due to outages
E
5.2
consumption
5.11
production
Economic management (Country Policy and Institutional Assessment) debt policy
5.9
total
3.8
sources
3.8
2012 World Development Indicators
417
INDEX OF INDICATORS time required transmission and distribution losses
5.3 5.11
Emissions
GDP per unit of energy use
3.9
per capita
3.7
total
3.7
See also Electricity; Fuels
carbon dioxide average annual growth intensity per capita per unit of GDP total
3.10
Enforcing contracts—see Business environment
3.9 1.3, 3.9
Enrollment—see Education
3.9 1.6, 3.9
Entry regulations for business—see Business environment
See also Carbon dioxide, emissions Environmental strategy or action plans, year adopted
methane agricultural, as share of total from energy processes, as share of total
3.10
total
3.10
nitrous oxide agricultural, as share of total
3.10
energy and industry, as share of total
3.10
total
3.10
other greenhouse gases
3.10
Equity flows foreign direct investment
6.10
portfolio equity
6.10
See also Private financial flows European Commission distribution of net aid from
6.13
Exchange rates
Employment children in employment
2.6, 5.8
in agriculture as share of total employment female male
3.2 1.5, 2.3 2.3
in industry, male and female
2.3
in services, male and female
2.3
to population ratio
2.4
vulnerable
1.2, 2.4
official, local currency units to U.S. dollar
4.16
purchasing power parity conversion factor
4.16
ratio of PPP conversion factor to official exchange rate
4.16
real effective
4.16
See also Purchasing power parity (PPP) Exports arms
5.7
documents required for
6.8
goods and services
See also Labor force; Unemployment
as share of GDP average annual growth
Endangered species—see Biological diversity; Plants, higher
total
4.8 4.a, 4.9 4.17
high-technology
Energy commodity price index
6.5
consumption, road sector
3.15
depletion, as share of GNI
4.11
share of manufactured exports total information and communications technology lead time
emissions—see Pollution imports, net
3.9
production
3.7
use
418
3.17
3.10
5.13 5.13 5.12 6.8
merchandise annual growth
6.1, 6.2
by regional trade blocs
6.6
alternative and nuclear energy
3.7
direction of trade
6.2
average annual growth
3.7
from high-income countries, by product
6.3
combustible renewables and waste, as share of total
3.7
from developing countries, by recipient
6.4
fossil fuel consumption, as share of total
3.7
structure
4.4
2012 World Development Indicators
total
4.4
value, average annual growth
6.1
volume, average annual growth
6.1
Foreign direct investment, netâ&#x20AC;&#x201D;see Investment; Private financial flows Forest
services
area
structure
4.6
as share of total land area
3.1
total
4.6
total
3.4
travel
4.6, 6.15
See also Trade Exposure to impact
deforestation, average annual
3.4
net depletion, as share of GNI
4.11
Fuels
land area with elevation of 5 meters or less
3.12
population affected by droughts, floods, and extreme temperature
3.12
population living in areas with elevation of five meters or less
3.12
consumption road sector
3.15
exports
F
as share of total merchandise exports
4.5
from high-income economies, as share of total exports
6.3
imports
Female-headed households Female participation in ownership
2.10 5.2
as share of total imports
4.4
by high-income economies, as share of total imports
6.3
prices
3.15
tariff rates applied by high-income countries
6.3
Fertility rate adolescent crude birth rate
2.19 2.1
desired
2.19
poorest and richest wealth quintile
2.24
total
2.19
G GEF benefits index for biodiversity
3.4
Gender differences in children in employment
Finance, firms using banks to finance investment
5.2
2.6, 5.8
in education
1.2, 2.12, 2.13, 2.14
in employment
2.3
by economic activity
Financial access, stability, and efficiency
2.3
automated teller machines
5.5
in HIV prevalence
2.22
bank capital to asset ratio
5.5
in labor force participation
2.2
bank nonperforming loans, ratio to total gross loans
5.5
in life expectancy at birth
1.5
borrowers from commercial banks
5.5
in literacy
commercial bank branches
5.5
adult
2.14
depositors with commercial banks
5.5
youth
2.14
in mortality Financial flows, net official
adult
2.23
child
2.23
from bilateral sources
6.11
in ownership of firms
from international financial institutions
6.11
in parliaments
from multilateral sources
6.11
in smoking
total
6.11
unemployment
See also Aid Foodâ&#x20AC;&#x201D;see Agriculture, production indexes; Commodity prices and price indexes
5.2 1.5 2.22
total
2.5
youth
2.10
nonagricultural wage employment
1.5
unpaid family workers
1.5
2012 World Development Indicators
419
INDEX OF INDICATORS vulnerable employment
2.4
U.S. dollars
1.1
total Gini index
2.9
PPP dollars
1.1, 1.6
U.S. dollars
1.1, 1.6, 4.10
Government, central cash surplus or deficit
4.12
debt as share of GDP
4.12
interest, as share of revenue
4.12
expense
H Health care antiretroviral therapy coverage
2.22
children sleeping under treated nets
2.18 2.18
as share of GDP
4.12
children with acute respiratory infection taken to health provider
by economic type
4.13
children with diarrhea who received oral rehydration and continued
net incurrence of liabilities, as share of GDP, domestic and foreign
4.12
feeding children with fever receiving antimalarial drugs
revenue
2.18 2.18
as share of GDP
4.12
HIV, prevalence
grants and other
4.14
hospital beds per 1,000 people
2.16
social contributions
4.14
immunization rate, child
2.18
nurses and midwives per 1,000 people
2.16
taxes as share of GDP by source, as share of revenue
5.6 4.14
1.3
outpatient visits per capita
2.16
physicians per 1,000 people
2.16
reproductive anemia, prevalence of, pregnant women
Greenhouse gases—see Emissions
births attended by skilled health staff contraceptive prevalence rate
Gross capital formation annual growth
4.9
as share of GDP
4.8
Gross domestic product (GDP) annual growth contribution of natural resources
1.1, 1.6, 4.1, 4.10 3.18
per capita, annual growth total
1.1, 1.6 4.2, 4.10
adolescent
2.19
total
2.19
lifetime risk of maternal death
2.19
low-birthweight babies
2.21
maternal mortality ratio unmet need for contraception
treatment success rate
1.5, 2.19 2.19 1.3, 2.22 2.18
Health expenditure
Gross national income (GNI)
as share of GDP
adjusted net national income
2.16
annual growth
4.10
external resources
2.16
total
4.10
out of pocket
2.16
4.10
per capita
2.16
public
2.16
annual growth per capita PPP dollars rank
1.1, 1.6 1.1
U.S. dollars
1.1, 1.6
Health information census, year last completed
2.17
completeness of vital registration
rank PPP dollars
420
1.3, 2.19, 5.8
tuberculosis incidence
Gross enrollment—see Education
2.19 1.3, 2.19
fertility rate
pregnant women receiving prenatal care
implicit deflator—see Prices
2.21
2012 World Development Indicators
1.1
birth registration
2.17
infant death
2.17
total death
2.17
health survey, year last completed
2.17
national health account number completed
2.17
year last completed
2.17
Hunger, depth
5.8
I Immunization rate, child DPT, share of children ages 12–23 months
2.18
measles, share of children ages 12–23 months
2.18
Health risks Imports
anemia, prevalence of children under age 5
2.21
arms
5.7
pregnant women
2.21
documents required for
6.8
2.22
energy, net, as share of total energy use
3.9
cause of death
goods and services
child malnutrition, prevalence stunting underweight wasting diabetes, prevalence HIV, prevalence low-birthweight babies
2.20 1.2, 2.20 2.20 2.22 1.3, 2.22 2.21
as share of GDP
4.8
average annual growth
4.9
total
4.17
information and communications technology goods lead time
5.12 6.8
merchandise
overweight children, prevalence
2.20
annual growth
smoking, prevalence, male and female
2.22
by developing countries, by partner
6.4
by high-income countries, by product
6.3
tuberculosis, incidence undernourishment, prevalence
1.3, 2.22 2.20
6.2
structure
4.5
tariffs
6.3, 6.7
total
Heavily indebted poor countries (HIPCs)
4.5
assistance
1.4
value, average annual growth
6.1
completion point
1.4
volume, average annual growth
6.1
decision point
1.4
Multilateral Debt Relief Initiative (MDRI) assistance
1.4
services structure
4.7
total
4.7
travel
HIV
See also Trade
prevalence female population ages 15–24, male and female total
4.7, 6.14
2.22 2.22 1.3, 2.22
Income distribution Gini index
2.9
percentage of Homicide rate, intentional
1.2, 2.9
5.8 Industry
Hospital beds—see Health care Housing conditions, national and urban durable dwelling units
3.14
home ownership
3.14
household size
3.14
multiunit dwellings
3.14
overcrowding
3.14
vacancy rate
3.14
annual growth
4.1
as share of GDP
4.2
employment, male and female
2.3
freshwater withdrawals for output
3.5
Inflation—see Prices Informal economy, firms formally registered when operations started
2012 World Development Indicators
5.2
421
INDEX OF INDICATORS Information and communications technology trade Innovation, internationally recognized certification ownership
5.11 5.2
L Labor force annual growth
Interest paymentsâ&#x20AC;&#x201D;see Government, central, debt Interest rates
2.2
armed forces
5.7
children at work
2.6
female
2.2
deposit
4.15
nonagricultural
1.5
lending
4.15
part-time
1.5
real
4.15
participation of population ages 15 and older, male and female
2.2
risk premium on lending
5.5
total
2.2
spread
5.5
See also Employment; Migration; Unemployment
Internally displaced persons
5.8
Land area arableâ&#x20AC;&#x201D;see Agriculture, land; Land use See also Protected areas; Surface area; and Exposure to impact
International Bank for Reconstruction and Development (IBRD) net financial flows from
6.11 Land use arable land
International Development Association (IDA) net concessional flows from Resource Allocation Index
6.11 5.8, 5.9
International Monetary Fund (IMF) net financial flows from
6.11
Internet fixed broadband
as share of total land
3.1
per 100 people
3.1
area under cereal production
3.2
by type
3.1
forest area, as share of total land
3.1
irrigated land
3.2
permanent cropland, as share of total land
3.1
total area
3.1
access tariff
5.12
subscriptions
5.12
Life expectancy at birth
international Internet bandwidth
5.12
male and female
secure servers
5.12
total
users
5.12
1.5 1.6, 2.23
Literacy adult
Investment
male and female
foreign direct, net inflows as share of GDP
6.10
total
6.10
infrastructure, private participation in energy
total
2.14 1.6
mathematics, students at the lowest level of proficiency on PISA
2.14
youth, male and female
2.14
5.1
telecommunications
5.1
transport
5.1
water and sanitation
5.1
See also Gross capital formation; Private financial flows
Logistics Performance Index
6.8
M Malaria
Iodized salt, consumption of
422
2012 World Development Indicators
2.21
children sleeping under treated nets
2.18
children with fever receiving antimalarial drugs
2.18
Malnutrition, in children under age 5
1.2, 2.20
Management time dealing with officials
by developing countries, by partner
5.2
6.4
food
4.5, 6.3
fuels
4.5, 6.3
information and communications technology goods Manufacturing
5.12
manufactures
4.5
annual growth
4.1
ores and metals
4.5
as share of GDP
4.2
ores and nonferrous materials
6.3
to low-income economies by high-income economies, by product
6.3
value added chemicals
4.3
to middle-income economies by high-income economies, by product 6.3
food, beverages, and tobacco
4.3
total
machinery and transport equipment
4.3
value, average annual growth
6.1
other
4.3
volume, average annual growth
6.1
structure
4.3
textiles and clothing
4.3
by developing countries, by partner
6.4
total
4.3
direction
6.2
growth
6.2
regional trade blocs
6.6
4.5
trade
See also Merchandise Market access to high-income countries goods admitted free of tariffs
1.4
support to agriculture
1.4
Metals and minerals commodity prices and price index
6.5
tariffs on exports from least developed countries agricultural products
1.4
clothing
1.4
Methane emissions agricultural as share of total
3.10
textiles
1.4
industrial as share of total
3.10
total
3.10
Merchandise exports
Migration
agricultural raw materials
4.4, 6.3
emigration of people with tertiary education to OECD countries
6.14
food
4.4, 6.3
international migrant stock, total
6.14 6.14
from developing countries, by partner
6.4
net
from regional trade blocs
6.6
See also Refugees; Remittances
fuels
4.4, 6.3
information and communications technology goods
5.12
information and communications technology services
5.12
Military armed forces personnel
manufactures
4.4
as share of labor force
5.7
ores and metals
4.4
total
5.7
ores and nonferrous materials
6.3
arms transfers
structure
4.4
exports
5.7
to low-income economies from high-income economies, by product
6.3
imports
5.7
to middle-income economies from high-income economies, by product total
military expenditure 6.3
as share of central government expenditure
4.4
as share of GDP
value, average annual growth
6.1
volume, average annual growth
6.1
within regional trade blocs
6.6
imports agricultural raw materials
5.7 5.7, 5.8
Millennium Development Goals, indicators for access to improved sanitation facilities access to improved water source
1.3, 2.18, 3.13, 5.8 2.18, 3.5, 5.8
4.5, 6.3
2012 World Development Indicators
423
INDEX OF INDICATORS average tariff imposed by developed countries on exports of least
incidence
developed countries
1.4
births attended by skilled health staff
treatment success rate
2.19
carbon dioxide emissions per capita
1.3, 3.9
children sleeping under treated nets
2.18
contraceptive prevalence rate
under-five mortality rate poorest and richest wealth quintile total
1.3, 2.19
employment to population ratio
undernourishment, prevalence
2.4
enrollment ratio, net, primary
unmet need for contraception
2.12
female to male enrollments, primary and secondary
vulnerable employment
1.2
fertility rate, adolescent
women in wage employment in the nonagricultural sector
1.3, 2.22 2.18 1.2, 2.22, 5.8 2.24 1.2, 2.23, 5.8 2.20 2.19 1.2, 2.4 1.5
2.19
goods admitted free of tariffs from least developed countries
1.4
Minerals depletion, as share of GNI
4.11
assistance
1.4
Monetary indicators
completion point
1.4
broad money
4.15
decision point
1.4
claims central government
4.15
other claims on domestic economy
4.15
heavily indebted poor countries (HIPCs)
Multilateral Debt Relief Initiative (MDRI) assistance nominal debt service relief committed
1.4 Mortality rate
immunization rate, child DPT
2.18
adult, male and female
measles
2.18
child, male and female
income or consumption, national share of poorest quintile infant mortality rate poorest and richest wealth quintile total Internet users per 100 people
2.22
crude death rate
2.24
infant
2.23
life expectancy at birth maternal
1.3, 5.12
labor productivity, GDP per person employed
lifetime risk of maternal death
2.4
literacy rate of 15- to 24-year-olds malnutrition, prevalence
children under age 5
1.2, 2.9
2.14
neonatal
2.23 2.23 1.2, 2.23, 5.8 2.1 2.23 2.23 1.3, 2.19, 5.8 2.19 2.23
1.2, 2.20
malaria
Motor vehicles
children under age 5 sleeping under treated nets
2.18
children under age 5 with fever who are treated with appropriate antimalarial drugs
2.18
maternal mortality ratio
1.3, 2.19, 5.8
mobile cellular subscriptions per 100 people
3.15
per 1,000 people
3.15
per kilometer of road
3.15
road density
3.15
See also Roads; Traffic
5.11
national parliament seats held by women
passenger cars
1.5
official development assistance
MUV G-5 index
6.5
MUV G-15 index
6.5
for basic social services as share of total sector allocable ODA commitments net disbursements, as share of donor GNI poverty gap pregnant women receiving prenatal care share of cohort reaching last grade of primary education support to agriculture telephone lines, fixed, per 100 people
1.4 1.4 2.7, 2.8 1.5, 2.19 2.13
N Natural resource depletion, as share of GNI
4.10
1.4 5.11
Net enrollmentâ&#x20AC;&#x201D;see Education
2.18
Newspapers, daily
tuberculosis case detection rate
424
2012 World Development Indicators
5.12
Nitrous oxide emissions agricultural as share of total
Plants, higher, threatened species
3.4
3.10
Industrial as share of total
3.10
total
3.10
Pollution carbon dioxide damage, as share of GNI
Nutrition anemia, prevalence of
average annual growth
children ages under 5
2.21
intensity
pregnant women
2.21
per capita
breastfeeding, exclusive
2.21
per unit of GDP
iodized salt consumption
2.21
total
malnutrition, child
4.11
emissions
1.2, 2.20
3.10 3.9 1.3, 3.9 3.9 1.6, 3.9
local damage
4.11
overweight children, prevalence
2.20
undernourishment, prevalence
2.20
agricultural, as share of total
vitamin A supplementation
2.21
from energy processes, as share of total
3.10
total
3.10
O
methane emissions
nitrogen dioxide, selected cities
3.10
3.16
nitrous oxide emissions
Official development assistance—see Aid
agricultural, as share of total
Official flows—see Aid; Financial flows, net
3.10
energy and industry, as share of total
3.10
total
3.10
organic water pollutants, emissions
P Particulate matter selected cities
3.16
urban-population-weighted PM10
3.15
Passenger cars per 1,000 people
3.15
Patent applications filed
5.13
Peacebuilding and peacekeeping operations operation name
5.8
troops, police, and military observers
5.8
Pension average, as share of per capita income
2.10
contributors
by industry
3.6
per day
3.6
per worker
3.6
particulate matter concentration selected cities
3.16
total
3.15
sulfur dioxide, selected cities
3.16
Population age dependency ratio, young and old
2.1
average annual growth
2.1
by age group, as share of total 0–14
2.11
5–64
2.1
65 and older
2.1
density
1.1, 1.6
female, as share of total
1.5
as share of labor force
2.10
as share of working-age population
2.10
annual growth
3.1
public expenditure on, as share of GDP
2.10
as share of total
3.1
rural
total Permits and licenses, time required to obtain operating license
5.2
1.1, 1.6, 2.1
urban as share of total
Physicians—see Health care
3.13
average annual growth
3.13
in largest city
3.13
2012 World Development Indicators
425
INDEX OF INDICATORS in selected cities
3.16
in urban agglomerations
3.13
total
3.13
See also Investment Productivity
See also Migration; Electricity; and Exposure to impact
agricultural
Portfolio—see Equity flows; Private financial flows Ports
3.3
labor
2.4
water
3.5
Protected areas
container traffic in
5.9
quality of infrastructure
6.8
marine
Poverty
as share of total surface area
3.4
total
3.4
terrestrial
international poverty line local currency
2.8
as share of total surface area
3.4
total
3.4
population living below $1.25 a day
2.8
$2 a day
2.8
national poverty line
Protecting investors disclosure index Public sector management and institutions (Country Policy and Institutional
population living below, national, rural, and urban
2.7
poverty gap, national, rural, and urban
2.7
Assessment)
Power—see Electricity, production Precipitation average annual
5.3
3.2, 3.12
efficiency of revenue mobilization
5.9
property rights and rule-based governance
5.9
public sector management and institutions cluster average
5.9
quality of budgetary and financial management
5.9
quality of public administration
5.9
transparency, accountability, and corruption in the public sector
5.9
see also Climate variability Purchasing power parity (PPP) Prenatal care, pregnant women receiving
1.5, 2.19
conversion factor gross national income
4.16 1.1, 1.6
Prices commodity prices and price indexes
6.5
consumer, annual growth
4.16
gasoline fuel
3.15
GDP implicit deflator, annual growth
4.16
net barter terms of trade
6.1
wholesale, annual growth
4.16
R Railways goods hauled by
5.10
lines, total
5.10
passengers carried
5.10
Refugees
Primary education—see Education Private financial flows
by country of asylum
5.8, 6.14
by country of origin
5.8, 6.14
internally displaced persons
debt flows bonds
6.10
commercial bank and other lending
6.10
Regional development banks, net financial flows from
foreign direct investment, net inflows
6.10
Regional trade agreements—see Trade blocs, regional
portfolio equity
6.10
equity flows
426
2012 World Development Indicators
5.8 6.11
Registering property
Sanitation, access to improved facilities, population with
number of procedures
5.3
total
time to register
5.3
urban and rural
Regulation and tax administration
1.3, 2.18, 5.8 3.13
Savings
management time dealing with officials
5.2
adjusted net
meeting with tax officials, number of times
5.2
gross, as share of GDP
4.11 4.8
gross, as share of GNI
4.11
Relative prices (PPP)—see Purchasing power parity (PPP) Schooling—see Education Remittances Science and technology
workers’ remittances and compensation of employees paid
6.14
scientific and technical journal articles
received
6.14
See also Research and development
Research and development
5.13
Secondary education—see Education
expenditures
5.13
researchers
5.13
technicians
5.13
Services employment, male and female
2.3
exports Reserves, gross international—see Balance of payments
computer, information and communications, and other
Resilience
insurance and financial services
4.6
structure
4.6
commercial services disaster risk reduction progress score
3.12
See also Electricity, access to Roads
4.6
total commercial
4.6
transport
4.6
travel
4.6
imports
goods hauled by
5.10
passengers carried
5.10
paved, as share of total
5.10
sectoral energy consumption
3.15
insurance and financial services
4.7
total network
5.10
structure
4.7
Royalty and license fees
computer, information and communications, and other commercial services
total commercial
4.7
transport
4.7 4.7
payments
5.13
travel
receipts
5.13
value added
Rural environment access to improved sanitation facilities access to an improved water source
4.7
annual growth
4.1
as share of GDP
4.2
3.13 3.5
Smoking, prevalence, male and female
annual growth
3.1
Social inclusion and equity policies (Country Policy and Institutional
as share of total
3.1
2.22
population
S S&P/Global Equity Indices
5.4
Assessment) building human resources
5.9
equity of public resource use
5.9
gender equity
5.9
policy and institutions for environmental sustainability
5.9
2012 World Development Indicators
427
INDEX OF INDICATORS social inclusion and equity cluster average
5.9
social protection and labor
5.9
weighted mean tariff
6.7
share of tariff lines with international peaks
6.7
share of tariff lines with specific rates
6.7
Starting a business—see Business environment Taxes and tax policies Stock markets listed domestic companies
business taxes 5.4
market capitalization
average number of times firms meet with tax officials
5.2
labor tax, as share of commercial profits
5.6 5.6
as share of GDP
5.4
number of payments
total
5.4
other taxes, as share of commercial profits
5.6
5.4
profit tax, as share of commercial profits
5.6
S&P/Global Equity Indices
5.4
time to prepare, file, and pay
5.6
turnover ratio
5.4
total tax rate, as share of commercial profits
5.6
market liquidity
goods and services taxes, domestic Steel products, commodity prices and price index
6.5
Structural policies (Country Policy and Institutional Assessment)
4.14
income, profit, and capital gains taxes
4.14
international trade taxes
4.14
other taxes
4.14
business regulatory environment
5.9
social contributions
4.14
financial sector
5.9
tax revenue collected by central government, as share of GDP
structural policies cluster average
5.9
trade
5.9
5.6
Technology—see Computers; Exports, high-technology; Internet; Research and development; Science and technology
Stunting, prevalence of
2.20 Telephones
Sulfur dioxide emissions—see Pollution Surface area
fixed 1.1, 1.6
See also Land use
international voice traffic
5.11
per 100 people
5.11
residential tariff
5.11
mobile cellular Suspended particulate matter—see Pollution
T Tariffs all products binding coverage
6.7
simple mean bound rate
6.7
simple mean tariff
6.7
weighted mean tariff applied rates on imports from low- and middle-income economies
6.7
weighted mean tariff on exports of least developed countries
428
2012 World Development Indicators
5.11
prepaid tariff
5.11
subscriptions per 100 people
5.11
mobile cellular and fixed subscriptions per employee
5.11
total revenue
5.11
Television, households with
5.12
Temperature—see Climate variability Terms of trade index, net barter
6.1
6.7 6.7
Tertiary education—see Education
1.4
primary products simple mean tariff
5.11
population covered
6.3
manufactured products simple mean tariff
international voice traffic
Threatened species—see Animal species; Biological diversity; Plants, higher 6.7
Tourism, international tourism expenditure
year of creation
6.6
year of entry into force of the most recent agreement
6.6
inbound as share of exports
6.15
total
6.15
outbound
Trademark applications filed
5.13
Trade policies—see Tariffs
as share of imports
6.15
total
6.15
Traffic—see Fuels; Motor vehicles; Roads
inbound
6.15
Transport—see Air transport; Ports; Railways; Roads
outbound
6.15
tourists
Travel—see Tourism, international Trade arms
5.7
barriers
6.7
Treaties, participation in
facilitation burden of customs procedures
6.8
documents
biological diversity
3.17
CFC control
3.17
climate change
3.17
Convention on International Trade on Endangered Species (CITES)
3.17
to export
6.8
Convention to Combat Desertification (CCD)
3.17
to import
6.8
Kyoto Protocol
3.17
6.8
Law of the Sea
3.17
Ozone layer
3.17
Stockholm Convention on Persistent Organic Pollutants
3.17
freight costs to the United States lead time to export
6.8
to import
6.8
liner shipping connectivity index
6.8
logistics performance index
6.8
case detection rate
quality of port infrastructure
6.8
incidence
information and communications technology
Tuberculosis
5.12
2.18 1.3, 2.22
treatment rate
2.18
merchandise direction of, by developing countries
6.4
direction of, by region
6.2
high-income economy with low- and middle-income economies, by product
U Undernourishment, prevalence of
2.20
Underweight, prevalence of
2.20
6.3
nominal growth, by region
6.2
regional trading blocs
6.6
structure
4.4, 4.5
total
4.4, 4.5
services
Unemployment by level of educational attainment, primary, secondary, tertiary
2.5
incidence of long-term, total, male, and female
2.5 2.5
structure
4.6, 4.7
total, male, and female
total
4.6, 4.7
youth, male, and female
1.3, 2.10
See also Balance of payments; Exports; Imports; Manufacturing; Urban environment
Merchandise; Terms of trade; Trade blocs
access to improved sanitation facilities access to an improved water source
Trade blocs, regional
3.13 3.5
emissions, selected cities
exports within bloc
6.6
total exports, by bloc
6.6
nitrogen dioxide
3.16
type of agreement
6.6
particulate matter
3.16
2012 World Development Indicators
429
INDEX OF INDICATORS sulfur dioxide
3.16
housing conditions
urban and rural
3.5
freshwater
durable dwelling units
3.14
home ownership
3.14
as share of internal resources
3.5
3.14
for agriculture
3.5
multiunit dwellings
3.14
for domestic use
3.5
overcrowding
3.14
for industry
3.5
vacancy rate
3.14
total
3.5
household size
population
annual withdrawals
internal renewable resources
as share of total
3.13
flows
3.5
average annual growth
3.13
per capita
3.5
in largest city
3.13
pollutionâ&#x20AC;&#x201D;see Pollution, organic water pollutants
in selected cities
3.16
productivity
in urban agglomerations
3.13
total
3.13
3.5
Women in development female-headed households
See also Pollution; Population; Sanitation; Water
V
1.5
life expectancy at birth
1.5
pregnant women receiving prenatal care
Value added
2.10
female population, as share of total
1.5, 2.19
teenage mothers
as share of GDP
poorest and richest wealth quintile
in agriculture
4.2
in industry
4.2
unpaid family workers
in manufacturing
4.2
vulnerable employment
2.4
women in nonagricultural sector
1.5
women in parliaments
1.5
in services
4.1, 4.2
per worker in agriculture
total
W
World Bank, net financial flows from
Wasting, prevalence of
2.20
See also International Bank for Reconstruction and Development; International Development Association
Water access to improved source of, population with total
2.18, 5.8
2012 World Development Indicators
1.5 1.5
3.3 Workforce, firms offering formal training
430
1.5 2.24
5.2 6.11
REGION MAP
The world by region
East Asia and Pacific American Samoa Cambodia China Fiji Indonesia Kiribati Korea, Dem. Rep. Lao PDR Malaysia Marshall Islands Micronesia, Fed. Sts. Mongolia Myanmar Palau Papua New Guinea Philippines Samoa Solomon Islands Thailand Timor-Leste Tonga Tuvalu Vanuatu Vietnam
Chile Colombia Costa Rica Cuba Dominica Dominican Republic Ecuador El Salvador Grenada Guatemala Guyana Haiti Honduras Jamaica Mexico Nicaragua Panama Paraguay Peru St. Kitts and Nevis St. Lucia St. Vincent & Grenadines Suriname Uruguay Venezuela, RB
Europe and Central Asia Albania Armenia Azerbaijan Belarus Bosnia and Herzegovina Bulgaria Georgia Kazakhstan Kosovo Kyrgyz Republic Latvia Lithuania Macedonia, FYR Moldova Montenegro Romania Russian Federation Serbia Tajikistan Turkey Turkmenistan Ukraine Uzbekistan
Middle East and North Africa Algeria Djibouti Egypt, Arab Rep. Iran, Islamic Rep. Iraq Jordan Lebanon Libya Morocco Syrian Arab Republic Tunisia West Bank and Gaza Yemen, Rep.
Latin America and the Caribbean Antigua and Barbuda Argentina Belize Bolivia Brazil
Sub-Saharan Africa Angola Benin Botswana Burkina Faso Burundi Cameroon
South Asia Afghanistan Bangladesh Bhutan India Maldives Nepal Pakistan Sri Lanka
Cape Verde Central African Republic Chad Comoros Congo, Dem. Rep. Congo, Rep. Côte d'Ivoire Eritrea Ethiopia Gabon Gambia, The Ghana Guinea Guinea-Bissau Kenya Lesotho Liberia Madagascar Malawi Mali Mauritania Mauritius Mayotte Mozambique Namibia Niger Nigeria Rwanda São Tomé and Príncipe Senegal Seychelles Sierra Leone Somalia South Africa South Sudan Sudan Swaziland Tanzania Togo Uganda Zambia Zimbabwe High-income OECD Australia Austria* Belgium* Canada Czech Republic Denmark Estonia* Finland* France* Germany* Greece* Hungary Iceland Ireland* Israel
Italy* Japan Korea, Rep. Luxembourg* Netherlands* New Zealand Norway Poland Portugal* Slovak Republic* Slovenia* Spain* Sweden Switzerland United Kingdom United States Other high income Andorra Aruba Bahamas, The Bahrain Barbados Bermuda Brunei Darussalam Cayman Islands Channel Islands Croatia Curaçao Cyprus* Equatorial Guinea Faeroe Islands French Polynesia Gibraltar Greenland Guam Hong Kong SAR, China Isle of Man Kuwait Liechtenstein Macao SAR, China Malta* Monaco New Caledonia Northern Mariana Islands Oman Puerto Rico Qatar San Marino Saudi Arabia Singapore Sint Maarten St. Martin Trinidad and Tobago Turks and Caicos Islands United Arab Emirates Virgin Islands (U.S.) *Member of the Euro area
The world by region
Classified according to World Bank analytical grouping
Low- and middle-income economies East Asia and Pacific
Middle East and North Africa
High-income economies
Europe and Central Asia
South Asia
OECD
Latin America and the Caribbean
Sub-Saharan Africa
Other
No data
Greenland (Den)
Iceland
Isle of Man (UK)
Canada
Ireland Channel Islands (UK)
Luxembourg Liechtenstein Switzerland Andorra United States
Norway
Faeroe Islands (Den)
The Netherlands
Sweden
Finland Russian Federation
Estonia Denmark Russian Latvia Fed. Lithuania United Belarus Germany Poland Kingdom Belgium Ukraine Moldova Romania France Italy
Kazakhstan
Bulgaria
Portugal
Spain Monaco
Turkey
Greece
Gibraltar (UK) Bermuda (UK)
Tunisia
Cyprus Lebanon Israel
Syrian Arab Rep.
West Bank and Gaza
Jordan
Malta
Morocco
Georgia Armenia
Azerbaijan
Cayman Is.(UK)
Libya
Former Spanish Sahara
Saudi Arabia
Cuba Belize Jamaica Guatemala Honduras El Salvador Nicaragua Costa Rica
Mauritania
Haiti
Cape Verde
Mali
Senegal The Gambia Guinea-Bissau Guinea
Panama
R.B. de Venezuela
Guyana Suriname
Sierra Leone Liberia
French Guiana (Fr)
Colombia
Niger
Benin Côte Ghana d’Ivoire
Cameroon
Central African Republic
Congo
Malawi
Zambia Zimbabwe
Tonga
Namibia Paraguay Germany
St. Maarten (Neth)
Antigua and Barbuda
U.S. Virgin Islands (US)
St. Kitts and Nevis
Curaçao (Neth)
Argentina
Dominica St. Lucia
St. Vincent and the Grenadines
Barbados Grenada Trinidad and Tobago
N. Mariana Islands (US) Guam (US)
Philippines
Federated States of Micronesia
Brunei Darussalam Malaysia
Marshall Islands
Palau
Maldives Nauru
Singapore
Botswana
South Africa
Poland
Kiribati
Comoros
Solomon Islands
Papua New Guinea
Indonesia
Tuvalu
Mayotte (Fr)
Madagascar
Vanuatu
Fiji
Mauritius Réunion (Fr)
Australia
New Caledonia (Fr)
Lesotho
Czech Republic Ukraine Slovak Republic Austria
Guadeloupe (Fr)
Martinique (Fr) Aruba (Neth)
Chile
Uruguay
Lao P.D.R.
Seychelles
Mozambique
Swaziland St. Martin (Fr)
Myanmar
Sri Lanka
Kenya
Rwanda Dem.Rep.of Burundi Congo Tanzania
Bolivia
Puerto Rico (US)
India
Vietnam Cambodia
Somalia Uganda
Gabon
Angola
American Samoa (US)
Bangladesh
Thailand
Brazil
Peru
Bhutan
Nepal
Rep. of Yemen
Ethiopia
South Sudan
Japan
Timor-Leste
French Polynesia (Fr)
Dominican Republic
Pakistan
Djibouti Nigeria
Kiribati
Samoa
Eritrea
Sudan
Burkina Faso
Togo Equatorial Guinea São Tomé and Príncipe
Ecuador
Chad
Rep.of Korea
China
Afghanistan
United Arab Emirates Oman
Turks and Caicos Is. (UK)
Mexico
Fiji
Arab Rep. of Egypt
Dem.People’s Rep.of Korea
Tajikistan
Bahrain Qatar
Algeria The Bahamas
Turkmenistan
Islamic Rep. of Iran Kuwait
Iraq
Mongolia Kyrgyz Rep.
Uzbekistan
Romania
Bosnia and Herzegovina San Marino Italy Montenegro Vatican City
New Zealand
Hungary
Slovenia Croatia
Serbia
Kosovo Bulgaria FYR Macedonia Albania Greece
R.B. de Venezuela
Antarctica
IBRD 39126 MARCH 2012
The World Bank 1818 H Street N.W. Washington, D.C. 20433 USA Telephone: 202 473 1000 Fax: 202 477 6391 Web site: data.worldbank.org Email: data@worldbank.org
ISBN 978-0-8213-8985-0
SKU 18985
The World Development Indicators • Includes more than 1,000 indicators for 158 economies • Provides definitions, sources, and other information about the data • Organizes the data into six thematic areas
WORLD VIEW
PEOPLE
ENVIRONMENT
Living standards and development progress
Gender, health, and employment
Natural resources and environmental changes
ECONOMY
STATES & MARKETS
GLOBAL LINKS
New opportunities for growth
Elements of a good investment climate
Evidence on globalization
Saved: 64 trees 26 million Btu of total energy 6,503 pounds of net greenhouse gases 29,321 gallons of waste water 1,859 pounds of solid waste