World Development Indicators 2011 Part 2 of 2 by World Bank Publications

SKU18709 18709 SKU 18709

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

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 and the Grenadines Suriname Uruguay Venezuela, RB

Europe and Central Asia Albania Armenia Azerbaijan Belarus Bosnia and Herzegovina Bulgaria Georgia Kazakhstan Kosovo Kyrgyz Republic 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 Chile

South Asia Afghanistan Bangladesh Bhutan India Maldives Nepal Pakistan Sri Lanka Sub-Saharan Africa Angola Benin Botswana Burkina Faso Burundi Cameroon

20433 20433USA USA 20433 USA Telephone: Telephone:202 202473 4731000 1000 Telephone: 202 473 1000 Fax: Fax:202 202477 4776391 6391 Fax: 202 477 6391 Web Website: site:data.worldbank.org data.worldbank.org Web site: data.worldbank.org

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 Principe Senegal Seychelles Sierra Leone Somalia South Africa 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 Cyprus * Equatorial Guinea Faeroe Islands French Polynesia Gibraltar Greenland Guam Hong Kong SAR, China Isle of Man Kuwait Latvia Liechtenstein Macao SAR, China Malta * Monaco Netherlands Antilles New Caledonia Northern Mariana Islands Oman Puerto Rico Qatar San Marino Saudi Arabia Singapore Taiwan, China Turks and Caicos Islands Trinidad and Tobago United Arab Emirates Virgin Islands (U.S.) * Member of the Euro area

Email: Email:data@worldbank.org data@worldbank.org Email: data@worldbank.org

The World World Development Development Indicators Indicators The Development Indicators Includes more more than than 800 800 indicators indicators for •• Includes than 800 indicators for 155 155 economies economies Provides definitions, definitions, sources, sources, and the data data •• Provides definitions, sources, and other other information information about about the Organizes the the data data into into six six thematic •• Organizes into six thematic areas areas

WORLD WORLD VIEW VIEW

PEOPLE PEOPLE

ENVIRONMENT ENVIRONMENT

Living standards Living standards standards and development and development development progress progress

Gender, Gender, health, health, and and employment employment

Natural Naturalresources resources Natural resources and andenvironmental environmental and environmental changes changes changes

ECONOMY ECONOMY

STATES STATES && MARKETS MARKETS

GLOBAL GLOBAL LINKS LINKS

New New opportunities opportunities opportunities for for growth growth

Elements Elements of of aa good good investment investment climate climate

Evidence Evidence Evidenceon on on globalization globalization globalization

Saved: Saved: Saved:91 91 91trees trees trees 29 29 29million million millionBtu Btu Btuofofof total total total energy energy energy 8,609 8,609 8,609pounds pounds poundsofofof net net net greenhouse greenhouse greenhouse gases gases gases 41,465 41,465 41,465gallons gallons gallons ofofof waste waste waste water water water 2,518 2,518 2,518pounds pounds poundsofofof solid solid solid waste waste waste

WORLD WORLD DEVELOPMENT DEVELOPMENT INDICATORS INDICATORS

The world by income

Low income Afghanistan Bangladesh Benin Burkina Faso Burundi Cambodia Central African Republic Chad Comoros Congo, Dem. Rep. Eritrea Ethiopia Gambia, The Ghana Guinea Guinea-Bissau Haiti Kenya Korea, Dem. Rep. Kyrgyz Republic Lao PDR Liberia Madagascar Malawi Mali Mauritania Mozambique Myanmar Nepal Niger Rwanda Sierra Leone Solomon Islands Somalia Tajikistan Tanzania Togo Uganda Zambia Zimbabwe Lower middle income Angola Armenia Belize Bhutan Bolivia Cameroon Cape Verde China Congo, Rep. Côte d'Ivoire Djibouti Ecuador Egypt, Arab Rep. El Salvador Georgia Guatemala Guyana

Honduras India Indonesia Iraq Jordan Kiribati Kosovo Lesotho Maldives Marshall Islands Micronesia, Fed. Sts. Moldova Mongolia Morocco Nicaragua Nigeria Pakistan Papua New Guinea Paraguay Philippines Samoa São Tomé and Principe Senegal Sri Lanka Sudan Swaziland Syrian Arab Republic Thailand Timor-Leste Tonga Tunisia Turkmenistan Tuvalu Ukraine Uzbekistan Vanuatu Vietnam West Bank and Gaza Yemen, Rep. Upper middle income Albania Algeria American Samoa Antigua and Barbuda Argentina Azerbaijan Belarus Bosnia and Herzegovina Botswana Brazil Bulgaria Chile Colombia Costa Rica Cuba Dominica Dominican Republic Fiji Gabon

Grenada Iran, Islamic Rep. Jamaica Kazakhstan Lebanon Libya Lithuania Macedonia, FYR Malaysia Mauritius Mayotte Mexico Montenegro Namibia Palau Panama Peru Romania Russian Federation Serbia Seychelles South Africa St. Kitts and Nevis St. Lucia St. Vincent and the Grenadines Suriname Turkey Uruguay Venezuela, RB High income Andorra Aruba Australia Austria Bahamas, The Bahrain Barbados Belgium Bermuda Brunei Darussalam Canada Cayman Islands Channel Islands Croatia 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 Latvia Liechtenstein Luxembourg Macao SAR, China Malta Monaco Netherlands Netherlands Antilles New Caledonia New Zealand Northern Mariana Islands Norway Oman Poland Portugal Puerto Rico Qatar San Marino Saudi Arabia Singapore Slovak Republic Slovenia Spain Sweden Switzerland Trinidad and Tobago Turks and Caicos Islands United Arab Emirates United Kingdom United States Virgin Islands (U.S.)

INCOME MAP

ISBN978-0-8213-8709-2 978-0-8213-8709-2 ISBN 978-0-8213-8709-2

WORLD DEVELOPMENT INDICATORS

REGION MAP

The TheWorld WorldBank Bank The World Bank 1818 1818HHHStreet StreetN.W. N.W. 1818 Street N.W. Washington, Washington,D.C. D.C. Washington, D.C.

Economy

Introduction

ecently revised data now confirm that in 2009 the world economy experienced the steepest global recession since the Great Depression. World gross domestic product (GDP) contracted 1.9 percent in 2009, with high-income economies contracting 3.3 percent and developing economies expanding just 2.7 percent, down from 8.6 percent in 2008. Among developing country regions, Europe and Central Asia fared the worst, contracting 5.8 percent (figure 4a). Contrast that with East Asia and Pacific, which grew at 7.4 percent, and South Asia, at 7 percent. The global economy rebounded in 2010, with domestic demand in developing countries accounting for 46 percent of global growth. Developing economies’ contribution to global growth has been rising since 2000 and was more stable than that of high-income economies during the recent recession (figure 4b). Preliminary estimates, often revised, indicate that the world economy grew 3.9 percent—2.8 percent in high-income economies and 7 percent in developing economies (figure 4c). Revisions to GDP Revisions to GDP usually occur one to two months after the initial release, as additional data sources become available. For example, the U.S. Bureau of Economic Analysis releases three versions of quarterly GDP estimates—advance (about a month after the quarter ends), preliminary (two months after), and final (three months after). Other countries follow a similar process, although the reporting lag varies. And some countries compile GDP only annually not quarterly. The differences between GDP estimates decline with each revision, and GDP data become more stable on average (figure 4d). More significant revisions to GDP involve new methodologies and new or improved data sources and data collection practices. Countries with advanced statistical capacity comprehensively revise GDP estimates every five years. These revisions take into account the latest recommendations of the Intersecretariat Working Group on National Accounts. They may also incorporate a change in the base year used for the constant price data (rebasing). Rebasing adjusts the weights used to compute aggregate measures by selecting a new set of relative component prices in the newly chosen base year. Comprehensive revisions of GDP estimates are usually higher as improved data sources increase the coverage of the economy and new weights for growing industries more accurately reflect contributions

Differences in GDP growth among developing country regions

4a 2008

GDP growth (percent)

2009

2010a

10 5 0 –5 –10

East Asia & Pacific

Europe & Central Asia

Latin America & Caribbean

Middle East & North Africa

South Asia

Sub-Saharan Africa

a. Data are preliminary estimates. Source: World Development Indicators data files.

Developing countries are contributing more to global growth Contribution to GDP growth (percent)

High-income economies

Developing economies

World GDP

6 4 2 0 –2 –4

2000

2001

2002

2003

2004

2005

2006

2007

2008

2009

2010a

a. Data are preliminary estimates. Source: World Development Indicators data files.

2011 World Development Indicators

189

Economies—both developing and high income—rebounded in 2010 GDP growth (percent) 10 Developing economies

5 World

0 High-income economies –5

2000

2001

2002

2003

2004

2005

2006

2007

2008

2009

2010a

a. Data are preliminary estimates. Source: World Development Indicators data files.

Revisions to GDP decline over time, and GDP data become more stable on average

Average difference in GDP (percent) 3

2000

2001

2002

2003

2004

2005

2006

2007

2008

Note: Average differences in current price GDP between World Development Indicators 2010 and 2011. Source: World Development Indicators data files.

Ghana’s revised GDP was 60 percent higher in the new base year, 2006 GDP ($ billions)

World Development Indicators 2010

World Development Indicators 2011

2000

2001

2002

2003

2004

2005

2006

2007

2008

2009

Source: World Development Indicators data files.

Revised data for Ghana show a larger share of services in GDP 2008 value added by industry (percent of GDP)

World Development Indicators 2010

World Development Indicators 2011

50 40

Broader measures of income and savings

30 20 10 0

Agriculture

Source: World Development Indicators data files.

190

to the economy. This has been the case for several countries that recently undertook such revisions to their national accounts statistics. In November 2010 the Ghana Statistical Service revised Ghana’s national accounts series, increasing GDP 60 percent in 2006, the new base year (figure 4e). Of the increase, 11 percentage points are in agriculture, 6 in industry, and 44 in services (figure 4f). Other countries have made similar revisions to their national accounts, incorporating improved methodology and data sources. Namibia revised its national accounts in 2008, resulting in 10–30 percent higher GDP estimates for 2000–07. Malawi revised its national accounts in 2007, raising GDP 37 percent. São Tomé and Príncipe revised its national accounts in 2006, resulting in 47.5 percent higher GDP in the new base year 2001. For more information on countries that have recently revised their national accounts data, see Primary data documentation. Many countries do not incorporate new sources of data into national accounts data compilation until they change the base year, which is the base or pricing period for constant price calculations. Such revisions can be substantial because of the long lag between rebasing exercises. The adjustments arising from rebasing can be reduced by incorporating new data sources in a timely manner and ensuring that the accounts are rebased at least every five years. Data users should be aware that rebasing creates a break in the time series. New data sources and methodologies are usually implemented only for recent years, creating a jump in GDP between the last year of the old data and the first year of the new. For constant price GDP these breaks can be eliminated by linking the old series to the new using historical growth rates. But for nominal GDP data the break in the time series cannot be avoided unless the statistics office revises historical series backward at a detailed level.

2011 World Development Indicators

Industry

Services

Two tables have been added to the Economy section this year. Table 4.10 contains new measures of adjusted net national income, and table 4.11 contains measures of adjusted net savings, previously included in the Environment section. Both tables follow recommendations of

economy

the recently published The Changing Wealth of Nations (World Bank 2011a). Adjusted net savings measures the change in a country’s national wealth. It begins with gross national savings and then adjusts for consumption of fixed capital, depletion of natural resources, changes in human capital, and damages from carbon dioxide and particulate emissions. If adjusted net savings is negative, capital stocks are declining and future well-being is reduced. The report argues that the key to increasing living standards is building national wealth through investment and national savings to finance the investment. The table on adjusted net national income presents growth rates of GDP, gross national income (GNI), and adjusted net national income. GNI is more useful than GDP for measuring the economic resources available to residents of an economy because it takes into account inflows of income (profits, wages, and rents) from outside the economy, net of outflows to other economies (box 4g). Adjusted net national income goes one step further by subtracting from GNI a charge for the consumption of fixed capital (or depreciation) and the depletion of natural resources. For some countries, adjusted net national income growth rates tell a story quite different from that of the more widely used GDP growth rates.

Changes to monetary indicators The monetary indicators in table 4.15 have been revised to reflect the International Monetary Fund’s (IMF) new presentation of monetary data for countries reporting in compliance with the Monetary and Financial Statistics Manual (IMF 2000) and Monetary and Financial Statistics Compilation Guide (IMF 2008). More than 120 countries report their monetary data under

Commission on the Measurement of Economic and Social Progress

Gross domestic product (GDP), the most quoted measure of economic activity, is often used as a measure of welfare. But as the Commission on the Measurement of Economic and Social Progress points out, GDP has many shortcomings as the sole measure of well-being. The commission’s report identified problems with the GDP measure itself and recommended including additional measures of the objective and subjective dimensions of well-being and measures of the sustainability of current consumption levels. The commission endorsed the adjusted net savings approach as the “relevant economic counterpart of the notion of sustainability” (Stiglitz, Sen, and Fitoussi 2009, p. 108). But it pointed out that the adjustment for environmental degradation has so far been limited mostly to carbon dioxide emissions. The report also notes the difficulties of pricing natural resources and environmental degradation. Other recommendations for improving GDP measurement include accounting more accurately for improvements in the quality of goods and services produced and the value of government services (usually based on inputs rather than on actual outputs produced).

this new presentation. A majority of these countries transmit the data on standardized report forms for the country’s monetary aggregates and for the assets and liabilities of the central bank, other depository corporations, and other financial corporations. This new presentation better classifies financial institution assets and liabilities by financial instrument, sector of the domestic economy, and residency. For many countries the new presentation provides broader institutional coverage of other depository corporations and monetary aggregates. In the new presentation, the IMF has adopted broad money as the flagship concept. Broad money consists of currency in circulation outside depository corporations, transferable deposits, and other liquid components. Table 4.15 has replaced money and quasi money with broad money. Claims on the private sector have been replaced with other claims on the domestic economy, consisting of the private sector plus state and local governments, public nonfinancial corporations, and other financial corporations. Claims on governments and other public entities have been replaced with net claims on the central government.

2011 World Development Indicators

191

Tables

4.a Albania Algeria Angola Argentinab Armenia Australia Austria Azerbaijan Bangladesh Belarus Belgium Bolivia Botswana Brazil Bulgaria Cambodia Cameroon Canada Chile China Hong Kong SAR, China Colombia Congo, Dem. Rep. Costa Rica Côte d’Ivoire Croatia Czech Republic Denmark Dominican Republic Ecuador Egypt, Arab Rep. El Salvador Estonia Ethiopia Finland France Gabon Gambia, The Georgia Germany Ghana Greece Guatemala Haiti Honduras Hungary India Indonesia Iran, Islamic Rep. Ireland Israel Italy Jamaica Japan Jordan

192

Recent economic performance Gross domestic product

Exports of goods and services

Imports of goods and services

GDP deflator

average annual % growth 2009 2010a

2.5 2.1 0.7 0.9 –14.4 1.3 –3.9 9.3 5.7 1.4 –2.8 3.4 –3.7 –0.6 –4.9 –1.9 2.0 –2.5 –1.5 9.1 –2.8 0.8 2.7 –1.5 3.6 –5.8 –4.2 –4.9 3.5 0.4 4.6 –3.5 –14.1 8.7 –8.0 –2.6 –1.0 4.6 –3.9 –4.7 4.7 –2.0 0.6 2.9 –1.9 –6.3 9.1 4.5 1.8 –7.1 0.8 –5.0 –3.0 –5.2 2.3

3.0 2.4 3.0 8.0 4.0 2.8 1.5 3.7 5.8 7.0 2.1 4.1 7.8 7.6 0.0 4.9 3.0 3.0 5.5 10.0 6.0 4.3 5.2 3.6 3.0 –0.8 1.7 2.1 4.4 2.3 5.1 1.3 1.0 9.0 3.0 1.6 5.1 5.0 5.5 3.5 6.6 –4.0 2.2 –8.5 2.4 0.3 9.5 5.9 1.5 –0.6 3.8 1.1 0.6 4.4 4.0

2011 World Development Indicators

5.9 –3.0 2.4 –6.4 –32.8 2.9 –16.1 2.8 0.0 –8.2 –11.4 –10.8 –28.0 –10.2 –10.3 –6.3 –4.8 –14.2 –5.6 –10.3 –10.1 –2.8 5.4 0.6 9.3 –16.2 –10.2 –9.7 –7.4 –6.4 –14.5 –16.4 –11.2 6.9 –20.5 –12.2 –4.9 2.5 –8.4 –14.3 12.6 –6.2 –6.2 9.9 –12.6 –9.1 –6.7 –9.7 8.5 –4.2 –11.9 –19.1 –10.8 –24.2 –2.7

12.7 3.0 10.0 12.8 8.5 15.0 8.2 11.0 –9.0 6.0 9.7 11.4 12.0 26.0 11.0 8.0 17.0 15.5 8.5 33.0 22.1 17.4 9.3 6.2 4.4 2.5 9.4 5.1 8.1 –2.0 11.8 9.4 5.4 11.7 6.8 6.6 7.0 5.2 11.0 10.7 8.9 0.5 9.9 –7.1 4.5 6.8 8.1 24.7 –3.0 1.7 17.8 8.0 5.7 28.7 5.2

–12.0 16.7 6.6 –19.0 –21.0 –9.0 –14.4 –5.3 –2.6 –8.6 –11.1 –10.2 –9.3 –11.5 –21.5 –4.9 –5.2 –13.9 –14.3 4.1 –8.8 –7.9 –11.9 –12.4 11.0 –20.7 –10.2 –12.5 –9.8 –8.0 –17.9 –23.3 –26.8 16.4 –18.1 –10.7 –2.8 3.8 –6.4 –9.4 –14.1 –18.6 –9.4 5.8 –26.0 –15.4 –7.3 –15.0 7.8 –9.7 –17.7 –14.5 –11.4 –16.7 –7.8

5.2 12.5 8.5 23.1 4.2 28.7 6.8 3.5 –12.5 3.4 8.2 12.3 8.9 35.1 3.0 12.6 12.0 14.6 25.5 35.0 22.5 21.4 10.8 13.1 5.0 1.5 10.4 1.0 11.8 5.0 12.0 15.2 4.0 4.4 3.5 5.2 4.8 3.1 9.0 9.1 10.5 –12.1 14.3 5.9 10.4 5.4 6.8 32.5 16.5 2.1 17.5 9.4 9.3 15.6 6.5

2.3 –9.4 –5.8 10.0 1.4 4.9 0.8 –16.8 6.5 3.9 1.1 –2.4 –5.7 5.7 4.1 5.1 –3.4 –2.1 4.2 –0.6 0.2 4.9 30.2 8.9 1.3 3.3 2.7 0.4 3.0 4.3 10.8 –1.0 –0.6 24.4 0.9 0.5 –19.0 2.4 –2.0 1.4 16.7 1.3 2.4 3.5 4.4 4.6 7.5 8.4 0.6 –3.2 5.2 2.1 6.5 –0.9 8.1

2.0 8.6 36.1 9.4 7.5 5.7 0.6 –2.7 10.7 6.4 –2.8 6.5 6.0 5.3 –0.6 3.9 3.4 2.7 6.6 1.7 0.3 4.8 21.7 7.9 1.3 1.6 1.6 5.3 7.1 4.4 11.2 3.6 –1.1 9.9 –0.4 1.4 9.1 4.8 7.4 1.6 10.6 4.8 6.4 12.6 10.5 2.7 11.5 6.2 15.0 0.6 4.6 1.6 16.7 –1.0 8.5

Current account balance

Gross international reserves

% of GDP 2009 2010a

months of import coverage 2010a

–15.6 –10.0 –10.0 2.8 –15.7 –4.2 2.9 23.7 3.7 –13.0 0.7 4.7 –4.4 –1.5 –9.8 –8.8 –5.1 –2.9 2.6 6.0 8.3 –2.1 –13.7 –1.8 7.2 –5.3 –1.1 3.6 –4.6 –0.5 –1.8 –1.8 4.7 –7.7 2.9 –2.0 .. 8.6 –11.3 5.0 –4.6 –10.9 0.0 –3.6 –3.1 –0.5 –1.9 2.0 3.4 –2.9 3.9 –3.1 –9.3 2.8 –5.0

–12.2 4.6 –5.1 1.8 –12.7 –2.2 3.9 27.2 2.4 –14.0 0.6 8.0 –2.1 –2.7 –2.4 –8.6 –2.7 –2.0 0.6 5.5 22.0 –2.7 –17.2 –3.2 4.1 –4.4 –2.7 5.6 –5.9 –0.8 –4.1 –3.1 4.0 –8.5 3.7 –2.1 12.7 5.2 –12.1 5.9 –3.6 –8.5 –2.5 –13.6 –4.7 –0.4 –3.8 2.6 6.1 –3.6 4.9 –3.6 –7.9 3.8 –4.6

$ millions 2010a

2,496 166,989 .. 52,208 1,859 42,268 22,339 6,409 11,175 5,025 26,779 .. .. 288,575 17,223 3,787 .. 57,151 27,827 2,711,162 266,055 28,076 1,768 4,630 3,502 14,133 42,328 75,077 3,501 2,622 36,517 2,897 2,567 .. 9,547 165,852 .. .. 2,264 215,978 .. 6,352 5,949 1,282 .. 44,988 300,480 92,815 .. 2,114 70,914 158,478 2,330 1,096,069 13,388

4.5 42.0 .. 10.2 6.7 1.8 1.4 7.0 6.4 2.0 0.9 .. .. 13.9 7.6 6.0 .. 1.4 5.4 21.6 6.7 6.6 7.3 4.1 4.8 7.1 3.9 6.7 2.7 1.1 5.7 3.9 2.3 .. 1.4 2.9 .. .. 5.0 2.0 .. 0.9 5.1 5.3 .. 5.5 9.7 7.1 .. 0.2 12.0 3.5 4.0 17.6 9.5

Kazakhstan Kenya Korea, Rep. Kuwait Latvia Lebanon Lithuania Malaysia Mauritius Mexico Morocco Namibia Nepal Netherlands New Zealand Nigeria Norway Oman Pakistan Panama Paraguay Peru Philippines Poland Portugal Romania Russian Federation Saudi Arabia Senegal Singapore Slovak Republic Slovenia South Africa Spain Sri Lanka Sudan Sweden Switzerland Syrian Arab Republic Tanzania Thailand Trinidad and Tobago Tunisia Turkey Uganda Ukraine United Kingdom United States Uruguay 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 2009 2010a

1.2 2.6 0.2 –4.0 –18.0 9.0 –15.0 –1.7 2.1 –6.5 4.9 –0.8 4.7 –4.0 –0.4 5.6 –1.6 3.6 3.6 2.4 –3.8 0.9 1.1 1.7 –2.6 –8.5 –7.9 0.6 2.2 –1.3 –6.2 –7.8 –1.8 –3.6 3.5 4.5 –5.1 –1.9 4.0 6.0 –2.2 –3.0 3.1 –4.7 7.1 –15.1 –4.9 –2.6 2.9 –3.3 5.3 3.8 6.4 5.7

5.5 5.0 6.2 1.9 –2.2 8.0 0.4 7.4 4.2 5.2 3.5 4.2 3.3 1.7 2.2 7.6 –0.2 4.8 4.4 5.7 8.5 8.0 6.8 3.5 1.4 –1.9 3.8 3.7 4.0 17.5 3.7 1.5 2.7 –0.4 7.1 5.9 5.2 2.7 5.0 7.0 7.5 2.2 3.8 8.1 6.3 4.3 1.7 2.8 7.9 –2.3 6.7 8.0 6.4 5.7

–6.2 –7.0 –0.8 –11.1 –13.9 5.3 –14.3 –10.4 –4.8 –14.8 –13.1 –14.0 38.4 –7.9 0.4 1.1 –3.9 –0.4 –3.3 –0.9 –12.8 –2.5 –13.4 –9.1 –11.7 –11.8 –4.7 –2.8 –8.8 –10.1 8.8 –19.3 –19.5 –11.6 –12.3 –4.4 –13.3 –8.7 5.6 15.5 –12.7 –3.8 –1.6 –5.3 16.2 –25.6 –10.1 –9.5 2.5 –12.9 11.1 –16.3 21.5 5.2

13.0 12.0 28.0 –2.0 4.0 20.0 6.5 28.0 –4.0 15.5 18.4 5.3 6.4 11.7 10.5 5.9 4.6 8.0 14.1 5.3 30.1 –4.1 23.0 6.4 7.4 12.0 5.2 1.5 6.8 29.7 6.9 1.4 6.5 7.8 2.0 7.2 12.2 6.7 –2.0 5.3 21.0 3.0 13.0 6.5 3.4 9.5 7.0 15.0 15.6 3.2 25.0 43.6 20.0 10.5

–15.9 –0.2 –8.2 –17.0 –34.2 6.5 –29.4 –12.3 –4.6 –18.2 –6.0 5.3 20.2 –8.5 –14.8 7.3 –11.4 –13.0 –15.2 –5.6 –13.2 –11.9 –1.9 –14.3 –10.8 –24.6 –30.4 –8.8 –17.1 –11.7 8.4 –7.9 –17.4 –17.8 –9.1 –7.3 –13.2 –5.4 6.4 14.1 –21.8 –4.1 6.7 –14.3 25.2 –38.6 –12.3 –13.8 –8.6 –19.6 6.7 –4.7 15.6 36.0

6.0 14.5 28.0 22.0 1.6 18.5 4.2 30.0 3.9 19.4 7.6 8.3 6.8 12.7 17.5 8.2 8.1 18.0 11.2 13.1 30.3 15.3 23.8 7.5 3.6 8.5 17.5 7.5 4.0 26.7 6.0 –4.1 12.7 7.0 11.5 7.2 15.0 8.3 4.5 6.2 32.0 4.2 16.1 16.0 10.5 5.5 9.4 18.8 19.2 –3.0 32.5 14.2 12.3 6.2

4.7 6.7 3.4 –14.7 –0.7 5.8 –2.1 –6.7 1.5 4.3 1.8 6.5 12.1 –0.3 1.7 –0.6 –4.0 –26.0 20.0 4.1 –0.1 3.0 2.6 3.7 0.1 6.5 2.5 –21.6 –0.5 –1.8 0.0 1.9 7.3 0.2 5.7 –0.8 2.0 0.3 –7.6 7.4 2.0 –15.7 2.9 5.2 16.5 13.4 1.4 0.9 5.9 8.4 6.0 –4.1 12.7 25.3

6.9 4.8 –0.5 13.9 –4.9 4.5 0.0 1.5 2.1 4.9 2.2 4.3 15.1 2.9 4.4 17.0 7.9 21.2 13.4 2.4 4.8 3.2 5.6 2.4 1.0 5.5 8.0 16.7 0.7 –2.2 2.9 –0.2 5.6 0.1 8.2 13.0 0.9 0.9 10.2 8.7 –1.9 4.6 3.8 7.1 6.1 9.2 3.0 0.6 7.1 38.5 12.5 13.8 –5.8 4.2

4.a

economy

Recent economic performance Current account balance

Gross international reserves

% of GDP 2009 2010a

months of import coverage 2010a

–3.7 –5.7 5.1 25.6 8.7 –21.9 4.4 16.5 –7.9 –0.7 –5.4 1.3 –0.1 4.6 –2.9 12.5 13.1 –0.6 –2.2 –0.2 0.6 0.2 5.3 –2.2 –10.3 –4.5 4.0 6.1 –13.6 17.9 –3.2 –1.5 –4.0 –5.5 –0.5 –7.1 7.7 7.9 –4.5 –8.5 8.3 21.8 –3.1 –2.3 –2.8 –1.5 –1.7 –2.7 0.7 2.6 –7.0 –9.7 –3.2 –1.8

3.8 –5.7 3.7 25.1 1.4 –23.6 2.6 14.7 –9.4 –1.0 –3.2 –1.6 –3.0 5.6 –2.5 10.7 11.8 8.5 –3.1 –6.1 –1.8 –1.7 5.3 –3.1 –10.6 –6.3 5.1 7.8 –14.3 22.6 –0.1 –2.2 –4.1 –6.0 –3.6 –1.9 6.7 7.7 –3.9 –8.3 6.0 25.7 –4.8 –5.9 –3.6 –2.2 –2.9 –3.3 –0.6 5.9 –15.5 –0.6 –4.5 –1.3

$ millions 2010a

28,281 4,327 292,143 24,805 7,604 44,476 6,836 106,501 2,619 120,583 23,585 .. .. 46,147 15,787 .. 50,036 13,025 17,256 .. 3,962 44,215 62,324 93,472 20,937 48,048 479,222 452,391 1,911 .. 2,156 1,108 43,820 31,872 7,240 .. 48,246 269,396 .. .. 172,028 .. .. 85,959 .. 34,571 82,365 488,928 7,744 27,700 .. 5,986 2,094 ..

8.4 4.1 7.0 8.2 7.9 27.3 4.1 6.9 5.7 4.7 7.7 .. .. 1.0 4.8 .. 5.3 7.3 5.7 .. 5.0 17.1 12.1 6.3 2.9 8.0 19.2 32.9 3.9 .. 0.3 0.5 5.8 1.0 6.7 .. 2.9 14.5 .. .. 10.4 .. .. 5.3 .. 7.2 1.4 2.3 9.7 5.9 .. 10.0 5.6 ..

a. Data are preliminary estimates based on World Bank staff estimates and National Sources. b. Private analysts estimate that consumer price index inflation was considerably higher for 2007–09 and believe that GDP volume growth has been significantly higher than official reports indicate since the last quarter of 2008. Source: World Development Indicators data files, the World Bank’s Global Economic Prospects 2011, and the International Monetary Fund’s International Financial Statistics. 2011 World Development Indicators

193

4.1

Growth of output Gross domestic product

average annual % growth 1990–2000 2000–09

Afghanistan Albania Algeria Angolaa Argentina Armenia Australia Austria Azerbaijan 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 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 Honduras

194

.. 3.8 1.9 1.6 4.3 –1.9 3.7 2.4 –6.3 4.8 –1.6 2.2 4.8 4.0 .. 5.0 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 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 1.8 4.3 2.2 4.2 4.4 1.2 0.5 3.2

2011 World Development Indicators

10.5 5.4 4.0 13.1 5.4b 10.5 3.3 2.0 17.9 5.9 8.4 1.7 4.0 4.1 5.0 4.4 3.6 5.4 5.4 3.0 9.0 3.3 2.1 0.8 10.2 4.1 10.9 4.7 4.5 5.2 4.0 5.1 0.8 3.9 6.7 4.1 1.2 5.5 5.0 4.9 2.6 0.2 5.9 8.5 2.5 1.5 2.1 5.2 7.4 1.0 5.8 3.6 3.7 3.0 1.0 0.7 4.9

Agriculture

average annual % growth 1990–2000 2000–09

.. 4.3 3.6 –1.4 3.5 0.5 3.1 –0.1 –1.7 2.9 –4.0 2.7 5.8 2.9 .. –0.5 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 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 .. 0.5 2.8 4.3 .. .. 2.2

4.9 1.4 4.6 14.0 2.5 6.6 0.0 1.3 5.3 3.3 5.2 –1.0 4.6 3.1 4.9 1.2 3.7 –2.5 6.2 –1.5 5.7 3.4 1.4 0.3 .. 5.2 4.4 –3.3 2.5 1.7 .. 3.5 1.4 2.0 –0.9 0.1 –1.8 3.2 3.7 3.3 3.6 2.7 –2.9 7.0 2.4 0.3 1.4 3.0 0.6 –0.3 .. –1.4 2.9 6.7 .. .. 3.3

Industry

average annual % growth 1990–2000 2000–09

.. –0.5 1.8 4.4 3.8 –7.8 2.7 2.5 –2.1 7.3 –1.8 1.8 4.1 4.1 .. 3.7 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 0.2 2.5 7.1 2.6 5.1 5.1 15.0 –2.4 4.1 3.8 1.1 1.6 1.0 –8.1 –0.1 .. 1.0 4.3 4.9 .. .. 3.6

14.5 4.4 3.3 13.4 6.1 11.3 2.6 2.3 23.1 7.8 12.3 0.7 3.8 5.3 6.8 2.5 2.8 5.9 7.3 –6.2 12.0 –0.4 0.1 –0.4 .. 2.7 11.8 –2.6 4.4 8.7 .. 5.1 –0.2 4.6 2.3 5.7 –0.5 2.4 4.2 5.3 1.7 0.6 8.6 9.3 3.6 0.5 0.9 7.4 10.0 0.3 .. 1.4 2.8 4.4 .. .. 4.1

Manufacturing

average annual % growth 1990–2000 2000–09

.. .. –2.1 –0.3 2.7 –4.3 1.8 2.5 –15.7 7.2 –0.7 .. 5.8 3.8 .. 4.7 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 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 .. .. 4.0

8.7 .. 2.6 20.2 5.8 4.6 1.3 2.9 10.8 7.9 10.8 .. 2.7 4.5 7.6 4.8 2.6 6.2 6.3 .. 11.3 .. –1.6 –0.1 .. 3.2 11.4 .. 4.0 6.3 .. 4.7 –1.7 3.7 –1.5 7.0 0.4 2.7 5.3 4.7 2.1 –6.0 8.9 7.2 4.1 0.1 3.1 .. 10.9 0.8 .. 1.7 2.8 3.1 .. .. 4.6

Services

average annual % growth 1990–2000 2000–09

.. 6.9 1.8 –2.2 4.5 6.4 4.2 2.5 –2.7 4.5 –0.4 2.0 4.2 4.3 .. 9.1 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 1.2 2.7 5.9 2.4 4.1 4.0 5.7 3.2 5.2 2.6 2.2 3.1 3.7 –0.3 2.9 .. 2.6 4.7 3.6 .. .. 3.8

13.5 8.3 5.3 12.1 4.7 12.1 3.7 2.1 10.6 6.1 5.9 2.0 3.2 3.1 4.4 5.6 3.8 6.1 5.5 10.4 9.5 6.2 3.0 –2.5 .. 4.6 11.6 5.3 4.7 11.2 .. 5.6 1.0 4.0 8.3 4.3 1.5 7.1 3.6 5.4 3.2 0.5 7.1 10.2 1.6 1.9 3.2 6.1 8.9 1.5 .. 4.7 4.4 –2.7 .. .. 6.2

Gross domestic product

average annual % growth 1990–2000 2000–09

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

1.5 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 3.2 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 4.2 ..

2.9 7.9 5.3 5.4 –0.3 3.9 3.6 0.5 1.5 1.1 6.9 8.8 4.4 .. 4.2 4.8 8.4 4.6 6.9 6.2 4.6 3.1 0.0 5.4 6.3 3.1 3.6 4.8 5.1 5.3 4.7 3.7 2.2 5.6 7.4 5.0 7.9 .. 5.3 3.7 1.7 2.5 3.3 4.3 6.6 2.1 4.5 5.2 6.9 3.4 3.4 6.0 4.9 4.4 0.8 .. 14.2

Agriculture

average annual % growth 1990–2000 2000–09

–1.9 3.2 2.0 3.2 .. 0.0 .. 2.1 –0.6 –1.3 –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 2.5 –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 1.7 0.5 –0.6 .. ..

5.3 2.9 3.4 5.9 .. –4.6 .. –0.2 –0.7 –0.3 8.3 4.6 2.2 .. 2.0 .. .. 1.8 3.3 2.7 1.4 –2.4 .. .. 1.7 2.2 2.4 2.4 3.5 4.8 0.9 –0.8 2.0 –0.6 5.9 5.8 8.2 .. 0.5 3.1 1.5 1.8 2.7 .. .. 2.4 .. 3.5 3.5 2.2 2.3 4.1 3.6 0.8 –0.3 .. ..

Industry

average annual % growth 1990–2000 2000–09

3.5 6.1 5.2 2.6 .. 11.6 .. 1.0 –0.8 –0.3 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 –2.5 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 3.5 7.1 3.1 .. ..

3.5 8.6 4.1 6.9 .. 4.0 .. –0.5 0.2 1.7 8.4 9.6 4.8 .. 5.4 .. .. 0.8 11.9 5.2 4.4 3.6 .. .. 9.6 3.5 4.2 5.5 3.5 4.5 5.0 1.7 1.3 –1.7 6.5 4.1 9.1 .. 6.2 2.8 0.9 1.9 3.7 .. .. –0.3 .. 6.8 5.7 4.1 1.8 6.5 4.0 5.8 –0.8 .. ..

4.1

Manufacturing

average annual % growth 1990–2000 2000–09

7.7 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 –9.7 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 9.9 2.7 .. ..

5.0 8.7 4.7 9.9 .. .. .. –1.1 –1.5 2.8 9.6 6.6 4.3 .. 6.3 .. .. –1.2 –1.9 3.1 2.2 5.7 .. .. 9.0 2.9 5.1 5.0 4.3 5.1 –1.4 0.4 1.1 1.3 7.1 3.1 7.9 .. 5.6 1.0 1.2 .. 4.8 .. .. 2.6 .. 8.7 1.5 3.8 1.2 6.2 3.9 8.5 –0.6 .. ..

economy

Growth of output

Services

average annual % growth 1990–2000 2000–09

1.3 7.7 4.0 3.8 .. 8.7 .. 1.6 3.8 1.8 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 8.2 3.0 4.9 6.3 2.9 0.7 0.7 3.1 5.0 .. 4.2 6.2 3.6 3.6 5.0 1.9 .. 3.8 5.0 4.4 4.5 –0.6 2.5 4.0 4.0 5.1 2.5 .. ..

2011 World Development Indicators

3.4 9.5 6.2 5.3 .. 4.4 .. 1.0 1.9 1.5 6.1 8.6 4.5 .. 3.7 .. .. 7.9 7.6 7.0 4.3 3.7 .. .. 7.4 3.0 3.6 6.5 6.4 6.5 5.5 5.7 2.6 10.5 8.7 5.0 7.0 .. 5.5 4.1 2.1 3.4 3.7 .. .. 3.0 .. 5.9 7.4 3.8 4.3 6.0 6.1 3.7 1.6 .. ..

195

4.1

Growth of output Gross domestic product

average annual % growth 1990–2000 2000–09

Romania Russian Federation Rwandaa Saudi Arabiaa Senegal Serbia Sierra Leone Singapore Slovak Republic Slovenia Somalia South Africa 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 7.6 2.2 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 7.2 –9.3 4.8 2.8 3.6 3.3 –0.2 1.6 7.9 7.3 6.0 0.5 2.3 2.9 w 3.1 3.9 6.5 2.1 3.9 8.5 –1.8 3.2 3.8 5.5 2.5 2.7 2.1

5.6 6.0 7.6 3.8 4.3 5.0 9.5 6.5 5.8 3.8 .. 4.1 2.8 5.5 7.3 2.6 2.4 1.9 4.4 8.2 7.1 4.6 2.4 2.5 7.4 4.9 4.9 13.9 7.8 5.6 7.0 2.0 2.0 3.4 6.9 4.9 7.6 –0.9 3.9 5.4 –7.5 2.9 w 5.4 6.4 8.5 4.4 6.4 9.4 5.9 3.8 4.7 7.3 5.1 2.0 1.5

Agriculture

average annual % growth 1990–2000 2000–09

–1.9 –4.9 2.5 1.6 2.4 .. .. .. 0.2 0.4 .. 1.0 3.1 1.8 7.4 0.9 –0.8 –0.9 6.0 –6.8 3.2 1.0 .. 4.0 2.7 2.3 1.3 –4.7 3.9 –5.6 13.2 –1.3 3.8 2.6 0.5 1.2 4.3 .. 5.6 4.2 4.3 1.9 w 2.9 2.4 3.1 0.9 2.4 3.4 –2.1 2.0 2.9 3.3 3.2 1.2 1.5

7.3 2.1 .. 1.4 2.0 .. .. 2.3 5.0 –0.7 .. 1.5 –0.2 2.8 2.4 1.3 3.5 0.3 3.8 7.7 4.4 2.3 .. 2.8 –7.2 2.6 1.5 14.3 2.3 3.1 3.6 0.6 2.1 2.9 6.5 3.6 3.8 .. .. 1.2 –10.8 2.5 w 3.6 3.6 3.8 3.0 3.6 4.1 3.0 3.0 4.4 3.0 3.2 0.9 0.0

Industry

average annual % growth 1990–2000 2000–09

–1.2 –7.1 –3.8 2.2 3.8 .. .. 7.8 3.7 1.6 .. 1.0 2.3 6.9 8.5 3.2 4.6 0.3 9.2 –11.4 3.1 5.7 .. 1.8 3.2 4.6 4.7 –2.7 12.0 –12.6 3.0 1.3 3.8 1.1 –3.4 1.2 11.9 .. 8.2 –4.2 0.4 2.4 w 3.4 4.5 8.7 1.3 4.5 11.0 –4.3 3.0 4.2 6.0 1.9 1.9 1.1

6.0 4.6 .. 3.6 3.3 .. .. 5.4 10.5 4.1 .. 2.9 1.3 5.5 10.2 1.7 2.8 2.1 2.4 9.2 9.5 5.6 .. 8.1 10.2 3.6 5.4 30.3 9.5 4.6 6.0 –0.6 0.9 4.0 4.7 3.3 9.6 .. .. 9.2 –5.8 2.8 w 7.4 7.2 9.6 3.9 7.2 10.2 6.2 3.2 3.6 8.2 4.9 1.1 0.7

Manufacturing

average annual % growth 1990–2000 2000–09

.. .. –5.8 5.6 3.1 .. .. .. 9.3 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 5.5 4.7 .. 13.9 –11.2 11.9 .. .. –0.1 0.7 4.5 11.2 .. 5.7 0.8 0.4 .. w 3.7 6.2 9.2 3.3 6.2 10.9 .. 2.9 4.3 6.4 2.2 .. 2.4

.. .. .. 5.9 1.4 .. .. .. 10.7 3.7 .. 3.1 –0.2 4.4 4.4 1.8 3.3 2.5 14.5 8.6 8.7 6.6 .. 7.5 9.5 3.6 5.3 .. 6.7 7.8 8.1 .. 2.4 6.2 2.3 3.6 11.3 .. .. 5.0 –6.6 4.0 w 6.4 7.6 9.8 3.6 7.6 10.2 .. 2.9 6.0 8.5 3.4 2.9 0.5

Services

average annual % growth 1990–2000 2000–09

0.9 –1.7 –0.9 2.2 3.0 .. .. 7.8 5.4 3.3 .. 3.0 2.7 5.7 1.9 3.9 1.8 1.2 1.5 –10.8 2.6 3.7 .. 3.9 3.2 5.3 4.0 –5.8 8.3 –8.1 7.2 3.5 3.6 1.5 0.4 –0.1 7.5 .. 5.0 2.5 3.0 3.2 w 2.9 4.3 6.8 3.0 4.3 8.6 0.3 3.5 3.3 6.9 2.6 3.0 2.5

5.2 7.0 .. 4.2 6.3 .. .. 6.2 2.4 4.0 .. 4.1 3.5 6.2 10.1 3.9 2.2 1.8 7.7 8.3 7.8 4.2 .. –0.7 5.3 5.9 5.3 16.0 8.5 5.8 9.5 2.9 2.3 3.4 8.5 5.9 7.5 .. .. 5.6 –4.8 2.9 w 5.9 6.6 9.3 4.5 6.6 10.0 6.3 3.9 5.5 8.7 4.8 2.2 1.9

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 higher than official reports indicate since the last quarter of 2008. c. Covers mainland Tanzania only.

196

2011 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 Classification (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 also include statistical discrepancies.

measures of output, measuring the growth of services remains difficult.

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 mea-

World Development Indicators adopted the termi-

organizations and central banks by visiting and

surement problem, especially in developing coun-

nology of the 1993 SNA in 2001. Although many

resident World Bank missions. Data for high

tries, where much economic activity is unrecorded.

countries continue to compile their national accounts

income economies are from Organisation for

A complete picture of the economy requires estimat-

according to the SNA version 3 (referred to as the

Economic Co-operation and Development (OECD)

ing household outputs produced for home use, sales

1968 SNA), more and more are adopting the 1993

data files. The United Nations Statistics Division

in informal markets, barter exchanges, and illicit or

SNA. Some low-income countries still use concepts

publishes detailed national accounts for UN mem-

deliberately unreported activities. The consistency

from the even older 1953 SNA guidelines, including

ber countries in National Accounts Statistics: Main

and completeness of such estimates depend on the

valuations such as factor cost, in describing major

Aggregates and Detailed Tables and publishes

skill and methods of the compiling statisticians.

economic aggregates. Countries that use the 1993

updates in the Monthly Bulletin of Statistics.

mates of inﬂation.

SNA are identiﬁed in Primary data documentation.

2011 World Development Indicators

197

4.2

Structure of output Gross domestic product

Agriculture

$ millions

198

% of GDP

1995

2009

1995

.. 2,424 41,764 5,040 258,032 1,468 371,091 238,314 3,052 37,940 13,973 284,142 2,009 6,715 1,867 4,774 768,951 13,069 2,380 1,000 3,441 8,733 590,517 1,122 1,446 71,349 728,007 144,230 92,507 5,643 2,116 11,722 11,000 22,046 30,428 55,257 181,984 16,358 20,206 60,159 9,500 578 4,353 7,606 130,700 1,569,983 4,959 382 2,694 2,522,792 6,457 131,718 14,657 3,694 254 2,695 3,911

14,483 12,015 140,577 75,493 307,155 8,714 924,843 381,084 43,019 89,360 49,037 471,161 6,656 17,340 17,042 11,823 1,594,490 48,722 8,141 1,325 10,447 22,186 1,336,068 2,006 6,839 163,669 4,985,461 210,568 234,045 10,575 9,580 29,240 23,304 63,034 62,705 190,274 309,596 46,788 57,249 188,413 21,101 1,873 19,084 28,526 237,989 2,649,390 11,062 733 10,744 3,330,032 26,169 329,924 37,322 4,103 837 6,479 14,318

.. 56 11 7 6 42 3 3 27 26 17 2 34 17 21 4 6 16 35 48 50 24 3 46 36 9 20 .. 15 57 10 14 25 7 9 5 3 10 .. 17 14 21 6 57 4 3 8 30 52 1 43 9 24 19 55 .. 22

2011 World Development Indicators

Industry

Manufacturing

% of GDP 2009

33 21 12 10 8 21 3 2 8 19 10 1 .. 14 8 3 6 6 .. .. 35 19 .. 56 14 3 10 .. 7 43 5 7 24 7 5 2 1 6 6 14 12 14 3 51 3 2 5 27 10 1 32 3 12 17 55 .. 12

Services

% of GDP

1995

2009

1995

2009

1995

2009

.. 23 50 66 28 32 29 31 34 25 37 28 15 33 26 51 28 28 21 19 15 31 31 21 14 35 47 15 32 17 45 30 21 32 23 38 25 36 .. 32 30 17 33 10 33 25 52 13 16 32 27 21 20 29 12 .. 31

22 20 55 59 32 35 29 29 60 29 42 22 .. 36 28 40 25 30 .. .. 23 31 .. 15 49 42 46 8 34 24 71 27 25 27 20 37 22 32 23 37 27 22 29 11 28 19 54 15 21 26 19 18 28 53 13 .. 27

.. 14 12 4 18 25 15 20 13 15 31 20 9 19 11 5 19 26 15 9 10 22 18 10 11 18 34 8 16 9 8 22 15 23 15 24 17 26 .. 17 23 9 21 5 25 .. 5 6 11 23 10 .. 14 4 8 .. 18

13 20 6 6 21 16 10 19 4 18 30 14 .. 14 13 4 16 15 .. .. 15 17 .. .. 7 13 34 2 14 5 4 19 18 16 10 23 13 24 10 16 21 6 17 4 18 11 4 5 12 19 7 10 20 5 10 .. 19

.. 22 39 26 66 26 68 67 39 49 46 70 51 50 54 45 67 56 43 33 36 45 66 33 51 55 33 85 53 26 45 57 55 61 68 57 71 54 .. 51 56 62 61 33 62 72 40 57 32 67 31 70 56 52 33 .. 48

45 60 34 31 61 45 68 69 32 53 48 78 .. 50 64 57 69 64 .. .. 42 50 .. 30 38 55 43 92 58 33 24 66 50 66 75 61 77 61 71 49 60 63 68 39 69 79 41 57 69 73 49 79 59 30 32 .. 60

Gross domestic product

Agriculture

$ millions

Industry

% of GDP

1995

2009

1995

44,656 356,299 202,132 90,829 10,114 67,061 96,065 1,126,041 5,813 5,264,380 6,727 20,374 9,046 .. 517,118 .. 27,192 1,661 1,764 5,236 11,719 814 135 25,541 7,905 4,449 3,160 1,397 88,832 2,466 1,415 4,040 286,698 1,753 1,227 32,986 2,247 .. 3,503 4,401 418,969 62,795 3,191 1,881 28,109 148,920 13,803 60,636 7,906 4,636 8,066 53,674 74,120 139,062 116,419 42,647 8,138

128,964 1,377,265 540,274 331,015 65,837 227,193 195,392 2,112,780 12,070 5,068,996 25,092 115,306 29,376 .. 832,512 5,387 148,024 4,578 5,939 26,195 34,528 1,579 876 62,360 37,206 9,221 8,590 4,727 193,093 8,996 3,024 8,589 874,810 5,405 4,202 91,375 9,790 .. 9,265 12,531 792,128 126,679 6,140 5,383 173,004 381,766 46,114 161,990 24,711 7,893 14,236 130,325 161,196 430,076 232,874 .. 98,313

7 26 17 18 9 7 .. 3 9 2 4 13 31 .. 6 .. 0 44 56 9 8 19 82 .. 11 13 27 30 13 50 37 10 6 33 41 15 35 60 12 42 3 7 23 40 .. 3 3 26 8 35 21 9 22 8 6 1 ..

Manufacturing

% of GDP 2009

4 18 16 10 .. 1 .. 2 6 1 3 6 23 .. 3 12 .. 29 35 3 5 8 61 2 4 11 29 31 10 37 21 4 4 10 24 16 31 .. 9 34 2 .. 19 .. 33 1 .. 22 6 36 19 7 15 4 2 .. ..

4.2

economy

Structure of output

Services

% of GDP

1995

2009

1995

2009

1995

2009

32 28 42 34 75 38 .. 30 37 34 29 31 16 .. 42 .. 55 20 19 30 25 43 5 .. 31 30 9 20 41 19 25 32 28 32 29 34 15 10 28 23 27 27 27 17 .. 34 46 24 18 34 23 31 32 35 28 44 ..

29 27 49 44 .. 31 .. 25 22 28 32 40 15 .. 37 20 .. 19 28 20 17 34 17 78 31 36 16 16 44 24 35 29 35 13 33 29 24 .. 33 16 24 .. 30 .. 41 40 .. 24 17 45 21 34 30 30 23 .. ..

24 18 24 12 1 30 .. 22 16 23 15 15 10 .. 28 .. 4 9 14 21 14 17 3 .. 19 23 8 16 26 8 8 23 21 26 12 19 8 7 13 10 17 18 19 6 .. 13 5 16 9 8 16 17 23 21 19 42 ..

22 15 27 11 .. 24 .. 16 9 20 20 11 9 .. 28 17 .. 13 9 10 9 17 13 4 18 23 14 10 25 3 4 19 17 13 5 16 14 .. 15 7 13 .. 20 .. .. 10 .. 17 6 6 13 14 20 16 13 .. ..

61 46 41 47 16 55 .. 66 54 64 67 56 53 .. 52 .. 45 37 25 61 68 38 13 .. 58 57 64 50 46 32 37 58 66 35 30 51 51 30 60 35 69 66 49 43 .. 63 51 50 74 31 56 60 46 57 66 55 ..

66 55 35 45 .. 68 .. 73 72 71 65 53 62 .. 61 68 .. 51 37 77 78 58 22 20 64 52 55 53 46 .. 45 67 61 77 44 55 45 .. 58 50 74 .. 51 .. 27 59 .. 54 77 20 59 59 55 66 75 .. ..

2011 World Development Indicators

199

4.2

Structure of output Gross domestic product

Agriculture

$ millions

Romania Russian Federation Rwandaa Saudi Arabiaa Senegal Serbia Sierra Leone Singapore Slovak Republic Slovenia Somalia South Africa 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

1995

2009

35,477 395,528 1,293 142,458 4,879 21,381 871 84,291 25,240 20,814 .. 151,113 596,751 13,030 13,830 1,699 253,680 315,940 11,397 1,232 5,255 168,019 .. 1,309 5,329 18,031 169,486 2,482 5,756 48,214 42,807 1,157,119 7,359,300 19,298 13,350 74,889 20,736 3,220 4,236 3,478 7,111 29,692,820 t 153,755 4,811,047 1,992,261 2,818,895 4,965,895 1,312,902 763,913 1,770,557 315,651 476,175 327,608 24,722,778 7,286,803

161,110 1,231,893 5,216 375,766 12,822 42,984 1,942 182,232 87,642 48,477 .. 285,366 1,460,250 41,979 54,681 3,001 406,072 491,924 52,177 4,978 21,368 263,772 558 2,855 21,204 39,561 614,603 19,947 16,043 113,545 230,252 2,174,530 14,119,000 31,511 32,104 326,133 97,180 .. 26,365 12,805 5,625 58,259,785 t 432,171 16,213,154 8,887,269 7,318,398 16,657,552 6,353,790 2,591,705 4,017,912 1,062,419 1,700,339 945,923 41,607,730 12,465,331

1995

21 7 44 6 21 .. 43 .. 6 4 .. 4 5 23 39 12 3 2 32 38 47 10 .. 38 2 11 16 17 49 15 3 2 2 9 32 6 27 .. 20 18 15 4w 37 14 21 8 15 19 14 7 16 26 18 2 3

a. Components are at producer prices. b. Covers mainland Tanzania only.

200

2011 World Development Indicators

Industry

Manufacturing

% of GDP 2009

7 5 34 3 17 13 51 .. 3 2 .. 3 3 13 30 7 2 1 21 22 29 12 .. .. 0 8 9 12 25 8 2 1 1 10 20 .. 21 .. .. 22 18 3w 26 10 13 6 10 11 8 6 11 18 13 1 2

1995

43 37 16 49 24 .. 39 35 38 35 .. 35 29 27 11 45 30 30 20 39 15 41 .. 22 47 29 33 63 14 43 52 31 26 29 28 41 29 .. 32 36 29 30 w 20 35 39 32 34 44 35 29 34 27 29 30 29

Services

% of GDP 2009

26 33 15 51 22 28 22 26 35 34 .. 31 26 30 26 49 25 27 34 24 24 43 .. .. 52 30 26 54 26 29 61 21 21 26 33 .. 40 .. .. 34 29 27 w 24 35 39 31 35 45 30 31 43 27 30 25 24

1995

29 .. 10 10 17 .. 9 27 27 26 .. 21 18 16 5 39 22 20 15 28 7 30 .. 10 9 19 23 40 7 35 10 21 19 20 12 15 15 .. 14 11 22 21 w 11 23 26 19 22 31 22 19 15 17 16 20 21

% of GDP 2009

22 15 6 10 13 .. .. 19 19 22 .. 15 13 18 7 44 16 19 13 11 10 34 .. .. 6 17 17 47 8 18 12 11 13 16 13 .. 20 .. .. 10 17 17 w 12 21 26 17 21 32 17 17 12 15 13 16 15

1995

36 56 40 45 55 .. 18 65 56 60 .. 61 66 50 51 43 66 68 48 22 38 50 .. 40 51 59 50 20 36 42 45 67 72 62 40 53 44 .. 48 46 56 65 w 43 51 40 60 51 36 51 64 50 46 53 68 68

2009

67 62 51 46 62 59 27 74 63 64 .. 66 71 58 44 43 73 72 45 54 47 45 .. .. 47 62 65 34 50 62 38 78 77 64 47 .. 39 .. .. 44 53 70 w 50 55 48 62 55 43 62 63 46 55 57 74 74

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 firms.

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 Classification (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-

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.

Agricultural production often must be estimated

2011 World Development Indicators

201

4.3

Structure of manufacturing Manufacturing value added

$ millions 1998 2009

Afghanistan Albania Algeria Angola Argentina Armenia Australia Austria Azerbaijan 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 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 Honduras

202

.. 268 4,372 407 53,326 377 51,505 37,828 370 6,887 4,487 45,588 200 1,189 497 253 117,276 2,180 387 64 436 1,843 104,352 91 188 13,540 324,603 8,868 13,770 370 136 2,972 2,499 4,163 3,103 14,416 24,894 5,136 2,912 14,403 2,569 64 870 373 29,158 209,123 252 22 307 449,216 672 12,338 2,631 132 19 .. 826

1,632 1,995 7,315 4,586 60,116 1,213 95,726 64,124 1,927 15,472 12,638 59,032 .. 2,014 1,816 475 216,924 6,424 .. .. 1,403 3,328 172,050 .. 381 19,665 1,691,153 4,971 30,690 582 429 5,034 4,187 8,789 4,955 39,662 34,971 10,577 5,316 28,712 4,319 102 2,393 1,071 37,557 253,608 479 32 1,073 567,902 1,759 29,718 6,937 201 44 .. 2,470

2011 World Development Indicators

Food, beverages, and tobacco

Textiles and clothing

Machinery and transport equipment

Chemicals

Other manufacturinga

% of total 1998 2007

.. 20 33 .. 26 .. .. 10 .. 24 .. 13 .. 35 .. 23 20 22 .. .. 7 35 14 .. .. 32 16 12 32 .. .. 46 42 .. .. 13 19 .. 22 16 29 49 17 55 8 13 .. .. 37 8 .. 24 .. .. .. .. 42

.. 17 .. .. .. .. 19 9 18 .. .. 12 .. .. .. 22 18 16 .. .. .. .. .. .. .. 14 12 14 27 .. .. 44 .. .. .. 9 17 .. 30 .. .. 44 12 41 6 14 .. .. 34 8 .. 22 .. .. .. .. ..

.. 27 8 .. 8 .. .. 5 .. 40 .. 6 .. 5 .. 8 7 13 .. .. 87 9 4 .. .. 4 12 22 10 .. .. 8 10 .. .. 6 3 .. 3 16 28 12 15 13 2 5 .. .. 1 3 .. 12 .. .. .. .. 22

.. 22 .. .. .. .. 3 2 1 .. .. 4 .. .. .. 5 6 12 .. .. .. .. .. .. .. 2 10 12 9 .. .. 5 .. .. .. 3 2 .. 4 .. .. 19 4 9 2 3 .. .. 2 2 .. 8 .. .. .. .. ..

.. 3 .. .. 13 .. .. 24 .. 3 .. 19 .. 0 .. 15 20 18 .. .. 0 1 29 .. .. 3 15 15 5 .. .. 3 2 .. .. 23 22 .. 2 12 2 1 10 1 30 26 .. .. 12 35 .. 11 .. .. .. .. 1

.. 3 .. .. .. .. 14 28 9 .. .. 19 .. .. .. .. 21 14 .. .. .. .. .. .. .. 2 24 13 6 .. .. 3 .. .. .. 29 19 .. 3 .. .. 1 10 5 32 24 .. .. 6 36 .. 10 .. .. .. .. ..

.. 5 11 .. 15 .. .. 7 .. 11 .. 18 .. 5 .. 5 13 9 .. .. 0 6 9 .. .. 10 11 3 17 .. .. 11 12 .. .. 6 10 .. 3 21 16 6 4 7 6 12 .. .. 7 10 .. 10 .. .. .. .. 5

.. 17 .. .. .. .. 7 7 5 .. .. 23 .. .. .. .. 11 7 .. .. .. .. .. .. .. 14 11 5 13 .. .. 10 .. .. .. 6 13 .. 5 .. .. 5 4 5 6 13 .. .. 8 10 .. 6 .. .. .. .. ..

.. 46 48 .. 38 .. .. 54 .. 21 .. 44 .. 55 .. 69 40 39 .. .. 7 49 44 .. .. 52 46 49 36 .. .. 32 34 .. .. 52 46 .. 69 35 25 31 53 24 54 44 .. .. 43 44 .. 43 .. .. .. .. 30

.. 41 .. .. .. .. 58 54 66 .. .. 43 .. .. .. 73 44 50 .. .. .. .. .. .. .. 69 43 55 45 .. .. 39 .. .. .. 53 50 .. 58 .. .. 31 69 40 54 45 .. .. 50 45 .. 54 .. .. .. .. ..

Manufacturing value added

$ millions 1998 2009

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

9,959 59,562 23,857 13,607 91 26,279 .. 236,315 914 868,624 1,047 2,659 1,540 .. 85,569 .. 1,037 233 216 965 2,144 140 17 .. 1,807 645 399 216 20,774 101 100 877 82,015 238 46 6,136 422 .. 369 436 58,120 8,495 538 128 .. 16,863 654 9,131 1,135 351 1,239 8,080 14,254 30,022 19,959 22,994 ..

28,619 190,333 142,532 29,832 .. 48,709 .. 306,459 973 970,204 4,416 12,536 2,801 .. 208,142 773 .. 570 478 2,278 2,645 243 105 3,879 7,562 1,816 1,115 447 49,213 195 115 1,483 144,431 568 176 12,909 1,219 .. 1,247 807 89,029 17,968 1,086 .. .. 32,575 .. 26,290 1,490 464 1,850 16,897 32,889 61,948 26,690 .. ..

4.3

economy

Structure of manufacturing Food, beverages, and tobacco

Textiles and clothing

Machinery andâ&#x20AC;Żtransport equipment

Chemicals

Other manufacturinga

% of total 1998 2007

15 13 21 13 31 17 12 9 48 11 27 .. 46 .. 9 .. 8 .. 46 26 26 .. .. .. 27 31 31 44 10 .. .. 22 24 .. 53 34 .. .. .. 35 18 30 .. .. 30 16 17 23 52 .. .. 26 35 25 12 10 4

11 9 26 10 .. 18 10 9 .. 11 23 .. 30 .. 6 .. .. .. .. 20 .. .. .. .. 23 18 0 .. 9 .. .. 31 25 39 38 30 .. .. .. .. 18 27 .. .. .. 20 8 22 .. .. .. 30 24 16 14 9 1

7 12 18 8 15 2 6 13 7 4 6 .. 8 .. 9 .. 5 .. 22 11 10 .. .. .. 18 21 33 8 4 .. .. 51 4 .. 33 18 .. .. .. 34 2 .. .. .. 11 2 7 26 7 .. .. 10 7 7 20 4 8

3 9 13 4 .. 0 3 10 .. 2 10 .. 4 .. 5 .. .. .. .. 7 .. .. .. .. 9 17 30 .. 2 .. .. 31 3 15 17 13 .. .. .. .. 2 2 .. .. .. 1 0 29 .. .. .. 12 6 4 12 1 2

27 15 14 16 2 16 24 23 .. 33 4 .. 4 .. 35 .. 2 .. 8 8 3 .. .. .. 12 9 .. 5 8 .. .. 1 23 .. 0 4 .. .. .. 0 15 .. .. .. 7 23 2 5 .. .. .. 4 21 16 15 5 0

31 19 18 24 .. 16 22 23 .. 37 3 .. 2 .. 46 .. .. .. .. 10 .. .. .. .. 10 4 1 .. 30 .. .. 1 18 4 1 5 .. .. .. .. 19 13 .. .. .. 25 1 8 .. .. .. 2 25 20 11 9 0

11 24 13 13 23 38 12 8 19 10 21 .. 8 .. 11 .. 3 .. 3 3 6 .. .. .. 3 8 6 16 11 .. .. 4 15 .. 2 15 .. .. .. 6 13 .. .. .. 26 8 7 16 7 .. .. 10 10 7 6 62 21

10 16 11 13 .. 33 20 7 .. 11 17 .. 4 .. 8 .. .. .. .. 4 .. .. .. .. 9 6 2 .. 15 .. .. .. 19 .. 3 16 .. .. .. .. 14 .. .. .. .. 9 12 14 .. .. .. 12 8 8 6 62 17

40 37 33 49 29 28 46 47 27 42 42 .. 34 .. 36 .. 83 .. 22 52 55 .. .. .. 40 31 30 28 67 .. .. 26 32 .. 12 28 .. .. .. 26 51 70 .. .. 26 51 67 30 34 .. .. 51 28 45 46 20 67

2011 World Development Indicators

44 47 32 50 .. 33 44 51 .. 39 48 .. 62 .. 35 .. .. .. .. 60 .. .. .. .. 48 55 67 .. 44 .. .. 37 35 42 41 35 .. .. .. .. 47 58 .. .. .. 45 79 26 .. .. .. 44 38 52 63 20 80

203

4.3

Structure of manufacturing Manufacturing value added

$ millions 1998 2009

Romania Russian Federation Rwanda Saudi Arabia Senegal Serbia Sierra Leone Singapore Slovak Republic Slovenia Somalia South Africa Spain Sri Lanka Sudan Swaziland Sweden Switzerland Syrian Arab Republic Tajikistan Tanzania b 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

9,601 .. 223 15,492 723 .. 26 18,839 6,036 4,860 .. 23,678 103,971 2,343 957 519 48,915 51,047 1,286 255 919 34,534 9 110 552 3,660 64,408 452 545 10,578 6,532 251,809 1,440,500 3,598 1,346 17,380 4,666 .. 638 372 923 5,516,751 t 20,369 1,085,340 535,090 563,006 1,105,587 425,997 .. 334,974 49,450 78,797 43,316 4,411,013 1,250,663

31,753 161,878 335 39,128 1,490 .. .. 33,499 15,375 10,566 .. 39,014 172,433 7,618 3,515 1,114 56,948 88,054 6,686 479 1,844 89,881 .. .. 1,334 6,527 92,715 9,158 1,190 17,992 24,643 217,594 1,779,474 4,377 3,979 .. 18,099 .. .. 1,192 826 9,102,310 t 44,786 3,432,566 2,342,311 1,036,562 3,479,229 2,036,104 .. 570,166 117,926 241,774 83,017 5,603,504 1,686,936

a. Includes unallocated data. b. Covers mainland Tanzania only.

204

2011 World Development Indicators

Food, beverages, and tobacco

Textiles and clothing

Machinery andâ&#x20AC;Żtransport equipment

Chemicals

Other manufacturinga

% of total 1998 2007

29 22 75 .. 44 .. .. 4 12 10 .. 18 15 39 .. .. 8 10 .. .. 45 25 .. .. 30 17 15 .. 65 .. .. 13 13 36 .. 22 30 15 45 .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. ..

15 15 .. 19 .. .. .. 2 7 7 .. 17 15 29 .. .. 7 .. .. .. 62 16 .. .. 11 .. 12 .. .. .. .. 16 14 42 .. .. .. 27 60 .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. ..

11 3 2 .. 3 .. .. 1 7 10 .. 6 7 30 .. .. 1 3 .. .. 0 12 .. .. 1 36 18 .. 5 .. .. 5 4 9 .. 2 22 23 5 .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. ..

12 2 .. 5 .. .. .. 1 3 6 .. 3 4 29 .. .. 1 .. .. .. 8 9 .. .. 1 .. 19 .. .. .. .. 3 2 7 .. .. .. 13 9 .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. ..

14 18 .. .. 0 .. .. 52 21 17 .. 14 20 4 .. .. 37 15 .. .. 2 27 .. .. 1 3 14 .. .. .. .. 26 30 3 .. 9 11 2 0 .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. ..

17 10 .. 6 .. .. .. 45 27 20 .. 14 17 0 .. .. 34 .. .. .. 1 35 .. .. 0 .. 20 .. .. .. .. 23 25 4 .. .. .. 1 0 .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. ..

5 9 6 .. 29 .. .. 13 9 11 .. 11 10 7 .. .. 9 .. .. .. 7 4 .. .. 26 11 8 .. 10 .. .. 10 12 8 .. 34 7 5 2 .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. ..

5 8 .. 27 .. .. .. 32 4 14 .. 7 8 14 .. .. 13 .. .. .. 2 6 .. .. 39 .. 7 .. .. .. .. 11 15 8 .. .. .. 4 4 .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. ..

40 48 17 .. 24 .. .. 30 52 51 .. 51 47 21 .. .. 46 71 .. .. 46 32 .. .. 42 33 45 .. 20 .. .. 46 41 44 .. 41 30 55 48 .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. ..

51 65 .. 43 .. .. .. 20 59 53 .. 58 55 27 .. .. 46 .. .. .. 29 34 .. .. 49 .. 42 .. .. .. .. 47 44 39 .. .. .. 55 27 .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. ..

About the data

4.3

economy

Structure of manufacturing Definitions

The data on the distribution of manufacturing value

revision 3. Concordances matching ISIC categories

• Manufacturing value added is the sum of gross

added by industry are provided by the United Nations

to national classification systems and to related

output less the value of intermediate inputs used

Industrial Development Organization (UNIDO). UNIDO

systems such as the Standard International Trade

in production for industries classified in ISIC major

obtains the data from a variety of national and inter-

Classification are available.

division D. • Food, beverages, and tobacco cor-

national sources, including the United Nations Sta-

In establishing classifications systems compil-

respond to ISIC divisions 15 and 16. • Textiles

tistics Division, the World Bank, the Organisation for

ers must define both the types of activities to be

and clothing correspond to ISIC divisions 17–19.

Economic Co-operation and Development, and the

described and the units whose activities are to

• Machinery and transport equipment correspond to

International Monetary Fund. To improve comparabil-

be reported. There are many possibilities, and the

ISIC divisions 29, 30, 32, 34, and 35. • Chemicals

ity over time and across countries, UNIDO supple-

choices affect how the statistics can be interpreted

correspond to ISIC division 24. • Other manufactur-

ments these data with information from industrial

and how useful they are in analyzing economic

ing is calculated as a residual. It covers wood and

censuses, statistics from national and international

behavior. The ISIC emphasizes commonalities in the

related products (ISIC division 20), paper and related

organizations, unpublished data that it collects in the

production process and is explicitly not intended to

products (ISIC divisions 21 and 22), petroleum and

field, and estimates by the UNIDO Secretariat. Nev-

measure outputs (for which there is a newly devel-

related products (ISIC division 23), basic metals and

ertheless, coverage may be incomplete, particularly

oped Central Product Classification). Nevertheless,

mineral products (ISIC division 27), fabricated metal

for the informal sector. When direct information on

the ISIC views an activity as defined by “a process

products and professional goods (ISIC division 28),

inputs and outputs is not available, estimates may

resulting in a homogeneous set of products” (United

and other industries (ISIC divisions 25, 26, 31, 33,

be used, which may result in errors in industry totals.

Nations 1990 [ISIC, series M, no. 4, rev. 3], p. 9).

36, and 37).

Moreover, countries use different reference periods

Firms typically use multiple processes to produce

(calendar or fiscal year) and valuation methods (basic

a product. For example, an automobile manufac-

or producer prices) to estimate value added. (See

turer engages in forging, welding, and painting as

About the data for table 4.2.)

well as advertising, accounting, and other service

The data on manufacturing value added in U.S. dol-

activities. Collecting data at such a detailed level

lars are from the World Bank’s national accounts files

is not practical, nor is it useful to record produc-

and may differ from those UNIDO uses to calculate

tion data at the highest level of a large, multiplant,

shares of value added by industry, in part because

multiproduct firm. The ISIC has therefore adopted as

of differences in exchange rates. Thus value added

the deﬁnition of an establishment “an enterprise or

in a particular industry estimated by applying the

part of an enterprise which independently engages in

shares to total manufacturing value added will not

one, or predominantly one, kind of economic activity

match those from UNIDO sources. Classification of

at or from one location . . . for which data are avail-

manufacturing industries in the table accords with

able . . .” (United Nations 1990, p. 25). By design,

the United Nations International Standard Industrial

this deﬁnition matches the reporting unit required

Classification (ISIC) revision 3. Editions of World

for the production accounts of the United Nations

Development Indicators prior to 2008 used revision

System of National Accounts. The ISIC system is

2, first published in 1948. Revision 3 was completed

described in the United Nations’ International Stan-

in 1989, and many countries now use it. But revi-

dard Industrial Classification of All Economic Activi-

sion 2 is still widely used for compiling cross-country

ties, Third Revision (1990). The discussion of the ISIC

data. UNIDO has converted these data to accord with

draws on Ryten (1998).

4.3a

Manufacturing continues to show strong growth in East Asia and Pacific through 2009 Value added in manufacturing (index, 1990 = 100) 600 East Asia & Pacific 500 400

South Asia

300 Latin America & Caribbean

Middle East & North Africa

Data sources

200

Data on manufacturing value added are from

100 Sub-Saharan Africa 0

1990

1995

2000

2005

2009

the World Bank’s National Accounts files. Data used to calculate shares of industry value added are provided to the World Bank in electronic files

Manufacturing continues to be the dominant sector in East Asia and Pacific, growing an average of about

by UNIDO. The most recent published source is

10.5 percent a year between 1990 and 2009.

UNIDO’s International Yearbook of Industrial Sta-

Source: World Development Indicators data files.

tistics 2010.

2011 World Development Indicators

205

4.4 Afghanistan Albania Algeria Angola Argentina Armenia Australia Austria Azerbaijan 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, Chinab Colombia Congo, Dem. Rep. Congo, Rep. Costa Rica Côte d’Ivoire Croatia Cuba 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 Honduras †Data for Taiwan, China

206

Structure of merchandise exports Merchandise exports

Food

Agricultural raw materials

Fuels

Ores and metals

Manufactures

$ millions 1995 2009

% of total 1995 2009

166 202 10,258 3,642 20,967 271 53,111 57,738 635 3,501 4,803 178,265a 420 1,100 152 2,142 46,506 5,355 276 105 855 1,651 192,197 171 243 16,024 148,780 173,871 10,056 1,563 1,172 3,453 3,806 4,517 1,600 21,335 50,906 3,780 4,307 3,450 1,652 86 1,840 422 40,490 301,162 2,713 16 151 523,461 1,724 11,054 2,155 702 24 110 1,769 113,047

560 1,088 45,194 40,080 55,668 698 154,234 137,672 21,097 15,084 21,283 369,854 1,000 4,848 3,929 3,458 152,995 16,455 850 64 4,200 3,000 316,713 120 2,800 53,735 1,201,534 329,422 32,853 3,100 5,600 8,788 8,900 10,474 3,109 113,437 93,344 5,463 13,799 23,062 3,797 15 9,031 1,596 62,798 484,725 5,100 15 1,135 1,126,383 5,500 20,093 7,214 1,010 115 576 5,196 203,675

2011 World Development Indicators

.. 11 1 .. 50 11 22 4 4 10 .. 10a 14 21 .. .. 29 18 25 91 .. 27 8 4 .. 24 8 3 31 .. 1 63 63 11 .. 6 24 19 53 10 57 .. 16 73 2 14 0 60 29 5 58 30 65 8 89 37 87 3

55 6 0 .. 50 20 14 7 4 7 11 10 .. 20 8 5 34 17 27 67 1 .. 11 .. .. 21 3 7 16 .. .. 25 48 13 .. 5 19 25 36 11 23 .. 10 77 2 12 .. 53 18 6 63 25 44 2 .. .. 54 1

.. 9 0 .. 4 5 8 3 8 3 .. 1a 75 10 .. .. 5 3 69 4 .. 28 9 20 .. 12 2 0 5 .. 8 5 20 5 .. 4 3 0 3 6 1 .. 10 13 8 1 13 1 3 1 15 4 4 1 11 0 3 2

8 3 0 .. 1 1 2 2 0 3 2 1 .. 1 6 0 4 1 60 5 1 .. 4 .. .. 5 0 2 4 .. .. 2 6 4 .. 1 2 1 4 2 1 .. 4 12 4 1 .. 1 2 1 9 3 3 5 .. .. 1 1

.. 3 95 .. 10 1 19 1 66 0 .. 3a 5 15 .. .. 1 7 0 0 .. 29 9 1 .. 0 4 0 28 .. 88 1 10 9 .. 4 3 0 36 37 0 .. 6 3 2 2 83 0 19 1 5 7 2 0 0 0 0 1

.. 12 98 .. 10 0 32 3 93 2 37 7 .. 40 13 0 9 13 0 2 0 .. 25 .. .. 1 2 4 51 .. .. 1 30 13 .. 4 8 0 50 44 3 .. 16 0 7 4 .. 0 3 2 2 9 4 2 .. .. 4 6

.. 12 1 .. 2 26 18 3 1 0 .. 4a 0 35 .. .. 10 10 0 1 .. 8 7 30 .. 48 2 1 1 .. 0 1 0 2 .. 3 1 0 0 6 3 .. 3 0 3 3 2 1 8 3 9 7 0 67 0 0 0 1

0 10 0 .. 4 47 27 3 0 0 1 3 .. 33 9 16 12 15 1 5 3 .. 7 .. .. 58 1 6 2 .. .. 1 0 4 .. 2 1 3 0 6 1 .. 2 1 4 2 .. 7 22 2 6 7 5 59 .. .. 4 2

.. 65 4 .. 34 54 30 88 20 85 .. 77a 6 19 .. .. 54 60 6 3 .. 8 63 45 .. 13 84 94 35 .. 3 25 7 74 .. 82 60 78 8 40 39 .. 65 11 83 79 2 36 41 87 13 50 28 24 0 62 9 93

18 70 2 .. 33 33 19 81 3 88 48 77 .. 6 61 78 39 53 12 21 96 .. 50 .. .. 11 94 79 28 .. .. 47 15 66 .. 87 65 70 9 37 72 .. 62 9 77 79 .. 39 55 82 19 54 43 32 .. .. 35 89

4.4

economy

Structure of merchandise exports Merchandise exports

Food

Agricultural rawâ&#x20AC;Żmaterials

Fuels

Ores and metals

Manufactures

$ millions 1995 2009

% of total 1995 2009

12,865 30,630 45,417 18,360 496 44,705 19,046 233,766 1,427 443,116 1,769 5,250 1,878 959 125,058 .. 12,785 409 311 1,305 816 160 820 8,975 2,705 1,204 507 405 73,914 441 488 1,538 79,542 745 473 6,881 168 860 1,409 345 203,171 13,645 466 288 12,342 41,992 6,068 8,029 625 2,654 919 5,575 17,502 22,895 22,783 .. 3,651

83,778 162,613 119,481 78,113 39,500 114,587 47,935 405,777 1,316 580,719 6,366 43,196 4,421 1,550 363,534 .. 50,328 1,439 940 7,688 4,187 750 150 35,600 16,452 2,692 1,140 920 157,433 2,100 1,370 1,942 229,637 1,288 1,903 13,863 2,147 6,710 3,553 813 498,330 24,932 1,391 900 52,500 120,880 27,651 17,680 948 4,328 3,167 26,885 38,436 134,466 43,358 .. 40,500

21 19 11 4 .. 19 5 7 22 0 25 10 56 .. 2 .. 0 23 .. 14 20 .. .. 0 18 18 69 90 10 23 57 29 8 72 2 31 66 .. .. 8 20 45 75 17 2 8 5 12 75 13 44 31 13 10 7 .. 0

8 8 17 .. 0 9 3 8 27 1 17 4 44 .. 1 .. 0 24 .. 17 16 .. .. .. 19 18 29 87 11 28 12 32 7 74 2 22 23 .. 23 25 15 56 87 18 5 6 3 17 84 .. 85 23 8 11 11 .. 0

2 1 7 1 .. 1 2 1 0 1 2 3 7 .. 1 .. 0 13 .. 23 2 .. .. 0 8 5 6 2 6 75 0 1 1 2 28 3 16 .. .. 1 4 19 3 1 2 2 0 4 0 20 36 3 1 3 5 .. 0

1 1 5 .. 0 0 1 1 0 1 0 0 13 .. 1 .. 0 4 .. 10 1 .. .. .. 2 1 5 4 2 42 0 1 0 1 12 2 3 .. 0 3 3 10 1 4 1 0 0 2 1 .. 4 1 1 1 2 .. 0

3 2 25 86 .. 0 0 1 1 1 0 25 6 .. 2 .. 95 11 .. 2 0 .. .. 95 11 0 1 0 7 0 1 0 10 1 0 2 2 .. .. 0 7 2 1 0 96 47 79 1 3 38 0 5 2 8 3 .. 82

2 13 28 .. 99 1 0 4 17 2 1 71 4 .. 6 .. 93 6 .. 5 0 .. .. .. 21 1 5 0 15 6 22 0 14 0 10 2 17 .. 0 0 8 5 1 2 90 65 79 4 1 .. 0 10 2 3 5 .. 94

5 3 6 1 .. 1 1 1 6 1 24 24 3 .. 1 .. 0 13 .. 1 8 .. .. 0 5 18 7 0 1 0 42 0 3 3 60 12 2 .. .. 0 3 5 1 80 0 9 2 0 1 25 0 46 4 7 2 .. 0

1 6 9 .. 0 1 1 2 8 3 9 11 2 .. 2 .. 0 3 .. 3 8 .. .. .. 1 3 3 1 2 1 60 1 3 2 70 9 4 .. 31 5 2 3 1 69 0 5 4 1 4 .. 1 49 4 4 3 .. 0

68 74 51 9 .. 72 89 89 71 95 49 38 28 .. 93 .. 5 40 .. 58 70 .. .. 5 58 58 14 7 75 2 0 70 78 23 10 51 13 .. .. 84 63 29 21 1 1 27 14 83 20 4 19 15 42 71 83 .. 17

2011 World Development Indicators

82 67 41 .. 0 86 94 83 47 88 73 14 37 .. 90 .. 6 34 .. 61 72 .. .. .. 55 51 57 9 70 22 0 65 76 23 6 65 12 .. 45 67 56 23 10 7 4 20 10 76 10 .. 11 16 86 80 72 .. 5

207

4.4 Romania Russian Federation Rwanda Saudi Arabia Senegal Serbia Sierra Leone Singaporeb Slovak Republic Slovenia Somalia South Africa 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

Structure of merchandise exports Merchandise exports

Food

Agricultural rawâ&#x20AC;Żmaterials

Fuels

Ores and metals

Manufactures

$ millions 1995 2009

% of total 1995 2009

7,910 40,633 81,095 303,388 54 193 50,040 192,296 993 2,180 .. 8,345 42 231 118,268 269,832 8,580 55,980 8,316 26,369 .. .. 62,603 27,853c 97,849 218,511 3,798 7,345 555 7,834 866 1,500 80,440 131,243 81,641 172,850 3,563 10,400 750 1,009 682 3,096 56,439 152,498 .. .. 378 800 2,455 9,126 5,475 14,445 21,637 102,129 1,880 6,595 460 2,478 13,128 39,703 28,364 175,000 237,953 352,491 584,743 1,056,043 2,106 5,386 3,430 10,735 18,457 57,595 5,449 57,096 .. .. 1,945 5,594 1,040 4,312 2,118 2,269 5,172,552 t 12,492,190 t 24,093 76,170 894,340 3,720,635 400,844 2,099,993 493,582 1,619,211 918,419 3,796,791 354,784 1,747,540 154,880 650,244 223,980 677,205 62,002 276,399 46,657 204,760 76,554 242,566 4,253,742 8,697,557 1,744,036 3,597,614

7 2 57 1 9 28 .. 4 6 4 .. 8c 15 21 44 .. 2 3 12 .. 65 19 .. 19 8 10 20 1 90 19 8 8 11 44 .. 3 30 .. 3 3 43 9w 31 14 14 15 15 11 8 20 6 17 18 8 11

7 3 42 1 30 19 .. 2 5 4 .. 10 16 26 6 21 5 4 22 .. 35 15 .. 16 3 9 11 .. 63 24 1 7 10 64 .. 0 20 .. 6 8 19 8w 25 11 9 12 11 8 8 18 .. 11 14 8 10

3 3 16 0 7 4 .. 1 4 2 .. 4c 2 4 47 .. 6 1 7 .. 23 5 .. 42 0 1 1 13 5 1 0 1 4 15 .. 0 3 .. 1 1 7 3w 10 3 3 4 3 4 3 3 1 2 7 2 2

2 2 2 0 1 2 .. 0 1 2 .. 2 1 3 1 7 4 0 1 .. 10 4 .. 9 0 0 0 .. 6 1 0 1 2 8 .. 0 3 .. 0 1 23 2w 8 2 2 2 2 2 2 2 .. 1 3 1 1

8 43 0 88 22 2 .. 7 4 1 .. 9c 2 0 0 .. 2 0 63 .. 0 1 .. 0 48 8 1 77 0 4 9 6 2 1 .. 77 18 .. 95 3 1 7w 2 12 8 15 11 6 29 15 73 1 36 6 2

6 67 0 88 24 3 .. 15 5 4 .. 11 4 0 92 1 6 3 39 .. 1 5 .. 0 79 14 4 .. 1 5 65 11 6 1 .. 96 20 .. 92 1 1 12 w 3 22 14 29 22 8 45 20 .. 11 37 9 4

3 10 12 1 12 15 .. 2 4 3 .. 8c 2 1 0 .. 3 3 1 .. 0 1 .. 32 0 2 3 1 1 7 55 3 3 1 .. 6 0 .. 1 87 12 3w 11 5 3 6 5 2 9 7 3 3 8 3 3

Note: Components may not sum to 100 percent because of unclassified trade. Exports of gold are excluded. a. Includes Luxembourg. b. Includes re-exports. c. Refers to the South African Customs Union (Botswana, Lesotho, Namibia, South Africa, and Swaziland).

208

2011 World Development Indicators

4 6 32 0 3 10 .. 1 2 3 .. 29 3 1 0 1 4 3 4 .. 25 1 .. 13 2 1 3 .. 2 6 1 3 4 0 .. 1 1 .. 0 81 22 4w 14 5 3 7 5 2 5 8 .. 5 15 3 2

78 26 14 10 48 49 .. 84 82 90 .. 44 c 78 73 6 .. 79 94 17 .. 10 73 .. 7 43 79 74 8 4 68 28 81 77 39 .. 14 44 .. 1 7 37 76 w 44 63 69 58 63 74 42 55 17 76 28 78 80

79 17 19 8 41 66 .. 74 87 87 .. 47 73 67 0 70 76 90 33 .. 25 75 .. 62 15 75 80 .. 27 63 4 72 67 26 .. 3 55 .. 2 8 34 70 w 50 59 71 48 59 80 37 51 .. 68 31 73 77

About the data

4.4

economy

Structure of merchandise exports Definitions

Data on merchandise trade are from customs

b and c are classified as re-exports. Because of dif-

• Merchandise exports are the f.o.b. value of goods

reports of goods moving into or out of an economy

ferences in reporting practices, data on exports may

provided to the rest of the world. • Food corresponds

or from reports of financial transactions related to

not be fully comparable across economies.

to the commodities in SITC sections 0 (food and live

merchandise trade recorded in the balance of pay-

The data on total exports of goods (merchandise)

animals), 1 (beverages and tobacco), and 4 (animal

ments. Because of differences in timing and defi-

are from the World Trade Organization (WTO), which

and vegetable oils and fats) and SITC division 22

nitions, trade flow estimates from customs reports

obtains data from national statistical offices and the

(oil seeds, oil nuts, and oil kernels). • Agricultural

and balance of payments may differ. Several inter-

IMF’s International Financial Statistics, supplemented

raw materials correspond to SITC section 2 (crude

national agencies process trade data, each correct-

by the Comtrade database and publications or data-

materials except fuels), excluding divisions 22, 27

ing unreported or misreported data, leading to other

bases of regional organizations, specialized agen-

(crude fertilizers and minerals excluding coal, petro-

differences.

cies, economic groups, and private sources (such as

leum, and precious stones), and 28 (metalliferous

The most detailed source of data on international

Eurostat, the Food and Agriculture Organization, and

ores and scrap). • Fuels correspond to SITC section

trade in goods is the United Nations Statistics Divi-

country reports of the Economist Intelligence Unit).

3 (mineral fuels). • Ores and metals correspond to

sion’s Commodity Trade (Comtrade) database. The

Country websites and email contact have improved

the commodities in SITC divisions 27, 28, and 68

International Monetary Fund (IMF) also collects

collection of up-to-date statistics, reducing the pro-

(nonferrous metals). • Manufactures correspond to

customs-based data on trade in goods. Exports are

portion of estimates. The WTO database now covers

the commodities in SITC sections 5 (chemicals), 6

recorded as the cost of the goods delivered to the

most major traders in Africa, Asia, and Latin America,

(basic manufactures), 7 (machinery and transport

frontier of the exporting country for shipment—the

which together with high-income countries account

equipment), and 8 (miscellaneous manufactured

free on board (f.o.b.) value. Many countries report

for nearly 95 percent of world trade. Reliability of

goods), excluding division 68.

trade data in U.S. dollars. When countries report in

data for countries in Europe and Central Asia has

local currency, the United Nations Statistics Division

also improved.

applies the average official exchange rate to the U.S. dollar for the period shown.

Export shares by major commodity group are from Comtrade. The values of total exports reported

Countries may report trade according to the gen-

here have not been fully reconciled with the esti-

eral or special system of trade. Under the general

mates from the national accounts or the balance

system exports comprise outward-moving goods that

of payments.

are (a) goods wholly or partly produced in the country;

The classification of commodity groups is based

(b) foreign goods, neither transformed nor declared

on the Standard International Trade Classification

for domestic consumption in the country, that move

(SITC) revision 3. Previous editions contained data

outward from customs storage; and (c) goods previ-

based on the SITC revision 1. Data for earlier years in

ously included as imports for domestic consumption

previous editions may differ because of this change

but subsequently exported without transformation.

in methodology. Concordance tables are available

Under the special system exports comprise cat-

to convert data reported in one system to another.

egories a and c. In some compilations categories Developing economies’ share of world merchandise exports continues to expand 1995 ($5.2 trillion)

High income 82% East Asia & Pacific 7% Europe & Central Asia 3% Latin America & Caribbean 4% Middle East & N. Africa 1% South Asia 1% Sub-Saharan Africa 2%

4.4a

2009 ($12.5 trillion)

Data sources Data on merchandise exports are from the WTO. Data on shares of exports by major commodity group are from Comtrade. The WTO publishes data

High income 70%

East Asia & Pacific 14%

on world trade in its Annual Report. The IMF publishes estimates of total exports of goods in its International Financial Statistics and Direction of

Europe & Central Asia 5% Latin America & Caribbean 5% Middle East & N. Africa 2% South Asia 2% Sub-Saharan Africa 2%

Developing economies’ share of world merchandise exports increased 12 percentage points from 1995 to 2009. East Asia and the Pacific was the biggest gainer, capturing an additional 7 percentage points. All other developing country regions also increased their share in world trade. Source: World Development Indicators data files and World Trade Organization.

Trade Statistics, as does the United Nations Statistics Division in its Monthly Bulletin of Statistics. And the United Nations Conference on Trade and Development publishes data on the structure of exports in its Handbook of Statistics. Tariff line records of exports are compiled in the United Nations Statistics Division’s Comtrade database.

2011 World Development Indicators

209

4.5

Structure of merchandise imports Merchandise imports

$ millions 1995 2009

Afghanistan Albania Algeria Angola Argentina Armenia Australia Austria Azerbaijan 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 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 Honduras †Data for Taiwan, China

210

387 714 10,100 1,468 20,122 674 61,283 66,237 668 6,694 5,564 164,934 a 746 1,424 1,082 1,911 54,137 5,660 455 234 1,187 1,199 168,426 175 365 15,900 132,084 196,072 13,853 871 670 4,036 2,931 7,352 2,825 25,085 45,939 5,170 4,152 11,760 3,329 454 2,546 1,145 29,470 289,391 882 182 392 463,872 1,906 25,898 3,292 819 133 653 1,879 103,558

3,970 4,548 39,294 17,000 38,780 3,304 165,471 143,382 6,514 21,833 28,563 351,945 2,040 4,410 8,773 4,728 133,669 23,330 2,083 402 6,200 4,250 329,904 300 1,950 42,427 1,005,688 352,241 32,898 3,600 2,900 11,395 6,050 21,203 9,623 105,179 82,947 12,283 15,093 44,946 7,255 540 10,122 7,963 60,753 559,817 2,200 304 4,378 938,295 8,140 59,858 11,531 1,400 230 2,050 7,788 174,371

2011 World Development Indicators

Food

Agricultural raw materials

Fuels

Ores and metals

Manufactures

% of total 1995 2009

.. 34 29 .. 5 31 5 6 39 17 .. 11a 27 10 .. .. 11 8 21 21 .. 17 6 16 24 7 7 5 9 .. 21 10 21 12 .. 7 12 .. 8 28 15 .. 14 14 6 11 19 36 36 10 8 16 12 31 44 .. 13 6

18 17 16 .. 4 19 6 8 16 22 8 9 .. 9 19 13 5 10 16 13 7 .. 8 .. .. 7 5 4 10 .. .. 7 23 10 .. 6 13 14 9 17 19 .. 12 11 7 9 .. 34 15 8 15 13 14 13 .. .. 19 5

.. 1 3 .. 2 0 2 3 1 3 .. 2a 3 2 .. .. 3 3 2 2 .. 3 2 10 1 2 5 2 3 .. 1 1 1 2 .. 3 3 .. 3 7 2 .. 3 2 4 3 1 1 0 3 1 2 2 1 0 .. 1 4

0 1 1 .. 1 1 1 2 1 8 1 1 .. 1 1 1 1 1 1 1 1 .. 1 .. .. 1 3 1 1 .. .. 1 1 1 .. 1 2 1 1 3 2 .. 2 1 2 1 .. 1 1 1 1 1 1 0 .. .. 1 1

.. 2 1 .. 4 27 5 4 4 8 .. 6a 9 5 .. .. 12 34 14 11 .. 3 4 9 18 9 4 2 3 .. 20 9 19 12 .. 8 3 .. 6 1 9 .. 11 11 9 7 4 14 39 6 6 7 12 19 16 .. 12 7

24 12 1 .. 6 16 13 11 1 11 40 12 .. 11 15 13 15 20 24 2 8 .. 10 .. .. 21 13 3 4 .. .. 9 25 17 .. 9 6 21 12 11 15 .. 19 16 15 13 .. 16 18 11 14 15 19 33 .. .. 19 21

.. 1 2 .. 2 0 1 4 2 2 .. 5a 1 3 .. .. 3 4 1 1 .. 2 3 2 1 2 4 2 2 .. 1 2 1 3 .. 4 2 .. 2 3 2 .. 1 1 6 4 1 0 0 4 0 3 1 1 0 .. 1 6

0 2 1 .. 2 4 1 4 1 3 3 3 .. 1 2 2 3 7 1 1 2 .. 2 .. .. 2 14 2 2 .. .. 1 1 2 .. 3 2 1 1 8 1 .. 1 1 5 2 .. 1 2 3 1 2 1 0 .. .. 1 7

.. 61 65 .. 86 39 86 82 53 69 .. 71a 59 82 .. .. 71 48 62 64 .. 76 83 64 56 79 79 88 78 .. 58 78 57 67 .. 77 73 .. 82 61 72 .. 71 72 74 76 75 46 24 73 77 71 73 47 40 .. 74 75

17 68 80 .. 86 59 76 75 79 54 45 73 .. 78 62 70 76 59 59 81 82 .. 77 .. .. 59 64 89 82 .. .. 60 49 70 .. 78 74 63 76 60 63 .. 60 72 64 74 .. 48 64 67 69 69 64 53 .. .. 60 65

Merchandise imports

$ millions 1995 2009

15,465 34,707 40,630 13,882 665 32,340 29,578 205,990 2,818 335,882 3,697 3,807 2,991 1,380 135,119 .. 7,790 522 589 1,815 7,278 1,107 510 5,392 3,650 1,719 628 475 77,691 772 431 1,976 74,427 840 415 10,023 704 1,348 1,616 1,333 185,232 13,957 975 374 8,222 32,968 4,379 11,515 2,510 1,452 3,144 7,584 28,341 29,050 32,610 .. 3,398

78,175 249,590 91,749 50,375 37,000 62,507 49,278 412,721 5,064 551,960 14,075 28,409 10,207 2,080 323,085 .. 17,920 3,037 1,260 9,765 16,574 1,950 552 10,150 18,234 5,043 3,250 1,700 123,832 2,644 1,430 3,728 241,515 3,278 2,131 32,892 3,764 4,316 5,120 4,392 445,496 25,545 3,477 1,500 39,000 69,292 18,020 31,710 7,801 3,200 6,940 21,706 45,878 146,626 69,844 .. 23,000

economy

4.5

Structure of merchandise imports Food

Agricultural rawâ&#x20AC;Żmaterials

Fuels

Ores and metals

Manufactures

% of total 1995 2009

6 4 9 21 .. 8 7 12 14 16 21 10 10 .. 6 .. 16 18 .. 10 21 .. .. 23 13 17 16 14 5 20 24 17 6 8 14 20 22 .. .. 12 14 7 18 32 18 7 20 18 11 .. 19 14 8 10 14 .. 9

5 4 9 .. .. 12 8 10 18 10 17 9 15 .. 5 .. 15 17 .. 17 15 .. .. .. 14 13 11 13 8 12 28 22 7 15 12 11 15 .. 14 15 11 11 18 25 12 8 11 11 12 .. 8 11 12 8 13 .. 6

3 4 6 2 .. 1 2 6 2 6 2 2 2 .. 6 .. 1 3 .. 2 2 .. .. 1 4 3 2 1 1 1 1 3 2 3 1 6 3 .. .. 3 2 1 1 1 1 3 1 6 1 .. 0 2 2 3 4 .. 1

1 2 3 .. .. 1 1 2 1 1 1 1 1 .. 2 .. 1 1 .. 1 1 .. .. .. 2 1 1 1 2 0 1 2 1 1 0 2 1 .. 1 2 1 1 1 5 1 1 1 4 0 .. 1 1 1 2 1 .. 0

12 24 8 2 .. 3 6 7 13 16 13 25 15 .. 14 .. 1 36 .. 21 9 .. .. 0 19 12 14 11 2 16 22 7 2 46 19 14 10 .. .. 12 8 5 18 13 1 3 2 16 14 .. 7 9 9 9 8 .. 1

8 34 20 .. .. 10 17 18 28 28 18 10 21 .. 28 .. 1 4 .. 16 21 .. .. .. 28 5 10 10 8 21 35 16 7 22 27 21 15 .. 14 17 13 15 22 17 1 5 5 28 17 .. 15 14 17 9 13 .. 1

4 7 4 3 .. 2 2 5 1 7 3 5 2 .. 6 .. 2 3 .. 1 2 .. .. 1 4 3 1 1 3 1 0 1 2 2 1 4 1 .. .. 3 3 3 1 3 2 6 2 3 1 .. 1 1 3 3 2 .. 2

2 6 3 .. .. 1 2 3 0 6 2 1 2 .. 7 .. 3 1 .. 2 2 .. .. .. 2 1 0 1 4 1 0 1 2 1 1 2 0 .. 1 3 2 1 0 2 2 5 3 4 1 .. 1 1 4 3 2 .. 3

75 54 73 71 .. 76 82 68 68 54 61 59 71 .. 68 .. 81 40 .. 66 66 .. .. 75 58 64 65 73 86 62 53 72 80 42 65 56 62 .. .. 46 72 83 63 51 77 81 70 57 73 .. 74 75 58 74 72 .. 87

2011 World Development Indicators

72 52 65 .. .. 68 72 65 51 52 60 80 60 .. 58 .. 81 50 .. 56 61 .. .. .. 53 62 78 74 76 65 36 59 80 61 60 63 55 .. 70 62 58 72 59 52 84 79 77 52 70 .. 76 72 67 74 62 .. 90

211

4.5

Structure of merchandise imports Merchandise imports

$ millions 1995 2009

Romania Russian Federation Rwanda Saudi Arabia Senegal Serbia Sierra Leone Singapore Slovak Republic Slovenia Somalia South Africa 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

10,278 54,247 60,945 191,803 236 1,227 28,091 95,567 1,412 4,713 .. 15,582 133 520 124,507 245,785 8,770 55,301 9,492 26,464 .. .. 73,172 30,546 b 113,537 287,567 5,306 10,207 1,218 9,691 1,008 1,600 65,036 119,839 80,152 155,706 4,709 16,300 810 2,569 1,675 6,347 70,786 133,801 .. .. 594 1,500 1,714 6,955 7,902 19,096 35,709 140,921 1,365 6,750 1,056 4,310 15,484 45,436 23,778 140,000 267,250 481,707 770,852 1,605,296 2,867 6,907 2,750 9,023 12,649 40,597 8,155 69,949 .. .. 1,582 8,500 700 3,793 2,660 2,900 5,228,194 t 12,595,548 t 36,735 127,386 947,153 3,519,888 434,758 2,038,080 512,441 1,475,992 983,905 3,647,212 366,062 1,493,538 163,415 626,665 240,278 668,496 77,167 289,612 60,322 323,199 78,377 253,161 4,244,063 8,955,148 1,647,277 3,519,840

Food

Agricultural rawâ&#x20AC;Żmaterials

Fuels

Ores and metals

Manufactures

% of total 1995 2009

8 18 19 17 25 14 .. 5 9 8 .. 7b 14 16 24 .. 7 6 17 .. 10 4 .. 18 16 13 7 24 16 8 15 10 5 10 .. 14 5 .. 29 10 6 9w 16 8 9 8 8 6 12 8 22 8 12 9 11

9 17 12 11 24 6 .. 3 7 9 .. 7 11 16 15 21 10 6 14 .. 9 6 .. 15 10 9 4 .. 13 11 7 11 5 10 .. 16 7 .. 28 6 22 8w 16 8 7 9 8 7 10 8 .. 7 11 8 10

2 1 3 1 2 4 .. 1 3 5 .. 2b 3 2 2 .. 2 2 3 .. 1 4 .. 2 1 4 6 0 3 2 0 2 2 4 .. 4 2 .. 2 2 2 3w 3 4 5 3 4 4 3 2 4 4 2 3 3

1 1 1 0 2 2 .. 0 1 3 .. 1 1 1 1 1 1 1 3 .. 1 2 .. 1 1 2 2 .. 1 1 0 1 1 2 .. 1 3 .. 1 1 0 1w 3 2 2 1 2 3 2 1 .. 2 1 1 1

21 3 12 0 30 14 .. 8 13 7 .. 8b 8 6 14 .. 6 3 1 .. 1 7 .. 30 1 7 13 3 2 48 4 4 8 10 .. 1 10 .. 8 13 9 7w 12 7 8 6 7 5 15 5 6 21 10 7 7

9 2 8 0 23 17 .. 24 12 11 .. 21 16 19 4 14 12 7 31 .. 23 19 .. 27 33 11 14 .. 19 32 1 10 17 24 .. 1 16 .. 21 14 13 15 w 16 14 18 11 14 14 14 10 .. 31 17 15 13

Note: Components may not sum to 100 percent because of unclassified trade. a. Includes Luxembourg. b. Refers to the South African Customs Union (Botswana, Lesotho, Namibia, South Africa, and Swaziland).

212

2011 World Development Indicators

4 2 3 4 1 7 .. 2 6 4 .. 2b 4 1 0 .. 4 3 1 .. 4 3 .. 1 6 3 6 2 2 3 6 3 3 1 .. 4 2 .. 1 2 2 4w 2 3 4 3 3 4 4 2 3 6 2 4 4

2 2 2 3 1 6 .. 2 2 4 .. 1 3 1 1 1 3 4 4 .. 1 4 .. 2 3 3 7 .. 1 3 5 3 2 1 .. 1 4 .. 1 13 5 3w 2 5 8 3 5 9 4 2 .. 5 2 3 3

63 45 64 76 42 60 .. 83 70 74 .. 78b 71 75 59 .. 80 85 76 .. 84 81 .. 49 76 73 68 71 78 38 75 80 79 74 .. 77 76 .. 59 72 78 75 w 66 75 72 77 75 78 57 78 66 56 73 75 73

75 76 76 36 50 69 .. 67 78 72 .. 64 68 62 78 63 70 81 47 .. 66 69 .. 55 53 75 64 .. 66 52 73 69 70 62 .. 79 70 .. 50 65 58 69 w 60 69 64 74 69 68 66 77 .. 53 66 69 68

About the data

4.5

economy

Structure of merchandise imports Definitions

Data on imports of goods are derived from the

and free trade zones. Goods transported through a

• Merchandise imports are the c.i.f. value of goods

same sources as data on exports. In principle, world

country en route to another are excluded.

purchased from the rest of the world valued in U.S.

exports and imports should be identical. Similarly,

The data on total imports of goods (merchandise)

dollars. • Food corresponds to the commodities in

exports from an economy should equal the sum of

in the table come from the World Trade Organization

SITC sections 0 (food and live animals), 1 (beverages

imports by the rest of the world from that economy.

(WTO). For further discussion of the WTO’s sources

and tobacco), and 4 (animal and vegetable oils and

But differences in timing and definitions result in dis-

and methodology, see About the data for table 4.4.

fats) and SITC division 22 (oil seeds, oil nuts, and oil

crepancies in reported values at all levels. For further

The import shares by major commodity group are

kernels). • Agricultural raw materials correspond to

discussion of indicators of merchandise trade, see

from the United Nations Statistics Division’s Com-

SITC section 2 (crude materials except fuels), exclud-

About the data for tables 4.4 and 6.2.

modity Trade (Comtrade) database. The values of

ing divisions 22, 27 (crude fertilizers and minerals

The value of imports is generally recorded as the

total imports reported here have not been fully recon-

excluding coal, petroleum, and precious stones),

cost of the goods when purchased by the importer

ciled with the estimates of imports of goods and ser-

and 28 (metalliferous ores and scrap). • Fuels cor-

plus the cost of transport and insurance to the fron-

vices from the national accounts (shown in table 4.8)

respond to SITC section 3 (mineral fuels). • Ores

tier of the importing country—the cost, insurance,

or those from the balance of payments (table 4.17).

and metals correspond to the commodities in SITC

and freight (c.i.f.) value, corresponding to the landed

The classification of commodity groups is based

divisions 27, 28, and 68 (nonferrous metals). • Man-

cost at the point of entry of foreign goods into the

on the Standard International Trade Classification

ufactures correspond to the commodities in SITC

country. A few countries, including Australia, Canada,

(SITC) revision 3. Previous editions contained data

sections 5 (chemicals), 6 (basic manufactures), 7

and the United States, collect import data on a free

based on the SITC revision 1. Data for earlier years in

(machinery and transport equipment), and 8 (miscel-

on board (f.o.b.) basis and adjust them for freight and

previous editions may differ because of this change

laneous manufactured goods), excluding division 68.

insurance costs. Many countries report trade data in

in methodology. Concordance tables are available

U.S. dollars. When countries report in local currency,

to convert data reported in one system to another.

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

4.5a

Top 10 developing economy exporters of merchandise goods in 2009 1995

Merchandise exports ($ billions)

2009

1,500

Data sources Data on merchandise imports are from the WTO.

1,200

Data on shares of imports by major commodity group are from Comtrade. The WTO publishes data

900

on world trade in its Annual Report. The International Monetary Fund publishes estimates of total

600

imports of goods in its International Financial Statistics and Direction of Trade Statistics, as does the

300

United Nations Statistics Division in its Monthly Bulletin of Statistics. And the United Nations Con-

0 China

Russian Mexico Federation

India

Malaysia

Brazil

Thailand Indonesia

Turkey

Iran, Islamic Rep.

ference on Trade and Development publishes data on the structure of imports in its Handbook of Sta-

China continues to dominate merchandise exports among developing economies. Even when developed

tistics. Tariff line records of imports are compiled

economies are included, China ranks as the second leading merchandise exporter.

in the United Nations Statistics Division’s Com-

Source: World Development Indicators data files and World Trade Organization.

trade database.

2011 World Development Indicators

213

4.6

Structure of service exports Commercial service exports

$ millions 1995 2009

214

.. 94 .. 113 3,676 27 16,076 31,692 166 469 466 35,466a 159 174 457 236 6,005 1,431 38 4 103 242 25,425 0 23 3,249 18,430 33,790 1,641 .. 61 957 426 2,223 .. 6,638 15,171 1,894 687 8,262 342 49 868 310 7,334 83,108 191 38 188 73,576 139 9,528 628 17 2 98 221

.. 2,348 .. 623 10,758 580 44,513 54,080 1,670 935 3,453 79,815a 328 498 1,396 842 26,245 6,889 109 2 1,592 1,158 57,476 .. .. 8,401 128,600 86,306 4,109 .. 303 3,694 816 11,889 .. 20,278 55,346 4,864 1,130 21,302 806 .. 4,368 1,676 27,536 142,487 .. 104 1,225 226,638 1,722 37,690 1,818 67 44 327 933

2011 World Development Indicators

Transport

Travel

% of total

Insurance and financial services

% of total

Computer, information, communications, and other commercial services

% of total

1995

2009

1995

2009

1995

2009

1995

2009

.. 19 .. 32 27 53 29 12 46 15 65 ..a 26 45 4 16 43 35 17 46 31 48 21 34 5 37 18 33 34 .. 52 14 29 32 .. 22 45 2 47 39 28 70 43 77 28 25 46 22 48 27 59 4 9 75 18 5 26

.. 11 .. 5 15 19 18 22 40 15 66 27a 4 13 20 10 15 21 19 22 12 41 15 .. .. 56 18 31 28 .. 4 8 29 9 .. 27 .. 9 31 31 34 .. 37 59 10 23 .. 19 51 23 19 50 14 22 0 .. 5

.. 69 .. 1 60 5 51 42 42 5 5 ..a 53 32 54 68 16 33 48 32 52 15 31 34 50 28 47 17 40 .. 22 71 21 61 .. 43 24 83 37 32 25 3 41 5 22 33 9 73 25 25 8 43 34 5 14 92 36

.. 78 .. 86 37 58 56 35 21 7 11 12a 72 56 49 54 20 55 57 62 74 19 24 .. .. 19 31 17 49 .. 18 49 14 76 .. 32 .. 83 59 50 40 .. 25 20 10 35 .. 60 39 15 56 39 65 4 87 96 66

.. 1 .. 9 0 7 5 4 0 0 0 ..a 7 10 3 8 17 0 0 0 0 7 11 20 2 7 10 9 6 .. 0 0 12 1 .. 1 .. 0 0 1 8 1 0 2 2 5 3 0 0 5 3 0 4 1 0 1 2

.. 0 .. 0 0 3 3 4 0 6 0 5a 2 14 1 4 7 3 2 14 0 2 11 .. .. 4 2 13 1 .. 31 0 0 1 .. 1 .. 1 0 1 4 .. 2 1 2 2 .. 0 2 7 1 2 2 9 1 0 2

.. 10 .. 59 12 41 15 42 12 80 30 ..a 14 14 39 7 24 32 35 21 18 30 37 12 44 28 24 41 19 .. 25 15 38 6 .. 34 31 15 16 28 39 27 16 16 48 37 41 5 27 44 30 52 54 18 82 2 36

.. 11 .. 9 48 21 22 38 39 72 23 55a 22 17 30 33 57 22 21 2 13 38 50 .. .. 22 49 39 23 .. 47 43 57 15 .. 40 .. 7 10 17 23 .. 36 20 78 41 .. 21 8 54 24 9 20 65 12 4 28

Commercial service exports

$ millions 1995 2009

5,086 6,763 5,342 533 .. 4,799 7,906 61,173 1,568 63,966 1,689 535 1,183 .. 22,133 .. 1,124 39 68 718 .. 30 .. 20 482 151 219 24 11,438 68 19 773 9,585 143 47 2,020 242 353 301 592 44,646 4,401 94 12 608 13,458 13 1,432 1,298 321 566 1,042 9,323 10,637 8,161 .. ..

18,419 90,193 13,238 .. 1,721 92,964 21,961 101,237 2,616 125,918 4,192 3,813 2,198 .. 57,304 .. 10,425 850 368 3,812 16,869 63 142 385 3,769 845 .. .. 28,727 442 .. 2,225 15,420 647 412 11,892 544 256 505 548 90,853 7,760 429 126 1,769 38,537 1,792 2,463 5,463 162 1,288 3,517 10,101 28,856 22,539 .. ..

Transport

Travel

% of total

Insurance and financial services

% of total

4.6 Computer, information, communications, and other commercial services

% of total

1995

2009

1995

2009

8 28 1 26 .. 22 25 18 16 35 25 66 59 .. 42 .. 84 40 23 92 .. 7 .. 63 60 32 30 28 22 32 9 26 12 30 32 20 25 6 .. 9 40 35 18 3 16 63 100 58 60 11 13 32 3 29 19 .. ..

19 12 18 .. 22 4 14 13 13 25 19 57 48 .. 51 .. 30 16 8 51 2 1 10 68 56 30 .. .. 15 7 .. 15 10 39 33 18 28 51 23 7 27 19 10 9 62 41 32 44 56 9 13 21 11 30 26 .. ..

58 38 98 13 .. 46 38 47 68 5 39 23 36 .. 23 .. 11 12 76 3 .. 91 .. 12 16 14 26 72 35 37 58 56 64 40 44 64 .. 43 92 30 15 53 52 58 3 17 81 8 24 8 24 41 12 22 59 .. ..

31 12 48 .. 0 5 17 40 74 8 69 25 31 .. 16 .. 2 54 73 19 40 64 87 13 30 26 .. .. 55 62 .. 50 73 26 57 56 36 18 72 68 14 59 81 62 34 11 39 11 27 1 16 58 23 31 43 .. ..

economy

Structure of service exports

% of total

1995

2009

3 3 0 9 .. 0 0 7 1 1 0 0 1 .. 0 .. 6 0 1 2 .. 1 .. .. 1 4 2 0 0 5 0 0 7 12 5 1 .. 0 1 0 1 0 2 0 1 4 0 1 6 1 5 7 1 8 5 .. ..

1 5 2 .. 0 20 0 9 2 4 0 4 1 .. 5 .. 1 2 3 7 2 1 .. 16 1 2 .. .. 2 1 .. 4 10 1 1 2 1 .. 1 0 2 1 1 7 1 5 1 6 7 7 2 9 1 2 2 .. ..

1995

2009

31 31 2 53 .. 32 36 29 15 59 36 12 3 .. 34 .. 0 48 1 3 .. 1 .. 25 23 51 42 0 44 25 33 19 17 19 19 14 75 51 6 61 44 13 27 39 80 16 0 33 10 80 57 19 84 41 18 .. ..

49 70 32 .. 78 70 68 38 11 62 12 14 20 .. 28 .. 66 28 16 23 56 34 3 3 14 43 .. .. 28 30 .. 30 6 34 9 25 35 31 4 25 57 21 8 21 3 44 28 39 9 84 69 12 65 37 30 .. ..

2011 World Development Indicators

215

4.6

Structure of service exports Commercial service exports

$ millions 1995 2009

Romania Russian Federation Rwanda Saudi Arabia Senegal Serbia Sierra Leone Singapore Slovak Republic Slovenia Somalia South Africa 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

1,476 9,737 10,567 41,068 11 249 3,475 9,335 364 1,177 .. 3,478 71 53 27,234 90,690 2,378 6,259 2,016 5,999 .. .. 4,414 11,656 40,019 122,101 800 1,874 82 392 150 191 15,336 59,073 25,179 72,309 1,632 3,770 .. 142 566 1,795 14,652 29,677 .. .. 64 253 331 918 2,401 5,241 14,475 32,758 79 .. 104 854 2,846 13,324 .. .. 77,549 236,254 198,501 475,979 1,309 2,132 .. .. 1,529 1,805 2,243 5,656 265 407 141 1,085 112 241 353 .. 1,228,960 t 3,417,725 t 6,429 21,036 174,925 641,508 87,678 377,784 87,180 264,293 180,841 660,929 62,745 220,270 35,079 131,431 38,013 98,855 .. .. 10,333 97,113 12,144 35,613 1,047,874 2,755,581 425,302 1,087,280

a. Includes Luxembourg.

216

2011 World Development Indicators

Transport

Travel

% of total 1995

32 36 61 .. 15 .. 14 30 26 25 .. 24 16 42 1 18 32 15 15 .. 0 17 .. 34 59 25 12 80 18 76 .. 21 23 31 .. 38 .. 0 22 64 26 27 w 28 25 21 27 25 17 38 24 .. 32 26 27 25

Insurance and financial services

% of total 2009

30 30 22 20 12 21 35 34 30 25 .. 12 15 46 4 4 16 8 5 50 19 19 .. 43 24 26 23 .. 4 47 .. 13 13 16 .. 39 .. 4 4 48 .. 21 w 20 21 21 22 21 17 33 18 .. 20 28 21 21

1995

40 41 22 .. 46 .. 80 28 26 54 .. 48 63 28 10 32 23 38 77 .. 89 55 .. 20 23 64 34 9 75 7 .. 26 38 47 .. 56 .. 96 35 26 51 33 w 28 45 46 43 44 49 34 51 .. 30 31 30 33

Computer, information, communications, and other commercial services

% of total 2009

13 23 70 64 46 25 48 10 37 42 .. 65 44 19 76 21 17 19 84 2 65 53 .. 16 43 53 65 .. 78 27 .. 13 25 62 .. 44 .. 66 83 41 .. 26 w 37 42 36 47 42 40 29 54 .. 13 53 22 24

% of total

1995

2009

5 1 0 .. 1 .. 0 15 5 1 .. 10 4 3 4 0 2 28 0 .. 0 1 .. 2 9 2 2 1 0 3 .. 18 4 1 .. 0 .. 0 0 0 0 5w 1 5 5 5 5 5 1 7 .. 2 6 6 4

2 4 1 13 1 1 1 12 6 2 .. 8 5 4 15 11 4 30 4 5 1 1 .. 5 25 2 3 .. 4 3 .. 28 15 4 .. 0 .. 0 0 2 .. 8w 3 4 2 5 4 1 3 7 .. 5 4 9 6

1995

23 23 18 .. 38 .. 6 27 43 21 .. 18 17 27 86 50 43 20 8 .. 11 28 .. 44 9 10 52 10 7 15 .. 35 35 21 .. 6 .. 4 43 10 23 36 w 44 27 30 25 28 31 27 18 .. 36 40 38 37

2009

56 44 8 3 40 53 15 44 26 31 .. 15 36 31 24 64 62 43 7 44 16 27 .. 36 8 18 9 .. 14 23 .. 46 47 18 .. 18 .. 30 13 9 .. 45 w 41 33 42 25 33 41 34 21 .. 62 16 48 49

About the data

4.6

economy

Structure of service exports Definitions

Balance of payments statistics, the main source of

affiliates. Another important dimension of service

• Commercial service exports are total service

information on international trade in services, have

trade not captured by conventional balance of pay-

exports minus exports of government services not

many weaknesses. Disaggregation of important

ments statistics is establishment trade—sales in

included elsewhere. • Transport covers all transport

components may be limited and varies considerably

the host country by foreign affiliates. By contrast,

services (sea, air, land, internal waterway, space,

across countries. There are inconsistencies in the

cross-border intrafirm transactions in merchandise

and pipeline) performed by residents of one economy

methods used to report items. And the recording of

may be reported as exports or imports in the balance

for those of another and involving the carriage of

major flows as net items is common (for example,

of payments.

passengers, movement of goods (freight), rental of

insurance transactions are often recorded as premi-

The data on exports of services in the table and on

carriers with crew, and related support and auxiliary

ums less claims). These factors contribute to a down-

imports of services in table 4.7, unlike those in edi-

services. Excluded are freight insurance, which is

ward bias in the value of the service trade reported

tions before 2000, include only commercial services

included in insurance services; goods procured in

in the balance of payments.

and exclude the category “government services not

ports by nonresident carriers and repairs of trans-

Efforts are being made to improve the coverage,

included elsewhere.” The data are compiled by the

port equipment, which are included in goods; repairs

quality, and consistency of these data. Eurostat and

IMF based on returns from national sources. Data on

of harbors, railway facilities, and airfield facilities,

the Organisation for Economic Co-operation and

total trade in goods and services from the IMF’s Bal-

which are included in construction services; and

Development, for example, are working together

ance of Payments database are shown in table 4.17.

rental of carriers without crew, which is included

to improve the collection of statistics on trade in

International transactions in services are defined

in other services. • Travel covers goods and ser-

services in member countries. In addition, the Inter-

by the IMF’s Balance of Payments Manual (1993) as

vices acquired from an economy by travelers in that

national Monetary Fund (IMF) has implemented

the economic output of intangible commodities that

economy for their own use during visits of less than

the new classification of trade in services intro-

may be produced, transferred, and consumed at the

one year for business or personal purposes. • Insur-

duced in the fifth edition of its Balance of Payments

same time. Definitions may vary among reporting

ance and financial services cover freight insurance

Manual (1993).

economies. Travel services include the goods and

on goods exported and other direct insurance such

Still, difficulties in capturing all the dimensions of

services consumed by travelers, such as meals,

as life insurance; financial intermediation services

international trade in services mean that the record

lodging, and transport (within the economy visited),

such as commissions, foreign exchange transac-

is likely to remain incomplete. Cross-border intrafirm

including car rental.

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 nonresidents; construction services; royalties and

4.6a

Top 10 developing economy exporters of commercial services in 2009 Commercial service exports ($ billions)

1995

2009

license fees; miscellaneous business, professional, and technical services; and personal, cultural, and recreational services.

150

120

0 China

India

Russian Federation

Turkey

Thailand Malaysia

Brazil

Egypt, Arab Rep.

Mexico

Lebanona

The top 10 developing country exporters of commercial services accounted for almost 68 percent of developing country commercial service exports and 13 percent of world commercial service exports. a. Data are unavailable for 1995. Source: International Monetary Fund balance of payments data files.

Data sources Data on exports of commercial services are from the IMF, which publishes balance of payments data in its International Financial Statistics and Balance of Payments Statistics Yearbook.

2011 World Development Indicators

217

4.7

Structure of service imports Commercial service imports

$ millions 1995 2009

218

.. 98 .. 1,665 6,992 52 16,979 27,552 297 1,192 276 33,134 a 235 321 262 440 13,161 1,278 116 62 181 485 32,985 114 174 3,524 24,635 24,962 2,813 .. 690 895 1,235 1,373 .. 4,860 13,945 957 1,141 4,511 488 45 420 337 9,418 64,523 832 47 249 128,865 331 4,003 672 252 27 236 326

.. 2,215 .. 18,210 11,445 839 47,613 36,894 3,297 3,202 2,031 73,008 500 993 625 1,040 44,074 5,037 564 160 939 2,081 77,579 .. .. 9,351 158,107 44,379 6,860 .. 3,523 1,407 2,324 3,812 .. 18,887 50,912 1,733 2,556 12,765 1,231 .. 2,496 2,190 25,687 126,425 .. 83 910 253,467 2,166 19,525 2,058 288 85 736 1,077

2011 World Development Indicators

Transport

Travel

% of total

Insurance and financial services

% of total

Computer, information, communications, and other commercial services

% of total

1995

2009

1995

2009

1995

2009

1995

2009

.. 61 .. 18 30 83 37 12 31 65 36 24 a 59 66 51 43 44 42 56 49 46 35 24 44 55 54 39 22 42 .. 19 41 50 28 .. 16 45 61 42 35 55 2 53 63 23 33 18 60 27 18 61 30 41 58 53 78 60

.. 15 .. 23 23 46 31 29 24 83 40 25 62 38 32 40 18 22 59 53 58 33 22 .. .. 52 29 34 34 .. 15 36 58 18 .. 21 .. 58 54 45 57 .. 33 67 19 26 .. 46 54 21 41 51 46 37 38 72 42

.. 7 .. 5 47 6 30 40 49 20 32 28a 15 15 31 33 26 15 20 41 5 22 31 38 15 20 15 54 31 .. 8 36 15 31 .. 34 31 18 21 28 15 7 22 8 24 25 17 30 63 47 6 33 21 8 14 15 18

.. 72 .. 1 39 39 39 29 11 8 29 25 13 29 38 22 25 35 11 39 11 17 31 .. .. 17 28 34 26 .. 5 26 15 27 .. 22 .. 20 21 20 15 .. 24 6 17 31 .. 11 20 32 27 17 35 5 54 9 27

.. 22 .. 3 7 10 7 6 1 6 4 10a 10 9 10 8 10 0 5 6 4 7 11 8 2 4 17 6 12 .. 7 5 11 3 .. 5 .. 10 6 5 11 0 5 7 5 6 9 6 8 2 6 5 9 7 5 2 2

.. 5 .. 4 5 7 3 4 3 2 4 4 5 13 4 4 8 8 17 3 5 4 12 .. .. 10 8 8 8 .. 5 9 0 5 .. 3 .. 9 6 11 15 .. 2 4 2 3 .. 8 14 4 4 8 10 9 5 1 6

.. 10 .. 75 16 1 26 43 19 10 29 38a 16 10 8 16 21 43 20 4 45 36 34 10 29 22 29 18 15 .. 67 18 23 38 .. 45 24 11 31 32 19 93 21 22 48 36 57 4 2 33 26 33 29 26 28 6 20

.. 9 .. 72 33 8 27 38 62 8 28 47 20 19 25 34 49 35 13 6 27 45 35 .. .. 20 35 24 32 .. 75 29 27 51 .. 54 .. 13 18 24 13 .. 41 22 62 41 .. 36 12 43 28 24 9 50 4 19 25

Commercial service imports

$ millions 1995 2009

3,765 10,062 13,230 2,192 .. 11,252 8,131 54,613 1,073 121,547 1,385 776 900 .. 25,394 .. 3,826 193 119 225 .. 58 .. 510 457 300 277 151 14,821 412 197 630 9,021 193 87 1,350 350 233 538 305 43,618 4,571 207 120 4,398 13,052 985 2,431 1,049 642 676 1,781 6,906 7,008 6,339 .. ..

16,407 80,274 27,625 .. 7,565 104,551 16,865 114,581 1,824 146,965 3,657 9,881 1,634 .. 74,978 .. 11,297 858 114 2,260 14,301 91 141 4,323 2,883 789 .. .. 27,257 1,022 .. 1,586 21,402 678 545 5,302 1,004 547 602 771 84,625 7,825 517 599 16,127 36,504 5,555 5,844 2,118 1,915 511 4,619 8,344 23,789 14,186 .. ..

Transport

Travel

% of total

Insurance and financial services

% of total

4.7

economy

Structure of service imports

Computer, information, communications, and other commercial services

% of total

1995

2009

1995

2009

1995

2009

1995

2009

13 57 37 43 .. 16 45 24 46 30 52 38 46 .. 38 .. 39 27 43 68 .. 75 .. 60 64 50 56 67 38 60 62 40 38 52 70 48 33 11 37 36 29 41 39 74 22 38 42 67 71 25 66 51 30 25 27 .. ..

17 44 44 .. 53 2 32 20 43 28 53 19 51 .. 31 .. 31 48 12 26 15 79 60 48 38 39 .. .. 34 63 .. 32 13 38 37 44 35 46 37 28 21 29 48 67 38 26 38 54 58 23 61 37 44 22 30 .. ..

40 10 16 11 .. 18 26 27 14 30 31 36 21 .. 25 .. 59 3 25 11 .. 23 .. 15 23 9 21 26 16 12 12 25 35 29 22 22 .. 8 17 45 27 28 19 11 21 32 5 18 12 9 20 17 6 6 33 .. ..

22 12 19 .. 10 8 17 24 12 17 29 11 14 .. 18 .. 66 31 72 35 28 15 20 37 41 13 .. .. 24 14 .. 22 33 36 39 21 21 7 18 56 25 33 28 11 25 34 16 12 16 2 25 24 29 31 27 .. ..

5 6 3 10 .. 1 3 10 9 2 6 0 10 .. 2 .. 2 4 4 7 .. 0 .. .. 1 21 4 0 0 1 1 5 12 9 0 4 2 1 9 3 3 5 3 3 3 6 5 4 9 3 12 10 2 14 9 .. ..

3 10 5 .. 27 14 2 5 11 6 8 6 8 .. 2 .. 1 2 â&#x20AC;&#x201C;4 5 2 0 2 14 2 4 .. .. 4 5 .. 5 52 3 3 5 2 .. 4 4 3 4 11 4 3 4 10 4 15 12 11 11 4 6 4 .. ..

43 28 43 36 .. 65 26 39 31 38 11 25 22 .. 36 .. 0 65 28 14 .. 2 .. 25 12 21 20 7 47 27 25 30 14 10 8 26 65 81 37 16 41 26 38 12 54 24 49 10 9 63 1 22 63 55 31 .. ..

57 35 32 .. 10 76 48 50 34 50 10 64 26 .. 49 .. 2 19 20 34 55 6 17 2 19 45 .. .. 38 18 .. 40 2 24 21 30 42 47 41 12 51 34 13 18 34 37 36 30 11 63 3 28 23 41 40 .. ..

2011 World Development Indicators

219

4.7

Structure of service imports Commercial service imports

$ millions 1995 2009

Romania Russian Federation Rwanda Saudi Arabia Senegal Serbia Sierra Leone Singapore Slovak Republic Slovenia Somalia South Africa 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

1,801 10,154 20,205 59,241 58 503 8,670 45,540 405 1,384 .. 3,406 79 107 21,111 82,189 1,800 7,933 1,429 4,330 .. .. 5,756 14,390 22,354 86,988 1,169 2,487 150 2,684 206 539 17,112 44,373 14,899 38,867 1,358 3,127 .. 289 729 1,685 18,629 37,541 .. .. 148 358 223 271 1,245 2,812 4,654 15,607 403 .. 563 1,408 1,334 11,070 .. .. 62,524 160,036 129,227 334,311 814 1,072 .. .. 4,654 9,223 2,304 7,044 349 786 604 2,038 282 674 645 .. 1,221,691 t 3,144,723 t 9,833 29,059 218,955 749,008 109,579 443,081 109,232 304,268 228,417 777,282 82,593 272,307 35,575 139,286 52,313 127,915 19,571 62,588 15,377 93,734 24,587 88,519 992,976 2,368,417 422,763 995,810

a. Includes Luxembourg.

220

2011 World Development Indicators

Transport

Travel

% of total 1995

34 16 73 25 57 .. 17 44 17 31 .. 40 31 58 27 16 28 35 57 .. 30 42 .. 71 42 45 30 40 38 34 .. 27 32 46 .. 31 .. 28 36 79 56 31 w 51 39 42 38 40 38 30 41 45 59 40 29 25

Insurance and financial services

% of total 2009

28 16 63 25 55 27 57 32 22 20 .. 41 20 62 51 33 16 19 58 49 36 45 .. 71 47 53 42 .. 61 32 .. 18 20 42 .. 44 .. 8 46 57 .. 25 w 58 32 38 27 33 35 30 24 47 51 42 22 23

1995

39 57 17 .. 18 .. 63 22 18 40 .. 32 20 16 29 21 32 50 37 .. 49 23 .. 12 31 20 20 18 14 16 .. 40 36 29 .. 37 .. 46 12 9 19 31 w 18 24 16 30 23 16 33 31 21 13 24 33 32

Computer, information, communications, and other commercial services

% of total 2009

15 35 14 41 13 28 12 19 26 31 .. 29 19 17 32 13 27 27 26 2 45 12 .. 5 28 15 27 .. 13 30 .. 32 24 31 .. 17 .. 68 11 6 .. 25 w 18 26 22 28 25 24 29 29 19 13 23 25 27

1995

5 0 0 3 7 .. 4 10 5 2 .. 14 7 5 0 4 1 1 6 .. 3 5 .. 4 8 6 8 7 4 7 .. 4 6 5 .. 3 .. 3 7 0 3 6w 5 9 10 8 9 10 5 10 .. 5 9 5 5

% of total 2009

7 4 1 6 11 4 9 6 14 4 .. 4 8 6 1 6 1 8 9 10 4 5 .. 10 3 10 13 .. 10 13 .. 7 21 6 .. 6 .. 1 9 11 .. 10 w 4 14 7 20 14 6 8 30 11 8 4 9 4

1995

22 26 10 72 18 .. 16 24 60 27 .. 14 41 21 44 59 38 14 6 .. 18 30 .. 12 19 28 42 35 43 43 .. 29 26 20 .. 30 .. 25 45 12 23 32 w 27 28 32 25 28 37 33 17 28 23 28 33 38

2009

51 45 22 28 21 40 22 42 37 45 .. 26 52 15 68 48 56 46 7 38 15 38 .. 14 22 23 19 .. 16 25 .. 44 35 21 .. 33 .. 23 35 26 .. 40 w 19 28 32 25 28 35 33 17 23 29 31 43 46

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,

The mix of commercial service imports by developing economies is changing 1995 ($228 billion)

Other 28%

Insurance and financial 9%

Transport 40%

4.7a

and technical services; and personal, cultural, and recreational services.

2009 ($777 billion)

Other 28%

Transport 33%

Travel 23% Insurance and financial 14% Travel 25%

Data sources Between 1995 and 2009 developing economies’ commercial service imports more than tripled. Insur-

Data on imports of commercial services are from

ance and financial services and travel services are displacing transport as the most important services

the IMF, which publishes balance of payments

imported.

data in its International Financial Statistics and

Source: International Monetary Fund balance of payments data files.

Balance of Payments Statistics Yearbook.

2011 World Development Indicators

221

4.8 Afghanistan Albania Algeria Angola Argentina Armenia Australia Austria Azerbaijan 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 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 Honduras

222

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 1995 2009

.. 87 55 34 69 109 60 56 77 83 59 54 82 76 .. 34 62 66 63 89 95 72 57 79 91 61 43 62 65 81 49 71 66 67 71 51 51 81 68 74 87 94 54 80 52 57 41 90 102 58 76 76 86 74 95 86 64

88 87 41 .. 59 82 57 54 37 77 56 52 .. 66 80 63 62 66 .. .. 74 72 59 93 79 60 35 62 64 74 42 62 72 57 54 51 49 85 69 76 92 86 53 88 54 58 41 78 83 59 82 75 86 75 83 .. 80

2011 World Development Indicators

.. 14 17 40 13 11 18 20 13 5 21 21 11 14 .. 29 21 17 25 19 6 9 21 15 7 10 14 8 15 5 13 14 11 26 24 21 25 5 13 11 9 44 26 8 23 24 12 14 11 20 12 15 6 8 6 7 9

9 10 14 .. 15 11 17 20 14 5 17 25 .. 15 23 24 22 16 .. .. 8 9 22 4 16 13 13 9 16 8 12 17 9 20 33 22 30 8 10 11 10 31 22 8 25 25 12 16 24 20 10 19 10 8 14 .. 19

.. 21 31 35 18 18 24 25 24 19 25 21 20 15 20 25 18 16 24 6 15 13 19 14 13 26 42 34 26 9 37 18 16 16 7 33 20 18 22 20 20 23 28 18 18 19 23 20 4 22 20 18 15 21 22 26 32

25 29 41 15 21 31 28 21 22 24 38 20 25 17 22 24 17 26 .. .. 21 18 21 11 34 19 48 23 23 30 25 20 11 27 11 22 17 15 32 19 13 11 19 22 18 19 28 26 12 16 20 16 13 22 23 27 20

.. 12 26 82 10 24 18 35 28 11 50 65 20 23 20 51 7 52 14 13 31 24 37 20 22 29 20 143 15 28 65 38 42 33 13 51 38 36 26 23 22 22 68 10 37 23 59 49 26 24 24 17 19 21 12 9 44

16 29 40 52 21 12 20 51 52 19 51 73 14 36 33 34 11 48 .. .. 60 27 29 14 42 38 27 194 16 10 72 43 42 36 20 70 48 22 37 25 22 4 71 11 37 23 52 30 30 41 31 19 23 41 26 14 42

.. 35 29 68 10 62 20 36 42 17 54 62 33 27 71 38 9 50 27 27 47 18 34 28 34 27 19 148 21 24 64 40 34 42 16 55 33 39 28 28 38 83 76 16 29 22 36 73 42 23 33 27 25 25 35 29 48

48 54 36 46 16 36 22 46 25 27 62 70 28 33 58 45 11 56 .. .. 63 31 30 22 70 30 22 187 18 22 51 42 34 39 18 64 44 30 48 32 38 20 65 29 35 25 33 50 49 36 41 29 33 45 47 44 61

.. 20 .. 78 16 –9 18 22 13 22 21 29 11 11 .. 36 16 15 29 6 6 14 18 11 12 25 42 .. 19 .. –2 15 12 11 .. 29 22 16 17 22 18 19 24 21 22 19 33 8 1 20 18 18 11 21 10 .. 27

.. 17 .. 10 23 20 21 24 45 39 25 22 11 23 13 16 15 16 .. .. 19 20 18 .. .. 22 54 31 18 .. 18 20 15 22 .. 20 22 10 24 17 11 .. 24 16 20 16 .. 19 0 21 16 3 12 8 .. .. 16

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 1995 2009

68 64 62 46 .. 54 56 58 70 55 65 71 70 .. 52 .. 43 75 .. 63 103 93 .. 59 68 70 90 79 48 83 77 63 67 57 56 68 90 .. 54 75 49 59 83 86 .. 50 51 72 52 44 76 71 74 60 65 .. 32

67 56 57 45 .. 52 57 60 81 60 83 50 76 .. 54 .. 28 86 66 61 79 79 202 23 65 81 80 62 50 77 72 75 67 87 55 57 84 .. 62 81 46 60 91 .. .. 43 34 80 49 69 78 64 74 61 67 .. 21

11 11 8 16 .. 16 28 18 11 15 24 14 15 .. 11 .. 32 20 .. 24 12 35 .. 22 21 19 7 21 12 10 11 14 10 27 13 17 8 .. 30 9 24 17 11 14 .. 22 25 12 15 17 10 10 11 20 17 .. 32

9 12 10 11 .. 19 24 22 16 20 24 12 16 .. 16 18 13 23 8 21 16 50 19 9 19 18 11 21 14 10 21 15 12 22 1 18 13 .. 24 11 29 20 12 .. .. 22 15 8 10 11 12 10 11 19 21 .. 25

21 27 32 29 .. 18 25 20 29 28 33 20 22 .. 38 .. 15 18 .. 14 36 76 .. 12 21 21 11 17 44 23 20 26 20 25 32 21 27 14 22 25 21 23 22 7 .. 22 15 19 30 22 26 25 22 19 24 .. 35

22 36 31 33 .. 14 16 19 21 20 15 30 21 .. 26 28 19 22 37 19 30 31 20 28 27 24 33 25 14 22 25 21 22 27 50 36 21 .. 27 30 18 18 23 .. .. 20 30 19 25 20 16 22 15 20 20 .. 39

46 11 26 22 .. 76 29 26 51 9 52 39 33 .. 29 .. 52 29 23 43 11 24 9 29 47 33 24 30 94 21 37 59 30 49 48 27 16 1 49 25 59 29 19 17 44 38 44 17 101 61 59 13 36 23 27 72 44

81 20 24 32 .. 89 35 24 35 13 43 42 25 .. 50 14 66 50 33 42 22 51 31 67 60 44 28 30 96 26 50 48 28 37 56 29 25 .. 47 16 69 28 35 .. 36 42 59 13 77 58 47 24 32 39 28 .. 47

46 12 28 13 .. 65 37 22 61 8 73 44 39 .. 30 .. 42 42 37 45 62 128 72 22 58 43 32 48 98 36 45 61 28 58 49 34 41 2 56 35 54 28 35 24 42 32 36 19 98 44 71 18 44 21 34 97 43

80 24 21 22 .. 74 32 24 53 12 65 34 38 .. 46 54 26 81 44 43 47 112 173 27 72 67 52 38 75 36 68 59 29 73 63 39 44 .. 60 37 62 27 61 .. 27 27 38 20 61 57 52 20 31 39 36 .. 31

17 27 28 37 .. 23 13 22 25 30 29 15 23 .. 36 .. 38 8 .. 14 .. 39 .. .. 12 13 2 8 34 15 14 25 19 18 35 17 9 .. 32 21 27 18 –1 –1 .. 26 10 21 30 35 18 16 19 20 24 .. ..

2011 World Development Indicators

15 35 23 .. .. 9 20 16 13 24 10 28 15 .. 30 .. 59 14 25 29 13 28 –2 67 15 18 .. .. 31 19 .. 17 22 19 42 31 9 .. 27 38 22 16 10 .. .. 32 39 22 35 20 12 23 40 19 10 .. ..

223

4.8

Structure of demand

Romania Russian Federation Rwanda Saudi Arabia Senegal Serbia Sierra Leone Singapore Slovak Republic Slovenia Somalia South Africa 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 1995 2009

68 52 97 47 80 73 88 41 52 60 .. 63 60 73 85 82 49 60 66 62 86 55 .. 77 53 63 68 44 85 55 48 63 68 73 51 69 74 98 71 72 65 61 w 81 60 55 63 60 48 61 66 63 67 69 61 57

61 54 81 38 83 74 84 43 47 55 .. 60 56 64 67 73 49 58 72 93 62 54 .. .. 49 63 72 49 76 65 46 65 71 68 56 64 66 .. .. 61 113 62 w 78 56 50 62 57 42 62 64 55 61 67 63 58

a. Covers mainland Tanzania only.

224

2011 World Development Indicators

14 19 10 24 13 23 14 8 22 19 .. 18 18 11 5 15 27 12 13 16 12 10 .. 12 12 16 11 12 11 21 16 20 15 12 22 7 8 18 14 15 18 17 w 9 14 12 15 14 13 16 15 15 10 16 17 20

15 20 15 25 9 19 14 10 20 20 .. 21 21 18 14 27 28 11 14 28 20 13 .. 9 10 13 15 10 11 19 10 23 17 13 18 13 6 .. .. 13 14 19 w 10 15 13 16 15 13 17 16 13 11 18 20 22

24 25 13 20 14 12 6 34 24 24 .. 18 22 26 14 16 17 23 27 29 20 42 .. 16 21 25 25 49 12 27 30 17 18 15 27 18 27 35 22 16 20 22 w 18 27 34 22 27 40 25 20 25 25 18 21 21

31 19 22 26 28 24 15 29 38 23 .. 19 24 25 25 17 17 20 16 22 30 22 .. .. 12 27 15 11 24 17 20 14 14 18 26 25 38 .. .. 22 2 19 w 24 28 37 20 28 40 19 20 28 33 21 17 19

28 29 5 38 31 17 19 .. 58 50 .. 23 22 36 5 60 40 36 31 66 24 42 .. 32 54 45 20 84 12 47 69 28 11 19 28 27 33 16 51 36 38 21 w 18 23 23 23 23 27 29 18 26 12 28 21 29

33 28 12 54 24 27 16 221 99 59 .. 27 23 21 15 60 49 52 34 13 23 68 .. 42 68 52 23 76 23 46 87 28 11 26 36 18 68 .. .. 36 36 24 w 23 27 29 25 27 35 30 21 38 19 30 24 36

33 26 26 28 37 24 26 .. 56 52 .. 22 22 46 10 74 33 31 38 72 42 49 .. 37 39 49 24 84 21 50 63 28 12 19 28 22 42 68 58 40 41 21 w 26 24 24 23 24 28 31 19 29 15 30 20 28

40 20 29 43 44 44 29 203 104 57 .. 28 26 28 21 76 42 41 36 56 35 58 .. 62 39 55 24 46 35 48 64 30 14 26 36 20 79 .. .. 32 65 24 w 36 26 28 24 26 30 29 21 33 24 34 24 35

19 28 20 20 8 .. â&#x20AC;&#x201C;3 53 27 23 .. 17 22 20 3 16 20 30 27 .. 7 34 .. 17 27 20 22 50 13 23 .. 15 16 14 .. 21 20 12 26 9 18 22 w 17 26 33 20 26 38 23 18 .. 25 16 21 21

29 23 15 32 16 17 8 45 29 22 .. 15 20 24 12 2 24 32 14 12 21 30 .. .. 31 23 13 .. 18 16 .. 12 10 17 .. 22 29 .. .. 19 .. 19 w 24 29 40 19 29 47 19 19 .. 34 15 16 19

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 final 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 final 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

2011 World Development Indicators

225

4.9

Growth of consumption and investment Household final consumption expenditure

General government final consumption expenditure

Gross capital formation

Goods and services

average annual % growth average annual average annual Total Per capita Exports Imports % growth % growth 1990–2000 2000–09 1990–2000 2000–09 1990–2000 2000–09 1990–2000 2000–09 1990–2000 2000–09 1990–2000 2000–09 average annual % growth

226

.. 1.3 –0.1 .. 2.8 –0.5 3.2 1.7 2.0 2.6 –0.5 1.8 2.6 3.6 .. 3.9 3.7 –2.6 5.7 .. 6.0 3.1 2.6 .. 1.5 7.3 8.9 3.8 2.4 –1.1 .. 5.1 4.1 2.3 4.0 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 .. 1.9 .. 2.2 4.2 5.2 .. .. 3.0

.. 5.3 3.6 .. 4.7 8.8 3.9 1.4 14.0 4.5 11.2 1.2 2.3 3.4 .. 6.7 3.6 6.3 4.5 .. 8.2 4.5 3.4 –0.9 2.7 5.5 7.7 3.7 4.0 .. .. 4.2 .. 3.5 5.0 3.6 2.1 6.7 5.4 4.4 3.3 1.6 6.8 10.7 3.1 2.1 4.5 .. .. 0.3 .. 3.8 3.8 4.1 .. .. 5.2

2011 World Development Indicators

.. 2.2 –1.9 .. 1.5 1.1 2.0 1.4 1.0 0.6 –0.3 1.6 –0.7 1.4 .. 1.4 2.2 –2.0 2.8 .. 3.4 0.5 1.6 .. –1.7 5.6 7.7 2.0 0.6 –3.8 .. 2.5 0.9 3.0 3.5 3.0 1.8 4.2 0.3 1.7 4.1 –6.6 2.1 0.4 1.4 1.2 –3.1 –0.2 .. 1.6 .. 1.4 1.8 2.0 .. .. 0.6

.. 4.9 2.1 .. 3.7 8.7 2.4 0.9 12.9 2.8 11.7 0.6 –1.1 1.5 .. 5.2 2.4 6.9 1.1 .. 6.4 2.1 2.3 –2.7 –0.8 4.4 7.1 3.1 2.4 .. .. 2.4 .. 3.5 4.9 3.3 1.7 5.1 4.3 2.5 2.9 –2.2 7.1 7.9 2.7 1.4 2.5 .. .. 0.4 .. 3.4 1.3 2.1 .. .. 3.1

.. 14.5 3.6 .. 2.2 –1.5 2.9 2.7 –4.8 4.7 –1.9 1.6 4.4 3.6 .. 6.9 1.0 –8.0 2.9 .. 7.2 0.7 0.3 .. –8.3 3.7 9.6 3.7 10.9 –20.4 .. 2.0 0.8 1.7 –2.9 –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 .. 1.9 .. 2.1 5.1 –0.5 .. .. 2.0

.. 7.9 9.0 .. 3.6 10.9 3.2 1.6 23.0 8.8 0.0 1.6 8.3 3.5 .. 4.9 3.2 2.0 8.7 .. 11.4 2.8 2.7 –1.3 2.7 4.8 8.8 1.6 4.0 .. .. 2.7 3.1 2.9 7.6 2.2 1.8 4.9 4.2 2.7 1.5 1.2 2.2 0.7 1.6 1.7 2.1 .. .. 0.9 .. 3.1 3.0 0.3 .. .. 6.6

.. 25.8 –0.6 .. 7.4 –1.9 5.1 2.3 41.6 9.2 –7.5 2.4 12.2 8.5 .. 5.3 4.2 –5.3 3.1 .. 10.3 0.4 4.6 .. 4.0 9.3 10.8 4.8 2.1 2.6 .. 5.1 8.1 7.2 0.7 4.6 5.7 11.7 –0.6 5.8 7.1 19.1 0.5 6.5 3.2 1.8 3.0 1.9 .. 1.1 .. 4.1 6.1 0.1 .. 9.0 6.9

.. 6.1 8.8 .. 11.1 18.3 7.6 1.2 19.3 7.8 18.8 3.0 7.7 3.9 5.3 3.0 4.0 13.5 9.0 .. 14.2 4.4 4.7 –0.1 –2.4 7.7 13.9 2.2 9.8 .. .. 5.8 2.5 9.2 8.8 2.9 1.3 1.7 7.8 7.3 0.7 –1.0 14.6 11.3 2.0 1.8 5.6 .. .. –0.1 .. 1.9 0.5 –0.5 .. 1.5 3.9

.. 18.9 3.2 .. 8.7 –18.4 7.7 5.8 5.7 13.1 –4.8 5.3 1.8 4.5 .. 4.9 5.9 4.3 4.4 .. 21.7 3.2 8.7 .. 2.3 9.4 15.5 7.8 5.0 –0.5 .. 10.9 1.9 6.3 –9.0 8.7 5.0 8.3 5.3 3.5 13.4 –2.5 11.0 7.1 10.3 6.9 2.1 0.1 .. 6.0 .. 7.6 6.1 0.3 .. 10.1 1.6

.. 9.8 2.3 .. 6.3 5.0 2.2 4.7 23.0 11.5 5.7 2.8 2.7 7.7 9.0 2.8 7.1 7.9 10.9 .. 15.2 –0.4 –0.4 –3.6 33.6 5.6 20.2 9.7 5.7 6.5 .. 6.9 2.4 3.8 12.2 10.5 3.4 1.1 6.1 16.8 2.9 –6.3 6.7 10.1 4.5 1.4 –2.0 1.1 .. 5.9 .. 2.9 2.1 2.3 .. 4.4 5.1

.. 15.7 –1.0 .. 15.6 –12.7 7.6 4.8 14.1 9.7 –8.7 5.0 2.1 6.0 .. 4.9 11.6 2.9 1.9 .. 14.8 5.1 7.1 .. –1.8 11.7 16.7 8.4 9.3 –2.4 .. 9.2 8.2 4.9 –2.9 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 .. 5.8 .. 7.4 9.2 –1.1 .. 19.4 3.8

.. 13.7 7.8 .. 9.2 8.6 9.2 3.9 19.7 8.8 10.9 2.9 1.8 5.6 2.6 4.8 7.5 10.5 7.2 .. 14.8 3.8 3.3 –3.9 –3.7 10.5 16.9 7.8 9.7 16.3 .. 5.4 3.9 5.7 10.1 9.1 5.3 2.4 8.7 14.4 3.3 –3.7 7.4 16.5 5.1 3.3 3.8 1.3 .. 4.7 .. 2.7 2.1 0.5 .. 2.1 5.4

Household final consumption expenditure

General government final consumption expenditure

Gross capital formation

4.9

economy

Growth of consumption and investment

Goods and services

–0.1 4.8 6.6 3.2 .. 5.6 5.0 1.6 .. 1.4 4.9 –7.5 3.6 .. 4.9 .. 4.5 –4.8 .. –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 .. .. 3.5 5.4 4.9 6.4 2.5 2.6 4.0 3.7 5.2 3.0 .. ..

3.8 6.9 4.3 7.4 .. 3.7 3.4 0.6 .. 1.0 7.5 9.3 4.0 .. 3.0 .. .. 9.9 –7.8 8.0 .. 9.5 .. .. 9.7 4.8 2.2 .. 7.5 0.9 7.4 5.6 3.2 7.9 .. 4.7 6.2 .. 5.7 .. 0.6 3.4 3.7 .. .. 3.7 .. 4.6 7.2 .. 3.0 5.2 5.1 3.7 1.5 .. ..

0.1 2.9 5.0 1.6 .. 4.7 2.5 1.5 .. 1.1 1.1 –6.4 0.6 .. 3.9 .. 0.6 –5.8 .. –2.7 –1.9 0.1 .. .. 6.1 1.7 –0.8 3.2 2.6 1.0 .. 3.9 2.2 10.0 .. 0.3 2.6 .. 2.3 .. 2.5 2.0 3.9 .. .. 3.0 2.6 2.3 4.2 –0.2 0.3 2.2 1.5 5.1 2.7 .. ..

4.1 5.4 2.9 5.8 .. 1.8 1.5 –0.1 .. 0.9 5.0 8.5 1.3 .. 2.6 .. .. 9.0 –9.4 8.6 .. 8.4 .. .. 10.3 4.6 –0.7 .. 5.6 –1.5 4.5 4.7 2.1 8.2 .. 3.5 3.6 .. 3.7 .. 0.3 2.0 2.3 .. .. 2.9 .. 2.2 5.4 .. 1.1 3.9 3.1 3.7 1.1 .. ..

0.9 6.6 0.1 1.6 .. 4.1 2.7 –0.2 .. 2.9 4.7 –7.1 6.9 .. 4.7 .. –2.4 –7.2 .. 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 .. .. 2.7 2.4 0.7 1.7 2.5 2.5 5.2 3.8 3.7 2.9 .. ..

1.3 5.7 8.2 3.6 .. 4.3 1.4 1.6 .. 1.6 6.7 7.8 2.3 .. 4.9 .. .. 4.2 9.7 2.1 .. 6.4 .. .. 4.3 0.0 5.5 .. 7.9 .. 3.1 3.8 0.8 5.9 .. 3.8 –4.6 .. 4.5 .. 3.2 4.1 2.7 .. .. 2.4 .. 8.3 3.6 .. 3.3 5.2 3.1 4.2 1.5 .. ..

9.6 6.9 –0.6 –0.1 .. 9.9 2.0 1.6 .. –0.8 0.3 –19.0 6.1 .. 3.4 .. 1.0 –1.1 .. –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 .. .. 6.0 4.0 1.8 10.4 1.9 0.7 7.4 4.1 10.6 5.9 .. ..

1.3 13.4 5.9 8.3 .. 1.5 2.3 0.3 .. –0.9 6.7 17.2 9.0 .. 3.1 .. .. 3.8 15.2 16.4 6.3 –0.5 .. .. 13.6 4.7 14.1 .. 2.1 6.2 23.8 5.3 0.4 9.8 .. 8.9 5.9 .. 9.4 .. 1.1 3.7 2.1 .. .. 5.0 .. 6.3 10.2 .. 3.0 10.5 1.3 5.9 –1.8 .. ..

9.9 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 .. .. 5.5 6.2 1.7 –0.4 5.1 3.1 8.5 7.8 11.3 5.7 1.6 ..

11.2 16.0 7.8 5.0 .. 4.2 5.9 0.4 .. 5.5 5.7 5.9 6.6 .. 10.6 .. .. 5.1 –7.6 7.1 10.2 10.0 .. .. 11.2 2.4 6.7 .. 5.3 6.3 –2.1 2.0 4.3 9.1 .. 6.4 16.0 .. 6.0 .. 4.1 2.2 8.3 .. .. 0.7 .. 7.1 7.8 .. 7.0 7.8 5.2 9.0 3.3 .. ..

11.4 14.4 5.7 –6.8 .. 14.5 7.6 4.4 .. 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 .. .. 5.8 5.9 2.5 1.2 3.4 2.9 9.0 7.8 16.7 7.6 4.5 ..

2011 World Development Indicators

10.0 16.5 8.6 13.2 .. 3.9 3.8 1.2 .. 2.5 6.9 5.6 8.3 .. 8.3 .. .. 16.0 –7.2 8.0 6.3 12.2 .. .. 14.0 4.0 9.3 .. 6.1 3.9 14.1 2.3 4.7 11.1 .. 8.3 6.2 .. 9.5 .. 3.8 4.5 5.1 .. .. 5.0 .. 7.3 6.9 .. 6.0 9.5 2.9 8.0 2.7 .. ..

227

4.9

Growth of consumption and investment Household final consumption expenditure

General government final consumption expenditure

Gross capital formation

Goods and services

Romania Russian Federation Rwanda Saudi Arabia Senegal Serbia Sierra Leone Singapore Slovak Republic Slovenia Somalia South Africa 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 .. .. 2.6 .. .. .. 6.0 3.9 .. 2.9 2.4 .. 3.7 7.3 1.5 1.1 3.0 –11.8 5.1 3.7 .. 5.0 0.7 4.3 3.8 .. 6.7 –6.9 7.1 3.0 3.7 5.0 .. 0.6 5.4 5.3 3.2 2.4 .. 3.0 w 2.9 4.1 5.7 3.0 4.0 7.4 0.5 3.6 2.8 4.6 3.3 2.8 2.0

6.2 9.9 .. 5.3 5.3 3.3 .. .. 5.3 3.2 .. 4.6 2.8 .. 5.9 2.0 2.2 1.4 7.5 6.1 6.2 4.0 .. 0.5 13.3 5.3 5.3 .. 1.9 12.1 .. 2.1 2.4 2.9 .. 8.7 7.8 –1.5 .. 0.1 .. 2.7 w 4.5 5.7 6.6 5.0 5.7 6.9 7.6 4.1 5.3 6.4 4.9 2.0 1.4

a. Covers mainland Tanzania only.

228

2011 World Development Indicators

1.7 –0.7 .. .. –0.2 .. .. .. 5.8 4.0 .. 0.6 2.0 .. 1.1 4.9 1.1 0.5 0.3 –13.1 2.0 2.7 .. 2.0 0.1 2.6 2.1 .. 3.3 –6.4 1.2 2.8 2.5 4.3 .. –1.5 3.9 1.1 –0.7 –0.5 .. 1.6 w 0.5 2.6 4.1 1.8 2.4 6.1 0.4 2.0 0.6 2.6 0.6 2.1 1.6

6.7 10.3 .. 2.9 2.5 3.6 .. .. 5.2 3.0 .. 3.4 1.2 .. 3.7 1.0 1.7 0.6 4.6 4.8 3.3 3.1 .. –2.1 12.9 4.3 3.9 .. –1.3 12.9 .. 1.5 1.4 2.8 .. 6.8 6.4 –4.9 .. –2.1 .. 1.5 w 2.2 4.5 5.3 4.1 4.3 6.0 7.5 2.9 3.3 4.8 2.4 1.3 0.8

0.8 –2.2 .. .. 0.9 .. .. .. 1.8 2.2 .. 0.3 2.7 10.5 5.5 7.1 0.7 0.5 2.0 –15.7 –8.8 5.1 .. 0.0 0.3 4.1 4.6 .. 7.7 –4.1 6.8 1.0 0.7 2.3 .. 3.7 3.2 12.7 1.7 –8.1 .. 1.7 w –1.3 3.3 6.6 1.4 3.3 8.0 –0.8 1.9 3.5 5.9 0.3 1.5 1.5

4.3 2.1 .. 7.6 –0.6 4.5 .. .. 3.3 3.2 .. 5.0 5.1 .. 8.4 6.0 0.9 1.2 8.4 1.6 13.5 5.3 .. 1.3 4.3 4.4 4.0 .. 4.0 2.6 .. 2.2 2.1 1.3 .. 7.0 7.7 1.3 .. 24.9 .. 2.6 w 6.8 5.4 7.4 3.7 5.5 8.4 3.3 3.3 3.6 6.1 5.1 2.1 1.9

–5.1 –19.1 .. .. 3.5 .. .. .. 7.7 10.4 .. 4.7 3.2 6.9 22.0 –4.7 2.0 0.7 3.3 –17.6 –1.1 –4.0 .. –0.1 12.5 3.6 4.7 .. 9.2 –18.5 5.5 4.7 7.6 6.1 –2.5 11.0 19.8 9.2 11.4 3.9 .. 3.3 w 5.5 2.6 6.3 –0.5 2.7 7.8 –11.2 5.4 1.2 6.5 4.6 3.4 2.2

11.5 9.0 .. 11.4 9.6 18.3 .. .. 7.8 7.5 .. 9.1 3.3 .. 11.2 –0.3 3.3 0.4 –0.4 7.3 12.8 4.8 .. 5.9 4.2 2.9 6.9 1.6 12.0 5.1 .. 1.6 0.0 6.6 4.7 11.2 12.3 –3.0 .. 6.6 .. 3.1 w 8.7 9.9 12.2 6.3 9.9 12.4 9.1 5.0 7.4 12.4 8.5 0.8 1.2

8.1 0.8 .. .. 4.1 .. .. .. 9.6 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 5.1 11.1 –2.4 15.4 –3.6 5.5 6.5 7.3 6.0 2.5 1.0 19.2 8.7 16.6 6.7 3.9 7.0 w 5.5 7.5 9.3 6.3 7.4 11.8 1.8 8.1 4.0 10.0 .. 6.9 6.8

9.6 7.1 .. 6.9 4.0 10.5 .. .. 11.0 9.1 .. 2.7 2.9 .. 14.3 5.2 4.6 4.7 6.5 9.5 11.6 5.8 .. 6.0 5.8 4.1 6.4 22.4 19.5 1.1 .. 3.1 4.5 7.8 4.9 –2.0 11.4 –3.1 .. 21.9 –10.7 5.9 w 9.7 10.4 14.7 5.5 10.4 14.5 7.2 5.0 7.7 14.6 .. 4.6 3.8

6.0 –6.1 .. .. 2.0 .. .. .. 12.4 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 3.8 10.8 7.2 9.7 –6.6 6.4 6.8 9.8 9.9 –0.4 8.2 19.5 7.5 8.3 15.5 3.1 7.0 w 5.3 6.4 8.3 5.1 6.4 10.9 –2.3 10.4 0.0 11.2 5.7 7.2 6.3

13.5 15.9 .. 16.9 7.8 10.7 .. .. 9.6 8.9 .. 8.1 4.7 .. 12.0 4.7 4.2 3.6 11.3 10.6 15.9 5.7 .. 3.1 9.5 3.6 8.8 15.2 11.2 5.2 .. 3.4 3.3 6.4 4.2 13.8 13.6 –2.3 .. 15.6 –5.8 5.7 w 9.4 10.7 12.9 8.5 10.7 12.6 11.9 6.7 9.9 14.8 8.8 4.3 3.8

About the data

4.9

economy

Growth of consumption and investment Definitions

Measures of growth in consumption and capital for-

the change in government employment. Neither

• Household final consumption expenditure is the

mation are subject to two kinds of inaccuracy. The

technique captures improvements in productivity

market value of all goods and services, including

first stems from the difficulty of measuring expendi-

or changes in the quality of government services.

durable products (such as cars and computers),

tures at current price levels, as described in About

Deflators for household consumption are usually cal-

purchased by households. It excludes purchases

the data for table 4.8. The second arises in deflat-

culated on the basis of the consumer price index.

of dwellings but includes imputed rent for owner-

ing current price data to measure volume growth,

Many countries estimate household consumption

occupied dwellings. It also includes government fees

where results depend on the relevance and reliabil-

as a residual that includes statistical discrepancies

for permits and licenses. Expenditures of nonproﬁt

ity of the price indexes and weights used. Measur-

associated with the estimation of other expenditure

institutions serving households are included, even

ing price changes is more difficult for investment

items, including changes in inventories; thus these

when reported separately. Household consumption

goods than for consumption goods because of the

estimates lack detailed breakdowns of household

expenditure may include any statistical discrepancy

one-time nature of many investments and because

consumption expenditures.

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 difficult. (An

diture per capita is household final consumption

example is c omputers—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 final 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 final 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 deflate 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, insurance, transport, travel, royalties, license fees, and

4.9a

GDP per capita is still lagging in some regions

other services (communication, construction, financial, information, business, personal, government

GDP per capita (2000 $) 5,000

services, and so on). They exclude compensation of Latin America & Caribbean

employees and investment income (factor services in the 1968 System of National Accounts) and transfer

4,000

payments. 3,000 Europe & Central Asia

2,000

Middle East & North Africa

Data sources

East Asia & Pacific

1,000

South Asia

Data on national accounts indicators for most

Sub-Saharan Africa

0 1990

1995

2000

2005

developing countries are collected from national 2009

statistical organizations and central banks by visiting and resident World Bank missions. Data for

Although GDP per capita has more than tripled in East Asia and Pacific between 1990 and 2009,

high-income economies are from Organisation for

it is still less than GDP per capita in Latin America and Carribean and in Europe and Central Asia.

Economic Co-operation and Development (OECD)

Source: World Development Indicators data files.

data files.

2011 World Development Indicators

229

4.10

Toward a broader measure of national income Gross domestic product

$ billions 2009

230

14.5 12.0 140.6 75.5 307.2 8.7 924.8 381.1 43.0 89.4 49.0 471.2 6.7 17.3 17.0 11.8 1,594.5 48.7 8.1 1.3 9.9 22.2 1,336.1 2.0 6.8 163.7 4,985.5 210.6 234.0 10.6 9.6 29.2 23.3 63.0 62.7 190.3 309.6 46.8 57.2 188.4 21.1 1.9 19.1 28.5 238.0 2,649.4 11.1 0.7 10.7 3,330.0 26.2 329.9 37.3 4.1 0.8 6.5 14.3

2011 World Development Indicators

Gross national income

$ billions 2009

10.6 11.9 139.6 67.5 297.7 8.9 900.7 377.1 40.3 97.5 47.9 475.0 6.6 16.7 17.4 11.3 1,562.4 46.6 8.0 1.3 9.4 22.1 1,317.3 2.0 6.1 153.4 5,028.8 216.9 224.5 9.8 6.9 28.8 22.4 60.5 61.8 178.1 318.3 45.0 56.1 188.6 20.4 1.9 18.5 28.5 238.1 2,671.2 9.5 0.7 10.6 3,377.0 25.9 320.8 36.1 3.7 0.8 .. 13.8

Adjustments

Consumption of fixed capital % of GNI 2009

Natural resource depletion % of GNI 2009

7.7 10.5 10.5 11.7 11.8 9.7 14.4 14.3 11.5 6.8 11.1 14.0 7.9 9.5 10.4 11.5 11.8 11.7 7.4 5.5 8.1 8.6 14.2 7.2 9.9 12.6 10.2 13.6 11.3 5.9 13.6 11.3 8.8 12.9 .. 13.6 16.5 11.1 10.7 9.6 10.5 6.8 12.8 6.7 17.0 14.2 13.2 7.5 8.8 13.8 8.6 13.9 10.1 7.7 7.4 .. 9.6

3.4 1.3 16.9 29.1 4.9 0.5 5.1 0.1 32.7 2.6 0.9 0.0 1.2 11.2 .. 2.8 3.1 1.1 1.6 10.6 0.2 4.8 2.3 0.0 25.2 10.0 3.1 0.0 6.2 10.7 50.6 0.2 3.1 0.8 3.3 0.3 1.5 0.5 9.9 7.3 0.5 0.8 0.7 4.5 0.1 0.0 29.2 1.0 0.1 0.1 6.9 0.2 1.2 6.6 0.0 .. 0.4

Adjusted net national income

Gross domestic product

Gross national income

Adjusted net national income

$ billions 2009

% growth 2000–2009

9.5 10.5 101.2 40.0 248.2 8.0 725.2 322.8 22.5 88.3 42.2 408.7 6.0 13.2 .. 9.7 1,330.0 40.7 7.3 1.1 8.6 19.1 1,100.4 1.8 4.1 118.8 4,355.8 188.5 185.3 8.2 2.5 25.5 19.7 52.2 52.8 153.3 261.1 39.8 44.5 156.9 18.2 1.7 16.0 25.3 197.6 2,292.1 5.5 0.6 9.6 2,908.2 19.7 275.8 32.0 3.2 0.8 .. 12.4

.. 5.4 4.0 13.1 5.4 10.5 3.3 2.0 17.9 5.9 8.4 1.7 4.0 4.1 5.0 4.4 3.6 5.4 5.4 3.0 9.0 3.3 2.1 0.8 10.2 4.1 10.9 4.7 4.5 5.2 4.0 5.1 0.8 3.9 6.7 4.1 1.2 5.5 5.0 4.9 2.6 0.2 5.9 8.5 2.5 1.5 2.1 5.2 7.4 1.0 5.8 3.6 3.7 3.0 1.0 0.7 4.9

.. 5.8 3.7 .. 5.1 10.5 3.6 1.9 19.4 5.3 8.6 1.9 3.9 4.3 5.9 4.2 3.4 6.1 6.0 .. 9.3 2.7 1.9 –0.9 20.2 5.0 10.6 4.4 4.7 5.5 .. 4.6 0.6 4.0 6.6 4.6 0.7 5.4 4.5 5.0 2.7 1.4 6.1 8.4 2.4 1.5 2.6 5.4 .. 0.6 .. 4.1 3.8 4.1 .. .. 4.8

.. 7.3 4.9 .. 5.4 11.4 3.3 2.0 19.8 5.9 10.6 1.3 3.6 3.0 .. 3.0 3.6 4.9 5.5 .. 10.2 4.4 2.9 –1.2 –5.5 4.9 9.9 4.2 4.3 7.5 .. 4.0 0.6 5.2 6.7 4.6 2.0 5.1 5.2 2.8 2.3 4.0 6.7 10.3 1.7 1.4 3.3 3.6 .. 1.4 .. 3.0 3.2 0.9 .. .. 3.0

Gross domestic product

$ billions 2009

129.0 1,377.3 540.3 331.0 65.8 227.2 195.4 2,112.8 12.1 5,069.0 25.1 115.3 29.4 .. 832.5 5.4 148.0 4.6 5.9 26.2 34.5 1.6 0.9 62.4 37.2 9.2 8.6 4.7 193.1 9.0 3.0 8.6 874.8 5.4 4.2 91.4 9.8 .. 9.3 12.5 792.1 126.7 6.1 5.4 173.0 381.8 46.1 162.0 24.7 7.9 14.2 130.3 161.2 430.1 232.9 .. 98.3

Gross national income

$ billions 2009

121.2 1,369.3 478.4 328.6 61.8 184.4 190.8 2,076.3 11.4 5,228.3 25.7 103.4 29.3 .. 836.9 5.6 158.1 4.4 5.8 28.1 35.4 1.9 0.6 62.0 37.2 9.0 8.5 4.7 188.9 9.0 3.0 8.9 860.2 5.7 4.0 89.5 9.7 .. 9.2 12.8 773.9 121.4 5.9 5.3 162.9 376.4 58.1 166.4 23.1 7.8 14.0 122.6 161.1 416.1 225.1 .. ..

Adjustments

Consumption of fixed capital % of GNI 2009

Natural resource depletion % of GNI 2009

13.0 8.6 10.9 10.7 10.1 17.7 13.6 14.0 11.2 13.5 10.3 12.7 7.4 .. 13.3 .. 5.2 8.4 8.4 11.3 11.2 6.4 8.2 11.9 12.0 10.9 7.3 7.4 11.6 7.7 8.1 10.9 11.7 8.5 9.5 10.1 7.1 .. 10.6 6.8 14.6 14.1 8.8 2.9 9.0 15.2 13.5 8.0 12.1 8.6 9.7 11.3 8.0 12.4 17.2 .. ..

0.2 4.2 6.5 17.9 45.7 0.1 0.2 0.1 0.7 0.0 1.1 22.0 1.2 .. 0.0 .. 37.0 0.5 0.0 0.3 0.0 1.4 11.0 30.5 0.2 0.1 0.2 0.9 7.9 0.0 18.8 0.0 5.4 0.2 11.1 1.4 3.8 .. 0.3 4.2 0.8 0.9 0.8 1.2 15.0 10.6 37.8 3.1 0.0 19.9 0.0 5.9 1.0 1.0 0.1 .. ..

4.10

economy

Toward a broader measure of national income Adjusted net national income

Gross domestic product

Gross national income

Adjusted net national income

$ billions 2009

% growth 2000–2009

105.2 1,194.1 395.3 234.7 27.4 151.8 164.4 1,784.1 10.1 4,521.8 22.8 67.6 26.8 .. 725.3 .. 91.3 4.0 5.3 24.8 31.4 1.8 0.5 35.8 32.7 8.0 7.9 4.3 152.3 8.3 2.2 7.9 713.2 5.2 3.1 79.2 8.6 .. 8.2 11.4 654.9 103.3 5.3 5.1 123.8 279.2 28.3 147.8 20.3 5.6 12.6 101.6 168.2 360.4 186.1 .. ..

4.0 7.8 5.1 6.2 .. 3.8 2.6 0.6 .. 0.8 6.7 9.9 4.2 .. 4.1 .. .. 4.3 6.6 6.0 3.9 0.8 .. .. 8.1 3.2 3.5 .. 4.5 5.9 3.2 3.3 2.1 5.0 .. 4.8 7.7 .. 5.7 .. 2.1 2.6 3.0 .. .. 2.2 .. 4.8 7.2 .. 3.7 6.7 4.1 4.7 1.0 .. ..

2011 World Development Indicators

3.1 7.5 3.0 6.7 .. 2.4 4.4 0.4 .. 1.5 6.9 9.1 5.1 .. 3.3 .. .. 4.8 1.0 8.2 4.7 8.9 .. .. 9.3 2.9 2.3 .. 7.2 5.7 5.1 2.2 1.5 6.1 .. 4.4 6.3 .. 6.1 .. 1.3 2.8 2.5 .. .. 3.7 .. 4.7 6.7 .. 3.3 5.2 4.3 4.2 0.5 .. ..

231

4.10

Toward a broader measure of national income Gross domestic product

$ billions 2009

Romania Russian Federation Rwanda Saudi Arabia Senegal Serbia Sierra Leone Singapore Slovak Republic Slovenia Somalia South Africa 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

161.1 1,231.9 5.2 375.8 12.8 43.0 1.9 182.2 87.6 48.5 .. 285.4 1,460.3 42.0 54.7 3.0 406.1 491.9 52.2 5.0 21.4 263.8 0.6 2.9 21.2 39.6 614.6 19.9 16.0 113.5 230.3 2,174.5 14,119.0 31.5 32.1 326.1 90.1 .. 26.4 12.8 5.6 58,252.1 w 431.5 16,206.0 8,880.2 7,318.4 16,649.8 6,346.0 2,591.7 4,017.9 1,062.4 1,700.3 945.9 41,607.7 12,465.3

a. Covers mainland Tanzania only.

232

2011 World Development Indicators

Gross national income

$ billions 2009

164.1 1,192.4 5.2 384.4 12.8 42.3 1.9 179.2 84.7 47.3 .. 279.0 1,430.2 41.5 49.3 2.9 413.4 512.3 50.9 4.9 21.4 252.0 2.9 2.8 20.7 37.3 606.9 19.2 15.7 111.1 .. 2,218.1 14,011.0 30.8 32.5 323.5 85.2 .. 24.9 11.4 5.2 57,867.2 w 433.8 16,112.0 8,952.6 7,173.2 16,558.2 6,307.5 2,521.8 3,921.9 1,192.9 1,702.0 904.2 41,369.3 12,368.9

Adjustments

Consumption of fixed capital % of GNI 2009

11.2 12.0 7.4 12.6 8.4 .. 6.8 14.1 13.0 13.5 .. 14.1 13.9 9.5 9.7 10.2 13.3 14.1 9.9 7.9 7.3 10.9 1.2 7.0 12.9 11.0 11.7 10.8 7.4 9.9 .. 13.5 14.3 12.0 8.4 12.2 8.8 .. 9.0 9.3 .. 13.1 w 7.2 10.7 9.9 11.8 10.7 10.3 11.7 11.7 10.4 8.4 10.6 14.1 14.2

Natural resource depletion % of GNI 2009

1.3 14.5 2.4 28.9 0.3 .. 2.1 0.0 0.3 0.2 .. 5.4 0.0 0.5 11.1 0.1 0.2 0.0 10.2 0.2 2.5 3.2 .. 3.6 28.2 4.6 0.2 .. 4.7 3.8 .. 1.2 0.7 0.4 17.8 9.8 7.2 .. 13.2 11.5 3.5 2.4 w 3.8 5.8 4.5 7.5 5.8 3.6 9.2 4.8 14.8 3.9 9.3 1.0 0.1

Adjusted net national income

Gross domestic product

Gross national income

Adjusted net national income

$ billions 2009

% growth 2000–2009

5.4 6.1 .. 3.4 4.1 5.3 .. .. 6.1 4.7 .. 4.1 2.9 .. 7.5 3.2 2.0 2.6 4.0 7.8 6.9 4.8 .. 2.3 8.3 5.0 4.8 14.0 7.8 5.6 .. 1.8 2.2 3.7 5.0 4.6 8.0 0.2 .. 7.4 –7.2 2.8 w 5.6 6.4 8.4 4.3 6.3 9.2 5.9 3.7 4.9 7.3 4.5 1.9 1.4

7.2 8.4 .. 6.2 .. .. .. .. 5.6 4.4 .. 3.8 2.7 .. 5.7 1.7 2.5 1.5 6.3 5.5 6.4 4.4 .. 3.0 5.7 3.7 4.0 .. 7.5 8.1 .. 2.1 1.4 2.8 –6.4 8.6 7.0 .. .. 5.3 –9.0 2.6 w 5.6 6.2 7.8 4.7 6.2 8.6 6.8 3.8 5.0 7.0 4.2 1.7 1.4

143.5 876.2 4.7 226.8 11.7 .. 1.7 154.0 73.4 40.8 .. 224.6 1,231.5 37.3 39.0 2.6 357.4 440.2 40.7 4.5 19.3 216.6 .. 2.5 12.2 31.5 534.7 .. 13.8 95.9 .. 1,892.3 11,909.0 27.0 24.0 252.4 71.5 .. 19.4 9.1 4.6 48,996.8 w 383.4 13,495.7 7,727.3 5,782.6 13,887.1 5,456.9 1,977.5 3,277.5 945.9 1,492.3 722.6 35,134.3 10,599.8

About the data

4.10

Definitions

An economy’s growth is typically measured by the

control of institutional units. The calculation of

• Gross domestic product is the sum of value

change in the volume of its output, as shown in table

adjusted net national income, which accounts for

added by all resident producers plus any product

4.1. However the widely tracked gross domestic prod-

net forest, energy, and mineral depletion, thus

taxes (less subsidies) not included in the valu-

uct (GDP) may not always be the most relevant sum-

remains within the SNA boundaries. This point is

ation of output. • Gross national income is GDP

mary of aggregated economic performance for all

critical because it allows for comparisons across

plus net receipts of primary income (compensation

economies, such as when production occurs at the

GDP, GNI, and adjusted net national income; such

of employees and property income) from abroad.

expense of consuming capital stock. For countries

comparisons reveal the impact of natural resource

• Consumption of fixed capital is the replacement

with significant exhaustible natural resources and

depletion, which is otherwise ignored by the popular

value of capital used up in production. • Natural

important foreign-investor presence, adjusted net

economic indicators.

resource depletion is the sum of net forest deple-

Adjusted net national income is particularly useful

tion, energy depletion, and mineral depletion. Net for-

in monitoring low-income, resource-rich economies,

est depletion is unit resource rents times the excess

The table presents three measures of economic

like many countries in Sub-Saharan Africa, because

of roundwood harvest over natural growth. Energy

progress: GDP, gross national income (GNI), and

such economies often see large natural resources

depletion is the ratio of the value of the stock of

adjusted net national income. GDP accounts for

depletion as well as substantial exports of resource

energy resources to the remaining reserve lifetime

all domestic production, regardless of whether the

rents to foreign mining companies. For recent years

(capped at 25 years). It covers coal, crude oil, and

income accrues to domestic or foreign institutions.

adjusted net national income gives a picture of eco-

natural gas. Mineral depletion is the ratio of the value

GNI accounts for the operation of foreign inves-

nomic growth that is strikingly different from the one

of the stock of mineral resources to the remaining

tors, who may be repatriating some of the income

provided by GDP.

reserve lifetime (capped at 25 years). It covers tin,

national income complements GDP in assessing economic progress (Hamilton and Ley 2010).

produced domestically. GNI comprises GDP plus

The key to increasing future consumption and

gold, lead, zinc, iron, copper, nickel, silver, bauxite,

net receipts of primary income from nonresident

thus the standard of living lies in increasing national

and phosphate. • Adjusted net national income is

sources. Adjusted net national income goes a step

wealth—including not only the traditional measures

GNI minus consumption of fixed capital and natural

further by subtracting from GNI a charge for the con-

of capital (such as produced and human capital),

resources depletion.

sumption of fixed capital (a calculation that yields

but also natural capital. Natural capital comprises

net national income) and for the depletion of natural

such assets as land, forests, and subsoil resources.

resources. The deduction for the depletion of natural

All three types of capital are key to sustaining eco-

resources, which covers net forest depletion, energy

nomic growth. By accounting for the consumption

depletion, and mineral depletion, reflects the decline

of fixed and natural capital depletion, adjusted net

in asset values associated with the extraction and

national income better measures the income avail-

harvest of natural resources. For more discussion

able for consumption or for investment to increase

of the estimates and methodology of produced capi-

a country’s future consumption. For a measure of

tal consumption and natural capital depletion, see

how comprehensive wealth is changing over time,

About the data in table 4.11.

see table 4.11.

The United Nations System of National Accounts

Methods of computing growth are described in Sta-

(SNA) includes nonproduced natural assets (such

tistical methods. For a detailed note on methodology,

as land, mineral resources, and forests) within the

see data.worldbank.org/.

asset boundary when they are under the effective GDP and adjusted net national income in Sub-Saharan Africa, 2000–09 (2000 $ billions)

4.10a

Data sources GNI and GDP are estimated by World Bank staff

(2000 $ billions)

based on national accounts data collected by

550

World Bank staff during economic missions or

GDP

reported by national statistical offices to other

500

international organizations such as the OECD. Data on consumption of fixed capital are from

450

the United Nations Statistics Division’s National

Adjusted net national income

400

Accounts Statistics: Main Aggregates and Detailed Tables, extrapolated to 2009. Data on energy, min-

350

eral, and forest depletion are estimates based on sources and methods in World Bank’s The

300 2000

2002

Source: World Development Indicators data files.

2004

2006

2008

2009

Changing Wealth of Nations: Measuring Sustainable Development in the New Millennium (2011a).

2011 World Development Indicators

233

economy

Toward a broader measure of national income

4.11 Afghanistan Albania Algeria Angola Argentina Armenia Australia Austria Azerbaijan 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 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 Honduras

234

Toward a broader measure of saving Gross savings

Consumption of fixed capital

Education expenditure

Net forest depletion

Energy depletion

Mineral depletion

Carbon dioxide damage

% of GNI 2009

.. 17.6 .. 10.9 23.9 19.5 22.4 24.4 48.0 35.3 25.7 21.7 10.6 23.8 12.9 17.1 15.0 16.7 .. .. 20.3 20.4 18.0 .. .. 23.0 53.2 30.3 19.2 .. 26.2 20.8 15.4 22.6 .. 21.8 21.4 10.5 24.1 16.7 11.7 .. 24.2 16.2 19.8 16.3 .. 20.0 0.2 21.2 15.7 3.4 12.9 8.5 .. .. 16.6

0.0 1.3 16.7 29.1 4.5 0.0 1.9 0.1 32.7 2.1 0.9 0.0 0.0 9.7 0.7 0.3 1.6 0.4 0.0 0.0 0.0 4.7 1.9 0.0 25.2 0.1 2.9 0.0 5.9 2.9 50.6 0.0 3.1 0.7 2.4 0.3 1.5 0.0 9.8 7.0 0.0 0.0 0.7 0.0 0.0 0.0 29.1 0.0 0.1 0.1 0.0 0.1 0.4 0.0 0.0 .. 0.0

0.0 0.0 0.1 0.0 0.3 0.5 3.1 0.0 0.0 0.0 0.0 0.0 0.0 1.5 0.9 2.5 1.5 0.7 0.0 0.8 0.0 0.1 0.4 0.0 0.0 9.9 0.2 0.0 0.3 7.9 0.0 0.1 0.0 0.0 1.0 0.0 0.0 0.5 0.0 0.1 0.0 0.0 0.0 0.1 0.1 0.0 0.1 0.0 0.0 0.0 4.8 0.0 0.0 3.7 0.0 .. 0.4

0.1 0.3 0.8 0.3 0.5 0.5 0.3 0.1 1.0 0.4 1.3 0.2 0.4 0.6 1.3 0.3 0.2 0.9 0.1 0.1 0.4 0.2 0.3 0.1 0.0 0.4 1.1 0.1 0.2 0.2 0.2 0.2 0.2 0.3 0.3 0.6 0.1 0.4 0.4 0.8 0.3 0.2 0.8 0.2 0.2 0.1 0.2 0.4 0.4 0.2 0.3 0.2 0.3 0.3 0.3 .. 0.5

0.7 0.2 0.2 1.2 1.1 1.6 0.0 0.1 0.3 0.4 0.0 0.1 0.3 1.0 0.1 0.2 0.1 0.8 0.6 0.1 0.3 0.4 0.0 0.2 1.0 0.5 0.8 .. 0.1 0.5 0.7 0.1 0.3 0.2 0.1 0.0 0.0 0.0 0.0 0.5 0.1 0.3 0.0 0.2 0.0 0.0 0.0 0.4 0.7 0.0 0.0 0.3 0.1 0.5 0.6 0.4 0.2

.. 8.2 .. –29.2 10.6 9.6 1.7 15.0 5.4 27.1 16.9 13.2 4.1 6.2 .. 9.6 4.6 6.1 .. .. 13.0 6.8 5.8 .. .. 3.2 39.7 .. 5.4 .. –44.7 15.2 7.3 12.3 .. 11.3 10.7 0.4 4.4 3.1 3.7 .. 14.4 8.3 8.1 7.0 .. 12.9 –7.1 11.4 –4.7 –7.9 4.0 –4.2 .. .. 9.5

2011 World Development Indicators

.. 2.8 4.5 2.3 4.9 2.2 4.5 5.2 2.9 2.0 4.4 5.8 3.3 4.7 .. 7.4 4.8 3.8 4.3 7.1 1.6 3.1 4.7 1.3 2.3 3.6 1.8 3.0 4.0 0.9 2.5 6.2 4.3 3.9 13.6 4.0 7.4 1.9 1.4 4.4 3.3 1.6 4.4 3.7 5.5 5.0 3.1 2.1 2.8 4.3 4.7 3.3 2.9 2.3 2.3 1.5 3.5

3.4 0.0 0.1 0.0 0.0 0.0 0.0 0.0 0.0 0.6 0.0 0.0 1.2 0.0 .. 0.0 0.0 0.0 1.6 9.8 0.2 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 0.1 0.5 0.8 0.0 4.4 0.0 0.0 0.0 1.0 0.0 0.0 2.1 0.0 0.8 2.9 0.0 .. 0.0

Local pollution Adjusted net damage savings

4.11

economy

Toward a broader measure of saving Gross savings

Consumption of fixed capital

Education expenditure

Net forest depletion

Energy depletion

Mineral depletion

Carbon dioxide damage

% of GNI 2009

16.0 35.2 26.2 .. .. 11.5 20.8 16.3 13.5 22.9 9.6 30.8 15.4 .. 30.1 .. 55.5 14.4 25.7 26.6 12.9 22.9 –2.7 66.8 15.1 18.8 .. .. 31.7 18.6 .. 16.2 22.1 17.8 44.7 31.8 9.2 .. 26.8 36.8 22.7 16.6 10.6 .. .. 32.6 40.3 21.5 37.4 19.7 12.3 24.0 35.0 19.2 10.4 .. ..

5.3 3.1 3.3 4.0 .. 5.2 5.7 4.1 6.2 3.2 5.6 4.4 6.6 .. 3.9 .. 3.2 5.2 1.1 5.6 1.6 9.4 3.1 .. 4.4 4.9 2.6 3.5 4.0 3.3 3.1 3.1 4.8 8.4 4.6 5.2 4.0 0.8 6.4 3.5 4.7 6.6 3.0 3.6 0.9 6.2 3.7 1.9 3.5 .. 3.6 2.4 2.5 4.8 5.3 .. ..

0.4 0.9 0.6 1.1 1.3 0.2 0.3 0.2 0.8 0.2 0.7 1.6 0.3 .. 0.5 0.0 0.4 1.1 0.2 0.2 0.4 0.0 1.0 0.8 0.3 1.0 0.2 0.2 0.8 0.1 0.5 0.3 0.4 0.7 2.1 0.4 0.2 .. 0.2 0.2 0.2 0.2 0.6 0.1 0.5 0.1 0.5 0.7 0.3 0.5 0.2 0.3 0.3 0.6 0.2 .. ..

0.0 0.5 0.5 0.5 2.6 0.0 0.1 0.1 0.2 0.2 0.2 0.1 0.1 0.8 0.3 .. 0.3 0.2 0.4 0.0 0.2 0.1 0.3 1.0 0.1 0.1 0.1 0.1 0.0 1.1 0.4 0.0 0.2 0.6 1.6 0.1 0.1 0.4 0.0 0.0 0.2 0.0 0.0 1.1 0.5 0.0 0.0 0.8 0.1 0.0 0.8 0.4 0.1 0.2 0.0 .. 0.1

4.5 24.1 11.0 .. .. –1.1 12.2 6.1 6.9 12.1 3.0 –1.2 13.1 .. 20.0 .. 15.7 9.4 17.8 20.4 2.7 24.4 –18.3 .. 6.0 11.6 .. .. 15.4 13.5 .. 8.0 9.1 16.2 24.9 25.0 2.0 .. 21.9 29.1 11.6 8.0 3.4 .. .. 12.8 –7.9 10.7 28.4 .. 5.2 8.6 28.0 9.7 –1.8 .. ..

0.0 0.9 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.2 .. 0.0 .. 0.0 0.0 0.0 0.3 0.0 1.4 10.4 0.0 0.1 0.1 0.2 0.9 0.0 0.0 0.5 0.0 0.0 0.1 0.0 0.0 0.5 .. 0.0 4.2 0.0 0.0 0.1 1.2 0.3 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.1 0.1 0.0 .. ..

0.2 2.2 5.3 17.7 45.7 0.0 0.1 0.1 0.0 0.0 0.1 20.8 0.0 .. 0.0 .. 37.0 0.5 0.0 0.0 0.0 0.0 0.0 30.4 0.1 0.0 0.0 0.0 7.9 0.0 0.0 0.0 5.1 0.1 3.8 0.0 3.2 .. 0.0 0.0 0.8 0.6 0.0 0.0 14.7 10.6 37.8 2.2 0.0 0.0 0.0 0.7 0.3 0.7 0.0 .. ..

0.0 1.1 1.2 0.2 0.0 0.0 0.1 0.0 0.7 0.0 1.0 1.2 0.0 .. 0.0 .. 0.0 0.0 0.0 0.0 0.0 0.0 0.7 0.0 0.0 0.0 0.0 0.0 0.0 0.0 18.3 0.0 0.3 0.0 7.3 1.4 0.0 .. 0.3 0.0 0.0 0.3 0.7 0.0 0.0 0.0 0.0 0.0 0.0 19.9 0.0 5.2 0.7 0.2 0.1 .. ..

Local pollution Adjusted net damage savings

2011 World Development Indicators

235

4.11 Romania Russian Federation Rwanda Saudi Arabia Senegal Serbia Sierra Leone Singapore Slovak Republic Slovenia Somalia South Africa 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

Toward a broader measure of saving Gross savings

Consumption of fixed capital

Education expenditure

Net forest depletion

Energy depletion

Mineral depletion

Carbon dioxide damage

% of GNI 2009

3.4 3.5 3.6 7.2 5.4 4.7 3.4 2.8 3.6 4.9 .. 5.4 4.0 2.6 0.9 7.2 6.1 4.8 2.6 3.2 2.4 4.6 1.6 4.5 4.0 6.7 2.6 .. 3.0 5.9 .. 5.1 4.8 2.3 9.4 3.6 2.8 .. 4.2 1.3 6.9 4.2 w 3.2 3.2 2.4 4.1 3.2 2.1 3.6 4.4 4.3 2.9 3.6 4.6 4.5

0.0 0.0 2.4 0.0 0.0 .. 1.7 0.0 0.3 0.1 .. 0.3 0.0 0.5 0.0 0.1 0.0 0.0 0.0 0.0 0.0 0.2 .. 2.3 0.0 0.1 0.0 .. 4.7 0.0 .. 0.0 0.0 0.4 0.0 0.0 0.2 .. 0.0 0.0 0.0 0.0 w 1.4 0.1 0.2 0.0 0.1 0.0 0.0 0.0 0.0 0.9 0.6 0.0 0.0

1.4 13.8 0.0 28.9 0.0 0.4 0.0 0.0 0.0 0.1 .. 2.8 0.0 0.0 11.1 0.0 0.0 0.0 10.0 0.2 0.2 3.0 0.0 0.0 28.2 3.5 0.2 30.4 0.0 3.8 .. 1.2 0.7 0.0 17.8 9.5 7.0 .. 13.2 0.0 2.2 2.0 w 1.2 5.1 4.0 6.4 5.0 3.3 8.7 3.5 14.5 2.1 7.5 0.9 0.1

0.0 0.7 0.0 0.0 0.3 0.0 0.4 0.0 0.0 0.0 .. 2.2 0.0 0.0 0.0 0.0 0.2 0.0 0.1 0.0 2.3 0.0 0.0 1.3 0.0 1.0 0.0 0.0 0.0 0.0 .. 0.0 0.1 0.0 0.0 0.3 0.0 .. 0.0 11.5 1.3 0.3 w 1.3 0.7 0.4 1.0 0.7 0.3 0.4 1.3 0.3 0.9 1.2 0.1 0.0

0.5 1.1 0.1 0.8 0.3 0.0 0.5 0.3 0.4 0.3 .. 1.2 0.2 0.2 0.2 0.3 0.1 0.1 1.1 1.1 0.2 0.9 0.0 0.4 1.4 0.5 0.3 2.1 0.1 2.4 .. 0.2 0.3 0.2 3.2 0.4 1.1 .. 0.7 0.2 1.1 0.4 w 0.3 0.8 1.0 0.6 0.8 1.0 0.9 0.3 0.9 0.9 0.6 0.2 0.2

0.0 0.1 0.1 0.7 0.5 .. 0.8 0.4 0.0 0.1 0.4 0.1 0.2 0.2 0.5 0.0 0.0 0.1 0.7 0.3 0.1 0.2 .. 0.1 0.2 0.1 0.6 0.9 0.0 0.1 0.5 0.0 0.1 1.1 0.3 0.0 0.3 .. .. 0.2 0.2 0.2 w 0.3 0.5 0.7 0.2 0.5 0.7 0.2 0.3 0.6 0.5 0.3 0.1 0.1

28.4 23.4 15.2 31.5 16.1 17.5 8.0 45.2 29.9 22.7 .. 15.8 19.9 24.3 13.5 2.5 23.6 31.0 13.9 12.4 21.1 31.0 .. .. 34.3 24.1 13.0 .. 17.9 15.9 .. 11.9 9.8 17.5 .. 21.8 31.2 .. .. 21.3 .. 21.1 w 23.9 33.2 43.3 20.0 33.0 48.7 21.1 18.9 .. 33.6 15.4 16.5 18.6

a. Covers mainland Tanzania only.

236

2011 World Development Indicators

Local pollution Adjusted net damage savings

% of GNI 2009

18.8 –0.8 8.8 –3.9 7.8 .. 1.2 33.0 19.8 13.6 .. 0.4 9.7 16.4 –7.1 –0.9 16.0 21.6 –14.1 6.2 13.5 20.5 .. .. –32.4 14.6 2.9 .. 8.6 5.6 .. 2.2 –0.8 6.1 .. 2.9 16.6 .. .. 1.4 .. 6.4 w .. 14.5 26.2 3.9 14.6 33.1 1.4 6.8 .. 21.6 –1.8 5.2 8.7

About the data

4.11

economy

Toward a broader measure of saving Definitions

Adjusted net savings measures the change in

of production. Natural resources give rise to rents

• Gross savings is the difference between gross

value of a specified set of assets, excluding capital

because they are not produced; in contrast, for pro-

national income and public and private consump-

gains. If a country’s net savings are positive and

duced goods and services competitive forces will

tion, plus net current transfers. • Consumption of

the accounting includes a sufficiently broad range

expand supply until economic profits are driven to

fixed capital is the replacement value of capital

of assets, economic theory suggests that the pres-

zero. For each type of resource and each country, unit

used up in production. • Education expenditure

ent value of social welfare is increasing. Conversely,

resource rents are derived by taking the difference

is public current operating expenditures in educa-

persistently negative adjusted net savings indicate

between world prices (to reflect the social oppor-

tion, including wages and salaries and excluding

that an economy is on an unsustainable path.

tunity cost of resource extraction) and the average

capital investments in buildings and equipment.

The table shows the extent to which today’s rents

unit extraction or harvest costs (including a “normal”

• Net forest depletion is unit resource rents times

from natural resources and changes in human capital

return on capital). Unit rents are then multiplied by

the excess of roundwood harvest over natural

are balanced by net savings—that is, this genera-

the physical quantity extracted or harvested to arrive

growth. • Energy depletion is the ratio of the value

tion’s bequest to future generations.

at total rent. To estimate the value of the resource,

of the stock of energy resources to the remaining

Adjusted net savings is derived from standard

rents are assumed to be constant over the life of the

reserve lifetime (capped at 25 years). It covers coal,

national accounting measures of gross savings

resource (the El Serafy approach), and the present

crude oil, and natural gas. • Mineral depletion is the

by making four adjustments. First, estimates of

value of the rent flow is calculated using a 4 percent

ratio of the value of the stock of mineral resources to

fixed capital consumption of produced assets are

social discount rate. For details on the estimation of

the remaining reserve lifetime (capped at 25 years).

deducted to obtain net savings. Second, current

natural wealth see World Bank (2011a).

It covers tin, gold, lead, zinc, iron, copper, nickel,

public expenditures on education are added to net

A positive net depletion figure for forest resources

silver, bauxite, and phosphate. • Carbon dioxide

savings (in standard national accounting these

implies that the harvest rate exceeds the rate of

damage is estimated at $20 per ton of carbon (the

expenditures are treated as consumption). Third,

natural growth; this is not the same as deforesta-

unit damage in 1995 U.S. dollars) times tons of

estimates of the depletion of a variety of natural

tion, which represents a change in land use (see

carbon emitted. • Particulate emissions damage

resources are deducted to reflect the decline in asset

Definitions for table 3.4). In principle, there should

is the willingness to pay to avoid illness and death

values associated with their extraction and harvest.

be an addition to savings in countries where growth

attributable to particulate emissions. • Adjusted net

And fourth, deductions are made for damages from

exceeds harvest, but empirical estimates suggest

savings is net savings plus education expenditure

carbon dioxide and particulate emissions.

that most of this net growth is in forested areas that

minus energy depletion, mineral depletion, net for-

The exercise treats public education expenditures

cannot currently be exploited economically. Because

est depletion, and carbon dioxide and particulate

as an addition to savings. However, because of the

the depletion estimates reflect only timber values,

emissions damage.

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.

Data on gross savings are from World Bank

tion should also consider private education expen-

The unit damage figure represents the present value

national accounts data files (see table 4.8).

diture, but data are not available for a large number

of global damage to economic assets and to human

Data on consumption of fixed capital are from

of countries.

welfare over the time the unit of pollution remains

the United Nations Statistics Division’s National

in the atmosphere.

Accounts Statistics: Main Aggregates and Detailed

While extensive, the accounting of natural

Data sources

resources depletion and pollution costs still has

Pollution damage from particulate emissions is

Tables, extrapolated to 2009. Data on educa-

some gaps. Key estimates missing on the resource

estimated by valuing the human health effects from

tion expenditure are from the United Nations

side include the value of fossil water extracted from

exposure to particulate matter pollution in urban

Educational, Scientific, and Cultural Organization

aquifers, net depletion of fish stocks, and depletion

areas. The estimates are calculated as willingness to

Institute for Statistics online database; missing

and degradation of soils. Important pollutants affect-

pay to avoid illness and death, from cardiopulmonary

data are estimated by World Bank staff. Data on

ing human health and economic assets are excluded

disease and lung cancer in adults and acute respira-

energy, mineral, and forest depletion are esti-

because no internationally comparable data are

tory infections in children, that are attributable to

mates based on sources and methods in World

widely available on damage from ground-level ozone

particulate emissions.

Bank (2011a). Data on carbon dioxide damage

or sulfur oxides.

Adjusted net savings aims to be as comprehensive

are from Fankhauser’s Valuing Climate Change:

Estimates of resource depletion are based on the

a measure as possible to provide a better under-

The Economics of the Greenhouse (1995). Data

“change in real wealth” method described in Hamil-

standing of the rate of country wealth creation or

on particulate emissions damage are from Pandey

ton and Ruta (2008), which estimates depletion as

depletion. To do so, it treats education as investment

and others’ “The Human Cost of Air Pollution: New

the ratio between the total value of the resource

and accounts for pollution damages to assets and

Estimates for Developing Countries” (2006). The

and the remaining reserve lifetime. The total value

human welfare, which goes outside the boundaries

conceptual underpinnings of the savings measure

of the resource is the present value of current and

of the United Nations System of National Accounts.

appear in Hamilton and Clemens’ “Genuine Sav-

future rents from resource extractions. An economic

For a detailed note on methodology, see data.

rent represents an excess return to a given factor

ings Rates in Developing Countries” (1999).

worldbank.org/.

2011 World Development Indicators

237

4.12

Central government finances Revenuea

Afghanistanb Albaniab Algeria Angola Argentina Armeniab Australia Austria Azerbaijanb 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 Czech Republicb Denmark Dominican Republic Ecuadorb Egypt, Arab Rep.b El Salvador Eritrea Estonia Ethiopiab Finland France Gabon Gambia, Theb Georgiab Germany Ghanab Greece Guatemalab Guineab Guinea-Bissau Haiti Honduras

238

Expense

Cash surplus or deficit

Net incurrence of liabilities

% of GDP

% of GDP 1995 2009

Domestic 1995 2009

1995

.. 21.2 .. .. .. .. .. 36.6 .. .. 30.0 41.5 .. .. .. 40.5 26.9 35.6 .. 19.3 .. 11.8 19.8 .. .. .. 5.4 .. .. 5.3 23.6 .. .. 36.8 .. 33.2 37.6 .. 30.9 34.8 .. .. 36.2 12.2 40.4 43.3 .. .. 12.2 29.9 17.0 35.3 8.4 11.2 .. .. ..

.. 25.6 .. .. .. .. .. 42.5 .. .. 28.7 45.7 .. .. .. 30.3 32.9 39.5 .. 23.6 .. 10.6 23.8 .. .. .. .. .. .. 8.2 29.8 .. .. 36.2 .. 32.6 41.5 .. 26.3 28.1 .. .. 32.8 12.0 49.7 47.6 .. .. 15.4 38.6 .. 44.3 7.6 12.1 .. .. ..

.. –8.9 .. .. .. .. .. –5.5 .. .. –2.7 –3.9 .. .. .. 4.9 –2.7 –5.1 .. –4.7 .. 0.2 –4.0 .. .. .. .. .. .. 0.0 –8.2 .. .. –1.1 .. –0.9 –3.7 .. 0.1 3.4 .. .. 1.6 –3.1 –7.5 –4.1 .. .. –4.3 –8.3 .. –9.1 –0.5 –4.3 .. .. ..

.. 7.4 .. .. .. .. .. .. .. .. 2.2 .. .. .. .. 0.2 .. 7.5 .. 3.1 .. –0.3 .. .. .. .. 1.6 .. .. 0.0 .. .. .. –2.3 .. –0.5 .. .. .. .. .. .. .. 1.8 8.9 .. .. .. 2.2 .. .. .. .. –0.1 .. .. ..

.. 2.1 .. .. .. .. .. .. .. .. 0.4 –0.5 .. .. .. –0.4 .. –0.8 .. 4.0 .. 0.3 .. .. .. .. .. .. .. 0.2 .. .. .. 0.7 .. –0.4 .. .. .. .. .. .. .. 2.6 0.2 .. .. .. 2.4 .. .. .. 0.4 4.5 .. .. ..

9.1 .. 36.6 .. .. 22.1 24.6 36.6 27.3 11.1 35.4 40.3 17.6 23.3 38.6 .. 23.1 32.3 14.0 .. 11.0 .. 17.4 .. .. 20.1 11.1 19.7 17.0 .. .. 24.7 18.7 34.1 .. 29.1 40.0 16.4 .. 27.0 17.5 .. 37.1 .. 39.0 40.5 .. .. 25.2 29.4 15.3 36.2 11.0 .. .. .. 20.8

2011 World Development Indicators

38.0 .. 25.0 .. .. 23.7 26.6 39.6 15.5 11.3 33.0 45.3 15.0 21.8 41.2 .. 25.6 31.6 13.0 .. 11.0 .. 19.2 .. .. 22.6 .. 18.9 19.5 .. .. 26.0 17.6 36.2 .. 37.3 42.4 16.2 .. 30.2 21.6 .. 36.8 .. 35.0 47.6 .. .. 31.0 31.7 17.9 50.7 12.6 .. .. .. 24.1

0.2 .. –4.4 .. .. –7.5 –2.4 –2.6 0.4 –1.7 0.2 –5.1 –4.5 1.2 –4.3 .. –3.5 –0.1 –4.8 .. –2.3 .. –1.9 .. .. –4.5 .. 0.6 –4.0 .. .. –3.4 0.9 –3.0 .. –6.1 –2.1 –3.8 .. –6.6 –5.0 .. –1.3 .. 4.6 –7.3 .. .. –7.8 –2.2 –5.6 –15.2 –3.2 .. .. .. –4.5

0.1 .. 5.9 .. .. 1.3 .. .. 0.0 3.1 –2.5 1.0 2.2 –0.2 3.7 .. 8.3 –0.4 4.5 .. –2.0 .. .. .. .. 0.8 0.4 1.0 5.8 .. .. .. .. 3.0 .. 2.9 .. 2.4 .. 9.9 2.0 .. .. .. –0.2 .. .. .. 1.3 3.1 2.8 .. 1.4 .. .. .. 5.0

Debt and interest payments

Foreign 2009

0.8 .. 0.0 .. .. 12.3 .. .. 0.2 0.4 8.4 6.5 2.1 –0.1 3.2 .. –0.1 0.5 2.9 .. 2.3 .. .. .. .. –0.4 0.0 –0.1 0.9 .. .. .. .. 2.2 .. 1.9 .. 1.5 .. –0.2 5.9 .. .. .. –0.6 .. .. .. 3.7 –0.2 2.6 .. 1.4 .. .. .. 1.0

Total debt % of GDP 2009

Interest % of revenue 2009

.. .. .. .. .. .. 24.1 70.7 .. .. 18.1 92.4 .. .. .. .. 61.0 .. .. .. .. .. 53.2 .. .. .. .. 30.5 59.3 .. .. .. .. .. .. 31.9 41.0 .. .. 79.5 48.5 .. 9.1 .. 36.2 82.8 .. .. 34.7 47.2 .. 138.5 23.3 .. .. .. ..

0.0 .. 1.0 .. .. 2.3 3.7 7.0 0.3 21.7 2.1 8.5 2.5 8.0 1.2 .. 20.7 2.2 2.2 .. 1.3 .. 10.1 .. .. 2.8 .. 0.3 18.9 .. .. 8.8 7.1 4.8 .. 4.1 4.9 9.7 .. 15.2 12.3 .. 0.6 .. 3.2 5.4 .. .. 3.4 5.5 15.2 14.3 12.6 .. .. .. 2.9

Revenuea

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

Expense

% of GDP 1995 2009

43.0 12.3 15.6 24.2 .. 35.5 .. 40.4 .. .. 28.2 14.0 21.6 .. 17.8 .. 36.8 16.7 .. 25.8 .. 57.1 .. .. .. .. .. .. 23.3 .. .. .. 15.3 28.4 19.0 .. .. 6.4 31.7 10.5 41.5 .. 12.8 .. .. .. 27.8 17.2 26.1 22.7 17.2 17.4 17.7 .. 33.2 .. ..

53.2 14.4 9.5 15.8 .. 37.5 .. 48.0 .. .. 26.1 18.7 25.8 .. 14.3 .. 44.0 25.6 .. 28.3 .. 39.4 .. .. .. .. .. .. 18.7 .. .. .. 15.0 38.4 13.8 .. .. .. 35.7 .. 50.8 .. 14.2 .. .. .. 32.4 19.1 22.0 24.5 14.5 17.4 15.9 .. 37.1 .. ..

40.5 11.9 15.4 31.9 .. 30.4 34.6 38.5 27.0 .. 23.5 9.2 20.5 .. 23.1 .. 47.1 19.2 13.9 24.9 22.5 66.4 0.4 .. 28.3 34.0 14.1 .. 23.3 17.1 .. 23.5 .. 33.1 29.2 33.1 .. .. 29.2 14.5 41.0 36.1 19.1 13.6 9.7 47.2 .. 14.0 .. .. 19.0 17.2 14.6 30.1 34.7 .. 47.2

45.3 16.2 15.7 24.7 .. 43.4 40.6 44.0 41.5 .. 28.6 16.9 21.7 .. 21.9 .. 21.9 19.3 11.3 34.8 29.5 52.1 0.3 .. 38.8 31.3 11.7 .. 22.7 14.6 .. 21.6 .. 38.3 28.8 27.9 .. .. 24.1 .. 45.6 32.1 20.9 11.8 7.2 35.9 .. 16.8 .. .. 17.1 17.1 18.6 35.8 43.2 .. 19.3

Cash surplus or deficit

% of GDP 1995 2009

–9.1 –2.2 1.7 1.1 .. –2.2 .. –7.5 .. .. 0.9 –1.8 –5.1 .. 2.4 .. –9.9 –10.8 .. –2.7 .. 5.8 .. .. .. .. .. .. 1.5 .. .. .. –0.6 –6.3 2.9 .. .. .. –5.0 .. –9.2 .. 0.6 .. .. .. –8.9 –5.3 1.5 –0.5 0.2 –1.3 –0.8 .. –5.1 .. ..

–4.0 –4.9 –1.7 0.6 .. –13.9 –4.3 –4.9 –15.9 .. –8.5 –2.0 –5.5 .. 0.0 .. 20.0 –1.4 –1.6 –6.4 –8.3 5.8 0.0 .. –9.0 –0.8 –1.9 .. –6.4 –2.1 .. 0.6 .. –5.7 –4.5 1.0 .. .. 2.0 .. –4.8 3.1 –2.3 –0.9 –1.7 10.7 .. –4.8 .. .. 0.1 –1.5 –3.9 –6.1 –8.7 .. 15.2

Net incurrence of liabilities

% of GDP

Domestic 1995 2009

1995

17.0 5.1 .. .. .. .. .. .. .. .. –2.5 0.8 3.9 .. –0.3 .. .. .. .. 2.4 .. 0.0 .. .. .. .. .. .. .. .. .. .. .. 3.0 1.6 .. .. .. .. 0.6 .. .. .. .. .. .. –0.1 .. .. 1.5 0.0 .. –0.5 .. –1.2 .. ..

0.2 0.0 –0.4 0.1 .. .. .. .. .. .. 6.1 2.8 –1.3 .. –0.1 .. .. .. .. 1.5 .. 7.2 .. .. .. .. .. .. .. .. .. .. 5.5 2.7 1.3 .. .. .. .. 2.5 .. .. 3.4 .. .. .. 0.0 .. .. –0.7 –0.8 3.9 –0.7 .. 4.2 .. ..

–1.9 5.6 0.9 1.4 .. .. .. .. 7.4 .. 7.6 2.8 3.0 .. 5.4 .. .. 0.5 –0.3 –2.7 11.8 –0.4 0.0 .. 1.9 –0.6 0.6 .. 6.5 –4.4 .. 3.1 .. 2.7 8.6 0.1 .. .. –0.8 3.2 .. .. .. –1.9 0.1 6.3 .. .. .. .. 1.3 0.2 1.2 1.6 3.4 .. ..

4.12 Debt and interest payments

Foreign 2009

5.8 0.2 0.4 0.0 .. .. .. .. 4.7 .. 1.2 0.5 0.1 .. –0.1 .. .. 7.7 2.1 15.1 0.3 1.6 0.0 .. 9.1 0.2 3.0 .. 0.9 2.6 .. 1.3 .. 3.3 5.2 1.7 .. .. –0.1 0.0 .. .. .. 2.4 .. –15.3 .. .. .. .. 0.1 1.1 2.0 3.6 5.9 .. ..

Total debt % of GDP 2009

Interest % of revenue 2009

81.7 53.0 28.3 .. .. 69.2 .. 118.9 115.8 157.7 57.9 9.5 .. .. .. .. .. .. .. 41.8 .. .. .. .. 33.3 .. .. .. 53.3 .. .. 38.9 .. 24.4 64.8 46.9 .. .. .. 43.7 58.3 37.9 .. .. 3.0 36.3 .. .. .. .. .. 23.6 .. 48.1 84.4 .. ..

10.6 28.5 10.9 0.6 .. 6.9 9.7 11.1 64.5 .. 8.7 2.5 10.4 .. 4.7 .. 0.0 3.3 3.2 3.8 48.7 1.3 2.1 .. 4.0 1.9 3.9 .. 9.0 1.7 .. 12.0 .. 4.0 1.6 3.1 .. .. 6.3 4.9 4.6 3.4 6.4 1.8 6.6 2.1 .. 41.7 .. .. 3.1 7.2 25.7 8.1 7.7 .. 2.1

2011 World Development Indicators

239

economy

Central government finances

4.12

Central government finances Revenuea

Expense

Cash surplus or deficit

Net incurrence of liabilities

Debt and interest payments

Total debt % of GDP

Interest % of revenue

2009

0.9 –0.2 .. .. .. 1.2 .. .. 3.0 –1.2 .. 1.0 4.8 –0.1 .. .. .. .. .. .. .. 0.0 .. –0.5 0.5 0.0 0.6 .. 1.8 4.9 .. .. 4.7 2.4 .. .. .. .. .. .. .. .. m .. 0.2 .. 0.9 .. 2.0 3.3 –0.2 0.1 0.3 .. .. 0.4

.. 8.6 .. .. .. .. .. 113.3 38.1 .. .. .. 46.5 85.0 .. .. 44.0 28.9 .. .. .. 28.6 .. .. 14.1 47.1 51.4 .. 32.7 .. .. 73.2 67.1 49.5 .. .. .. .. .. .. .. .. m .. .. .. .. .. .. .. .. .. 56.5 .. 55.7 69.9

% of GDP % of GDP

Romania Russian Federation Rwandab Saudi Arabia Senegalb Serbiab Sierra Leoneb Singaporeb Slovak Republic Sloveniab Somalia South Africa 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, RB b 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

% of GDP

1995

2009

1995

2009

.. .. 10.6 .. 15.2 .. 9.4 26.7 .. 35.8 .. .. 32.0 20.4 7.2 .. 38.6 22.6 22.9 9.3 .. .. .. .. 27.2 30.0 .. .. 10.6 .. 10.1 35.2 .. 27.6 .. 16.9 .. .. 17.3 20.0 26.7 .. w .. 14.6 10.7 .. 14.6 7.2 .. 21.2 .. 13.1 .. .. 34.9

30.9 35.4 .. .. .. 36.3 11.6 18.2 28.5 37.5 .. 28.2 22.4 14.9 .. .. 34.7 18.4 .. .. .. 18.6 .. 18.8 36.1 31.4 21.8 .. 12.4 34.5 .. 35.9 15.9 29.4 .. .. .. .. .. 17.6 .. 24.3 w .. 20.0 14.7 .. 19.8 13.4 29.3 .. 30.6 12.1 24.5 24.7 34.5

.. .. 15.0 .. .. .. .. 12.4 .. 34.3 .. .. 37.1 26.0 6.8 .. .. 25.7 .. 11.4 .. .. .. .. 25.3 28.4 .. .. .. .. 11.0 40.4 .. 27.1 .. 18.5 .. .. 19.1 21.4 32.1 .. w .. .. .. .. .. .. .. 23.3 .. 15.3 .. .. 42.3

33.8 30.9 .. .. .. 37.7 22.5 15.2 37.6 42.7 .. 33.0 30.7 19.2 .. .. .. 17.0 .. .. .. 19.6 .. 17.4 28.4 29.9 27.3 .. 13.7 40.6 .. 46.4 26.3 29.3 .. .. .. .. .. 22.9 .. 31.1 w .. .. .. .. .. .. 30.1 .. 27.3 16.0 24.2 32.2 39.8

% of GDP 1995

2009

.. .. –5.6 .. .. .. .. 19.8 .. –0.1 .. .. –5.8 –7.6 –0.4 .. .. –0.6 .. –3.3 .. .. .. .. –0.1 –2.4 .. .. .. .. 0.5 –5.5 .. –1.2 .. –2.3 .. .. –3.9 –3.1 –5.4 .. w .. .. .. .. .. .. .. –1.4 .. –2.7 .. .. –7.4

–4.6 5.3 .. .. .. –2.6 –3.1 1.7 –7.3 –5.5 .. –4.9 –8.6 –6.6 .. .. .. 1.3 .. .. .. –3.0 .. –0.6 2.3 –1.7 –5.5 .. –0.9 –5.6 .. –10.9 –10.4 –1.5 .. .. .. .. .. –0.8 .. –7.1 w .. .. .. .. .. .. –0.4 .. –3.0 –4.6 –1.0 –7.7 –5.2

a. Excludes grants. b. Data were reported on a cash basis and have been adjusted to the accrual framework.

240

2011 World Development Indicators

Foreign

Domestic 1995 2009

1995

.. .. 2.9 .. .. .. 0.3 10.3 .. –0.4 .. .. .. 5.2 0.3 .. .. –0.5 .. 0.1 .. .. .. .. 2.8 0.9 .. .. .. .. .. .. .. 7.9 .. 1.1 .. .. .. 28.0 –1.4 .. m .. .. .. .. .. .. .. .. .. 3.8 .. .. ..

.. .. .. .. .. .. .. 0.0 .. 0.3 .. .. .. 3.2 .. .. .. .. .. 2.3 .. .. .. .. 2.6 2.9 .. .. .. .. .. .. .. 1.1 .. 0.1 .. .. .. 16.2 1.6 .. m .. .. .. .. .. .. .. .. .. 1.1 .. .. ..

2.4 0.8 .. .. .. 2.8 .. 13.7 2.9 12.4 .. 7.0 6.4 6.9 .. .. .. 2.0 .. .. .. 5.3 .. 2.7 –0.6 0.3 6.1 .. 1.5 6.7 .. .. 6.5 3.8 .. .. .. .. .. .. .. .. m .. 0.9 0.5 3.7 0.6 1.2 1.9 0.1 6.7 3.2 .. .. 0.8

2.0 1.3 .. .. .. 2.0 8.3 0.1 4.7 2.9 .. 8.4 6.1 31.0 .. .. .. 3.5 .. .. .. 5.8 .. 4.0 5.0 7.0 24.1 .. 7.7 3.1 .. 5.3 11.4 9.3 .. .. .. 1.1 .. 7.2 .. 5.4 m .. 7.0 6.0 7.2 4.5 5.8 2.5 9.1 7.0 21.7 .. 5.3 6.1

About the data

4.12

Definitions

Tables 4.12–4.14 present an overview of the size and

borrowing for temporary periods can also be used.

• Revenue is cash receipts from taxes, social con-

role of central governments relative to national econo-

Government excludes public corporations and quasi

tributions, and other revenues such as ﬁnes, fees,

mies. The tables are based on the concepts and recom-

corporations (such as the central bank).

rent, and income from property or sales. Grants, usu-

mendations of the second edition of the International

Units of government at many levels meet this defini-

ally considered revenue, are excluded. • Expense is

Monetary Fund’s (IMF) Government Finance Statistics

tion, from local administrative units to the national

cash payments for government operating activities in

Manual 2001. Before 2005 World Development Indica-

government, but inadequate statistical coverage pre-

providing goods and services. It includes compensa-

tors reported data derived on the basis of the 1986

cludes presenting subnational data. Although data for

tion of employees, interest and subsidies, grants,

manual’s cash-based method. The 2001 manual,

general government under the 2001 manual are avail-

social benefits, and other expenses such as rent

harmonized with the 1993 United Nations System of

able for a few countries, only data for the central gov-

and dividends. • Cash surplus or deficit is revenue

National Accounts, recommends an accrual account-

ernment are shown to minimize disparities. Still, differ-

(including grants) minus expense, minus net acquisi-

ing method, focusing on all economic events affecting

ent accounting concepts of central government make

tion of nonfinancial assets. In editions before 2005

assets, liabilities, revenues, and expenses, not only

cross-country comparisons potentially misleading.

nonfinancial assets were included under revenue

those represented by cash transactions. It takes all

Central government can refer to consolidated or bud-

and expenditure in gross terms. This cash surplus

stocks into account, so that stock data at the end of an

getary accounting. For most countries central govern-

or deficit is close to the earlier overall budget balance

accounting period equal stock data at the beginning of

ment finance data have been consolidated into one

(still missing is lending minus repayments, which are

the period plus flows over the period. The 1986 manual

account, but for others only budgetary central gov-

included as a financing item under net acquisition

considered only the debt stock data. Further, the new

ernment accounts are available. Countries reporting

of financial assets). • Net incurrence of liabilities

manual no longer distinguishes between current and

budgetary data are noted in Primary data documenta-

is domestic financing (obtained from residents) and

capital revenue or expenditures, and it introduces the

tion. Because budgetary accounts may not include

foreign financing (obtained from nonresidents), or

concepts of nonfinancial and financial assets. Most

all central government units (such as social security

the means by which a government provides financial

countries still follow the 1986 manual, however. The

funds), they usually provide an incomplete picture.

resources to cover a budget deficit or allocates finan-

IMF has reclassified historical Government Finance Sta-

Data on government revenue and expense are col-

cial resources arising from a budget surplus. The net

tistics Yearbook data to conform to the 2001 manual’s

lected by the IMF through questionnaires to member

incurrence of liabilities should be offset by the net

format. Because of reporting differences, the reclassi-

countries and by the Organisation for Economic Co-

acquisition of financial assets (a third financing item).

fied data understate both revenue and expense.

operation and Development. Despite IMF efforts to

The difference between the cash surplus or deficit

The 2001 manual describes government’s eco-

standardize data collection, statistics are often incom-

and the three financing items is the net change in

nomic functions as the provision of goods and ser-

plete, untimely, and not comparable across countries.

the stock of cash. • Total debt is the entire stock of

vices on a nonmarket basis for collective or individual

Government finance statistics are reported in local

direct government fixed-term contractual obligations

consumption, and the redistribution of income and

currency. The indicators here are shown as percent-

to others outstanding on a particular date. It includes

wealth through transfer payments. Government

ages of GDP. Many countries report government

domestic and foreign liabilities such as currency and

activities are financed mainly by taxation and other

finance data by fiscal year; see Primary data docu-

money deposits, securities other than shares, and

income transfers, though other financing such as

mentation for information on fiscal year end by country.

loans. It is the gross amount of government liabilities reduced by the amount of equity and financial

Twenty selected economies had a central government debt to GDP ratio of 65 percent or higher

derivatives held by the government. Because debt

4.12a

Central government debt, 2009 (percent of GDP) 160

is a stock rather than a flow, it is measured as of a given date, usually the last day of the ﬁscal year. • Interest payments are interest payments on government debt—including long-term bonds, long-term loans, and other debt instruments—to domestic and

120

foreign residents.

Data sources Data on central government finances are from the

IMF’s Government Finance Statistics database. Each country’s accounts are reported using the system of common definitions and classifications Gr

Ja pa n ee ce Ita Ja ly m ai Si c ng a ap or e Ic el an d St . K Cy pr itt u s s & Ne Ba vis rb ad o Be s lg iu m Sr iL an ka Po r tu ga l Fr an c Hu e ng ar Eg y yp t, Mal A t a Un ra ite b R e d Ki p. ng do m Au st ria Un Ire ite lan d d St at es

Note: Data are for the most recent year for 2005–2009. Source: International Monetary Fund, Government Finance Statistics data files, and World Development Indicators data files.

in the IMF’s Government Finance Statistics Manual 2001. See these sources for complete and authoritative explanations of concepts, definitions, and data sources.

2011 World Development Indicators

241

economy

Central government finances

4.13 Afghanistana Albaniaa Algeria Angola Argentina Armeniaa Australia Austria Azerbaijana 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 Czech Republica Denmark Dominican Republic Ecuadora Egypt, Arab Rep.a El Salvador Eritrea Estonia Ethiopiaa Finland France Gabon Gambia, Thea Georgiaa Germany Ghanaa Greece Guatemalaa Guineaa Guinea-Bissau Haiti Honduras

242

Central government expenses Goods and services

Compensation of employees

Interest payments

Subsidies and other transfers

Other expense

% of expense 1995 2009

.. 18 .. .. .. .. .. 5 .. .. 39 3 .. .. .. 32 5 18 .. 20 .. 17 8 .. .. .. .. .. .. 37 7 .. .. 35 .. 7 8 .. 6 18 .. .. 21 35 8 8 .. .. 52 4 .. 10 15 17 .. .. ..

2011 World Development Indicators

72 .. 11 .. .. 13 10 6 9 12 12 3 18 14 23 .. 13 9 19 .. 32 .. 8 .. .. 10 .. 26 6 .. .. 11 29 8 .. 6 9 15 .. 8 15 .. 13 .. 10 6 .. .. 19 5 16 12 15 .. .. .. 17

.. 14 .. .. .. .. .. 14 .. .. 5 7 .. .. .. 30 8 7 .. 30 .. 40 10 .. .. .. .. .. .. 58 35 .. .. 27 .. 9 12 .. 49 22 .. .. 23 40 9 23 .. .. 11 5 .. 21 50 34 .. .. ..

23 .. 34 .. .. 25 10 14 12 19 11 7 47 22 28 .. 19 19 46 .. 43 .. 12 .. .. 20 .. 22 16 .. .. 46 38 26 .. 8 13 31 .. 25 36 .. 21 .. 10 21 .. .. 17 5 40 24 29 .. .. .. 54

.. 9 .. .. .. .. .. 9 .. .. 1 18 .. .. .. 2 45 37 .. 6 .. 26 24 .. .. .. .. .. .. 1 47 .. .. 3 .. 3 14 .. 26 26 .. .. 1 15 8 6 .. .. 10 6 .. 25 12 28 .. .. ..

0 .. 1 .. .. 2 3 7 1 22 2 8 3 10 1 .. 19 2 3 .. 2 .. 9 .. .. 2 .. 0 17 .. .. 8 9 5 .. 3 5 10 .. 14 10 .. 1 .. 4 5 .. .. 3 5 16 10 11 .. .. .. 3

.. 59 .. .. .. .. .. 68 .. .. 55 71 .. .. .. 36 45 38 .. 14 .. 14 57 .. .. .. .. .. .. 2 10 .. .. 32 .. 75 59 .. .. 6 .. .. 39 18 68 59 .. .. 26 67 .. 38 18 9 .. .. ..

4 .. 45 .. .. 37 73 71 18 35 70 53 30 47 44 .. 49 64 11 .. 21 .. 69 .. .. 51 .. 17 47 .. .. 21 16 56 .. 72 17 39 .. 45 22 .. 48 .. 71 54 .. .. 49 81 28 50 33 .. .. .. 7

.. 0 .. .. .. .. .. 6 .. .. 0 2 .. .. .. 2 1 2 .. 10 .. .. 3 .. .. .. .. .. .. .. .. .. .. 3 .. 5 10 .. .. .. .. .. 4 0 11 6 .. .. .. 20 .. 8 6 1 .. .. ..

0 .. 8 .. .. 23 6 5 61 12 6 0 2 7 4 .. 0 6 21 .. 2 .. 3 .. .. 19 .. 38 15 .. .. 14 7 5 .. 11 2 5 .. 9 18 .. 4 .. 8 2 .. .. 12 4 12 7 12 .. .. .. 19

Hungary Indiaa Indonesiaa Iran, Islamic Rep.a Iraq Ireland Israel Italy Jamaica Japan Jordana Kazakhstana Kenyaa Korea, Dem. Rep. Korea, Rep.a Kosovo Kuwaita Kyrgyz Republica Lao PDR Latviaa Lebanon Lesothoa Liberiaa Libya Lithuania Macedonia, FYRa Madagascar Malawi Malaysiaa Mali Mauritania Mauritius Mexicoa Moldovaa Mongoliaa Moroccoa Mozambique Myanmara Namibiaa Nepala Netherlands New Zealand Nicaraguaa Niger Nigeriaa Norway Omana Pakistana Panamaa Papua New Guineaa Paraguaya Perua Philippinesa Poland Portugal Puerto Rico Qatara

4.13

Goods and services

Compensation of employees

Interest payments

Subsidies and other transfers

Other expense

% of expense 1995 2009

8 14 22 21 .. 5 .. 4 .. .. 7 .. 15 .. 16 .. 34 32 .. 20 .. 32 .. .. .. .. .. .. 14 .. .. .. 9 10 30 .. .. .. 28 .. 5 .. 14 .. .. .. 55 .. 16 19 12 20 15 .. 9 .. ..

10 11 9 11 .. 10 27 4 6 .. 11 19 20 .. 11 .. 10 30 27 8 3 42 37 .. 10 28 15 .. 17 31 .. 12 .. 19 20 9 .. .. 20 .. 8 30 13 30 15 11 .. 22 .. .. 9 20 28 5 7 .. 25

10 10 20 56 .. 15 .. 14 .. .. 67 .. 28 .. 15 .. 33 36 .. 20 .. 45 .. .. .. .. .. .. 34 .. .. .. 19 8 12 .. .. .. 53 .. 8 .. 25 .. .. .. 30 .. 45 36 51 19 34 .. 30 .. ..

13 10 14 40 .. 23 25 15 14 .. 50 8 37 .. 10 .. 16 29 49 15 21 35 36 .. 16 17 40 .. 28 34 .. 34 .. 15 33 48 .. .. 45 .. 7 25 39 30 24 16 .. 4 .. .. 50 18 30 12 24 .. 32

17 27 17 0 .. 14 .. 24 .. .. 11 3 46 .. 3 .. 5 5 .. 3 .. 5 .. .. .. .. .. .. 14 .. .. .. 19 11 2 .. .. .. 1 .. 9 .. 17 .. .. .. 7 28 8 20 5 19 33 .. 15 .. ..

10 21 11 1 .. 5 9 10 43 .. 8 2 10 .. 5 .. 0 4 5 3 38 2 2 .. 3 2 7 .. 9 2 .. 14 .. 4 2 4 .. .. 8 .. 4 4 7 3 9 3 .. 35 .. .. 4 7 20 7 6 .. 5

56 33 40 .. .. 33 .. 54 .. .. 12 58 .. .. 63 .. 21 27 .. 56 .. 8 .. .. .. .. .. .. 36 .. .. .. .. 71 56 .. .. .. .. .. 77 .. 29 .. .. .. 8 2 30 26 31 33 15 .. 43 .. ..

63 51 54 34 .. 40 32 66 6 .. 30 69 31 .. 57 .. 58 34 10 70 36 14 24 .. 68 49 25 .. 46 15 .. 31 .. 56 45 27 .. .. 13 .. 79 38 36 9 53 67 .. 21 .. .. 29 47 20 71 51 .. 21

13 0 2 .. .. 1 .. 6 .. .. 4 .. 2 .. 3 .. 7 .. .. 0 .. 3 .. .. .. .. .. .. 1 .. .. .. .. 1 0 .. .. .. 4 .. 3 .. 14 .. .. .. 0 .. 1 1 0 8 .. .. 7 .. ..

2011 World Development Indicators

8 7 12 14 .. 1 9 6 31 .. 2 2 1 .. 17 .. 15 3 10 4 2 6 .. .. 6 4 14 .. 0 17 .. 10 .. 6 1 13 .. .. 14 .. 4 7 5 28 .. 5 .. 18 .. .. 9 7 2 7 1 .. 16

243

economy

Central government expenses

4.13 Romania Russian Federation Rwandaa Saudi Arabia Senegala Serbiaa Sierra Leonea Singaporea Slovak Republic Sloveniaa Somalia South Africa 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 1995 2009

.. .. 52 .. .. .. .. 38 .. 19 .. .. 5 23 44 .. .. 24 .. 47 .. .. .. .. 20 7 .. .. .. .. 48 14 .. 13 .. 6 .. .. 8 32 16 .. m .. .. .. .. .. .. .. .. .. .. .. 10 5

13 12 .. .. .. 13 24 36 7 13 .. 13 4 14 .. .. .. 6 .. .. .. 31 .. 24 14 7 10 .. 31 12 .. 18 15 12 .. .. .. 12 .. 32 .. 12 m .. 12 15 12 15 27 13 13 9 17 .. 9 7

.. .. 36 .. .. .. .. 39 .. 21 .. .. 14 20 38 .. .. 6 .. 8 .. .. .. .. 36 37 .. .. .. .. 33 15 .. 17 .. 22 .. .. 67 35 34 .. m .. .. .. .. .. .. .. .. .. .. .. 15 14

19 16 .. .. .. 26 28 27 12 20 .. 13 8 28 .. .. .. 6 .. .. .. 36 .. 40 21 36 23 .. 14 13 .. 14 12 25 .. .. .. 67 .. 30 .. 21 m .. 25 31 20 27 33 17 27 36 14 .. 14 15

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.

244

2011 World Development Indicators

.. .. 12 .. .. .. .. 8 .. 3 .. .. 11 22 8 .. .. 4 .. 12 .. .. .. .. 20 13 .. .. .. .. .. 9 .. 6 .. 27 .. .. 16 16 31 .. m .. .. .. .. .. .. .. .. .. 27 .. 9 10

2 1 .. .. .. 2 7 0 4 3 .. 7 5 25 .. .. .. 4 .. .. .. 5 .. 5 6 7 20 .. 9 3 .. 4 7 9 .. .. .. 1 .. 7 .. 5m .. 7 7 7 6 5 2 9 7 21 .. 5 5

.. .. 5 .. .. .. .. 15 .. 55 .. .. 42 24 10 .. .. 66 .. 33 .. .. .. .. 24 36 .. .. .. .. .. 57 .. 64 .. 61 .. .. 8 19 19 .. m .. .. .. .. .. .. .. .. .. 24 .. 56 55

60 68 .. .. .. 58 23 0 68 62 .. 63 80 23 .. .. .. 83 .. .. .. 28 .. 18 38 38 44 .. 45 70 .. 53 62 47 .. .. .. 18 .. 24 .. 46 m .. 45 36 47 37 28 58 35 36 28 .. 62 62

.. .. .. .. .. .. .. .. .. 3 .. .. 2 10 .. .. .. 0 .. .. .. .. .. .. 1 7 .. .. .. .. .. 8 .. 0 .. 2 .. .. 0 0 .. .. m .. .. .. .. .. .. .. .. .. .. .. 4 5

8 10 .. .. .. 1 18 .. 14 3 .. 4 5 10 .. .. .. 3 .. .. .. 3 .. 13 21 13 5 .. 1 2 .. 12 6 7 .. .. .. 1 .. 7 .. 6m .. 7 8 6 7 2 6 13 9 10 .. 5 4

About the data

4.13

Definitions

The term expense has replaced expenditure in the

to households are shown as subsidies and other

• Goods and services are all government payments

table since the 2005 edition of World Development

transfers, and other expenses. The economic clas-

in exchange for goods and services used for the

Indicators in accordance with use in the International

sification can be problematic. For example, subsidies

production of market and nonmarket goods and ser-

Monetary Fund’s (IMF) Government Finance Statis-

to public corporations or banks may be disguised

vices. Own-account capital formation is excluded.

tics Manual 2001. Government expenses include all

as capital financing or hidden in special contractual

• Compensation of employees is all payments in

nonrepayable payments, whether current or capital,

pricing for goods and services. For further discussion

cash, as well as in kind (such as food and hous-

requited or unrequited. The concept of total central

of government finance statistics, see About the data

ing), to employees in return for services rendered,

government expense as presented in the IMF’s Gov-

for tables 4.12 and 4.14.

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

miscellaneous expenses, including provision for con-

the table shows whether the government produces

sumption 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 Interest payments are a large part of government expenses for some developing economies

4.13a

Central government interest payments as a share of total expense, 2009 (percent) 50 40 30 20

Data sources

Data on central government expenses are from the s

rit

a bi

an Gh

az il Br

lo Co

ke y Tu r

in es Se yc he lle s

di a

ili

ka an

la d ng

& s itt

.K St

Ne vis

n no

is ta Pa k

ba Le

Interest payments accounted for more than 14 percent of total expenses in 2009 for 15 countries.

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

Source: International Monetary Fund, Government Finance Statistics data files.

data sources.

2011 World Development Indicators

245

economy

Central government expenses

4.14

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

% of revenue 1995 2009

1995

Afghanistana Albaniaa Algeria Angola Argentina Armeniaa Australia Austria Azerbaijana 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 Czech Republica Denmark Dominican Republic Ecuadora Egypt, Arab Rep.a El Salvador Eritrea Estonia Ethiopiaa Finland France Gabon Gambia, Thea Georgiaa Germany Ghanaa Greece Guatemalaa Guineaa Guinea-Bissau Haiti Honduras

246

2009

.. 8 .. .. .. .. .. 21 .. .. 16 36 .. .. .. 21 14 17 .. 14 .. 17 48 .. .. .. 9 .. .. 21 6 .. .. 11 .. 15 37 .. 50 17 .. .. 18 19 16 17 .. .. 7 16 15 17 19 8 .. .. ..

2011 World Development Indicators

4 .. 60 .. .. 20 65 23 33 19 6 34 17 10 5 .. 30 16 14 .. 11 .. 55 .. .. 28 26 44 26 .. .. 17 15 10 .. 15 45 22 .. 28 27 .. 8 .. 20 22 .. .. 32 16 23 21 29 .. .. .. 20

.. 39 .. .. .. .. .. 22 .. .. 33 23 .. .. .. 4 24 28 .. 30 .. 25 18 .. .. .. 61 .. .. 12 21 .. .. 42 .. 32 40 .. 26 13 .. .. 35 13 31 25 .. .. 48 20 31 32 46 4 .. .. ..

3 .. 28 .. .. 41 23 23 23 29 29 24 39 43 43 .. 33 45 37 .. 36 .. 15 .. .. 46 55 9 32 .. .. 32 20 43 .. 27 36 54 .. 22 39 .. 39 .. 32 23 .. .. 51 24 29 29 56 .. .. .. 39

.. 14 .. .. .. .. .. 0 .. .. 6 .. .. .. .. 15 2 8 .. 20 .. 28 3 .. .. .. 7 .. .. 21 18 .. .. 9 .. 4 .. .. 11 10 .. .. 0 27 0 0 .. .. 10 .. 24 0 23 62 .. .. ..

5 .. 4 .. .. 3 2 0 4 24 16 .. 18 3 0 .. 2 1 12 .. 16 .. 1 .. .. 1 5 0 5 .. .. 4 33 2 .. 0 .. 10 .. 5 5 .. .. .. .. 0 .. .. 1 .. 16 0 7 .. .. .. 3

.. 1 .. .. .. .. .. 5 .. .. 11 2 .. .. .. 0 4 3 .. 1 .. 3 .. .. .. .. 0 .. .. 5 1 .. .. 1 .. 1 8 .. 1 10 .. .. 0 3 1 3 .. .. .. 0 .. 3 3 2 .. .. ..

0 .. 1 .. .. 8 0 5 1 3 3 0 6 9 2 .. 2 0 2 .. 0 .. .. .. .. 2 3 13 5 .. .. 3 8 2 .. 1 5 5 .. 2 0 .. .. .. 2 4 .. .. 1 .. .. 3 2 .. .. .. 1

.. 15 .. .. .. .. .. 43 .. .. 31 36 .. .. .. .. 31 21 .. 5 .. 2 21 .. .. .. .. .. .. 1 .. .. .. 33 .. 40 4 .. .. 10 .. .. 34 1 34 47 .. .. 13 58 .. 31 2 1 .. .. ..

0 .. .. .. .. 14 .. 42 .. .. 33 37 2 7 39 .. 26 23 .. .. .. .. 24 .. .. 7 .. 0 6 .. .. 34 6 35 .. 45 3 2 .. .. 12 .. 36 .. 31 45 .. .. 17 55 .. 36 3 .. .. .. 13

.. 22 .. .. .. .. .. 9 .. .. 3 3 .. .. .. 59 26 23 .. 30 .. 25 10 .. .. .. 22 .. .. 41 54 .. .. 4 .. 8 11 .. 12 41 .. .. .. 36 17 8 .. .. 22 6 9 16 6 23 .. .. ..

88 .. 6 .. .. 14 10 7 39 24 13 3 18 28 11 .. 6 16 36 .. 37 .. 8 .. .. 16 12 34 25 .. .. 10 18 9 .. 12 .. 8 .. 43 17 .. .. .. 15 6 .. .. 15 4 32 12 4 .. .. .. 23

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

% of revenue 1995 2009

1995

Hungary Indiaa Indonesiaa Iran, Islamic Rep.a Iraq Ireland Israel Italy Jamaica Japan Jordana Kazakhstana Kenyaa Korea, Dem. Rep. Korea, Rep.a Kosovo Kuwaita 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

16 23 52 12 .. 37 .. 32 .. .. 10 11 35 .. 31 .. 1 26 .. 7 .. 15 .. .. .. .. .. .. 38 .. .. .. 27 6 31 .. .. 20 27 10 26 .. 9 .. .. .. 21 18 20 40 15 15 33 .. 23 .. ..

2009

23 47 37 19 .. 33 26 32 25 .. 17 24 40 .. 28 .. 1 12 21 8 15 17 28 .. 10 13 12 .. 46 19 .. 23 .. 1 21 28 .. .. 28 14 26 57 29 12 1 28 .. 25 .. .. 16 30 39 14 23 .. 40

28 28 32 5 .. .. .. 21 .. .. 23 28 40 .. 32 .. 0 56 .. 41 .. 12 .. .. .. .. .. .. 27 .. .. .. 54 38 18 .. .. 26 32 33 24 .. 52 .. .. .. 1 27 17 8 36 46 26 .. 33 .. ..

32 23 31 3 .. .. 31 20 37 .. 38 20 41 .. 26 .. .. 42 46 35 44 12 15 .. 36 40 15 .. 16 29 .. 46 .. 46 30 31 .. .. 19 35 27 26 49 18 2 24 .. 32 .. .. 43 39 29 37 31 .. ..

10 24 5 9 .. 0 .. .. .. .. 22 3 14 .. 7 .. 2 5 .. 3 .. 49 .. .. .. .. .. .. 12 .. .. .. 4 5 9 .. .. 12 28 26 .. .. 7 .. .. .. 3 24 11 27 18 10 29 .. 0 .. ..

0 13 2 6 .. 0 1 .. 7 .. 6 6 10 .. 4 .. 1 9 9 0 6 57 39 .. .. 5 31 .. 2 10 .. 2 .. 4 6 6 .. .. 44 16 .. 3 4 26 .. 0 .. 8 .. .. 7 2 20 0 0 .. 2

1 0 1 1 .. 2 .. 5 .. .. 9 5 1 .. 10 .. 0 1 .. 0 .. 1 .. .. .. .. .. .. 6 .. .. .. 2 1 0 .. .. .. 2 4 2 .. 0 .. .. .. 2 7 3 2 4 8 4 .. 2 .. ..

1 0 4 1 .. 2 5 7 10 .. 3 0 1 .. 9 .. 0 .. 1 0 10 3 1 .. 0 0 6 .. 3 10 .. 7 .. 0 0 5 .. .. 1 5 2 0 0 3 .. 1 .. 0 .. .. 1 6 .. 1 2 .. ..

35 0 .. 6 .. 17 .. 35 .. .. .. 48 0 .. 8 .. .. .. .. 35 .. .. .. .. .. .. .. .. .. .. .. .. 14 38 15 .. .. .. .. .. 40 .. .. .. .. .. .. .. 16 0 6 10 .. .. 30 .. ..

32 0 .. 19 .. 22 17 36 3 .. 0 .. .. .. 16 .. .. .. .. 31 1 .. .. .. 42 29 4 .. .. .. .. 4 .. 33 17 12 .. .. 0 .. 35 0 .. .. .. 21 .. .. .. .. 7 10 .. 37 33 .. ..

9 25 10 66 .. .. .. 6 .. .. 36 6 10 .. 12 .. 97 11 .. 13 .. 24 .. .. .. .. .. .. 17 .. .. .. 16 2 27 .. .. 42 11 27 8 .. 31 .. .. .. 74 24 34 23 22 11 8 .. .. .. ..

2011 World Development Indicators

12 18 26 52 .. .. 19 5 18 .. 36 51 8 .. 17 .. 98 37 22 26 23 11 18 .. 13 13 32 .. 33 31 .. 17 .. 16 26 17 .. .. 7 29 10 15 18 41 97 26 .. 35 .. .. 26 13 13 10 .. .. 58

247

economy

Central government revenues

4.14

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

% of revenue 1995 2009

1995

Romania Russian Federation Rwandaa Saudi Arabia Senegala Serbiaa Sierra Leonea Singaporea Slovak Republic Sloveniaa Somalia South Africa 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

2009

.. .. 11 .. 17 .. 15 26 .. 13 .. .. 28 12 17 .. 13 11 23 6 .. .. .. .. 50 16 .. .. 10 .. .. 37 .. 10 .. 38 .. .. 17 27 36 .. m .. .. 17 .. .. 32 .. .. 16 15 .. 21 22

22 1 .. .. .. 9 17 36 9 13 .. 53 24 18 .. .. 11 24 .. .. .. 38 .. 17 63 27 26 .. 22 10 .. 36 47 18 .. .. .. 2 .. 33 .. 23 m .. 25 26 23 21 37 10 27 27 19 .. 24 23

.. .. 25 .. 19 .. 34 20 .. 33 .. .. 21 49 41 .. 33 21 37 63 .. .. .. .. 26 20 .. .. 45 .. 15 32 .. 32 .. 33 .. .. 10 22 22 .. m .. .. 27 .. .. 26 .. .. 16 31 .. 28 24

35 16 .. .. .. 43 25 26 33 33 .. 32 13 45 .. .. 37 26 .. .. .. 38 .. 34 13 31 51 .. 47 34 .. 28 3 41 .. .. .. 21 .. 36 .. 32 m .. 36 33 36 36 31 42 39 31 29 .. 27 27

.. .. 23 .. 36 .. 39 1 .. 9 .. .. 0 17 27 .. .. 1 13 12 .. .. .. .. 6 28 .. .. 7 .. .. .. .. 4 .. 9 .. .. 18 36 17 .. m .. .. 16 .. .. 10 .. .. 16 24 .. .. 0

0 18 .. .. .. 5 14 0 0 0 .. 3 .. 14 .. .. .. 6 .. .. .. 5 .. 18 4 6 1 .. 10 2 .. .. 1 3 .. .. .. 11 .. 8 .. 5m .. 5 6 4 7 6 4 4 6 13 .. 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.

248

2011 World Development Indicators

.. .. 3 .. 2 .. 0 15 .. 0 .. .. 0 4 1 .. 4 2 8 0 .. .. .. .. 1 4 .. .. 2 .. .. 6 .. 10 .. 0 .. .. 3 0 3 .. m .. .. 2 .. .. 2 .. .. 6 4 .. 2 2

0 0 .. .. .. 0 .. 14 0 0 .. 2 0 8 .. .. 13 3 .. .. .. 1 .. 3 8 4 5 .. 0 0 .. 7 1 2 .. .. .. 0 .. 0 .. 2m .. 2 1 2 2 1 0 2 3 0 .. 2 2

.. .. 2 .. .. .. .. .. .. 42 .. .. 40 1 .. .. 32 49 0 13 .. .. .. .. 2 15 .. .. .. .. 1 20 .. 31 .. 4 .. .. .. 0 2 .. m .. .. .. .. .. .. .. .. .. .. .. 34 36

33 17 .. .. .. 35 .. .. 43 41 .. 2 58 1 .. .. 25 36 .. .. .. 5 .. .. 4 19 .. .. .. 37 .. 23 43 30 .. .. .. 0 .. .. .. .. m .. .. .. 22 .. .. 29 10 6 0 .. 36 37

.. .. 36 .. 26 .. 12 38 .. 3 .. .. .. 18 14 .. .. 17 19 5 .. .. .. .. 15 17 .. .. 37 .. 84 5 .. 8 .. 19 .. .. 51 15 19 .. m .. .. 23 .. .. 23 .. .. 38 25 .. 10 7

10 48 .. .. .. 7 44 24 15 12 .. 8 4 14 .. .. .. 5 .. .. .. 14 .. 28 9 12 16 .. 22 17 .. 6 6 6 .. .. .. 66 .. 23 .. 17 m .. 17 17 16 18 26 16 17 23 29 .. 12 8

About the data

4.14

Definitions

The International Monetary Fund (IMF) classifies

Direct taxes tend to be progressive, whereas indirect

• Taxes on income, profits, and capital gains are

government revenues as taxes, grants, and property

taxes are proportional.

levied on the actual or presumptive net income

income. Taxes are classified by the base on which

Social security taxes do not reflect compulsory pay-

of individuals, on the profits of corporations and

the tax is levied, grants by the source, and property

ments made by employers to provident funds or other

enterprises, and on capital gains, whether real-

income by type (for example, interest, dividends,

agencies with a like purpose. Similarly, expenditures

ized or not, on land, securities, and other assets.

or rent). The most important source of revenue is

from such funds are not reflected in government

taxes. Grants are unrequited, nonrepayable, non-

expenses (see table 4.13). For further discussion of

compulsory receipts from other government units

taxes and tax policies, see About the data for table

and foreign governments or from international orga-

5.6. For further discussion of government revenues

nizations. Transactions are generally recorded on an

and expenditures, see About the data for tables 4.12

accrual basis.

and 4.13.

Intra-governmental payments are eliminated in consolidation. • Taxes on goods and services include general sales and turnover or value added taxes, selective excises on goods, selective taxes on services, taxes on the use of goods or property, taxes on extraction and production of minerals, and profits of fiscal monopolies. • Taxes on international

The IMF’s Government Finance Statistics Manual

trade include import duties, export duties, profits

2001 describes taxes as compulsory, unrequited

of export or import monopolies, exchange profits,

payments made to governments by individuals, busi-

and exchange taxes. • Other taxes include employer

nesses, or institutions. Taxes are classified in six

payroll or labor taxes, taxes on property, and taxes

major groups by the base on which the tax is levied:

not allocable to other categories, such as penalties

income, profits, and capital gains; payroll and work-

for late payment or nonpayment of taxes. • Social

force; property; goods and services; international

contributions include social security contributions by

trade and transactions; and other. However, the dis-

employees, employers, and self-employed individu-

tinctions are not always clear. Taxes levied on the

als, and other contributions whose source cannot be determined. They also include actual or imputed

income and profits of individuals and corporations

contributions to social insurance schemes operated

are classified as direct taxes, and taxes and duties

by governments. • Grants and other revenue include

levied on goods and services are classified as indi-

grants from other foreign governments, international

rect taxes. This distinction may be a useful simplifica-

organizations, and other government units; interest;

tion, but it has no particular analytical significance

dividends; rent; requited, nonrepayable receipts

except with respect to the capacity to fix tax rates.

for public purposes (such as fines, administrative

4.14a

Rich economies rely more on direct taxes

fees, and entrepreneurial income from government ownership of property); and voluntary, unrequited, nonrepayable receipts other than grants.

Taxes on income and capital gains as a share of central government revenue, 2009 (percent) 70

Data sources

Data on central government revenues are from the 10

IMF’s Government Finance Statistics database. Each country’s accounts are reported using the

0 100

10,000

1,000

100,000

GNI per capita ($, log scale) Low income

Middle income

system of common definitions and classifications in the IMF’s Government Finance Statistics Manual 2001. The IMF receives additional information

High income

from the Organisation for Economic Co-operation

High-income economies tend to tax income and property, whereas low-income economies tend to rely

and Development on the tax revenues of some of

on indirect taxes on international trade and goods and services. But there are exceptions in all groups.

its members. See the IMF sources for complete

Note: Data are for the most recent year for 2005–09. Source: International Monetary Fund, Government Finance Statistics data files, and World Development Indicators data files.

and authoritative explanations of concepts, definitions, and data sources.

2011 World Development Indicators

249

economy

Central government revenues

4.15

Monetary indicators Broad money

annual % growth 2000 2009

Afghanistan Albania Algeria Angolaa Argentinaa Armenia Australiaa Austriab Azerbaijan 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 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 Haiti Honduras

250

.. 12.0 14.1 303.7 1.5 38.6 3.7 .. 73.4 19.3 219.3 .. 26.0 1.6 11.3 1.4 19.7 30.8 6.2 15.5 26.9 19.1 6.6 2.4 19.4 9.1 12.3 9.3 3.6 40.0 58.5 24.0 –1.9 29.1 .. 16.0 –12.1 16.8 47.0 11.6 1.6 17.3 25.7 13.1 .. .. 18.3 34.8 39.2 .. 54.2 .. 21.4 12.9 60.8 20.3 15.4

33.0 6.8 1.6 62.6 17.0 16.4 0.5 .. –0.3 20.3 25.9 .. 8.0 11.8 –0.1 –1.3 15.8 4.2 22.3 14.5 35.6 6.3 15.1 13.3 1.1 1.3 28.4 5.2 8.1 50.4 5.0 8.0 17.2 –0.6 .. 0.2 7.0 13.4 10.1 9.5 2.1 15.7 –0.1 23.4 .. .. 2.1 19.4 8.2 .. 39.2 .. 11.3 .. 6.9 10.3 0.6

2011 World Development Indicators

Claims on domestic economy

Claims on central government

Annual growth % of broad money 2000 2009

.. 0.9 8.4 35.8 –2.9 0.3 13.3 .. –23.9 10.7 59.9 .. 8.5 –1.3 10.3 10.3 8.3 6.5 8.3 15.0 5.4 7.4 3.6 2.9 0.4 4.1 9.5 1.7 8.9 3.8 –23.0 14.1 2.9 21.3 .. –11.0 26.1 13.2 –10.8 4.1 2.6 3.7 .. 3.0 .. .. 6.2 4.2 18.7 .. 7.5 .. 4.2 2.3 5.5 12.3 7.9

8.0 5.4 7.7 33.3 5.1 15.0 8.7 .. 13.2 13.3 64.6 .. 6.7 6.0 –3.8 5.3 6.7 4.2 1.0 8.3 6.3 4.5 23.3 2.8 5.7 –0.6 22.7 3.6 2.7 19.2 5.2 4.9 6.0 –0.6 .. 0.7 –4.4 5.3 5.5 0.5 –4.1 0.2 –9.0 17.7 .. .. –2.6 5.4 –18.1 .. 30.4 .. –2.4 .. 3.9 6.2 7.1

.. 4.8 –11.6 –413.7 –0.8 –5.7 –1.8 .. 15.4 5.6 22.2 .. 0.9 3.1 –0.4 –56.2 13.5 8.5 5.3 –22.6 –6.9 –12.3 2.4 6.8 15.1 4.0 0.0 0.4 6.0 –34.0 –11.7 –0.2 –7.6 2.0 .. 2.6 3.0 2.8 –28.1 7.7 2.3 25.7 –3.2 19.8 .. .. –42.2 2.7 19.8 .. 32.9 .. 10.2 7.9 16.2 13.8 –2.6

–9.5 2.4 0.2 48.1 18.9 –11.8 –2.7 .. 4.3 1.3 –40.9 .. 7.5 –3.0 –0.1 18.7 1.2 2.5 2.7 13.0 5.7 0.9 4.7 –0.3 72.5 0.6 0.6 8.8 7.2 –14.5 12.0 2.8 7.4 0.2 .. 3.9 6.3 8.0 8.8 10.5 –1.3 11.9 –3.6 2.5 .. .. 4.0 5.2 11.0 .. 22.1 .. 6.8 .. –13.3 –12.4 4.8

Interest rate

% Lending

Deposit

Real

2000

2009

2000

2009

2000

2009

.. 8.3 7.5 39.6 8.3 18.1 4.2 2.2 12.9 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 .. 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 12.1 15.9

.. 6.8 1.8 7.6 11.6 8.7 2.8 .. 12.2 8.2 10.7 .. 3.5 3.4 3.6 7.5 9.3 6.2 3.5 .. 1.7 3.3 0.1 3.3 3.3 2.0 2.3 0.0 6.1 15.9 3.3 7.0 3.5 3.2 .. 1.3 .. 7.8 4.8 6.5 .. .. 4.8 4.7 .. 1.9 3.3 15.5 10.3 .. 17.1 .. 5.6 .. 3.5 1.1 10.8

.. 22.1 10.0 103.2 11.1 31.6 9.3 5.6 19.7 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 .. 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 .. 19.1 26.8

15.0 12.7 8.0 15.7 15.7 18.8 6.0 .. 20.0 14.6 11.7 9.2 .. 12.4 7.9 13.8 44.7 11.3 .. 14.1 .. 15.0 2.4 15.0 15.0 7.3 5.3 5.0 13.0 65.4 15.0 19.7 .. 11.6 .. 6.0 .. 18.1 12.1 12.0 .. .. 9.4 8.0 .. .. 15.0 27.0 25.5 .. .. .. 13.8 .. .. 17.3 19.4

.. 17.0 –11.7 –60.8 9.9 33.4 6.5 5.2 6.4 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 .. 5.6 4.9 18.6 26.0 7.9 10.5 .. 2.4 3.8 2.9 5.2 –4.8 19.6 26.8 10.4 .. 8.6 13.2 7.4 .. 7.3 –3.1

36.1 10.1 19.2 22.8 5.2 17.1 1.0 .. 44.2 7.6 7.5 7.1 .. 15.1 7.9 20.6 36.8 7.0 .. 0.4 .. 12.7 4.6 12.2 9.4 2.9 6.0 4.8 7.7 27.0 14.4 9.9 .. 8.0 .. 3.2 .. 14.7 6.3 1.0 .. .. 10.0 –17.2 .. .. 9.3 24.1 28.1 .. .. .. 11.2 .. .. 13.3 14.4

Broad money

annual % growth 2000 2009

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 Madagascar a 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 Puerto Rico Qatar

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 12.2 16.1 9.2 –4.5 41.7 17.6 8.4 38.3 42.5 13.2 18.8 .. 1.5 9.4 12.4 48.1 8.7 6.0 12.1 9.3 5.0 2.8 –0.4 8.1 11.6 .. .. 10.7

3.3 18.0 13.0 27.7 26.7 .. 6.1 .. 5.4 2.1 24.3 19.5 16.5 .. 12.2 11.2 13.4 33.2 18.3 –2.7 19.6 17.7 43.4 17.4 0.6 5.5 11.3 24.6 7.7 14.6 .. 8.1 11.5 3.2 26.9 5.8 32.6 30.6 5.9 29.4 .. –0.6 14.3 18.7 14.4 .. 4.7 14.8 10.3 21.9 22.2 2.6 10.0 8.1 .. .. 16.9

Claims on domestic economy

Claims on central government

Annual growth % of broad money 2000 2009

14.5 9.9 7.2 15.8 .. .. 10.7 .. 9.1 –5.4 3.2 32.2 4.7 .. 21.9 12.1 8.5 3.5 22.4 31.2 2.9 6.6 –10.0 0.2 14.4 2.7 7.9 16.5 5.5 –1.5 41.1 5.8 10.1 24.4 29.6 3.6 11.9 13.9 19.4 –4.6 .. 8.0 7.0 14.8 5.8 18.0 1.1 2.0 –8.4 1.2 1.7 –2.7 2.2 .. .. .. –1.7

–4.2 7.8 6.9 10.2 2.0 .. –0.5 .. 2.6 –2.9 0.8 14.1 11.5 .. 3.9 7.6 7.1 29.2 19.4 –25.9 4.8 7.2 17.1 2.0 –12.9 3.1 3.7 19.3 5.5 7.0 .. 0.8 8.1 –4.0 1.2 9.0 32.7 5.2 11.3 26.4 .. 1.3 –7.4 12.1 17.5 .. 7.3 8.0 2.5 8.4 14.8 0.8 5.4 7.9 .. .. 2.9

–2.0 4.7 17.2 –7.9 .. .. –4.8 .. –2.3 2.6 –1.2 –3.2 –2.1 .. –1.4 –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 –5.0 –64.3 –4.7 3.5 –5.7 –7.1 3.6 6.9 25.0 –4.0 2.6 .. –0.9 10.0 –14.1 –43.0 –4.8 9.5 2.6 0.2 –4.6 4.7 2.3 1.5 –5.8 .. .. –23.1

0.1 9.4 2.5 2.0 33.6 .. 1.1 .. 9.4 4.4 2.5 –4.7 8.2 .. 2.2 1.8 –1.0 –8.8 –3.4 –9.6 4.5 –0.5 47.7 1.8 –4.1 1.3 8.9 21.2 3.3 –13.3 .. 1.1 4.1 4.0 –6.4 –1.1 0.2 29.9 –4.1 –1.6 .. 2.7 7.0 28.9 12.7 .. 1.4 7.4 –0.6 10.1 –3.5 0.3 2.5 1.7 .. .. 26.7

4.15

Interest rate

% Lending

Deposit

Real

2000

2009

2000

2009

2000

2009

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 .. 0.0

5.8 .. 9.3 13.1 7.8 .. 1.1 .. 7.0 0.4 4.9 .. 6.0 .. 3.5 4.0 2.8 3.9 4.7 8.0 7.3 4.9 4.1 2.5 4.8 7.0 11.5 3.5 2.1 3.5 8.0 8.4 2.0 14.9 13.3 3.8 9.5 12.0 6.2 2.5 2.6 4.0 6.0 3.5 13.3 2.3 4.1 8.7 3.5 2.3 1.5 2.8 2.7 2.2 .. .. 4.2

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 9.3 18.1 .. 21.3 8.9 10.1 .. 10.5 17.5 26.8 30.0 10.9 20.0 5.2 .. ..

11.0 12.2 14.5 12.0 15.6 .. 3.7 4.8 16.4 1.7 9.2 .. 14.8 .. 5.6 14.1 6.2 23.0 24.0 16.2 9.6 13.0 14.2 6.0 8.4 10.1 45.0 25.3 5.1 .. 23.5 19.3 7.1 20.5 21.7 .. 15.7 17.0 11.1 8.0 2.0 10.4 14.0 .. 18.4 4.3 7.4 14.5 8.2 10.1 28.3 21.0 8.6 5.5 .. .. 7.0

0.9 8.5 –1.7 .. .. –1.1 11.1 5.0 11.5 3.9 12.2 .. 15.3 .. 3.4 .. –9.7 19.5 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 8.6 14.0 6.3 12.5 –9.0 4.8 0.6 5.9 8.8 .. –12.2 –5.8 –8.3 .. 11.9 3.9 13.1 25.4 4.3 12.0 1.8 .. ..

6.1 4.3 5.6 11.3 61.5 .. –1.4 2.6 9.3 2.7 1.1 .. 7.6 .. 2.2 18.1 2.5 20.5 14.4 17.1 3.5 9.2 6.3 57.8 10.8 7.1 33.8 15.6 12.6 .. 15.1 17.5 2.7 18.1 21.3 .. 12.0 .. 4.4 –3.6 2.3 8.6 –2.0 .. 19.1 8.7 –16.0 –4.6 4.0 14.2 28.4 17.5 5.9 3.9 .. .. 31.0

2011 World Development Indicators

251

economy

Monetary indicators

4.15

Monetary indicators Broad money

annual % growth 2000 2009

Romania Russian Federation Rwandaa Saudi Arabiaa Senegala Serbia Sierra Leonea Singaporea Slovak Republicb Sloveniab Somalia South Africa 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

40.8 57.9 15.6 4.5 10.7 160.8 12.1 –2.0 .. .. .. 7.2 .. 12.9 36.9 –6.6 1.9 –16.9 19.0 63.3 14.8 4.9 41.1 15.2 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

9.0 16.4 .. 10.8 11.4 21.3 27.5 11.3 .. .. .. 1.8 .. 18.7 23.7 26.8 2.5 7.6 8.6 –3.6 17.7 6.8 39.3 16.0 30.6 12.5 12.7 .. 17.5 –5.5 9.8 0.0 –0.6 –2.6 .. 26.1 26.2 .. 12.8 7.7 111.3

Claims on domestic economy

Claims on central government

Annual growth % of broad money 2000 2009

20.0 33.2 10.3 3.3 19.1 –71.0 1.6 5.1 .. .. .. –11.8 .. 9.1 16.9 16.9 8.5 –1.2 –4.1 8.2 12.2 6.2 45.7 0.5 8.8 23.7 16.2 10.8 8.2 30.9 8.7 17.4 5.0 45.1 .. 14.3 29.6 .. 3.6 –11.4 27.2

1.9 2.1 .. 0.0 2.6 18.1 14.2 1.6 .. .. .. 0.1 .. –4.6 13.6 12.5 3.8 5.1 8.6 145.1 5.8 3.6 0.6 9.7 –3.1 9.7 9.4 .. 10.1 –3.4 1.4 –2.6 –1.3 –10.3 .. 18.6 35.0 .. –1.2 –3.4 56.4

–1.1 –18.1 –11.4 –3.5 –3.9 22.5 54.6 –1.6 .. .. .. 0.2 .. 12.5 33.9 1.7 2.4 2.1 –6.1 36.6 0.7 0.5 –36.8 –0.5 –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

10.7 14.0 .. 8.9 4.3 4.9 4.0 8.9 .. .. .. 5.5 .. 4.4 13.0 17.4 1.6 0.6 1.4 –9.8 6.2 0.9 12.1 6.3 25.3 1.4 12.4 .. 0.4 9.4 13.3 7.9 4.5 3.0 .. –1.9 7.0 .. 26.2 16.2 –28.7

Interest rate

% Lending

Deposit

Real

2000

2009

2000

2009

2000

2009

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 .. 14.0 20.2 50.2

12.0 8.6 6.7 .. 3.5 11.8 9.7 0.3 3.7 1.4 .. 8.5 .. 10.6 .. 5.4 .. 0.1 6.4 5.8 8.0 1.0 0.8 3.5 3.4 .. 17.6 .. 9.8 13.8 .. .. .. 4.4 .. 16.4 12.7 .. 10.7 7.1 121.5

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

17.3 15.3 16.5 .. .. 11.8 24.5 5.4 5.8 5.9 .. 11.7 .. 15.7 .. 11.4 .. 2.8 10.0 22.9 15.0 6.0 11.2 .. 11.9 .. .. .. 21.0 20.9 .. 0.6 3.3 15.3 .. 19.9 10.1 .. 18.0 22.1 579.0

6.7 –9.6 20.6 .. .. –40.1 19.0 2.0 5.0 9.9 .. 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 –9.9 4.7 6.9 41.1 .. –3.3 6.9 .. –4.9 6.7 67.8

10.1 12.5 3.3 .. .. 1.6 12.0 7.4 2.8 4.0 .. 4.1 .. 9.5 .. 5.6 .. 2.5 19.0 8.5 7.1 3.9 1.1 .. 32.8 .. .. .. 3.8 6.6 .. –0.7 2.3 8.9 .. 10.6 3.8 .. 23.1 8.3 ..

a. For these countries data reported under Claims on domestic economy include claims on private sector only. b. As members of the European Monetary Union, these countries share a single currency, the euro.

252

2011 World Development Indicators

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

supply of money lie at the heart of a country’s

tives and the net liabilities of the banking system

currency outside banks; demand deposits other

financial system. There are several commonly used

can also be difficult. The quality of commercial bank

than those of the central government; the time,

definitions of the money supply. The narrowest,

reporting also may be adversely affected by delays in

savings, and foreign currency deposits of resident

M1, encompasses currency held by the public and

reports from bank branches, especially in countries

sectors other than the central government; bank

demand deposits with banks. M2 includes M1 plus

where branch accounts are not computerized. Thus

and traveler’s checks; and other securities such

time and savings deposits with banks that require

the data in the balance sheets of commercial banks

as certificates of deposit and commercial paper.

prior notice for withdrawal. M3 includes M2 as well

may be based on preliminary estimates subject to

Change in broad money is measured as the differ-

as various money market instruments, such as cer-

constant revision. This problem is likely to be even

ence in end-of-year totals relative to the preceding

tificates of deposit issued by banks, bank deposits

more serious for nonbank financial intermediaries.

year. For countries reporting under the old presen-

denominated in foreign currency, and deposits with

Many interest rates coexist in an economy, reflect-

tation of monetary statistics and for all countries

financial institutions other than banks. However

ing competitive conditions, the terms governing

prior to 2001, data are based on money plus quasi

defined, money is a liability of the banking system,

loans and deposits, and differences in the position

money. • Claims on domestic economy (IFS line

distinguished from other bank liabilities by the spe-

and status of creditors and debtors. In some econo-

32S..ZK) include gross credit from the financial

cial role it plays as a medium of exchange, a unit of

mies interest rates are set by regulation or adminis-

system to households, nonprofit institutions serv-

account, and a store of value.

trative fiat. In economies with imperfect markets, or

ing households, nonfinancial corporations, state

The banking system’s assets include its net for-

where reported nominal rates are not indicative of

and local governments, and social security funds.

eign assets and net domestic credit. Net domestic

effective rates, it may be difficult to obtain data on

For countries where claims on domestic economy

credit includes credit extended to the private sector

interest rates that reflect actual market transactions.

are not available, data are claims on private sec-

and general government and credit extended to the

Deposit and lending rates are collected by the Inter-

tor (IFS line 32D..ZK or 32D..ZF) • Claims on cen-

nonfinancial public sector in the form of investments

national Monetary Fund (IMF) as representative inter-

tral government (IFS line 32AN..ZK) include loans

in short- and long-term government securities and

est rates offered by banks to resident customers.

to central government institutions net of 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 (i –

tral bank lending to the government often playing the

P) / (1 + P), where i is the nominal lending interest

most important role. The central bank can regulate

rate and P is the inflation rate (as measured by the

lending to the private sector in several ways—for

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 the IMF’s Monetary and

controlling the availability of credit through changes

Financial Statistics Manual 2000. The presentation

in the reserve requirements imposed on banks and

for countries that report data in accordance with the

Data on monetary and financial statistics are

ceilings on the credit provided by banks to the pri-

IMF’s International Financial Statistics (IFS) remains

published by the IMF in its monthly International

vate sector.

the same.

Financial Statistics and annual International Finan-

Data sources

Monetary accounts are derived from the balance

cial Statistics Yearbook. The IMF collects data on

sheets of financial institutions—the central bank,

the financial systems of its member countries. The

commercial banks, and nonbank financial interme-

World Bank receives data from the IMF in elec-

diaries. Although these balance sheets are usually reliable, they are subject to errors of classification,

tronic files that may contain more recent revisions Data sources than the published sources. The discussion of

valuation, and timing and to differences in account-

monetary indicators draws from an IMF publication

ing practices. For example, whether interest income

by Marcello Caiola, A Manual for Country Econo-

is recorded on an accrual or a cash basis can make

mists (1995). Also see the IMF’s Monetary and

a substantial difference, as can the treatment of non-

Financial Statistics Manual (2000) for guidelines

performing assets. Valuation errors typically arise

for the presentation of monetary and financial sta-

for foreign exchange transactions, particularly in

tistics. Data on real interest rates are derived from

countries with flexible exchange rates or in countries

World Bank data on the GDP deflator.

that have undergone currency devaluation during the

2011 World Development Indicators

253

economy

Monetary indicators

4.16 Afghanistan Albania Algeria Angola Argentina Armenia Australia Austriac Azerbaijan Bangladesh Belarus Belgiumc 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 Czech Republic Denmark Dominican Republic Ecuador Egypt, Arab Rep. El Salvador Eritrea Estonia Ethiopia Finland c Francec Gabon Gambia, The Georgia Germanyc Ghana Greecec Guatemala Guinea Guinea-Bissau Haiti Honduras

254

Exchange rates and prices Official exchange rate

Purchasing power parity (PPP) conversion factor

local currency units to $ 2009 2010a

local currency units to international $ 1995 2009

50.23 94.98 72.65 79.33 3.71 363.28 1.28 0.72 0.80 69.04 2,789.49 0.72 472.19 7.02 1.41 7.16 2.00 1.41 472.19 1,230.18 4,139.33 472.19 1.14 472.19 472.19 560.86 6.83 7.75 2,166.79 809.79 472.19 573.29 472.19 5.28 .. 19.06 5.36 36.03 .. 5.54 8.75 15.38 11.26 11.78 0.72 0.72 472.19 26.64 1.67 0.72 1.41 0.72 8.16 .. 472.19 41.20 18.90

45.21 .. 104.95 24.4 74.25 15.3 92.35 0.0 3.96 1.0 360.50 116.6 1.01 1.3 0.76 0.9 0.80 0.2 70.63 19.2 3,010.98 3.4 0.76 0.9 496.24 187.4 7.02 1.7 1.48 0.6 6.58 1.4 1.70 0.7 1.48 0.0 496.24 189.5 1,230.91 126.6 4,096.00 1,142.3 496.24 241.1 1.01 1.2 496.24 271.9 496.24 163.1 474.78 264.1 6.65 3.4 7.77 7.9 1,925.90 417.8 907.62 0.0 496.24 149.2 512.34 103.0 496.24 261.8 5.59 3.1 .. .. 19.03 11.1 5.64 8.5 37.41 7.3 .. 0.4 5.74 1.2 8.75 0.4 15.38 1.9 11.82 4.8 .. 2.1 0.76 1.0 0.76 1.0 496.24 187.9 28.12 3.9 1.76 0.4 0.76 1.0 1.49 0.1 0.76 0.6 7.98 2.9 .. 747.4 496.24 58.6 39.90 5.8 18.90 3.0

2011 World Development Indicators

18.1 41.5 35.8 55.7 2.0 194.5 1.5 0.8 0.4 26.8 1,085.6 0.9 233.3 2.8 0.7 3.2 1.6 0.7 205.5 500.6 1,526.8 243.3 1.2 282.8 221.6 377.1 3.8 5.4 1,233.7 414.3 289.8 329.5 306.9 3.8 .. 13.5 8.0 19.7 0.5 2.2 0.5 9.8 8.1 4.3 0.9 0.9 245.7 8.1 0.9 0.8 1.0 0.7 4.6 2,066.8 229.0 23.1 9.4

Ratio of PPP conversion factor to market exchange rate

2009

0.4 0.4 0.5 0.7 0.5 0.5 1.1 1.2 0.5 0.4 0.4 1.2 0.5 0.4 0.5 0.5 0.8 0.5 0.4 0.4 0.4 0.5 1.1 0.6 0.5 0.7 0.6 0.7 0.6 0.5 0.6 0.6 0.7 0.7 .. 0.7 1.5 0.6 0.5 0.4 0.5 0.6 0.7 0.4 1.3 1.2 0.5 0.3 0.5 1.1 0.7 1.0 0.6 0.4 0.5 0.6 0.5

Real effective exchange rate

GDP implicit deflator

Consumer price index

Wholesale price index

Index average annual average annual average annual 2000 = 100 % growth % growth % growth 2009 1990–2000 2000–09 1990–2000 2000–09 1990–2000 2000–09

.. .. 102.1 .. .. 124.4 100.8 101.5 .. .. .. 104.3 .. 127.6 .. .. .. 126.0 .. 109.4 .. 108.0 96.8 115.8 .. 100.3 119.8 .. 113.1 597.2 .. 108.4 105.7 108.6 .. 120.4 105.6 96.2 98.8 .. .. .. .. .. 103.8 101.8 105.3 104.4 124.3 102.3 91.9 106.9 .. .. .. .. ..

.. 37.7 18.5 739.4 5.2 212.5 1.4 1.6 203.0 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 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 1.7 26.7 9.2 10.4 5.5 32.5 18.1 19.9

9.0 3.5 8.6 41.1 12.9b 4.5 4.1 1.7 9.9 5.2 23.1 2.1 3.4 6.9 3.9 9.0 8.3 6.0 2.5 10.4 5.0 2.1 2.6 2.7 5.6 6.3 4.3 –1.3 6.1 27.2 7.4 10.2 3.5 3.9 3.3 2.2 2.3 13.7 9.1 8.3 3.6 18.6 5.3 10.8 1.1 2.1 5.0 9.8 7.0 1.1 27.2 3.1 5.4 16.1 11.8 15.3 6.4

.. 27.8 17.3 711.0 8.9 70.5 2.1 2.2 179.7 5.5 271.3 1.9 8.7 8.7 .. 10.4 199.5 117.5 5.5 16.1 6.3 6.5 1.7 5.3 6.9 .. 8.6 5.9 20.2 930.2 9.3 15.6 7.2 86.3 .. 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 21.9 18.8

9.5 2.8 3.0 41.1 10.0 4.0 3.0 2.0 8.3 6.8 18.7 2.1 3.2 5.3 .. 8.9 6.9 6.4 3.1 9.2 6.0 2.5 2.1 3.2 2.7 .. 2.3 0.3 5.8 26.9 3.4 11.2 3.0 2.9 .. 2.5 2.0 14.6 6.6 8.0 3.9 .. 4.4 12.3 1.5 1.8 2.1 7.6 7.0 1.7 16.2 3.2 7.3 .. 2.4 16.5 7.9

.. .. .. .. 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 .. 8.2 1.1 .. .. 6.1 .. .. 8.1 .. 0.9 .. .. .. .. 0.4 .. 3.6 .. .. .. .. ..

.. 4.5 4.0 .. 15.7 1.3 3.6 2.4 .. .. 22.5 2.9 .. .. .. .. 10.0 6.2 .. .. .. .. 1.4 4.4 .. 6.5 .. –0.2 4.9 .. .. 13.0 .. 3.0 .. 2.3 2.4 .. 7.9 9.6 4.7 .. 3.4 .. 2.1 1.8 .. .. 6.7 2.5 .. 4.3 .. .. .. .. ..

Hungary India Indonesia Iran, Islamic Rep. Iraq Irelandc Israel Italyc 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 Netherlandsc New Zealand Nicaragua Niger Nigeria Norway Oman Pakistan Panama Papua New Guinea Paraguay Peru Philippines Poland Portugalc Puerto Rico Qatar

Official exchange rate

Purchasing power parity (PPP) conversion factor

local currency units to $ 2009 2010a

local currency units to international $ 1995 2009

202.34 48.41 10,389.94 9,864.30 1,170.00 0.72 3.93 0.72 87.89 93.57 0.71 147.50 77.35 .. 1,276.93 0.72 0.29 42.90 8,516.05 0.51 1,507.50 8.47 68.29 1.25 2.48 44.10 1,956.21 141.17 3.52 472.19 262.37 31.96 13.51 11.11 1,437.80 8.06 27.52 5.52 8.47 77.55 0.72 1.60 20.34 472.19 148.90 6.29 0.38 81.71 1.00 2.76 4,965.39 3.01 47.68 3.12 0.72 .. 3.64

209.67 61.7 45.16 10.8 8,948.00 1,031.3 10,364.64 567.2 1,170.00 252.5 0.76 0.8 3.60 2.8 0.76 0.8 85.67 14.6 83.43 175.0 0.71 0.4 147.41 17.5 80.57 15.8 .. .. 1,146.23 709.6 0.76 .. 0.28 0.2 47.00 3.5 8,245.42 327.6 0.53 0.2 1,507.50 774.7 6.84 2.1 71.85 0.6 1.23 .. 2.61 1.2 46.55 18.0 2,117.83 287.5 150.80 4.2 3.13 1.4 496.24 226.7 .. 62.4 30.54 10.5 12.40 2.9 12.15 1.2 1,256.47 158.6 8.43 4.9 35.64 4.0 5.42 .. 6.84 2.2 72.38 15.4 0.76 0.9 1.29 1.5 21.84 3.5 496.24 203.1 148.57 15.5 5.98 9.2 0.38 0.2 85.77 10.1 1.00 0.5 2.64 0.7 4,667.57 948.9 2.82 1.2 43.95 14.1 3.02 1.2 0.76 0.7 .. .. 3.64 ..

128.2 17.2 5,813.6 3,875.0 689.4 0.9 3.7 0.8 52.0 114.7 0.5 93.0 36.3 .. 804.7 .. 0.3 16.2 3,548.2 0.4 942.9 4.5 38.2 0.7 1.6 17.8 852.8 55.1 1.8 275.4 125.0 16.8 7.7 5.9 643.7 5.0 13.0 .. 5.6 28.4 0.9 1.5 8.2 241.0 75.6 8.9 0.3 28.8 0.6 1.4 2,462.5 1.6 23.6 1.9 0.6 .. 2.8

Ratio of PPP conversion factor to market exchange rate

2009

0.6 0.4 0.6 0.4 0.6 1.3 1.0 1.1 0.6 1.2 0.8 0.6 0.5 .. 0.6 .. 0.9 0.4 0.4 0.7 0.6 0.5 0.6 0.6 0.6 0.4 0.4 0.4 0.5 0.6 0.5 0.5 0.6 0.5 0.5 0.6 0.5 .. 0.7 0.4 1.2 1.0 0.4 0.5 0.5 1.4 0.9 0.4 0.6 0.5 0.5 0.5 0.5 0.6 0.9 .. 0.8

Real effective exchange rate

GDP implicit deflator

Consumer price index

Wholesale price index

Index average annual average annual average annual 2000 = 100 % growth % growth % growth 2009 1990–2000 2000–09 1990–2000 2000–09 1990–2000 2000–09

103.8 .. .. 142.1 .. 107.4 110.2 103.2 .. 101.4 .. .. .. .. .. .. .. .. .. .. .. 93.2 .. .. .. 104.2 .. 107.5 103.3 .. .. .. .. 135.3 .. 102.4 .. .. .. .. 102.7 86.4 107.8 .. 109.4 97.8 .. 98.6 .. 116.1 135.7 .. 121.3 98.5 102.1 .. ..

19.6 8.1 15.8 27.7 .. 3.6 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 57.8 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 8.4 24.7 5.2 3.0 ..

4.9 5.6 11.1 16.4 11.6 2.1 1.3 2.6 11.2 –1.1 6.1 14.9 6.0 .. 2.2 0.8 9.8 8.3 8.9 8.8 2.6 8.1 10.3 17.9 4.1 3.8 11.2 17.0 4.0 4.5 10.8 6.0 7.8 11.0 14.6 2.0 8.0 .. 7.1 6.6 2.1 3.1 7.7 3.1 15.3 4.6 9.8 8.5 2.4 6.5 10.2 3.5 5.1 2.7 2.6 .. 10.6

20.3 9.1 13.7 26.0 .. 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 .. 2.8

5.5 5.3 9.1 15.4 .. 3.2 1.8 2.3 11.7 –0.1 4.4 8.6 11.3 .. 3.1 1.5 3.4 6.9 8.3 6.5 .. 7.8 .. 0.4 3.1 2.4 10.7 12.2 2.4 2.5 7.3 6.3 4.5 10.8 8.7 2.0 10.9 22.4 5.9 6.2 1.9 2.7 8.8 2.8 12.5 1.8 2.9 8.0 2.5 5.9 8.4 2.4 5.5 2.5 2.7 .. 7.2

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 .. .. ..

2011 World Development Indicators

3.5 5.1 11.2 10.8 .. –0.1 4.5 2.7 .. 0.7 9.1 13.3 .. .. 2.5 .. 2.5 10.2 .. 7.3 .. .. .. .. 4.8 2.5 .. .. 4.8 .. .. .. 6.1 .. .. .. .. .. .. .. 2.7 3.3 .. .. .. 7.9 .. 8.9 3.8 .. 10.3 2.8 7.0 2.7 2.6 .. ..

255

economy

4.16

Exchange rates and prices

4.16 Romania Russian Federation Rwanda Saudi Arabia Senegal Serbia Sierra Leone Singapore Slovak Republicc Sloveniac Somalia South Africa Spainc 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 $ 2009 2010a

local currency units to international $ 1995 2009

3.05 3.24 0.1 31.74 30.85 1.7 568.28 594.45 126.3 3.75 3.75 1.8 472.19 496.24 251.9 67.58 80.39 2.9 3,385.65 .. 379.5 1.45 1.31 1.3 0.72 0.76 0.4 0.72 0.76 0.4 .. .. .. 8.47 6.84 2.3 0.72 0.76 0.7 114.94 111.11 18.2 2.30 .. 0.3 8.47 6.84 2.2 7.65 6.85 9.4 1.09 0.97 2.0 11.23 11.23 12.8 4.14 4.40 0.0 1,320.31 1,462.88 159.4 34.29 30.12 15.1 .. .. .. 472.19 496.24 238.5 6.32 6.37 2.8 1.35 1.45 0.5 1.55 1.52 0.0 .. .. 0.0 2,030.31 .. 500.3 7.79 7.96 0.3 3.67 3.67 1.7 0.64 0.64 0.6 1.00 1.00 1.0 22.57 19.99 5.5 .. .. 11.2 2.15 2.59 0.1 17,065.08 18,932.00 3,168.8 .. .. .. 202.85 214.40 22.1 5,046.11 4,735.74 404.0 .. .. ..

1.6 14.6 261.0 2.4 265.2 33.4 1,399.7 1.1 0.5 0.6 .. 4.8 0.7 49.8 1.4 4.2 8.9 1.5 24.4 1.5 487.3 16.7 0.6 239.5 3.9 0.6 0.9 1.5 767.5 3.2 3.2 0.6 1.0 16.1 602.5 2.0 6,434.3 .. 91.8 3,492.7 ..

Ratio of PPP conversion factor to market exchange rate

2009

0.5 0.5 0.5 0.6 0.6 0.5 0.4 0.7 0.7 0.9 .. 0.6 1.0 0.4 0.6 0.5 1.2 1.4 0.5 0.4 0.4 0.5 0.6 0.5 0.6 0.5 0.6 0.5 0.4 0.4 0.9 1.0 1.0 0.7 0.4 0.9 0.4 .. 0.5 0.7 ..

Real effective exchange rate

GDP implicit deflator

Consumer price index

Wholesale price index

Index average annual average annual average annual 2000 = 100 % growth % growth % growth 2009 1990–2000 2000–09 1990–2000 2000–09 1990–2000 2000–09

102.2 115.3 .. 103.8 .. .. 104.5 107.9 137.2 .. .. 87.8 106.2 .. .. .. 89.5 101.6 .. .. .. .. .. 104.8 123.7 94.2 .. .. 103.2 96.8 .. 80.8 95.2 120.9 .. 191.2 .. .. .. 119.5 ..

98.0 161.5 14.3 1.6 6.0 .. 31.9 1.3 11.1 29.3 .. 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 4.4 81.7 408.2 11.6 271.0 2.2 2.8 2.0 32.6 245.8 45.3 15.2 5.7 22.4 52.1 –3.9

15.9 15.8 10.5 7.6 2.8 16.5 9.5 1.2 3.4 4.0 .. 7.2 3.7 10.7 10.0 7.9 1.7 1.2 8.0 20.9 7.3 3.2 4.5 1.4 6.5 3.2 15.3 13.0 5.6 16.4 10.2 2.6 2.6 8.4 24.7 25.0 8.3 3.4 13.0 16.4 4.1

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 .. 2.9 2.7 33.9 .. 49.0 4.1 .. 26.3 57.0 29.0

11.5 12.5 8.9 2.2 2.2 15.4 .. 1.5 4.8 4.2 .. 5.7 3.1 11.1 8.6 7.3 1.5 1.0 6.2 12.7 6.5 2.9 5.1 2.8 6.5 3.3 16.9 .. 6.7 10.9 .. 2.9 2.7 9.1 .. 21.2 7.8 .. 11.4 15.9 497.7

93.8 99.8 .. 1.3 .. .. .. –1.0 9.5 9.1 .. 7.7 2.4 8.1 .. .. 2.5 –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

15.3 15.7 .. 2.5 .. .. .. 2.8 4.7 3.9 .. 6.7 3.2 12.4 .. .. 2.9 1.1 3.2 .. .. 5.5 .. .. 3.8 4.5 16.9 .. .. 14.6 .. 1.8 4.2 13.6 .. 26.0 .. .. .. .. ..

Note: The differences in the growth rates of the GDP deflator and the 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. Private analysts estimate that consumer price index inflation was considerably higher for 2007–09 and that GDP volume growth has been significantly lower than official reports indicate since the last quarter of 2008. c. As members of the euro area, these countries share a single currency, the euro.

256

2011 World Development Indicators

About the data

4.16

Definitions

In a market-based economy, household, producer,

cost indicator of relative normalized unit labor costs

• Official exchange rate is the exchange rate deter-

and government choices about resource allocation

in manufacturing. For selected other countries the

mined by national authorities or the rate determined

are influenced by relative prices, including the real

nominal effective exchange rate index is based on

in the legally sanctioned exchange market. It is cal-

exchange rate, real wages, real interest rates, and

manufactured goods and primary products trade with

culated as an annual average based on monthly aver-

other prices in the economy. Relative prices also

partner or competitor countries. For these countries

ages (local currency units relative to the U.S. dollar).

largely reflect these agents’ choices. Thus relative

the real effective exchange rate index is the nomi-

• Purchasing power parity (PPP) conversion factor

prices convey vital information about the interaction

nal index adjusted for relative changes in consumer

is the number of units of a country’s currency required

of economic agents in an economy and with the rest

prices; an increase represents an appreciation of

to buy the same amount of goods and services in the

of the world.

the local currency. Because of conceptual and data

domestic market that a U.S. dollar would buy in the

limitations, changes in real effective exchange rates

United States. • Ratio of PPP conversion factor to

should be interpreted with caution.

market exchange rate is the result obtained by divid-

The exchange rate is the price of one currency in terms of another. Official exchange rates and exchange rate arrangements are established by

Inflation is measured by the rate of increase in a

ing the PPP conversion factor by the market exchange

governments. Other exchange rates recognized by

price index, but actual price change can be nega-

rate. • Real effective exchange rate is the nominal

governments include market rates, which are deter-

tive. The index used depends on the prices being

effective exchange rate (a measure of the value of a

mined largely by legal market forces, and for coun-

examined. The GDP deflator reflects price changes

currency against a weighted average of several for-

tries with multiple exchange arrangements, principal

for total GDP. The most general measure of the over-

eign currencies) divided by a price deflator or index

rates, secondary rates, and tertiary rates.

all price level, it accounts for changes in government

of costs. • GDP implicit deflator measures the aver-

Official or market exchange rates are often used

consumption, capital formation (including inventory

age annual rate of price change in the economy as a

to convert economic statistics in local currencies to

appreciation), international trade, and the main com-

whole for the periods shown. • Consumer price index

a common currency in order to make comparisons

ponent, household final consumption expenditure.

reflects changes in the cost to the average consumer

across countries. Since market rates reflect at best

The GDP deflator is usually derived implicitly as the

of acquiring a basket of goods and services that may

the relative prices of tradable goods, the volume of

ratio of current to constant price GDP—or a Paasche

be fixed or may change at specified intervals, such

goods and services that a U.S. dollar buys in the

index. It is defective as a general measure of inflation

as yearly. The Laspeyres formula is generally used.

United States may not correspond to what a U.S.

for policy use because of long lags in deriving esti-

• Wholesale price index refers to a mix of agricul-

dollar converted to another country’s currency at

mates and because it is often an annual measure.

tural and industrial goods at various stages of pro-

the official exchange rate would buy in that country,

Consumer price indexes are produced more fre-

particularly when nontradable goods and services

quently and so are more current. They are also con-

account for a significant share of a country’s output.

structed explicitly, based on surveys of the cost of

An alternative exchange rate—the purchasing power

a defined basket of consumer goods and services.

parity (PPP) conversion factor—is preferred because

Nevertheless, consumer price indexes should be

it reflects differences in price levels for both tradable

interpreted with caution. The definition of a house-

and nontradable goods and services and therefore

hold, the basket of goods, and the geographic (urban

provides a more meaningful comparison of real out-

or rural) and income group coverage of consumer

put. See table 1.1 for further discussion.

price surveys can vary widely by country. In addi-

The ratio of the PPP conversion factor to the official

tion, weights are derived from household expendi-

exchange rate—the national price level or compara-

ture surveys, which, for budgetary reasons, tend to

tive price level—measures differences in the price

be conducted infrequently in developing countries,

level at the gross domestic product (GDP) level. The

impairing comparability over time. Although useful for

price level index tends to be lower in poorer coun-

measuring consumer price inflation within a country,

tries and to rise with income. The real effective

consumer price indexes are of less value in compar-

exchange rate is a nominal effective exchange rate

ing countries.

duction and distribution, including import duties. The Laspeyres formula is generally used.

index adjusted for relative movements in national

Wholesale price indexes are based on the prices

price or cost indicators of the home country, selected

at the first commercial transaction of commodities

countries, and the euro area. A nominal effective

that are important in a country’s output or consump-

exchange rate index is the ratio (expressed on the

tion. Prices are farm-gate for agricultural commodi-

base 2000 = 100) of an index of a currency’s period-

ties and ex-factory for industrial goods. Preference

Data on official and real effective exchange rates

average exchange rate to a weighted geometric aver-

is given to indexes with the broadest coverage of

and consumer and wholesale price indexes are

age of exchange rates for currencies of selected

the economy. The least squares method is used to

from the International Monetary Fund’s Interna-

countries and the euro area. For most high-income

calculate growth rates of the GDP implicit deflator,

tional Financial Statistics. PPP conversion fac-

countries weights are derived from industrial coun-

consumer price index, and wholesale price index.

tors and GDP deflators are from the World Bank’s

try trade in manufactured goods. Data are compiled

Data sources

data files.

from the nominal effective exchange rate index and a

2011 World Development Indicators

257

economy

Exchange rates and prices

4.17

Balance of payments current account Goods and services

$ millions

Exports 1995

Afghanistan Albania Algeria Angola Argentina Armenia Australia Austria Azerbaijan 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 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 Honduras †Data for Taiwan, China

258

2009

.. .. 304 3,458 .. .. 3,836 41,451 24,987 66,563 300 1,338 69,710 234,298 89,906 189,999 785 22,847 4,431 17,011 5,269 24,843 334,175b 190,686b 614 1,630 1,234 5,433 .. 5,480 2,421 4,179 52,641 180,723 6,776 23,270 272 744 129 116 969 5,927 2,040 5,313 219,501 383,759 179 .. 190 .. 19,358 62,242 147,240 1,333,346 .. 408,142 12,294 38,222 .. .. 1,374 6,127 4,451 12,566 4,337 11,478 6,972 22,626 .. .. 28,202 132,920 65,655 147,276 5,731 10,465 5,196 15,574 13,260 44,609 2,040 4,696 135 .. 2,573 13,539 768 3,433 47,973 90,571 362,717 617,335 2,945 .. 175 278 575 3,207 600,347 1,376,861 1,582 7,809 15,523 59,150 2,823 9,220 700 1,122 30 172 192 933 1,635 6,028 128,369 235,091

2011 World Development Indicators

Imports 1995

2009

Net income

Net current transfers

Current account balance

Total reservesa

$ millions 1995 2009

.. .. .. 836 6,495 44 .. .. .. 3,519 41,829 –767 26,066 48,951 –4,636 726 3,688 40 74,841 242,311 –14,036 92,055 175,559 –1,597 1,290 9,872 –6 7,589 23,165 68 5,752 30,360 –51 178,798 b 328,387b ..b 895 2,400 –8 1,574 5,159 –207 .. 9,464 .. 2,050 5,131 –32 63,293 174,679 –11,105 6,502 27,196 –432 483 2,858 –29 259 520 –13 1,375 6,898 –57 1,608 6,540 –412 200,991 407,655 –22,721 244 .. –23 411 .. –7 18,301 49,335 –2,714 135,282 1,113,234 –11,774 .. 393,077 .. 16,012 38,404 –1,596 .. .. .. 1,346 6,386 –695 4,717 12,286 –226 3,806 8,803 –787 9,152 24,900 –53 .. .. .. 30,044 122,069 –104 57,860 134,738 –4,549 6,137 14,160 –769 5,708 16,876 –930 17,140 53,842 –405 3,623 7,966 –67 498 .. 8 2,860 12,435 3 1,446 9,046 –19 37,705 83,807 –4,440 333,746 663,242 –8,964 1,723 .. –665 230 343 –5 1,413 5,266 127 586,662 1,212,133 –2,814 2,120 10,789 –129 24,711 84,204 –1,684 3,728 12,726 –159 1,011 1,391 –85 89 284 –21 802 2,813 –31 1,852 8,641 –226 124,171 202,629 4,188

.. .. .. .. –145 477 1,307 –12 .. .. .. .. –6,823 156 –370 –295 –9,013 597 34 –5,118 166 168 814 –218 –39,399 –109 –374 –19,277 –1,148 –1,702 –2,296 –5,448 –3,519 111 722 –401 –1,376 2,265 10,875 –824 –1,114 76 242 –458 6,641b 7,822b –8,907b ..b –11 121 245 –167 –674 244 1,213 –303 535 .. 2,275 .. –452 –39 878 300 –33,684 3,621 3,338 –18,136 –2,116 132 1,291 –26 –4 255 409 15 –17 153 257 10 –468 277 574 –186 –303 69 393 90 –12,591 –117 –1,892 –4,328 .. 63 .. –25 .. 191 .. –38 –10,306 307 1,616 –1,350 43,282 1,435 33,748 1,618 5,530 .. –3,177 .. –9,432 799 4,614 –4,516 .. .. .. .. –1,885 42 –38 –625 –1,176 134 359 –358 –890 –237 –115 –492 –2,491 802 1,450 –1,431 .. .. .. .. –12,194 572 –805 –1,374 3,933 –1,391 –5,248 1,855 –1,769 992 3,305 –183 –1,463 442 2,497 –1,000 –2,076 4,031 7,960 –254 –664 1,389 3,561 –262 .. 324 .. –31 –529 126 318 –158 –37 736 3,459 39 2,394 –597 –2,344 5,231 31,844 –9,167 –37,796 10,840 .. –42 .. 515 –8 52 135 –8 –118 197 967 –514 47,352 –38,768 –46,610 –27,897 –296 523 2,078 –144 –12,516 8,008 1,657 –2,864 –1,111 491 4,626 –572 –168 179 34 –216 –15 46 98 –35 13 553 1,635 –87 –487 243 2,652 –201 12,512 –2,912 .. 5,474

.. .. .. –1,875 265 2,369 .. 4,164 155,112 –7,572 213 13,664 8,632 15,979 48,007 –1,369 111 2,004 –47,786 14,952 41,742 10,995 23,369 17,904 10,178 121 5,364 3,345 2,376 10,342 –6,389 377 5,640 3,522b 24,120 b 23,862b –536 198 1,230 813 1,005 8,575 –1,175 80 3,245 –526 4,695 8,704 –24,302 51,477 238,539 –4,751 1,635 18,522 –1,709 347 1,296 –164 216 323 –866 192 3,286 –1,137 15 3,676 –38,380 16,369 54,356 .. 238 211 .. 147 617 4,217 14,860 25,292 297,142 80,288 2,452,899 17,418 55,424 255,841 –5,001 8,452 24,987 157 1,615 .. –2,181 64 3,806 –537 1,060 4,068 1,670 529 3,267 –3,314 1,896 14,895 .. .. .. –2,147 14,613 41,608 11,222 11,652 76,618 –2,159 373 2,905 –268 1,788 3,792 –3,349 17,122 34,897 –373 940 3,122 .. 40 58 893 583 3,981 –2,191 815 1,781 6,814 10,657 11,429 –51,857 58,510 131,786 .. 153 1,993 63 106 224 –1,210 199 2,110 165,471 121,816 179,040 –1,198 804 .. –35,913 16,119 5,486 8 783 5,205 –403 87 .. –29 20 169 –232 199 790 –449 270 2,492 42,911 95,559 363,010

Goods and services

Exports

$ millions

Imports

1995

2009

1995

19,765 38,013 52,923 18,953 .. 49,439 27,478 295,618 3,394 493,991 3,479 5,975 3,526 .. 147,761 .. 14,215 448 408 2,088 .. 199 .. 7,513 3,191 1,302 749 470 83,369 529 504 2,349 89,321 884 508 9,044 411 1,307 1,734 1,029 241,517 17,883 662 321 12,342 56,058 6,078 10,214 7,610 2,992 4,802 6,622 26,795 35,716 32,260 .. ..

100,098 258,822 133,255 .. 65,695 199,942 67,877 509,797 4,038 673,615 10,915 48,258 7,414 .. 432,097 .. 61,692 2,560 1,444 11,231 21,600 789 454 37,440 20,309 3,548 .. .. 186,424 2,551 .. 4,181 245,206 2,000 2,300 26,381 2,464 .. 4,057 1,493 518,122 33,210 2,857 1,043 61,545 160,687 29,443 22,220 16,652 4,579 7,253 30,538 47,611 171,071 67,268 .. ..

19,916 48,225 54,461 15,113 .. 42,169 35,287 250,319 3,729 419,556 4,903 6,102 5,922 .. 155,104 .. 12,615 726 748 2,193 .. 1,046 .. 5,755 3,902 1,773 987 660 86,851 991 510 2,454 82,168 1,006 521 11,243 1,055 2,020 2,100 1,624 216,558 17,248 1,150 457 12,841 46,848 5,035 14,185 7,768 1,905 5,200 9,597 33,317 33,825 39,545 .. ..

2009

4.17

economy

Balance of payments current account Net income

Net current transfers

Current account balance

Total reservesa

$ millions 1995 2009

93,412 –1,701 –7,890 328,036 –3,734 –6,514 112,233 –5,874 –15,140 .. –478 .. 37,731 .. 2,106 166,569 –7,325 –38,752 63,129 –2,654 –4,558 520,563 –15,644 –38,480 6,356 –371 –668 650,364 44,285 131,339 16,300 –279 612 38,877 –146 –12,729 11,314 –219 –58 .. .. .. 393,172 –1,303 4,554 .. .. .. 30,679 4,881 7,726 3,680 –35 –190 1,581 –6 –47 11,486 19 1,655 30,215 .. –767 1,792 314 424 1,704 .. –128 27,065 133 578 20,605 –13 318 5,665 –30 –128 .. –167 .. .. –44 .. 144,873 –4,144 –4,170 3,760 –41 –313 .. –48 .. 5,106 –19 27 257,976 –12,689 –14,925 3,989 –18 303 2,632 –25 –195 37,307 –1,318 –1,495 4,305 –140 –95 .. –110 .. 5,128 139 –70 5,086 9 158 459,194 7,247 –12,001 31,953 –3,955 –5,148 4,482 –372 –235 1,951 –47 26 47,843 –2,878 –10,020 104,496 –1,919 –1,660 21,607 –374 –2,810 35,008 –1,939 –3,619 15,446 –466 –1,460 4,802 –488 –625 7,374 110 –312 25,777 –2,482 –7,371 54,950 3,662 –69 170,631 –1,995 –16,575 83,259 21 –10,952 .. .. .. .. .. ..

203 8,382 981 –4 .. 1,776 5,673 –4,579 607 –7,676 1,444 59 1,037 .. –19 .. –1,465 79 110 71 .. 210 .. –220 109 213 129 157 –1,017 219 76 101 3,960 56 77 2,330 339 562 403 230 –6,434 255 138 31 799 –2,059 –1,469 2,562 153 75 195 832 880 958 7,132 .. ..

505 49,102 4,861 .. –2,936 –1,109 7,402 –16,952 1,860 –12,397 3,523 –900 2,297 .. –811 .. –10,133 1,208 193 883 1,827 547 1,101 –1,572 1,625 1,599 .. .. –5,580 455 .. 224 21,468 1,221 186 7,451 764 .. 1,261 3,426 –10,345 267 1,018 230 17,977 –4,408 –5,313 12,824 210 176 519 2,856 15,960 6,537 2,992 .. ..

–1,650 –5,563 –6,431 3,358 .. 1,721 –4,790 25,076 –99 111,044 –259 –213 –1,578 .. –8,665 .. 5,016 –235 –237 –16 .. –323 .. 1,672 –614 –288 –276 –78 –8,644 –284 22 –22 –1,576 –85 39 –1,186 –445 –261 176 –356 25,773 –3,065 –722 –152 –2,578 5,233 –801 –3,349 –471 674 –92 –4,625 –1,980 854 –132 .. ..

–699 –26,626 10,743 .. 27,133 –6,488 7,592 –66,199 –1,126 142,194 –1,251 –4,248 –1,661 .. 42,668 .. 28,605 –102 9 2,284 –7,555 –32 –277 9,381 1,646 –646 .. .. 31,801 –1,066 .. –675 –6,228 –465 –342 –4,971 –1,171 .. 120 –10 36,581 –3,624 –841 –651 21,659 50,122 –287 –3,583 –44 –672 86 247 8,552 –9,598 –23,952 .. ..

12,017 22,865 14,908 .. 8,347 8,770 8,123 60,690 681 192,620 2,279 1,660 384 .. 32,804 .. 4,543 134 99 602 8,100 457 28 7,415 829 275 109 115 24,699 323 90 887 17,046 257 158 3,874 195 651 221 646 47,162 4,410 142 95 1,709 22,976 1,943 2,528 781 267 1,106 8,653 7,781 14,957 22,063 .. 848

2011 World Development Indicators

44,181 284,683 66,119 .. 46,461 2,151 60,611 131,497 2,076 1,048,991 12,135 23,183 3,850 .. 270,437 830 23,028 1,584 1,010 6,902 39,132 .. 372 103,754 6,657 2,288 1,135 163 96,704 1,604 238 2,316 99,889 1,480 1,327 23,568 2,181 .. 2,051 .. 39,284 15,594 1,573 656 45,510 48,859 12,204 13,606 3,028 2,629 3,862 33,225 44,206 79,522 15,829 .. 18,804

259

4.17

Balance of payments current account Goods and services

$ millions

Exports 1995

2009

Romania 9,404 50,491 Russian Federation 92,987 344,934 Rwanda 75 534 Saudi Arabia 53,450 201,964 Senegal 1,506 3,500 Serbia .. 11,858 Sierra Leone 128 323 Singapore 159,488 364,332 Slovak Republic 10,969 61,792 Slovenia 10,377 28,542 Somalia .. .. South Africa 34,402 78,563 Spain 133,910 346,893 Sri Lanka 4,617 8,977 Sudan 681 8,226 Swaziland 1,020 1,860 Sweden 95,525 194,516 Switzerland 123,320 280,162 Syrian Arab Republic 5,757 19,374 Tajikistan .. 1,218 Tanzania 1,265 5,219 Thailand 70,292 180,653 Timor-Leste .. .. Togo 465 1,136 Trinidad and Tobago 2,799 19,622 Tunisia 7,979 19,917 Turkey 36,581 142,865 Turkmenistan 1,774 .. Uganda 664 3,954 Ukraine 17,090 54,253 United Arab Emirates .. .. United Kingdom 322,114 595,914 United States 794,397 1,570,797 Uruguay 3,507 8,557 Uzbekistan .. .. Venezuela, RB 20,753 59,600 Vietnam 9,498 62,752 West Bank and Gaza 764 1,168 Yemen, Rep. 2,160 7,092 Zambia 1,222 4,560 Zimbabwe 2,344 .. World 6,395,661 t 15,641,184 t Low income 29,028 104,191 Middle income 1,087,422 4,483,392 Lower middle income 492,428 2,563,013 Upper middle income 594,996 1,906,819 Low & middle income 1,115,105 4,583,161 East Asia & Pacific 397,583 1,969,911 Europe & Central Asia 193,610 795,858 Latin America & Carib. 273,265 796,196 Middle East & N. Africa .. .. South Asia 58,893 310,779 Sub-Saharan Africa 89,266 296,829 High income 5,304,481 11,224,885 Euro area 2,100,300 4,450,297

Imports 1995

2009

11,306 60,470 82,809 253,233 374 1,479 44,874 160,639 1,821 7,020 .. 18,486 260 628 144,904 325,605 10,658 61,806 10,749 27,980 .. .. 33,375 80,816 135,000 374,259 5,982 11,708 1,238 11,212 1,274 2,344 81,142 165,275 108,916 243,800 5,541 19,309 .. 3,062 2,139 7,543 82,246 155,777 .. .. 671 1,666 2,110 9,948 8,811 21,091 40,113 151,453 1,796 .. 1,490 5,210 18,280 56,206 .. .. 327,000 650,834 890,784 1,945,705 3,568 7,794 .. .. 16,905 48,064 12,334 72,446 2,789 4,962 2,471 10,001 1,338 4,119 2,515 .. 6,248,111 t 15,144,783 t 46,738 149,627 1,137,135 4,125,043 532,363 2,390,741 604,453 1,722,447 1,182,581 4,271,461 413,806 1,684,481 205,686 759,347 288,584 781,728 106,423 334,137 78,652 407,949 99,774 327,513 5,072,079 11,020,075 1,977,018 4,275,187

Net income

Net current transfers

Current account balance

Total reservesa

$ millions 1995 2009

–241 –3,372 7 2,800 –124 .. –30 541 –14 201 .. –2,875 –5,402 –137 –3 81 –6,473 10,708 –560 .. –110 –2,114 .. –34 –390 –716 –3,204 17 –96 –434 .. 3,393 20,899 –227 .. –1,943 –384 607 –561 –249 –294 .. .. .. .. .. .. .. .. .. .. .. .. .. ..

a. International reserves including gold valued at London gold price. b. Includes Luxembourg.

260

2011 World Development Indicators

–2,968 –39,474 –37 8,613 –48 –710 –36 –3,061 –1,837 –1,081 .. –6,389 –42,120 –488 –2,402 –123 7,303 14,922 –1,149 –71 –175 –7,499 .. –15 –1,202 –2,011 –8,121 .. –329 –2,440 .. 40,655 121,418 –689 .. –2,652 –3,028 911 –1,171 –1,363 .. .. .. .. .. .. .. .. .. .. .. .. .. .. ..

369 5,649 –1,774 157 –2,862 6,963 350 604 57 –16,694 –27,172 –5,318 195 1,685 –244 .. 4,925 .. 43 148 –118 –894 –3,037 14,230 93 –959 390 95 –202 –75 .. .. .. –645 –2,684 –2,493 4,525 –10,889 –1,967 732 3,005 –770 60 1,480 –500 144 192 –30 –2,970 –5,083 4,940 –4,409 –12,312 20,703 607 1,150 263 .. 1,735 .. 395 683 –590 487 4,484 –13,582 .. .. .. 118 324 –122 –4 47 294 774 1,951 –774 4,398 2,299 –2,338 5 .. 0 639 1,133 –281 472 2,661 –1,152 .. .. .. –11,943 –22,786 –13,436 –38,073 –124,944 –113,561 76 140 –213 .. .. .. 109 –323 2,014 1,200 6,448 –2,020 435 3,418 –984 1,056 1,515 184 182 516 –182 40 .. –425 .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. ..

–7,298 2,624 49,365 18,024 –379 99 22,765 10,399 –1,884 272 –2,412 .. –193 35 32,628 68,816 –2,810 3,863 –720 1,821 .. .. –11,327 4,464 –80,375 40,531 –215 2,112 –3,908 163 –414 298 31,460 25,870 38,972 68,620 66 448 –180 39 –1,816 270 21,861 36,939 .. .. –222 130 8,519 379 –1,234 1,689 –14,410 13,891 .. 1,168 –451 459 –1,732 1,069 .. 7,778 –37,050 49,144 –378,435 175,996 215 1,813 .. .. 8,561 10,715 –6,274 1,324 535 .. –2,565 638 –406 223 .. 888 .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. ..

44,383 439,342 743 420,984 2,123 15,228 405 187,803 1,804 1,078 .. 39,603 28,051 5,354 1,094 959 47,255 134,566 18,300 .. 3,470 138,419 250 703 9,245 11,294 74,933 .. 2,994 26,501 36,104 66,550 404,099 8,038 .. 34,318 16,447 .. 6,990 1,892 .. .. .. .. .. .. .. .. .. .. .. .. .. .. ..

About the data

4.17

Definitions

The balance of payments records an economy’s

system, external debt records, information provided

• Exports and imports of goods and services are all

transactions with the rest of the world. Balance of

by enterprises, surveys to estimate service transac-

transactions between residents of an economy and

payments accounts are divided into two groups:

tions, and foreign exchange records. Differences in

the rest of the world involving a change in ownership

the current account, which records transactions in

collection methods—such as in timing, deﬁnitions

of general merchandise, goods sent for processing

goods, services, income, and current transfers, and

of residence and ownership, and the exchange rate

and repairs, nonmonetary gold, and services. • Net

the capital and financial account, which records capi-

used to value transactions—contribute to net errors

income is receipts and payments of employee com-

tal transfers, acquisition or disposal of nonproduced,

and omissions. In addition, smuggling and other ille-

pensation for nonresident workers, and investment

nonfinancial assets, and transactions in financial

gal or quasi-legal transactions may be unrecorded or

income (receipts and payments on direct investment,

assets and liabilities. The table presents data from

misrecorded. For further discussion of issues relat-

portfolio investment, and other investments and

the current account plus gross international reserves.

ing to the recording of data on trade in goods and

receipts on reserve assets). Income derived from

services, see About the data for tables 4.4–4.7.

the use of intangible assets is recorded under busi-

The balance of payments is a double-entry accounting system that shows all flows of goods and

The concepts and definitions underlying the data in

ness services. • Net current transfers are recorded

services into and out of an economy; all transfers

the table are based on the fifth edition of the Inter-

in the balance of payments whenever an economy

that are the counterpart of real resources or financial

national Monetary Fund’s (IMF) Balance of Payments

provides or receives goods, services, income, or

claims provided to or by the rest of the world without

Manual (1993). That edition redefined as capital

financial items without a quid pro quo. All transfers

a quid pro quo, such as donations and grants; and

transfers some transactions previously included in the

not considered to be capital are current. • Current

all changes in residents’ claims on and liabilities to

current account, such as debt forgiveness, migrants’

account balance is the sum of net exports of goods

nonresidents that arise from economic transactions.

capital transfers, and foreign aid to acquire capital

and services, net income, and net current transfers.

All transactions are recorded twice—once as a credit

goods. Thus the current account balance now reflects

• Total reserves are holdings of monetary gold, spe-

and once as a debit. In principle the net balance

more accurately net current transfer receipts in addi-

cial drawing rights, reserves of IMF members held by

should be zero, but in practice the accounts often do

tion to transactions in goods, services (previously

the IMF, and holdings of foreign exchange under the

not balance, requiring inclusion of a balancing item,

nonfactor services), and income (previously factor

control of monetary authorities. The gold component

net errors and omissions.

income). Many countries maintain their data collection

of these reserves is valued at year-end (December

Discrepancies may arise in the balance of pay-

systems according to the fourth edition of the Balance

31) London prices ($386.75 an ounce in 1995 and

ments because there is no single source for balance

of Payments Manual (1977). Where necessary, the IMF

$1,087.50 an ounce in 2009).

of payments data and therefore no way to ensure

converts such reported data to conform to the fifth

that the data are fully consistent. Sources include

edition (see Primary data documentation). Values are

customs data, monetary accounts of the banking

in U.S. dollars converted at market exchange rates.

4.17a

Top 15 economies with the largest reserves in 2009 Total reserves ($ billions)

Share of world total (%) 2009

Annual change (%) 2008–09

Months of imports 2009

2008

2009

China

1,966

2,453

26.1

24.8

25.0

Japan

1,031

1,049

11.2

1.8

18.1

426

439

4.7

3.0

16.1 29.4

Russian Federation Saudi Arabia

451

421

4.5

–6.7

United States

294

404

4.3

37.4

2.0

Taiwan, China

304

363

3.9

19.6

20.7

India

257

285

3.0

10.6

9.8

Korea, Rep.

202

270

2.9

34.2

8.0

Hong Kong SAR, China

183

256

2.7

40.2

6.3

Data sources Data on the balance of payments are published in the IMF’s Balance of Payments Statistics Yearbook and International Financial Statistics. The World Data sources Bank exchanges data with the IMF through elec-

Brazil

194

239

2.5

23.1

13.2

Singapore

174

188

2.0

7.8

5.9

Germany

139

179

1.9

29.1

1.5

Algeria

148

155

1.7

4.7

Thailand

111

138

1.5

24.7

9.8

the IMF’s Balance of Payments Manual, fifth edition

135

1.4

81.6

5.0

(1993), Balance of Payments Textbook (1996), and

Switzerland

Source: International Monetary Fund, International Financial Statistics data files.

tronic files that in most cases are more timely and cover a longer period than the published sources. More information about the design and compilation of the balance of payments can be found in

Balance of Payments Compilation Guide (1995).

2011 World Development Indicators

261

economy

Balance of payments current account

States and Markets

Introduction

ew firm creation recently declined sharply in most countries, according to the 2010 World Bank Group Entrepreneurial Snapshots. The economic and financial crisis that began in 2008 increased unemployment in many countries, and the fight against poverty could be hampered as spending for human and productive capital is strained. Governments around the world face fiscal deficits and pressure to improve public spending and accelerate business reforms. Partnership between the private sector, which employs people and makes investments, and a capable public sector, which creates a stable regulatory environment, is a key ingredient to successful development. This section includes a range of indicators showing how effective and accountable government, together with a vibrant private sector, produces employment opportunities and services that empower poor people. Its 13 tables cover cross-cutting themes: private sector development, public sector policies, infrastructure, information, communications, telecommunications, and science and technology. New data show that business reforms are making it easier to do business and create new firms and that more-inclusive financial systems are removing barriers to economic growth and development.

Businesses are created faster in a good business environment The World Bank Group Entrepreneurship Snapshots (www.enterprisesurveys.org), which cover 112 countries, show that new businesses are created faster in countries with good governance, low corporate taxes, minimal red tape, and a strong legal and regulatory environment. Countries with well developed financial markets also have higher new firm creation than countries with less developed financial markets. The downside is that countries with well developed financial markets also had steeper declines in new firm creation during the recent financial and economic crisis, probably due to the credit crunch. High-income countries created more new limited liability firms—more than 4 per 1,000 working-age people, compared with only about 0.3 in low-income countries. Data on business entry and density are in table 5.1. The Doing Business database (www.doing business.org) shows that between June 2009 and May 2010, 117 countries adopted 216 business

regulation reforms, making it easier to start and operate businesses, strengthening property rights, and improving commercial dispute resolution and bankruptcy procedures. Using data from the Enterprise Snapshots and Doing Business to analyze whether some reforms are more important than others, Klapper and Love (2010a) find that small reforms that reduce costs, time, or number of procedures to register a business by less than 40 percent do not have a significant impact on new firm registration. This suggests that “token” reforms do not boost private sector activity and that countries with weak business environments require larger reforms to increase new firm registration. They find that two reforms occurring simultaneously tend to have more impact than two reforms occuring sequentially over a longer period.

Forty countries made it easier to pay taxes between 2009 and 2010 The World Bank’s Doing Business project collects information for 183 countries on tax payments, time spent paying taxes, and the total tax rate borne by a standard firm. In cooperation with PricewaterhouseCoopers, the project collects information on business tax systems around the world, allowing governments to benchmark their tax system with others to identify good practices, and researchers to analyze the impact of higher corporate tax rates on business start-ups and investments. Over June 2009–May 2010, 40 countries made tax compliance easier, reducing costs for firms and encouraging job creation. Higher tax compliance costs are associated with larger informal sectors and more corruption, ultimately limiting employment, investment, and growth. Keeping rules simple

2011 World Development Indicators

263

and clear improves compliance and reduces tax evasion. And better compliance keeps the system working and supports government programs and services. In the past six years more than 60 percent of the countries covered by the Doing Business project made paying taxes easier or lowered the tax burden for local enterprises. Countries that make paying taxes easy for domestic firms usually offer electronic systems for tax filing and payment, have one tax per tax base, and use a filing system based on self-assessment. In high-income countries the average business spends about 180 hours a year preparing, filing, and paying taxes; in Latin America and the Caribbean, more than 400 hours a year (figure 5a). Previous editions of World Development Indicators included data on the highest marginal corporate tax rate (the statutory rate of corporate income tax). It is not a comprehensive indicator of the amount of tax a company pays, however, because it is only one of the many taxes businesses pay. Generous tax allowances in some countries significantly reduce the corporate income tax paid, while The average business in Latin America and the Caribbean spends about 400 hours a year in preparing, filing, and paying business taxes, 2009

Time to prepare, file, and pay taxes (hours a year) 500 400 300 200 100 0

East Asia & Pacific

Europe & Central Asia

Latin America Middle East & & Caribbean North Africa

South Asia

Sub-Saharan Africa

High income

Source: Doing Business 2011.

Firms in East Asia and the Pacific have the lowest business tax rate, 2010

Total tax rate (% of commercial profits) 80 60 40

East Asia & Pacific

Europe & Central Asia

Latin America Middle East & & Caribbean North Africa

Source: Doing Business 2011.

264

Benchmarking the quality of the business environment— Doing Business and Enterprise Surveys are complementary The World Bank’s Enterprise Surveys are based on firm-level surveys of a representative sample of the nonagricultural private sector in a country. The surveys cover a broad range of business environment topics including corruption, infrastructure, crime, competition, performance measures, and access to finance. Data from Enterprise Surveys are presented in table 5.2. The Doing Business project uses indicator sets and rankings to measure business regulations and quantify the ease of doing business across countries. The indicators cover common transactions such as starting a business or registering property based on standardized case studies. Data are collected through surveys of local experts on business transactions and reflect the country’s laws and regulations. Data on Doing Business indicators are in tables 5.3 and 5.6. Box 5c compares the data sources, coverage, and information collected by Enterprise Surveys and the Doing Business project.

About half the world’s households do not have deposit accounts in formal financial institutions

20 0

disallowances in others can increase the effective rate. In this year’s edition table 5.6 on tax policies includes the total business tax rate as a percent of commercial profit, with details on corporate taxes, labor taxes paid by the employer, social contributions, and other taxes. The total tax rate is a comprehensive measure of the cost of all the taxes a business bears. It differs from the statutory tax rate, which merely provides the factor to be applied to the tax base. In computing the total tax rate, tax payable is divided by commercial profit. The total tax rate is lowest in East Asia and Pacific and is highest in Sub-Saharan Africa (figure 5b). Note that these tax rates are “de jure” tax rates based on case studies of a “standardized business” as defined by the Doing Business project.

2011 World Development Indicators

South Asia

Sub-Saharan Africa

High income

Financial exclusion is a barrier to economic development. Evidence from household surveys indicates that access to basic financial

states and markets

services such as savings, payments, and credit can make an important difference in poor people’s lives. For firms, lack of access to finance is often the main obstacle to growth. In an increasingly digitized and globalized world many countries are promoting access to financial services—from establishing a credit facility for indigenous farmers in rural areas to introducing broad consumer protection legislation. Although financial inclusion mandates, from consumer protection to rural finance promotion, are on the agenda of many financial regulators, insufficient authority and resources to provide broad financial access limit implementation capacity in many developing countries. Nevertheless, more than 70 percent of financial regulators in developing countries have programs to protect consumers, and almost 60 percent promote financial literacy. Five new financial indicators from Financial Access 2010 (www.cgap.org/financialindicators) are included in table 5.5 this year: commercial bank deposits, commercial bank loans, commercial bank branches, automated teller machines (ATMs), and point-of-sale terminals. Although many nonbank institutions (cooperatives, specialized state financial institutions, and microfinance institutions) provide financial services, the most complete information available to central banks and financial regulators is on commercial banks, which account for 85 percent of deposits and 96 percent of accounts. Although financial inclusion, measured as people with commercial bank accounts, is high in some developing country regions such as East Asia and Pacific, it remains low in Sub-Saharan Africa (figure 5d). Access to deposit and credit services varies by region. Access is greater in countries with higher incomes, better infrastructure, and a well functioning legal environment. People without access to bank accounts and credit from regulated institutions have to rely on informal nonregulated financial services, often more costly and less reliable. Low- and middle- income countries lag behind high-income countries in the number of bank branches, ATMs, and point-of-sale terminals, but the number of ATMs exceeds the number of bank branches in low-income countries. And new technology, including the expansion of electronic payments through mobile and Internet banking, offer hope for bringing financial services to the unbanked.

Two approaches to collecting business environment data: Doing Business and Enterprise Surveys

Topic

Enterprise Surveys

Global coverage

125 countries

Doing Business

Data source

Collects firm-level data; face-toface interview with owner or top manager. Businesses surveyed include manufacturing, retail, construction, transport, communications, and other services

Collects information through surveys administered by local experts (lawyers, accountants, and architects). The information is confirmed through the underlying laws and regulations

Number of observations

150–360 observations in smaller countries; 1,200–1,800 interviews in larger countries

Underlying laws and regulations in addition to an average of 39 surveys per country

183 countries

Geographical Main cities or regions of coverage within economic activity a country

Main (most populous) business city and subnational studies in other cities

Information gathered

Time and cost to complete common business transactions based on standardized case studies; underlying laws and regulations

Objective data on the business environment as experienced by firms, performance measures, firm characteristics, and perceptions regarding obstacles to growth

Business characteristics; approxiStandardized business; 10 mately 20 Investment climate topics business regulation topics Examples of data

Hard data: number of days to obtain a construction permit. Soft data: opinion on whether access to land is an obstacle faced by the establishment

Hard data: laws and regulations, number of procedures, and costs to build a warehouse. Soft data: experts’ estimates on the number of days required for each procedure

Inference from the data

Stratified random sampling design of the surveys, which ensures that data are representative of the universe of formal firms (with five or more employees)

Standardized case studies that relate to a common business situation, which makes comparisons and benchmarks valid across countries

Measures what happens to existing firms—their actual experiences with investment climate issues such as payment of taxes. Also surveys obstacles to business growth

Expectations of a standardized firm following official legal requirements and costs. For instance, “paying taxes” measures the number of payments, time to file, and tax rates

Measures what happens in practice in the normal course of business; for instance, whether a firm pays a bribe when obtaining an import license and the actual time it takes to obtain the license

Assumes that firms comply with all formal regulations and minimize information gathering time and that all regulations are enforced. Measures what would happen if the firm complied with all regulatory requirements in a lawful manner.

Can be used to identify potential areas of reform in the business environment as well as assess the impact of reforms on businesses.

Can be used to identify areas for reform based on bottlenecks or weaknesses in specific areas of private sector regulation and learn from practices in other countries.

Source: Summary of www.enterprisesurveys.org/Methodology/Compare.aspx.

People living in developing countries of East Asia and Pacific have more commercial bank accounts than those in other developing country regions, 2009

Deposit accounts in commercial banks (median per 1,000 adults) 2,000 1,500 1,000

Developing country median

500 0

East Asia & Pacific

Europe & Central Asia

Latin America Middle East & & Caribbean North Africa

South Asia

Sub-Saharan Africa

High income

Source: Financial Access 2010, CGAP and World Bank.

2011 World Development Indicators

265

Tables

5.1

Private sector in the economy Investment commitments in infrastructure projects with private participationa

$ millions Telecommunications

266

Energy

2000–05

2006–09

2000–05

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 3,561.6 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 6.6 173.8 .. 156.5 .. 560.1 50.6 21.9 18.0 135.0

1,040.4 670.0 1,925.0 1,129.0 5,033.6 488.8 .. .. 1,283.5 3,729.8 2,219.2 .. 399.7 284.7 1,086.6 183.9 31,121.4 1,866.5 680.6 0.0 436.9 701.4 .. 20.8 246.4 4,167.6 0.0 .. 5,294.7 880.0 330.7 .. 885.4 3,035.0 0.0 .. .. 220.1 1,764.7 8,864.0 901.9 0.0 .. .. .. .. 278.8 35.0 612.2 .. 2,916.0 .. 1,511.4 242.2 96.4 306.0 930.5

1.6 790.6 962.0 45.0 3,826.9 74.0 .. .. 375.2 501.5 .. .. 590.0 884.4 .. .. 26,171.6 3,253.5 .. .. 82.1 91.8 .. .. 0.0 1,590.5 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 .. .. 5.5 358.8

2011 World Development Indicators

2006–09

Transport 2000–05

.. .. 664.0 308.0 2,320.0 120.9 9.4 .. 3,479.0 203.6 127.0 63.0 .. .. .. .. .. .. 243.5 0.0 1,875.0 .. .. .. .. .. 137.3 16.6 800.0 .. .. .. 46,690.5 3,398.4 2,246.7 2.1 .. .. .. .. 695.8 125.3 440.0 0.0 .. .. .. .. .. .. 2,397.7 4,821.2 7,170.5 15,350.1 .. .. 944.6 1,005.4 .. .. .. .. 190.0 465.2 0.0 176.4 85.0 451.0 60.0 0.0 .. .. .. .. 0.0 898.9 129.0 685.0 469.0 821.5 0.0 .. .. .. .. .. 4.0 .. .. .. .. .. 0.0 177.4 0.0 .. 634.2 .. .. .. 100.0 10.0 .. .. 263.8 .. .. .. .. .. .. .. .. 120.0

2006–09

.. .. 269.0 53.0 1,402.6 715.0 .. .. .. 0.0 4.0 .. .. .. .. .. 22,086.9 536.2 .. .. 40.1 .. .. .. .. 1,311.1 15,795.0 .. 2,344.4 .. 735.0 373.0 .. 492.0 .. .. .. 879.9 766.0 1,370.0 .. .. .. .. .. .. 3.9 .. 573.0 .. .. .. .. 159.0 .. .. ..

Domestic credit to private sector

Water and sanitation 2000–05

.. 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 .. .. .. .. .. 207.9

2006–09

.. 0.0 1,572.0 .. .. 0.0 .. .. .. .. .. .. .. .. .. .. 1,365.4 .. .. .. .. 0.0 .. .. .. 3.1 3,992.2 .. 305.0 .. .. .. 0.0 .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. 435.0 .. .. .. 6.7 .. .. 0.0 ..

Businesses registered

% of GDP

New

Entry density

2009

9.1 37.0 16.2 21.2 13.5 23.1 127.8 126.9 19.6 41.5 37.3 97.9 22.2 37.0 57.3 25.5 54.0 75.6 17.5 21.7 24.5 11.3 128.6 7.0 5.2 97.5 127.3 158.0 29.9 7.5 4.8 49.4 17.1 66.3 .. 55.3 231.6 21.3 25.3 36.2 41.3 16.6 110.2 17.8 94.4 110.3 10.1 18.9 31.2 112.3 15.9 91.7 25.4 .. 5.6 14.5 52.6

.. 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 .. 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 .. 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 .. .. .. ..

Investment commitments in infrastructure projects with private participationa

$ millions Telecommunications

Energy

2000–05

2006–09

2000–05

2006–09

5,172.8 20,030.5 6,557.2 695.0 984.0 .. .. .. 700.3 .. 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 3,777.0 82.6 92.1 413.0 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 .. .. ..

1,523.3 33,682.4 9,748.1 1,506.0 4,521.0 .. .. .. 301.6 .. 648.6 3,170.2 2,973.8 400.0 .. .. .. 115.9 135.0 468.1 0.0 30.6 73.8 .. 490.2 489.6 304.8 197.7 1,700.0 583.0 133.1 102.1 12,622.6 392.3 0.0 2,549.6 156.2 .. 8.5 26.0 .. .. 380.1 251.7 11,348.1 .. .. 8,706.5 1,224.0 150.0 591.4 2,485.0 4,177.0 7,750.0 .. .. ..

851.6 8,369.2 1,860.5 650.0 .. .. .. .. 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 .. .. ..

1,707.0 50,754.4 3,779.3 .. 590.0 .. .. .. 78.0 .. 989.0 0.0 332.7 .. .. .. .. .. 1,425.0 184.0 .. .. .. .. 417.6 655.0 .. .. 384.5 .. .. .. 1,483.0 68.0 .. .. .. 556.1 .. .. .. .. 95.0 .. 280.0 .. .. 4,058.2 576.7 .. .. 1,142.9 9,463.3 2,475.4 .. .. ..

Transport 2000–05

3,297.5 4,172.2 159.2 .. .. .. .. .. 565.0 .. 0.0 231.0 .. .. .. .. .. .. 0.0 .. 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 .. .. ..

2006–09

1,588.0 23,012.8 1,731.5 .. .. .. .. .. .. .. 1,380.0 31.0 404.0 .. .. .. .. .. .. 135.0 .. .. .. .. .. 295.0 17.5 .. 1,379.0 .. .. .. 11,434.1 60.0 .. 200.0 0.0 .. .. .. .. .. .. .. 644.1 .. .. 923.7 0.0 .. .. 3,157.6 678.9 3,642.3 .. .. ..

Domestic credit to private sector

Water and sanitation 2000–05

0.0 112.9 44.8 .. .. .. .. .. .. .. 169.0 .. .. .. .. .. .. 0.0 .. .. 0.0 .. .. .. .. .. .. .. 6,502.2 .. .. .. 523.7 .. .. .. .. .. 0.0 .. .. .. .. 3.4 .. .. .. .. .. .. .. 152.0 0.0 64.3 .. .. ..

2006–09

0.0 241.7 20.2 .. .. .. .. .. .. .. 951.0 .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. 0.0 .. .. 0.0 303.8 .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. 530.5 0.8 .. .. ..

% of GDP 2009

71.3 46.8 27.6 36.7 6.4 230.3 84.5 110.8 28.5 171.0 71.7 50.3 31.5 .. 107.6 36.1 63.3 15.1 9.5 107.8 73.9 13.5 16.1 10.9 70.9 44.3 11.5 14.2 117.1 17.4 .. 85.1 23.3 36.2 43.9 64.4 25.1 .. 46.8 59.4 215.3 147.0 34.4 12.2 37.6 .. 49.0 23.5 85.7 32.1 29.1 24.1 30.3 52.9 187.8 .. 51.5

5.1

states and markets

Private sector in the economy

Businesses registered

New 2009

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 .. ..

2011 World Development Indicators

Entry density 2009

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 .. ..

267

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 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

2006–09

Energy 2000–05

3,906.9 4,188.9 1,240.8 22,049.4 24,525.8 1,726.0 72.3 351.0 1.6 .. .. .. 593.1 1,333.0 93.3 563.5 3,297.4 .. 48.8 111.2 .. .. .. .. .. .. .. .. .. .. 13.4 0.0 .. 10,519.5 7,714.0 1,251.3 .. .. .. 766.1 1,444.5 270.8 747.7 1,748.3 .. 27.7 48.3 .. .. .. .. .. .. .. 583.0 307.7 .. 8.5 125.0 16.0 515.3 1,484.5 348.0 5,602.7 3,106.0 4,693.3 0.0 0.0 .. 0.0 44.0 657.7 .. .. .. 751.0 2,805.0 30.0 12,788.6 12,068.7 6,754.8 20.0 158.1 .. 387.6 1,463.0 113.9 3,162.9 4,508.8 160.0 .. .. .. .. .. .. .. .. .. 114.2 158.5 330.0 285.6 942.1 .. 3,337.0 2,619.8 39.5 430.0 1,593.7 2,360.6 279.8 47.0 150.0 376.8 392.2 .. 208.3 624.0 3.0 72.0 343.0 .. .. s .. s .. s 6,362.3 20,932.3 .. 227,575.0 248,323.5 107,077.9 84,109.2 27,585.0 38,840.9 143,465.8 134,452.2 49,324.0 233,937.3 269,255.8 87,324.8 29,862.2 4,662.0 31,290.4 50,274.6 62,911.8 5,316.0 81,401.1 72,021.9 45,682.0 13,435.4 23,566.1 .. 29,314.5 48,647.1 9,533.6 24,654.4 40,481.6 .. .. .. .. .. .. ..

2006–09

Transport 2000–05

6,288.7 .. 27,214.2 109.4 .. .. .. .. .. 55.4 .. .. 1.2 .. .. .. .. .. .. .. .. .. 9.9 504.7 .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. 28.4 27.7 2,341.0 939.0 .. .. 190.0 .. .. .. .. .. 8,862.7 3,118.6 .. .. 1,000.6 .. 54.0 .. .. .. .. .. .. .. .. 251.1 .. .. .. 34.0 297.0 20.0 .. .. 15.8 .. .. 15.6 .. .. .. s .. s .. .. 191,687.3 50,686.8 82,564.1 26,511.7 109,123.2 5,696.9 196,264.6 5,403.2 26,112.4 21,800.1 47,981.4 .. 57,940.1 16,150.3 .. .. 55,257.1 4,285.0 .. .. .. .. .. ..

2006–09

Domestic credit to private sector

Water and sanitation 2000–05

2006–09

116.8 116.0 41.0 191.0 904.7 1,241.7 .. .. .. .. .. .. 398.0 0.0 0.0 .. 0.0 .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. 3,483.0 31.3 0.0 .. .. .. .. .. .. 30.0 .. 120.7 .. .. .. .. .. .. .. .. .. 82.0 .. .. .. .. .. 134.0 8.5 .. .. 522.7 18.8 .. .. .. .. .. .. .. .. .. 840.0 .. .. 4,138.5 .. .. .. .. .. 404.0 0.0 .. 130.0 100.0 102.0 .. .. .. .. .. .. .. .. .. .. 368.0 .. 25.0 0.0 .. .. 15.0 .. 965.0 266.0 .. .. .. .. 220.0 .. .. .. 0.0 .. .. .. .. .. s .. s .. s .. .. .. 105,160.9 16,175.1 6,654.9 51,724.0 3,704.9 5,271.6 41,208.8 407.0 .. 86,781.7 .. .. 20,589.5 10,840.9 4,561.7 .. .. .. 43,755.5 2,516.1 .. .. .. .. 23,936.5 112.9 241.7 .. .. .. .. .. .. .. .. ..

% of GDP

New

2009

47.1 56,698 45.3 261,633 .. 3,028 52.1 .. 24.7 1,636 42.2 9,715 9.3 .. 103.2 26,416 44.7 15,825 94.0 5,836 .. .. 147.1 24,700 211.5 79,757 24.8 4,223 12.3 .. 25.0 .. 139.3 24,228 174.8 25,250 20.3 .. 29.0 2,171 15.3 .. 116.3 27,520 18.6 .. 21.9 125 31.5 .. 68.4 9,079 36.5 44,472 .. .. 13.1 11,152 73.3 19,300 93.0 .. 213.5 330,100 202.9 .. 20.6 4,664 .. 14,428 21.7 .. 112.7 .. .. .. 7.4 .. 12.0 5,509 .. .. 138.2 w 26.4 72.8 92.9 47.8 72.0 117.1 45.0 40.8 34.5 43.5 65.1 165.1 133.0

a. Data refer to total for the period shown. Includes infrastructure projects with private sector participation that reached financial closure in 1990–2009.

268

2011 World Development Indicators

Businesses registered

Entry density 2009

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 Statistics

ments are the sum of investments in physical assets

easing fiscal constraints, improving the efficiency

or, when unavailable, from its monetary survey. The

and payments to the government. Investments in

of infrastructure services, and extending delivery

monetary survey includes monetary authorities (the

physical assets are resources the project company

to poor people. Developing countries have been in

central bank), deposit money banks, and other bank-

commits to invest during the contract period in new

the forefront, pioneering better approaches to infra-

ing institutions, such as finance companies, develop-

facilities or in expansion and modernization of exist-

structure services and reaping the benefits of greater

ment banks, and savings and loan institutions. Credit

ing facilities. Payments to the government are the

competition and customer focus.

to the private sector may sometimes include credit

resources the project company spends on acquir-

to state-owned or partially state-owned enterprises.

ing government assets such as state-owned enter-

The data on investment in infrastructure projects with private participation refer to all investment (pub-

Entrepreneurship is essential to the dynamism of

prises, rights to provide services in a specific area, or

lic and private) in projects in which a private com-

the modern market economy, and a greater entry rate

use of specific radio spectrums. • Domestic credit

pany assumes operating risk during the operating

of new businesses can foster competition and eco-

to private sector is financial resources provided

period or development and operating risk during the

nomic growth. The table includes data on business

to the private sector—such as through loans, pur-

contract period. Investment refers to commitments

registrations from the 2008 World Bank Group Entre-

chases of nonequity securities, and trade credits and

not disbursements. Foreign state-owned companies

preneurship Survey, which includes entrepreneurial

other accounts receivable—that establish a claim for

are considered private entities for the purposes of

activity in more than 100 countries for 2000–08.

repayment. For some countries these claims include

this measure.

Survey data are used to analyze firm creation, its

credit to public enterprises. • New businesses regis-

Investments are classified into two types: invest-

relationship to economic growth and poverty reduc-

tered are the number of limited liability corporations

ments in physical assets—the resources a com-

tion, and the impact of regulatory and institutional

registered in the calendar year. • Entry density is the

pany commits to invest in expanding and modern-

reforms. The 2008 survey improves on earlier sur-

number of newly registered limited liability corpora-

izing facilities—and payments to the government to

veys’ methodology and country coverage for better

tions per 1,000 people ages 15–64.

acquire state-owned enterprises or rights to provide

cross-country comparability. Data on total and newly

services in a specific area or to use part of the radio

registered businesses were collected directly from

spectrum.

national registrars of companies. For cross-country

The data are from the World Bank’s Private Par-

comparability, only limited liability corporations

ticipation in Infrastructure (PPI) Project database,

that operate in the formal sector are included. For

which tracks infrastructure projects with private par-

additional information on sources, methodology,

ticipation in developing countries. It provides infor-

calculation of entrepreneurship rates, and data limi-

mation on more than 4,600 infrastructure projects

tations see http://econ.worldbank.org/research/

in 137 developing economies from 1984 to 2009.

entrepreneurship.

Data sources Data on investment commitments in infra-

The database contains more than 30 fields per proj-

structure projects with private participation are

ect record, including country, financial closure year,

from the World Bank’s PPI Project database

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 Financial

location, contract duration, private sponsors, bidding

Statistics. Data on business registration are from

process, and development bank support. Data on the

the World Bank’s Entrepreneurship Survey and

projects are compiled from publicly available infor-

database (http://econ.worldbank.org/research/

mation. The database aims to be as comprehensive

entrepreneurship).

as possible, but some projects—particularly those

2011 World Development Indicators

269

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

270

2008 2007 2007 2006 2006 2009

2009 2007 2008 2009 2006 2009 2006 2009 2009 2009 2006 2007 2009

2009 2006 2003 2006 2010 2009 2005 2009 2007 2009 2005 2006 2008 2006 2009 2009 2006

2009 2006 2008 2005 2007 2005 2006 2006 2006 2006

6.8 18.7 25.1 7.1 13.8 10.3 .. .. 3.0 3.2 13.6 .. 20.7 13.5 11.2 5.0 18.7 10.6 22.2 5.7 5.6 7.0 .. .. 20.8 9.0 18.3 .. 14.3 29.4 6.0 9.6 1.8 10.9 .. 10.4 .. 8.8 17.3 8.8 9.2 0.5 5.5 3.8 .. .. 2.8 7.3 2.1 1.2 4.0 1.8 9.2 2.7 2.9 .. 4.6

2011 World Development Indicators

1.2 3.9 2.3 3.3 2.2 2.1 .. .. 2.1 1.3 1.1 .. 1.2 1.7 1.0 0.9 1.2 2.2 1.5 1.8 1.0 4.4 .. .. 3.4 3.0 14.4 .. 0.6 8.0 2.7 0.5 3.6 0.7 .. 1.5 .. 0.5 0.6 3.4 2.7 0.2 0.4 1.1 .. .. 15.2 2.5 0.6 1.3 4.1 1.7 2.1 2.8 3.4 .. 1.5

Permits Corruption and licenses

Crime

Informality

Firms formally registered when operations started % of firms

Time required to obtain operating license

Informal payments to public officials

Losses due to theft, robbery, vandalism, and arson

days

% of firms

% of sales

13.8 21.2 19.3 24.1 78.3 20.0 .. .. 15.8 6.0 38.2 .. 64.3 26.0 21.4 13.7 83.5 20.8 35.8 27.3 .. 30.0 .. .. 24.3 67.7 11.6 .. 28.2 40.0 .. .. 14.5 26.5 .. 19.9 .. .. 19.9 90.6 35.4 .. 8.3 11.4 .. .. 12.1 8.4 11.8 .. 6.4 .. 75.4 13.0 30.4 .. 31.6

41.5 57.7 64.7 46.8 18.7 11.6 .. .. 32.0 85.1 13.5 .. 54.5 32.4 8.1 27.6 9.7 8.5 8.5 56.5 61.2 50.8 .. .. 41.8 8.2 72.6 .. 8.2 65.7 49.2 33.8 30.6 14.5 .. 8.7 .. 26.3 21.5 15.2 34.3 0.0 1.6 12.4 .. .. 26.1 52.4 4.1 .. 38.8 21.6 15.7 84.8 62.7 .. 16.7

1.5 0.5 0.9 0.4 1.5 0.6 .. .. 0.3 0.1 0.4 .. 1.9 0.9 0.2 1.3 1.7 0.5 0.3 1.1 0.0 1.7 .. .. 2.5 0.6 0.1 .. 0.7 1.8 3.3 0.4 3.4 0.2 .. 0.4 .. 0.7 0.9 3.0 2.6 0.0 0.9 1.4 .. .. 0.4 2.7 0.7 0.5 0.9 0.0 1.5 2.0 1.1 .. 2.2

88.0 89.4 98.3 .. 93.8 96.2 .. .. 85.1 .. 98.5 .. 87.9 90.5 98.6 .. 95.8 98.5 77.7 .. 87.5 82.1 .. .. 77.1 97.8 .. .. 85.6 61.9 84.3 .. 56.4 98.1 .. 98.0 .. .. 91.1 14.3 79.5 100.0 97.4 .. .. .. 63.7 .. 99.6 .. 66.4 .. 91.3 .. .. .. 89.4

Gender

Finance

Firms with Firms using banks to female finance participation in ownership investment % of firms

% of firms

2.8 10.8 15.0 23.4 30.3 31.8 .. .. 10.8 16.1 52.9 .. 43.9 41.1 32.8 40.9 59.3 33.9 19.2 34.8 .. 15.7 .. .. 40.1 27.8 .. .. 43.0 38.9 31.8 65.3 61.9 33.5 .. 25.0 .. .. 32.7 34.0 39.6 4.2 36.3 30.9 .. .. 33.1 21.3 40.8 20.3 44.0 24.4 28.4 25.4 19.9 .. 39.9

1.4 12.4 8.9 2.1 6.9 31.9 .. .. 19.0 24.7 35.8 .. 4.2 22.2 59.7 11.3 48.4 34.7 25.6 12.3 11.3 31.4 .. .. 4.2 29.1 28.8 .. 30.6 6.7 7.7 14.9 13.9 60.0 .. 33.4 .. 12.5 24.0 5.6 17.3 11.9 41.5 11.0 .. .. 6.3 7.6 38.2 45.0 16.0 25.9 12.8 0.9 0.7 .. 8.5

Infrastructure Innovation

Trade

Average Inter time to nationally recognized clear direct exports quality Value lost due to electrical certification through customs ownership outages % of sales

6.5 13.7 4.0 3.7 1.6 1.8 .. .. 1.8 10.6 0.8 .. 7.5 4.4 1.9 1.4 3.0 1.6 5.8 10.7 2.4 4.9 .. .. 3.3 1.8 1.3 .. 2.3 22.7 16.4 1.9 5.0 0.8 .. 0.6 .. 15.2 2.7 3.4 2.9 0.2 0.5 0.9 .. .. 1.7 11.8 1.4 .. 6.0 .. 4.5 14.0 5.3 .. 3.8

% of firms

8.5 24.6 5.0 5.1 26.9 26.9 .. .. 18.2 7.8 13.9 .. 7.3 13.8 30.1 12.7 25.7 19.9 14.4 7.1 2.8 20.4 .. .. 43.3 22.0 35.9 .. 5.9 8.5 19.6 10.5 4.3 16.5 .. 43.5 .. 9.6 18.2 21.1 11.0 15.1 21.2 4.2 .. .. 18.6 22.2 16.0 .. 6.8 11.7 8.0 5.2 8.4 .. 16.5

days

14.6 1.9 14.1 16.5 5.5 3.3 .. .. 1.9 8.4 2.6 .. 9.6 15.3 1.4 1.4 15.9 4.2 7.4 .. 1.5 15.1 .. .. 11.9 5.8 6.6 .. 7.0 18.0 .. 3.5 16.6 1.3 .. 5.7 .. 11.4 7.0 6.2 2.5 9.6 1.8 4.3 .. .. 3.8 5.0 3.8 4.7 7.8 5.5 4.5 4.3 5.6 .. 6.0

Workforce

Firms offering formal traininga % of firms

14.6 19.9 17.3 19.4 52.2 30.4 .. .. 10.5 16.2 44.4 .. 32.4 53.9 66.5 37.7 52.9 30.7 24.8 22.1 48.4 25.5 .. .. 43.4 46.9 84.8 .. 39.5 24.1 37.5 46.4 19.1 28.0 .. 70.7 .. 53.3 61.6 21.7 49.6 26.1 69.3 38.2 .. .. 30.9 25.6 14.5 35.4 33.0 20.0 28.1 21.1 12.4 .. 33.3

Survey year

Regulations and tax

Time dealing with officials

Average number of times % of management meeting with tax officials time

2009 2006 2009

2005

2005 2006 2009 2007 2005 2009 2009 2009 2009 2009 2009 2009 2009 2009 2009 2009 2007 2007 2006 2009 2006 2009 2009 2007 2007 2006 2009

2006 2009 2007 2003 2007 2006 2006 2006 2009 2009 2005

13.5 6.7 1.9 .. .. 2.3 .. .. 6.3 .. 6.7 4.7 5.1 .. 0.1 9.8 .. 4.9 1.6 9.7 8.9 5.6 7.5 .. 9.3 14.5 17.1 3.5 7.8 2.4 5.8 9.4 20.5 7.0 12.1 11.4 3.3 .. 2.9 6.5 .. .. 9.3 21.1 6.1 .. .. 2.2 10.3 .. 7.9 13.5 9.1 12.8 1.1 .. ..

0.8 2.6 0.2 .. .. 1.3 .. .. 1.8 .. 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 2.7 2.6 1.6 1.8 0.5 0.6 1.9 2.0 0.9 1.9 .. 0.3 1.3 .. .. 1.3 1.6 3.0 .. 4.4 1.6 1.4 .. 0.7 1.4 1.6 0.6 1.6 .. ..

Permits Corruption andÂ licenses

Crime

Informality

Firms formally registered when operations started % of firms

Time required to obtain operating license

Informal payments to public officials

Losses due to theft, robbery, vandalism, and arson

days

% of firms

% of sales

35.6 .. 21.1 .. .. .. .. .. .. .. 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 11.2 13.9 43.5 3.4 35.2 .. 9.6 14.5 .. .. 19.7 39.7 12.1 .. 11.8 16.4 41.2 .. 37.8 81.1 10.6 14.6 .. .. ..

4.0 47.5 14.6 .. .. 8.3 .. .. 17.7 .. 18.1 23.3 79.2 .. 14.1 2.2 .. 37.5 39.8 11.3 23.0 14.0 55.2 .. 8.5 11.5 19.2 10.8 .. 28.9 82.1 1.6 22.6 25.4 30.4 13.4 14.8 .. 11.4 15.2 .. .. 17.2 35.2 40.9 .. 33.2 27.2 25.4 .. 84.8 11.3 18.6 5.0 14.5 .. ..

0.1 0.1 0.3 .. .. 0.3 .. .. 1.1 .. 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 0.6 0.6 1.4 0.7 0.4 0.6 0.0 1.8 .. 1.3 0.9 .. .. 0.9 0.9 4.1 .. .. 0.5 0.5 .. 0.9 0.4 1.1 0.5 0.2 .. ..

100.0 .. 29.1 .. .. .. .. .. .. .. .. 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 85.4 .. 84.2 94.1 97.9 90.1 86.0 85.9 .. .. 94.0 .. .. 85.4 90.5 .. .. .. .. 98.0 .. 94.0 99.2 97.5 99.3 .. .. ..

Gender

Finance

Firms with Firms using banks to female finance participation in ownership investment % of firms

% of firms

42.4 9.1 42.8 .. .. 41.6 .. .. 32.2 .. 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 18.4 17.3 16.9 24.8 53.1 52.0 13.1 24.4 .. 33.4 27.4 .. .. 41.4 17.6 20.0 .. .. 6.7 37.1 .. 44.8 32.8 69.4 47.9 50.8 .. ..

48.7 46.7 11.7 .. .. 37.4 .. .. 37.0 .. 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 7.0 3.2 37.5 2.6 30.8 26.5 12.3 10.5 .. 8.1 17.5 .. .. 13.0 9.3 2.7 .. 31.0 9.7 19.2 .. 8.2 30.9 22.0 40.7 24.4 .. ..

Infrastructure Innovation

Trade

Average InterÂ time to nationally recognized clear direct exports quality Value lost due to electrical certification through customs ownership outages % of sales

0.9 6.6 2.4 .. .. 1.5 .. .. 11.8 .. 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 17.0 3.0 1.8 1.6 2.2 2.4 2.0 0.8 1.3 2.4 .. 0.7 27.0 .. .. 8.7 1.9 8.9 .. 4.2 9.9 2.4 .. 2.5 3.2 3.4 1.9 .. .. ..

% of firms

39.4 22.5 2.9 .. .. 17.2 .. .. 16.4 .. 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 8.6 5.9 11.1 20.3 9.1 16.7 17.3 18.7 .. 17.6 3.1 .. .. 18.7 4.6 8.5 .. 10.8 9.6 14.7 .. 7.1 14.6 15.7 17.3 12.7 .. ..

days

4.3 15.1 2.4 .. .. 2.6 .. .. 4.3 .. 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 4.9 2.7 4.8 3.9 10.3 5.2 2.4 18.6 1.8 10.1 .. 1.4 5.6 .. .. 5.0 2.6 7.5 .. 3.4 4.8 5.7 .. 5.5 5.4 8.1 6.0 7.2 .. ..

2011 World Development Indicators

states and markets

5.2

Business environment: Enterprise Surveys

Workforce

Firms offering formal traininga % of firms

14.8 15.9 4.7 .. .. 73.2 .. .. 53.5 .. 23.9 40.9 40.7 .. 39.5 24.6 .. 29.7 11.1 43.4 52.4 42.5 17.0 .. 46.0 19.0 27.0 48.4 50.1 22.5 25.5 25.6 24.6 33.1 61.2 24.7 22.1 .. 44.5 8.8 .. .. 28.9 32.1 25.7 .. 20.9 6.7 43.9 .. 46.9 57.7 31.1 60.9 31.9 .. ..

271

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

2009 2009 2006 2007 2009 2009 2009 2009 2007 2005 2004 2006

2009 2008 2006 2006 2009 2009

2008 2006 2008

2006 2008 2006 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 4.1 2.7 .. .. 27.1 .. 5.2 11.3 .. .. .. 7.0 11.1 33.6 4.9 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 .. .. .. 0.7 0.7 2.9 1.1 1.7 7.3 1.9 ..

Permits Corruption andÂ licenses

Crime

Informality

Firms formally registered when operations started % of firms

Time required to obtain operating license

Informal payments to public officials

Losses due to theft, robbery, vandalism, and arson

days

% of firms

% of sales

9.8 29.4 20.0 .. 18.1 18.0 18.8 .. 9.1 5.4 .. 15.1 4.4 16.3 .. 40.6 .. .. 83.8 40.5 49.5 .. 19.4 16.7 .. .. 17.7 .. 51.7 22.9 .. .. .. 7.3 56.2 .. 52.1 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 1.5 2.4 .. .. 0.4 .. 1.0 0.6 .. .. .. 0.7 0.7 1.4 0.3 1.2 0.6 1.0 ..

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 .. .. .. 133.8 9.1 41.6 15.9 21.3 6.5 48.3 ..

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 .. .. .. 97.8 100.0 97.3 87.5 .. 81.7 96.2 ..

Gender

Firms with Firms using banks to female finance participation in ownership investment % of firms

% of firms

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 .. .. .. 41.6 39.8 .. 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 .. .. .. 6.9 8.2 35.7 21.5 4.2 4.2 10.2 ..

Note: Enterprise surveys are updated several times a year; see www.enterprisesurveys.org for the most recent updates. a. For survey data collected in 2006 and 2007, data refer to the manufacturing module only.

272

2011 World Development Indicators

Finance

Infrastructure Innovation

Trade

Average InterÂ time to nationally recognized clear direct exports quality Value lost due to electrical certification through customs ownership outages % of sales

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 .. .. .. 0.9 5.4 4.4 3.7 4.6 13.2 3.7 ..

% of firms

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 .. .. .. 6.8 1.3 12.5 16.7 18.2 4.4 17.2 ..

days

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 .. .. .. 2.5 5.1 14.1 4.5 6.0 6.2 2.3 ..

Workforce

Firms offering formal traininga % of firms

24.9 52.2 27.6 .. 16.3 36.5 18.6 .. 33.1 47.5 .. 36.8 51.3 32.6 .. 51.0 .. .. 38.3 21.1 36.5 75.3 49.7 31.0 .. .. 28.8 .. 35.0 24.8 .. .. .. 24.6 9.6 42.3 43.6 26.5 12.9 26.0 ..

About the data

5.2

states and markets

Business environment: Enterprise Surveys Definitions

The World Bank Group’s Enterprise Survey gath-

The reliability and availability of infrastructure ben-

• Survey year is the year in which the underlying data

ers firm-level data on the business environment

efit households and support development. Firms with

were collected. • Time dealing with officials is the

to assess constraints to private sector growth and

access to modern and efficient infrastructure—tele-

average percentage of senior management’s time

enterprise performance. Standardized surveys are

communications, electricity, and transport—can be

that is spent in a typical week dealing with require-

conducted all over the world, and data are available

more productive. Firm-level innovation and use of

ments imposed by government regulations. • Aver-

on more than 120,000 firms in 125 countries. The

modern technology may help firms compete.

age number of times meeting with tax officials is

survey covers 11 dimensions of the business envi-

Delays in clearing customs can be costly, deterring

the average number of visits or required meetings

ronment, including regulation, corruption, crime,

firms from engaging in trade or making them uncom-

with tax officials. • Time required to obtain operat-

informality, finance, infrastructure, trade. For some

petitive globally. Ill-considered labor regulations dis-

ing license is the average wait to obtain an operating

countries, firm-level panel data are available, making

courage firms from creating jobs, and while employed

license from the day applied for to the day granted.

it possible to track changes in the business environ-

workers may benefit, unemployed, low-skilled, and

• Informal payments to public officials are the

ment over time.

informally employed workers will not. A trained labor

percentage of firms that answered positively to the

force enables firms to thrive, compete, innovate, and

question “Was a gift or informal payment expected

adopt new technology.

or requested during a meeting with tax officials?”

Firms evaluating investment options, governments interested in improving business conditions, and economists seeking to explain economic perfor-

The data in the table are from Enterprise Surveys

• Losses due to theft, robbery, vandalism, and

mance have all grappled with defining and measur-

implemented by the World Bank’s Financial and Pri-

arson are the estimated losses from those causes

ing the business environment. The firm-level data

vate Sector Development Enterprise Analysis Unit. All

that occurred on establishments’ premises as a

from Enterprise Surveys provide a useful tool for

economies in East Asia and Pacific, Europe and Cen-

percentage of annual sales. • Firms formally regis-

benchmarking economies across a large number of

tral Asia, Latin America and the Caribbean, Middle

tered when operations started are the percentage

indicators measured at the firm level.

East and North Africa, and Sub-Saharan Africa (for

of firms formally registered when they started opera-

Most countries can improve regulation and taxa-

2009) and Afghanistan, Bangladesh, and India draw

tions in the country. Firms not formally registered (the

tion without compromising broader social interests.

a sample of registered nonagricultural businesses,

residual) are in the informal sector of the economy.

Excessive regulation may harm business perfor-

excluding those in the financial and public sectors.

• Firms with female participation in ownership are

mance and growth. For example, time spent with

Samples for other economies are drawn only from the

the percentage of firms with a woman among the own-

tax officials is a burden firms may face in paying

manufacturing sector and are footnoted in the table.

ers. • Firms using banks to finance investment are

taxes. The business environment suffers when gov-

Typical Enterprise Survey sample sizes range from

the percentage of firms that invested in fixed assets

ernments increase uncertainty and risks or impose

150 to 1,800, depending on the size of the economy.

during the last fiscal year that used banks to finance

unnecessary costs and unsound regulation and taxa-

In each country samples are selected by stratified

fixed assets. • Value lost due to electrical outages

tion. Time to obtain licenses and permits and the

random sampling, unless otherwise noted. Stratified

is losses that resulted from power outages as a per-

associated red tape constrain firm operations.

random sampling allows indicators to be computed

centage of annual sales. • Internationally recognized

In some countries doing business requires informal

by sector, firm size, and region and increases the

quality certification ownership is the percentage of

payments to “get things done” in customs, taxes,

precision of economywide indicators compared with

firms that have an internationally recognized quality

licenses, regulations, services, and the like. Such

alternative simple random sampling. Stratification

certification, such as International Organization for

corruption harms the business environment by dis-

by sector of activity divides the economy into manu-

Standardization 9000, 9001, 9002, or 14000 or

torting policymaking, undermining government cred-

facturing and retail and other services sectors. For

Hazard Analysis and Critical Control Points. • Aver-

ibility, and diverting public resources. Crime, theft,

medium-size and large economies the manufacturing

age time to clear direct exports through customs

and disorder also impose costs on businesses and

sector is further stratified by industry. Firm size is

is the average number of days to clear direct exports

society.

stratified into small (5–19 employees), medium-size

through customs. • Firms offering formal training

In many developing countries informal businesses

(20–99 employees), and large (more than 99 employ-

are the percentage of firms offering formal training

operate without formal registration. These firms have

ees). Geographic stratification divides the national

programs for their permanent, full-time employees.

less access to financial and public services and can

economy into the main centers of economic activity.

engage in fewer types of contracts and investments, constraining growth. Equal opportunities for men and women contribute to development. Female participation in firm ownership is a measure of women’s integration as decision makers. Financial markets connect firms to lenders and investors, allowing firms to grow their businesses:

Data sources

creditworthy firms can obtain credit from financial

Data on the business environment are from the

intermediaries at competitive prices. But too often

World Bank Group’s Enterprise Surveys website

market imperfections and government-induced distor-

(www.enterprisesurveys.org).

tions limit access to credit and thus restrain growth.

2011 World Development Indicators

273

5.3

Business environment: Doing Business indicators Starting a business

Number of procedures June 2010

274

4 5 14 8 14 6 2 8 6 7 5 3 7 15 12 10 15 4 4 11 9 6 1 8 13 8 14 3 9 10 10 12 10 6 .. 9 4 8 13 6 8 13 5 5 3 5 9 8 3 9 7 15 12 13 17 13 13

Time required days June 2010

7 5 24 68 26 15 2 28 8 19 5 4 31 50 55 61 120 18 14 32 85 19 5 22 75 22 38 6 14 84 160 60 40 7 .. 20 6 19 56 7 17 84 7 9 14 7 58 27 3 15 12 19 37 41 216 105 14

2011 World Development Indicators

Registering property

Cost % of per capita income June 2010

26.7 16.8 12.9 163.0 14.2 3.1 0.7 5.2 3.1 33.3 1.6 5.4 152.6 100.8 17.7 2.2 7.3 1.6 49.8 129.3 128.3 51.2 0.4 228.4 226.9 6.8 4.5 2.0 14.7 735.1 111.4 10.5 133.0 8.6 .. 9.3 0.0 19.2 32.6 6.3 45.0 69.2 1.9 14.1 1.1 0.9 21.9 199.6 5.0 4.8 20.3 20.7 49.1 146.6 183.3 212.0 47.2

Number of procedures June 2010

9 6 11 7 6 3 5 3 4 8 3 8 4 7 7 5 14 8 4 5 7 5 6 5 6 6 4 5 7 6 6 6 6 5 .. 4 3 7 9 7 5 11 3 10 3 8 7 5 1 5 5 11 4 6 9 5 7

Time required days June 2010

250 42 47 184 52 7 5 21 11 245 15 79 120 92 33 16 42 15 59 94 56 93 17 75 44 31 29 36 20 54 55 21 62 104 .. 43 42 60 16 72 31 78 18 41 14 59 39 66 2 40 34 22 23 104 211 405 23

Dealing with construction permits

Number of procedures to build a warehouse June 2010

13 24 22 12 28 20 16 14 31 14 16 14 15 17 16 24 18 24 15 25 23 14 14 21 14 18 37 7 10 14 17 23 21 13 .. 36 6 17 19 25 34 .. 14 12 18 13 16 17 10 12 18 15 22 32 15 11 17

Time required to build a warehouse days June 2010

340 331 240 328 338 137 221 194 207 231 151 169 320 249 255 167 411 139 122 212 709 213 75 239 164 155 336 67 50 128 169 191 592 315 .. 150 69 214 155 218 155 .. 134 128 66 137 210 146 98 100 220 169 178 255 167 1,179 106

Enforcing contracts

Number of procedures June 2010

47 39 46 46 36 49 28 25 39 41 28 26 42 40 37 29 45 39 37 44 44 43 36 43 41 36 34 24 34 43 44 40 33 38 .. 27 35 34 39 41 30 39 36 37 32 29 38 32 36 30 36 39 31 50 40 35 45

Time required days June 2010

1,642 390 630 1,011 590 285 395 397 237 1,442 225 505 825 591 595 625 616 564 446 832 401 800 570 660 743 480 406 280 1,346 625 560 852 770 561 .. 611 410 460 588 1,010 786 405 425 620 375 331 1,070 434 285 394 487 819 1,459 276 1,140 508 900

Protecting Closing a investors business

Disclosure index 0–10 (least to most disclosure) June 2010

Time to resolve insolvency years June 2010

1 8 6 5 6 5 8 3 7 6 5 8 6 1 3 7 6 10 6 4 5 6 8 6 6 8 10 10 8 3 6 2 6 1 .. 2 7 5 1 8 5 4 8 4 6 10 6 2 8 5 7 1 3 6 6 2 0

.. .. 2.5 6.2 2.8 1.9 1.0 1.1 2.7 4.0 5.8 0.9 4.0 1.8 3.3 1.7 4.0 3.3 4.0 .. .. 3.2 0.8 4.8 .. 4.5 1.7 1.1 3.0 5.2 3.3 3.5 2.2 3.1 .. 3.2 1.1 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 3.8

Starting a business

Number of procedures June 2010

4 12 9 6 11 4 5 6 6 8 8 6 11 .. 8 10 13 2 7 5 5 7 5 .. 6 3 2 10 9 6 9 5 6 8 7 6 9 .. 10 7 6 1 6 9 8 5 5 10 6 6 7 6 15 6 6 7 8

Time required days June 2010

4 29 47 8 77 13 34 6 8 23 13 19 33 .. 14 58 35 10 100 16 9 40 20 .. 22 3 7 39 17 8 19 6 9 10 13 12 13 .. 66 31 8 1 39 17 31 7 12 21 9 51 35 27 38 32 6 7 12

Registering property

Cost % of per capita income June 2010

8.2 56.5 22.3 4.0 107.8 0.4 4.3 18.5 5.2 7.5 44.6 1.0 38.3 .. 14.7 28.7 1.3 3.7 11.3 1.5 75.0 26.0 54.6 .. 2.8 2.5 12.9 108.4 17.5 79.7 33.6 3.8 12.3 10.9 3.2 15.8 13.9 .. 18.5 46.6 5.7 0.4 117.9 118.6 78.9 1.8 3.3 10.7 10.3 17.7 55.1 13.6 29.7 17.5 6.5 0.7 9.7

Number of procedures June 2010

4 5 6 9 5 5 7 8 6 6 7 4 8 .. 7 8 8 4 9 6 8 6 10 .. 3 5 7 6 5 5 4 4 5 5 5 8 8 .. 9 3 5 2 8 4 13 1 2 6 8 4 6 4 8 6 1 8 10

Time required days June 2010

17 44 22 36 51 38 144 27 37 14 21 40 64 .. 11 33 55 5 135 42 25 101 50 .. 3 58 74 49 56 29 49 26 74 5 11 47 42 .. 23 5 7 2 124 35 82 3 16 50 32 72 46 7 33 152 1 194 16

Dealing with construction permits

Enforcing contracts

Number of procedures to build a warehouse June 2010

Time required to build a warehouse days June 2010

Number of procedures June 2010

31 37 14 17 14 11 20 14 10 15 19 34 11 .. 13 21 25 13 24 24 21 15 24 .. 17 21 16 21 25 15 25 18 11 30 21 19 17 .. 12 15 18 7 17 17 18 14 15 12 20 24 13 19 26 32 19 22 19

189 195 160 322 215 192 235 257 156 187 87 219 120 .. 34 320 104 143 172 186 218 601 77 .. 162 146 178 268 261 168 201 107 105 292 215 163 381 .. 139 424 230 65 219 265 350 252 186 223 116 217 179 188 169 311 272 209 76

35 46 40 39 51 20 35 41 35 30 38 38 40 .. 35 53 50 39 42 27 37 41 41 .. 30 37 38 42 30 36 46 36 38 31 32 40 30 .. 33 39 26 30 35 39 40 33 51 47 31 42 38 41 37 38 31 39 43

Time required days June 2010

395 1,420 570 505 520 515 890 1,210 655 360 689 390 465 .. 230 420 566 260 443 309 721 785 1,280 .. 275 370 871 312 585 620 370 645 415 365 314 615 730 .. 270 735 514 216 540 545 457 280 598 976 686 591 591 428 842 830 547 620 570

5.3

states and markets

Business environment: Doing Business indicators

Protecting Closing a investors business

Disclosure index 0â&#x20AC;&#x201C;10 (least to most disclosure) June 2010

Time to resolve insolvency years June 2010

2 7 10 5 4 10 7 7 4 7 5 8 3 .. 7 3 7 8 2 5 9 2 4 .. 5 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

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 2.9 .. 2.6 2.3 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

2011 World Development Indicators

275

5.3

Business environment: Doing Business indicators Starting a business

Romania Russian Federation Rwanda Saudi Arabia Senegal Serbia Sierra Leone Singapore Slovak Republic Slovenia Somalia South Africa 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

Number of procedures June 2010

Time required days June 2010

Cost % of per capita income June 2010

6 9 2 4 4 7 6 3 6 2 .. 6 10 4 10 12 3 6 7 8 12 7 10 7 9 10 6 .. 18 10 8 6 6 11 7 17 9 11 6 6 9 8u 8 8 8 8 8 8 6 9 8 7 9 6 6

10 30 3 5 8 13 12 3 16 6 .. 22 47 35 36 56 15 20 13 27 29 32 83 75 43 11 6 .. 25 27 15 13 6 65 15 141 44 49 12 18 90 34 u 41 39 35 43 39 40 18 60 23 25 43 18 14

2.6 3.6 8.8 7.0 63.1 7.9 110.7 0.7 1.9 0.0 .. 6.0 15.1 5.4 33.6 33.0 0.6 2.1 38.1 36.9 30.9 5.6 18.4 178.1 0.8 5.0 17.2 .. 94.4 6.1 6.4 0.7 1.4 42.1 11.9 30.2 12.1 93.7 82.1 27.9 182.8 40.7 u 107.9 31.7 44.4 16.1 52.6 31.5 8.9 39.6 54.6 24.5 95.2 7.3 6.7

Note: Regional aggregates are for developing countries only.

276

Registering property

2011 World Development Indicators

Dealing with construction permits

Number of procedures June 2010

Time required days June 2010

Number of procedures to build a warehouse June 2010

Time required to build a warehouse days June 2010

8 6 4 2 6 6 7 3 3 6 .. 6 4 8 6 9 1 4 4 6 9 2 .. 5 8 4 6 .. 13 10 1 2 4 8 12 8 4 7 6 5 5 6u 7 6 6 6 6 5 6 7 7 6 7 5 5

48 43 55 2 122 91 86 5 17 113 .. 24 18 83 9 44 7 16 19 37 73 2 .. 295 162 39 6 .. 77 117 2 8 12 66 78 47 57 47 19 40 31 58 u 94 54 65 41 65 99 36 62 39 100 69 38 35

17 53 14 12 16 20 25 11 13 14 .. 17 11 22 19 14 8 14 26 30 22 11 22 15 20 20 25 .. 18 22 17 11 19 30 28 11 13 21 15 17 17 18 u 18 19 18 19 19 19 23 16 20 18 18 17 14

228 540 195 89 210 279 252 25 287 199 .. 174 233 214 271 116 116 154 128 228 328 156 208 277 261 97 188 .. 171 374 64 95 40 234 274 395 194 199 107 254 1,012 207 u 275 201 197 206 221 181 235 220 181 241 240 169 227

Enforcing contracts

Protecting Closing a investors business

Number of procedures June 2010

Time required days June 2010

Disclosure index 0â&#x20AC;&#x201C;10 (least to most disclosure) June 2010

31 37 24 43 44 36 40 21 31 32 .. 30 39 40 53 40 30 31 55 34 38 36 51 41 42 39 35 .. 38 30 49 28 32 41 42 29 34 44 36 35 38 38 u 39 39 40 38 39 37 38 39 42 44 39 35 31

512 281 230 635 780 635 515 150 565 1,290 .. 600 515 1,318 810 972 508 417 872 430 462 479 1,285 588 1,340 565 420 .. 490 345 537 399 300 720 195 510 295 540 520 471 410 605 u 613 638 679 588 631 564 382 698 701 1,053 641 532 602

9 6 7 9 6 7 6 10 3 3 .. 8 5 4 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 5 6 5 5 7 4 6 4 5 6 5

Time to resolve insolvency years June 2010

3.3 3.8 .. 1.5 3.0 2.7 2.6 0.8 4.0 2.0 .. 2.0 1.0 1.7 .. 2.0 2.0 3.0 4.1 1.7 3.0 2.7 .. 3.0 .. 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 2.9 3.3 3.1 2.9 3.2 3.5 4.5 3.4 2.1 1.6

About the data

5.3

states and markets

Business environment: Doing Business indicators Definitions

The economic health of a country is measured not

The Doing Business project encompasses two

• Number of procedures for starting a business is the

only in macroeconomic terms but also by other

types of data: data from readings of laws and regu-

number of procedures required to start a business,

factors that shape daily economic activity such as

lations and data on time and motion indicators that

including interactions to obtain necessary permits and

laws, regulations, and institutional arrangements.

measure efficiency in achieving a regulatory goal.

licenses and to complete all inscriptions, verifications,

The Doing Business indicators measure business

Within the time and motion indicators cost estimates

and notifications to start operations for businesses

regulation, gauge regulatory outcomes, and measure

are recorded from official fee schedules where appli-

with specific characteristics of ownership, size, and

the extent of legal protection of property, the flex-

cable. The data from surveys are subjected to numer-

type of production. • Time required for starting a

ibility of employment regulation, and the tax burden

ous tests for robustness, which lead to revision or

business is the number of calendar days to complete

on businesses.

expansion of the information collected.

the procedures for legally operating a business using

The table presents a subset of Doing Business

The Doing Business methodology has limitations

the fastest procedure, independent of cost. • Cost

indicators covering 6 of the 10 sets of indicators:

that should be considered when interpreting the

for starting a business is normalized as a percentage

starting a business, registering property, dealing with

data. First, the data collected refer to businesses

of gross national income (GNI) per capita. It includes

construction permits, enforcing contracts, protecting

in the economy’s largest city and may not represent

all official fees and fees for legal or professional ser-

investors, and closing a business. Table 5.5 includes

regulations in other locations of the economy. To

vices if such services are required by law. • Number of

Doing Business measures of getting credit, and table

address this limitation, subnational indicators are

procedures for registering property is the number of

5.6 presents data on paying taxes.

being collected for selected economies. These sub-

procedures required for a business to legally transfer

The fundamental premise of the Doing Business

national studies point to significant differences in

property. • Time required for registering property is

project is that economic activity requires good rules

the speed of reform and the ease of doing business

the number of calendar days for a business to legally

and regulations that are efficient, accessible to all

across cities in the same economy. Second, the data

transfer property. • Number of procedures for deal-

who need to use them, and simple to implement.

often focus on a specific business form—generally

ing with licenses to build a warehouse is the number

Thus some Doing Business indicators give a higher

a limited liability company of a specified size—and

of interactions of a company’s employees or manag-

score for more regulation, such as stricter disclosure

may not represent regulation for other types of busi-

ers with external parties, including government staff,

requirements in related-party transactions, and oth-

nesses such as sole proprietorships. Third, transac-

public inspectors, notaries, land registry and cadastre

ers give a higher score for simplified regulations,

tions described in a standardized business case refer

staff, and technical experts apart from architects and

such as a one-stop shop for completing business

to a specific set of issues and may not represent the

engineers. • Time required for dealing with construc-

startup formalities.

full set of issues a business encounters. Fourth, the

tion permits to build a warehouse is the number of

In constructing the indicators, it is assumed that

time measures involve an element of judgment by the

calendar days to complete the required procedures

entrepreneurs know about all regulations and comply

expert respondents. When sources indicate different

for building a warehouse using the fastest procedure,

with them; in practice, entrepreneurs may not be

estimates, the Doing Business time indicators repre-

independent of cost. • Number of procedures for

aware of all required procedures or may avoid legally

sent the median values of several responses given

enforcing contracts is the number of independent

required procedures altogether. But where regula-

under the assumptions of the standardized case.

actions, mandated by law or court regulation, that

tion is particularly onerous, levels of informality are

Fifth, the methodology assumes that a business has

demand interaction between the parties to a con-

higher, which comes at a cost: firms in the informal

full information on what is required and does not

tract or between them and the judge or court officer.

sector usually grow more slowly, have less access

waste time when completing procedures.

• Time required for enforcing contracts is the number

to credit, and employ fewer workers—and those

of calendar days from the time of the filing of a law-

workers remain outside the protections of labor law.

suit in court to the final determination and payment.

The indicators in the table can help policymakers

• Extent of disclosure index measures the degree

understand the business environment in a country

to which investors are protected through disclosure

and—along with information from other sources such

of ownership and financial information. Higher values

as the World Bank’s Enterprise Surveys—provide

indicate more disclosure. • Time to resolve insolvency

insights into potential areas of reform.

is the number of years from time of filing for insolvency

Doing Business data are collected with a standardized survey that uses a simple business case

in court until resolution of distressed assets and payment of creditors.

to ensure comparability across economies and over time—with assumptions about the legal form of the business, its size, its location, and nature of its operation. Surveys in 183 countries are administered through more than 8,200 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

sionals who routinely administer or advise on legal

(www.doingbusiness.org).

and regulatory requirements.

2011 World Development Indicators

277

5.4

Stock markets Market capitalization

$ millions 2000

278

.. .. .. .. 166,068 2 372,794 29,935 3 1,186 .. 182,481 .. 1,742 .. 978 226,152 617 .. .. .. .. 841,385 .. .. 60,401 580,991 623,398 9,560 .. .. 2,924 1,185 2,742 .. 11,002 107,666 .. 704 28,741 2,041 .. 1,846 .. 293,635 1,446,634 .. .. 24 1,270,243 502 110,839 172 .. .. .. 458

% of GDP 2010

2000

.. .. .. .. 63,910 28 1,454,547 67,683 .. 47,000 .. 269,342 .. 3,388 .. 4,076 1,545,566 7,276 .. .. .. .. 2,160,229 .. .. 341,584 4,762,837 2,711,334 208,502 .. .. 1,445 7,099 24,912 .. 43,056 231,746 .. 5,263 82,495 4,227 .. 2,260 .. 118,160 1,926,488 .. .. 1,060 1,429,707 3,531 72,639 .. .. .. .. ..

.. .. .. .. 58.4 0.1 89.4 15.7 0.1 2.5 .. 78.5 .. 20.7 .. 17.4 35.1 4.8 .. .. .. .. 116.1 .. .. 80.3 48.5 368.6 9.5 .. .. 18.3 11.4 12.8 .. 19.4 67.3 .. 4.4 28.8 15.5 .. 32.5 .. 241.2 108.9 .. .. 0.8 66.8 10.1 88.3 0.9 .. .. .. 8.8

2011 World Development Indicators

2009

.. .. .. .. 15.9 1.6 136.1 14.1 .. 7.9 .. 55.5 .. 16.1 .. 33.8 73.2 14.6 .. .. .. .. 125.8 .. .. 128.0 100.4 1,088.3 57.0 .. .. 5.0 26.4 40.7 .. 27.7 60.4 .. 7.4 47.7 21.0 .. 13.9 .. 38.2 74.4 .. .. 6.8 39.0 9.6 16.6 .. .. .. .. ..

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

2000

2009

2000

2010

2000

2010

2009

2010

.. .. .. .. 2.1 0.0 54.3 4.9 .. 1.6 .. 16.4 .. 0.8 .. 0.8 15.7 0.4 .. .. .. .. 87.6 .. .. 8.1 60.2 223.4 0.4 .. .. 0.7 0.3 0.9 .. 11.6 57.2 .. 0.1 11.1 0.2 .. 5.7 .. 169.8 81.6 .. .. 0.1 56.3 0.2 75.7 0.1 .. .. .. ..

.. .. .. .. 0.9 0.0 82.4 6.7 .. 16.3 .. 27.1 .. 0.1 .. 0.9 40.7 0.8 .. .. .. .. 92.8 .. .. 23.0 179.6 707.4 5.5 .. .. 0.1 0.6 2.3 .. 10.8 47.9 .. 2.4 28.0 .. .. 2.0 .. 38.3 51.6 .. .. 0.0 38.7 0.2 15.7 .. .. .. .. ..

.. .. .. .. 4.8 11.9 56.5 29.8 .. 74.8 .. 20.7 .. 5.7 .. 4.7 44.6 8.7 .. .. .. .. 77.3 .. .. 9.5 158.3 61.3 3.8 .. .. 4.0 2.5 7.1 .. 57.7 86.0 .. 2.0 36.1 1.2 .. 18.0 .. 64.3 74.1 .. .. 11.3 79.1 1.4 60.4 6.4 .. .. .. ..

.. .. .. .. 4.6 0.2 90.1 79.4 .. 54.4 .. 42.0 .. 0.4 .. 3.5 66.4 2.8 .. .. .. .. 71.1 .. .. 19.7 164.4 63.9 13.4 .. .. 2.8 2.0 4.1 .. 29.4 69.1 .. 3.8 43.0 .. .. 13.1 .. 97.4 42.5 .. .. 0.3 103.0 3.4 67.7 .. .. .. .. ..

.. .. .. .. 127 105 1,330 97 2 221 .. 174 .. 26 .. 16 459 503 .. .. .. .. 1,418 .. .. 258 1,086 779 126 .. .. 21 41 64 .. 131 225 .. 30 1,076 40 .. 23 .. 154 808 .. .. 269 1,022 22 329 7 .. .. .. 94

.. .. .. .. 101 2 1,913 72 .. 302 .. 161 .. 38 .. 21 373 390 .. .. .. .. 3,805 .. .. 227 2,063 1,396 84 .. .. 9 38 221 .. 16 196 .. 40 211 61 .. 15 .. 123 901 .. .. 143 571 35 287 .. .. .. .. ..

.. .. .. .. 97.8 a .. 72.4 57.0 .. 38.6a .. 54.5 .. .. .. 24.3a 125.1 17.2a .. .. .. .. 57.5 .. .. 84.0 66.3 67.1 75.7a .. .. .. –10.7a 31.1a .. 23.0 40.6 .. –13.1a 35.6 .. .. 32.9a .. 17.5 25.6b .. .. .. 25.8 c –42.7a 22.1 .. .. .. .. ..

.. .. .. .. 55.3a .. 12.5 10.9 .. 37.6a .. 0.5 .. .. .. –6.8a 6.5 –15.2a .. .. .. .. 22.0 .. .. 47.2 6.9 21.3 44.1a .. .. .. 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 .. .. .. .. ..

Market capitalization

$ millions 2000

12,021 148,064 26,834 7,350 .. 81,882 64,081 768,364 3,582 3,157,222 4,943 1,342 1,283 .. 171,587 .. 20,772 4 .. 563 1,583 .. .. .. 1,588 7 .. .. 116,935 .. .. 1,331 125,204 38 37 10,899 .. .. 311 790 640,456 18,866 .. .. 4,237 65,034 3,463 6,581 2,794 1,520 224 10,562 25,957 31,279 60,681 .. 5,152

% of GDP 2010

27,708 1,615,860 360,388 86,616 .. 33,722 218,055 318,140 6,626 4,099,591 30,864 60,742 14,461 .. 1,089,217 .. 119,621 79 .. 1,252 12,586 .. .. .. 5,661 2,647 .. 1,363 410,534 .. .. 6,506 454,345 .. 1,093 69,153 .. .. 1,176 4,843 661,204 36,295 .. .. 50,883 250,922 20,267 38,169 10,917 9,742 42 99,831 157,321 190,235 81,996 .. 123,592

2000

25.1 32.2 16.3 7.3 .. 84.8 51.4 70.0 39.8 67.6 58.4 7.3 10.1 .. 32.2 .. 55.1 0.3 .. 7.2 9.2 .. .. .. 13.9 0.2 .. .. 124.7 .. .. 29.0 21.5 3.2 3.4 29.4 .. .. 8.0 14.4 166.3 36.7 .. .. 9.2 38.6 17.4 8.9 24.0 49.3 3.5 19.8 34.2 18.3 51.9 .. 29.0

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

2009

2000

2009

2000

2010

2000

2010

2009

2010

21.9 85.6 33.0 19.1 .. 13.2 93.2 15.0 51.4 66.6 127.0 50.0 36.6 .. 100.5 .. 72.4 1.6 .. 7.0 37.3 .. .. .. 12.0 10.0 .. 29.3 132.6 .. .. 55.2 38.9 .. 10.2 68.8 .. .. 9.1 43.8 68.5 52.9 .. .. 19.3 59.5 37.5 20.5 32.6 116.1 0.3 53.5 49.7 31.5 42.4 .. 89.4

25.4 110.8 8.7 1.1 .. 14.9 18.8 70.9 0.8 57.7 4.9 0.5 0.4 .. 200.2 .. 11.2 1.7 .. 2.9 0.7 .. .. .. 1.8 3.3 .. .. 62.4 .. .. 1.6 7.8 1.9 0.7 3.0 .. .. 0.6 0.6 175.9 21.0 .. .. 0.6 35.7 2.8 44.6 1.3 0.0 0.1 2.9 10.8 8.5 46.5 .. 1.3

20.1 79.1 21.3 5.2 .. 8.1 45.2 21.8 1.0 82.7 54.4 3.5 1.7 .. 190.0 .. 82.9 1.5 .. 0.1 3.0 .. .. .. 0.8 0.7 .. 0.4 37.8 .. .. 3.8 8.8 0.2 0.4 32.2 .. .. 0.2 1.8 76.3 29.4 .. .. 2.6 64.9 12.6 14.5 0.2 0.2 0.1 2.4 10.7 13.0 19.7 .. 25.9

85.8 306.5 31.5 7.4 .. 19.2 36.6 104.0 2.5 69.9 7.7 4.9 3.5 .. 376.6 .. 21.3 580.6 .. 47.8 6.7 .. .. .. 14.8 1,612.9 .. .. 44.6 .. .. 5.1 32.5 80.2 23.2 8.9 .. .. 4.4 5.4 101.4 45.9 .. .. 7.3 93.4 14.2 486.8 4.7 0.1 3.5 12.7 24.1 48.1 85.5 .. 4.5

94.5 75.6 48.1 22.9 .. 52.9 66.7 169.7 3.3 114.5 30.1 3.9 8.6 .. 168.9 .. 38.8 11.9 .. 1.8 14.7 .. .. .. 5.8 2.0 .. 1.5 27.1 .. .. 6.4 27.3 .. 6.4 16.3 .. .. 1.8 1.9 98.4 20.8 .. .. 12.5 90.8 18.2 36.2 2.0 .. .. 4.7 22.6 47.6 34.6 .. 17.3

60 5,937 290 304 .. 76 654 291 46 2,561 163 23 57 .. 1,308 .. 77 80 .. 64 12 .. .. .. 54 1 .. .. 795 .. .. 40 179 34 410 53 .. .. 13 110 234 142 .. .. 195 191 131 762 29 7 56 230 228 225 109 .. 22

48 4,987 420 341 .. 50 596 291 39 3,553 277 60 53 .. 1,781 .. 215 11 .. 33 10 .. .. .. 39 34 .. 14 957 .. .. 86 130 .. 336 73 .. .. 7 190 113 102 .. .. 215 195 120 644 34 10 50 199 251 569 47 .. 43

73.0 94.1 130.1 .. .. 44.7 56.8 23.1 –15.8a 16.4 d –13.9a 1.5a 0.6a .. 67.2 .. –10.4 a .. .. 2.2a 43.4 a .. .. .. 36.7a .. .. .. 46.7 .. .. 44.2a 55.8 .. .. –1.7 .. .. 22.6a .. 41.7 40.4 .. .. –35.4 a 91.4 22.0a 56.7a 15.4 a .. .. 79.3 71.5 41.9 35.0 .. 5.1a

–10.8 18.7 37.9 .. .. –7.7 7.4 –17.4 22.4 a 9.6d –8.6a –1.0a 33.8a .. 25.3 .. 29.1a .. .. 39.4 a –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 12.8a .. .. 51.3 56.7 11.3 –16.6 .. 27.7a

2011 World Development Indicators

279

5.4

Stock markets Market capitalization

$ millions 2000

% of GDP 2010

Romania 1,069 32,385 Russian Federation 38,922 1,004,525 Rwanda .. .. Saudi Arabia 67,171 353,414 Senegal .. .. Serbia 734 9,690 Sierra Leone .. .. Singapore 152,827 370,091 Slovak Republic 1,217 4,150 Slovenia 2,547 9,428 Somalia .. .. South Africa 204,952 1,012,538 Spain 504,219 1,171,615 Sri Lanka 1,074 19,924 Sudan .. .. Swaziland 73 .. Sweden 328,339 581,174 Switzerland 792,316 1,229,357 Syrian Arab Republic .. .. Tajikistan .. .. Tanzania 233 1,264 Thailand 29,489 277,732 Timor-Leste .. .. Togo .. .. Trinidad and Tobago 4,330 12,158 Tunisia 2,828 10,682 Turkey 69,659 306,662 Turkmenistan .. .. Uganda 35 .. Ukraine 1,881 39,457 United Arab Emirates 5,727 104,669 United Kingdom 2,576,992 3,107,038 United States 15,104,037 17,138,978 Uruguay 161 157 Uzbekistan 32 .. Venezuela, RB 8,128 3,991 Vietnam .. 20,385 West Bank and Gaza 765 2,450 Yemen, Rep. .. .. Zambia 236 2,817 Zimbabwe 2,432 11,476 World 32,187,124 s 56,172,634 s Low income .. 86,835 Middle income 1,941,548 13,277,006 Lower middle income 879,123 7,570,880 Upper middle income 1,062,425 5,706,126 Low & middle income 1,948,214 13,363,841 East Asia & Pacific 780,487 6,001,435 Europe & Central Asia 115,145 1,473,816 Latin America & Carib. 620,023 2,750,758 Middle East & N. Africa 57,110 294,845 South Asia 157,695 1,725,795 Sub-Saharan Africa 217,754 1,117,191 High income 30,238,910 42,808,793 Euro area 5,435,393 6,276,893

2000

2.9 15.0 .. 35.6 .. 4.9 .. 164.8 4.2 12.8 .. 154.2 86.8 6.6 .. 4.9 132.8 317.0 .. .. 2.3 24.0 .. .. 53.1 14.5 26.1 .. 0.6 6.0 8.1 174.4 152.6 0.7 0.2 6.9 .. 18.6 .. 7.3 36.8 101.7 w .. 36.5 36.2 36.8 36.1 47.1 17.5 31.7 19.9 26.1 89.8 115.2 86.8

Market liquidity

Turnover ratio

Value of shares traded % of GDP

Value of shares traded % of market capitalization

2009

2000

18.8 69.9 .. 84.8 .. 26.8 .. 170.5 5.3 24.3 .. 247.0 88.8 19.4 .. 6.9 106.5 217.7 .. .. 5.4 52.4 .. .. 52.6 23.1 36.7 .. .. 14.8 47.6 128.6 106.8 0.4 .. 2.7 21.8 .. .. 17.4 161.4 85.2 w 37.7 73.2 82.2 62.1 72.6 91.0 50.8 52.9 38.0 73.3 154.1 89.9 49.3

0.6 7.8 .. 9.2 .. 0.1 .. 98.7 3.1 2.3 .. 58.3 169.8 0.9 .. 0.0 157.7 243.7 .. .. 0.4 19.0 .. .. 1.7 3.2 67.2 .. 0.0 0.9 0.2 124.2 321.9 0.0 0.1 0.6 .. 4.6 .. 0.2 4.2 151.4 w .. 34.5 54.6 17.5 34.0 49.8 30.1 8.4 5.1 90.2 32.3 175.5 80.2

2009

1.2 55.4 .. 89.7 .. 1.3 .. 138.4 0.2 2.1 .. 120.0 109.5 2.1 .. .. 96.1 161.7 .. .. 0.1 51.2 .. .. 1.1 3.2 39.6 .. .. 0.5 28.5 156.5 331.0 0.0 0.0 0.0 6.8 .. .. 0.8 16.1 142.5 w 7.9 82.7 124.3 31.5 81.9 149.0 38.3 20.9 16.2 67.0 48.1 165.3 45.6

2000

24.3 36.6 .. 27.1 .. .. .. 52.1 78.7 19.7 .. 33.2 210.7 10.8 .. 0.3 111.2 82.0 .. .. 19.4 52.9 .. .. 3.1 22.6 196.5 .. 1.7 19.2 1.8 66.6 200.8 0.9 25.7 8.8 .. 23.4 .. 3.1 11.3 140.2 w 18.3 93.8 162.2 44.2 93.5 116.2 131.0 27.1 21.4 308.8 31.7 143.0 90.1

2010

5.4 85.7 .. 60.5 .. 2.2 .. 82.9 3.9 2.6 .. 39.6 76.0 23.6 .. .. 86.8 75.6 .. .. .. 104.8 .. .. 1.2 17.2 158.4 .. .. 7.5 25.6 101.9 189.1 .. .. 0.8 141.4 18.7 .. .. .. 122.0 w 32.5 101.1 132.4 55.7 100.8 146.0 91.2 46.1 27.7 73.5 37.1 128.5 75.0

Listed domestic companies

number 2000

2010

S&P/Global Equity Indices

% change 2009

5,555 1,383 26.1a 249 345 106.6 .. .. .. 75 146 28.5e .. .. .. 6 7 .. .. .. .. 418 461 76.7 493 90 –23.1a 38 71 16.1a .. .. .. 616 360 53.7 1,019 3,310 29.0 239 241 118.0a .. .. .. 6 5 .. 292 331 66.0 252 246 24.5 .. .. .. .. .. .. 4 11 .. 381 541 72.8 .. .. .. .. .. .. 27 37 –10.2a 44 54 40.6a 315 337 99.6 .. .. .. 2 8 .. 139 183 31.1a 54 101 24.6a 1,904 2,056 35.2f 7,524 4,279 23.5g 16 6 .. 5 .. .. 85 55 .. .. 164 46.9a 24 41 .. .. .. .. 9 19 16.7a 69 76 –83.8 47,751 s 47,071 s .. 719 21,522 16,778 11,444 11,088 10,078 5,690 22,094 17,497 3,190 4,758 7,199 2,963 1,672 1,457 1,676 1,007 7,269 6,364 1,088 948 25,657 29,574 5,051 6,278

2010

–6.6a 21.7 .. 9.0e .. .. .. 18.4 5.4 a –20.3a .. 32.1 –24.5 84.6a .. .. 32.6 11.0 .. .. .. 52.1 .. .. 0.8 a 11.7a 21.4 .. .. 53.8a –6.8a 5.2f 12.8g .. .. .. 0.5a .. .. 17.4 a ..

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 Saudi Arabia country index. f. Refers to the FTSE 100. g. Refers to the S&P 500 index.

280

2011 World Development Indicators

About the data

5.4

states and markets

Stock markets Definitions

The development of an economy’s financial markets

countries. Market capitalization shows the overall

• Market capitalization (also known as market

is closely related to its overall development. Well

size of the stock market in U.S. dollars and as a

value) is the share price times the number of shares

functioning financial systems provide good and eas-

percentage of GDP. The number of listed domestic

outstanding. • Market liquidity is the total value

ily accessible information. That lowers transaction

companies is another measure of market size. Mar-

of shares traded during the period divided by gross

costs, which in turn improves resource allocation and

ket size is positively correlated with the ability to

domestic product (GDP). This indicator complements

boosts economic growth. Both banking systems and

mobilize capital and diversify risk.

the market capitalization ratio by showing whether

stock markets enhance growth, the main factor in

Market liquidity, the ability to easily buy and sell

market size is matched by trading. • Turnover ratio

poverty reduction. At low levels of economic develop-

securities, is measured by dividing the total value

is the total value of shares traded during the period

ment commercial banks tend to dominate the finan-

of shares traded by GDP. The turnover ratio—the

divided by the average market capitalization for the

cial system, while at higher levels domestic stock

value of shares traded as a percentage of market

period. Average market capitalization is calculated as

markets tend to become more active and efficient

capitalization—is also a measure of liquidity as well

the average of the end-of-period values for the cur-

relative to domestic banks.

as of transaction costs. (High turnover indicates low

rent period and the previous period. • Listed domes-

Open economies with sound macroeconomic poli-

transaction costs.) The turnover ratio complements

tic companies are the domestically incorporated

cies, good legal systems, and shareholder protection

the ratio of value traded to GDP, because the turn-

companies listed on the country’s stock exchanges

attract capital and therefore have larger financial mar-

over ratio is related to the size of the market and the

at the end of the year. This indicator does not include

kets. Recent research on stock market development

value traded ratio to the size of the economy. A small,

investment companies, mutual funds, or other col-

shows that modern communications technology and

liquid market will have a high turnover ratio but a low

lective investment vehicles. • S&P/Global Equity

increased financial integration have resulted in more

value of shares traded ratio. Liquidity is an impor-

Indices measure the U.S. dollar price change in the

cross-border capital flows, a stronger presence of

tant attribute of stock markets because, in theory,

stock markets.

financial firms around the world, and the migration of

liquid markets improve the allocation of capital and

stock exchange activities to international exchanges.

enhance prospects for long-term economic growth.

Many firms in emerging markets now cross-list on inter-

A more comprehensive measure of liquidity would

national exchanges, which provides them with lower

include trading costs and the time and uncertainty

cost capital and more liquidity-traded shares. However,

in finding a counterpart in settling trades.

this also means that exchanges in emerging markets

Standard & Poor’s Index Services, the source for

may not have enough financial activity to sustain them,

all the data in the table, provides regular updates on

putting pressure on them to rethink their operations.

21 emerging stock markets and 36 frontier markets.

The indicators in the table are from Standard &

Standard & Poor’s maintains a series of indexes for

Poor’s Emerging Markets Data Base. They include

investors interested in investing in stock markets in

measures of size (market capitalization, number of

developing countries. The S&P/IFCI index, Standard

listed domestic companies) and liquidity (value of

& Poor’s leading emerging markets index, is designed

shares traded as a percentage of gross domestic

to be sufficiently investable to support index tracking

product, value of shares traded as a percentage of

portfolios in emerging market stocks that are legally

market capitalization). The comparability of such indi-

and practically open to foreign portfolio investment.

cators across countries may be limited by concep-

The S&P/Frontier BMI measures the performance of

tual and statistical weaknesses, such as inaccurate

36 smaller and less liquid markets. The individual

reporting and differences in accounting standards.

country indexes include all publicly listed equities

The percentage change in stock market prices in U.S.

representing an aggregate of at least 80 percent or

dollars for developing economies is from Standard

more of market capitalization in each market. These

& Poor’s Global Equity Indices (S&P IFCI) and Stan-

indexes are widely used benchmarks for international

dard & Poor’s Frontier Broad Market Index (BMI). The

portfolio management. See www.standardandpoors.

percentage change for France, Germany, Japan, the

com for further information on the indexes. Data sources

United Kingdom, and the United States is from local

Because markets included in Standard & Poor’s

stock market prices. The indicator is an important

emerging markets category vary widely in level of

Data on stock markets are from Standard & Poor’s

measure of overall performance. Regulatory and

development, it is best to look at the entire category

Global Stock Markets Factbook 2010, which draws

institutional factors that can affect investor confi-

to identify the most significant market trends. And it

on the Emerging Markets Data Base, supple-

dence, such as entry and exit restrictions, the exis-

is useful to remember that stock market trends may

mented by other data from Standard & Poor’s.

tence of a securities and exchange commission, and

be distorted by currency conversions, especially when

The firm collects data through an annual survey

the quality of laws to protect investors, may influence

a currency has registered a significant devaluation.

of the world’s stock exchanges, supplemented by

the functioning of stock markets but are not included in the table. Stock market size can be measured in various

About the data is based on Demirgüç-Kunt and

information provided by its network of correspon-

Levine (1996), Beck and Levine (2001), and Claes-

dents and by Reuters. Data on GDP are from the

sens, Klingebiel, and Schmukler (2002).

World Bank’s national accounts data files.

ways, and each may produce a different ranking of

2011 World Development Indicators

281

5.5

Financial access, stability, and efficiency Getting credit Strength of legal rights index 0–10 (weak to strong)

Financial access and outreach

Deposit Loan accounts accounts Depth of at at Commercial Automated Pointcredit commercial commercial bank teller of-sale information banks banks branches machines terminals index per per per per per 0–6 100,000 100,000 100,000 1,000 1,000 (low to high) adults adults adults adults adults

June 2010 June 2010

282

6 9 3 4 4 6 9 7 6 7 3 7 3 1 5 7 3 8 3 2 8 3 6 3 3 4 6 10 5 3 3 5 3 6 .. 6 9 3 3 3 5 2 7 4 7 7 3 5 7 7 8 3 8 3 3 3 6

0 4 2 3 6 5 5 6 5 2 5 4 1 6 5 4 5 6 1 1 0 2 6 2 1 5 4 5 5 0 2 5 1 4 .. 5 4 6 5 6 6 0 5 2 5 4 2 0 6 6 3 5 6 0 1 2 6

2011 World Development Indicators

2009

.. 451 683 .. 875 572 .. 2,442 702 319 .. 3,725 .. 274 380 481 .. 1,987 .. 21 76 .. .. .. .. 746 .. .. 1,151 6 .. .. .. .. .. 1,680 .. .. 494 .. 737 .. 2,752 82 .. .. .. 269 661 .. 270 3,219 1,050 .. .. 330 744

4 102 .. .. 503 192 .. .. .. 42 .. .. .. 72 344 80 390 456 .. 1 25 .. .. .. .. 629 .. .. .. .. .. .. .. .. .. .. .. 310 .. .. .. .. 1,022 1 .. .. .. 44 349 .. .. 1,297 374 .. .. 11 ..

1.1 21.4 5.3 5.5 13.3 15.7 31.8 .. 8.6 5.2 44.9 50.0 .. 6.3 25.0 6.9 12.2 88.1 .. 1.7 3.7 .. 23.7 .. .. 15.0 .. 24.4 13.7 0.3 .. .. .. 33.2 .. 22.4 46.7 10.0 1.6 .. 8.2 .. 22.2 1.2 18.5 23.0 .. 5.5 18.6 16.3 4.4 38.8 33.1 .. .. .. 1.5

2009

0.18 26.87 4.13 7.82 33.04 22.22 159.30 118.37 23.05 .. 29.71 85.96 .. 15.11 27.14 29.26 110.19 78.22 .. 0.04 .. .. 202.78 .. .. 55.56 .. .. 26.31 .. .. 53.35 .. 88.62 .. 38.40 70.42 27.21 26.01 .. 22.86 .. 89.09 .. 38.74 102.55 .. 1.48 28.77 79.74 4.16 76.06 22.18 .. .. 0.58 21.89

Ratio of Bank bank noncapital to asset performing loans to total ratio gross loans

Domestic credit provided by banking sector

Interest rate spread

Risk premium on lending Prime lending rate minus treasury bill rate percentage points 2009

% of GDP

Lending rate minus deposit rate percentage points

2009

.. 123 8 25 .. 94 3,939 4,890 112 .. 165 1,086 .. 33 502 .. 1,471 683 .. 0 36 .. 2,202 .. .. 450 .. .. .. .. .. 0 .. 2,121 .. 651 2,023 .. .. .. 250 .. 1,417 .. 66 2,153 .. 5 169 799 4 3,827 486 .. .. .. ..

.. 8.7 .. .. 13.3 21.0 5.0 7.0 .. 6.5 16.6 4.5 .. 8.7 15.2 .. 9.5 10.8 .. .. .. .. 5.7 .. .. 7.4 5.6 12.7 13.6 .. .. 13.9 .. 13.9 .. 6.1 5.7 9.1 7.7 6.4 13.2 .. 8.5 .. 6.4 4.5 16.2 .. 18.3 4.8 17.0 6.1 10.5 .. .. .. ..

1.5 68.5 -8.9 29.2 28.0 19.9 143.6 141.1 23.1 60.4 34.6 119.3 19.1 49.5 58.3 -1.0 97.5 69.4 15.2 36.5 19.0 6.9 178.1 17.2 8.3 98.8 145.2 166.8 37.2 7.6 -15.9 54.3 22.8 76.9 .. 62.4 223.0 40.6 18.9 75.4 44.5 112.1 106.2 37.1 98.7 128.4 7.5 38.7 33.2 131.8 27.9 112.7 37.7 .. 4.9 25.8 54.1

.. 5.9 6.3 8.1 4.1 10.1 3.2 .. 7.8 6.4 1.0 .. .. 8.9 4.3 6.3 35.4 5.2 .. .. .. 10.8 2.3 10.8 10.8 5.2 3.1 5.0 6.9 49.5 10.8 12.8 .. 8.4 .. 4.7 .. 10.3 7.1 5.5 .. .. 4.6 3.3 .. .. 10.8 11.5 15.2 .. .. .. 8.3 .. .. 16.2 8.6

.. 10.5 .. .. 3.0 4.8 1.2 2.3 .. 11.2 4.2 2.7 .. 3.5 5.9 .. 4.2 6.4 .. .. .. .. 1.3 .. .. 3.0 1.6 1.1 4.1 .. .. 2.0 .. 7.8 .. 4.6 0.3 4.0 2.9 13.4 3.6 .. 5.2 .. 0.7 3.6 9.8 .. 6.3 3.3 16.2 7.7 2.7 .. .. .. ..

.. 6.4 7.3 .. .. 9.3 2.9 .. 16.7 .. .. 5.6 .. 9.5 .. .. 34.9 6.2 .. .. .. .. 2.0 .. .. .. .. 4.9 .. .. .. .. .. .. .. 4.7 .. .. .. 2.1 .. .. .. 7.3 .. .. .. .. 19.5 .. .. .. .. .. .. .. ..

Getting credit Strength of legal rights index 0–10 (weak to strong)

Financial access and outreach

June 2010 June 2010

7 8 3 4 3 8 9 3 8 7 4 4 10 .. 7 8 4 10 4 9 3 6 4 .. 5 7 2 7 10 3 3 5 5 8 6 3 2 .. 8 6 6 10 3 3 8 7 4 6 6 5 3 7 3 9 3 7 3

5 4 4 4 0 5 5 5 0 6 2 5 4 .. 6 4 4 3 0 5 5 0 1 .. 6 4 0 0 6 1 1 3 6 0 3 5 4 .. 5 2 5 5 5 1 0 4 2 4 6 3 6 6 3 4 5 5 2

2009

1,571 680 484 .. .. .. 2,254 763 1,172 .. 814 .. 296 .. .. .. .. 115 .. 1,219 1,310 199 .. .. 2,142 1,302 34 124 2,227 .. 37 2,110 1,014 .. 1,935 277 112 .. 466 165 1,772 .. 198 .. .. .. .. 226 757 .. 80 716 517 1,527 .. 1,026 ..

.. 124 181 .. .. .. 1,055 597 215 .. 160 .. 70 .. .. .. .. 25 .. 687 .. 18 .. .. 381 962 21 17 973 .. .. 417 .. .. 272 .. 20 .. 356 38 .. .. 185 .. .. .. .. 47 435 .. 89 367 .. .. .. .. ..

17.1 9.3 6.7 28.8 .. 34.1 19.8 53.0 7.2 12.5 16.2 21.6 4.0 .. 12.6 .. 15.1 6.3 1.7 12.0 29.1 1.9 .. .. 28.8 22.1 1.0 1.8 11.6 .. 3.8 19.4 14.0 9.7 56.7 11.6 2.9 .. 7.3 3.2 26.1 31.7 6.8 .. .. 35.0 22.1 7.5 18.9 2.8 6.2 7.5 10.5 32.6 55.9 16.6 ..

2009

54.24 3.55 13.44 23.97 .. .. 47.38 93.93 21.89 .. .. 52.83 6.67 .. .. .. 50.05 .. 3.06 .. 38.55 7.13 .. .. 51.69 45.98 0.96 1.48 43.25 .. 0.74 37.71 40.15 .. 18.18 16.65 4.32 .. 27.31 1.13 63.78 72.34 .. .. .. 59.73 .. 3.39 36.94 .. .. 17.67 13.33 42.16 189.60 43.33 ..

Ratio of Bank bank noncapital to asset performing loans to total ratio gross loans

Domestic credit provided by banking sector

5.5 Interest rate spread

Risk premium on lending Prime lending rate minus treasury bill rate percentage points 2009

% of GDP

Lending rate minus deposit rate percentage points

2009

585 .. 120 1,353 .. .. .. 2,386 674 .. .. 173 .. .. .. .. 904 .. .. .. 1,293 .. .. .. 1,413 1,297 2 2 941 .. .. 647 .. .. 448 46 34 .. 217 .. 2,286 3,916 .. .. .. 2,827 .. 47 427 .. .. 40 .. 253 2,548 1,398 ..

8.5 6.4 10.3 .. .. 5.6 6.0 8.0 .. 4.7 11.0 -9.3 12.7 .. 10.9 .. 12.1 .. .. 7.4 7.0 7.9 .. .. 7.9 11.4 .. .. 9.0 .. .. .. 9.7 16.0 .. 7.6 7.7 .. 7.9 .. 4.3 .. .. .. 18.4 6.0 13.5 10.1 11.7 .. 8.7 9.9 11.1 9.0 6.5 .. ..

79.9 69.4 36.9 37.2 -16.3 219.8 78.1 141.6 59.8 320.5 99.3 54.6 44.8 .. 112.4 14.3 65.1 14.0 10.5 93.2 165.0 -15.5 149.5 -65.9 69.3 44.0 11.6 32.0 137.4 10.7 .. 109.7 44.1 41.6 32.2 100.5 22.8 .. 43.5 69.6 224.4 154.2 67.5 12.2 35.9 .. 41.9 48.4 81.6 39.1 25.5 18.1 49.4 61.5 196.1 .. 75.7

5.2 .. 5.2 -1.1 7.8 .. 2.6 .. 9.5 1.3 4.3 .. 8.8 .. 2.2 10.1 3.3 19.2 19.3 8.2 2.3 8.2 10.1 3.5 3.6 3.0 33.5 21.8 3.0 .. 15.5 10.8 5.1 5.6 8.4 .. 6.2 5.0 4.9 5.5 -0.6 6.3 8.0 .. 5.1 2.0 3.3 5.9 4.8 7.8 26.8 18.2 5.8 .. .. .. 2.8

6.7 2.3 3.3 .. .. 9.0 1.5 7.0 .. 1.7 6.7 21.2 7.9 .. 1.2 4.4 9.7 .. .. 16.4 6.0 4.0 .. .. 19.3 8.9 .. .. 3.7 .. .. .. 3.1 16.3 .. 5.5 1.8 .. 2.7 .. .. .. .. .. 6.6 1.5 3.5 12.2 1.4 .. 1.6 2.7 4.1 7.6 3.2 .. ..

states and markets

Financial access, stability, and efficiency

2011 World Development Indicators

2.6 .. .. .. 1.8 .. 2.3 3.8 -3.5 1.6 .. .. 7.4 .. .. .. 5.2 12.5 11.5 5.8 4.7 5.2 .. .. -0.1 .. 37.4 15.1 3.0 .. 13.1 .. 1.6 9.2 15.0 .. 5.1 .. 2.9 1.7 .. 7.6 .. .. 14.6 .. .. 2.0 .. 3.0 .. .. 5.3 .. .. .. ..

283

5.5

Financial access, stability, and efficiency Getting credit Strength of legal rights index 0–10 (weak to strong)

Financial access and outreach

Deposit Loan accounts accounts Depth of at at Commercial Automated Pointcredit commercial commercial bank teller of-sale banks banks branches machines terminals information per per per per per index 100,000 100,000 100,000 1,000 1,000 0–6 adults adults adults adults adults (low to high)

June 2010 June 2010

Romania Russian Federation Rwanda Saudi Arabia Senegal Serbia Sierra Leone Singapore Slovak Republic Slovenia Somalia South Africa 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

284

8 3 8 5 3 8 6 10 9 5 .. 9 6 4 5 6 5 8 1 3 8 4 1 3 8 3 4 .. 7 9 4 9 8 5 2 2 8 0 2 9 6 5.5 u 4.9 5.1 4.6 5.7 5.0 5.8 6.3 5.2 2.5 5.4 4.6 6.7 6.3

5 5 4 6 1 5 0 4 4 2 .. 6 5 5 0 5 4 5 2 0 0 5 0 1 4 5 5 .. 4 3 5 6 6 6 3 0 5 3 2 5 0 3.0 u 1.3 3.1 2.6 3.6 2.6 1.9 4.1 3.4 3.1 2.1 1.6 4.3 4.1

2011 World Development Indicators

2009

.. .. 202 .. .. .. .. 2,305 .. 1,394 .. 788 741 1,652 .. 270 .. .. 157 .. .. 1,498 .. .. .. 672 1,851 .. 154 3,755 .. .. 1,761 507 .. 518 .. .. 106 293 139

431 .. 2 .. .. .. .. 899 .. .. .. 297 310 487 .. 98 .. .. 23 .. .. 276 .. .. .. 176 315 .. 21 .. .. .. .. 439 .. 484 .. .. 6 19 ..

27.6 2.9 3.1 .. .. 44.9 .. 11.0 25.7 15.7 .. 8.0 40.5 9.1 .. 2.9 22.8 .. 2.2 3.9 1.8 10.9 .. .. .. 13.6 17.3 .. 1.9 3.3 .. .. 35.4 13.9 .. 18.5 3.3 .. 1.8 3.5 2.8

2009

50.63 65.60 0.38 .. .. 41.31 .. 50.64 47.76 99.47 .. 54.85 157.10 10.46 .. 15.96 36.94 93.70 0.95 2.97 2.63 65.48 .. .. .. 14.26 40.99 .. 2.24 70.09 .. 127.07 169.23 30.57 .. 27.99 .. .. 2.44 4.54 ..

2009

460 275 1 .. .. 959 .. 1,887 611 1,925 .. .. 3,523 .. .. 52 .. 2,004 .. 2 11 .. .. .. .. 172 3,046 .. 3 293 .. 2,177 2,156 275 .. .. .. .. 17 11 ..

Ratio of Bank bank noncapital to asset performing loans to total ratio gross loans

Domestic credit provided by banking sector

Interest rate spread

Risk premium on lending Prime lending rate minus treasury bill rate percentage points 2009

% of GDP

Lending rate minus deposit rate percentage points

2009

15.3 9.7 13.1 3.3 18.7 15.5 16.5 2.3 5.3 2.3 .. 5.9 5.1 .. .. 8.1 2.0 0.4 .. .. .. 5.3 .. .. .. 13.2 5.6 .. 4.2 40.2 4.8 3.5 5.4 1.0 .. 3.0 .. .. .. .. .. 4.2 m .. 4.8 5.1 4.2 5.3 .. 9.3 3.0 .. 10.5 .. 3.4 3.6

52.7 33.8 .. 0.6 26.6 44.8 10.7 91.2 53.8 94.5 .. 183.5 228.4 39.6 20.0 9.1 143.8 191.0 45.1 27.5 18.1 136.9 -18.4 30.2 26.5 75.2 63.0 .. 11.2 88.5 114.5 228.9 230.5 27.9 .. 20.5 123.0 .. 19.3 18.5 .. 169.0 w 35.1 89.4 110.3 63.3 88.4 134.2 47.1 67.1 40.9 65.6 78.5 201.8 152.0

5.3 6.7 9.8 .. .. 0.0 14.8 5.1 4.3 4.5 .. 3.2 .. 5.1 .. 6.0 .. 2.7 3.7 17.1 7.1 4.9 10.3 .. 8.5 .. .. .. 11.2 7.1 .. .. .. 10.9 .. 3.5 3.1 .. 7.3 15.0 457.5 6.2 m 11.5 6.3 7.3 5.5 6.8 7.1 5.7 7.7 4.3 5.9 8.5 .. ..

7.6 15.7 13.0 11.9 9.3 21.0 18.9 10.5 9.6 8.3 .. 6.7 6.8 .. .. 16.9 5.0 5.5 .. .. .. 9.8 .. .. .. .. 13.3 .. 13.4 13.1 16.0 5.4 11.0 8.9 .. 9.4 .. .. .. .. .. 9.4 m .. 10.1 10.0 9.7 .. .. 13.3 9.6 .. 6.4 .. 6.8 6.5

6.4 .. 8.9 .. .. 1.4 9.0 5.0 .. 4.8 .. 3.9 .. 2.7 .. 3.4 .. 2.8 .. .. 7.9 4.7 .. .. 9.2 .. .. .. 13.9 .. .. 0.1 3.1 3.4 .. .. 2.0 .. 4.5 6.7 330.2

5.5

states and markets

Financial access, stability, and efficiency About the data Access to finance can expand opportunities for all with

all nonfinancial and financial assets. Data are from

consumer loans, business loans, trade loans, student

higher levels of access and use of banking services

internally consistent financial statements.

loans, emergency loans, agricultural loans, and the

associated with lower financing obstacles for people

The ratio of bank nonperforming loans to total gross

like. • Commercial banks branches are retail locations

and businesses. A stable financial system that pro-

loans, a measure of bank health and efficiency, helps

offering a wide array of face-to-face and automated

motes efficient savings and investment is also crucial

identify problems with asset quality in the loan portfo-

financial services. • Automated teller machines are

for a thriving democracy and market economy.

lio. A high ratio may signal deterioration of the credit

computerized telecommunications devices that pro-

There are several aspects of access to financial ser-

portfolio. International guidelines recommend that

vide clients of a financial institution with access to

vices: availability, cost, and quality of services. The

loans be classified as nonperforming when payments

financial transactions in a public place. • Point-of-sale

development and growth of credit markets depend on

of principal and interest are 90 days or more past

terminals are the equipment used to manage the sell-

access to timely, reliable, and accurate data on bor-

due or when future payments are not expected to be

ing process by a salesperson-accessible interface in

rowers’ credit experiences. Access to credit can be

received in full. Domestic credit provided by the bank-

the location where a transaction takes place. • Bank

improved by making it easy to create and enforce col-

ing sector as a share of GDP is a measure of bank-

capital to asset ratio is the ratio of bank capital and

lateral agreements and increasing information about

ing sector depth and financial sector development in

reserves to total assets. Capital and reserves include

potential borrowers’ creditworthiness. Lenders look at

terms of size. In a few countries governments may hold

funds contributed by owners, retained earnings, gen-

a borrower’s credit history and collateral. Where credit

international reserves as deposits in the banking sys-

eral and special reserves, provisions, and valuation

registries and effective collateral laws are absent—

tem rather than in the central bank. Since the claims

adjustments. • Ratio of bank nonperforming loans to

as in many developing countries—banks make fewer

on the central government are a net item (claims on

total gross loans is the value of nonperforming loans

loans. Indicators that cover getting credit include the

the central government minus central government

divided by the total value of the loan portfolio (including

strength of legal rights index and the depth of credit

deposits), this net figure may be negative, resulting

nonperforming loans before the deduction of loan loss

information index.

in a negative figure of domestic credit provided by the

provisions). The amount recorded as nonperforming

banking sector.

should be the gross value of the loan as recorded

The “unbanked” have to resort to informal services to manage their money—saving under the

The interest rate spread—the margin between

on the balance sheet, not just the amount overdue.

mattress, borrowing from family and friends, or

the cost of mobilizing liabilities and the earnings on

• Domestic credit provided by banking sector is all

money lenders—that are usually less reliable and

assets—is a measure of financial sector efficiency in

credit to various sectors on a gross basis, except to

more costly than formal banking institutions. The

intermediation. A narrow interest rate spread means

the central government, which is net. The banking

table presents data on financial access cover-

low transaction costs, which reduces the cost of funds

sector includes monetary authorities, deposit money

ing deposits and loans, and outreach indicators

for investment, crucial to economic growth.

banks, and other banking institutions for which data

The risk premium on lending is the spread between

are available. • Interest rate spread is the interest rate

the lending rate to the private sector and the “risk-

charged by banks on loans to prime customers minus

Data on financial access cover 142 coun-

free” government rate. Spreads are expressed as

the interest rate paid by commercial or similar banks

tries and present indicators on savings, credit,

annual averages. A small spread indicates that the

for demand, time, or savings deposits. • Risk premium

and payment services in banks and regulated

market considers its best corporate customers to be

on lending is the interest rate charged by banks on

nonbank financial institutions. Data were col-

low risk. A negative rate indicates that the market

loans to prime private sector customers minus the

lected for commercial banks and regulated

considers its best corporate clients to be lower risk

“risk-free” treasury bill interest rate at which short-

nonbank financial institutions such as cooperatives,

than the government.

term government securities are issued or traded in

such as the number of branches, automatic teller machines, and point-of-sale terminals.

credit unions, specialized state financial institutions, and microfinance institutions.

Definitions

the market.

The size and mobility of international capital flows

• Strength of legal rights index measures the degree

make it increasingly important to monitor the strength

to which collateral and bankruptcy laws protect the

of financial systems. Robust financial systems can

rights of borrowers and lenders and thus facilitate

increase economic activity and welfare, but instability

lending. Higher values indicate that the laws are bet-

in the financial system can disrupt financial activity and

ter designed to expand access to credit. • Depth of

impose widespread costs on the economy. The ratio

credit information index measures rules affecting

Data sources

of bank capital to assets, a measure of bank solvency

the scope, accessibility, and quality of information

Data on getting credit are from the World Bank’s

and resiliency, shows the extent to which banks can

available through public or private credit registries.

Doing Business project (www.doingbusiness.org).

deal with unexpected losses. Capital includes tier 1

Higher values indicate the availability of more credit

Data on financial access and outreach are from

capital (paid-up shares and common stock), a com-

information. • Deposit accounts are accounts at com-

the Consultative Group to Assist the Poor and the

mon feature in all countries’ banking systems, and

mercial banks that allow money to be deposited and

World Bank Group’s Financial Access 2010. Data

total regulatory capital, which includes several types of

withdrawn by the account holder. The major types of

on bank capital and nonperforming loans are from

subordinated debt instruments that need not be repaid

deposits are checking accounts, savings accounts,

the IMF’s Global Financial Stability Report. Data

if the funds are required to maintain minimum capital

and time deposits. • Loan accounts at commer-

on credit and interest rates are from the IMF’s

levels (tier 2 and tier 3 capital). Total assets include

cial banks include loans from banks to individuals,

International Financial Statistics.

businesses, and others, including home mortgages,

2011 World Development Indicators

285

5.6

Tax policies Tax revenue collected by central government

% of GDP

286

Taxes payable by businesses

Number of payments

Time to prepare, file, and pay taxes hours

Profit tax % of commercial profits

Labor tax and contributions % of commercial profits

Other taxes % of commercial profits

Total tax rate % of commercial profits

June 2010

2000

2009

June 2010

.. 16.1 .. .. 9.8a .. 23.0a 19.9a .. 7.6 16.6 27.4a 15.5a 13.2a .. .. 14.0 17.9 10.5a 13.6 8.2a 11.2 15.3a .. .. 16.7a 6.8 9.1a 11.0a 3.5 5.9 .. .. 22.4 .. 15.4 30.8 a .. .. 13.4 10.7a .. 15.8 a 8.1 24.7a 23.2a .. .. 7.7 11.9a 17.2 23.3a 10.1 11.1 .. .. ..

7.3 .. 34.3a .. .. 16.4 22.1a 18.7a 16.7 8.6 19.4 24.0a 16.1a 17.0a 19.6a .. 15.6 20.9 12.9a .. 9.6a .. 11.8a .. .. 15.3a 10.3 13.0a 11.9a .. .. 13.9a 16.4 a 19.1 .. 13.5 34.5a 14.9a .. 15.7 12.5a .. 17.6a .. 21.3a 19.6a .. .. 23.2 12.0a 12.5 19.1a 10.4 .. .. .. 14.4 a

8 44 34 31 9 50 11 22 18 21 82 11 55 42 51 19 10 17 46 32 39 44 8 54 54 9 7 3 20 32 61 42 64 17 .. 12 9 9 8 29 53 18 7 19 8 7 26 50 18 16 33 10 24 56 46 42 47

2011 World Development Indicators

275 360 451 282 453 581 109 170 306 302 798 156 270 1,080 422 152 2,600 616 270 211 173 654 131 504 732 316 398 80 208 336 606 272 270 196 .. 557 135 324 654 433 320 216 81 198 243 132 488 376 387 215 224 224 344 416 208 160 224

0.0 8.5 6.6 24.6 2.8 16.6 25.9 15.7 13.8 25.7 22.0 4.8 14.8 0.0 5.3 15.9 21.4 4.6 16.1 19.4 18.9 29.9 9.8 176.8 31.3 18.0 6.0 18.7 17.7 58.9 0.0 18.9 8.8 11.4 .. 7.4 21.9 20.5 18.4 13.2 17.0 8.8 8.0 26.8 15.9 8.2 18.4 41.4 13.3 23.0 18.1 13.9 25.9 19.4 14.9 23.3 26.7

0.0 27.3 29.7 9.0 29.4 23.0 20.7 34.6 24.8 0.0 39.3 50.4 27.3 15.5 12.6 0.0 40.9 20.4 22.6 7.8 0.1 18.3 12.6 8.1 28.4 3.8 49.6 5.3 33.9 7.9 32.9 29.5 20.1 19.4 .. 38.4 3.6 18.3 13.7 25.8 17.2 0.0 39.2 0.0 27.7 51.7 22.7 12.9 0.0 22.0 14.1 31.7 14.3 24.5 24.8 12.4 10.7

36.4 4.9 35.7 19.5 76.0 1.1 1.3 5.1 2.2 9.2 19.2 1.8 23.9 64.6 5.0 3.6 6.6 3.9 6.2 126.2 3.5 0.9 6.9 18.9 5.7 3.2 7.9 0.1 27.1 272.8 32.6 6.6 15.5 1.6 .. 3.0 3.7 1.8 3.2 3.6 0.8 75.8 2.4 4.3 1.0 5.9 2.3 238.0 2.0 3.3 0.5 1.6 0.7 10.8 6.1 4.3 10.9

36.4 40.6 72.0 53.2 108.2 40.7 47.9 55.5 40.9 35.0 80.4 57.0 66.0 80.0 23.0 19.5 69.0 29.0 44.9 153.4 22.5 49.1 29.2 203.8 65.4 25.0 63.5 24.1 78.7 339.7 65.5 55.0 44.4 32.5 .. 48.8 29.2 40.7 35.3 42.6 35.0 84.5 49.6 31.1 44.6 65.8 43.5 292.3 15.3 48.2 32.7 47.2 40.9 54.6 45.9 40.1 48.3

Tax revenue collected by central government

% of GDP

5.6

states and markets

Tax policies Taxes payable by businesses

Number of payments

Time to prepare, file, and pay taxes hours

Profit tax % of commercial profits

Labor tax and contributions % of commercial profits

Other taxes % of commercial profits

Total tax rate % of commercial profits

2000

2009

June 2010

21.9a

23.5a

9.0 11.6 6.3 .. 26.0a 28.7a 23.2a .. .. 19.0 10.2 16.8 .. 15.4 .. 1.3 11.7 .. 14.2 11.9a 37.4 .. .. 14.6a .. 11.3a .. 13.7 13.2a .. .. 11.7 14.7 14.5 19.9a .. 3.0 27.5 8.7 22.3a 29.2a 13.8 .. .. 27.4 a 7.2 10.1 10.2 19.0 10.9 12.2 13.7 16.0a 20.6a .. ..

9.8 11.4 9.3 .. 20.8a 23.0a 23.0a 21.9a 9.2a 16.2 8.1 19.6 .. 15.5 21.1 0.9 15.4 12.5 12.6 17.3a 60.0 0.3 .. 13.8a 19.7 13.0a .. 15.7 14.7a .. 19.2a .. 17.8 18.0 23.8a .. .. 27.3 12.2 22.7a 30.8a 17.8 11.5a 0.3 25.4 a .. 9.3 .. .. 13.0 13.4 12.8 16.4 a 19.7a .. 19.8

14 56 51 20 13 9 33 15 72 14 26 9 41 .. 14 33 15 48 34 7 19 21 32 .. 11 40 23 19 12 59 38 7 6 48 43 28 37 .. 37 34 9 8 64 41 35 4 14 47 62 33 35 9 47 29 8 16 3

277 258 266 344 312 76 235 285 414 355 101 271 393 .. 250 163 118 202 362 293 180 324 158 .. 175 119 201 157 145 270 696 161 404 228 192 358 230 .. 375 338 134 192 222 270 938 87 62 560 482 194 311 380 195 325 298 218 36

16.7 24.0 26.6 17.8 14.9 11.9 23.8 22.8 28.6 27.9 15.2 16.3 33.1 .. 15.3 10.2 4.7 8.9 25.2 6.5 6.1 16.4 0.0 .. 0.0 6.3 15.8 23.3 16.7 12.9 44.2 11.8 23.1 0.0 9.5 18.1 27.7 .. 4.0 16.2 20.9 30.4 24.8 20.1 21.8 24.4 9.7 14.3 17.0 22.0 9.6 26.0 21.3 17.7 14.9 26.3 0.0

34.4 18.2 10.6 25.9 13.5 11.6 5.3 43.4 13.0 14.7 12.4 11.5 6.8 .. 12.9 5.6 10.7 21.5 5.6 27.2 24.1 0.0 5.4 .. 35.1 0.6 20.3 1.1 15.6 32.6 17.6 5.0 26.1 30.2 12.4 22.2 4.5 .. 1.0 11.3 17.9 3.0 19.2 19.6 9.7 15.9 11.8 15.0 22.6 11.7 18.6 11.0 10.3 22.1 26.8 14.4 11.3

2.2 21.1 0.1 0.4 0.0 3.0 2.6 2.4 8.5 6.0 3.6 1.9 9.9 .. 1.6 0.6 0.0 26.7 2.9 4.8 0.0 3.2 38.3 .. 3.6 3.8 1.6 0.7 1.4 6.7 6.6 7.3 1.3 0.7 1.0 1.4 2.1 .. 4.6 10.7 1.7 0.9 19.2 6.8 0.7 1.3 0.1 2.3 10.5 8.6 6.7 3.2 14.2 2.5 1.6 27.0 0.0

53.3 63.3 37.3 44.1 28.4 26.5 31.7 68.6 50.1 48.6 31.2 29.6 49.7 .. 29.8 16.5 15.5 57.2 33.7 38.5 30.2 19.6 43.7 .. 38.7 10.6 37.7 25.1 33.7 52.2 68.4 24.1 50.5 30.9 23.0 41.7 34.3 .. 9.6 38.2 40.5 34.3 63.2 46.5 32.2 41.6 21.6 31.6 50.1 42.3 35.0 40.2 45.8 42.3 43.3 67.7 11.3

2011 World Development Indicators

287

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 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

2000

2009

11.7a 13.6a .. .. 16.1 .. 10.2 15.4 .. 20.6 .. 24.0a 16.2a 14.5 6.4 24.9 23.6a 11.1 .. 7.7 .. .. .. .. 22.1 21.3 .. .. 10.4 14.1 1.7 28.4a 12.5a 14.7 .. 13.3 .. .. 9.4 18.6 .. 15.5 w 10.4 10.9 8.2 .. 10.9 7.7 .. 13.0 12.0 9.3 .. 16.4 19.1

17.9a 12.9a .. .. .. 21.0 10.8 13.8 12.4 a 18.3 .. 25.4 a 8.5a 13.3 .. .. 21.5a 10.9 .. .. .. 15.1a .. 17.0a 31.6 21.9 18.9a .. 12.0 16.4 .. 26.0a 8.2a 18.8 .. .. .. .. .. 17.1 .. 14.2 w 11.6 14.1 11.3 15.8 14.0 11.1 15.0 .. 17.5 9.7 17.9 14.2 17.1

Taxes payable by businesses

Number of payments

Time to prepare, file, and pay taxes hours

Profit tax % of commercial profits

Labor tax and contributions % of commercial profits

Other taxes % of commercial profits

Total tax rate % of commercial profits

June 2010

222 320 148 79 666 279 357 84 257 260 .. 200 197 256 180 104 122 63 336 224 172 264 276 270 210 144 223 .. 161 657 12 110 187 336 205 864 941 154 248 132 242 282 u 271 337 326 351 319 233 340 408 263 283 311 179 190

10.4 9.0 21.2 2.1 14.8 11.6 0.0 7.4 7.0 14.8 .. 24.4 20.9 27.4 13.8 28.1 16.4 8.9 23.2 17.7 19.9 28.9 0.0 8.8 21.6 15.0 17.0 .. 23.3 10.4 0.0 23.1 27.6 23.6 1.6 10.0 12.5 16.2 35.1 1.7 24.0 17.9 u 24.8 17.1 16.3 18.0 19.2 18.4 10.0 21.4 16.6 17.8 23.3 14.3 13.9

32.3 31.8 5.7 12.4 24.1 20.2 11.3 14.9 39.6 18.2 .. 2.5 35.0 16.9 19.2 4.0 36.6 17.5 19.3 28.5 18.0 5.7 0.0 28.3 5.8 25.2 23.1 .. 11.3 43.3 14.1 10.8 10.0 15.6 27.1 18.0 20.3 0.0 11.3 10.4 6.2 16.3 u 12.6 15.8 14.3 17.5 14.9 10.3 22.7 15.3 18.9 7.8 13.2 20.1 29.2

2.2 5.7 4.4 0.0 7.0 2.2 224.3 3.1 2.1 2.4 .. 3.7 0.7 20.3 3.1 4.7 1.6 3.6 0.5 39.9 7.3 2.8 0.2 13.7 5.8 22.5 4.4 .. 1.1 1.8 0.0 3.3 9.2 2.9 66.9 24.6 0.3 0.6 1.4 4.0 10.1 13.7 u 39.2 8.6 9.6 7.5 17.0 7.8 9.6 11.2 6.1 14.2 31.7 4.2 2.4

44.9 46.5 31.3 14.5 46.0 34.0 235.6 25.4 48.7 35.4 .. 30.5 56.5 64.7 36.1 36.8 54.6 30.1 42.9 86.0 45.2 37.4 0.2 50.8 33.1 62.8 44.5 .. 35.7 55.5 14.1 37.3 46.8 42.0 95.6 52.6 33.1 16.8 47.8 16.1 40.3 47.8 u 76.5 41.5 40.2 43.0 51.1 36.5 42.2 47.9 41.6 39.9 68.2 38.6 45.5

113 11 26 14 59 66 29 5 31 22 .. 9 8 62 42 33 2 19 20 54 48 23 6 53 40 8 15 .. 32 135 14 8 11 53 44 70 32 27 44 37 49 30 u 38 34 36 32 35 27 47 34 25 31 37 15 15

Note: Regional aggregates for Taxes payable by businesses are for developing countries only. 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.

288

2011 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 reflect 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 2011.

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 PricewaterhouseCoopers,

on a cash basis, the IMF adjusts reported data to the

The level of taxation is typically measured by tax

which measures the taxes that are borne by compa-

GFSM 2001 accrual framework. These countries are

revenue as a share of gross domestic product (GDP).

nies and affect their income statements. However,

footnoted in the table. • Number of tax payments

Comparing levels of taxation across countries pro-

PricewaterhouseCoopers bases its calculation on

by businesses is the total number of taxes paid by

vides a quick overview of the fiscal obligations and

data from the largest companies in the economy,

businesses during one year. When electronic filing is

incentives facing the private sector. The table shows

while Doing Business focuses on a standardized

available, the tax is counted as paid once a year even

only central government data, which may significantly

medium-sized company.

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 the business. • Labor tax and contributions is

Tax revenue ratios tend to rise with income, with

the amount of taxes and mandatory contributions on

higher income countries relying on taxes to finance

labor paid by the business. • Other taxes includes

a much broader range of social services and social

the amounts paid for property taxes, turnover taxes,

security than lower income countries are able to.

and other small taxes such as municipal fees and

The total tax rate payable by businesses provides

vehicle and fuel taxes. • Total tax rate measures

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 the business in the second year of opera-

rate, which is the factor applied to the tax base. In

tion, expressed as a share of commercial profits.

computing business tax rates, actual tax payable is

Doing Business 2011 reports the total tax rate for

divided by commercial profit. The indicators cover-

fiscal 2009. Taxes withheld (such as sales or value

ing taxes payable by businesses measure all taxes

added tax or personal income tax) but not paid by

and contributions that are government mandated

the company are excluded. For further details on the

(at any level—federal, state, or local), apply to stan-

method used for assessing the total tax payable, see

dardized businesses, and have an impact in their

the World Bank’s Doing Business 2011.

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 government-mandated contributions paid

Data sources

by the employer to a requited private pension fund

Data on central government tax revenue are from

or workers insurance fund but exclude value-added

print and electronic editions of the IMF’s Govern-

taxes because they do not affect the accounting prof-

ment Finance Statistics Yearbook. Data on taxes

its of the business—that is, they are not reflected in

payable by businesses are from Doing Business

the income statement.

2011 (www.doingbusiness.org).

2011 World Development Indicators

289

5.7

Military expenditures and arms transfers Military expenditures

% of central government expenditure

% of GDP

290

Armed forces personnel

thousands

2000

2009

2000

2009

2000

2009

.. 1.2 3.4 6.4 1.1 3.6 1.9 1.0 2.3 1.4 1.3 1.4 0.6 1.9 3.6 3.3 1.8 2.7 1.2 6.0 2.2 1.3 1.1 1.0 1.9 3.7 1.8a .. 2.8 1.0 1.4 .. .. 3.1 .. 2.0 1.5 0.7 1.7 3.2 0.9 36.4 1.4 7.6 1.3 2.5 1.8 0.8 0.6 1.5 1.0 4.3 0.8 1.5 4.4 .. 0.5

1.8 2.1 3.8 4.2 0.8 4.0 1.9 0.9 3.5 1.1 1.8 1.1 1.0 1.6 1.5 3.1 1.6 2.3 1.3 3.8 1.2 1.5 1.4 1.8 6.4 3.1 2.0a .. 4.1 1.1 1.2 .. 1.6 1.8 3.2 1.5 1.4 0.6 3.3 2.1 0.6 .. 2.3 1.3 1.5 2.4 1.1 0.7 5.6 1.4 0.4 4.0 0.4 .. .. .. 0.8

.. 5.4 .. .. 5.5 .. 7.8 2.5 .. 14.9 5.3 3.2 4.7 7.6 .. .. 8.1 8.6 9.8 30.3 16.8 12.4 6.0 .. .. 17.7 19.8a .. 15.6 11.4 5.9 .. .. 7.8 .. 6.1 4.3 .. .. 12.3 4.3 .. 4.7 29.7 3.7 5.7 .. .. 5.3 4.7 3.3 9.8 7.5 11.8 .. .. ..

4.6 .. 15.0 .. .. 17.1 7.3 2.3 22.9 10.0 5.5 2.5 6.8 7.9 3.8 .. 6.4 7.2 10.4 .. 13.9 .. 7.5 .. .. 13.6 16.1a .. 20.9 .. .. .. 8.8 5.0 .. 4.1 3.3 3.8 .. 7.1 3.0 .. 6.2 .. 3.8 5.1 .. .. 18.1 4.3 2.4 7.9 3.5 .. .. .. 3.2

400 68 305 118 102 42 52 41 87 137 91 39 7 70 76 10 673 114 11 46 360 22 69 5 35 117 3,910 .. 247 93 15 15 15 101 85 63 22 40 58 679 29 200 8 353 35 389 7 1 33 221 8 163 53 19 9 5 14

256 15 334 117 104 56 57 26 82 221 183 39 7 83 11 11 713 65 11 51 191 23 66 3 35 104 2,945 .. 442 159 12 10 19 22 76 27 19 40 59 866 33 202 5 138 25 342 7 1 32 251 16 143 34 19 6 0 20

2011 World Development Indicators

Arms transfers

% of labor force 2000

5.4 5.2 2.7 1.9 0.6 2.9 0.5 1.0 2.5 0.2 1.9 0.9 0.3 2.0 4.1 1.3 0.8 3.2 0.2 1.4 6.1 0.4 0.4 0.3 1.1 1.9 0.5 .. 1.6 0.5 1.2 1.0 0.2 5.1 1.8 1.2 0.8 1.1 1.2 3.1 1.3 14.5 1.2 1.2 1.3 1.5 1.2 0.1 1.4 0.5 0.1 3.3 1.3 0.5 1.7 0.1 0.6

Trend indicator values 1990 $ millions Exports Imports

2009

2000

2009

2.7 1.0 2.3 1.4 0.5 3.4 0.5 0.6 2.0 0.3 3.7 0.8 0.2 1.8 0.5 1.1 0.7 1.8 0.2 1.1 2.4 0.3 0.3 0.2 0.8 1.4 0.4 .. 2.3 0.6 0.8 0.5 0.2 1.1 1.5 0.5 0.6 0.9 1.0 3.2 1.3 9.4 0.8 0.3 0.9 1.2 0.9 0.1 1.4 0.6 0.1 2.8 0.6 0.4 1.0 0.0 0.7

.. .. .. 2 2 .. 43 21 .. .. 295 24 .. .. 4 .. 26 2 .. .. 1 .. 110 .. .. 1 272 .. .. .. .. .. .. 2 .. 78 20 .. .. .. .. 0 .. .. 9 1,055 .. .. 54 1,603 .. 2 .. .. .. .. ..

.. .. .. .. .. .. 51 33 .. .. 292 217 .. .. .. .. 49 7 .. .. .. .. 177 .. .. 133 870 .. .. .. .. 0 .. .. .. 19 12 .. .. .. .. .. .. .. 40 1,851 .. .. .. 2,473 .. .. .. .. .. .. ..

2000

2009

33 .. 418 200 209 2 364 25 3 205 41 39 6 19 25 52 124 7 .. 1 .. 1 550 .. 15 179 2,015 .. 62 74 0 .. 33 70 .. 16 64 13 12 788 16 17 27 124 516 106 .. .. 6 135 1 710 1 19 .. .. ..

344 25 942 11 11 1 757 330 49 12 .. 84 2 5 .. 10 210 153 1 .. 4 1 80 .. 23 231 595 .. 250 .. 0 .. .. 3 .. 5 47 6 46 217 4 4 56 .. 70 149 21 .. 81 137 13 1,269 0 0 .. 1 0

Military expenditures

% of central government expenditure

% of GDP 2000

1.7 3.1 1.0 3.8 .. 0.7 7.8 2.0 0.5 1.0 6.2 0.8 1.3 .. 2.6 .. 7.1 2.9 0.8 0.9 5.4 4.1 .. 3.2 1.7 1.9 1.2 0.7 1.6 2.4 3.5 0.2 0.6 0.4 2.2 2.3 1.3 2.3 2.4 1.0 1.6 1.2 0.8 1.1 0.8 1.7 10.6 4.0 1.0 0.9 1.1 2.0 1.1 1.8 1.9 .. 4.7

Armed forces personnel

2009

2000

1.3 2.7 0.9 2.7 6.3 0.6 6.9 1.7 0.6 1.0 5.5 1.2 1.9 .. 2.9 .. 3.2 3.6 0.4 2.6 4.1 2.8 0.8 1.2 1.7 2.1 1.1 1.2 2.0 2.0 3.8 0.2 0.5 0.5 1.4 3.3 0.9 .. 3.3 1.6 1.5 1.1 0.7 .. 0.9 1.5 8.7 3.0 .. 0.5 0.9 1.2 0.8 2.0 2.0 .. 2.2

4.1 19.5 5.8 22.5 .. 2.6 17.6 5.2 .. .. 23.1 5.7 7.8 .. 15.6 .. 24.9 18.0 .. 3.2 17.7 7.8 .. .. 6.5 .. 11.5 .. 9.9 20.7 .. .. 3.7 1.4 9.5 12.0 .. .. 8.3 .. 4.0 3.5 4.7 .. .. 5.3 40.4 23.4 4.6 2.9 6.4 10.9 6.2 5.4 5.1 .. ..

2009

2.9 16.6 5.6 12.2 .. 1.5 17.0 3.9 1.6 .. 19.3 6.9 8.7 .. 13.2 .. 7.5 21.4 3.6 7.5 14.0 3.1 .. .. 4.4 5.8 9.3 .. 8.9 13.4 .. .. .. 1.2 5.8 12.0 .. .. 10.7 .. 3.4 3.1 3.2 .. 10.8 4.1 .. 18.0 .. .. 5.2 6.7 4.6 5.7 4.6 .. 13.7

thousands 2000

2009

58 2,372 492 753 479 12 181 503 3 249 149 99 27 1,244 688 .. 20 14 129 9 77 2 15 77 17 24 29 6 116 15 21 2 208 13 16 241 6 429 9 90 57 9 16 11 107 27 48 900 12 4 35 193 149 239 91 .. 12

42 2,626 582 563 659 10 185 327 3 260 111 81 29 1,379 660 .. 23 20 129 6 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 26 47 921 12 3 25 192 166 121 91 .. 12

Arms transfers

% of labor force 2000

1.4 0.6 0.5 3.4 8.0 0.7 7.2 2.2 0.3 0.4 10.4 1.3 0.2 11.2 3.0 .. 1.8 0.7 5.2 0.8 6.5 0.2 1.3 4.2 1.0 2.8 0.4 0.1 1.2 0.5 2.0 0.3 0.5 0.7 1.4 2.4 0.1 1.7 1.5 0.9 0.7 0.5 0.9 0.3 0.3 1.1 5.4 2.2 0.9 0.2 1.5 1.7 0.5 1.4 1.7 .. 3.6

5.7

states and markets

Military expenditures and arms transfers

2009

1.0 0.6 0.5 1.9 8.6 0.5 6.0 1.3 0.2 0.4 5.7 0.9 0.2 11.2 2.7 .. 1.5 0.8 4.2 0.5 5.4 0.2 0.1 3.2 1.6 0.9 0.2 0.1 1.1 0.3 1.5 0.4 0.7 0.5 1.2 2.1 0.1 1.9 1.9 1.2 0.5 0.4 0.5 0.2 0.3 1.0 4.3 1.6 0.8 0.1 0.8 1.4 0.4 0.7 1.6 .. 1.2

Trend indicator values 1990 $ millions Exports Imports 2000

34 16 16 0 .. .. 354 189 .. .. .. 19 .. 13 8 .. 99 .. .. .. 45 .. .. 11 3 .. .. 1 8 .. .. .. .. 6 .. .. .. .. .. .. 280 1 .. .. .. 3 .. 3 .. .. .. 10 .. 45 .. .. 9

2009

6 22 .. 5 .. 4 760 588 .. .. 44 .. .. .. 163 .. .. 16 .. .. .. .. .. 12 .. .. .. .. .. .. .. .. .. 11 .. .. .. .. .. .. 608 .. .. .. .. 17 .. .. .. .. .. .. 4 93 40 .. ..

2000

2009

14 911 171 415 .. 0 357 37 5 431 130 147 9 18 1,262 .. 238 .. 7 3 4 6 8 145 5 11 .. .. 30 7 31 .. 227 .. .. 123 0 3 18 11 141 45 .. .. 38 263 120 158 0 .. 6 24 9 159 2 .. 11

2 2,116 452 91 365 1 158 112 2 391 195 49 35 5 1,172 .. 17 .. 7 0 47 .. .. 11 26 .. .. .. 1,494 7 .. .. 57 .. 12 49 .. 3 10 .. 243 48 .. 0 73 576 93 1,146 .. .. .. 33 4 94 431 .. 285

2011 World Development Indicators

291

5.7

Military expenditures and arms transfers Military expenditures

% of GDP

Romania Russian Federation Rwanda Saudi Arabia Senegal Serbia Sierra Leone Singapore Slovak Republic Slovenia Somalia South Africa 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

Armed forces personnel

% of central government expenditure

thousands

2000

2009

2000

2009

2000

2.5 3.7 3.5 10.6 1.3 5.5 3.7 4.7 1.7 1.1 .. 1.6 1.2 5.0 4.7 1.6 2.0 1.1 5.3 1.2 1.3 1.4 .. .. .. 1.7 3.7 2.9 2.5 3.6 9.4 2.4 3.0 1.3 1.2 1.5 .. .. 5.0 1.8 5.2 2.3 w 2.2 2.1 2.2 2.0 2.1 1.7 3.4 1.4 3.5 3.1 2.0 2.3 1.8

1.4 4.3 1.4 11.0 1.6 2.2 2.3 4.3 1.5 1.8 .. 1.4 1.3 3.5 .. 2.1 1.3 0.8 4.2 .. 1.0 1.8 11.8 2.0 .. 1.4 2.8 .. 2.2 2.9 5.6 2.7 4.7 1.6 .. 1.3 2.2 .. 4.4 1.7 2.8 2.6 w 1.5 2.2 2.1 2.2 2.1 1.9 3.3 1.5 3.5 2.6 1.7 2.8 1.7

8.9 19.3 .. .. 10.4 .. 12.8 28.7 .. 2.9 .. 5.6 3.9 21.9 53.0 7.3 .. 4.2 .. 13.4 .. .. .. .. .. 6.2 .. .. 16.0 13.5 .. 6.6 15.6 5.0 .. 7.1 .. .. 23.9 10.3 .. 10.2 w .. 15.0 18.0 .. 15.0 18.7 .. 7.2 12.7 19.9 .. 10.1 4.8

4.4 14.0 .. .. .. 5.9 11.2 27.9 4.0 4.1 .. 4.4 4.1 18.5 .. .. .. 4.7 .. .. .. 9.1 .. 13.0 .. 4.6 10.1 .. 15.9 7.0 .. 5.8 17.8 5.3 .. .. .. .. .. 5.7 .. 10.0 w .. 12.2 14.3 9.8 12.2 14.6 12.0 .. 12.3 16.5 .. 9.9 4.2

283 1,427 76 217 15 136 4 169 41 14 50 72 242 204 120 3 88 28 425 7 35 417 .. 8 8 47 828 15 51 420 66 213 1,455 25 79 79 524 .. 136 23 62 29,353 s 4,040 18,924 12,446 6,478 22,965 7,794 3,871 2,084 3,379 4,114 1,724 6,388 1,869

2009

152 1,495 35 249 19 29 11 148 17 12 2 77 222 223 127 .. 22 26 403 16 28 420 1 9 4 48 613 22 47 215 51 178 1,564 25 87 115 495 56 138 17 51 27,924 s 3,845 18,350 12,108 6,242 22,195 6,978 3,227 2,439 3,591 4,404 1,554 5,729 1,569

Arms transfers

% of labor force 2000

2009

2.4 2.0 2.0 3.4 0.4 .. 0.2 8.2 1.6 1.4 1.7 0.5 1.3 2.6 1.1 0.8 2.0 0.7 8.6 0.4 0.2 1.2 .. 0.4 1.3 1.5 3.6 0.8 0.5 1.8 3.5 0.7 1.0 1.6 0.9 0.8 1.4 .. 3.2 0.6 1.2 1.1 w 1.3 1.0 0.8 1.6 1.0 0.8 2.1 0.9 3.8 0.8 0.7 1.2 1.3

1.6 2.0 0.7 2.9 0.3 .. 0.5 5.5 0.6 1.2 0.1 0.4 1.0 2.7 0.9 .. 0.4 0.6 5.8 0.6 0.1 1.1 0.3 0.3 0.6 1.2 2.4 0.9 0.3 0.9 1.8 0.6 1.0 1.5 0.7 0.9 1.1 5.9 2.2 0.3 1.0 0.9 w 1.0 0.8 0.7 1.4 0.8 0.6 1.7 0.9 3.1 0.7 0.5 1.0 1.0

Trend indicator values 1990 $ millions Exports Imports 2000

2009

3 3 3,985 4,469 .. .. .. .. .. .. .. .. .. .. 10 124 92 8 .. .. .. .. 18 154 46 925 .. .. .. .. .. .. 306 353 176 270 .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. 15 36 .. .. .. .. 288 214 3 .. 1,484 1,024 7,220 6,795 1 .. .. 90 .. 17 .. .. .. .. .. .. .. .. 3 .. .. s .. s .. .. .. .. 983 1,251 .. .. .. .. 389 870 4,667 4,830 .. .. .. .. 19 22 .. .. 13,136 16,637 3,319 6,779

2000

2009

23 56 .. 1 14 6 80 626 .. 3 .. .. 13 .. 622 1,729 2 1 1 6 1 .. 16 139 332 430 274 64 107 39 1 .. 210 46 14 31 19 175 .. 7 .. 0 90 34 .. .. .. .. 10 6 11 8 1,170 675 .. 47 6 1 .. .. 243 604 829 288 301 831 4 37 6 .. 108 172 5 44 .. 14 158 45 27 3 2 .. 18,088 s 22,223 s 572 329 8,353 10,467 5,109 5,682 3,244 4,785 8,925 10,889 2,339 2,644 .. 1,162 970 1,058 2,056 2,065 1,548 3,606 647 354 9,163 11,334 2,075 3,322

Note: For some countries data are partial or uncertain or based on rough estimates. See SIPRI (2010). a. Estimates differ from statistics of the government of China, which has published the following estimates: military expenditure as 1.2 percent of GDP in 2000 and 1.4 percent in 2008 and 7.6 percent of national government expenditure in 2000 and 6.7 percent in 2008 (see National Bureau of Statistics of China, www.stats.gov.cn).

292

2011 World Development Indicators

About the data

5.7

states and markets

Military expenditures and arms transfers Definitions

Although national defense is an important function of

always strictly comparable across countries. How-

• Military expenditures are SIPRI data derived from

government and security from external threats that

ever, SIPRI puts a high priority on ensuring that the data

the NATO deﬁnition, which includes all current and

contributes to economic development, high levels of

series for each country is comparable over time. More

capital expenditures on the armed forces, including

military expenditures for defense or civil conflicts bur-

information on SIPRI’s military expenditure project can

peacekeeping forces; defense ministries and other gov-

den the economy and may impede growth. Data on

be found at www.sipri.org/contents/milap/.

ernment agencies engaged in defense projects; para-

military expenditures as a share of gross domestic

Data on armed forces refer to military personnel on

military forces, if judged to be trained and equipped

product (GDP) are a rough indicator of the portion of

active duty, including paramilitary forces. Because

for military operations; and military space activities.

national resources used for military activities and of

data exclude personnel not on active duty, they

Such expenditures include military and civil person-

the burden on the national economy. As an “input”

underestimate the share of the labor force working

nel, including retirement pensions and social services

measure military expenditures are not directly related

for the defense establishment. Governments rarely

for military personnel; operation and maintenance;

to the “output” of military activities, capabilities, or

report the size of their armed forces, so such data

procurement; military research and development;

security. Comparisons of military spending between

typically come from intelligence sources.

and military aid (in the military expenditures of the

countries should take into account the many fac-

SIPRI’s Arms Transfers Programme collects data

donor country). Excluded are civil defense and current

tors that influence perceptions of vulnerability and

on arms transfers from open sources. Since publicly

expenditures for previous military activities, such as

risk, including historical and cultural traditions, the

available information is inadequate for tracking all

for veterans beneﬁts, demobilization, and weapons

length of borders that need defending, the quality of

weapons and other military equipment, SIPRI covers

conversion and destruction. This deﬁnition cannot be

relations with neighbors, and the role of the armed

only what it terms major conventional weapons. Data

applied for all countries, however, since that would

forces in the body politic.

cover the supply of weapons through sales, aid, gifts,

require more detailed information than is available

Data on military spending reported by governments

and manufacturing licenses; therefore the term arms

about military budgets and off-budget military expen-

are not compiled using standard definitions. They

transfers rather than arms trade is used. SIPRI data

ditures (for example, whether military budgets cover

are often incomplete and unreliable. Even in coun-

also cover weapons supplied to or from rebel forces

civil defense, reserves and auxiliary forces, police and

tries where the parliament vigilantly reviews bud-

in an armed conﬂict as well as arms deliveries for

paramilitary forces, and military pensions). • Armed

gets and spending, military expenditures and arms

which neither the supplier nor the recipient can be

forces personnel are active duty military personnel,

transfers rarely receive close scrutiny or full, public

identiﬁed with acceptable certainty; these data are

including paramilitary forces if the training, organiza-

disclosure (see Ball 1984 and Happe and Wakeman-

available in SIPRI’s database.

tion, equipment, and control suggest they may be used

Linn 1994). Therefore, the Stockholm International

SIPRI’s estimates of arms transfers are designed

to support or replace regular military forces. Reserve

Peace Research Institute (SIPRI) has adopted a defi-

as a trend-measuring device in which similar weap-

forces, which are not fully staffed or operational in

nition of military expenditure derived from the North

ons have similar values, reﬂecting both the quantity

peace time, are not included. The data also exclude

Atlantic Treaty Organization (NATO) definition (see

and quality of weapons transferred. SIPRI cautions

civilians in the defense establishment and so are not

Definitions). The data on military expenditures as a

that the estimated values do not reﬂect ﬁnancial

consistent with the data on military expenditures on

share of GDP and as a share of central government

value (payments for weapons transferred) because

personnel. • Arms transfers cover the supply of military

expenditure are estimated by SIPRI. Central govern-

reliable data on the value of the transfer are not avail-

weapons through sales, aid, gifts, and manufacturing

ment expenditures are from the International Mon-

able, and even when values are known, the transfer

licenses. Weapons must be transferred voluntarily by

etary Fund (IMF). Therefore the data in the table may

usually includes more than the actual conventional

the supplier, have a military purpose, and be destined

differ from comparable data published by national

weapons, such as spares, support systems, and

for the armed forces, paramilitary forces, or intelligence

governments.

training, and details of the ﬁnancial arrangements

agencies of another country. The trends shown in the

(such as credit and loan conditions and discounts)

table are based on actual deliveries only. Data cover

are usually not known.

major conventional weapons such as aircraft, armored

SIPRI’s primary source of military expenditure data is official data provided by national governments. These data are derived from national budget docu-

Given these measurement issues, SIPRI’s method

vehicles, artillery, radar systems and other sensors,

ments, defense white papers, and other public docu-

of estimating the transfer of military resources

missiles, and ships designed for military use, as well as

ments from official government agencies, including

includes an evaluation of the technical parameters

some major components such as turrets for armored

governments’ responses to questionnaires sent by

of the weapons. Weapons for which a price is not

vehicles and engines. Excluded are transfers of other

SIPRI, the United Nations, or the Organization for

known are compared with the same weapons for

military equipment such as most small arms and light

Security and Co-operation in Europe. Secondary

which actual acquisition prices are available (core

weapons, trucks, small artillery, ammunition, support

sources include international statistics, such as

weapons) or for the closest match. These weapons

equipment, technology transfers, and other services.

those of NATO and the IMF’s Government Finance

are assigned a value in an index that reﬂects their

Statistics Yearbook. Other secondary sources include

military resource value in relation to the core weap-

country reports of the Economist Intelligence Unit,

ons. These matches are based on such characteris-

Data on military expenditures are from SIPRI’s Year-

country reports by IMF staff, and specialist journals

tics as size, performance, and type of electronics,

book 2010: Armaments, Disarmament, and Interna-

and newspapers.

and adjustments are made for secondhand weap-

tional Security. Data on armed forces personnel are

In the many cases where SIPRI cannot make inde-

ons. More information on SIPRI’s Arms Transfers

from the International Institute for Strategic Stud-

pendent estimates, it uses the national data pro-

Programme is available at www.sipri.org/research/

ies’ The Military Balance 2011. Data on arms trans-

vided. Because of the differences in definitions and

armaments/transfers.

fers are from SIPRI’s Arms Transfers Programme

the difficulty in verifying the accuracy and complete-

Data sources

(www.sipri.org/research/armaments/transfers).

ness of data, data on military expenditures are not

2011 World Development Indicators

293

5.8

Fragile situations International Development Association Resource Allocation Index

Peacebuilding and peacekeeping

Operation namea

1–6 (low to high)

December 2010

2009

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 Myanmar Nepal São Tomé and Príncipe Sierra Leone Solomon Islands Somalia Sudan Tajikistan Timor-Leste Togo West Bank and Gaza Western Saharaj Yemen, Rep. Zimbabwe Fragile situations Low income

2.8 2.8 3.7 3.1 2.6 2.5 2.5 2.7 2.8 2.8 2.2 4.4 2.8 2.6 2.9 .. 3.1 3.4 2.8 .. 3.3 2.9 3.2 2.8 .. 2.5 3.2 2.9 2.8 .. .. 3.2 1.9

UNAMA BINUB MINURCATe MINURCAT MONUC UNOCI MINUSTAH UNAMI UNMIK UNMIL UNMIN RAMSI UNMISg UNMIT MINURSO

Battlerelated deaths

Troops, police, and military observers number December 2010

16 .. .. 4 3 .. .. 19,105 .. 9,071 .. .. .. .. 11,984 235 .. 16 9,392 .. 72 .. .. 580 .. 10,416 .. 1,517 .. .. 242 .. ..

number

Intentional homicides per 100,000 people Law enforcement Public and criminal health justice sources sources

Military expenditures

% of GDP

2000–08b

2004

2004–08c

2009

26,589 3,534 0 4,937 350 4,328 0 75,118 116 1,265 57 648 1,174 0 244 124,002 0 0 2,487 2,833 11,520 0 212 0 3,983 12,363 0 0 0 0 .. 0 0 275,761 s 146,844

3.4 38.6 1.9 37.4 29.8 19.2 11.9 35.0 19.9 50.8 16.1 3.7 16.9 17.6 21.8 7.3 6.6 .. 17.4 15.6 13.6 5.3 37.2 1.5 3.2 27.2 1.9h 12.5 14.3 .. .. 2.5 34.3 21.1 w 17.6

.. 5.0 1.9 .. .. .. .. .. .. 0.4 .. 7.6 0.4 .. .. .. .. .. .. .. 2.2 .. 2.6 .. .. .. 2.3 .. .. 3.9 .. 4.0 8.7 .. ..

1.8 4.2 1.5 3.8 1.8 6.4 .. 1.1 1.2 1.6 .. 5.6 .. .. 0.0 6.3 .. .. 0.8 .. 1.6 .. 2.3 .. .. .. .. 11.8 2.0 .. .. 4.4 2.8 3.2 w 1.4

Business environment

Survey year

2008 2006 2009 2006 2009 2010 2009 2009 2009 2008 2006 2006 2009 2009 2009 2009 2008 2009 2009 2006 2010

Losses due to theft, robbery, vandalism, and arson

Firms formally registered when operations started

% of sales

% of firms

1.5 0.4 0.2 1.1 .. 2.5 .. 1.8 3.3 3.4 0.0 0.7 2.0 1.1 .. .. .. 0.3 2.8 .. 0.9 .. 0.8 .. .. .. 0.3 1.5 2.4 1.2 .. 0.6 ..

88.0 .. 98.6 .. .. 77.1 .. 61.9 84.3 56.4 100.0 99.6 .. .. .. .. .. 89.2 73.8 .. 94.0 .. 89.2 .. .. .. 92.7 91.8 75.8 .. .. 81.7 ..

Note: The countries with fragile situations in the table are primarily International Development Association–eligible countries and nonmember or inactive countries and territories with 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 between 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 an interim one, and the World Bank will continue to improve and refine its understanding of fragility. a. UNAMA is United Nations Assistance Mission in Afghanistan, BINUB is Bureau Intégré des Nations Unies au Burundi (United Nations Integrated Office in Burundi), MINURCAT is United Nations Mission in the Central African Republic and Chad, MONUC is United Nations Organization Mission in DR Congo, UNOCI is United Nations Operation in Côte d'Ivoire, 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, UNMIN is United Nations Mission in Nepal, RAMSI is Regional Assistance Mission to Solomon Islands, UNMIS is United Nations Missions in Sudan, UNMIT is United Nations Integrated Mission in Timor-Leste, and MINURSO is United Nations Mission for the Referendum in Western Sahara. b. Total over the period. c. Data are for the most recent year available. d. Average over the period. e. Includes peacekeepers in Chad. The mission ended in 2010. f. The Internal Displacement Monitoring Centre's (IDMC) high estimate; the low estimate is 50,000. g. Does not include 22,444 troops, police, and military observers from the African Union–UN Hybrid Operation in Darfur. h. Data are for 2005. i. 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 UN High Commissioner for Refugees. j. The designation Western Sahara is used instead of Former Spanish Sahara (the designation used on the maps on the 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. k. IDMC's high estimate; the low estimate is 570,000.

294

2011 World Development Indicators

Children in employment

Refugees

Internally displaced persons

Access to an improved water source

Access to improved sanitation facilities

Maternal mortality ratio

Under-five Depth of mortality hunger rate

per 100,000 live births

Survey year

% of children ages 7–14

.. 2001 30.1 2006 10.6 2005 11.7 2000 67.0 2004 60.4 .. 2000 39.8 2005 30.1 2006 45.7 .. 2006 31.8 1994 48.3 2006 50.5 2005 33.4 2006 14.7 .. .. 2007 37.4 .. 1999 47.2 .. 2007 14.9 .. 2006 43.5 2000 19.1 2005 8.9 .. 2006 38.7 .. .. 2006 18.3 1999 14.3

By country of origin

By country of asylum

number

2009

2,887,123 141,021 70,018 94,239 159,554 55,014 268 455,852 20,544 23,153 209,168 15,020 10,920 1,109 24,116 1,785,212 33 .. 71,599 406,669 5,108 33 15,417 66 678,309 368,195 562 7 18,378 95,201 .. 1,934 22,449 7,636,291 s 5,427,548

37 14,734 7,132 24,967 27,047 338,495 .. 185,809 111,411 24,604 4,751 870 15,325 7,898 3 35,218 .. .. 6,952 .. 108,461 .. 9,051 .. 1,815 186,292 2,679 1 8,531 1,885,188i .. 170,854 3,995 3,182,120 s 1,893,823

% of % of population population

297,000 20,000 114,000 100,000 162,000 168,000 .. 1,900,000 7,800 621,000 10,000 230,000 .. .. .. 2,764,000 .. 19,700 .. 470,000 70,000 f .. .. .. 1,500,000 4,900,000 .. 400 1,500 .. 160,000 175,000 1,000,000k 14,047,900 s ..

2008

48 50 99 72 67 50 95 46 71 80 61 98 71 61 63 79 61 .. 68 71 88 89 49 69 30 57 70 69 60 91 .. 62 82 64 w 64

2008

National estimates

Modeled estimates

per 1,000

2004–09c

2008

2009

37 57 95 46 34 9 36 23 30 23 14 95 19 21 17 73 31 .. 17 81 31 26 13 29 23 34 94 50 12 89 .. 52 44 43 w 35

.. .. 3 615 543 1,099 .. 549 781 543 .. 14 980 405 630 84 .. .. 994 316 281 148 857 .. 1,044 1,107 38 .. .. .. .. .. 555 .. ..

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 64 370 350 .. .. 210 790 640 w 580

states and markets

5.8

Fragile situations

Primary gross enrollment ratio

kilocalories per person % of relevant age group per day

199 161 14 166 171 209 104 199 128 119 55 29 142 193 87 44 46 .. 112 71 48 78 192 36 180 108 61 56 98 30 .. 66 90 132 w 118

2005–07d

.. 320 140 380 300 310 300 410 230 230 350 150 260 250 430 .. 180 .. 340 230 220 160 340 180 .. 240 240 260 280 190 .. 270 300 290 w 285

2009

104 128 109 147 89 90 119 90 120 74 48 108 90 120 .. 103 116 .. 91 116 .. 131 158 107 33 74 102 113 115 79 .. 85 .. 94 w 104

About the data The table focuses on countries with fragile situations

According to the Geneva Declaration on Armed Vio-

have to build their own institutions tailored to their

and highlights the links among weak institutions,

lence and Development, more than 740,000 people

own needs. Peacekeeping operations in post-conflict

poor development outcomes, fragility, and risk of

die each year because of the violence associated with

situations have been effective in reducing the risks

conflict. These countries and territories often have

armed conflict and large- and small-scale criminality.

of reversion to conflict.

weak institutions that are ill-equipped to handle eco-

Recovery and rebuilding can take years, and the chal-

The countries with fragile situations in the table

nomic shocks, natural disasters, and illegal trade

lenges are numerous: infrastructure to be rebuilt,

are primarily International Development Association–

or to resist conflict, which increasingly spills across

persistently high crime, widespread health problems,

eligible countries and nonmember or inactive coun-

borders. Organized violence, including violent crime,

education systems in disrepair, and landmines to be

tries or territories of the World Bank with a 3.2 or

interrupts economic and social development through

cleared. Most countries emerging from conflict lack

lower harmonized average of the World Bank’s Country

lost human and social capital, disrupted services,

the capacity to rebuild the economy. Thus, capacity

Policy and Institutional Assessment rating and the cor-

displaced populations and reduced confidence for

building is one of the first tasks for restoring growth

responding rating by a regional development bank or

future investment. As a result, countries with fragile

and is linked to building peace and creating the con-

that have had a UN or regional peacebuilding mission

situations achieve lower development outcomes and

ditions that lead to sustained poverty reduction. The

(for example, by the African Union, European Union,

make slower progress toward the Millennium Develop-

World Bank and other international development agen-

or Organization of American States) or peacekeeping

ment Goals.

cies can help, but countries with fragile situations

mission (for example, by the African Union, European

2011 World Development Indicators

295

5.8

Fragile situations

About the data (continued) Union, North Atlantic Treaty Organization (NATO), or

fewer types of contracts and investments, constrain-

• Troops, police, and military observers in peace-

Organization of American States) during the last three

ing growth. The table presents data on the loss of

building and peacekeeping refer to people active in

years. Peacebuilding and peacekeeping involve many

sales due to theft, robbery, vandalism, and arson and

peacebuilding and peacekeeping as part of an official

elements—military, police, and civilian—working

on the percentage of firms operating informally. For

operation. Peacekeepers deploy to war-torn regions

together to lay the foundations for sustainable peace.

further information on enterprise surveys, see About

where no one else is willing or able to go to prevent

The list of countries and territories with fragile situa-

the data for table 5.2.

conflict from returning or escalating. • Battle-related

As the table shows, the human toll of armed vio-

deaths are deaths of members of warring parties in

lence across various contexts is severe. Additionally,

battle-related conflicts. Typically, battle-related

An armed conflict is a contested incompatibility

in countries with fragile situations weak institutional

deaths occur in warfare involving the armed forces

that concerns a government or territory where the

capacity often results in poor performance and fail-

of the warring parties (battlefield fighting, guerrilla

use of armed force between two parties (one of them

ure to meet expectations of effective service deliv-

activities, and all kinds of bombardments of military

the government) results in at least 25 battle-related

ery. Failure to deliver water, health, and education

units, cities, and villages). The targets are usually

deaths in a calendar year. There were 35 active

services can weaken struggling governments. The

the military and its installations or state institutions

armed conflicts in 26 locations in 2009. Separate

table includes several indicators related to living con-

and state representatives, but there is often sub-

measures are presented for intentional homicides—

ditions in fragile situations: children in employment,

stantial collateral damage of civilians killed in cross-

unlawful deaths purposefully inflicted on a person

refugees, internally displaced persons, access to

fire, indiscriminate bombings, and other military

by another person—which exclude deaths arising

water and sanitation, maternal and under-five mortal-

activities. All deaths—civilian as well as military—

from armed conflict. One measure draws from inter-

ity, depth of hunger, and primary school enrollment.

incurred in such situations are counted as bat-

national public health data sources, while the other

For more detailed information on these indicators,

tlerelated deaths. • Intentional homicides are esti-

draws from estimates by the United Nations Office on

see About the data for table 2.6 (children in employ-

mates of unlawful homicides purposely inflicted as

Drugs and Crime, which obtains data from national

ment), table 6.18 (refugees), table 2.18 (access to

a result of domestic disputes, interpersonal violence,

and international law enforcement and criminal jus-

improved water and sanitation), table 2.19 (maternal

violent conflicts over land resources, intergang vio-

tice sources. Data from these two sources measure

mortality), table 2.22 (under-five mortality), and table

lence over turf or control, and predatory violence and

different phenomena and are therefore unlikely to

2.12 (primary school enrollment).

killing by armed groups. Intentional homicide does

tions is an interim one, and the World Bank will continue to improve and refine its understanding of fragility.

provide identical numbers. Data on military expenditures reported by governments are not compiled using standard definitions

not include all intentional killing; the difference is Definitions

usually in the organization of the killing. Individuals or small groups usually commit homicide, whereas

and are often incomplete and unreliable. Even in

• International Development Association Resource

killing in armed conflict is usually committed by fairly

countries where the parliament vigilantly reviews

Allocation Index is from the Country Policy and Insti-

cohesive groups of up to several hundred members

budgets and spending, military expenditures and

tutional Assessment rating, which is the average

and is thus usually excluded. Data are from interna-

arms transfers rarely receive close scrutiny or full

score of four clusters of indicators designed to mea-

tional public health organizations such as the World

public disclosure. Data are from the Stockholm

sure macroeconomic, governance, social, and struc-

Health Organization (WHO) and the Pan American

International Peace Research Institute (SIPRI), which

tural dimensions of development: economic manage-

Health Organization and from the United Nations

uses NATO’s pre-2004 definition of military expen-

ment, structural policies, policies for social inclusion

Survey of Crime Trends and Operations of Criminal

diture (see Definitions). Therefore, the data in the

and equity, and public sector management and insti-

Justice Systems (CTS), which draws from national

table may differ from comparable data published by

tutions (see table 5.9). Countries are rated on a

and international law enforcement and criminal jus-

national governments. For a more detailed discus-

scale of 1 (low) to 6 (high). • Peacebuilding and

tice sources. • Military expenditures are SIPRI data

sion of military expenditures, see About the data for

peacekeeping refer to operations that engage in

derived from NATO's pre-2004 deﬁnition, which

table 5.7.

peacebuilding (reducing the risk of lapsing or relaps-

includes all current and capital expenditures on the

Along with public sector efforts, private sector

ing into conflict by strengthening national capacities

armed forces, including peacekeeping forces;

development and investment, especially in competi-

for conflict management and laying the foundation

defense ministries and other government agencies

tive markets, has tremendous potential to contribute

for sustainable peace and development) or peace-

engaged in defense projects; paramilitary forces, if

to growth and poverty reduction. The World Bank’s

keeping (providing essential security to preserve the

judged to be trained and equipped for military opera-

Enterprise Surveys review the business environment,

peace where fighting has been halted and to assist

tions; and military space activities. Such expendi-

assessing constraints to private sector growth and

in implementing agreements achieved by the peace-

tures include military and civil personnel, including

enterprise performance. In some countries doing

makers). UN peacekeeping operations are authorized

retirement pensions and social services for military

business requires informal payments to “get things

by the UN Secretary-General and planned, managed,

personnel; operation and maintenance; procure-

done” in customs, taxes, licenses, regulations, ser-

directed, and supported by the United Nations

ment; military research and development; and mili-

vices, and the like. Crime, theft, and disorder also

Department of Peacekeeping Operations and the

tary aid (in the military expenditures of the donor

impose costs on businesses and society. And in

Department of Field Support. The UN Charter gives

country). Excluded are civil defense and current

many developing countries informal businesses oper-

the Security Council primary responsibility for main-

expenditures for previous military activities, such as

ate without licenses. These firms have less access

taining international peace and security, including

for veterans beneﬁts, demobilization, and weapons

to financial and public services and can engage in

the establishment of a UN peacekeeping operation.

conversion and destruction. This definition cannot

296

2011 World Development Indicators

5.8

states and markets

Fragile situations be applied to all countries, however, since the neces-

disposal facilities that can effectively prevent

on intentional homicides are from the UN Office

sary detailed information is missing in some cases

human, animal, and insect contact with excreta.

on Drugs and Crime’s International Homicide Sta-

for military budgets and off-budget military expendi-

Improved facilities range from protected pit latrines

tistics database. Data on military expenditures are

tures (for example, whether military budgets cover

to flush toilets. • Maternal mortality ratio is the

from SIPRI’s Yearbook 2010: Armaments, Disar-

civil defense, reserves and auxiliary forces, police

number of women who die from pregnancy-related

mament, and International Security and database

and paramilitary forces, and military pensions).

causes during pregnancy and childbirth per 100,000

(www.sipri.org/databases/milex). Data on the

• Survey year is the year in which the underlying

live births. National estimates are based on national

business environment are from the World Bank’s

data were collected. • Losses due to theft, robbery,

surveys, vital registration records, and surveillance

Enterprise Surveys (www.enterprisesurveys.

vandalism, and arson are the estimated losses from

data or are derived from community and hospital

org). Data on children in employment are esti-

those causes that occurred on business establish-

records. Modeled estimates are based on an exer-

mates produced by the Understanding Children’s

ment premises calculated as a percentage of annual

cise by the WHO, United Nations Children’s Fund

Work project based on household survey data

sales. • Firms formally registered when operations

(UNICEF), United Nations Population Fund (UNFPA),

sets made available by the International Labour

started are the percentage of firms formally regis-

and the World Bank. See About the data for table

Organization’s International Programme on the

tered when they started operations in the country.

2.19 for further details. • Under-five mortality rate

Elimination of Child Labour under its Statistical

• Children in employment are children involved in

is the probability per 1,000 that a newborn baby will

Monitoring Programme on Child Labour, UNICEF

any economic activity for at least one hour in the

die before reaching age 5, if subject to current age-

under its Multiple Indicator Cluster Survey pro-

reference week of the survey. • Refugees are people

specific mortality rates. • Depth of hunger, or the

gram, the World Bank under its Living Standards

who are recognized as refugees under the 1951 Con-

intensity of food deprivation, indicates how much

Measurement Study program, and national sta-

vention Relating to the Status of Refugees or its

people who are food-deprived fall short of minimum

tistical offices (see table 2.6). Data on refugees

1967 Protocol, the 1969 Organization of African

food needs in terms of dietary energy. It is measured

are from the UNHCR’s Statistical Yearbook 2009,

Unity Convention Governing the Specific Aspects of

by comparing the average amount of dietary energy

complemented by statistics on Palestinian refu-

Refugee Problems in Africa, people recognized as

that undernourished people get from the foods they

gees under the mandate of the United Nations

refugees in accordance with the UN Refugee Agency

eat with the minimum amount of dietary energy they

Relief and Works Agency for Palestine Refugees

(UNHCR) statute, people granted refugee-like human-

need to maintain body weight and undertake light

in the Near East as published on its website (www.

itarian status, and people provided temporary protec-

activity. Depth of hunger is low when it is less than

unrwa.org). Data on internally displaced persons

tion. Asylum seekers—people who have applied for

200 kilocalories per person per day and high when

are from the Internal Displacement Monitoring

asylum or refugee status and who have not yet

it is above 300. • Primary gross enrollment ratio is

Centre. Data on access to water and sanitation

received a decision, or who are registered as asylum

the ratio of total enrollment, regardless of age, to the

are from the WHO and UNICEF’s Progress on Sani-

seekers—are excluded. Palestinian refugees are

population of the age group that officially corre-

tation and Drinking Water (2010). National esti-

people (and their descendants) whose residence was

sponds to the primary level of education. Primary

mates of maternal mortality are from UNICEF’s The

Palestine between June 1946 and May 1948 and

education provides children with basic reading, writ-

State of the World’s Children 2009 and Childinfo

who lost their homes and means of livelihood as a

ing, and mathematics skills along with an elementary

and Demographic and Health Surveys by Macro

result of the 1948 Arab-Israeli conflict. • Country of

understanding of such subjects as history, geogra-

International. Modeled estimates for maternal

origin refers to the nationality or country of citizen-

phy, natural science, social science, art, and music.

mortality are from WHO, UNICEF, UNFPA, and

ship of a claimant. • Country of asylum is the country

the World Bank’s Trends in Maternal Mortality in

where an asylum claim was filed and granted. • Inter-

1990–2008 (2010). Data on under-five mortal-

nally displaced persons are people or groups of people who have been forced or obliged to flee or to

Data sources

ity estimates by the Inter-agency Group for Child Mortality Estimation (which comprises UNICEF,

leave their homes or places of habitual residence, in

Data on the International Development Asso-

WHO, the World Bank, United Nations Population

particular as a result of armed conflict, or to avoid

ciation Resource Allocation Index are from

Division, and other universities and research insti-

the effects of armed conflict, situations of general-

the World Bank Group’s International Develop-

tutes) and are based mainly on household surveys,

ized violence, violations of human rights, or natural

ment Association database (www.worldbank.

censuses, and vital registration data, supple-

or human-made disasters and who have not crossed

org/ida). Data on peacebuilding and peace-

mented by the World Bank’s Human Development

an international border. • Access to an improved

keeping operations are from the UN Depart-

Network estimates based on vital registration and

water source refers to people with reasonable

ment of Peacekeeping Operations. Data on

sample registration data (see table 2.22). Data on

access to water from an improved source, such as

battle-related deaths are primarily from the

depth of hunger are from the Food and Agriculture

piped water into a dwelling, public tap, tubewell, pro-

Peace Research Institute Oslo/Uppsala Conflict

Organization’sFood Security Statistics (www.fao.

tected dug well, and rainwater collection. Reasonable

Data Program (UCDP) Armed Conflict Dataset (v.4-

org/economic/ess/food-security-statistics/en/).

access is the availability of at least 20 liters a person

2010) 1946-2009 (www.pcr.uu.se/research/ucdp/

Data on primary gross enrollment are from the

a day from a source within 1 kilometer of the dwell-

datasets), supplemented with data from the UCDP

United Nations Educational, Scientific, and Cul-

ing. • Access to improved sanitation facilities refers

Battle-Related Deaths Dataset (v.5-2010). Data

tural Organization’s Institute for Statistics.

to people with at least adequate access to excreta

2011 World Development Indicators

297

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)

Macroeconomic management

Fiscal policy

Debt policy

Average

2009

2.8 2.8 4.2 3.8 3.5 3.5 3.9 3.8 3.7 3.8 3.1 3.3 3.2 4.2 2.6 2.5 2.5 2.7 2.8 2.8 3.2 3.8 2.2 3.4 3.3 4.4 3.8 3.7 2.8 2.6 3.4 2.9 3.5 3.8 3.7 3.1 3.4 3.7

3.5 3.0 5.0 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 2.5 3.5 4.0 3.0 4.5 4.5 2.5 3.5 4.5

3.0 3.0 5.0 4.5 4.0 3.5 4.5 4.0 3.5 4.5 3.5 3.5 4.0 4.5 3.0 2.5 2.0 3.5 3.0 2.5 3.0 4.5 2.0 4.0 3.5 4.5 3.5 2.5 2.5 2.5 3.0 3.5 3.5 3.5 4.0 3.0 3.0 4.0

3.5 3.0 5.0 5.0 4.0 3.5 4.5 4.5 4.0 4.0 3.0 3.5 3.0 4.5 2.5 2.5 2.0 2.5 2.5 2.5 2.5 3.0 1.5 3.5 3.0 5.0 4.0 3.0 2.0 1.5 4.0 2.5 4.0 4.0 4.0 5.0 3.5 4.0

3.3 3.0 5.0 4.5 4.0 3.7 4.5 4.2 3.8 4.3 3.3 3.8 3.7 4.5 3.0 2.5 2.3 3.2 3.0 2.8 3.0 3.8 1.8 3.7 3.5 4.7 3.7 3.0 2.3 2.2 3.5 3.3 3.5 4.0 4.2 3.5 3.3 4.2

Trade

Financial sector

Business regulatory environment

Average

2009

3.0 4.0 4.5 4.0 3.5 4.0 3.0 5.0 4.0 4.0 4.0 4.0 3.5 4.0 3.5 3.0 3.0 3.5 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

2.5 2.5 4.0 3.5 3.5 3.5 3.0 4.0 4.0 3.0 2.5 2.5 3.0 4.0 2.5 3.0 2.5 2.0 3.0 3.0 3.5 3.5 1.0 3.0 3.0 3.5 4.0 4.0 3.0 3.0 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 2.5 2.5 2.0 2.5 3.0 3.5 4.5 2.0 3.5 3.5 5.5 4.0 4.0 3.0 2.5 3.0 2.5 3.5 3.5 4.0 3.0 3.5 3.5

2.7 2.8 4.2 3.8 3.5 3.7 3.2 3.8 4.0 3.5 3.0 3.3 3.2 3.8 2.7 2.8 2.7 2.5 3.0 3.3 3.7 4.0 1.5 3.2 3.3 5.0 4.0 4.2 3.3 3.2 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

terms. The IRAI is a key element in the country per-

part of the World Bank Group that helps the poorest

the mid-1970s by World Bank staff. Over time the cri-

formance rating.

countries reduce poverty by providing concessional loans

teria have been revised from a largely macroeconomic

The CPIA exercise is intended to capture the quality

and grants for programs aimed at boosting economic

focus to include governance aspects and a broader

of a country’s policies and institutional arrangements,

growth and improving living conditions. IDA funding helps

coverage of social and structural dimensions. Country

focusing on key elements that are within the country’s

these countries deal with the complex challenges they

performance is assessed against a set of 16 criteria

control, rather than on outcomes (such as economic

face in meeting the Millennium Development Goals.

grouped into four clusters: economic management,

growth rates) that are influenced by events beyond

The World Bank’s IDA Resource Allocation Index

structural policies, policies for social inclusion and

the country’s control. More specifically, the CPIA

(IRAI), presented in the table, is based on the results

equity, and public sector management and institu-

measures the extent to which a country’s policy and

of the annual Country Policy and Institutional Assess-

tions. IDA resources are allocated to a country on per

institutional framework supports sustainable growth

ment (CPIA) exercise, which covers the IDA-eligible

capita terms based on its IDA country performance

and poverty reduction and, consequently, the effective

countries. The table does not include Myanmar and

rating and, to a limited extent, based on its per capita

use of development assistance.

Somalia because they were not rated in the 2009

gross national income. This ensures that good per-

All criteria within each cluster receive equal weight,

exercise even though they are IDA eligible. Country

formers receive a higher IDA allocation in per capita

and each cluster has a 25 percent weight in the overall

298

2011 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)

Macroeconomic management

Fiscal policy

Debt policy

Average

2009

3.2 3.5 2.8 3.5 3.4 3.4 3.7 3.2 3.7 3.4 3.7 3.3 3.7 3.3 3.5 3.2 3.3 3.8 4.1 2.9 3.7 3.2 2.8 3.5 3.8 3.8 2.5 3.2 3.8 2.9 2.8 3.5 3.9 3.3 3.4 3.8 3.2 3.4 1.9

4.0 4.0 3.5 4.0 3.0 2.5 4.5 3.5 3.5 3.5 4.5 3.5 4.0 4.0 4.0 3.0 4.0 4.0 4.0 3.0 4.0 4.0 3.5 3.0 4.0 4.0 3.5 3.5 4.5 3.0 3.0 3.0 4.5 4.0 4.0 4.5 3.5 4.0 2.0

4.0 4.0 3.5 3.0 3.5 2.0 4.0 2.5 3.5 3.0 4.5 3.5 4.0 3.5 4.5 3.0 3.5 4.0 4.0 3.0 4.0 3.5 2.5 3.0 3.5 3.5 3.0 3.5 4.5 3.5 3.0 3.0 4.5 4.0 3.5 4.5 2.5 3.0 2.0

3.0 4.0 2.5 4.0 3.0 3.0 4.5 3.5 4.0 3.0 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 3.5 2.5 3.0 4.5 4.0 4.5 4.0 3.5 3.5 1.0

3.7 4.0 3.2 3.7 3.2 2.5 4.3 3.2 3.7 3.2 4.5 3.3 4.2 3.8 4.3 3.2 4.0 3.8 4.3 2.8 4.0 3.7 3.0 3.2 3.7 3.7 2.7 3.5 4.3 3.3 2.8 3.0 4.5 4.0 4.0 4.3 3.2 3.5 1.7

states and markets

5.9

Public policies and institutions

Trade

Financial sector

Business regulatory environment

Average

2009

3.5 3.5 3.0 4.0 4.0 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 3.5 3.5 4.5 4.0 3.0

2.0 3.5 2.5 3.0 3.0 3.0 3.0 2.5 3.5 2.0 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 3.5 3.5 3.5 2.5 2.5 4.0 2.5 2.5 3.5 3.5 3.0 3.0 3.0 2.0 3.5 1.5

3.0 3.0 3.0 3.5 3.5 4.0 3.5 3.5 3.5 3.5 3.0 3.0 3.5 3.0 3.5 4.0 3.0 4.0 3.5 2.5 4.0 3.0 2.5 4.0 4.5 4.5 3.0 3.0 3.5 1.5 3.0 3.0 4.0 3.0 3.5 3.5 3.5 3.0 2.0

2.8 3.3 2.8 3.5 3.5 3.7 3.5 3.3 3.8 3.3 3.7 3.2 3.7 3.3 3.5 3.7 3.5 3.8 4.2 3.0 3.8 3.2 2.8 3.7 4.0 4.0 2.7 3.2 3.8 2.8 3.2 3.8 3.8 2.8 3.3 3.3 3.3 3.5 2.2

score, which is obtained by averaging the average

criteria are designed in a developmentally neutral man-

two key phases. In the benchmarking phase a small

scores of the four clusters. For each of the 16 criteria

ner. Accordingly, higher scores can be attained by a

representative sample of countries drawn from all

countries are rated on a scale of 1 (low) to 6 (high).

country that, given its stage of development, has a

regions is rated. Country teams prepare proposals

The scores depend on the level of performance in

policy and institutional framework that more strongly

that are reviewed first at the regional level and then

a given year assessed against the criteria, rather

fosters growth and poverty reduction.

in a Bankwide review process. A similar process is

than on changes in performance compared with the

The country teams that prepare the ratings are very

followed to assess the performance of the remaining

previous year. All 16 CPIA criteria contain a detailed

familiar with the country, and their assessments are

countries, using the benchmark countries’ scores as

description of each rating level. In assessing country

based on country diagnostic studies prepared by the

guideposts. The final ratings are determined following

performance, World Bank staff evaluate the country’s

World Bank or other development organizations and

a Bankwide review. The overall numerical IRAI score

performance on each of the criteria and assign a rat-

on their own professional judgment. An early con-

and the separate criteria scores were first publicly

ing. The ratings reflect a variety of indicators, observa-

sultation is conducted with country authorities to

disclosed in June 2006.

tions, and judgments based on country knowledge and

make sure that the assessments are informed by

on relevant publicly available indicators. In interpreting

up-to-date information. To ensure that scores are

the assessment scores, it should be noted that the

consistent across countries, the process involves

See IDA’s website at www.worldbank.org/ida for more information.

2011 World Development Indicators

299

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)

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

2009

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 3.5 3.5 3.0 3.5 4.5 4.0 4.5 3.5 2.5 4.0 3.0 4.0 3.5 3.0 2.5 3.5 4.5

3.0 2.5 4.5 4.0 3.5 3.0 4.0 4.0 3.5 4.0 3.5 3.0 3.0 4.5 2.5 2.5 2.5 3.0 2.5 2.0 3.0 3.5 2.5 4.5 3.5 4.5 4.0 3.5 3.0 3.0 3.5 3.0 4.0 4.0 3.5 3.5 3.5 3.5

3.0 2.5 4.0 4.0 4.0 3.5 4.0 4.0 3.5 3.5 3.0 3.5 3.5 4.5 2.5 2.5 3.0 3.0 3.0 2.5 3.5 4.0 3.5 4.0 3.5 4.5 4.5 4.0 3.0 2.0 4.0 2.5 3.5 4.0 4.0 2.5 2.5 3.5

2.5 3.0 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 3.0 2.5 2.5 3.0 3.5 2.5 3.5 2.5 4.5 3.5 3.5 3.0 2.5 3.0 2.5 3.5 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 2.5 3.0 2.5 3.5 3.5 3.5 3.0 3.0 3.0

2.6 2.9 4.1 3.8 3.6 3.3 4.0 3.8 3.7 3.6 3.3 3.3 3.1 4.3 2.5 2.4 2.6 2.8 2.7 2.4 3.2 3.6 2.8 3.6 3.3 4.2 3.9 3.9 3.0 2.5 3.5 2.7 3.7 3.7 3.5 2.9 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 4.0 3.0 3.5 3.5 3.5 3.5 4.5 3.0 3.5 3.0 4.0 2.5 2.0 2.0 2.5 2.5 2.5 3.0 3.5 2.5 3.5 3.0 4.0 3.5 4.0 3.0 2.5 3.5 3.0 4.0 4.0 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 4.0 3.5 4.0 3.5 3.5 3.5 4.5 4.5 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 2.5 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 2.5 2.5 4.0 2.0 2.5 2.0 3.0 4.0 4.0 2.0 2.5 3.0 2.5 3.0 3.5 3.0 3.0 3.0 2.5

2.4 2.4 3.7 3.2 3.0 3.3 3.9 3.3 3.3 3.7 2.6 2.7 2.9 4.0 2.4 2.2 2.4 2.2 2.6 2.6 2.8 3.8 2.7 3.2 2.9 3.8 3.8 3.7 2.6 2.6 3.1 2.5 3.3 3.7 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: Macroe conomic 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

300

2011 World Development Indicators

5.9

Policies for social inclusion and equity

Public sector management and institutions

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

2009

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 3.5 3.5 3.0 3.5 3.0 3.0 4.0 3.5 4.0 2.0 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 3.0 4.0 3.5 4.0 3.5 3.5 3.5 3.5 3.5 4.0 3.5 3.5 3.5 3.5 3.5 4.5 4.5 3.0 3.5 3.0 2.5 3.5 4.0 3.5 2.5 3.5 4.0 3.0 2.0 4.0 4.0 3.5 3.5 4.5 3.5 3.5 1.5

3.0 3.5 2.5 3.5 3.5 3.5 3.5 3.5 4.0 4.0 3.5 4.0 3.5 3.5 3.0 3.0 2.5 4.5 4.0 3.0 3.5 3.5 3.0 4.5 4.0 4.0 2.5 3.0 4.0 2.5 3.0 4.0 4.0 4.0 2.5 4.0 3.0 4.0 1.0

2.5 3.0 2.5 3.5 3.5 3.5 3.5 3.0 3.5 3.5 3.0 3.0 3.5 3.0 3.5 3.0 3.0 3.5 3.5 2.5 3.0 3.5 2.5 3.5 3.5 3.5 2.5 3.5 3.5 2.5 3.0 3.0 3.5 3.5 2.0 3.5 3.5 3.0 1.0

4.0 3.0 2.0 3.5 3.5 4.0 3.0 3.0 3.5 3.0 3.0 3.5 3.5 3.0 3.0 3.0 2.0 3.5 4.0 2.5 3.5 2.5 2.0 3.5 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

3.4 3.3 2.5 3.6 3.5 3.8 3.4 3.4 3.9 3.5 3.3 3.7 3.5 3.1 3.2 2.9 2.7 3.9 3.9 2.8 3.4 3.1 2.6 3.8 3.7 3.7 2.3 3.4 3.7 2.8 2.7 3.4 3.8 3.7 2.9 4.0 3.1 3.5 1.6

3.0 3.5 2.5 3.5 3.5 4.0 3.5 3.0 3.5 3.0 3.0 2.5 3.0 3.0 2.5 2.5 2.0 3.0 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 3.5 3.5 2.5 3.5 3.5 2.5 3.0 1.5

3.5 3.0 2.5 3.0 3.0 3.0 3.5 3.0 4.0 4.0 4.0 3.0 4.0 3.5 3.0 3.5 3.0 4.0 3.5 3.0 3.0 3.5 2.5 4.0 3.5 3.5 2.0 3.0 3.5 3.0 2.5 3.5 4.0 3.5 3.5 4.0 3.5 3.5 2.0

3.0 4.0 3.5 4.0 4.0 4.0 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 2.5 2.5 3.5 4.5 4.0 3.0 3.0 4.0 3.0 3.0 4.0 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 2.5 3.5 4.0 3.0 3.5 3.0 2.0 3.0 3.5 3.5 2.5 3.0 3.5 2.5 2.0 3.5 3.0 3.0 3.0 3.5 3.0 3.0 1.5

2.0 3.5 3.0 2.5 3.0 3.0 3.5 2.5 3.0 3.0 3.0 3.0 3.0 2.5 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 3.0 3.0 2.0 3.5 2.5 1.5 3.0 3.0 3.0 3.0 1.5

2.9 3.4 2.8 3.3 3.4 3.5 3.4 3.0 3.4 3.4 3.4 3.0 3.4 3.1 2.9 3.0 2.8 3.5 4.0 3.1 3.4 2.9 2.6 3.4 4.0 3.8 2.2 2.7 3.5 2.7 2.4 3.6 3.3 2.8 3.3 3.6 3.0 3.2 2.0

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 pattern 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 facilit ated 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,

electorate, the legislature, and the judiciary and the

Data sources Data on public policies and institutions are from the World Bank Group’s CPIA database available at www.worldbank.org/ida.

2011 World Development Indicators

301

5.10

Transport services Roads

302

Total road network km

Paved roads %

Passengers carried million passengerkm

2000–08a

42,150 18,000 111,261 51,429 231,374 7,704 818,356 110,778 52,942 239,226 94,797 153,595 19,000 62,479 21,846 25,798 1,751,868 40,231 92,495 12,322 38,257 51,346 1,409,000 24,307 40,000 79,814 3,730,164 2,040 164,183 153,497 17,000 38,049 81,996 29,248 .. 130,573 73,257 12,600 43,670 104,918 10,029 4,010 58,034 44,359 78,860 951,200 9,170 3,742 20,329 644,288 57,614 116,711 14,095 44,348 3,455 4,160 13,600

29.3 39.0 73.5 10.4 30.0 90.5 .. 100.0 50.6 9.5 88.6 78.2 9.5 7.0 52.3 32.6 5.5 98.4 4.2 10.4 6.3 8.4 39.9 .. 0.8 20.2 53.5 100.0 .. 1.8 7.1 25.3 7.9 86.9 49.0 100.0 100.0 49.4 14.8 86.9 19.8 21.8 28.8 13.7 65.5 100.0 10.2 19.3 94.1 100.0 14.9 91.8 34.5 9.8 27.9 24.3 20.4

2011 World Development Indicators

.. 197 .. 166,045 .. 2,742 302,369 69,000 14,041 .. 8,184 132,404 .. .. .. .. .. 13,688 .. .. 201 .. 493,814 .. .. .. 1,247,611 .. 157 .. .. 27 .. 4,093 6,551 88,468 70,173 .. 11,819 12,793 .. .. 3,190 219,113 71,800 769,000 .. 16 5,269 949,306 .. .. .. .. .. .. ..

Railways

Goods hauled million ton-km 2000–08a

.. 2,200 .. 4,709 .. 179 189,847 26,411 9,947 .. 22,767 46,891 .. .. 300 .. .. 11,843 .. .. .. .. 129,600 .. .. .. 3,286,819 .. 39,726 .. .. 1 .. 11,042 2,222 50,877 10,717 .. 1,193 .. .. .. 7,641 2,456 28,500 313,000 .. .. 586 472,700 .. 18,360 .. .. .. .. ..

Passengers carried million Rail lines total route- passengerkm km 2000–09a

.. 423 4,723 .. 25,023 845 9,674 5,784 2,079 2,835 5,510 3,578 758 2,866 1,016 888 29,817 4,150 622 .. 650 977 58,345 .. .. 5,352 65,491 .. 1,672 3,641 795 .. 639 2,723 5,076 9,539 2,131 .. .. 5,195 .. .. 929 .. 5,919 33,778 810 .. 1,566 33,706 953 1,552 .. .. .. .. ..

2000–09a

.. 32 1,141 .. 6,979 27 1,546 10,210 1,025 5,609 7,401 10,493 .. 313 61 94 .. 2,144 .. .. 45 377 2,901 .. .. 840 787,890 .. .. 35 211 .. 10 1,835 1,285 6,462 7,312 .. .. 40,837 .. .. 274 .. 3,876 87,667 95 .. 626 76,772 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

2000–09a

2009

.. 231 4,371 275 5,695 653 50,027 8,521 840 1,409 333 4,859 .. 1,537 80 234 67,946 798 79 .. 184 466 52,584 .. .. 8,097 229,062 23,973 12,115 .. .. 933 .. 1,679 780 5,048 6,773 .. 2,897 6,216 1,997 .. 396 2,914 7,423 58,318 525 .. 294 103,397 .. 8,795 .. .. .. .. ..

.. 0 4 64 112 6 2,769 342 7 0 1 1,427 .. 7 0 0 1,782 2 0 .. 1 23 1,347 .. .. 1,179 11,976 13,293 2,420 .. .. 9 .. 2 27 22 14 .. 3 180 15 .. 1 424 484 6,625 62 .. 2 10,188 .. 31 .. .. .. .. ..

.. 46 1,184 .. 12,025 354 62,083 20,202 7,592 870 42,742 6,542 36 1,060 988 674 267,700 3,152 .. .. 92 978 258,280 .. .. 4,032 2,523,917 .. 11,884 182 234 .. 675 2,641 1,351 11,249 2,030 .. .. 3,840 .. .. 5,780 .. 8,872 26,482 2,485 .. 5,417 93,946 181 538 .. .. .. .. ..

.. .. .. .. 1,555 .. 6,197 .. .. 1,182 .. 9,701 .. .. .. .. 6,246 .. .. .. .. .. 4,175 .. .. 2,814 105,977 21,040 2,042 .. .. 876 .. .. .. .. .. 1,263 1,001 6,250 .. .. .. .. 1,064 4,491 .. .. .. 12,765 .. 935 906 .. .. .. ..

.. 5 53 3 75 8 403 139 10 16 6 250 .. 19 1 6 752 11 1 .. 3 10 1,198 .. .. 97 2,140 150 196 .. .. 33 .. 25 11 78 86 .. 46 56 19 .. 9 44 105 772 5 .. 5 1,081 .. 113 .. .. .. .. ..

Roads

Total road network km

Paved roads %

Passengers carried million passengerkm

2000–08a

197,534 4,236,429 437,759 174,301 45,550 96,424 18,096 487,700 22,210 1,200,858 7,816 93,612 63,265 25,554 104,237 .. 5,749 34,000 34,994 69,684 6,970 5,940 10,600 83,200 81,030 13,922 49,827 15,451 98,722 18,912 11,066 2,028 366,096 12,778 49,250 58,256 30,331 27,000 66,467 17,782 136,135 93,911 20,333 18,948 193,200 93,247 53,430 260,420 13,727 19,600 29,500 102,887 200,037 383,313 82,900 25,645 7,790

37.7 49.3 59.1 73.3 84.3 100.0 100.0 100.0 73.3 79.6 100.0 89.9 14.1 2.8 78.5 .. 85.0 91.1 13.5 100.0 .. 18.3 6.2 57.2 28.6 56.5 11.6 45.0 82.8 19.0 26.8 98.0 35.3 85.8 3.5 67.8 20.8 11.9 12.8 55.9 90.0 65.9 12.0 20.7 15.0 80.5 43.5 65.4 38.1 3.5 50.8 13.9 9.9 68.2 86.0 95.0 90.0

20,449 .. .. .. .. .. .. 97,560 .. 947,562 .. 106,878 .. .. 97,854 .. .. 6,468 2,113 17,966 .. .. .. .. 42,739 1,239 .. .. .. .. .. .. 463,865 1,640 1,215 .. .. .. 47 .. .. .. 123 .. .. 63,362 .. 263,788 .. .. .. .. .. 26,791 .. .. ..

Railways

Goods hauled million ton-km 2000–08a

35,743 .. .. .. .. 15,900 .. 192,700 .. 327,632 .. 63,481 22 .. 12,545 .. .. 903 287 12,344 .. .. .. .. 20,419 3,978 .. .. .. .. .. .. 227,290 1,577 782 794 .. .. 591 .. 77,100 .. .. .. .. 17,564 .. 129,249 .. .. .. .. .. 174,223 46,406 10 ..

Passengers carried million Rail lines total route- passengerkm km 2000–09a

7,793 63,273 3,370 7,555 2,025 1,919 1,005 16,959 .. 20,036 294 14,205 1,917 .. 3,378 .. .. 417 .. 1,885 .. .. .. .. 1,767 699 854 797 1,665 733 728 .. 26,704 1,157 1,814 2,110 3,116 .. .. .. 2,886 .. .. .. 3,528 4,114 .. 7,791 .. .. .. 2,020 479 19,764 2,842 .. ..

2000–09a

5,708 838,032 14,344 15,312 54 1,683 1,968 45,590 .. 253,555 .. 14,860 226 .. 31,298 .. .. 106 .. 75 .. .. .. .. 357 154 10 44 1,527 196 47 .. 449 423 1,009 4,190 114 4,163 .. .. 15,400 .. .. .. 174 2,877 .. 24,731 .. .. .. 78 83 16,454 3,766 .. ..

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

2000–09a

2009

447 551,448 4,390 20,540 121 79 1,055 13,569 .. 22,100 353 197,302 1,399 .. 9,273 .. .. 745 .. 18,693 .. .. .. .. 11,888 497 12 33 1,384 189 7,566 .. 71,136 1,017 7,852 4,111 695 885 .. .. 4,331 4,078 .. .. 77 2,092 .. 6,187 .. .. .. 900 1 29,940 872 .. ..

.. 7,889 6,394 2,206 .. 817 2,033 9,532 1,690 16,286 .. .. .. .. 16,054 .. .. .. .. .. 995 .. .. .. .. .. .. .. 15,843 .. .. .. 2,869 .. .. 1,222 .. .. .. .. 10,066 2,955 .. .. .. .. 3,768 2,058 4,597 .. .. 1,335 4,116 859 1,042 1,674 ..

46 602 330 134 .. 528 48 383 17 642 32 19 34 2 256 .. 18 5 10 27 14 .. .. 10 12 1 10 4 182 .. 1 11 222 5 5 62 11 28 5 7 292 217 .. .. 17 110 26 51 66 21 10 66 87 83 124 .. 77

2,953 54,446 27,421 13,053 .. 77,747 4,605 33,195 1,380 86,897 2,324 1,193 2,949 101 34,169 .. 2,597 309 303 1,302 1,308 .. .. 1,147 617 87 500 157 23,766 .. 142 1,093 15,728 402 257 4,931 490 1,527 455 484 29,109 12,104 .. .. 1,365 8,786 2,361 5,303 6,348 847 428 5,843 10,481 4,279 9,904 .. 10,211

2011 World Development Indicators

10 1,235 277 96 .. 121 985 400 10 10,486 163 15 272 2 15,163 .. 281 2 2 18 94 .. .. 0 7 0 14 1 2,853 .. 0 153 714 1 3 63 6 3 0 6 4,520 799 .. .. 8 14 39 304 0 19 0 257 227 55 314 .. 2,276

303

5.10

Transport services Roads

Romania Russian Federation Rwanda Saudi Arabia Senegal Serbia Sierra Leone Singapore Slovak Republic Slovenia Somalia South Africa 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

2000–08a

198,817 963,000 14,008 221,372 14,805 40,130 11,300 3,325 43,848 38,872 22,100 362,099 667,064 97,286 11,900 3,594 574,741 71,355 64,983 27,767 87,524 180,053 .. 11,652 8,320 19,371 426,951 24,000 70,746 169,502 4,080 419,634 6,506,221 77,732 81,600 96,155 160,089 5,147 71,300 66,781 97,267

30.2 20,194 80.1 78,000 19.0 .. 21.5 .. 29.3 .. 47.7 4,719 8.0 .. 100.0 5,964 87.0 32,214 100.0 815 11.8 .. 17.3 .. 99.0 397,117 81.0 21,067 36.3 .. 30.0 .. 23.6 108,100 100.0 93,675 91.0 589 .. 150 7.4 .. 98.5 .. .. .. 21.0 .. 51.1 .. 75.2 .. .. 206,098 81.2 .. 23.0 .. 97.8 60,671 100.0 .. 100.0 736,000 67.4 7,980,611 .. 2,032 87.3 56,674 33.6 .. 47.6 49,372 100.0 .. 8.7 .. 22.0 .. 19.0 .. 49.0 m .. m 12.0 .. 35.4 .. 36.3 .. 36.8 .. 24.3 .. 11.4 .. .. 27,816 22.0 .. 81.0 .. 51.8 .. 12.1 .. 93.4 .. 100.0 69,000

Railways

Goods hauled million ton-km 2000–08a

56,377 206,000 .. .. .. 1,112 .. .. 22,114 16,261 .. 434 132,868 .. .. .. 42,400 16,226 .. 14,572 .. .. .. .. .. 16,611 181,935 .. .. 36,866 .. 173,077 1,889,923 .. 21,038 .. 24,647 .. .. .. .. ..m .. .. .. .. .. .. 21,038 .. .. .. .. 29,505 45,032

Passengers carried million Rail lines total route- passengerkm km 2000–09a

2000–09a

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

2000–09a

2009

3,268 34,403 .. 17,508 573 927 22 18,427 3,441 953 .. 12,504 49,289 2,418 607 .. 5,824 14,701 1,343 765 684 19,619 .. .. 1,014 2,279 31,339 1,706 64 3,428 31,762 102,465 679,423d 564 1,850 5,121 11,074 .. 1,050 62 261 2,270,901 s 13,439 664,804 398,922 265,882 678,243 326,294 83,523 138,460 38,022 64,196 27,749 1,592,658 399,964

4 2,306 .. 1,838 0 2 8 7,391 0 3 .. 676 1,080 279 42 .. 16 1,058 11 6 1 2,133 .. .. 70 14 856 9 27 63 8,960 6,615 61,684 d 4 76 2 312 .. 26 .. 7 202,136 s 783 31,329 17,548 13,781 32,112 17,878 3,365 6,576 653 1,825 1,815 170,024 33,950

10,776 5,975 8,902 85,194 153,500 1,865,305 .. .. .. 1,020 337 1,748 906 129 384 4,058 683 3,013 .. .. .. .. .. .. 3,623 2,247 6,465 1,228 840 2,668 .. .. .. 22,051 13,865 113,342 15,043 22,959 7,348 1,463 4,767 135 4,508 34 766 300 .. .. 9,946 7,038 11,500 3,544 17,417 12,460 1,801 1,120 2,370 616 45 1,282 475b 728b 2,600 b 4,429 8,037 3,161 .. .. .. .. .. .. .. .. .. 1,991 1,493 2,073 8,686 5,374 9,681 3,095 1,685 11,547 259 .. 218 21,678 48,327 196,188 .. .. .. 16,173 51,467 12,512 226,205 9,476 2,431,181c 2,993 15 284 4,230 2,832 24,238 336 .. 81 2,347 4,129 3,807 .. .. .. .. .. .. 1,273 183 .. 2,583 .. 1,580 .. s 2,264 m 5,321 m .. .. .. .. 1,343 4,072 .. 1,917 4,049 .. 1,083 5,812 .. .. 3,910 .. 4,248 3,483 171,322 1,025 7,592 .. .. .. .. 1,493 2,222 .. 24,731 3,529 .. .. .. .. 7,038 8,872 130,021 10,210 6,542

1,381 58 2,178 475 .. .. 4,431 157 .. 0 .. 17 .. 0 25,866 84 .. 32 .. 25 .. .. 3,726 151 10,193 548 3,464 17 .. 7 .. .. 1,251 62 .. 168 685 19 .. 10 .. 21 5,898 124 .. .. .. .. .. 14 .. 24 4,522 272 .. 15 .. 0 1,112 59 14,425 171 5,987 1,004 34,300 9,182d .. 9 .. 23 1,168 124 4,751 84 .. .. .. 15 .. 4 .. 6 443,740 s 26,379 s .. 228 206,537 7,169 150,612 3,954 55,926 3,215 207,719 7,398 142,980 3,093 11,018 1,018 28,362 1,794 .. 419 14,593 700 .. 373 236,021 18,981 62,931 4,488

a. Data are for the latest year available in the period shown. b. Includes Tazara railway. c. Refers to class 1 railways only. d. Covers only carriers designated by the U.S. Department of Transportation as major and national air carriers.

304

2011 World Development Indicators

About the data

5.10

Definitions

Transport infrastructure—highways, railways, ports

But when traffic is merely transshipment, much of

• Total road network covers motorways, highways,

and waterways, and airports and air traffic control

the economic benefit goes to the terminal operator

main or national roads, secondary or regional roads,

systems—and the services that flow from it are cru-

and ancillary services for ships and containers rather

and all other roads in a country. • Paved roads are

cial to the activities of households, producers, and

than to the country more broadly. In transshipment

roads surfaced with crushed stone (macadam) and

governments. Because performance indicators vary

centers empty containers may account for as much

hydrocarbon binder or bituminized agents, with con-

widely by transport mode and focus (whether physical

as 40 percent of traffic.

crete, or with cobblestones. • Passengers carried

infrastructure or the services flowing from that infra-

The air transport data represent the total (interna-

by road are the number of passengers transported

structure), highly specialized and carefully specified

tional and domestic) scheduled traffic carried by the

by road times kilometers traveled. • Goods hauled

indicators are required. The table provides selected

air carriers registered in a country. Countries submit

by road are the volume of goods transported by road

indicators of the size, extent, and productivity of

air transport data to ICAO on the basis of standard

vehicles, measured in millions of metric tons times

roads, railways, and air transport systems and of the

instructions and definitions issued by ICAO. In many

kilometers traveled. • Rail lines are the length of rail-

volume of traffic in these modes as well as in ports.

cases, however, the data include estimates by ICAO

way route available for train service, irrespective of

Data for transport sectors are not always inter-

for nonreporting carriers. Where possible, these esti-

the number of parallel tracks. • Passengers carried

nationally comparable. Unlike for demographic sta-

mates are based on previous submissions supple-

by railway are the number of passengers transported

tistics, national income accounts, and international

mented by information published by the air carriers,

by rail times kilometers traveled. • Goods hauled

trade data, the collection of infrastructure data has

such as flight schedules.

by railway are the volume of goods transported by

not been “internationalized.” But data on roads are

The data cover the air traffic carried on scheduled

railway, measured in metric tons times kilometers

collected by the International Road Federation (IRF)

services, but changes in air transport regulations

traveled. • Port container traffic measures the flow

and data on air transport by the International Civil

in Europe have made it more difficult to classify

of containers from land to sea transport modes and

Aviation Organization (ICAO).

traffic as scheduled or nonscheduled. Thus recent

vice versa in twenty-foot-equivalent units (TEUs), a

National road associations are the primary source

increases shown for some European countries may

standard-size container. Data cover coastal shipping

of IRF data. In countries where a national road asso-

be due to changes in the classification of air traffic

as well as international journeys. Transshipment traf-

ciation is lacking or does not respond, other agencies

rather than actual growth. For countries with few air

fic is counted as two lifts at the intermediate port

are contacted, such as road directorates, ministries

carriers or only one, the addition or discontinuation

(once to off-load and again as an outbound lift) and

of transport or public works, or central statistical

of a home-based air carrier may cause significant

includes empty units. • Registered carrier depar-

offices. As a result, definitions and data collection

changes in air traffic.

tures worldwide are domestic takeoffs and takeoffs

methods and quality differ, and the compiled data

abroad of air carriers registered in the country. • Pas-

are of uneven quality. Moreover, the quality of trans-

sengers carried by air include both domestic and

port service (reliability, transit time, and condition of

international passengers of air carriers registered

goods delivered) is rarely measured, though it may be

in the country. • Air freight is the volume of freight,

as important as quantity in assessing an economy’s

express, and diplomatic bags carried on each flight

transport system.

stage (operation of an aircraft from takeoff to its next

Unlike the road sector, where numerous qualified motor vehicle operators can operate anywhere on

landing), measured in metric tons times kilometers traveled.

the road network, railways are a restricted transport system with vehicles confined to a fixed guideway. Considering the cost and service characteristics, railways generally are best suited to carry—and can effectively compete for—bulk commodities and con-

Data sources

tainerized freight for distances of 500–5,000 kilo-

Data on roads are from the IRF’s World Road

meters, and passengers for distances of 50–1,000

Statistics, supplemented by World Bank staff

kilometers. Below these limits road transport

estimates. Data on railways are from a database

tends to be more competitive, while above these

maintained by the World Bank’s Transport, Water,

limits air transport for passengers and freight and

and Information and Communication Technologies

sea transport for freight tend to be more competi-

Department, Transport Division, based on data

tive. The railways indicators in the table focus on

from the International Union of Railways. Data on

scale and output measures: total route-kilometers,

port container traffic are from Containerisation

passenger-kilometers, and goods (freight) hauled in

International’s Containerisation International Year-

ton-kilometers.

book. Data on air transport are from the ICAO’s

Measures of port container traffic, much of it commodities of medium to high value added, give

Civil Aviation Statistics of the World and ICAO staff estimates.

some indication of economic growth in a country.

2011 World Development Indicators

305

states and markets

Transport services

5.11

Power and communications Telephonesa

Electric power Access and use

306

.. 1,372 957 189 2,789 1,578 11,217 8,218 2,317 208 3,427 8,523 76 561 2,467 1,503 2,232 4,594 .. .. 113 263 17,061 .. .. 3,319 2,455 5,866 974 95 150 1,866 186 3,878 1,327 6,464 6,460 1,377 1,137 1,425 953 .. 6,348 42 16,350 7,931 1,158 .. 1,678 7,149 268 5,723 543 .. .. 23 708

Affordability and efficiency

Telecom munications revenue % of GDP

Mobile cellular and fixed-line subscribers per employee

Total

2008

2009

2008

2009

2008

.. 50 18 15 13 15 7 5 13 5 11 5 .. 13 17 52 17 10 .. .. 13 10 8 .. .. 9 6 13 19 11 77 10 24 14 16 6 6 11 20 11 2 .. 11 9 4 6 18 .. 13 5 22 8 14 .. .. 53 21

0 12 7 2 24 20 41 39 16 1 41 39 1 8 27 7 21 29 1 0 0 2 54 0 0 21 24 60 16 0 1 33 1 42 10 20 37 10 15 12 18 1 37 1 27 57 2 3 15 59 1 53 10 0 0 1 11

40 132 94 44 129 85 111 141 88 31 100 115 56 72 86 96 90 140 21 10 38 38 68 4 24 97 56 174 92 15 59 43 63 136 4 136 134 86 100 67 123 3 203 5 144 95 93 84 67 128 63 118 123 56 35 36 103

1 127 15 .. 42 .. .. .. .. 6 .. .. 12 80 109 115 .. 27 11 .. .. 4 .. .. .. 35 9 1,435 142 .. .. 120 .. 229 .. 136 210 .. 3 27 578 17 .. 2 .. 242 .. .. 44 .. 6 .. .. .. .. .. 39

7 263 34 .. .. .. .. .. 77 .. .. .. 309 .. .. .. .. 105 .. .. .. .. .. .. .. 43 .. 1,435 .. 6 .. 132 .. 302 .. 197 357 .. .. 44 510 29 .. 5 .. 301 .. .. 268 .. 61 .. 206 .. .. .. 224

75 99 82 40 94 88 99 99 99 90 99 100 80 46 99 99 91 100 61 80 87 58 98 19 24 100 97 100 83 50 53 69 59 100 77 100 .. .. 84 95 95 80 100 10 100 99 79 85 98 99 73 100 76 80 65 .. 90

.. 6.0 4.2 16.6 3.9 4.1 26.0 27.3 2.5 1.6 1.0 33.6 10.0 23.5 8.8 18.0 13.4 13.8 11.5 .. 7.8 14.1 18.3 10.1 .. 23.6 2.3 7.1 5.7 .. .. 4.1 21.7 19.2 13.2 29.3 24.5 12.3 1.3 3.0 11.5 .. 13.2 0.9 18.5 29.3 .. 2.4 3.5 32.7 3.8 25.4 7.8 3.0 .. .. ..

.. 13.4 6.3 11.0 13.7 5.8 34.9 6.8 4.4 1.3 3.4 20.8 14.8 7.3 9.4 8.1 34.6 17.6 14.4 .. 5.0 14.0 17.7 12.9 .. 10.2 3.7 0.8 9.5 .. .. 2.3 11.5 18.4 22.7 17.7 6.5 8.5 9.4 4.1 7.1 .. 12.3 2.4 13.4 35.2 .. 6.3 7.6 9.5 4.3 23.6 7.3 3.1 .. .. ..

0.0 6.0 2.5 .. 3.1 4.5 3.4 1.7 2.4 .. 2.1 2.8 1.0 6.8 5.5 2.9 4.5 5.1 4.0 3.1 .. 3.1 2.5 .. .. .. 2.5b 3.6 3.7 7.4 .. 1.8 5.5 4.6 .. 3.8 2.4 .. 4.1 3.7 4.8 3.0 4.5 1.3 2.3 2.0 2.0 .. 6.9 2.5 .. 3.7 .. .. .. .. 7.2

58 871 285 .. 1,929 .. 346 843 484 .. .. 732 1,652 376 567 1,018 358 565 .. 492 1,712 1,050 .. 293 .. 592 1,310 980 .. 3,628 .. 497 3,274 892 .. 812 543 .. 513 855 2,275 117 742 233 708 695 .. 466 355 787 1,780 813 .. .. .. .. 391

Transmission and Consumption distribution losses per capita % of output kWh 2008

Quality Population covered by mobile cellular network %

2011 World Development Indicators

per 100 people Mobile cellular Fixed subscriptions lines

International voice traffic minutes per person Fixed lines

$ per month Mobile cellular Residential prepaid fixed-line tariff tariff

Telephonesa

Electric power Access and use

3,989 566 591 2,423 1,164 6,301 7,054 5,661 2,552 8,071 2,087 4,689 155 820 8,853 .. 16,747 1,449 .. 3,087 2,267 .. .. 3,909 3,557 3,723 .. .. 3,490 .. .. .. 2,020 1,287 1,473 736 461 97 1,797 89 7,226 9,492 457 .. 126 24,867 4,894 436 1,646 .. 1,002 1,032 588 3,732 4,822 .. 15,682

Quality

Affordability and efficiency Mobile cellular and fixed-line subscribers per employee

Total

Population covered by mobile cellular network %

2008

2009

2008

2009

2008

10 23 10 18 7 8 2 7 12 5 14 9 15 16 4 .. 12 31 .. 15 16 .. .. 14 8 23 .. .. 3 .. .. .. 17 53 11 11 9 27 18 19 4 7 24 .. 9 7 13 21 14 .. 5 8 13 8 9 .. 7

31 3 15 35 4 47 44 35 11 35 8 24 2 5 40 .. 20 9 2 29 18 2 0 17 22 22 1 1 16 1 2 30 18 32 7 11 0 2 7 3 44 43 4 0 1 39 11 2 16 1 6 10 4 25 38 22 20

118 45 69 72 63 109 121 150 110 90 101 94 49 0 98 .. 107 84 51 99 36 32 21 78 149 95 31 16 111 29 66 85 78 77 84 79 26 1 56 26 128 109 56 17 47 111 140 61 164 13 88 85 81 117 143 68 175

120 .. .. .. 0 .. 413 .. 39 .. 67 47 3 .. 33 .. .. .. .. .. .. .. .. 65 57 159 1 .. .. 2 4 100 174 155 5 21 .. .. .. .. .. 310 39 .. 1 .. 30 .. 61 .. 35 .. .. .. .. .. ..

159 .. .. .. .. .. .. .. 224 .. 258 52 6 .. 64 .. .. .. .. .. 190 .. .. .. 132 256 8 .. .. 13 57 215 .. 457 .. 87 .. 3 .. .. .. .. .. .. 26 .. 431 .. 118 .. .. 113 .. 32 .. .. ..

99 61 90 95 72 99 100 100 95 100 99 94 83 0 94 .. 100 24 .. 99 100 55 .. 71 100 100 23 93 92 22 62 99 100 98 82b 98 44 10 95 60 b 98 97 .. 45 83 .. 96 90 83 .. .. 95 99 99 99 .. 100

24.0 3.1 5.6 0.2 .. 43.8 17.0 28.2 9.6 22.8 9.4 1.9 10.1 .. 5.2 .. 8.6 1.3 3.8 11.2 10.3 12.8 .. .. 14.3 13.4 12.2 3.3 4.8 9.4 11.9 5.6 17.3 2.9 0.7 23.5 13.1 0.9 13.0 3.0 27.8 33.1 4.7 12.9 5.7 29.4 12.8 2.9 12.0 4.0 6.6 14.3 15.9 17.4 27.5 .. 9.1

15.4 1.4 2.8 3.6 .. 20.9 13.8 18.4 5.6 44.3 5.7 8.8 7.5 .. 12.2 .. 7.8 2.9 3.5 7.3 15.8 12.9 .. .. 8.6 13.4 10.5 10.8 4.9 10.0 9.9 4.5 8.6 8.2 3.6 22.2 8.0 12.8 12.8 1.2 29.7 27.9 14.0 15.3 10.4 8.7 6.2 1.0 5.0 17.8 5.3 8.9 6.2 9.6 9.2 .. 8.6

3.8 1.9 .. .. .. 2.5 4.0 2.9 1.4 3.1 6.3 2.9 6.4 .. 4.7 .. .. 4.8 .. 4.0 .. .. 8.2 .. 2.8 6.3 3.9 3.6 .. 4.3 6.9 3.6 2.7 10.1 6.7b 5.1 1.2 .. .. 1.0 0.7 2.9 .. .. 3.4 1.2 2.5 2.7 3.2 .. 4.8 3.1 .. 3.9 4.5 .. 1.7

1,127 .. .. 913 1,098 .. .. 1,657 .. 12 1,132b 253 2,354 .. 657 .. .. 311 748 697 .. .. .. 1,717 402 1,065 2,427 .. .. 2,059 2,842 .. 838 294 341b .. .. 90 .. 565 .. 605 .. .. .. .. 967 50 380 .. 799 624 .. 396 1,534 .. 597

Transmission and Consumption distribution losses per capita % of output kWh 2008

5.11

states and markets

Power and communications

per 100 people Mobile cellular Fixed subscriptions lines

Inter足national voice traffic minutes per person Fixed lines

$ per month Mobile cellular Residential prepaid fixed-line tariff tariff

Telecom足 munications revenue % of GDP

2011 World Development Indicators

307

5.11

Power and communications Telephonesa

Electric power Access and use

Transmission and Consumption distribution losses per capita % of output kWh 2008

Romania Russian Federation Rwanda Saudi Arabia Senegal Serbia Sierra Leone Singapore Slovak Republic Slovenia Somalia South Africa 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

2,488 6,435 .. 7,527 158 4,284 .. 8,185 5,268 6,920 .. 4,759 6,315 409 96 .. 14,869 8,307 1,521 2,072 84 2,079 .. 99 5,789 1,298 2,308 2,273 .. 3,534 16,891 6,061 13,654 2,393 1,646 3,074 799 .. 220 602 1,022 2,875 w 231 1,670 1,318 3,001 1,505 1,972 4,052 1,907 1,494 503 531 9,518 6,970

2008

11 11 .. 9 20 16 .. 5 3 5 .. 9 5 11 12 .. 7 6 24 18 19 6 .. .. 2 12 14 14 .. 12 12 7 6 20 9 28 10 .. 23 23 6 8w 15 11 10 13 11 6 12 16 15 22 11 6 5

per 100 people Mobile cellular Fixed subscriptions lines 2009

25 32 0 16 2 42 1 37 19 51 1 9 44 17 1 4 55 60 18 4 0 10 .. 3 24 12 22 9 1 28 34 54 50 29 7 24 35 9 5 1 3 18 w 1 15 13 22 13 20 25 18 16 3 1 45 48

2009

118 162 24 177 55 135 20 133 101 103 7 94 111 69 36 55 123 120 46 70 40 123 .. 33 147 93 84 29 29 120 232 130 97 114 59 99 101 30 16 34 24 69 w 27 67 58 101 61 62 119 90 67 45 37 111 123

Quality

Affordability and efficiency

Fixed lines

Total

Population covered by mobile cellular network %

2008

2009

124 .. 8 .. 101 203b .. .. 228 220 .. .. .. .. 13 41 .. .. .. .. 1 .. .. 28 443 .. 60 .. .. .. .. .. 216 125 .. 79 .. .. .. .. 19 .. w .. .. .. .. .. .. .. .. .. .. .. .. ..

98 95 92 98 85 94b 70 100 100 100 .. 100 99 95 66 91 98 100 96 .. 65 38 .. 85 100 100 b 100 14 100 100 100 100 100 100 93 90 70 95 68 50 75 80 w 53 80 77 94 76 93 91 92 93 61 56 99 99

19.3 5.4 8.1 9.2 24.0 3.9 .. 7.7 22.7 19.6 .. 21.6 28.5 4.7 3.9 4.9 26.2 31.5 1.3 0.9 12.2 8.3 .. 12.8 19.5 2.8 13.8 .. 9.9 2.8 4.1 24.1 12.8 12.5 1.1 9.0 2.1 .. 0.7 24.6 .. 10.1 m 8.8 5.7 4.7 10.0 6.6 4.0 3.9 10.6 3.0 3.0 11.5 22.8 27.6

10.6 5.8 6.6 7.4 8.3 5.2 .. 3.9 24.9 15.8 .. 12.6 31.6 0.9 3.4 12.8 14.8 33.7 7.6 2.9 10.2 2.4 .. 12.4 6.5 7.2 23.9 .. 7.9 4.3 4.1 16.5 15.3 12.7 1.1 28.6 3.2 .. 4.8 12.7 .. 8.7 m 8.0 7.6 7.1 8.8 7.9 3.7 7.6 8.8 6.3 1.2 10.4 14.8 19.6

International voice traffic minutes per person

41 .. 11 .. 27 136b .. 1,531 123 96 .. .. .. 34 6 .. .. .. 78 .. 0 .. .. 6 .. 79 39 .. 7 0 .. .. .. .. .. .. .. .. .. .. 22 .. w .. .. .. .. .. 9 .. .. 27 .. .. .. ..

$ per month Mobile cellular Residential prepaid fixed-line tariff tariff

Telecom munications revenue % of GDP

Mobile cellular and fixed-line subscribers per employee

2008

3.4 2.6 3.0 2.7 9.8 4.9b .. 2.6 3.3 3.3 .. 7.3 4.1 .. 3.2 4.5 2.7 3.3 3.0 .. .. 4.0 7.9 7.4 2.6 4.3 2.3 .. .. 5.7 3.1 4.3 2.8 3.2 2.5 3.5 .. .. .. 2.6 .. 3.1 w .. 3.2 3.0 3.3 3.2 2.6 2.8 3.8 .. 2.0 .. 3.0 2.6

564 .. 1,952 1,618 1,859 883b .. .. 665 644 .. .. 855 919 2,168 1,118 894 601 409 .. .. 1,957 .. 1,059 .. 1,004 2,145 .. .. .. 924 .. 416 692 739 1,500 .. 880 .. .. 711 755 m .. 665 .. 576 624 .. 462 586 880 565 .. 765 765

a. Data are from the International Telecommunication Union’s (ITU) World Telecommunication Report database. Please cite ITU for third-party use of these data. b. Data are for 2009.

308

2011 World Development Indicators

About the data

5.11

Definitions

The quality of an economy’s infrastructure, includ-

Access to telephone services rose on an unprece-

• Electric power consumption per capita measures

ing power and communications, is an important ele-

dented scale over the past 15 years. This growth was

the production of power plants and combined heat

ment in investment decisions for both domestic and

driven primarily by wireless technologies and liberal-

and power plants less transmission, distribution,

foreign investors. Government effort alone is not

ization of telecommunications markets, which have

and transformation losses and own use by heat and

enough to meet the need for investments in modern

enabled faster and less costly network rollout. In

power plants divided by midyear population. • Elec-

infrastructure; public-private partnerships, especially

2002 the number of mobile phones in the world sur-

tric power transmission and distribution losses are

those involving local providers and ﬁnanciers, are

passed the number of fixed telephones. The Interna-

losses in transmission between sources of supply

critical for lowering costs and delivering value for

tional Telecommunication Union (ITU) estimates that

and points of distribution and in distribution to con-

money. In telecommunications, competition in the

there were 5 billion mobile subscriptions globally in

sumers, including pilferage. • Fixed telephone lines

marketplace, along with sound regulation, is lower-

2010. No technology has ever spread faster around

are telephone lines connecting a subscriber to the

ing costs, improving quality, and easing access to

the world. Mobile communications have a particu-

telephone exchange equipment. • Mobile cellular

services around the globe.

larly important impact in rural areas. The mobility,

telephone subscriptions are subscriptions to a pub-

An economy’s production and consumption of elec-

ease of use, flexible deployment, and relatively low

lic mobile telephone service using cellular technol-

tricity are basic indicators of its size and level of

and declining rollout costs of wireless technologies

ogy, which provide access to the public switched

development. Although a few countries export elec-

enable them to reach rural populations with low lev-

telephone network. Post-paid and prepaid subscrip-

tric power, most production is for domestic consump-

els of income and literacy. The next billion mobile

tions are included. • International voice traffic is

tion. Expanding the supply of electricity to meet the

subscribers will consist mainly of the rural poor.

the sum of international incoming and outgoing tele-

growing demand of increasingly urbanized and indus-

Access is the key to delivering telecommunications

phone traffic (in minutes) divided by total population.

trialized economies without incurring unacceptable

services to people. If the service is not affordable to

• Population covered by mobile cellular network is

social, economic, and environmental costs is one

most people, then goals of universal usage will not

the percentage of people that live in areas served by

of the great challenges facing developing countries.

be met. Two indicators of telecommunications afford-

a mobile cellular signal regardless of whether they

Data on electric power production and consump-

ability are presented in the table: fixed-line telephone

use it. • Residential fixed-line tariff is the monthly

tion are collected from national energy agencies by

service tariff and prepaid mobile cellular service tar-

subscription charge plus the cost of 30 three-minute

the International Energy Agency (IEA) and adjusted

iff. Telecommunications efficiency is measured by

local calls (15 peak and 15 off-peak). • Mobile cel-

by the IEA to meet international deﬁnitions (for data

total telecommunications revenue divided by GDP

lular prepaid tariff is based on the Organisation for

on electricity production, see table 3.10). Electricity

and by mobile cellular and fixed-line telephone sub-

Economic Co-operation and Development’s low-user

consumption is equivalent to production less power

scribers per employee.

definition, which includes the cost of monthly mobile

plants’ own use and transmission, distribution, and

Operators have traditionally been the main source

use for 25 outgoing calls per month spread over the

transformation losses less exports plus imports. It

of telecommunications data, so information on sub-

same mobile network, other mobile networks, and

includes consumption by auxiliary stations, losses

scribers has been widely available for most coun-

mobile to fixed-line calls and during peak, off-peak,

in transformers that are considered integral parts

tries. This gives a general idea of access, but a

and weekend times as well as 30 text messages

of those stations, and electricity produced by pump-

more precise measure is the penetration rate—the

per month. • Telecommunications revenue is the

ing installations. Where data are available, it covers

share of households with access to telecommunica-

revenue from the provision of telecommunications

electricity generated by primary sources of energy—

tions. During the past few years more information

services such as fixed-line, mobile, and data divided

coal, oil, gas, nuclear, hydro, geothermal, wind, tide

on information and communication technology use

by GDP. • Mobile cellular and fixed-line subscribers

and wave, and combustible renewables. Neither pro-

has become available from household and business

per employee are telephone subscribers (fixed-line

duction nor consumption data capture the reliability

surveys. Also important are data on actual use of

plus mobile) divided by the total number of telecom-

of supplies, including breakdowns, load factors, and

telecommunications equipment. Ideally, statistics

munications employees.

frequency of outages.

on telecommunications (and other information and

Over the past decade new ﬁnancing and technol-

communications technologies) should be compiled

ogy, along with privatization and liberalization, have

for all three measures: subscription and possession,

spurred dramatic growth in telecommunications

access, and use. The quality of data varies among

in many countries. With the rapid development of

reporting countries as a result of differences in regu-

mobile telephony and the global expansion of the

lations covering data provision and availability.

Data sources Data on electricity consumption and losses are

Internet, information and communication technolo-

from the IEA’s Energy Statistics and Balances of

gies are increasingly recognized as essential tools of

Non-OECD Countries 2010, the IEA’s Energy Sta-

development, contributing to global integration and

tistics of OECD Countries 2010, and the United

enhancing public sector effectiveness, efficiency,

Nations Statistics Division’s Energy Statistics

and transparency. The table presents telecommuni-

Yearbook. Data on telecommunications are from

cations indicators covering access and use, quality,

the ITU’s World Telecommunication Development

and affordability and efficiency.

Report database and TeleGeography.

2011 World Development Indicators

309

states and markets

Power and communications

5.12

The information age Daily Households newspapers with televisiona

Personal computers and the Internet Access and use

per 100 people

310

Quality Affordability International Fixed Internet Fixed broadband bandwidtha broadband Internet a Internet subscribers bits per second per access tariff a per 100 capita people $ per month

per 1,000 people

Personal computersa

Internet usersa

2000–05b

2008

2009

0.4 4.6 .. 0.6 .. .. .. .. 8.0 2.3 .. .. 0.7 .. 6.4 6.2 .. 11.0 0.6 0.9 0.4 .. 94.3 .. .. .. 5.7 69.3 11.2 .. .. .. .. .. 5.6 .. 54.9 .. 13.0 3.9 .. 1.0 25.5 0.7 .. 65.2 3.4 3.5 5.5 65.6 1.1 9.4 .. .. .. 5.1 2.5

3.4 41.2 13.5 3.3 30.4 6.8 72.0 73.5 42.0 0.4 45.9 75.2 2.2 11.2 37.7 6.2 39.2 44.8 1.1 0.8 0.5 3.8 77.7 0.5 1.7 34.0 28.8 61.4 45.5 0.6 6.7 34.5 4.6 50.4 14.3 63.7 85.9 26.8 15.1 20.0 14.4 4.9 72.3 0.5 83.9 71.3 6.7 7.6 30.5 79.5 5.4 44.1 16.3 0.9 2.3 10.0 9.8

0.00 2.85 2.34 0.11 8.80 0.19 24.69 22.45 1.14 0.03 11.30 29.05 0.02 2.86 7.76 0.77 7.51 12.91 0.04 0.00 0.20 0.00 29.55 0.00 0.00 9.81 7.78 29.42 4.64 0.00 0.00 6.01 0.05 15.45 0.02 19.26 37.46 3.93 1.77 1.30 2.42 0.00 25.25 0.00 29.33 30.98 0.20 0.02 3.52 30.53 0.11 16.99 0.78 0.00 0.00 0.00 0.00

.. 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 .. .. .. .. .. .. ..

.. .. .. 36 .. 97 .. 97 99 30 95 99 25 69 97 .. 97 98 18 .. .. 32 99 .. .. 100 .. 99 88 14 .. 96 .. 97 88 .. 98 77 83 97 83 .. 98 .. 93 97 .. .. .. 95 43 100 69 .. .. 25 68

2011 World Development Indicators

2009

550 1,902 .. 17 2,320 .. 5,457 20,323 1,399 4 2,277 24,945 35 225 1,195 220 2,108 37,657 15 2 19 23 16,193 .. 1 4,076 651 560,989 2,940 1 0 4,333 40 15,892 27 7,075 34,506 1,387 484 1,172 243 6 12,680 3 17,221 29,356 141 38 752 25,654 97 4,537 186 0 1 16 241

2009

.. 22 15 157 31 31 26 36 49 50 7 29 118 35 19 62 28 15 91 .. 89 89 25 1,329 .. 48 18 13 35 .. .. 6 44 21 1,630 43 29 26 40 8 20 .. 28 487 39 36 .. 307 42 43 44 24 34 503 .. .. ..

Application Secure Internet servers per million people December 2010

1 9 1 3 26 17 1,761 857 5 0 9 490 0 8 16 9 41 73 0 0 2 1 1,237 0 .. 53 2 455 14 0 1 108 1 168 0 318 1,866 15 15 2 13 .. 434 0 1,246 306 8 3 13 874 2 124 10 0 1 1 8

Information and communications technology trade

Goods Imports Exports % of total % of total goods goods imports exports 2009

.. 1.1 0.0 .. 0.4 1.5 1.4 5.5 0.0 0.6 0.7 2.8 .. 0.0 0.6 0.4 1.8 3.6 0.0 1.9 0.1 .. 4.4 .. .. 0.2 29.5 44.6 0.3 .. .. 18.7 0.4 5.1 .. 15.6 4.8 3.6 0.2 1.8 2.9 .. 5.8 0.7 12.6 5.6 .. 0.4 0.4 6.8 0.1 3.0 0.7 0.0 .. .. 0.2

2009

0.4 5.4 4.9 .. 11.2 5.0 11.4 7.0 8.5 5.7 2.4 4.3 .. 4.6 3.7 5.5 11.4 6.4 2.0 10.9 4.0 .. 9.6 .. .. 6.8 24.0 43.6 9.9 .. .. 17.9 4.5 6.3 .. 16.7 8.9 5.4 7.5 4.4 5.5 .. 6.5 9.5 11.3 7.8 .. 4.0 7.8 9.3 7.3 5.9 6.3 5.8 .. .. 6.6

Services Exports % of total service exports 2009

.. 5.7 .. 5.4 12.2 16.1 4.9 6.5 4.7 11.5 9.0 9.8 0.7 12.6 9.2 3.3 2.0 5.6 11.6 0.0 6.5 6.6 11.2 .. .. 2.8 6.0 1.7 7.4 .. .. 21.9 0.0 3.6 .. 8.9 .. 4.1 4.9 4.7 16.9 .. 8.6 5.3 25.4 4.3 .. 17.8 2.6 8.4 0.0 2.2 14.1 21.6 0.2 2.5 26.8

Daily Households newspapers with televisiona

Personal computers and the Internet Access and use

per 100 people

per 1,000 people

2000â&#x20AC;&#x201C;05b

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 .. .. ..

Quality Affordability International Fixed Internet Fixed broadband bandwidtha broadband Internet a Internet subscribers bits per second per access tariff a per 100 capita people $ per month

Personal computersa

Internet usersa

2008

2009

99 55 69 .. .. 98 90 94 .. 99 97 .. .. .. .. .. .. 99 .. 99 .. .. 9 .. 98 99 .. 9 97 22 .. 96 93 .. 88 .. .. .. 37 33 98 99 67 10 39 95 88 58 83 .. 85 73 71 98 99 .. ..

25.6 3.3 2.0 10.6 .. 58.2 .. .. .. .. 7.6 .. .. .. 57.6 .. .. .. .. 32.7 10.2 .. .. .. 24.2 36.8 .. .. 23.1 0.8 4.5 17.6 14.4 11.4 24.6 5.7 .. 0.9 23.9 .. 91.2 52.6 .. .. .. 62.9 16.9 .. 6.3 .. .. .. 7.2 16.9 18.2 .. 15.7

61.6 5.3 8.7 38.3 1.0 68.4 49.7 48.5 58.6 77.7 29.3 33.4 10.0 0.0 80.9 .. 39.4 41.2 4.7 66.7 23.7 3.7 0.5 5.5 58.8 51.8 1.6 4.7 57.6 1.9 2.3 22.7 26.5 35.9 13.1 32.2 2.7 0.2 5.9 2.1 90.0 83.4 3.5 0.8 28.4 91.8 43.5 12.0 27.8 1.9 15.8 27.7 6.5 58.8 48.6 25.2 28.3

18.76 0.67 0.74 0.55 0.00 21.94 24.86 19.59 4.16 24.86 3.42 8.61 0.02 0.00 33.54 .. 1.61 0.10 0.13 11.48 5.26 0.02 .. 0.16 18.98 10.59 0.02 0.02 6.09 0.07 0.27 7.25 9.24 5.19 0.91 1.49 0.05 0.03 0.02 0.26 35.70 22.73 0.82 0.01 0.05 37.19 1.44 0.37 5.82 0.00 2.22 2.79 1.87 13.54 17.54 10.78 9.22

5,987 32 110 151 3 15,261 2,003 12,989 741 5,770 1,811 1,342 477 0 6,065 .. 871 112 142 3,537 223 5 .. 50 14,300 17 12 5 5,097 51 76 364 312 6,660 2,920 1,600 56 20 27 5 78,156 4,544 144 11 5 26,904 1,365 43 15,964 2 662 2,646 113 2,748 4,790 1,764 2,044

2009

30 5 21 30 .. 36 7 29 22 37 30 17 40 .. 25 .. 19 48 194 25 23 50 .. .. 15 14 102 493 19 55 58 17 16 13 8 17 80 28 47 22 36 29 34 266 105 51 31 15 17 142 22 36 22 14 29 .. 55

Application Secure Internet servers per million people December 2010

166 2 2 1 0 1,005 399 154 39 650 20 5 3 0 1,167 .. 133 1 1 173 28 0 1 1 176 24 0 0 42 1 2 87 22 13 11 3 1 0 14 2 2,276 1,489 8 0 1 1,653 27 1 127 3 7 14 7 211 174 84 99

5.12

Information and communications technology trade

Goods Imports Exports % of total % of total goods goods imports exports 2009

24.6 3.8 5.7 .. .. 11.5 19.2 3.0 0.8 14.7 3.1 0.1 1.3 .. 22.6 .. 0.4 0.3 .. 6.1 3.0 .. .. .. 2.9 0.5 1.6 0.3 38.1 0.2 .. 0.6 22.9 7.5 0.1 4.6 0.4 .. 0.6 0.2 12.6 1.8 0.4 0.7 0.0 2.4 1.5 0.3 0.0 .. 0.2 0.1 54.2 7.5 4.6 .. 0.0

2009

18.8 8.8 9.7 .. .. 14.0 11.0 6.7 3.9 12.0 5.4 4.3 6.2 .. 14.6 .. 7.2 2.6 .. 6.2 3.5 .. .. .. 4.3 5.5 3.9 5.3 32.0 3.6 1.6 4.2 20.9 5.1 5.1 6.0 3.9 .. 4.9 5.6 13.5 9.4 4.4 3.6 7.2 8.8 3.2 3.7 7.3 .. 21.6 8.3 34.5 8.9 6.6 .. 8.2

2011 World Development Indicators

Services Exports % of total service exports 2009

8.8 53.1 8.4 .. 0.6 37.1 36.1 2.4 7.1 1.2 .. 3.0 14.5 .. 1.5 .. 60.9 1.2 8.5 5.9 2.9 .. .. 2.6 3.8 14.3 .. .. 7.0 23.2 .. 3.7 1.3 20.2 3.0 7.5 5.8 .. 2.4 .. 11.3 4.8 7.2 11.6 1.6 8.6 .. 12.0 4.8 1.2 1.4 3.5 16.2 5.3 4.5 .. ..

311

states and markets

The information age

5.12

The information age Daily Households newspapers with televisiona

Personal computers and the Internet Access and use

per 100 people

Romania Russian Federation Rwanda Saudi Arabia Senegal Serbia Sierra Leone Singapore Slovak Republic Slovenia Somalia South Africa 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

Personal computersa

2000â&#x20AC;&#x201C;05b

2008

70 92 .. .. 9 .. .. 361 126 173 .. 30 144 26 .. 24 481 420 .. .. 2 .. .. 2 149 23 .. 9 .. 131 .. 290 193 .. .. 93 .. 10 4 5 .. 105 w .. 68 71 .. 59 74 .. 64 .. 68 .. 255 201

97 .. 3 .. 46 .. 10 .. 99 99 .. 69 100 76 .. 35 94 92 .. .. 9 92 .. .. .. .. 98 .. 7 97 94 99 .. 91 .. 95 .. 95 .. 24 31 .. m .. .. .. 93 .. .. .. 85 .. 55 .. 98 98

19.2 13.3 0.3 69.3 .. 25.8 .. 74.3 58.1 42.5 .. .. 39.3 .. 10.7 3.7 88.1 96.2 9.0 .. .. .. .. .. 13.2 9.7 6.1 .. 1.7 4.5 33.1 80.2 80.5 .. 3.1 .. 9.6 .. 2.8 .. 7.6 15.3 w .. 5.5 4.5 .. 5.1 5.6 9.8 .. 5.7 3.3 .. 65.4 56.0

Internet usersa 2009

36.2 42.1 4.5 38.6 7.4 56.1 0.3 73.3 75.0 63.6 1.2 9.0 61.2 8.7 9.9 7.6 90.3 70.9 18.7 10.1 1.5 25.8 .. 5.4 36.2 33.5 35.3 1.6 9.8 33.3 82.2 83.2 78.1 55.5 16.9 31.2 27.5 8.8 1.8 6.3 11.4 27.1 w 2.7 20.9 17.2 34.6 18.1 24.1 36.4 31.5 21.5 5.5 8.8 72.3 67.3

Quality Affordability International Fixed Internet Fixed broadband bandwidtha broadband Internet a Internet subscribers bits per second per access tariff a per 100 capita people $ per month 2009

13.05 9.09 0.08 5.66 0.47 8.07 0.00 22.52 14.36 22.79 0.00 0.98 21.05 0.84 0.11 0.13 40.85 33.91 0.16 0.05 0.02 1.47 .. 0.04 7.84 3.57 8.54 0.05 0.02 4.15 15.01 29.68 27.78 7.33 0.32 6.56 3.04 5.76 0.00 0.06 0.14 7.30 w 0.04 4.07 3.37 6.69 3.53 5.81 7.66 6.62 1.25 0.55 0.13 25.78 25.90

2009

18,271 573 35 1,731 372 12,660 .. 22,783 7,567 6,720 .. 70 11,008 190 322 35 49,828 29,413 261 37 2 818 .. 23 7,916 2,699 4,323 48 36 206 13,233 39,664 11,279 903 46 628 581 313 28 8 17 3,526 w 7 348 151 1,120 299 742 1,087 1,408 323 31 31 19,521 32,455

Application Secure Internet servers per million people

Information and communications technology trade

Goods Imports Exports % of total % of total goods goods imports exports

2009

December 2010

2009

7 13 88 27 40 14 .. 17 29 22 .. 27 29 4 23 858 35 33 31 364 64 19 .. 186 13 12 18 .. 194 7 41 24 20 18 199 31 15 .. 220 51 .. 30 m 90 22 30 19 31 21 17 30 23 15 88 29 29

40 20 1 18 1 20 0 523 128 301 0 63 233 4 0 10 1,266 1,876 0 0 0 13 1 2 72 14 95 0 1 13 243 1,396 1,443 45 0 7 3 4 0 1 1 156 w 1 9 3 32 8 3 33 27 2 2 5 906 545

8.4 0.6 1.4 0.3 0.4 2.2 .. 35.4 17.5 3.8 .. 2.0 3.0 1.0 0.0 0.1 10.0 3.3 0.2 .. 0.6 19.8 .. 0.1 0.2 6.0 2.3 .. 4.9 1.3 2.0 8.6 13.0 0.1 .. 0.1 3.8 .. 0.1 0.1 0.6 13.0 w 0.6 16.3 21.3 12.2 16.2 28.9 1.5 11.6 .. 3.0 1.0 12.2 6.6

9.3 8.4 12.3 4.6 4.5 5.4 .. 28.2 14.7 5.6 .. 9.8 8.4 3.6 4.7 3.6 11.5 6.6 1.4 .. 6.9 18.1 .. 4.2 4.0 7.5 5.9 .. 9.3 2.6 5.3 10.5 15.1 7.0 .. 9.3 7.1 .. 2.5 3.7 4.8 13.9 w .. 16.6 18.4 15.1 16.4 24.4 6.6 15.2 .. 7.4 7.8 13.3 8.6

Services Exports % of total service exports 2009

18.9 6.3 0.1 .. 15.6 8.0 2.2 2.9 8.0 7.2 .. 3.9 6.6 17.2 1.2 11.3 14.8 .. 4.4 19.6 2.7 .. .. 18.6 .. 4.9 1.9 .. 6.1 5.6 .. 7.9 4.6 9.5 .. 7.4 .. 5.4 8.5 8.0 .. 9.1 w 6.5 13.3 19.9 5.4 13.1 6.8 6.1 5.5 .. 49.9 4.5 8.1 9.8

a. Data are from the International Telecommunicaton Unionâ&#x20AC;&#x2122;s (ITU) World Telecommunication Development Report database. Please cite the ITU for third party use of these data. b. Data are for the most recent year available.

312

2011 World Development Indicators

About the data

5.12

Definitions

The digital and information revolution has changed

counts reported by Internet service providers by a

• Daily newspapers are newspapers issued at least

the way the world learns, communicates, does busi-

multiplier. This method may undercount actual

four times a week that report mainly on events in the

ness, and treats illnesses. New information and

users, particularly in developing countries, where

24-hour period before going to press. The indicator is

communications technologies (ICT) offer vast oppor-

many commercial subscribers rent out computers

average circulation (or copies printed) per 1,000 peo-

tunities for progress in all walks of life in all coun-

connected to the Internet or prepaid cards are used

ple. • Households with television are the percentage

tries—opportunities for economic growth, improved

to access the Internet.

of households with a television set. • Personal com-

health, better service delivery, learning through dis-

Broadband refers to technologies that provide

puters are self-contained computers designed for use

tance education, and social and cultural advances.

Internet speeds of at least 256 kilobits a second

by a single individual, including laptops and notebooks

Comparable statistics on access, use, quality,

of upstream and downstream capacity and includes

and excluding terminals connected to mainframe and

and affordability of ICT are needed to formulate

digital subscriber lines, cable modems, satellite

minicomputers intended primarily for shared use and

growth-enabling policies for the sector and to moni-

broadband Internet, fiber-to-home Internet access,

devices such as smart phones and personal digital

tor and evaluate the sector’s impact on develop-

ethernet local access networks, and wireless area

assistants. • Internet users are people with access

ment. Although basic access data are available for

networks. Bandwidth refers to the range of frequen-

to the worldwide network. • Fixed broadband Internet

many countries, in most developing countries little

cies available for signals. The higher the bandwidth,

subscribers are the number of broadband subscrib-

is known about who uses ICT; what they are used for

the more information that can be transmitted at

ers with a digital subscriber line, cable modem, or

(school, work, business, research, government); and

one time. Reporting countries may have different

other high-speed technology. • International Internet

how they affect people and businesses. The global

definitions of broadband, so data are not strictly

bandwidth is the contracted capacity of international

Partnership on Measuring ICT for Development is

comparable.

connections between countries for transmitting

helping to set standards, harmonize information and

The number of secure Internet servers, from the

Internet traffic. • Fixed broadband Internet access

communications technology statistics, and build sta-

Netcraft Secure Server Survey, indicates how many

tariff is the lowest sampled cost per 100 kilobits a

tistical capacity in developing countries. For more

companies conduct encrypted transactions over the

second per month and are calculated from low- and

information see www.itu.int/ITU-D/ict/partnership/.

Internet. The survey examines the use of encrypted

high-speed monthly service charges. Monthly charges

Data on daily newspapers in circulation are from

transactions through extensive automated explora-

do not include installation fees or modem rentals.

United Nations Educational, Scientific, and Cultural

tion, tallying the number of Web sites using a secure

• Secure Internet servers are servers using encryp-

Organization (UNESCO) Institute for Statistics surveys

socket layer (SSL). The country of origin of more than

tion technology in Internet transactions. • Information

on circulation, online newspapers, journalists, com-

a third of the 1.5 million distinct valid third-party

and communication technology goods exports and

munity newspapers, and news agencies.

certificates is unknown. Some countries, such as the

imports include telecommunications, audio and video,

Estimates of households with television are derived

Republic of Korea, use application layers to estab-

computer and related equipment; electronic compo-

from household surveys. Some countries report only

lish the encryption channel, which is SSL equivalent;

nents; and other information and communication

the number of households with a color television set,

these data are reported in the table.

technology goods. Software is excluded. • Informa-

and so the true number may be higher than reported.

Information and communication technology goods

tion and communication technology service exports

Estimates of personal computers are from an

exports and imports are defined by the Working

include computer and communications services (tele-

annual International Telecommunication Union (ITU)

Party on Indicators for the Information Society and

communications and postal and courier services) and

questionnaire sent to member states, supplemented

are reported in the Organisation for Economic Co-

information services (computer data and news-related

by other sources. Many governments lack the capac-

operation and Development’s Guide to Measuring the

service transactions).

ity to survey all places where personal computers

Information Society (2005). Information and commu-

are used (homes, schools, businesses, government

nication technology service exports data are based

offices, libraries, Internet cafes) so most estimates

on the International Monetary Fund’s (IMF) Balance of

are derived from the number of personal computers

Payments Statistics Yearbook classification.

Data sources Data on newspapers are compiled by the UNESCO

sold each year. Annual shipment data can also be

Institute for Statistics. Data on televisions, per-

multiplied by an estimated average useful lifespan

sonal computers, Internet users, Internet broad-

before replacement to approximate the number of

band users and cost, and Internet bandwidth are

personal computers. There is no precise method

from the ITU’s World Telecommunication Develop-

for determining replacement rates, but in general

ment Report database and TeleGeography. Data

personal computers are replaced every three to five

on secure Internet servers are from Netcraft (www.

years.

netcraft.com/) and official government sources.

Data on Internet users and related indicators

Data on information and communication tech-

(broadband and bandwidth) are based on nation-

nology goods trade are from the United Nations

ally reported data to the ITU. Some countries derive

Statistics Division’s Commodity Trade (Comtrade)

these data from surveys, but since survey questions

database. Data on information and communication

and definitions differ, the estimates may not be

technology service exports are from the IMF’s Bal-

strictly comparable. Countries without surveys gen-

ance of Payments Statistics database.

erally derive their estimates by multiplying subscriber

2011 World Development Indicators

313

states and markets

The information age

5.13

Science and technology Researchers Technicians Scientific Expenditures in R&D in R&D and for R&D technical journal articles

314

per million people

2000–08 d

.. .. 170 .. 980 .. 4,224 4,123 .. .. .. 3,435 .. 120 197 .. 694 1,499 .. .. 17 .. 4,260 .. .. 833 1,071 2,650 126 .. 34 122 66 1,514 .. 2,886 5,670 .. 69 617 49 .. 2,966 21 7,707 3,496 .. .. .. 3,532 .. 1,873 29 .. .. .. ..

.. .. 35 .. 196 .. .. 1,960 .. .. .. 1,407 .. .. 71 .. .. 476 .. .. 13 .. 1,690 .. .. 302 .. 459 .. .. 37 .. .. 605 .. 1,466 2,166 .. 20 378 .. .. 617 12 .. 1,880 .. .. .. 1,301 .. 764 37 .. .. .. ..

2011 World Development Indicators

2007

4 12 481 3 3,362 175 17,831 4,825 97 235 412 7,071 43 51 54 62 11,885 801 43 3 26 154 27,800 4 3 1,740 56,806 .. 489 7 21 100 37 1,102 244 3,689 5,236 8 66 1,934 5 8 502 149 4,989 30,740 16 17 129 44,408 109 4,980 22 4 10 4 6

High-technology exports

Royalty and license fees

% of GDP

$ millions

% of manufactured exports

2000–08 d

2009

.. .. 0.07 .. 0.51 0.21 2.06 2.66 0.17 .. 0.96 1.92 .. 0.28 0.03 0.50 1.10 0.49 0.11 .. 0.05 .. 1.84 .. .. 0.68 1.44 0.81 0.16 0.48 .. 0.32 .. 0.90 0.49 1.47 2.72 .. 0.15 0.23 0.09 .. 1.29 0.17 3.46 2.02 .. .. 0.18 2.54 .. 0.57 0.06 .. .. .. 0.04

.. 10 4 .. 1,548 7 3,550 12,097 6 97 315 29,676 0 15 76 24 8,316 714 0 2 4 3 25,080 .. .. 266 348,295 1,849 466 .. .. 1,682 187 756 248 15,200 10,743 177 51 95 136 .. 656 7 8,599 83,827 71 0 21 142,449 6 1,212 141 0 .. .. 7

.. 1 1 .. 9 4 13 11 1 1 3 10 0 5 3 1 14 8 1 12 0 3 18 .. .. 4 31 31 5 .. .. 41 12 11 35 16 18 5 4 1 5 .. 10 4 18 23 32 1 3 16 1 11 5 0 .. .. 1

.. 6 .. 0 106 0 703 752 2 0 9 2,376 0 3 12 1 434 9 0 0 0 0 3,221 .. .. 59 429 380 48 .. .. 1 0 32 .. 96 .. 0 0 0 0 .. 24 2 1,738 9,397 .. 0 7 13,785 0 48 13 0 .. 3 0

.. 14 .. 0 1,331 0 3,026 1,280 19 11 73 2,144 3 19 6 12 2,512 117 0 0 8 0 7,716 .. .. 461 11,065 1,610 258 .. .. 65 0 213 .. 726 .. 53 47 285 26 .. 46 3 1,282 5,274 .. 0 9 14,104 0 654 86 0 0 0 18

Patent applications fileda,b

$ millions

Trademark applications fileda,c

Receipts

Payments

Residents

Nonresidents

Total

2009

.. .. 84 .. .. 116 2,821 2,263 222 29 1,510 669 .. .. 59 .. 4,023 242 .. .. .. .. 5,067 .. .. 531 229,096 149 121 .. .. .. .. 250 69 789 1,518 .. .. 490 .. .. 76 12 1,806 14,295 .. .. 250 47,859 .. 698 7 .. .. .. ..

.. .. 765 .. .. 11 23,525 292 5 270 220 148 .. .. 12 .. 17,802 24 .. .. .. .. 32,410 .. .. 3,421 85,477 11,708 1,860 .. .. .. .. 68 189 92 131 .. 794 1,452 .. .. 20 25 127 1,809 .. .. 218 11,724 .. 22 322 .. .. .. ..

.. 3,072 2,144 .. 73,717 4,398 8,611 11,699 3,221 8,232 5,403 25,566e .. 6,081 3,786 712 119,841 7,904 .. .. 2,866 .. 40,956 .. .. 33,026 808,546 24,754 23,952 .. .. 11,754 .. 5,990 1,450 11,047 8,329 5,208 12,605 2,828 .. .. 3,230 719 5,564 84,213 .. 327 4,382 74,676 677 2,458 11,003 .. 6 .. 7,403

Researchers Technicians Scientific Expenditures in R&D in R&D and for R&D technical journal articles

per million people

2000–08 d

1,733 137 205 706 .. 3,090 .. 1,616 .. 5,573 .. .. .. .. 4,627 .. 166 .. 16 1,935 .. 10 .. .. 2,547 521 50 .. 372 42 .. .. 353 726 .. 647 16 18 .. 59 3,089 4,365 .. 8 .. 5,468 .. 152 144 .. 71 .. 81 1,623 3,799 .. ..

512 94 .. .. .. 684 .. .. .. 589 .. .. .. .. 720 .. 33 .. .. 543 .. 11 .. .. 553 75 15 .. 44 13 .. .. 186 117 .. 48 35 137 .. 137 1,764 894 .. 10 .. .. .. 64 106 .. .. .. 10 191 403 .. ..

2007

2,452 18,194 198 4,366 73 2,487 6,623 26,544 49 52,896 344 106 262 10 18,467 .. 242 16 12 147 238 3 0 30 456 58 48 63 808 19 3 18 4,223 70 21 378 24 13 14 72 14,210 3,173 11 22 427 4,079 129 741 78 21 12 153 195 7,136 3,424 .. 48

High-technology exports

Royalty and license fees

% of GDP

$ millions

% of manufactured exports

2000–08 d

2009

0.96 0.80 0.05 0.67 .. 1.42 4.86 1.18 0.06 3.44 0.34 0.22 .. .. 3.21 .. 0.09 0.23 0.04 0.61 .. 0.06 .. .. 0.80 0.21 0.14 .. 0.64 .. .. 0.37 0.37 0.55 0.23 0.64 0.53 0.16 .. .. 1.63 1.21 0.05 .. .. 1.62 .. 0.67 0.21 .. 0.09 0.15 0.12 0.61 1.51 .. ..

17,444 10,143 5,940 375 0 24,738 10,268 25,988 4 99,210 49 1,802 78 .. 103,400 .. 6 11 .. 363 138 .. .. .. 931 42 10 3 51,560 3 .. 13 37,354 10 7 646 24 .. 21 2 58,450 504 7 2 46 4,694 7 227 0 .. 38 87 21,531 7,172 1,288 .. 0

26 9 13 6 0 25 23 8 1 20 1 30 5 .. 32 .. 0 5 .. 8 7 .. .. .. 10 3 2 3 47 3 .. 1 22 5 8 7 10 .. 1 0 24 10 6 8 3 20 0 2 0 .. 11 3 66 5 4 .. 0

862 193 38 .. 1,312 1,697 761 1,115 9 21,698 0 0 19 .. 3,185 .. 0 4 0 7 0 18 .. 0 0 6 .. .. 266 0 .. 0 656 4 0 2 0 .. 0 .. 5,473 159 0 0 0 637 .. 6 0 .. 295 2 2 103 148 .. ..

1,369 1,860 1,530 .. 396 34,873 897 1,899 45 16,835 0 64 21 .. 7,049 .. 0 12 0 26 1 .. .. 0 29 20 .. .. 1,133 2 .. 5 0 11 1 49 4 .. 6 .. 4,073 529 0 0 208 553 .. 90 25 .. 2 147 421 1,542 507 .. ..

5.13

Patent applications fileda,b

$ millions

Trademark applications fileda,c

Receipts

Payments

Residents

Nonresidents

Total

2009

757 5,314 282 5,970 .. 908 1,387 8,814 21 295,315 59 11 38 7,956 127,316 .. .. 135 .. 114 .. .. .. .. 91 34 1 .. 818 .. .. 2 822 134 103 177 18 .. .. .. 2,575 1,555 .. .. .. 1,140 .. 170 .. 1 .. 37 216 2,899 381 .. ..

30 23,626 4,324 557 .. 53 5,387 903 132 53,281 507 162 33 55 36,207 .. .. 3 .. 37 .. .. .. .. 16 406 43 .. 4,485 .. .. 22 13,459 5 110 834 22 .. .. .. 279 4,803 .. .. .. 4,280 .. 1,375 371 45 .. 657 3,095 241 24 .. ..

2011 World Development Indicators

6,671 130,172 52,649 3,013 .. 4,091 10,742 40,702 1,708 110,622 9,145 3,500 1,430 1,351 134,211 .. .. 2,580 .. 3,566 .. 634 489 .. 4,465 3,788 1,605 804 24,070 .. .. 24 75,250 5,046 1,399 3,774 870 .. 858 1,132 .. 16,190 5,975 .. .. 13,607 2,103 14,872 8,553 612 .. 24,825 14,912 17,877 2,681 .. ..

315

states and markets

Science and technology

5.13

Science and technology Researchers Technicians Scientific Expenditures in R&D in R&D and for R&D technical journal articles

Romania Russian Federation Rwanda Saudi Arabia Senegal Serbia Sierra Leone Singapore Slovak Republic Slovenia Somalia South Africa 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 million people

2000–08 d

2007

908 3,191 .. .. 276 1,196 .. 6,088 2,331 3,490 .. 393 2,944 93 .. .. 5,239 3,436 .. .. .. 311 .. 34 .. 1,588 680 .. .. 1,458 .. 4,269 4,663 346 .. 187 115 .. .. .. .. 1,281 w .. 596 479 1,112 579 1,071 2,064 487 .. 129 .. 3,945 2,977

216 493 .. .. .. 299 .. 529 392 1,696 .. 123 1,143 65 .. .. 1,871 2,317 .. .. .. 160 .. 17 .. 43 102 .. .. 325 .. 893 .. .. .. .. .. .. .. .. .. .. w .. .. .. .. .. .. 351 .. .. 87 .. .. 1,376

1,252 13,953 12 589 68 1,057 3 3,792 971 1,280 0 2,805 20,981 125 36 4 9,914 9,190 80 22 123 1,728 .. 12 67 757 8,638 2 164 1,847 214 47,121 209,695 215 166 497 283 .. 18 36 80 758,132 s 1,690 144,072 85,227 58,845 145,762 60,164 29,335 23,240 8,700 19,375 4,946 612,370 167,647

High-technology exports

% of GDP

$ millions

% of manufactured exports

2000–08 d

2009

0.59 3,230 1.03 4,576 .. 11 0.05 40 0.09 104 0.35 .. .. .. 2.52 97,207 0.47 3,171 1.66 1,264 .. .. 0.93 1,418 1.34 10,841 0.17 44 0.29 11 .. 0 3.75 17,059 2.90 38,556 .. 83 0.06 .. .. 24 0.25 28,655 .. .. .. 0 0.06 3 1.02 663 0.72 1,463 .. .. 0.39 5 0.85 1,519 .. 29 1.88 57,178 2.82 141,519 0.64 73 .. .. .. 66 0.19 1,685 .. .. .. 0 0.03 6 .. 7 2.07 w 1,858,138 s .. .. 0.98 576,048 1.19 414,058 0.79 117,380 0.98 540,234 1.44 .. 0.88 16,275 0.68 50,434 .. 1,571 0.79 .. .. 3,260 2.29 1,116,596 1.68 392,305

Royalty and license fees

Patent applications fileda,b

$ millions

Trademark applications fileda,c

Receipts

Payments

Residents

Nonresidents

Total

2009

10 193 339 9 494 4,107 31 0 1 0 .. .. 14 0 9 .. 63 144 .. 1 1 49 1,340 11,686 5 92 155 7 36 290 .. .. .. 6 48 1,658 5 1,041 3,449 1 0 0 34 0 0 0 0 116 17 4,709 1,832 25 .. .. 2 0 30 .. 1 0 4 0 0 26 145 2,250 .. .. .. 0 0 5 0 .. .. 6 25 14 2 .. 648 .. .. .. 1 3 3 3 112 644 3 .. .. 23 12,928 9,498 23 89,791 25,230 5 0 17 .. .. .. 4 0 352 5 .. .. .. 0 1 0 33 –5 2 0 0 1 .. .. 20 w 181,636 s 188,861 s 3 34 67 20 3,767 32,422 25 1,336 18,747 14 2,431 13,675 20 3,800 32,489 32 881 16,411 9 923 6,257 13 1,629 5,477 2 60 343 8 208 1,962 6 99 2,039 19 177,835 156,372 16 38,296 70,574

1,054 25,598 .. 128 .. 319 .. 750 176 373 .. .. 3,596 201 3 .. 2,549 1,684 124 11 .. 802 .. .. .. .. 2,555 .. 6 2,434 .. 15,985 224,912 33 238 .. .. .. 11 .. .. 994,324 s .. 179,049 134,475 36,842 185,505 237,052 33,042 5,287 .. 5,580 .. 764,583 84,182

37 12,966 .. 642 .. 40 .. 7,986 63 12 .. 10,753 207 264 13 .. 306 394 133 1 .. 5,939 .. .. 551 .. 177 .. 1 2,380 .. 6,480 231,194 706 174 .. .. .. 24 .. .. 634,131 s .. 198,050 131,207 55,416 198,493 85,532 16,049 41,517 .. 25,831 .. 406,316 15,710

12,977 49,189 238 .. .. 7,237 750 15,332 5,534 4,073 .. 26,621 46,711 5,916 743 680 12,706 28,945 2,432 2,496 556 36,087 .. .. .. .. 71,466 2,337 .. 8,568 .. 33,542 266,845 11,501 4,541 .. 4,187 .. 4,518 795 .. 2,884,372 s .. 1,559,267 955,629 603,638 1,575,589 890,552 214,396 313,022 14,191 151,906 .. 1,104,532 313,484

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 African Intellectual Property Organization (448 by nonresidents), European Patent Office (134,580 by nonresidents), and the Eurasian Patent Organization (2,801 by nonresidents). c. Excludes applications filed under the auspices of the Office for Harmonization in the Internal Market (88,086). d. Data are for the most recent year available. e. Includes Luxembourg and the Netherlands.

316

2011 World Development Indicators

About the data

5.13

Definitions

The United Nations Educational, Scientific, and

for filing patent applications. An applicant files an

• Researchers in R&D are professionals engaged

Cultural Organization (UNESCO) Institute for Statis-

international application for which the 142 eligible

in conceiving of or creating new knowledge, prod-

tics collects data on researchers, technicians, and

countries in 2009 are automatically designated.

ucts, processes, methods, and systems and in

expenditure on R&D through surveys and from other

The application is searched and published, and,

managing the projects concerned. Postgraduate

international sources. R&D covers basic research,

optionally, a supplementary international search or

doctoral students (ISCED97 level 6) engaged in R&D

applied research, and experimental development.

preliminary examination can be conducted. In the

are considered researchers. • Technicians in R&D

Data on researchers and technicians are calculated

national or regional phase the applicant requests

and equivalent staff are people whose main tasks

as full-time equivalents.

national processing of the application and initiates

require technical knowledge and experience in engi-

Scientific and technical article counts are from jour-

the national search and granting procedure in the

neering, physical and life sciences (technicians),

nals classified by the Institute for Scientific Informa-

countries where protection is sought. International

and social sciences and humanities (equivalent

tion’s Science Citation Index (SCI) and Social Sci-

applications under the treaty provide for a national

staff). They engage in R&D by performing scientific

ences Citation Index (SSCI). Counts are based on

patent grant only—there is no international patent.

and technical tasks involving the application of

fractional assignments; articles with authors from

The national filing represents the applicant’s seeking

concepts and operational methods, normally under

different countries are allocated proportionately to

of patent protection for a given territory, whereas

researcher supervision. • Scientific and technical

each country (see Definitions for fields covered).

international filings, while representing a legal right,

journal articles are published articles in physics,

The SCI and SSCI databases cover the core set of

do not accurately reflect where patent protection is

biology, chemistry, mathematics, clinical medicine,

scientific journals but may exclude some of local

sought. Resident filings are those from residents of

biomedical research, engineering and technology,

importance and may reflect some bias toward Eng-

the country concerned. Nonresident filings are from

and earth and space sciences. • Expenditures for

lish-language journals.

applicants abroad. For regional offices such as the

R&D are current and capital expenditures on creative

R&D expenditures include all expenditures for R&D

European Patent Office, applications from residents

work undertaken to increase the stock of knowledge,

performed within a country, including capital costs

of any member state of the regional patent conven-

including on humanity, culture, and society, and the

and current costs (wages and associated costs of

tion are considered nonresident filings. Some offices

use of knowledge to devise new applications. • High-

researchers, technicians, and supporting staff and

(notably the U.S. Patent and Trademark Office) use

technology exports are products with high R&D inten-

other current costs, including noncapital purchases

the residence of the inventor rather than the appli-

sity, such as in aerospace, computers, pharmaceuti-

of materials, supplies, and R&D equipment such as

cant to classify filings. For further information on

cals, scientific instruments, and electrical machinery.

utilities, reference materials, subscriptions to librar-

the PCT, see the PCT Yearly Review at http://www.

• Royalty and license fees are payments and receipts

ies and scientific societies, and lab materials).

wipo.int/export/sites/www/ipstats/en/statistics/

between residents and nonresidents for authorized

pct/pdf/901e_2009.pdf.

use of intangible, nonproduced, nonfinancial assets

The method for determining high-technology exports was developed by the Organisation for Eco-

A trademark is a distinctive sign identifying goods

and proprietary rights (such as patents, copyrights,

nomic Co-operation and Development in collabora-

or services as produced or provided by a specific

trademarks, and industrial processes) and for

tion with Eurostat. It takes a “product approach” (as

person or enterprise. A trademark protects the owner

the use, through licensing, of produced originals

distinguished from a “sectoral approach”) based on

of the mark by ensuring the exclusive right to use it

of prototypes (such as films and manuscripts).

R&D intensity (expenditure divided by total sales)

to identify goods or services or to authorize another

• Patent applications filed are patent applications

for groups of products from Germany, Italy, Japan,

to use it. Period of protection varies, but a trade-

filed at a national or regional office; an international

the Netherlands, Sweden, and the United States.

mark can be renewed indefinitely for an additional

patent application (or PCT filing) is in the interna-

Because industrial sectors specializing in a few high-

fee. Detailed components of trademark filings, avail-

tional phase of the PCT. • Trademark applications

technology products may also produce low-technol-

able on the World Development Indicators CD-ROM

filed are applications to register a trademark with a

ogy products, the product approach is more appro-

and WDI Online, include applications filed by direct

national or regional IP office.

priate for international trade. The method takes only

residents (domestic applicants filing directly at a

R&D intensity into account, but other characteristics

given national or regional intellectual property [IP]

Data sources

of high technology are also important, such as know-

office); direct nonresident (applicants from abroad

Data on R&D are provided by the UNESCO Institute

how, scientific personnel, and technology embodied

filing directly at a given national or regional IP office);

for Statistics. Data on scientific and technical journal

in patents. Considering these characteristics would

aggregate direct (applicants not identified as direct

articles are from the U.S. National Science Board’s

yield a different list (see Hatzichronoglou 1997).

resident or direct nonresident by the national or

Science and Engineering Indicators 2010. Data on

A patent is an exclusive right granted for a specified

regional office); and Madrid (designations received

high-technology exports are from the United Nations

period (generally 20 years) for a new way of doing

by the national or regional IP office based on inter-

Statistics Division’s Commodity Trade (Comtrade)

something or a new technical solution to a problem—

national applications filed via the World Intellectual

database. Data on royalty and license fees are

an invention. The invention must be of practical use

Property Organization (WIPO)–administered Madrid

from the International Monetary Fund’s Balance of

and display a characteristic unknown in the existing

System). Data are based on information supplied to

Payments Statistics Yearbook. Data on patents and

body of knowledge in its field.

WIPO by IP offices in annual surveys supplemented

trademarks are from the World Intellectual Property

by data in national IP office reports. Data may be

Organization’s World Intellectual Property Indicators

missing for some offices or periods.

(2010) and www.wipo.int/econ_stat.

Most countries have systems to protect patentable inventions. The international Patent Coop-

states and markets

Science and technology

eration Treaty (PCT) provides a two-phase system

2011 World Development Indicators

317

Text figures, tables, and boxes

Global Links

Introduction

he past three years show dramatically how events in one part of the world can affect people in the rest of the world, though sometimes with a lag. The financial crisis that struck high-income economies in 2008 reached low- and middle-income economies in 2009. World exports of goods and services fell 20 percent, from $19.6 trillion in 2008 to $15.6 trillion in 2009, more in high-income economies and somewhat less in low- and middle-income economies. Developing economies’ share of world exports increased by 1 percentage point over 2008, continuing a rising trend from 19 percent in 2000 to 27 percent in 2009. Imports of goods and services by high-income economies fell 22 percent, from $14.0 trillion in 2008 to $10.9 trillion in 2009; imports by low and middle income economies fell 19 percent. The financial crisis also reduced the external financing available to developing economies from private sources, which dropped to $521 billion in 2009 from the record high of $932 billion in 2007. Net inflows of foreign direct investment dropped to $359 billion in 2009 from a high of $597 billion in 2008. In contrast, net inflows of portfolio equity investments rose to $108 billion following net outflows of $53 billion in 2008. Bond issuances, which dropped from $88 billion in 2007 to $24 billion in 2008, recovered in 2009 to reach $51 billion. But commercial and traderelated lending, which declined from $195 billion in 2007 to $172 billion in 2008, dried up in 2009, dropping to $1.7 billion. Total debt flows from private creditors fell 70 percent in 2009, to $59 billion. But net flows from official creditors reached $171 billion in 2009, a 50 percent increase over 2008, driven by such multilateral institutions as the International Monetary Fund (IMF) and the World Bank. Global food prices soared again in 2010 and 2011, with some commodities exceeding their record high in 2008. The World Bank food price index (table 6.6) averaged 311 in February 2011, exceeding the June 2008 record of 293. Food price inflation has accelerated in several low- and middleincome economies, where consumers often spend more than half their income on food. During the 12 months ending in August 2010, food prices rose 13.2 percent a year in Indonesia, 10.4 percent a year in India, and 9.6 percent a year in Bangladesh. The financial crisis has also demonstrated the need for more data and more frequently updated data to monitor global transactions. The World Development Indicators database contains more

than 400 indicators for monitoring exchanges between economies on an annual basis, and the topics covered have expanded each year. Many others are not included in the database because of their structure or limited country coverage, but they are necessary for understanding global links. Most high-income economies and some low- and middleincome economies now produce economic statistics on a quarterly or monthly basis. This introduction highlights some of these data.

Data sources for bilateral trade flows World Development Indicators publishes data on merchandise trade values by commodity groups (tables 4.4 and 4.5), values of trade in services (tables 4.6 and 4.7), intra- and extra-regional trade (table 6.5), merchandise trade indices (table 6.2), tariff rates (table 6.8), and indicators for measuring trade facilitation (table 6.9). Demand is rising for more detailed data, such as trade flows by partner economies and by commodities and sectors. Table 6a summarizes the main sources of data on bilateral trade flows. Some of these databases are accessible through the World Integrated Trade Solutions platform (http://wits.worldbank.org).

Barriers to trade in services Trade in services makes up 22 percent of world trade, up from 20 percent in 2000. In developing economies the nominal value of trade in services grew 16 percent a year over 2000–09, doubling the rate of growth over 1990–2000 and surpassing that of high-income economies, which grew at 11 percent a year over 2000–09. Despite this growing

2011 World Development Indicators

319

Source of data for bilateral trade flows

Compiling organization

International Monetary Fund

Name of publication and database

Country coverage

Direction of Trade Statistics database

Most developing and developed economies

Data coverage

Periodicity

Merchandise trade data, no breakdowns of sectors and partners. Available through subscription

Quarterly and annual

Links

http://www2. imfstatistics.org/DOT/ This is a link to a 5-day trial

United Nations Conference on Trade and Development

UNCTADstat Merchandise Trade Matrix

Most developing and developed economies

Merchandise trade by partner economies and by product groups

Annual

http://unctadstat. unctad.org/

United Nations Statistics Division

Commodity Trade Statistics (Comtrade)

Most developing and developed economies

Merchandise trade by partner economies and by commodity classifications

Annual

http://wits.worldbank. org/wits/

Organisation for Economic Coâ&#x20AC;&#x2018;operation and Development (OECD)

Monthly Statistics of International Trade

OECD member economies

Total merchandise trade by partners

Monthly

http://stats.oecd.org/

International Trade by Commodity Statistics

OECD member economies plus EU

Merchandise trade by partners and by products

Annual

Trade in services

OECD member economies plus EU and a few more economies

Trade in services by partners and by service category

Annual

External Trade database

27 EU members

Merchandise trade by partners and by products

Monthly, quarterly, and annual

Eurostat

importance, little is known about policies affecting services trade, a major impediment to the analysis of trade policy and trade flows. To address this gap, the World Bank has built the Services Policy Restrictiveness Database, with information on 102 countries for five major service sectors disaggregated by subsectors and relevant modes of supply in each subsector. So far, the information focuses mainly on discriminatory policy measures affecting foreign service providers. The full database will be released in the second quarter of 2011 at http://econ.worldbank.org/programs/trade/ services. Restrictiveness is assessed by the newly created Services Trade Restrictiveness Index score. The index reveals patterns of restrictiveness by major service sector and across 6b

Trade in professional services faces the highest barriers Services trade restrictiveness index, 0 (fully open) to 100 (entirely closed)

High-income economies

Low- and middle-income economies

50 40 30 20 10 0

Financial services

Professional services

Retail services

Telecommunications services

Transportation services

Note: Aggregate values are the simple average of individual country scores. Data are for 102 countries. Source: World Bank Services Policy Restrictiveness Database.

320

2011 World Development Indicators

Extract databases are available under â&#x20AC;&#x153;International Trade and balance of Paymentsâ&#x20AC;? theme Full databases are subscription based http://epp.eurostat. ec.europa.eu/portal/ page/portal/external_ trade/data/database

low- and middle-income and high-income economies (figure 6b). In both high-income and lowand middle-income economies, professional services (including the movement of individuals) face the highest trade barriers, followed by transportation services. High-income economies exhibit more open financial, telecommunications, and retail distribution sectors than do low- and middle-income economies (Borchert, Gootiiz, and Mattoo forthcoming).

Foreign direct investment Countries are increasingly compiling more data on foreign direct investment (FDI) transactions and stocks. Despite recent improvements, however, deficiencies in coverage remain. For example, if recording of FDI transactions were complete and comparable, the total outflows of FDI from investing economies would equal the total inflows recorded by the recipient economies. But in 2009 the divergence between outflows and inflows of FDI at the global level was about $82 billion (7 percent of global outflows; figure 6c). The discrepancies arise from differences in reporting practices. For example, some countries include reinvested earnings in their outflow statistics while others do not include them in their inflow statistics. Furthermore, corporate accounting practices and valuation methods may differ by reporters. Discrepancies exist among FDI statistics published by various international agencies,

global links

even when the agencies adopt common methodological standards. Such discrepancies may reflect differences in comparability and timing of FDI data reported by different countries, discrepancies in sector coverage, and lags in reporting revisions. Recognizing these issues, the IMF is leading a worldwide statistical data collection effort to improve the quality of FDI data (the Coordinated Direct Investment Survey; http://cdis.imf.org). Preliminary results were released in December 2010. Data on FDI are published in table 6.12. These data cover FDI net inflows received by the reporting economy from foreign residents, and FDI net outflows by the reporting economy residents. Breakdowns of FDI transactions and investment positions by sector and partner, increasingly sought by users, are not published in World Development Indicators but are available from other sources. Table 6d summarizes the availability of FDI statistics for some of the main data compilers.

Bilateral remittance flows World Development Indicators publishes data on total workersâ&#x20AC;&#x2122; remittances and compensation of employees received and sent by the reporting economies (table 6.18). Data coverage and quality have been improving, but inconsistencies and lack of reporting remain. For example, if all economies reported completely and consistently, the sum of remittances flows recorded by receiving economies would equal the sum of remittance flows recorded by sending economies. But as of 2009 there was a discrepancy of $127 billion (30 percent of total inflows; figure 6e). Large amounts of remittance flows are sent through private and informal channels that are not officially recorded. No comprehensive dataset is available on the bilateral flow of remittances. Bilateral remittance flows estimated through approximation and allocation methods using the proportions of migrant stocks in destination and sending countries or the incomes of destination and sending countries are available at www.worldbank.org/prospects/migrationandremittances (Ratha and Shaw 2007). The data shed light on patterns of remittance flows, but the estimates are sensitive to the assumptions and allocation method chosen.

Bilateral migration stocks Because migration data come mostly from destination countries, the quality of global migration

Discrepancies persist in measures of FDI net flows

Global FDI net flows ($ trillions) 3.0

Inflows

Outflows

2.5 2.0 1.5 1.0 0.5 0.0

2004

2005

2006

2007

2008

2009

Source: World Development Indicators data files.

data depends on how well the destination countries survey migrants within their borders. Systematic recording of migrants is difficult, especially for countries with weak statistical capacity and for those affected by civil disorder and natural disasters. Moreover, ensuring the comparability of migration data is a long-standing challenge, in part because destination countries classify migrants using various criteria. Many countries compile migration data based on immigrantsâ&#x20AC;&#x2122; nationality, while others collect data based on the immigrantsâ&#x20AC;&#x2122; place of birth. World Development Indicators publishes aggregate data on international migrant stocks and net migration estimated by the United Nations Population Division based on population censuses supplemented by border statistics, administrative records, surveys, and refugee registrations (tables 6.1 and 6.18). Efforts to produce complete data on bilateral migration have been rare. A 2008 database on immigrants in OECD countries contains data on bilateral migrant stock for OECD members (http://stats.oecd.org/). The dataset includes sociodemographic information such as age, gender, education, and occupation. A series of studies have published data on OECD immigrants by educational attainment (Docquier and Marfouk 2006), gender and educational attainment (Docquier, Lowell, and Marfouk 2009), and age of entry and educational attainment (Beine, Docquier, and Marfouk 2006). Global bilateral databases have been constructed for the 2000 census round (Parsons and others 2007) and for bilateral migration and remittance flows (Ratha and Shaw 2007). The United Nations Population Division in cooperation with the World Bank, the United Nations Statistics Division, and the Universities of Nottingham and Sussex created the Global 2011 World Development Indicators

321

Source of data on FDI

Compiling organization

Name of publication and database

Country coverage

Data coverage

Periodicity

Links

Balance of Payments Statistics Yearbook and database

Most developing and developed economies

Aggregate FDI flows and stock by reporting economy. By-partner, bysector breakdowns are not available. Available through subscription

Quarterly and annual

http://www2. imfstatistics.org/BOP

World Investment Report and Foreign Direct Investment database

Most developing and developed economies

Aggregate FDI flows and stock by reporting economy

Annual

http://unctadstat. unctad.org

Transnational Corporations Statistics database

Transnational Corporations Worldwide

Detailed data on transactions of transnational corporations and mergers and acquisitions, by partner and by sector; available through data extract service

Annual

www.unctad.org/ Templates/Page.asp ?intItemID=3159& lang=1

Organisation for Economic Cooperation and Development (OECD)

International Direct Investment database

32 OECD member economies

FDI stock (annual) and flows (annual and quarterly) by partner economies and by sectors. Full dataset is available to subscribers

Quarterly and annual

http://stats.oecd.org/

Eurostat

European Union Foreign Direct Investment Yearbook and database

27 EU members

Aggregate and bilateral FDI flows and stock, by partner and by sector

Annual

http://epp.eurostat. ec.europa.eu/ portal/page/portal/ balance_of_payments/ data/database

Association of Southeast Asian Nations

Foreign Direct Investment Statistics

10 ASEAN member economies

Bilateral FDI inflows and outflows

Annual

www.aseansec. org/18144.htm

Centre d’Etudes Prospectives et d’Informations Internationales

Foreign Direct Investment database

96 countries of the GTAP 6.2 database for stocks and 70 countries for flows

Harmonized bilateral flows and stocks of FDI for 26 sectors. Data are gap filled using gravity-based regressions and raw data from IMF, UNCTAD, OECD, and Eurostat.

Annual for 2004 only

www.cepii.fr/ anglaisgraph/ bdd/fdi.htm

Financial Times

FDI database

All countries with greenfield FDI projects;

Greenfield FDI projects since 2003; subscription based. Methodology differs significantly from balance of payments and international investment position standards. The data are based on press reports.

Daily

www.fdimarkets.com

Mergers and acquisitions activity worldwide covering an array of transactions

Information for mergers and acquisitions activity, including information on target and acquiror, deal value, and financials.

Monthly

www.dealogic.com

International Monetary Fund (IMF)

United Nations Conference on Trade and Development (UNCTAD)

FDI Intelligence

Dealogic

M&A Analytics

Migration database (www.unmigration.org) in 2008. It contains all publicly available data from more than 230 destination countries and territories over the last five decades on international migrants, classified by age, gender, place of birth, and country of citizenship. However, it still does not include all raw data points needed for a global migration matrix. These raw data were assembled to construct a global bilateral migration matrix using empirical methods to fill holes in the data (Özden and others forthcoming). The resulting database covers 226 origin and 226 destination countries

322

2011 World Development Indicators

This is a link to 5-day trial

Extract databases are available under Globalisation theme

(forthcoming at www.data.worldbank.org/data -catalog). Construction of such a matrix entails formidable challenges, including selecting the most relevant sources, allocating migrants who “originated” in aggregate geographic regions and migrants of unknown origins to specific countries, and accounting for varying survey dates and definitions. Of all cell-level values in the final matrix, about 12–14 percent are from raw census data, 40–60 percent are based primarily on raw data scaled to United Nations Population Division estimates of migrant stocks or augmented by the disaggregation of aggregate categories,

global links

and the remaining 26–48 percent are estimated through interpolation and extrapolation. This new dataset reveals that the total stock of migrants increased from 92 million in 1960 to 165 million in 2000. The number of migrants from high-income economies remained stable, while the number from low- and middle-income economies rose from 14 million in 1960 to 60 million in 2000 (figure 6f). The increase was driven largely by an increase in migrants residing in the United States (up 24 million) and Western Europe (up 22 million).

At least 30 percent of remittance inflows go unrecorded by the sending economies

Global remittance flows ($ billions)

Inflows

Outflows

500 400 300 200 100 0

2004

2005

2006

2007

2008

2009

Note: Incudes workers’ remittances and compensation of employees. Source: World Development Indicators data files.

Public sector debt World Development Indicators publishes data on public and publicly guaranteed external debt (tables 6.10, 6.11, and 6.13). But these data present only a portion of total public sector debt, much of which is held by domestic creditors. Domestic debt data are important for economic policymaking because of the implications for local financial markets. To fill the gap, the World Bank and the IMF launched an online Quarterly Public Sector Debt database in 2009 (http://data.worldbank.org/data-catalog). The database provides data on clearly defined tiers of debt for central, state, and local government in developing or emerging market economies as well as on extrabudgetary agencies and funds. It also includes debt data by instruments, valuation methods, maturity types, and creditors. The level and composition of public sector debt are affected by many external and domestic economic factors. The recent global financial crisis limited the private sector’s ability to borrow. The public sector, usually more creditworthy, increased external borrowing to stimulate sluggish domestic economies. Most external financing for developing economies in 2009 was provided by official multilateral institutions such as the IMF and the World Bank. After the Asian financial crises in the late 1990s many governments switched from external to domestic borrowing to reduce their exposure to exchange rate fluctuations, dramatically increasing the size of domestic debt in emerging market economies. Today, domestic debt represents about 78 percent of the total general government debt in developing economies with data. Comparison with earlier period is not possible due to lack of data. Emerging market economies have also issued local currency–denominated debt to correct currency and maturity mismatches. In September 2010 the estimated local currency

debt among developing economies averaged 67 percent of total government debt (excluding Brazil and China, with upwards of 96 percent). Financing needed to support fiscal deficits led to a significant increase in the ratio of sovereign debt to GDP. Among developing economies, central government debt for 2009 averaged 46 percent of GDP, up from 42 percent in 2009. Brazil, which undertook aggressive countercyclical spending and tax cuts to stimulate the economy, had the highest share of gross debt in gross domestic product (about 70 percent; figure 6g). Migrants originating from low- and middle-income economies and residing in high-income economies rose fivefold over 1960–2000

International migrant stock by origin and destination, millions 80 60

Low- and middle-income to low- and middle-income Low- and middle-income to high-income

High-income to high-income

High-income to low- and middle-income 0

1960

1970

1980

1990

2000

Source: Özden and others forthcoming.

The ratio of central government debt to GDP has increased for most economies, 2007–10

Central government debt (percent of GDP) 80 Brazil Pakistan

60 40

Mauritius

Kenya

Turkey Mexico

Honduras Lithuania

Q3 2007

Q3 2008

Q3 2009

Q3 2010a

a. Derived using 2009 GDP because 2010 GDP was not available. Source: World Bank Quarterly Public Sector Debt database

2011 World Development Indicators

323

Tables

6.1

Integration with the global economy Trade

International finance

Financing through international capital markets % of GDP Gross Merchandise Services inflows

324

Movement of people

Communication

% of GDP

Emigration of Workers’ people with tertiary International remittances International education to Internet Foreign direct and voice OECD countries bandwidtha International investment compensation traffic a migrant stock % of population age bits per Net Net of employees Net migration 25 and older with % of total second minutes inflows outflows received tertiary education population per capita per person thousands

2009

2005–10

2010

2000

2008

31.3 46.9 60.1 75.6 30.7 45.9 34.6 73.8 64.2 41.3 101.6 153.2 45.7 53.4 74.5 69.2 18.0 81.7 36.0 35.2 105.3 32.7 48.4 20.9 69.5 58.8 44.3 323.7 28.1 63.4 88.7 69.0 64.2 50.3 30.9 114.9 56.9 37.9 50.5 36.1 52.4 29.6 100.4 33.5 51.9 39.4 66.0 43.5 51.3 62.0 52.1 24.2 50.2 58.7 41.2 40.5 90.7

.. 38.6 .. 26.2 7.4 16.6 9.0 24.1 11.9 6.0 11.3 33.0 12.8 8.8 11.9 15.9 4.7 24.5 8.7 17.1 26.8 15.2 10.3 .. .. 11.1 5.8 62.1 4.8 .. 46.1 17.5 14.8 25.0 .. 20.7 34.3 14.6 6.7 18.8 9.9 .. 36.4 14.4 22.4 10.2 .. 25.5 21.3 14.6 18.0 17.5 10.7 9.8 15.2 17.9 14.1

0.0 0.0 0.0 2.2 0.2 0.0 .. .. 0.1 0.2 0.5 .. 0.0 0.0 0.0 0.0 3.9 0.0 0.0 0.0 0.0 0.6 .. 0.0 0.0 3.2 1.0 .. 3.4 0.0 0.0 0.0 0.0 .. 0.0 .. .. 0.0 0.0 1.4 0.0 0.0 .. 0.0 .. .. 0.4 0.0 0.0 .. 4.7 .. 0.0 0.0 0.0 0.0 0.2

1.3 8.1 2.0 2.9 1.3 8.9 2.4 2.3 1.1 0.8 3.8 –8.2 1.4 2.4 1.4 2.1 1.6 9.4 2.1 0.0 5.4 1.5 1.5 2.1 6.8 7.8 1.6 24.9 3.1 9.0 21.7 4.6 1.6 4.7 .. 1.4 0.9 4.4 0.6 3.6 2.0 0.0 9.2 0.8 0.0 2.3 0.3 5.4 6.1 1.2 6.4 0.7 1.6 1.2 1.7 0.6 3.5

.. 0.3 .. 0.0 0.2 0.6 3.7 1.4 0.8 0.0 0.2 –16.7 –0.1 0.0 –0.1 0.0 –0.6 –0.3 0.6 0.0 0.2 1.8 3.0 .. .. 4.9 0.9 30.4 1.3 .. .. 0.0 0.0 2.1 .. 0.7 2.1 0.0 0.0 0.3 –0.6 .. 8.2 0.0 1.6 5.6 .. 0.0 0.0 1.8 0.0 0.6 0.1 0.0 –0.1 0.0 0.0

.. 11.0 1.5b 0.1 0.2 8.8 0.4b 0.9 3.0 11.8 0.7 2.2 3.6b 6.2 12.2 0.7 0.3 3.2 1.2b 2.1 3.4 0.7 .. .. .. 0.0 1.0 b 0.2 1.8 .. 0.1b 1.8 0.8 2.3 .. 0.6 0.3 7.4 4.4 3.8 16.5 .. 1.7 0.9 0.4 0.6 0.1b 10.9 6.6 0.3 0.4 0.6 10.8 1.6 5.6 21.2 17.6

1,000 –75 –140 80 30 –75 500 160 –50 –570 0 200 50 –100 –10 15 –229 –50 –65 323 –5 –19 1,050 5 –75 30 –1,731c 113 –120 –100 –50 30 –145 10 –194 226 30 –140 –350 –340 –280 55 0 –300 55 500 5 15 –250 550 –51 150 –200 –300 –12 –140 –100

0.3 2.8 0.7 0.3 3.6 10.5 21.1 15.6 3.0 0.7 11.3 9.0 2.5 1.5 0.7 5.8 0.4 1.4 6.4 0.7 2.2 1.0 21.1 1.8 3.4 1.9 0.1c 38.9 0.2 0.7 3.8 10.5 11.2 15.8 0.1 4.3 8.7 4.2 2.9 0.3 0.7 0.3 13.6 0.6 4.2 10.6 18.9 16.6 4.0 13.2 7.6 10.0 0.4 3.8 1.2 0.4 0.3

22.6 17.5 9.5 3.7 2.8 8.9 2.7 13.5 1.8 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 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 24.8

7 263 34 .. .. .. .. .. 77 .. .. .. 309 .. .. .. .. 105 .. .. .. .. .. .. .. 43 .. 1,435 .. 6 .. 132 .. 302 .. 197 357 .. .. 44 510 29 .. 5 .. 301 .. .. 268 .. 61 .. 206 .. .. .. 224

2011 World Development Indicators

2009

Trade

International finance

Financing through international capital markets % of GDP Gross Merchandise Services inflows

2009

125.6 29.9 39.1 38.8 116.2 77.9 49.8 38.7 52.9 22.3 81.5 62.1 49.8 .. 82.5 .. 75.9 97.8 37.0 66.6 60.1 171.0 80.1 73.4 93.2 83.9 51.1 55.4 145.7 52.7 92.6 66.0 53.9 84.5 96.0 51.2 60.4 .. 93.6 41.5 119.2 39.8 79.3 44.6 52.9 49.8 99.0 30.5 35.4 95.4 71.0 37.3 52.3 65.4 48.6 .. 64.6

27.3 12.5 7.7 .. 11.5 86.9 20.0 10.4 37.5 5.5 33.3 12.4 16.1 .. 16.1 .. 18.0 37.7 8.6 23.4 90.4 12.5 162.0 8.7 18.4 18.3 .. .. 29.1 17.0 .. 44.8 4.5 25.6 23.1 21.1 17.1 .. 12.2 11.5 22.6 12.5 16.7 13.7 11.4 19.8 15.9 6.4 31.2 26.9 13.9 6.5 11.6 12.4 15.9 .. ..

2009

.. 1.6 2.3 0.0 0.0 .. .. .. 9.0 .. 0.0 2.1 0.2 .. .. 0.0 .. 0.0 0.0 0.0 2.7 0.0 0.0 0.0 6.4 2.6 0.0 0.0 5.8 0.0 0.0 0.0 3.1 0.0 0.1 0.0 0.6 .. 0.0 0.0 .. .. 0.0 0.0 0.7 .. .. 0.2 8.8 58.3 0.0 2.6 4.5 3.8 .. .. ..

Movement of people

6.1

global links

Integration with the global economy

Communication

% of GDP

2009

2.2 2.5 0.9 0.9 1.6 11.1 2.0 1.4 4.5 0.2 9.5 11.8 0.5 .. 0.2 7.5 0.0 4.1 5.4 0.4 13.9 4.0 24.9 2.7 0.6 2.7 6.3 1.3 0.7 1.2 –1.3 3.0 1.7 2.4 14.8 2.2 9.0 .. 5.3 0.3 4.2 –1.0 7.1 13.7 3.3 3.0 4.8 1.5 7.2 5.4 1.4 3.7 1.2 3.2 1.2 .. ..

2.1 1.1 0.5 .. 0.0 10.6 0.6 2.1 0.5 1.5 0.3 2.7 0.2 .. 1.3 .. 6.1 0.0 0.0 –0.2 3.3 0.0 0.0 1.9 0.5 0.1 .. .. 4.2 0.0 .. 0.4 0.9 0.1 1.3 0.5 0.0 .. 0.0 .. 3.5 –0.5 0.0 0.5 0.1 7.1 0.9 0.0 0.0 0.1 0.1 0.3 0.2 1.2 0.5 .. ..

1.7 3.6 1.3 0.3b 0.1 0.3 0.6 0.1 15.8 0.0 14.3 0.1 5.7b .. 0.3 .. .. 21.7b 0.6 2.3 21.9 26.2 6.2b 0.0 b 3.1 4.1 0.1b 0.0 b 0.6 4.5b 0.1b 2.5b 2.5 22.4 4.8 6.9 1.1 .. 0.1 23.8 0.5 0.5 12.5 1.7 5.5 b 0.2 0.1 5.4 0.7 0.2 4.3 1.8 12.3 1.9 1.5 .. ..

2005–10

75 –1,000 –730 –500 –577 200 85 1,650 –100 150 250 –100 –189 0 –30 .. 120 –75 –75 –10 –13 –36 248 20 –100 –10 –5 –20 130 –202 10 0 –2,430 –172 –10 –425 –20 –500 –1 –100 100 50 –200 –28 –300 135 20 –1,416 11 0 –40 –625 –900 –120 200 –21 562

2010

2000

2008

2009

3.7 0.5 0.1 2.9 0.3 20.2 38.8 7.4 1.1 1.7 48.8 19.1 2.0 0.2 1.1 .. 73.3 4.2 0.3 14.9 17.8 0.3 2.3 10.4 3.9 6.3 0.2 1.8 8.4 1.2 2.9 3.3 0.7 11.4 0.4 0.2 1.9 0.2 6.3 3.2 10.5 22.0 0.7 1.3 0.7 9.9 28.4 2.4 3.4 0.4 2.5 0.1 0.5 2.2 8.6 8.1 86.5

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

159 .. .. .. .. .. .. .. 224 .. 258 52 6 .. 64 .. .. .. .. .. 190 .. .. .. 132 256 8 .. .. 13 57 215 .. 457 .. 87 .. 3 .. .. .. .. .. .. 26 .. 431 .. 118 .. .. 113 .. 32 .. .. ..

2011 World Development Indicators

325

6.1

Integration with the global economy Trade

International finance

Financing through international capital markets % of GDP Gross Merchandise Services inflows 2009

Romania Russian Federation Rwanda Saudi Arabia Senegal Serbia Sierra Leone Singapore Slovak Republic Slovenia Somalia South Africa 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

58.9 40.2 27.2 76.6 53.8 55.7 38.7 282.9 127.0 109.0 .. 47.6 34.7 41.8 32.1 103.3 61.8 66.8 51.2 71.9 44.2 108.5 .. 80.6 75.8 84.8 39.5 66.9 42.3 75.0 136.8 38.4 18.8 39.0 61.5 30.1 141.0 .. 53.5 63.3 91.9 42.8 w 48.9 44.7 46.7 42.3 44.8 51.5 48.3 33.6 53.5 31.0 52.7 42.0 57.1

2009

12.4 8.4 16.5 22.1 20.6 16.2 8.7 95.1 16.3 21.6 .. 9.4 14.4 10.5 5.6 25.4 25.6 23.0 13.3 9.5 16.7 25.7 .. 22.2 4.9 21.4 8.2 .. 14.9 22.3 .. 18.6 6.1 10.5 .. 3.6 14.1 .. 12.8 7.4 .. 11.2 w 13.3 8.9 9.2 8.5 9.0 7.9 10.5 5.9 .. 11.5 13.4 12.5 16.9

2009

0.1 2.4 0.0 .. 2.8 0.0 0.0 .. .. .. .. 2.7 .. 1.3 0.0 0.0 .. .. 0.1 0.0 0.0 0.3 0.0 19.9 .. 0.1 1.7 0.0 0.0 0.9 .. .. .. 1.6 0.0 1.5 1.5 .. 0.0 0.5 0.0 .. w 0.6 1.8 1.2 2.5 1.8 1.4 1.8 2.9 0.3 1.3 1.4 .. ..

Movement of people

Communication

% of GDP

2009

3.9 3.0 2.3 2.8 1.6 4.5 3.8 9.2 0.0 –1.2 .. 1.9 0.4 1.0 4.9 2.2 2.8 5.6 2.7 0.3 1.9 1.9 .. 1.8 3.3 4.0 1.4 6.8 3.8 4.2 .. 3.4 1.0 4.0 2.3 –1.0 8.4 .. 0.5 5.5 1.1 1.8 w 2.7 2.2 2.0 2.4 2.2 1.6 3.3 1.9 2.6 2.3 3.1 2.0 3.0

0.1 3.6 0.0 0.6 1.0 0.1 0.0 3.3 0.5 0.3 .. 0.5 0.5 0.0 0.0 0.2 7.9 6.8 0.0 0.0 0.0 1.6 .. –0.5 2.7 0.2 0.3 .. 0.0 0.1 .. 2.0 1.9 0.0 .. 0.6 0.8 .. 0.0 0.0 .. 2.1 w 0.0 0.9 0.8 1.1 0.9 1.0 2.0 0.3 .. 0.9 0.2 2.8 3.8

3.1 0.4 1.8 0.1 10.6 12.6b,d 2.4 .. 1.9 0.6 .. 0.3 0.7 8.0 5.5b 3.1 0.2 0.5 2.6b 35.1 0.1 0.6 .. 10.7b 0.5b 5.0 0.2 .. 4.7 4.5 .. 0.3 0.0 0.3 .. 0.0 7.4b .. 4.4 0.3 .. 0.8 w 6.6 1.8 2.4 1.1 1.9 1.4 1.4 1.4 3.2 4.5 2.5 0.3 0.5

2005–10

–200 250 15 150 –100 0 60 500 20 22 –250 700 1,750 –300 135 –6 150 100 800 –200 –300 300 10 –5 –20 –20 –44 –25 –135 –80 343 948 5,052 –50 –400 40 –200 –10 –135 –85 –700 ..e s –2,737 –13,203 –9,231 –3,972 –15,941 –3,781 –1,671 –5,214 –1,089 –2,376 –1,810 15,894 5,607

2010

0.6 8.7 4.5 28.0 1.6 7.2 1.8 38.3 2.4 7.9 0.2 3.7 13.8 1.7 1.7 3.4 13.9 22.6 10.2 4.0 1.5 1.7 1.2 2.7 2.6 0.3 1.9 4.0 1.9 11.5 70.0 10.4 13.8 2.4 4.2 3.5 0.1 46.3 2.1 1.8 2.9 3.1 w 1.5 1.4 0.9 3.3 1.4 0.3 6.8 1.1 3.6 0.8 2.1 12.0 11.0

2000

2008

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 13.1 6.8 6.6 7.0 7.1 7.0 3.4 10.6 10.5 5.3 12.6 4.1 7.1

124 .. 8 .. 101 203 .. .. 228 220 .. .. .. .. 13 41 .. .. .. .. 1 .. .. 28 443 .. 60 .. .. .. .. .. 216 125 .. 79 .. .. .. .. 19 .. .. .. .. .. .. .. .. .. .. .. .. .. ..

2009

a. Data are from the International Telecommunication Union’s (ITU) World Telecommunication Development Report database. Please cite the ITU for third-party use of these data. b. World Bank estimate. c. Includes Taiwan, China. d. Includes Montenegro. e. World totals computed by the United Nations sum to zero, but because the aggregates shown here refer to World Bank definitions, regional and income group totals do not equal zero.

326

2011 World Development Indicators

About the data

6.1

global links

Integration with the global economy Definitions

Globalization—the integration of the world econ-

statistics agencies (see About the data for table

• Trade in merchandise is the sum of merchandise

omy— has been a persistent theme of the past 25

6.12). FDI data are recorded on a directional basis,

exports and imports. • Trade in services is the sum

years. Growth of cross-border economic activity has

as an inward flow to the economy of the direct invest-

of services exports and imports. • Financing through

changed countries’ economic structure and political

ment enterprise, and as an outward flow from the

international capital markets is the sum of the abso-

and social organization. Not all effects of globaliza-

economy of the direct investor. Net flows refer to

lute values of new bond issuance, syndicated bank

tion can be measured directly. But the scope and

new investments during the reporting period netted

lending, and new equity placements. • Foreign direct

pace of change can be monitored along four key

against disinvestments.

investment net inflows and outflows are net inflows

The data on workers’ remittances and compensa-

and outflows of FDI (equity capital, reinvestment of

tion of employees are the sum of three items defined

earnings, and other short- and long-term capital).

Trade data are based on gross flows that capture

in the IMF’s Balance of Payments Manual, 5th edi-

• Workers’ remittances and compensation of employ-

the two-way flow of goods and services. In conven-

tion: workers’ remittances, compensation of employ-

ees received are current transfers by migrant work-

tional balance of payments accounting, exports

ees, and migrants’ transfers. The distinction among

ers and wages and salaries of nonresident workers.

are recorded as a credit and imports as a debit.

these three items is not always consistent in the

• Net migration is the number of immigrants minus

The data on merchandise trade are from the World

data reported by countries to the IMF. In some cases

the number of emigrants, including citizens and nonciti-

Trade Organization (WTO), which obtains data from

countries compile data on the basis of the citizenship

zens, for the five-year period. • International migrant

national statistical offices and the International

of migrant workers rather than their residency status.

stock is the number of people born in a country other

Monetary Fund’s (IMF) International Financial Sta-

Some countries also report remittances entirely as

than that in which they live, including refugees. • Emi-

tistics, supplemented by the Comtrade database and

worker’s remittances or compensation of employees.

gration of people with tertiary education to OECD

publications or databases of regional organizations,

Following the fifth edition of the Balance of Payments

countries is adults ages 25 and older, residing in an

specialized agencies, economic groups, and private

Manual in 1993, migrants’ transfers are considered

OECD country other than that in which they were born,

sources. Because of differences in timing and defi-

a capital transaction, but previous editions regarded

with at least one year of tertiary education. • Interna-

nitions, trade flow estimates from customs reports

them as current transfers. For these reasons the

tional voice traffic is the sum of international incoming

and balance of payments may differ. See tables 4.4

figures presented in the table take all three items

and outgoing telephone traffic (in minutes) divided by

and 4.5 for data on the main trade components of

into account. See About the data for table 6.18 for

total population. • International Internet bandwidth

merchandise trade and tables 4.6 and 4.7 for the

more information.

is the contracted capacity of international connections

dimensions: trade in goods and services, financial flows, movement of people, and communication.

same data on services trade.

Migration has increased in importance, accounting

between countries for transmitting Internet traffic.

Financing through international capital markets

for a substantial part of global integration. Data on

includes gross bond issuance, bank lending, and new

net migration are estimated by the United Nations

equity placement as reported by Dealogic, a com-

Population Division, based on data on immigrant

pany specializing in the investment banking industry.

stock and on fertility and mortality assumptions, tak-

Data on merchandise trade are from the WTO’s

In financial accounting inward investment is a credit

ing into account the migration history of a country or

Annual Report. Data on trade in services are from the

and outward investment a debit. Gross flow is a bet-

area, the migration policy of a country, and the influx

International Monetary Fund’s (IMF) Balance of Pay-

ter measure of integration than net flow because

of refugees in recent periods. The estimates of the

ments database. Data on international capital market

gross flow shows the total value of financial trans-

international migrant stock are derived from data on

financing are based on data from Dealogic. Data on

actions over a period, while net flow is the sum of

people who reside in one country but were born in

FDI are based on balance of payments data from the

credits and debits and represents a balance in which

another, mainly from population censuses (see About

IMF, supplemented by staff estimates using data from

many transactions are canceled out. Components of

the data and Definitions for table 6.18).

the United Nations Conference on Trade and Develop-

Data sources

One negative effect of migration is “brain drain”—

ment and official national sources. Data on workers’

emigration of highly educated people. The table

remittances are World Bank staff estimates based

Foreign direct investment (FDI) includes equity

shows data on emigration of people with tertiary

on IMF balance of payments data. Data on net migra-

investment, reinvested earnings, and short- and

education, drawn from Docquier, Lowell, and Mar-

tion are from the United Nations Population Division’s

long-term loans between parent firms and foreign

fouk (2009), who analyzed skilled migration using

World Population Prospects: The 2008 Revision. Data

affiliates. Distinguished from other kinds of interna-

data from censuses and registers of Organisation

on international migrant stock are from the United

tional investment, FDI establishes a lasting interest

for Economic Development and Co-operation (OECD)

Nations Population Division’s Trends in Total Migrant

in or effective management control over an enter-

countries and provide data disaggregated by gender

Stock: The 2008 Revision. Data on emigration of

prise in another country. FDI may be understated

for 1990 and 2000.

people with tertiary education are from Docquier, Low-

financing through international capital markets are reported in U.S. dollars by market sources.

in developing countries because some fail to report

Well developed communications infrastructure

ell, and Marfouk’s “A Gendered Assessment of Highly

reinvested earnings and because the definition of

attracts investments and allows investors to capi-

Skilled Emigration” (2009). Data on international voice

long-term loans differs by country. However, data

talize on benefits of the digital age. See About the

traffic and international Internet bandwidth are from

quality and coverage are improving as a result of

data for tables 5.11 and 5.12 for more information.

the International Telecommunication Union’s World

continuous efforts by international and national

Telecomunication Development Report database.

2011 World Development Indicators

327

6.2

Growth of merchandise trade Export volume

Import volume

Export value

Import value

average annual % growth

1990–2000

Afghanistana Albania Algeria Angola Argentina Armenia Australiaa Austriaa Azerbaijan Bangladesh Belarusb Belgiuma Benin Bolivia Bosnia and Herzegovina Botswana Brazil Bulgaria Burkina Faso Burundi Cambodia Cameroon Canadaa Central African Republic Chada Chile China† Hong Kong SAR, China Colombia Congo, Dem. Rep. Congo, Rep. Costa Rica Côte d’Ivoire Croatia Cuba Czech Republic Denmarka Dominican Republic Ecuador Egypt, Arab Rep. El Salvador Eritrea Estonia Ethiopia Finlanda Francea Gabon Gambia, The Georgia Germanya Ghana Greecea Guatemala Guinea Guinea-Bissaua Haiti Honduras †Data for Taiwan, China

328

2000–09

.. .. 2.8 6.2 8.4 .. 7.3 6.2 .. 12.9 .. 6.0 1.0 2.8 .. 4.8 5.1 .. 13.2 8.6 .. 0.3 9.1 20.0 –0.9 11.1 13.8 8.4 4.5 –1.8 6.6 14.0 5.0 .. .. .. 5.4 3.9 6.3 –0.2 2.9 –28.3 .. 10.5 .. 8.3 5.2 –11.6 .. 6.5 7.7 8.9 8.5 5.0 12.2 12.6 2.5 3.1

2011 World Development Indicators

15.2 .. –0.1 12.9 5.9 .. 7.6 4.9 .. 11.0 6.0 2.9 5.9 9.9 .. 2.9 7.6 .. 11.7 –4.2 12.8 –1.8 –0.7 –3.7 31.7 5.1 21.9 7.1 6.6 8.3 1.2 7.6 0.9 .. 1.5 .. 2.7 –0.7 8.2 10.2 2.4 –8.7 .. 7.8 .. 4.9 –1.2 –3.0 .. 5.6 4.8 .. 8.8 –7.4 3.6 5.9 3.5 7.6

1990–2000

.. .. –0.8 7.1 17.7 .. 9.2 5.6 .. 5.9 .. 5.7 8.2 9.1 .. 4.0 16.7 .. 3.6 4.0 .. 5.0 9.0 4.3 2.0 10.7 12.8 8.9 8.5 4.6 4.9 14.9 –0.3 .. .. .. 5.8 11.6 5.9 1.8 7.6 –3.2 .. 7.3 .. 6.6 2.5 0.1 .. 4.9 8.6 9.3 10.0 –1.4 –16.0 13.3 12.7 4.8

2000–09

3.9 .. 12.7 20.1 11.3 .. 7.5 4.2 .. 4.6 10.1 3.5 6.8 7.8 .. 5.1 7.2 .. 7.3 10.4 9.4 3.7 3.3 5.7 6.5 11.3 15.4 6.9 11.4 14.6 17.7 7.6 6.6 .. 8.2 .. 3.7 2.7 12.3 8.8 4.2 –5.2 .. 17.5 .. 6.4 7.2 2.2 .. 5.1 10.0 .. 5.8 3.4 7.2 2.3 4.3 2.3

1990–2000

–0.2 .. 2.0 6.2 10.1 .. 5.7 .. .. 15.8 .. 4.8 3.3 4.3 .. 4.8 5.9 .. 12.9 –4.3 26.9 –3.6 9.4 3.5 –3.5 9.4 14.5 8.3 7.3 –7.2 7.5 17.0 6.1 .. –1.7 .. 4.1 4.2 6.8 0.7 9.0 –31.0 .. 10.7 .. 4.9 0.8 –12.3 .. 3.7 9.0 8.2 10.1 0.6 18.6 12.2 5.3 7.2

Net barter terms of trade index

2000 = 100

2000–09

1990–2000

2000–09

1995

2009

24.0 .. 16.3 30.7 12.2 .. 20.0 .. .. 12.5 18.4 10.9 14.0 21.9 .. 7.4 16.2 .. 17.1 6.2 15.2 11.2 5.6 –0.5 49.6 18.3 23.7 7.9 14.9 18.8 16.9 7.5 12.4 .. 11.9 .. 9.8 2.3 17.3 24.6 4.7 –5.1 .. 17.8 .. 10.5 13.8 2.1 .. 11.6 16.6 .. 13.1 6.6 9.1 8.6 5.8 8.0

20.6 .. –1.3 7.8 17.0 .. 8.7 .. .. 10.4 .. 5.3 9.7 9.7 .. 4.2 12.6 .. 3.6 –6.9 25.2 2.1 8.9 0.2 0.5 10.3 13.0 8.8 9.7 –0.5 8.7 13.9 3.0 .. 2.5 .. 4.9 12.0 7.8 4.7 10.9 –0.2 .. 7.3 .. 3.7 2.2 0.2 .. 2.9 8.3 8.2 10.4 –2.6 –15.7 14.4 12.8 8.5

9.4 .. 18.8 24.6 14.4 .. 12.3 .. .. 13.0 19.4 11.4 16.1 13.3 .. 11.5 14.3 .. 15.6 15.9 15.1 13.1 7.3 12.7 12.2 15.3 21.6 8.2 15.8 21.5 24.4 9.9 15.7 .. 13.7 .. 10.7 6.4 17.8 17.2 7.3 1.7 .. 25.1 .. 12.2 12.4 9.8 .. 10.9 16.5 .. 11.5 10.5 17.5 9.8 9.3 7.8

.. .. 57.9 80.8 91.6 .. 99.4 .. .. 111.8 .. 104.3 106.6 89.4 .. 89.3 110.4 .. 131.0 163.6 .. 90.4 103.2 193.0 92.6 135.6 101.9 99.1 86.8 79.8 52.0 104.6 122.0 .. .. .. 102.1 98.2 80.6 116.3 121.1 101.7 .. 151.0 110.6 106.4 125.4 100.0 .. 107.5 106.7 89.6 117.9 89.6 102.7 113.2 96.3 89.9

107.6 .. 161.0 170.8 126.0 .. 163.0 .. .. 64.5 121.0 103.1 83.1 136.9 .. 79.1 107.8 .. 78.6 137.9 85.0 121.6 114.8 78.5 136.0 166.7 79.7 97.6 114.4 112.0 147.5 87.2 140.4 .. 111.1 .. 102.9 96.8 109.7 128.1 99.1 73.3 .. 121.1 83.1 99.8 155.3 85.5 .. 105.9 178.4 90.8 91.4 143.3 66.0 70.6 81.9 69.2

Export volume

Import volume

Export value

Import value

average annual % growth

1990–2000

Hungarya India Indonesia Iran, Islamic Rep. Iraqa Irelanda Israela Italya Jamaica Japana Jordan Kazakhstana Kenya Korea, Dem. Rep.a Korea, Rep. Kosovo Kuwait Kyrgyz Republic Lao PDR Latviaa Lebanon Lesotho Liberiaa Libya Lithuania Macedonia, FYR Madagascar Malawi Malaysia Mali Mauritania Mauritius Mexico Moldova Mongolia Morocco Mozambique Myanmar Namibia Nepal Netherlandsa New Zealanda Nicaragua Niger Nigeria Norwaya Oman Pakistan Panama Papua New Guinea Paraguay Peru Philippines Polanda Portugala Puerto Rico Qatar

10.1 6.9 9.1 .. .. 15.2 9.7 4.8 2.2 2.6 4.7 .. 3.9 .. 15.8 .. .. .. .. 7.2 .. 13.3 .. .. .. .. 4.1 2.7 13.6 10.3 1.9 2.7 15.5 .. .. 7.5 15.2 15.5 2.4 .. 8.0 4.7 10.4 3.1 3.3 6.6 4.0 2.5 6.0 –7.7 –0.2 9.4 16.0 9.8 0.3 .. ..

2000–09

10.7 12.3 8.7 2.4 1.0 1.9 3.7 0.3 0.3 2.4 4.2 .. 5.1 4.3 12.4 .. 4.6 .. 9.9 .. 12.9 14.7 –6.3 4.5 .. .. 2.8 5.7 5.8 2.0 10.4 3.2 2.7 .. 4.5 0.1 12.5 6.7 7.3 –1.5 4.6 3.0 9.1 –2.6 3.2 0.2 –1.3 7.0 1.5 –3.5 14.5 8.1 2.6 11.8 –2.0 .. 4.8

1990–2000

11.6 9.0 2.9 .. .. 11.3 8.9 4.2 .. 5.3 3.8 .. 7.4 .. 10.0 .. .. .. .. .. .. 3.1 .. 0.0 .. .. 4.5 –2.4 10.6 6.4 4.2 3.4 13.2 .. .. 7.2 1.0 13.8 7.7 .. 8.4 6.0 9.3 –2.1 2.5 7.8 .. 2.4 7.8 .. 5.4 10.6 11.3 19.0 0.5 .. ..

2000–09

8.0 18.4 5.7 10.8 6.8 0.9 1.8 0.6 1.2 1.7 6.8 .. 8.6 –3.1 7.2 .. 9.8 .. 7.7 .. 4.0 7.7 5.4 16.6 .. .. 10.6 8.0 5.2 8.7 11.9 6.6 3.5 .. 12.0 8.6 8.7 –1.0 11.0 2.9 4.5 5.8 5.7 10.0 14.6 5.7 11.8 8.0 9.7 7.0 15.5 9.7 0.6 9.4 –1.4 .. 25.6

1990–2000

10.1 5.3 8.1 1.2 118.9 13.8 10.0 4.6 2.2 2.1 6.6 .. 6.3 –8.5 10.1 .. 16.5 .. 15.4 11.8 4.6 12.4 –14.5 –2.6 .. .. 8.5 0.9 12.2 6.3 –1.9 2.2 16.1 .. 0.7 7.2 10.2 14.4 0.9 11.0 5.7 4.3 10.3 0.0 2.9 5.7 5.7 4.3 9.4 3.7 1.7 8.9 18.8 9.5 –3.0 .. 10.1

6.2

global links

Growth of merchandise trade

Net barter terms of trade index

2000–09

1990–2000

2000–09

16.2 20.3 10.9 18.0 17.2 5.4 8.7 9.3 6.0 3.2 16.3 .. 12.6 10.9 12.7 .. 20.5 .. 18.3 .. 21.7 15.0 –0.4 21.2 .. .. 6.0 10.9 9.6 15.4 24.0 3.1 7.0 .. 21.3 10.3 22.4 17.2 14.9 4.0 11.5 9.6 12.2 15.9 19.7 12.8 14.7 10.0 3.4 13.8 19.0 21.6 3.0 21.8 4.0 .. 21.4

11.8 7.9 2.7 –4.8 70.3 10.9 8.2 3.2 6.9 5.2 5.1 .. 6.0 1.0 7.1 .. 5.5 .. 12.7 .. 8.7 2.0 2.6 –1.4 .. .. 6.4 –0.6 9.5 4.7 –1.6 3.3 14.2 .. 0.5 5.5 1.1 22.6 3.9 9.3 5.5 5.9 11.6 0.8 3.1 4.4 6.1 3.1 8.7 –0.8 6.7 12.7 12.5 17.0 –2.5 .. 7.4

14.3 25.3 15.6 18.4 13.7 5.4 7.1 10.2 9.2 8.0 17.2 .. 17.7 6.3 13.0 .. 14.4 .. 14.5 .. 11.9 12.4 9.9 23.9 .. .. 17.6 15.4 8.7 17.1 18.7 9.5 6.8 .. 20.6 16.3 16.3 6.5 15.5 12.4 11.1 10.5 11.0 18.6 21.7 12.5 18.1 18.3 13.6 15.8 19.4 17.3 5.6 18.4 4.5 .. 30.9

2000 = 100 1995

2009

104.3 108.0 90.4 .. .. 103.9 92.1 96.6 .. 114.9 115.6 .. 103.9 .. 138.5 .. .. .. .. .. .. 100.0 .. .. .. .. 79.6 105.7 108.6 109.6 102.2 88.5 92.5 .. .. 89.1 151.1 214.3 82.6 .. 97.6 99.0 128.9 121.4 55.6 60.3 .. 119.2 100.0 .. 118.3 123.4 80.2 102.4 104.7 .. ..

95.6 99.4 63.2 132.4 140.8 96.6 102.7 103.3 77.1 74.4 120.4 .. 94.7 83.9 68.6 .. 156.1 .. 103.9 .. 109.1 78.3 111.4 140.4 .. .. 75.5 94.2 99.7 165.4 150.9 81.3 104.0 .. 170.2 137.4 98.2 117.1 113.5 80.7 102.5 111.0 83.9 185.2 145.3 128.6 150.1 63.4 92.4 164.1 104.9 129.1 72.0 107.1 107.6 .. 173.1

2011 World Development Indicators

329

6.2

Growth of merchandise trade Export volume

Import volume

Export value

Import value

average annual % growth

1990–2000

Romania Russian Federation Rwanda Saudi Arabia Senegal Serbia Sierra Leonea Singapore Slovak Republic Slovenia Somaliaa South Africa Spaina Sri Lanka Sudan Swaziland Swedena Switzerlanda Syrian Arab Republic Tajikistan Tanzania Thailand Timor-Leste Togo Trinidad and Tobago Tunisia Turkey Turkmenistan Uganda Ukraine United Arab Emirates United Kingdoma United Statesa Uruguay Uzbekistan Venezuela, RB Vietnam West Bank and Gaza Yemen, Rep. Zambia Zimbabwe

2000–09

.. .. –8.0 2.9 10.6 .. .. 11.7 .. .. .. 4.5 11.4 7.4 12.6 4.0 8.9 3.7 2.2 .. 6.0 9.6 .. 9.1 .. 5.7 10.7 .. 17.8 .. .. 6.3 6.6 6.1 .. 5.2 .. .. .. 6.1 8.8

.. .. 3.6 0.5 0.8 .. 28.7 10.9 .. .. 0.4 0.5 3.1 3.1 8.3 2.5 3.5 3.7 0.4 .. 6.4 7.4 .. 3.2 2.8 7.7 11.5 .. 15.7 .. 7.8 1.1 4.0 8.1 .. –1.9 11.8 .. –4.7 8.9 –5.1

1990–2000

.. .. 0.8 .. 4.9 .. .. 8.3 .. .. .. 7.6 9.3 8.0 8.4 3.1 6.4 4.2 .. .. –2.0 2.6 .. 6.0 .. 4.3 11.1 .. 22.4 .. .. 6.5 9.1 10.5 .. 4.8 .. .. 4.4 2.9 8.0

2000–09

.. .. 15.2 11.4 7.1 .. 3.1 8.0 .. .. 5.4 6.6 4.7 1.7 17.9 4.3 4.4 2.5 12.5 .. 11.8 7.8 .. –1.7 2.8 5.0 9.8 .. 8.8 .. 15.9 3.2 2.9 5.9 .. 12.3 12.8 .. 9.5 15.1 –2.1

1990–2000

.. .. –4.0 3.1 4.0 .. .. 9.9 .. .. 2.3 2.5 8.6 11.3 14.0 5.9 7.4 4.4 0.9 .. 6.4 10.5 .. 6.6 6.8 6.0 9.1 .. 15.6 .. 6.6 6.2 7.2 5.2 .. 5.4 22.7 .. 20.6 –4.6 3.4

2011 World Development Indicators

2000 = 100

2000–09

1990–2000

2000–09

1995

2009

.. .. 17.8 17.6 9.4 .. 35.2 12.6 .. .. 8.3 12.8 10.6 6.3 24.7 8.9 9.7 5.7 14.1 .. 17.0 12.6 .. 9.9 17.9 13.4 19.3 .. 25.9 .. 20.9 6.2 6.6 14.3 .. 13.9 19.7 .. 9.9 25.7 2.8

.. .. –1.7 0.8 3.6 .. .. 7.8 .. .. 4.5 5.9 6.2 8.9 9.8 5.0 5.4 3.6 3.6 .. 0.1 5.0 .. 5.5 12.1 5.2 10.3 .. 21.0 .. 10.7 6.5 9.5 10.1 .. 5.2 22.7 .. 0.6 1.3 1.9

.. .. 22.7 17.0 16.5 .. 14.3 12.0 .. .. 13.0 15.7 11.8 9.1 23.5 10.2 12.1 4.4 20.5 .. 20.5 13.0 .. 14.8 12.1 11.7 18.4 .. 16.0 .. 21.6 7.9 6.5 13.3 .. 15.7 21.2 .. 18.4 21.8 7.4

.. .. 110.1 .. 156.3 .. .. 104.4 .. .. .. 106.0 104.3 99.0 100.0 100.0 110.2 96.4 .. .. 98.0 116.0 .. 99.1 .. 95.8 105.7 .. 197.2 .. .. 100.1 103.3 116.2 .. 63.4 .. .. .. 189.7 96.8

.. .. 155.3 175.6 99.2 .. 64.6 82.6 .. .. 101.3 135.0 107.2 78.5 152.5 112.8 89.6 106.6 148.3 .. 121.1 97.1 .. 28.6 131.0 94.3 95.0 .. 120.4 .. 134.7 104.0 99.0 98.5 .. 187.1 97.4 .. 126.6 155.9 90.9

a. Data are from the International Monetary Fund’s International Financial Statistics database. b. Data are from national sources.

330

Net barter terms of trade index

About the data

6.2

global links

Growth of merchandise trade Definitions

Data on international trade in goods are available

from national and international sources such as the

• Export and import volumes are indexes of the

from each country’s balance of payments and

IMF’s International Financial Statistics database,

quantity of goods traded. They are derived from

customs records. While the balance of payments

the United Nations Economic Commission for Latin

UNCTAD’s volume index series and are the ratio of

focuses on the financial transactions that accom-

America and the Caribbean, the U.S. Bureau of Labor

the export or import value indexes to the correspond-

pany trade, customs data record the direction of

Statistics, Japan Customs and Bank of Japan, and

ing unit value indexes. Unit value indexes are based

trade and the physical quantities and value of goods

UNCTAD’s Commodity Price Statistics. The IMF also

on data reported by countries that demonstrate

entering or leaving the customs area. Customs data

compiles data on trade prices and volumes in its

consistency under UNCTAD quality controls, supple-

may differ from data recorded in the balance of pay-

International Financial Statistics (IFS) database.

mented by UNCTAD’s estimates using the previous

ments because of differences in valuation and time

Unless otherwise noted, the growth rates and

year’s trade values at the Standard International

of recording. The 1993 United Nations System of

terms of trade in the table were calculated from

Trade Classification three-digit level as weights.

National Accounts and the fifth edition of the Inter-

index numbers compiled by UNCTAD. The growth

To improve data coverage, especially for the latest

national Monetary Fund’s (IMF) Balance of Payments

rates and terms of trade for selected economies

periods, UNCTAD constructs a set of average prices

Manual (1993) attempted to reconcile definitions and

were calculated from index numbers compiled in

indexes at the three-digit product classification of the

reporting standards for international trade statistics,

the IMF’s International Financial Statistics. In some

Standard International Trade Classification revision 3

but differences in sources, timing, and national prac-

cases price and volume indexes from different

using UNCTAD’s Commodity Price Statistics, interna-

tices limit comparability. Real growth rates derived

sources vary significantly as a result of differences

tional and national sources, and UNCTAD secretariat

from trade volume indexes and terms of trade based

in estimation procedures. Because the IMF does not

estimates and calculates unit value indexes at the

on unit price indexes may therefore differ from those

publish trade value indexes, for selected economies

country level using the current year’s trade values as

derived from national accounts aggregates.

the trade value indexes were derived from the vol-

weights. For economies for which UNCTAD does not

ume and price indexes. All indexes are rescaled to

publish data, the export and import volume indexes

a 2000 base year.

(lines 72 and 73) in the IMF’s International Financial

Trade in goods, or merchandise trade, includes all goods that add to or subtract from an economy’s material resources. Trade data are collected on the

The terms of trade measures the relative prices of

Statistics are used to calculate the average annual

basis of a country’s customs area, which in most

a country’s exports and imports. There are several

growth rates. • Export and import values are the cur-

cases is the same as its geographic area. Goods

ways to calculate it. The most common is the net

rent value of exports (free on board, f.o.b.) or imports

provided as part of foreign aid are included, but

barter (or commodity) terms of trade index, or the

(cost, insurance, and freight, c.i.f.), converted to U.S.

goods destined for extraterritorial agencies (such

ratio of the export price index to the import price

dollars and expressed as a percentage of the aver-

as embassies) are not.

index. When a country’s net barter terms of trade

age for the base period (2000). UNCTAD’s export or

index increases, its exports become more valuable

import value indexes are reported for most econo-

or its imports cheaper.

mies. For selected economies for which UNCTAD

Collecting and tabulating trade statistics are difficult. Some developing countries lack the capacity to report timely data, especially landlocked coun-

does not publish data, the value indexes are derived

tries and countries whose territorial boundaries are

from export or import volume indexes (lines 72 and

porous. Their trade has to be estimated from the data

73) and corresponding unit value indexes of exports

reported by their partners. (For further discussion of

or imports (lines 74 and 75) in the IMF’s International

the use of partner country reports, see About the

Financial Statistics. • Net barter terms of trade index

data for table 6.3.) Countries that belong to common

is calculated as the percentage ratio of the export

customs unions may need to collect data through

unit value indexes to the import unit value indexes,

direct inquiry of companies. Economic or political

measured relative to the base year 2000.

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,

Data sources

which maintains the Commodity Trade (Comtrade)

Data on trade indexes are from UNCTAD’s annual

and Monthly Bulletin of Statistics databases. The

Handbook of Statistics for most economies and

United Nations Conference on Trade and Develop-

from the IMF’s International Financial Statistics for

ment (UNCTAD) compiles international trade sta-

selected economies.

tistics, including price, value, and volume indexes,

2011 World Development Indicators

331

6.3

Direction and growth of merchandise trade

Direction of trade High-income importers

% of world trade, 2009

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

European Union

Japan

United States

Other highincome

Total

28.5 21.9 0.6 1.8 4.2 6.5 2.3 1.8 1.9 0.9 0.7 0.3 0.8 0.2 0.4 0.3 0.4 0.1 35.0

2.3 0.4 .. 0.4 1.5 1.6 1.3 0.8 0.1 0.1 0.1 0.0 0.1 0.0 0.0 0.0 0.1 0.0 4.0

6.5 2.3 0.8 .. 3.4 5.4 2.3 1.8 0.1 0.1 2.2 0.1 0.2 0.1 0.2 0.1 0.3 0.0 11.8

13.1 4.6 1.5 3.1 3.8 6.2 4.2 2.9 0.6 0.3 0.5 0.2 0.2 0.0 0.5 0.4 0.2 0.1 19.3

50.4 29.2 2.8 5.3 13.0 19.7 10.2 7.3 2.7 1.3 3.5 0.6 1.3 0.3 1.1 0.9 1.0 0.3 70.1

Low- and middle-income importers

% of world trade, 2009

332

East Asia & Pacific

Europe & Central Asia

Latin America & Caribbean

Middle East & N. Africa

South Asia

Sub-Saharan Africa

Total

8.2 1.2 1.4 0.8 4.8 2.7 1.7 0.6 0.2 0.2 0.4 0.2 0.2 0.0 0.2 0.2 0.1 0.1 11.7

2.6 2.0 0.0 0.1 0.4 1.6 0.5 0.4 1.0 0.3 0.1 0.0 0.1 0.0 0.0 0.0 0.0 0.0 4.3

3.3 0.7 0.2 1.8 0.5 1.7 0.5 0.4 0.0 0.0 1.0 0.3 0.0 0.0 0.0 0.0 0.1 0.0 5.2

1.4 0.9 0.0 0.1 0.4 0.9 0.3 0.2 0.3 0.1 0.1 0.0 0.2 0.0 0.1 0.1 0.0 0.0 2.3

1.5 0.4 0.1 0.2 0.9 1.1 0.5 0.3 0.1 0.0 0.1 0.0 0.2 0.0 0.1 0.1 0.1 0.0 2.5

1.0 0.6 0.1 0.1 0.3 0.8 0.3 0.3 0.0 0.0 0.1 0.0 0.0 0.0 0.1 0.1 0.2 0.1 1.8

18.2 5.7 1.8 3.2 7.5 9.2 3.9 2.4 1.6 0.6 1.8 0.6 0.7 0.1 0.5 0.4 0.7 0.2 27.5

2011 World Development Indicators

6.3

global links

Direction and growth of merchandise trade Nominal growth of trade High-income importers

average annual % growth, 1999–2009

European Union

Japan

United States

Other highincome

Total

9.1 9.3 2.8 5.4 11.3 16.7 18.9 26.4 19.8 20.3 12.5 12.6 13.3 13.8 14.5 16.5 11.7 10.4 10.2

6.6 4.5 .. 0.1 9.9 11.3 10.7 13.3 17.1 16.9 11.5 10.5 14.0 22.9 6.7 8.1 19.5 22.0 8.3

3.8 5.8 –0.6 .. 3.9 11.0 15.2 21.8 7.8 4.6 7.1 7.3 19.9 26.4 8.6 10.8 18.2 14.8 6.4

9.1 10.8 6.9 5.5 11.8 17.4 17.3 22.7 18.5 17.5 15.7 19.8 17.1 25.1 20.5 22.9 14.3 14.3 11.1

8.1 9.1 3.4 5.0 8.6 14.6 16.1 21.9 18.6 18.3 9.0 12.0 14.9 17.5 14.6 17.2 14.6 13.3 9.6

Low- and middle-income importers

average annual % growth, 1999–2009

East Asia & Pacific

Europe & Central Asia

Latin America & Caribbean

Middle East & N. Africa

South Asia

Sub-Saharan Africa

Total

15.4 15.8 12.6 12.0 17.0 22.6 21.4 27.4 18.7 19.0 30.9 32.2 25.0 42.4 26.1 27.9 20.5 28.0 17.3

19.4 19.0 27.1 13.5 22.7 22.9 37.9 40.9 20.1 19.3 20.3 21.9 17.8 12.5 15.9 14.2 24.7 18.7 20.8

8.0 8.8 8.9 6.5 12.6 17.6 26.6 31.3 20.4 21.0 14.5 16.6 15.2 8.4 21.8 24.2 21.8 12.0 10.7

13.6 11.8 11.9 12.1 19.2 23.2 25.5 30.5 22.9 21.5 17.4 20.8 24.9 21.6 23.7 26.8 13.8 20.4 16.4

19.6 15.9 12.1 20.3 22.5 25.2 27.2 35.3 23.2 19.8 25.1 20.3 34.1 60.7 19.9 20.1 15.8 20.7 21.2

13.0 12.0 10.8 13.1 15.6 21.7 26.9 31.7 22.1 14.6 24.1 26.8 25.0 10.1 24.6 25.8 15.7 12.9 16.0

14.2 14.7 12.4 8.8 17.5 22.1 25.2 32.2 20.7 19.6 17.9 20.6 24.7 17.2 22.7 24.2 21.0 17.0 16.3

a. Data for 1999 are based on imports from South Africa reported by other economies because data on exports for South Africa were not available.

2011 World Development Indicators

333

6.3

Direction and growth of merchandise trade

About the data

Definitions

The table provides estimates of the flow of trade in

Most countries report their trade data in national

• Merchandise trade includes all trade in goods;

goods between groups of economies. The data are

currencies, which are converted into U.S. dollars

trade in services is excluded. • High-income econo-

from the International Monetary Fund’s (IMF) Direc-

using the IMF’s published period average exchange

mies are those classified as such by the World Bank

tion of Trade database. All high-income economies

rate (series rf or rh, monthly averages of the mar-

(see front cover flap). • European Union is defined

and major developing economies report trade on

ket or official rates) for the reporting country or, if

as all high-income EU members: Austria, Belgium,

a timely basis, covering about 85 percent of trade

unavailable, monthly average rates in New York.

Cyprus, Czech Republic, Denmark, Estonia, Finland,

for recent years. Trade by less timely reporters and

Because imports are reported at cost, insurance,

France, Germany, Greece, Hungary, Ireland, Italy, Lux-

by countries that do not report is estimated using

and freight (c.i.f.) valuations, and exports at free on

embourg, Malta, the Netherlands, Portugal, Slovak

reports of trading partner countries. Because the

board (f.o.b.) valuations, the IMF adjusts country

Republic, Slovenia, Spain, Sweden, and the United

largest exporting and importing countries are reli-

reports of import values by dividing them by 1.10

Kingdom. • Other high-income economies include

able reporters, a large portion of the missing trade

to estimate equivalent export values. The accuracy

all high-income economies (both Organisation for

flows can be estimated from partner reports. Part-

of this approximation depends on the set of part-

Economic Co-operation and Development members

ner country data may introduce discrepancies due to

ners and the items traded. Other factors affecting

and others) except the high-income European Union,

smuggling, confidentiality, different exchange rates,

the accuracy of trade data include lags in reporting,

Japan, and the United States. • Low- and middle-

overreporting of transit trade, inclusion or exclusion

recording differences across countries, and whether

income regional groupings are based on World

of freight rates, and different points of valuation and

the country reports trade according to the general or

Bank classifications (see back cover flap for regional

times of recording.

special system of trade. (For further discussion of

groupings) and may differ from those used by other

the measurement of exports and imports, see About

organizations.

In addition, estimates of trade within the European Union (EU) have been significantly affected by

the data for tables 4.4 and 4.5.)

changes in reporting methods following the creation

The regional trade flows in the table are calculated

of a customs union. The current system for collect-

from current price values. The growth rates are in

ing data on trade between EU members—Intrastat,

nominal terms; that is, they include the effects of

introduced in 1993—has less exhaustive coverage

changes in both volumes and prices.

than the previous customs–based system and has resulted in some problems of asymmetry (estimated imports are about 5 percent less than exports). Despite these issues, only a small portion of world trade is estimated to be omitted from the IMF’s Direction of Trade Statistics Yearbook and Direction of Trade database.

More than half of the world’s merchandise trade takes place between high-income economies. But low- and middle-income economies’ participation in the global trade has increased in the past 15 years

1996

Low- and middle-income to low- and middle-income 4.5%

Unspecified 3.0%

6.3a

2009

Low- and middle-income to low- and middle-income 9.2%

Unspecified 2.5%

Low- and middle-income to high-income 14.1%

High-income to low- and middleincome 16.0%

High-income to high-income 62.4%

Low- and middle-income to high-income 19.7%

High-income to high-income 50.4%

High-income to low- and middleincome 18.2%

Data sources Data on the direction and growth of merchandise trade were calculated using the IMF’s Direction

Trade among low- and middle-income economies accounted for about 9.2 percent of the world’s merchandise trade in 2009, compared with 4.5 percent in 1996. The share of trade from low- and middleincome economies to high-income economies increased 9.8 percentage points between 1996 and 2009. Source: World Bank staff calculations based on data from the International Monetary Fund’s Direction of Trade database.

334

2011 World Development Indicators

of Trade database. Regional and income group classifications are according to the World Bank classification of economies as of July 1, 2010, and are as shown on the cover flaps of this report.

6.4

global links

High-income economy trade with low- and middle-income economies Exports to low-income economies High-income economies

Total ($ billions) % of total exports Food Cereals Agricultural raw materials Ores and nonferrous metals Fuels Crude petroleum Petroleum products Manufactured goods Chemical products Iron and steel Machinery and transport equipment Furniture Textiles Footwear Other Miscellaneous goods

European Union

Japan

United States

1999

2009

1999

2009

1999

2009

1999

2009

32.0

86.9

15.7

40.2

3.5

6.1

3.4

12.0

12.5 4.0 2.5 1.0 4.9 0.1 4.4 77.1 12.3 2.6 44.2 0.4 5.9 0.2 11.6 2.0

10.5 4.1 2.1 1.4 11.5 0.4 10.7 67.3 11.0 2.9 42.0 0.3 2.2 0.1 9.0 7.2

14.2 3.2 1.8 0.9 3.1 0.1 2.7 78.4 15.2 2.3 43.6 0.6 2.5 0.2 14.0 1.5

9.8 3.0 1.5 1.3 15.6 0.0 15.3 68.0 12.0 2.2 40.4 0.4 1.7 0.2 11.1 3.8

0.4 0.2 1.2 0.6 0.3 0.0 0.3 96.4 3.4 6.9 74.2 0.1 3.0 0.0 8.8 1.2

0.3 0.2 2.3 0.7 0.3 0.0 0.1 94.4 3.1 8.1 74.5 0.1 1.2 0.0 7.4 2.1

25.2 17.4 4.8 0.6 1.8 0.0 1.2 62.0 10.6 0.8 37.9 0.3 5.2 0.2 6.8 5.5

17.2 12.6 4.6 1.5 5.9 0.0 5.1 58.7 7.3 1.2 41.9 0.2 1.0 0.2 6.9 12.2

40.2

100.4

20.1

47.7

2.1

2.9

11.8

34.5

23.0 0.7 5.5 5.1 23.3 21.2 1.7 41.5 0.6 0.5 1.9 0.2 30.1 0.4 7.9 1.7

15.1 0.7 2.4 5.1 39.5 34.1 1.6 33.2 0.8 0.1 1.6 0.2 27.4 0.7 2.4 4.8

31.9 0.3 6.8 5.6 13.3 12.5 0.5 41.2 0.9 0.4 2.4 0.2 25.5 0.5 11.3 1.2

22.0 0.3 3.6 5.0 29.6 23.5 0.2 38.9 1.2 0.1 2.9 0.1 31.0 0.9 2.7 0.9

37.1 0.0 9.9 17.1 8.9 7.6 0.1 24.0 0.3 2.3 1.6 0.1 15.0 1.7 2.9 3.0

23.7 0.0 2.7 23.3 23.0 5.3 5.0 26.4 0.6 0.8 0.8 0.1 14.3 7.6 2.1 1.0

7.5 0.1 1.1 2.1 41.4 36.3 4.6 47.6 0.1 0.4 0.3 0.2 42.3 0.0 4.4 0.4

4.3 0.1 0.6 0.6 64.3 61.2 2.8 29.5 0.2 0.0 0.1 0.3 28.0 0.1 1.0 0.7

3.0 9.1 2.6 0.7 1.3 2.0 1.2 6.3 2.1 1.1 0.2 0.1 0.0 3.6 6.4 0.7 0.0

1.3 2.6 5.3 0.1 0.0 0.5 0.0 1.1 1.3 0.2 0.0 0.0 0.0 1.6 4.4 1.1 0.0

5.2 3.8 2.7 0.4 0.1 0.4 0.3 0.9 5.9 0.3 0.4 0.2 0.6 10.7 12.5 1.0 0.4

Imports from low-income economies Total ($ billions) % of total imports Food Cereals Agricultural raw materials Ores and nonferrous metals Fuels Crude petroleum Petroleum products Manufactured goods Chemical products Iron and steel Machinery and transport equipment Furniture Textiles Footwear Other Miscellaneous goods

Simple applied tariff rates on imports from low-income economies (%)a Average Food Cereals Agricultural raw materials Ores and nonferrous metals Fuels Crude petroleum Petroleum products Manufactured goods Chemical products Iron and steel Machinery and transport equipment Furniture Textiles Footwear Other Miscellaneous goods

4.3 6.8 16.9 3.3 1.2 3.1 1.3 4.5 4.1 2.7 4.2 1.7 3.2 7.5 7.2 2.1 0.7

2.7 3.0 5.8 1.6 1.1 1.2 0.5 1.6 2.8 2.1 2.4 1.3 2.2 4.5 4.4 1.6 0.8

1.3 3.1 24.0 0.1 0.2 0.2 0.0 0.4 1.1 1.2 0.8 0.4 0.1 2.5 2.9 0.6 0.2

0.8 0.7 0.1 0.1 0.2 0.0 0.0 0.0 0.9 0.3 0.2 0.2 0.1 2.4 1.7 0.2 0.2

2011 World Development Indicators

3.5 2.3 0.4 0.2 0.1 0.2 0.0 0.4 4.0 0.1 0.0 0.1 0.9 7.3 8.7 0.7 0.0

335

6.4

High-income economy trade with low- and middle-income economies

Exports to middle-income economies High-income economies

European Union

Japan

United States

1999

2009

1999

2009

1999

2009

1999

2009

646.4

1845.4

224.9

700.5

89.0

222.8

184.4

346.7

6.7 1.7 1.8 2.0 3.1 0.5 1.9 83.8 11.7 2.5 48.8 0.5 6.2 0.1 14.1 2.6

6.5 1.4 1.9 4.5 6.2 0.5 4.4 75.2 14.0 3.4 41.8 0.4 2.6 0.2 13.3 5.7

8.1 1.5 1.3 1.5 1.7 0.2 1.3 85.7 13.7 2.4 46.3 0.9 5.4 0.3 16.7 1.6

6.1 1.1 1.5 2.7 3.2 0.1 2.7 82.5 14.6 3.3 45.4 0.7 3.3 0.4 14.8 4.0

0.4 0.1 1.0 1.9 0.5 0.0 0.4 93.3 8.2 5.9 63.9 0.1 3.6 0.0 11.6 2.9

0.4 0.0 1.1 3.8 1.6 0.0 1.4 88.2 9.9 6.8 58.0 0.3 1.7 0.0 11.6 4.8

8.2 3.0 2.1 1.5 2.1 0.0 1.5 81.6 10.5 1.0 50.0 0.7 5.7 0.1 13.7 4.4

12.8 2.8 3.6 3.9 7.1 0.0 5.4 63.4 14.4 1.5 33.2 0.3 1.9 0.0 12.1 9.3

1,010.3

2,816.6

285.1

998.3

106.5

243.4

364.4

796.0

10.0 0.4 2.3 4.8 13.3 9.0 1.9 67.8 2.9 1.9 29.2 1.7 13.9 2.6 15.7 1.8

7.6 0.5 1.1 3.7 19.8 12.7 3.6 64.0 3.6 1.7 31.8 1.8 9.5 1.7 14.2 3.8

14.2 0.3 3.4 6.3 18.8 13.0 2.5 56.4 3.7 2.1 18.6 1.4 15.3 2.1 13.3 0.9

9.4 0.4 1.3 3.3 25.6 16.2 3.8 56.9 3.7 1.8 24.6 1.6 11.0 2.0 12.3 3.4

15.6 0.4 4.3 8.8 13.8 6.6 1.1 56.2 2.7 1.0 21.6 1.5 14.9 1.7 12.7 1.3

9.2 0.3 2.0 8.7 16.6 7.0 1.8 62.0 4.1 1.0 27.8 1.7 12.0 1.4 14.0 1.6

6.5 0.2 1.2 2.7 11.3 8.9 2.1 75.5 2.0 1.6 36.9 2.4 12.6 3.2 16.8 2.8

5.9 0.3 0.7 1.8 19.1 15.8 2.9 69.6 2.9 1.0 36.2 2.7 9.4 2.1 15.3 2.8

1.1 2.9 0.7 0.4 0.5 0.1 0.0 0.1 1.0 0.6 0.1 0.2 0.0 3.3 3.4 0.3 0.5

2.9 13.5 10.0 0.9 0.1 1.3 1.2 4.2 1.6 0.6 0.1 0.0 0.0 4.2 19.7 0.4 0.0

2.2 6.9 10.5 0.5 0.0 0.2 0.0 0.6 1.8 0.3 0.2 0.0 0.1 4.9 16.9 0.7 0.0

3.4 3.6 2.3 0.5 0.3 0.6 0.5 1.7 3.6 1.2 2.0 0.4 0.3 10.3 13.3 0.9 0.5

2.5 2.9 1.1 0.4 0.4 1.3 0.0 3.0 2.5 1.1 0.3 0.5 0.4 6.8 8.0 0.8 0.3

Imports from middle-income economies Total ($ billions) % of total imports Food Cereals Agricultural raw materials Ores and nonferrous metals Fuels Crude petroleum Petroleum products Manufactured goods Chemical products Iron and steel Machinery and transport equipment Furniture Textiles Footwear Other Miscellaneous goods

Simple applied tariff rates on imports from middle-income economies (%)a Average Food Cereals Agricultural raw materials Ores and nonferrous metals Fuels Crude petroleum Petroleum products Manufactured goods Chemical products Iron and steel Machinery and transport equipment Furniture Textiles Footwear Other Miscellaneous goods

5.6 10.3 15.2 2.5 1.9 2.8 1.5 5.5 5.2 3.6 3.6 3.0 5.0 9.7 11.6 3.8 1.7

a. Includes ad valorem equivalents of specific rates.

336

2011 World Development Indicators

3.2 4.3 6.7 1.9 1.3 1.5 0.4 2.1 3.1 2.0 1.6 1.9 3.2 6.0 6.4 2.3 0.9

3.9 9.7 22.1 1.0 1.4 0.9 0.0 2.9 3.4 3.3 2.3 1.6 0.7 7.6 8.6 2.3 1.2

About the data

6.4

global links

High-income economy trade with low- and middle-income economies Definitions

Developing economies are becoming increasingly

trade between developing economies has grown

The product groups in the table are defined in accor-

important in the global trading system. Since the

substantially over the past decade, a result of their

dance with SITC revision 2: food (0, 1, 22, and 4) and

early 1990s trade between high-income economies

increasing share of world output and liberalization of

cereals (04); agricultural raw materials (2 excluding

and low- and middle-income economies has grown

trade, among other influences.

22, 27, and 28); ores and nonferrous metals (27, 28,

faster than trade among high-income economies.

Yet trade barriers remain high. The table includes

and 68); fuels (3), crude petroleum (crude petroleum

The increased trade benefits consumers and pro-

information about tariff rates by selected product

oils and oils obtained from bituminous minerals;

ducers. But as was apparent at the World Trade Orga-

groups. Applied tariff rates are the tariffs in effect

333), and petroleum products (noncrude petroleum

nization’s (WTO) Ministerial Conferences in Doha,

for partners in preferential trade agreements such

and preparations; 334); manufactured goods (5–8

Qatar, in October 2001; Cancun, Mexico, in Septem-

as the North American Free Trade Agreement. When

excluding 68), chemical products (5), iron and steel

ber 2003; and Hong Kong SAR, China, in December

these rates are unavailable, most favored nation

(67), machinery and transport equipment (7), furni-

2005, achieving a more pro-development outcome

rates are used. The difference between most favored

ture (82), textiles (65 and 84), footwear (85), and

from trade remains a challenge. Doing so will require

nation and applied rates can be substantial. Simple

other manufactured goods (6 and 8 excluding 65,

strengthening international consultation. After the

averages of applied rates are shown because they

67, 68, 82, 84, and 85); and miscellaneous goods

Doha meetings negotiations were launched on ser-

are generally a better indicator of tariff protection

(9). • Exports are all merchandise exports by high-

vices, agriculture, manufactures, WTO rules, the

than weighted average rates are.

income economies to low-income and middle-income

environment, dispute settlement, intellectual prop-

The data on trade flows are from the United Nations

economies as recorded in the United Nations Sta-

erty rights protection, and disciplines on regional

Statistics Division’s Commodity Trade (Comtrade)

tistics Division’s Comtrade database. Exports are

integration. At the most recent negotiations in Hong

database. Partner country reports by high-income

recorded free on board (f.o.b.). • Imports are all

Kong SAR, China, trade ministers agreed to eliminate

economies were used for both exports and imports.

merchandise imports by high-income economies

subsidies of agricultural exports by 2013; to abolish

Because of differences in sources of data, timing,

from low–income and middle-income economies as

cotton export subsidies and grant unlimited export

and treatment of missing data, the numbers in the

recorded in the United Nations Statistics Division’s

access to selected cotton-growing countries in Sub-

table may not be fully comparable with those used

Commodity Trade (Comtrade) database. Imports

Saharan Africa; to cut more domestic farm supports

to calculate the direction of trade statistics in tables

include insurance and freight charges (c.i.f.). • High-,

in the European Union, Japan, and the United States;

6.3 and 6.5 or the aggregate flows in tables 4.4, 4.5,

middle-, and low-income economies are those

and to offer more aid to developing countries to help

and 6.2. Tariff data are from United Nations Confer-

classified as such by the World Bank as of July 1,

them compete in global trade.

ence on Trade and Development (UNCTAD)’s Trade

2010 (see front cover flap). • European Union is

Trade flows between high-income and low- and

Analysis and Information System (TRAINS) database.

defined as all high-income EU members: Austria, Bel-

middle-income economies reflect the changing mix of

Tariff line data were matched to Standard Interna-

gium, Cyprus, Czech Republic, Denmark, Estonia,

exports to and imports from developing economies.

tional Trade Classification (SITC) revision 2 codes to

Finland, France, Germany, Greece, Hungary, Ireland,

While food and primary commodities have continued

define commodity groups. For further discussion of

Italy, Luxembourg, Malta, the Netherlands, Portugal,

to fall as a share of high-income economies’ imports,

merchandise trade statistics, see About the data for

Slovak Republic, Slovenia, Spain, Sweden, and the

manufactures as a share of goods imports from both

tables 4.4, 4.5, 6.2, 6.3, and 6.5, and for informa-

United Kingdom.

low- and middle-income economies have grown. And

tion about tariff barriers, see table 6.8.

Low-income economies have a small market share in the global market of various commodities

6.4a

Exports from upper middle-income economies Exports from lower middle-income economies Exports from low-income economies

Share of world exports (percent) 70 60 50 40 30 20 10

Data sources

0 1990 2009

1990 2009

Agricultural products

Manufactured goods

Textiles

Fuels

Clothing

Footwear

Data on trade values are from United Nations Statistics Division’s Comtrade database. Data

Low-income economies specialize in labor-intensive sectors, but their share in the global market of labor intensive products is very small. Lower middle-income economies provided most of the textiles, clothing, and footwear traded globally in 2009. High-income economies accounted for the majority of trade in agricultural products and manufactured goods.

on tariffs are from UNCTAD’s TRAINS database

Source: World Bank staff estimates, based on data from United Nations Statistics Division’s Comtrade database.

at http://wits.worldbank.org.

and are calculated by World Bank staff using the World Integrated Trade Solution system, available

2011 World Development Indicators

337

6.5

Direction of trade of developing economies Exports

Imports

% of total merchandise exports To developing economies Within region 1999 2009

East Asia & Pacific 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 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

338

8.1 w 7.4 4.2 8.9 11.1 7.5 .. 9.9 .. 20.1 8.0 8.7 13.2 20.7 22.5 w 3.3 24.8 31.0 65.6 5.6 25.8 58.3 29.0 42.7 19.5 29.0 66.4 11.7 20.0 .. 46.1 8.7 52.1 38.5 51.4 14.4 w 45.1 38.4 23.0 20.3 24.1 12.4 8.8 2.8 21.6 60.9 22.2 2.5 7.7 2.9 3.2 31.9 23.6 62.0 16.2 53.0 15.2

11.9 w 3.6 6.6 19.6 22.4 50.8 .. 23.8 .. 57.6 10.1 16.2 27.0 20.2 19.9 w 5.1 35.7 12.4 46.5 5.9 27.4 66.3 26.1 80.0 24.5 31.5 62.3 16.0 14.5 32.3 36.7 13.3 45.2 42.9 69.3 18.8 w 42.3 64.4 22.4 16.3 29.3 26.2 24.9 17.4 42.3 44.0 37.7 9.7 29.0 5.0 6.3 47.7 45.9 69.6 15.5 44.1 12.0

2011 World Development Indicators

Outside region 1999 2009

7.2 w 0.3 8.6 0.1 8.1 42.0 0.6 6.6 14.5 13.4 0.1 1.8 6.0 6.2 11.3 w 0.1 10.5 5.3 11.3 .. 5.7 5.8 14.6 .. 1.3 1.6 1.7 8.1 11.5 .. .. 11.6 .. 21.8 .. 4.0 w 15.3 0.6 10.9 5.3 1.1 0.7 36.0 0.5 5.5 1.5 1.0 0.8 0.1 7.1 0.3 0.1 0.8 0.6 8.6 9.2 ..

15.5 w 1.4 17.7 1.5 14.1 35.7 0.2 10.9 4.1 22.1 1.3 2.5 11.9 6.7 14.2 w 6.9 7.0 12.6 10.6 .. 6.9 4.4 18.1 .. 3.6 1.8 1.9 6.6 12.2 1.9 .. 24.1 .. 26.0 .. 13.9 w 23.6 3.7 28.2 28.7 6.7 12.1 31.6 2.6 6.4 1.2 2.2 2.1 3.0 3.9 1.9 0.6 10.7 14.4 17.9 22.2 10.5

% of total merchandise imports From developing economies

To high-income economies 1999 2009

Within region 1999 2009

Outside region 1999 2009

From high-income economies 1999 2009

83.9 w 60.5 87.2 80.6 80.8 50.5 32.1 83.5 28.4 53.2 63.3 88.2 79.2 72.0 64.0 w 96.6 56.6 62.3 22.8 91.7 66.5 35.6 49.5 47.8 79.1 68.8 32.0 79.7 67.2 .. 50.7 74.9 25.2 39.6 40.2 77.6 w 39.5 59.4 64.3 67.9 73.4 26.9 55.3 96.5 72.2 37.1 74.2 96.9 82.6 89.5 96.1 62.4 73.6 30.3 75.1 36.8 63.2

11.0 w 38.0 6.8 8.0 14.6 34.8 81.8 12.7 19.1 49.2 11.6 12.4 14.7 17.8 27.9 w 12.3 32.4 46.3 66.5 4.0 28.4 55.7 46.9 46.6 25.3 31.2 59.6 12.9 29.7 .. 78.9 11.2 51.9 60.0 33.9 14.3 w 30.4 41.8 19.0 28.1 25.9 20.1 17.0 17.5 32.7 40.0 29.3 15.2 15.1 11.7 2.3 48.6 24.4 54.9 30.4 47.8 18.2

8.9 w 1.8 8.0 2.2 7.9 25.4 1.0 3.1 37.1 1.7 1.7 4.2 7.3 4.9 12.9 w 1.1 13.6 7.6 3.4 .. 7.1 3.4 6.6 10.1 4.0 4.3 1.6 5.4 13.8 .. .. 12.2 .. 5.3 .. 3.5 w 9.4 2.3 9.9 8.6 4.4 3.3 17.5 1.7 4.4 2.1 3.9 3.9 2.8 3.7 3.4 0.4 1.2 3.2 3.3 8.9 0.2

80.5 w 60.0 82.8 89.1 76.8 39.8 16.0 82.8 53.8 49.0 85.8 82.2 76.0 77.0 61.1 w 86.5 50.4 45.7 30.1 95.8 64.0 40.9 46.4 41.7 69.1 64.5 38.9 80.3 56.1 .. 18.6 72.9 35.0 34.5 63.4 78.0 w 58.4 55.7 71.0 49.9 68.7 41.1 65.5 80.7 61.9 56.3 65.7 80.5 72.2 81.4 93.7 45.4 60.7 41.7 66.2 42.8 69.4

73.7 w 96.5 77.9 51.2 64.0 13.7 18.0 65.5 34.3 14.3 50.4 79.7 60.7 69.1 55.7 w 87.7 56.3 77.3 42.9 92.4 64.7 29.4 43.2 9.5 72.8 55.2 34.9 77.9 55.9 57.4 15.1 59.1 36.3 29.8 13.1 66.0 w 32.6 31.5 48.0 51.4 63.1 61.3 43.5 71.1 50.7 58.3 56.7 87.9 68.0 89.9 94.5 50.9 43.0 14.1 81.2 33.5 56.0

15.9 w 54.6 9.1 18.7 26.4 43.2 83.5 28.1 27.0 66.7 24.3 26.2 27.0 37.2 26.1 w 16.8 43.1 46.2 65.5 9.9 33.1 53.8 41.7 22.6 33.9 33.3 51.5 15.5 12.8 20.6 62.2 21.0 42.8 48.1 41.9 19.2 w 40.1 67.2 17.1 29.5 25.9 22.5 43.5 25.1 39.9 41.4 34.6 34.8 44.4 24.5 4.3 53.5 9.2 48.5 33.5 52.6 38.4

18.0 w 2.7 17.1 3.4 10.0 50.3 1.7 6.6 41.1 4.8 1.2 5.1 7.1 7.8 14.7 w 8.3 19.2 14.7 6.3 .. 7.8 9.1 27.7 71.5 4.4 11.2 11.0 8.7 23.4 4.7 .. 23.1 .. 12.0 .. 12.8 w 17.5 4.6 26.8 14.9 15.8 9.6 23.2 7.6 11.7 6.2 8.9 11.4 7.8 7.3 18.5 12.1 15.7 32.7 23.2 20.6 10.9

64.1 w 44.0 65.4 76.0 63.4 6.5 13.5 64.7 32.0 28.4 73.3 68.7 64.3 53.5 54.6 w 72.9 37.7 39.5 26.8 89.2 59.1 37.8 30.7 6.0 62.4 55.6 37.6 77.1 62.2 64.6 15.8 55.6 35.4 40.3 36.9 62.1 w 38.1 28.0 56.1 45.0 55.0 63.5 33.4 63.6 47.3 61.4 55.0 53.7 47.8 66.2 80.7 35.5 68.5 18.4 48.1 28.3 47.3

Exports

Imports

% of total merchandise exports To developing economies Within region 1999 2009

Middle East & N. Africa Algeria 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. Cote dâ&#x20AC;&#x2122;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

3.2 w 1.8 7.2 .. 4.1 20.8 17.6 4.1 2.4 8.5 5.7 0.9 4.3 w 46.6 1.9 4.2 29.6 4.5 3.1 13.3 w 1.0 5.9 10.2 2.1 6.9 1.4 5.5 .. 1.1 1.6 24.8 1.7 0.9 18.1 7.8 4.7 1.6 30.7 2.4 5.8 19.4 5.7 11.3 6.7 45.1 39.1 10.6 4.8 25.5 .. 0.7 16.3 10.4 16.7 24.0 3.1 35.6 29.1

8.0 w 3.0 21.1 2.1 2.4 31.2 40.5 3.7 3.3 52.5 11.8 2.7 5.4 w 48.3 2.6 4.5 64.6 12.4 5.7 13.7 w 3.9 30.8 15.5 8.8 12.8 9.2 0.4 .. 21.0 1.3 27.6 5.1 3.2 5.9 10.3 2.6 26.8 34.3 18.3 4.8 19.7 9.2 14.1 14.2 16.5 26.9 10.9 56.5 44.3 9.3 4.2 18.7 1.6 18.1 58.8 46.8 22.8 49.1

Outside region 1999 2009

13.0 w 15.1 11.9 13.7 5.9 35.4 12.2 8.5 10.4 11.9 5.9 62.0 14.7 w 17.6 4.4 17.5 .. 12.0 10.4 13.8 w 8.9 68.7 31.6 .. 8.4 14.6 .. .. 0.6 8.1 16.5 18.4 10.1 4.7 12.1 1.1 .. 16.2 9.3 8.5 9.0 32.3 9.0 0.9 13.2 0.3 27.6 14.7 18.8 .. 30.9 8.5 24.2 24.1 30.5 5.0 12.2 14.5

25.8 w 15.6 22.8 42.1 29.0 22.2 10.5 14.1 22.3 5.7 7.5 73.5 25.1 w 19.3 6.8 27.4 .. 23.8 16.0 27.9 w 47.1 54.7 37.9 12.5 18.1 32.8 .. .. 46.9 33.2 7.7 24.9 24.9 61.7 24.6 24.1 .. 14.8 8.9 11.0 32.6 50.9 46.9 3.5 8.3 0.5 24.8 19.8 11.7 11.0 21.4 21.8 77.0 28.4 32.5 7.3 16.0 20.6

6.5

global links

Direction of trade of developing economies

% of total merchandise imports From developing economies

To high-income economies 1999 2009

Within region 1999 2009

Outside region 1999 2009

From high-income economies 1999 2009

78.2 w 83.1 65.6 73.4 90.0 40.8 69.4 87.4 80.7 76.2 84.1 35.8 78.8 w 35.8 78.7 78.2 60.2 81.2 82.4 66.4 w 90.1 25.1 55.8 72.3 84.1 84.0 81.3 93.4 98.0 88.0 58.7 70.5 83.3 77.2 74.0 90.0 16.8 51.9 88.3 74.6 71.2 60.1 78.6 92.4 40.6 60.6 61.2 43.3 49.1 66.9 68.4 60.1 65.2 57.3 41.1 92.0 32.0 54.8

3.5 w 1.5 1.1 .. 12.8 15.3 6.0 11.4 1.7 4.8 4.4 4.0 3.8 w 24.8 13.5 0.9 14.0 2.3 10.1 12.0 w 11.5 24.3 39.5 23.8 20.0 18.0 31.6 .. 50.6 12.9 16.8 2.3 5.7 8.4 23.9 11.1 15.4 9.5 4.6 8.7 67.5 23.7 6.3 13.9 29.9 31.0 3.8 27.8 12.3 11.4 12.9 3.7 4.0 18.8 21.4 49.4 55.8 46.0

12.3 w 17.2 21.7 22.3 37.1 17.1 18.5 9.9 9.0 26.8 7.8 20.7 10.7 w 35.2 16.4 29.6 .. 23.3 14.3 12.7 w 12.7 16.9 5.1 8.1 10.7 7.9 .. .. 5.6 9.0 14.3 19.1 2.6 24.2 15.0 16.3 .. 15.2 1.8 22.1 6.3 5.8 17.2 24.1 7.1 21.1 24.6 6.2 19.6 11.9 61.1 14.4 37.1 23.4 9.7 10.3 4.5 6.6

72.7 w 81.4 69.3 65.6 50.1 65.4 73.9 78.6 79.0 45.9 85.9 72.8 66.8 w 40.0 52.5 69.5 45.1 72.8 61.4 70.3 w 75.8 58.5 51.1 57.8 68.1 57.9 62.4 51.4 41.5 64.3 63.0 70.5 90.9 67.4 60.4 72.5 44.1 74.6 93.5 59.5 25.1 38.6 68.3 62.0 20.2 46.2 71.3 47.3 66.5 72.0 15.0 81.7 58.8 57.7 65.2 39.7 35.3 39.1

61.1 w 81.4 52.9 39.8 68.6 44.6 48.3 82.1 73.1 41.8 77.9 22.8 67.4 w 32.4 76.8 65.3 29.6 61.9 115.8 57.9 w 49.0 14.5 43.1 66.7 68.4 58.0 96.0 66.5 31.9 65.3 84.4 55.1 56.0 32.4 53.2 51.8 2.4 42.9 72.9 76.2 47.3 28.3 37.8 82.4 63.2 72.8 63.1 23.0 37.9 75.7 74.4 60.3 21.3 44.6 8.1 43.2 63.8 30.4

7.4 w 3.2 2.9 0.6 22.9 10.7 14.2 11.9 6.2 17.4 8.6 3.9 3.6 w 30.1 14.3 0.6 52.9 4.2 19.9 11.8 w 5.1 7.2 37.2 24.2 18.6 14.9 19.1 .. 48.1 4.7 26.8 2.7 9.7 16.3 24.5 5.8 18.7 12.2 0.9 8.4 56.4 27.2 5.4 11.4 36.6 17.6 4.7 42.0 15.9 24.5 8.5 7.0 6.5 16.2 16.0 25.2 60.2 73.3

22.1 w 32.9 34.7 38.8 42.5 28.2 26.1 27.9 18.3 36.2 16.0 43.9 15.5 w 24.5 36.1 39.7 .. 31.8 35.5 22.7 w 32.7 58.4 13.8 18.9 26.5 14.3 .. .. 15.2 30.0 26.3 35.1 16.8 53.4 33.0 17.3 .. 33.6 19.0 46.7 16.1 10.9 35.6 46.0 18.8 31.7 27.2 12.4 32.8 32.9 65.0 35.3 48.7 39.5 31.6 23.1 9.7 8.7

59.9 w 64.5 60.9 59.5 34.7 60.9 58.6 60.1 75.8 46.5 74.8 51.3 58.1 w 45.5 43.5 59.4 15.8 63.1 61.2 52.3 w 62.7 34.5 44.5 46.9 55.1 43.8 55.9 46.4 36.7 63.9 53.3 33.8 72.3 30.4 41.8 33.6 36.6 53.4 80.1 37.2 27.7 32.9 49.8 42.6 32.1 51.0 52.0 44.7 74.0 38.2 14.1 58.0 41.3 40.7 50.5 51.8 30.2 14.0

Note: Bilateral trade data are not available for Timor-Leste, Kosovo, West Bank and Gaza, Botswana, Eritrea, Lesotho, Namibia, and Swaziland. Components may not sum to 100 percent because of trade with unspecified partners or with economies not covered by World Bank classification.

2011 World Development Indicators

339

6.5

Direction of trade of developing economies

About the data Developing economies are an increasingly important

those in Sub-Saharan Africa—are not well recorded,

affinity. The direction of trade is also influenced by

part of the global trading system. Their share of world

and the value of trade among developing economies

preferential trade agreements that a country has

trade rose from 15 percent in 1990 to 30 percent

may be understated. The table does not include some

made with other economies. Though formal agree-

in 2009. And trade between high-income economies

developing economies because data on their bilateral

ments on trade liberalization do not automatically

and low- and middle-income economies has grown

trade flows are not available. Data on the direction

increase trade, they nevertheless affect the direction

faster than trade between high-income economies.

of trade between selected high-income economies

of trade between the participating economies. Table

This increased trade benefits both producers and

are presented and discussed in tables 6.3 and 6.4.

6.7 illustrates the size of existing regional trade blocs

consumers in developing and high-income economies.

At the regional level most exports from developing

The table shows trade in goods between develop-

economies are to high-income economies, but the

Although global integration has increased, develop-

ing economies in the same region and other regions

share of intraregional trade is increasing. Geographic

ing economies still face trade barriers when accessing

and between developing economies and high-income

patterns of trade vary widely by country and commod-

other markets (see table 6.8).

economies. Data on exports and imports are from

ity. Larger shares of exports from oil- and resource-

the International Monetary Fund’s (IMF) Direction of

rich economies are to high-income economies.

that have formal preferential trade agreements.

Definitions

Trade database and should be broadly consistent with

The relative importance of intraregional trade is

• Exports to developing economies within region

data from other sources, such as the United Nations

higher for both landlocked countries and small coun-

are the sum of merchandise exports from the report-

Statistics Division’s Commodity Trade (Comtrade)

tries with close trade links to the largest regional

ing economy to other developing economies in the

database. All high-income economies and major devel-

economy. For most developing economies—especially

same World Bank region as a percentage of total

oping economies report trade to the IMF on a timely

smaller ones—there is a “geographic bias” favoring

merchandise exports by the economy. • Exports to

basis, covering about 85 percent of trade for recent

intraregional trade. Despite the broad trend toward

developing economies outside region are the sum

years. Trade by less timely reporters and by countries

globalization and the reduction of trade barriers,

of merchandise exports from the reporting econ-

that do not report is estimated using reports of trading

the relative share of intraregional trade increased

omy to other developing economies in other World

partner countries. Therefore, data on trade between

for most economies between 1999 and 2009. This

Bank regions as a percentage of total merchandise

developing and high-income economies shown in the

is due partly to trade-related advantages, such as

exports by the economy. • Exports to high-income

table should be generally complete. But trade flows

proximity, lower transport costs, increased knowledge

economies are the sum of merchandise exports from

between many developing economies—particularly

from repeated interaction, and cultural and historical

the reporting economy to high-income economies as a percentage of total merchandise exports by the

6.5a

Developing economies are trading more with other developing economies

economy. • Imports from developing economies within region are the sum of merchandise imports by

Low-income economies

Share of merchandise exports (percent) 100

the reporting economy from other developing economies in the same World Bank region as a percentage of total merchandise imports by the economy.

Exports to high-income economies

• Imports from developing economies outside region are the sum of merchandise imports by the

reporting economy from other developing economies Exports to developing economies within region

25 0

Exports to developing economies outside region

1990

1995

2000

2005

2009

Middle-income economies

in other World Bank regions as a percentage of total merchandise imports by the economy. • Imports from high-income economies are the sum of merchandise imports by the reporting economy from high-income economies as a percentage of total merchandise imports by the economy.

100 Exports to high-income economies

Data sources

50 25 0

Data on merchandise trade flows are published in

Exports to developing economies within region Exports to developing economies outside region

1990

1995

2000

2005

2009

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

Share of merchandise exports to high-income economies have been declining for both low- and middle-income

of Trade database. Regional and income group

economies. On the other hand, their exports to other developing economies have increased, especially

classifications are according to the World Bank

exports to developing economies within the same region.

classification of economies as of July 1, 2010,

Source: World Bank staff calculations based on data from International Monetary Fund’s Direction of Trade database.

and are as shown on the cover flaps of this report.

340

2011 World Development Indicators

1970

World Bank commodity price index (2000= 100) Energy 19 Nonenergy commodities 183 Agriculture 188 Beverages 230 Food 201 Fats and oils 237 Grains 204 Other food 151 Raw materials 136 Timber 97 Other raw materials 179 Fertilizers 82 Metals and minerals 185 Base metals 200 .. Steel productsa Commodity prices (2000 prices) Energy Coal, Australian ($/mt) .. Natural gas, Europe ($/mmBtu) .. Natural gas, U.S. ($/mmBtu) 0.57 Natural gas, liquefied, Japan ($mmBtu) .. Petroleum, avg., spot ($/bbl) 4 Beverages (cents/kg) Cocoa 233 Coffee, Arabica 397 Coffee, robusta 316 Tea, avg., 3 auctions 289 Tea, Colombo auctions 217 Tea, Kolkata auctions 343 Tea, Mombasa auctions 307 Food Fats and oils ($/mt) Coconut oil 1,376 779 Copraa Groundnut oil 1,312 Palm oil 901 .. Palmkernell oila Soybeans 405 Soybean meal 355 Soybean oil 992 Grains ($/mt) Barley .. Maize 202 Rice, Thailand, 5% 438 .. Rice, Thailand, 25% a .. Rice, Thailand, A1a 179 Sorghuma 218 Wheat, Canadaa Wheat, U.S., hard red winter 190 197 Wheat, U.S., soft red winter a

1980

1990

1995

153 177 195 273 199 196 199 205 143 92 198 177 141 145 134

79 115 113 117 116 105 121 124 105 88 124 98 122 124 131

49 5.21 1.91 7.02 45

6.6

global links

Primary commodity prices

2000

2004

2005

2006

2007

2008

2009

2010

53 117 122 136 117 126 124 101 125 105 146 110 106 112 118

100 100 100 100 100 100 100 100 100 100 100 100 100 100 100

123 121 118 109 123 134 115 117 109 90 129 125 126 127 153

171 135 121 125 121 120 115 129 119 100 140 148 162 152 170

197 172 134 130 131 123 134 140 143 113 177 151 251 253 162

209 192 154 145 158 178 161 127 149 117 185 205 268 272 155

274 218 184 168 198 222 225 142 157 120 196 453 261 230 231

179 178 165 184 171 181 179 152 141 116 168 245 197 174 190

225 224 192 210 186 203 179 170 197 119 282 232 288 247 190

39 2.48 1.65 3.54 22

33 2.26 1.43 2.86 14

26 3.86 4.31 4.71 28

48 3.88 5.35 4.66 34

43 5.74 8.09 5.44 48

44 7.57 6.01 6.32 57

56 7.30 5.96 6.56 61

102 10.72 7.09 10.04 78

60 7.27 3.30 7.46 52

82 6.87 3.64 9.00 66

321 427 400 205 137 253 224

123 192 115 200 182 273 144

119 277 230 124 118 145 108

91 192 91 188 179 181 203

141 161 72 153 162 156 141

140 230 101 150 167 147 134

142 225 133 168 171 157 175

167 232 163 174 215 164 142

206 247 186 194 223 180 177

241 265 137 227 262 210 210

260 358 144 239 273 233 212

831 558 1,059 719 .. 365 324 737

327 224 937 282 .. 240 195 435

556 364 823 521 .. 215 164 519

450 305 714 310 444 212 189 338

600 409 1,054 428 588 278 219 559

560 376 963 383 569 249 195 495

542 360 867 427 519 240 187 535

784 518 1,154 666 758 328 263 752

979 653 1,705 759 904 418 340 1,007

606 401 988 570 585 365 340 709

932 622 1,164 747 982 373 314 833

96 154 506 .. .. 159 235 213 208

78 106 263 254 152 101 152 132 125

86 103 266 247 218 99 172 147 139

77 89 202 173 143 88 147 114 99

90 102 216 205 186 100 169 142 131

86 90 260 241 198 87 179 138 123

104 109 272 248 196 110 194 172 142

147 140 279 262 232 139 256 218 204

160 178 520 425 386 166 364 261 217

107 138 463 382 273 126 251 187 155

131 154 405 366 318 137 259 185 190

2011 World Development Indicators

341

6.6

Primary commodity prices

1970

Commodity prices (continued) (2000 prices) Food (continued) Other food Bananas, U.S. ($/mt) Beef (cents/kg) Chicken meat (cents/kg) Fishmeal ($/mt)a Oranges ($/mt) Shrimp. Mexico (cents/kg) Sugar, EU domestic (cents/kg) Sugar, U.S. domestic (cents/kg) Sugar, world (cents/kg) Agricultural raw materials Cotton A index (cents/kg) Logs, Cameroon ($/cu. m)a Logs, Malaysia ($/cu. m) Rubber, Singapore (cents/kg) Rubber, TSR 20 (cents/kg)a Plywood (cents/sheet)a Sawnwood, Malaysia ($/cu. m) 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 (cents/dmtu) Iron ore, spot, cfr China ($/dmtu) Lead (cents/kg) Nickel ($/mt) Silver (cents/toz)a Tin (cents/kg) Zinc (cents/kg) MUV G-5 index

573 452 .. 682 582 .. 39 57 29

1980

1990

467 340 85 621 482 1,420 60 82 78

526 249 96 401 516 1,039 57 50 27

1995

369 158 92 411 441 1,253 57 42 24

2000

424 193 119 413 363 1,515 56 43 18

2004

476 228 138 589 780 928 61 41 14

2005

547 238 135 664 794 939 60 43 20

2006

2007

605 228 124 1,040 741 915 58 44 29

577 222 134 1,005 817 862 58 39 19

2008

675 251 136 906 886 855 56 37 23

2010

707 220 143 1,027 759 789 44 46 33

720 278 143 1,399 857 1,033 37 66 39

219 149 149 141 .. 357 608 3,727 615

252 310 241 176 .. 338 489 2,806 661

177 334 172 84 .. 345 518 3,297 792

177 282 212 131 .. 485 614 2,194 708

130 275 190 67 63 448 595 2,976 664

124 301 179 116 110 422 528 2,488 582

110 304 184 135 126 462 599 2,533 577

113 285 214 186 174 532 670 2,653 624

119 325 229 193 184 547 688 2,830 655

126 421 234 207 202 516 711 2,871 656

115 352 240 160 150 471 673 3,541 513

189 355 231 303 280 472 703 3,570 719

187 38 109 147 63

274 58 143 222 237

167 39 95 128 116

180 29 98 124 155

154 44 123 138 101

201 37 113 169 159

224 38 144 183 199

233 40 156 180 199

369 61 171 289 264

774 276 456 703 394

270 102 526 215 208

415 102 275 317 239

1,926 4,904 125 34 .. 105 9,860 614 1,273 102

1,795 2,690 750 35 .. 112 8,037 2,544 2,068 94

1,593 2,586 373 32 .. 79 8,614 475 591 147

1,499 2,437 319 24 .. 52 6,830 431 516 86

1,549 1,813 279 29 .. 45 8,638 500 544 113

1,558 2,602 372 34 .. 80 12,551 607 773 95

1,724 3,340 404 59 .. 89 13,387 666 670 125

2,297 6,007 540 69 .. 115 21,675 1,034 785 293

2,252 6,076 595 72 108 220 31,778 1,145 1,241 277

2,058 5,564 697 112 125 167 16,888 1,200 1,481 150

1,390 4,300 812 84 69 144 12,237 1,227 1,133 138

1,802 6,248 1,016 134 126 178 18,084 1,675 1,692 179

103

120

100

110

112

117

125

120

121

Note: bbl = barrel, cu. m = cubic meter, dmtu = dry metric ton unit, kg = kilogram, mmBtu = million British thermal unit, mt = metric ton, toz = troy ounce. a. Series not included in the nonenergy index.

342

2009

2011 World Development Indicators

About the data

6.6

global links

Primary commodity prices Definitions

Primary commodities—raw or partially processed

commodity price index contains 41 price series for

• Energy price index is the composite price index for

materials that will be transformed into finished

34 nonenergy commodities.

coal, petroleum, and natural gas, weighted by exports

goods—are often developing countries’ most impor-

Separate indexes are compiled for energy and steel

of each commodity from low- and middle-income

tant exports, and commodity revenues can affect liv-

products, which are not included in the nonenergy

countries. • Nonenergy commodity price index cov-

ing standards. Price data are collected from various

commodity price index.

ers the 34 nonenergy primary commodities that

sources, including international commodity study

The MUV index is a composite index of prices

make up the agriculture, fertilizer, and metals and

groups, government agencies, industry trade jour-

for manufactured exports from the five major (G-5)

minerals indexes. • Agriculture includes beverages,

nals, and Bloomberg and Datastream. Prices are

industrial economies (France, Germany, Japan, the

food, and agricultural raw materials. • Beverages

compiled in U.S. dollars or converted to U.S. dollars

United Kingdom, and the United States) to low- and

include cocoa, coffee, and tea. • Food includes

when quoted in local currencies.

middle-income economies, valued in U.S. dollars.

fats and oils, grains, and other food items. Fats

The table is based on frequently updated price

The index covers products in groups 5–8 of SITC

and oils include coconut oil, groundnut oil, palm oil,

reports. Prices are those received by exporters when

revision 1. For the MUV G-5 index, unit value indexes

soybeans, soybean oil, and soybean meal. Grains

available, or the prices paid by importers or trade

in local currency for each country are converted to

include barley, maize, rice, and wheat. Other food

unit values. Annual price series are generally simple

U.S. dollars using market exchange rates and are

items include bananas, beef, chicken meat, oranges,

averages based on higher frequency data. The con-

combined using weights determined by each coun-

shrimp, and sugar. • Agricultural raw materials

stant price series in the table are deflated by the

try’s export share in the base year (1995). The export

include timber and other raw materials. Timber

manufactures unit value (MUV) index for the Group

shares were 8.2 percent for France, 17.4 percent

includes tropical hard logs and sawnwood. Other

of Five (G-5) countries (see below).

for Germany, 35.6 percent for Japan, 6.6 percent

raw materials include cotton, natural rubber, and

for the United Kingdom, and 32.2 percent for the

tobacco. • Fertilizers include phosphate, phosphate

United States.

rock, potassium, and nitrogenous products. • Met-

Commodity price indexes are calculated as Laspeyres index numbers; the fixed weights are the 2002–04 average export values for low- and middle-

als and minerals include base metals and iron ore.

income economies (based on 2001 gross national

• Base metals include aluminum, copper, lead,

income) rebased to 2000. Data for exports are from

nickel, tin, and zinc. • Steel products price index

the United Nations Statistics Division’s Commod-

is the composite price index for eight steel prod-

ity Trade Statistics (Comtrade) database Standard

ucts based on quotations free on board (f.o.b.)

International Trade Classification (SITC) revision 3,

Japan excluding shipments to the United States

the Food and Agriculture Organization’s FAOSTAT

for all years and to China prior to 2001, weighted

database, the International Energy Agency data-

by product shares of apparent combined consump-

base, BP’s Statistical Review of World Energy, the

tion (volume of deliveries) for Germany, Japan, and

World Bureau of Metal Statistics, and World Bank

the United States. • Commodity prices—for defi-

staff estimates.

nitions and sources, see “Commodity price data”

Each index in the table represents a fixed basket of

(also known as the “Pink Sheet”) at the World Bank

primary commodity exports over time. The nonenergy

Prospects for Development website (www.worldbank. org/prospects, click on Products). • MUV G-5 index is the manufactures unit value index for G-5 country

6.6a

Primary commodity prices soared again in 2010

exports to low- and middle-income economies.

World Bank commodity price index, current prices (2000 = 100) 500 Energy 400 Raw materials 300 Food 200

100 2005

Data sources Data on commodity prices and the MUV G-5 2006

2007

2008

2009

2010

2011

index are compiled by the World Bank’s Develop-

The food commodity price index started rising again in the beginning of 2009, and by the end of February

ment Prospects Group. Monthly updates of com-

2011 exceeded the record high in June 2008. The price index for raw materials reached new highs, and

modity prices are available at www.worldbank.

the energy price index also rose throughout 2009 and 2010.

org/prospects and http://data.worldbank.org/

Source: World Bank commodity price data.

data-catalog.

2011 World Development Indicators

343

6.7

Regional trade blocs

Merchandise exports within bloc

Year of creation High-income and lowand middle-income economies 1989 APECb EEA 1994 EFTA 1960 European Union 1957 NAFTA 1994 SPARTECA 1981 Trans-Pacific SEP 2006 East Asia and Pacific and South Asia APTA 1975 ASEAN 1967 MSG 1993 PICTA 2001 SAARC 1985 Europe, Central Asia, and Middle East Agadir Agreement 2004 CEFTA 1992 CEZ 2003 CIS 1991 EAEC 1997 ECO 1985 GCC 1981 PAFTA (GAFTA) 1997 UMA 1989 Latin America and the Caribbean Andean Community 1969 CACM 1961 CARICOM 1973 LAIA (ALADI) 1980 MERCOSUR 1991 OECS 1981 Sub-Saharan Africa CEMAC 1994 CEPGL 1976 COMESA 1994 EAC 1996 ECCAS 1983 ECOWAS 1975 Indian Ocean Commission 1984 SADC 1992 UEMOA 1994

Year of entry into Type force of the of most most recent recent agreement agreementa

$ millions 1990

1995

2000

2005

2007

2008

2009

1994 2002 1958 1994 1981 2006

None EIA EIA EIA, CU FTA PTA EIA, FTA

1976 1992 1994 2003 2006

PTA FTA PTA FTA FTA

2,429 27,365 5 4 945

21,728 79,544 18 4 2,081

37,895 98,060 22 8 2,894

127,340 165,458 51 22 8,619

193,951 216,727 78 34 12,747

233,617 251,285 89 38 13,177

204,745 198,915 78 34 11,095

1994 2004 1994 2000 2003 2003c 1998 1994 c

NNA FTA FTA FTA CU PTA CU FTA NNA

156 .. .. .. .. 1,243 6,906 13,204 958

226 619 10,154 31,277 10,919 4,746 6,832 12,948 1,109

294 1,187 13,283 28,422 13,936 4,518 8,029 16,188 1,041

635 2,847 23,469 58,113 24,818 12,579 15,408 41,659 1,885

1,046 6,160 43,003 98,050 45,714 22,064 24,372 61,100 2,695

1,913 7,543 47,731 123,052 51,186 26,739 31,514 82,267 4,570

2,075 5,083 19,094 60,389 21,872 18,412 21,849 61,881 3,422

1988 1961 1997 1981 2005 1981c

CU CU EIA PTA EIA NNA

544 667 456 13,350 4,127 29

1,788 1,594 877 35,986 14,199 39

2,046 2,655 1,078 44,253 17,829 38

4,572 4,311 2,235 71,711 21,128 68

5,926 5,637 3,112 110,006 32,421 104

7,029 6,475 3,808 143,283 46,657 118

5,785 5,287 2,716 98,510 32,689 104

1999

CU NNA FTA CU NNA PTA NNA FTA CU

139 7 1,146 335 160 1,532 63 1,655 621

120 8 1,367 628 157 1,875 113 3,615 560

96 10 1,443 689 182 2,715 106 4,427 741

201 20 2,695 1,075 255 5,497 162 7,799 1,390

305 29 4,021 1,385 385 6,717 214 12,051 1,735

355 73 6,676 1,797 449 9,355 217 16,011 2,281

300 64 6,114 1,572 378 7,312 183 11,697 1,927

1994 2000 2004 c 1993 2005c 2000 2000

901,560 1,688,708 2,261,791 3,318,699 4,192,784 4,606,339 3,738,989 1,079,711 1,463,232 1,714,018 3,037,759 4,025,418 4,446,686 3,392,597 782 925 831 1,252 2,196 2,910 2,006 1,032,397 1,404,255 1,641,609 2,905,551 3,846,547 4,233,112 3,237,024 226,273 394,472 676,141 824,359 951,258 1,013,245 768,820 5,299 9,135 8,579 15,201 18,617 20,263 17,079 1,110 2,614 1,438 2,345 3,290 4,262 3,548

Note: Regional bloc memberships are as follows: Agadir Agreement, the Arab Republic of Egypt, Jordan, Morocco, and Tunisia; Andean Community, Bolivia, Colombia, Ecuador, and Peru; Arab Maghreb Union (UMA), Algeria, Libyan Arab Republic, Mauritania, Morocco, and Tunisia; Asia Pacific Economic Cooperation (APEC), Australia, Brunei Darussalam, Canada, Chile, China, Hong Kong SAR, China, Indonesia, Japan, the Republic of Korea, Malaysia, Mexico, New Zealand, Papua New Guinea, Peru, the Philippines, the Russian Federation, Singapore, Taiwan (China), Thailand, the United States, and Vietnam; Asia-Pacific Trade Agreement (APTA; formerly Bangkok Agreement), Bangladesh, China, India, the Republic of Korea, the Lao People’s Democratic Republic, and Sri Lanka; Association of South East Asian Nations (ASEAN), Brunei Darussalam, Cambodia, Indonesia, the Lao People’s Democratic Republic, Malaysia, Myanmar, the Philippines, Singapore, Thailand, and Vietnam; Caribbean Community and Common Market (CARICOM), Antigua and Barbuda, the Bahamas, Barbados, Belize, Dominica, Grenada, Guyana, Haiti, Jamaica, Montserrat, St. Kitts and Nevis, St. Lucia, St. Vincent and the Grenadines, Suriname, and Trinidad and Tobago; Central American Common Market (CACM), Costa Rica, El Salvador, Guatemala, Honduras, and Nicaragua; Central European Free Trade Area (CEFTA), Albania, Bosnia and Herzegovina, Croatia, Kosovo, Macedonia, Moldova, Montenegro, and Serbia; Common Economic Zone (CEZ), Belarus, Kazakhstan, and the Russian Federation; Common Market for Eastern and Southern Africa (COMESA), Burundi, Comoros, the Democratic Republic of Congo, Djibouti, the Arab Republic of Egypt, Eritrea, Ethiopia, Kenya, Libyan Arab Republic, Madagascar, Malawi, Mauritius, Rwanda, Seychelles, Sudan, Swaziland, Uganda, Zambia, and Zimbabwe; Commonwealth of Independent States (CIS), Armenia, Azerbaijan, Belarus, Georgia, Kazakhstan, Kyrgyz Republic, Moldova, the Russian Federation, Tajikistan, Turkmenistan, Ukraine, and Uzbekistan; East African Community (EAC), Burundi, Kenya, Rwanda, Tanzania, and Uganda; Economic and Monetary Community of Central Africa (CEMAC; formerly Central African Customs and Economic Union [UDEAC]), Cameroon, the Central African Republic, Chad, the Republic of Congo, Equatorial Guinea, and Gabon; Economic Community of Central African States (ECCAS), Angola, Burundi, Cameroon, the Central African Republic, Chad, the Democratic Republic of Congo, the Republic of Congo, Equatorial Guinea, Gabon, and São Tomé and Príncipe; Economic Community of the Great Lakes Countries (CEPGL), Burundi, the Democratic Republic of Congo, and Rwanda; Economic Community of West African States (ECOWAS), Benin, Burkina Faso, Cape Verde, Côte d’Ivoire, the Gambia, Ghana, Guinea, Guinea-Bissau, Liberia, Mali, Niger, Nigeria, Senegal, Sierra Leone, and Togo; Economic Cooperation Organization (ECO), Afghanistan, Azerbaijan, the Islamic Republic of Iran, Kazakhstan, the Kyrgyz Republic, Pakistan, Tajikistan, Turkey, Turkmenistan, and

344

2011 World Development Indicators

6.7

global links

Regional trade blocs Merchandise exports within bloc

Year of entry into Type force of the of most most recent recent agreement agreementa

% of total bloc exports 1990

1995

2000

2005

2007

2008

2009

1994 2002 1958 1994 1981 2006

None EIA EIA EIA, CU FTA PTA EIA, FTA

68.3 68.8 0.8 67.3 41.4 10.5 1.5

71.7 67.9 0.7 66.5 46.2 12.9 1.7

73.0 69.0 0.6 67.7 55.7 10.7 0.8

70.8 73.0 0.5 71.6 55.7 11.4 0.8

67.3 73.3 0.7 71.9 51.3 10.5 0.8

65.2 72.8 0.8 71.4 49.5 8.9 1.0

66.3 71.9 0.7 70.4 48.0 9.1 1.0

1976 1992 1994 2003 2006

PTA FTA PTA FTA FTA

1.6 18.9 0.3 0.3 3.5

6.8 24.4 0.4 0.1 4.5

8.0 23.0 0.6 0.3 4.6

11.0 25.3 0.8 0.4 6.6

11.0 25.2 0.8 0.4 6.6

11.4 25.5 0.8 0.4 5.9

11.6 24.5 0.8 0.4 5.4

1994 2004 1994 2000 2003 2003c 1998 1994 c

NNA FTA FTA FTA CU PTA CU FTA NNA

1.3 .. .. .. .. 3.2 8.0 10.2 2.9

1.4 9.0 11.6 28.4 12.3 7.9 6.8 9.8 3.8

1.4 14.5 11.0 19.8 11.5 5.6 4.9 7.2 2.2

1.8 16.3 8.4 17.7 8.9 6.9 4.4 9.2 1.9

2.0 21.2 10.4 20.1 10.9 8.0 5.0 9.4 2.0

2.7 22.4 8.8 18.0 9.3 6.8 4.5 8.9 2.5

3.8 20.2 5.6 14.8 6.3 7.2 5.1 10.6 3.1

1988 1961 1997 1981 2005 1981c

CU CU EIA PTA EIA NNA

4.0 15.3 8.0 11.6 8.9 8.1

8.6 21.8 12.0 17.3 20.3 12.6

7.7 19.6 14.4 13.2 20.0 10.0

9.0 23.2 12.1 13.6 12.9 11.5

7.8 23.5 13.1 15.3 14.7 12.1

7.5 24.8 12.9 16.5 14.7 12.0

7.5 22.3 13.7 15.5 15.2 13.0

1999

CU NNA FTA CU NNA PTA NNA FTA CU

2.3 0.5 4.7 17.7 1.4 8.0 3.9 6.6 13.0

2.1 0.5 6.1 19.5 1.5 9.0 5.9 10.2 10.3

1.0 0.8 4.6 22.6 1.0 7.6 4.4 9.5 13.1

0.9 1.2 4.6 18.0 0.6 9.3 4.9 9.3 13.4

1.1 1.4 4.5 17.8 0.6 7.8 5.8 10.2 14.9

0.8 1.9 5.3 19.2 0.4 8.5 5.7 10.3 15.9

1.2 2.2 7.2 18.9 0.6 9.9 5.8 11.0 13.2

1994 2000 2004c 1993 2005c 2000 2000

Uzbekistan; Eurasian Economic Community (EAEC), Belarus, Kazakhstan, Kyrgyz Republic, the Russian Federation, Tajikistan, and Uzbekistan; European Economic Area (EEA), European Union plus Iceland, Liechtenstein, and Norway; European Free Trade Association (EFTA), Iceland, Liechtenstein, Norway, and Switzerland; European Union (EU; formerly European Economic Community and European Community), Austria, Belgium, Bulgaria, Cyprus, Czech Republic, Denmark, Estonia, Finland, France, Germany, Greece, Hungary, Ireland, Italy, Latvia, Lithuania, Luxembourg, Malta, the Netherlands, Poland, Portugal, Romania, Slovak Republic, Slovenia, Spain, Sweden, and the United Kingdom; Gulf Cooperation Council (GCC), Bahrain, Kuwait, Oman, Qatar, Saudi Arabia, and the United Arab Emirates; Indian Ocean Commission, Comoros, Madagascar, Mauritius, Réunion, and Seychelles; Latin American Integration Association (LAIA; formerly Latin American Free Trade Area), Argentina, Bolivia, Brazil, Chile, Colombia, Cuba, Ecuador, Mexico, Paraguay, Peru, Uruguay, and Bolivarian Republic of Venezuela; Melanesian Spearhead Group (MSG), Fiji, Papua New Guinea, Solomon Islands, and Vanuatu; North American Free Trade Agreement (NAFTA), Canada, Mexico, and the United States; Organization of Eastern Caribbean States (OECS), Anguilla, Antigua and Barbuda, British Virgin Islands, Dominica, Grenada, Montserrat, St. Kitts and Nevis, St. Lucia, and St. Vincent and the Grenadines; Pacific Island Countries Trade Agreement (PICTA), Cook Islands, Kiribati, Nauru, Niue, Papua New Guinea, Samoa, Solomon Islands, Tonga, Tuvalu, and Vanuatu; Pan-Arab Free Trade Area (PAFTA; also known as Greater Arab Trade Area [GAFTA]), Bahrain, Egypt, Iraq, Jordan, Kuwait, Lebanon, Libya, Morocco, Oman, Qatar, Saudi Arabia, Sudan, Syrian Arab Republic, Tunisia, the United Arab Emirates, and Yemen; South Asian Association for Regional Cooperation (SAARC), Afghanistan, Bangladesh, Bhutan, India, Maldives, Nepal, Pakistan, and Sri Lanka; South Pacific Regional Trade and Economic Cooperation Agreement (SPARTECA), Australia, Cook Islands, Fiji, Kiribati, Marshall Islands, Federated States of Micronesia, Nauru, New Zealand, Niue, Papua New Guinea, Solomon Islands, Tonga, Tuvalu, Vanuatu, and Western Samoa; Southern African Development Community (SADC), Angola, Botswana, the Democratic Republic of Congo, Lesotho, Madagascar, Malawi, Mauritius, Mozambique, Namibia, Seychelles, South Africa, Swaziland, Tanzania, Zambia, and Zimbabwe; Southern Common Market (MERCOSUR), Argentina, Brazil, Paraguay, Uruguay, and Bolivarian Republic of Venezuela; Trans-Pacific Strategic Economic Partnership (Trans-Pacific SEP), Brunei Darussalam, Chile, New Zealand, and Singapore; West African Economic and Monetary Union (WAEMU or UEMOA), Benin, Burkina Faso, Côte d’Ivoire, Guinea-Bissau, Mali, Niger, Senegal, and Togo.

2011 World Development Indicators

345

6.7

Regional trade blocs

Merchandise exports by bloc

Year of entry into Type force of the of most most recent recent agreement agreementa

% of world exports 1990

1995

2000

2005

2007

2008

2009

39.0 46.4 2.9 45.3 16.2 1.5 2.2

46.4 42.4 2.4 41.5 16.8 1.4 3.0

48.5 38.9 2.2 38.0 19.0 1.3 2.7

45.1 40.1 2.3 39.1 14.3 1.3 2.9

44.8 39.5 2.3 38.5 13.4 1.3 2.9

44.1 38.1 2.3 37.0 12.8 1.4 2.8

45.7 38.3 2.4 37.3 13.0 1.5 2.9

1994 2002 1958 1994 1981 2006

None EIA EIA EIA, CU FTA PTA EIA, FTA

1976 1992 1994 2003 2006

PTA FTA PTA FTA FTA

4.5 4.3 0.1 0.0 0.8

6.3 6.4 0.1 0.1 0.9

7.5 6.7 0.1 0.0 1.0

11.2 6.3 0.1 0.1 1.3

12.7 6.2 0.1 0.1 1.4

12.8 6.1 0.1 0.1 1.4

14.3 6.6 0.1 0.1 1.7

1994 2004 1994 2000 2003 2003c 1998 1994 c

NNA FTA FTA FTA CU PTA CU FTA NNA

0.3 .. .. .. .. 1.1 2.6 3.8 1.0

0.3 0.1 1.7 2.2 1.7 1.2 2.0 2.6 0.6

0.3 0.1 1.9 2.2 1.9 1.3 2.6 3.5 0.8

0.3 0.2 2.7 3.2 2.7 1.8 3.3 4.4 0.9

0.4 0.2 3.0 3.5 3.0 2.0 3.5 4.7 1.0

0.4 0.2 3.4 4.3 3.4 2.5 4.3 5.8 1.1

0.4 0.2 2.8 3.3 2.8 2.1 3.5 4.7 0.9

1988 1961 1997 1981 2005 1981c

CU CU EIA PTA EIA NNA

0.4 0.1 0.2 3.4 1.4 0.0

0.4 0.1 0.1 4.1 1.4 0.0

0.4 0.2 0.1 5.3 1.4 0.0

0.5 0.2 0.2 5.1 1.6 0.0

0.5 0.2 0.2 5.2 1.6 0.0

0.6 0.2 0.2 5.4 2.0 0.0

0.6 0.2 0.2 5.2 1.7 0.0

1999

CU NNA FTA CU NNA PTA NNA FTA CU

0.2 0.0 0.7 0.1 0.3 0.6 0.0 0.7 0.1

0.1 0.0 0.4 0.1 0.2 0.4 0.0 0.7 0.1

0.1 0.0 0.5 0.0 0.3 0.6 0.0 0.7 0.1

0.2 0.0 0.6 0.1 0.4 0.6 0.0 0.8 0.1

0.2 0.0 0.6 0.1 0.5 0.6 0.0 0.9 0.1

0.3 0.0 0.8 0.1 0.7 0.7 0.0 1.0 0.1

0.2 0.0 0.7 0.1 0.5 0.6 0.0 0.9 0.1

1994 2000 2004c 1993 2005c 2000 2000

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. Years of the most recent agreement are collected from the official website of the trade bloc.

346

2011 World Development Indicators

6.7

global links

Regional trade blocs About the data Trade blocs are groups of countries that have estab-

preferential arrangements, it is included because of

one trade bloc, so shares of world exports exceed

lished preferential arrangements governing trade

the volume of trade between its members.

100 percent. Exports include all commodity trade,

between members. Although in some cases the pref-

The data on country exports are from the Interna-

which may include items not specified in trade bloc

erences—such as lower tariff duties or exemptions

tional Monetary Fund’s (IMF) Direction of Trade data-

agreements. Differences from previously published

from quantitative restrictions—may be no greater than

base and should be broadly consistent with those

estimates may be due to changes in membership or

those available to other trading partners, such arrange-

from sources such as the United Nations Statistics

revisions in underlying data.

ments are intended to encourage exports by bloc mem-

Division’s Commodity Trade (Comtrade) database. All

bers to one another—sometimes called intratrade.

high-income economies and major developing econo-

Definitions

Most countries are members of a regional trade

mies report trade to the IMF on a timely basis, cover-

• Merchandise exports within bloc are the sum of

bloc, and more than a third of the world’s trade takes

ing about 85 percent of trade for recent years. Trade

merchandise exports by members of a trade bloc to

place within such arrangements. While trade blocs

by less timely reporters and by countries that do not

other members of the bloc. They are shown both in

vary in structure, they all have the same objective:

report is estimated using reports of trading partner

U.S. dollars and as a percentage of total merchan-

to reduce trade barriers between member countries.

countries. Therefore, data on trade between develop-

dise exports by the bloc. • Merchandise exports by

But effective integration requires more than reduc-

ing and high-income economies shown in the table

bloc as a share of world exports are the bloc’s total

ing tariffs and quotas. Economic gains from compe-

should be generally complete. But trade flows between

merchandise exports (within the bloc and to the rest

tition and scale may not be achieved unless other

many developing countries—particularly those in Sub-

of the world) as a share of total merchandise exports

barriers that divide markets and impede the free flow

Saharan Africa—are not well recorded, and the value of

by all economies in the world. • Type of most recent

of goods, services, and investments are lifted. For

trade among developing countries may be understated.

agreement includes customs union, under which

example, many regional trade blocs retain contingent

Membership in the trade blocs shown is based

members substantially eliminate all tariff and nontariff

protections on intrabloc trade, including antidumping,

on the most recent information available (see Data

barriers among themselves and establish a common

countervailing duties, and “emergency protection” to

sources). Other types of preferential trade agreements

external tariff for nonmembers; economic integration

address balance of payments problems or protect an

may have entered into force earlier than those shown

agreement, which liberalizes trade in services among

industry from import surges. Other barriers include

in the table and may still be effective. Unless other-

members and covers a substantial number of sec-

differing product standards, discrimination in public

wise indicated in the footnotes, information on the type

tors, affects a sufficient volume of trade, includes

procurement, and cumbersome border formalities.

of agreement and date of enforcement are based on

substantial modes of supply, and is nondiscriminatory

Membership in a regional trade bloc may reduce

the World Trade Organization’s (WTO) list of regional

(in the sense that similarly situated service suppliers

the frictional costs of trade, increase the credibility

trade agreements. Information on trade agreements

are treated the same); free trade agreement, under

of reform initiatives, and strengthen security among

not notified to the WTO was collected from the Global

which members substantially eliminate all tariff and

partners. But making it work effectively is challenging.

Preferential Trade Agreements database (box 6.7a)

nontariff barriers but set tariffs on imports from non-

All economic sectors may be affected, and some may

and from official websites of the trade blocs.

members; preferential trade agreement, which is an

expand while others contract, so it is important to

Although bloc exports have been calculated back

agreement notified to the WTO that is not a free trade

weigh the potential costs and benefits of membership.

to 1990 on the basis of current membership, several

agreement, a customs union, or an economic integra-

The table shows the value of merchandise intra-

blocs came into existence after that and membership

tion agreement; and not notified agreement, which is

trade (service exports are excluded) for important

may have changed over time. For this reason, and

a preferential trade arrangement established among

regional trade blocs and the size of intratrade rela-

because systems of preferences also change over

member countries that is not notified to the World

tive to each bloc’s exports of goods and the share

time, intratrade in earlier years may not have been

Trade Organization (the agreement may be functionally

of the bloc’s exports in world exports. Although the

affected by the same preferences as in recent years.

equivalent to any of the other agreements).

Asia Pacific Economic Cooperation (APEC) has no

In addition, some countries belong to more than

Global Preferential Trade Agreements Database

6.7a

Data sources

The Global Preferential Trade Agreement Database (GPTAD) provides information on preferential trade

Data on merchandise trade flows are published in

agreements around the world, including those not notified to the World Trade Organization (WTO). It is

the IMF’s Direction of Trade Statistics Yearbook and

designed to help trade policymakers, scholars, and business operators better understand and navigate the

Direction of Trade Statistics Quarterly; the data in

world of preferential trade agreements. The GPTAD is updated regularly and currently comprises more than

the table were calculated using the IMF’s Direction

330 preferential trade agreements in their original language, which have been indexed by WTO criteria and

of Trade database. Data on trade bloc membership

can be downloaded as PDFs. Users can search by provision or keyword, compare provisions across multiple

are from the World Bank Policy Research Report

agreements, and sort agreements by membership, date of signature, in-force status, and other key criteria.

Trade Blocs (2000), UNCTAD’s Trade and Develop-

The database was developed jointly by the World Bank and the Center for International Business at the

ment Report 2007, WTO’s Regional Trade Agree-

Tuck School of Business at Dartmouth College. It is supported by the Multidonor Trust Fund for Trade and

ments Information System, and the World Bank

Development with financing from the governments of Finland, Norway, Sweden, and the United Kingdom.

and the Center for International Business at the

The GPTAD is integrated with the World Integrated Trade Solution database and is part of the World Bank’s

Tuck School of Business at Dartmouth College’s

Open Data initiative (http://wits.worldbank.org/gptad/).

Global Preferential Trade Agreements Database.

2011 World Development Indicators

347

6.8

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 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

348

Most recent year

Binding coverage

2008 2009 2009 2009 2009 2010 2008 2010 2009 2006 2009 2008 2007 2009 2009 2010 2009 2007 2010 2009 2010 2010 2010 2010 2010 2008 2009 2010 2010 2007 2009 2010 2009 2010 2010 2010 2008 2009 2007 2009 2010 2010 2010 2009 2007 2008 2010 2009 2010 2007 2006 2009 2010 2009 2009 2009 2009 2009 2009 2010

.. 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 94.7 100.0 100.0 99.3 100.0 .. .. .. 100.0 51.4 .. 100.0 13.7 100.0 14.4 100.0

2011 World Development Indicators

Simple mean bound rate

.. 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 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

Simple mean tariff

Weighted mean tariff

6.2 5.7 14.2 7.4 13.8 11.4 3.7 2.9 8.3 28.5 4.3 13.9 15.1 8.0 11.2 13.3 18.1 18.2 9.6 3.7 8.8 13.4 3.8 12.4 9.8 12.4 18.4 3.3 14.7 17.5 17.6 4.9 8.2 0.0 0.0 11.2 7.8 12.9 18.6 4.8 13.1 2.4 10.5 20.6 11.9 9.0 9.3 12.6 5.1 18.3 9.6 18.1 1.8 11.9 6.8 18.7 18.7 0.5 13.0 5.3

6.4 5.1 8.6 7.4 14.6 6.2 2.3 1.9 3.9 23.9 3.6 13.0 14.8 2.3 5.9 15.4 27.8 17.8 5.4 2.0 5.2 7.6 4.1 8.8 5.5 9.9 15.0 1.0 11.6 13.6 14.7 4.0 4.2 0.0 0.0 8.9 7.8 11.0 14.7 2.4 7.3 1.2 8.7 15.2 7.9 4.9 6.0 8.0 5.5 15.6 5.4 9.7 1.4 10.1 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 77.4 0.2 38.0 44.9 27.2 30.1 50.2 66.7 50.7 11.9 5.7 20.2 26.4 20.8 44.5 29.8 19.7 52.5 7.2 44.3 47.4 47.4 0.0 13.4 0.0 0.0 19.8 42.8 42.5 52.6 0.7 47.9 4.1 11.6 69.4 43.3 28.8 20.2 18.3 1.9 52.3 22.4 55.4 1.1 20.9 28.1 53.1 91.2 0.0 40.5 6.0

0.0 0.0 0.0 0.0 0.0 0.0 0.3 0.0 0.0 0.0 0.0 0.0 0.6 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

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 24.4 6.7 16.3 26.3 6.8 17.2 15.5 10.0 43.5 8.4 1.6 6.1 8.1 0.2 11.4 15.4 13.8 20.5 2.1 16.2 18.9 22.5 4.4 8.1 0.0 0.0 10.9 4.2 14.2 21.9 6.3 15.1 4.5 11.1 15.9 19.2 11.6 9.0 37.5 8.4 21.5 9.2 19.2 2.4 13.7 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 15.1 6.9 8.8 21.9 0.6 4.0 12.4 16.1 44.9 5.8 1.3 0.5 1.5 0.1 8.1 9.4 11.8 12.9 0.3 12.2 13.8 17.2 2.7 1.7 0.0 0.0 8.8 3.8 10.8 18.6 3.3 5.4 1.9 6.2 8.7 5.7 4.5 4.3 6.2 7.4 21.4 3.5 5.6 0.6 7.7 2.7 15.1 12.8 1.0 8.9 2.0

6.1 5.5 14.0 6.7 13.0 11.8 3.5 3.1 8.0 29.4 4.0 13.5 13.4 8.2 10.1 12.9 19.5 15.6 9.6 3.9 9.0 13.9 4.4 12.5 9.1 12.1 18.1 3.5 14.3 17.3 16.7 4.9 8.1 0.0 0.0 11.2 8.7 12.6 18.1 4.6 12.8 2.1 10.4 21.4 10.5 8.6 9.3 9.3 4.7 17.7 9.5 17.9 1.6 11.6 7.3 18.3 19.1 0.1 12.4 4.7

6.3 4.9 8.8 5.9 14.5 7.0 2.4 2.5 3.9 29.7 3.1 14.0 12.2 4.3 9.3 17.0 28.8 16.0 5.2 2.5 6.6 9.6 5.0 9.2 4.5 9.6 16.0 1.3 10.9 13.3 13.8 4.8 5.5 0.0 0.0 8.8 10.3 11.1 14.1 2.0 9.3 0.9 9.8 18.6 9.3 5.2 6.7 9.1 4.2 14.3 7.1 12.8 1.9 12.8 5.2 14.3 16.9 0.0 8.5 2.7

All products

Primary products

6.8

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

2008 2009 2009 2010 2008 2009 2009 2009 2009 2009 2008 2009 2007 2010 2009 2008 2010 2010 2009 2009 2008 2007 2010 2006 2009 2008 2009 2009 2009 2010 2007 2009 2009 2010 2008 2009 2009 2009 2009 2008 2010 2009 2010 2009 2010 2010 2009 2009 2009 2006 2009 2008 2010 2010 2010

Binding coverage

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 ..

Simple mean bound rate

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 ..

Simple mean tariff

Weighted mean tariff

10.6 4.4 13.5 13.3 10.7 3.0 6.4 1.9 10.2 5.2 24.8 .. 5.5 9.2 2.6 9.7 4.3 12.1 .. 10.3 .. 4.1 3.6 9.3 5.6 9.5 .. 0.0 4.3 12.1 13.0 5.3 21.7 12.8 12.6 2.0 5.3 7.8 4.2 4.9 2.2 9.1 7.7 4.0 6.3 12.8 2.5 4.4 13.0 10.9 0.4 3.6 14.8 2.6 7.6 4.8 8.1 4.8 5.3 ..

8.8 2.7 11.9 9.9 6.8 5.1 6.5 0.9 7.9 3.1 19.6 .. 3.2 9.0 1.6 5.2 2.7 9.2 .. 8.7 .. 4.2 8.4 13.2 4.8 10.5 .. 0.0 3.2 8.3 7.0 3.1 20.6 8.4 10.1 1.0 1.8 6.1 3.0 5.1 3.2 7.1 4.5 3.2 1.8 14.3 1.6 2.6 9.1 10.6 0.3 3.2 9.5 2.2 7.6 2.6 3.7 2.5 4.8 ..

Share of tariff Share of tariff lines lines with international with specific rates peaks

43.3 18.1 56.1 51.8 41.3 5.1 0.5 5.7 6.6 11.4 56.5 .. 1.1 36.1 8.6 29.5 8.8 36.6 .. 7.0 .. 0.0 0.9 20.4 11.6 21.6 .. 0.0 14.5 41.1 47.5 16.3 88.1 47.9 49.0 10.4 2.6 6.4 7.7 0.1 5.4 23.6 25.4 4.1 16.7 50.4 0.0 17.1 48.9 34.9 0.5 0.2 45.3 0.5 2.8 24.4 18.3 10.0 5.4 ..

0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 .. 0.0 0.0 0.0 0.0 11.5 0.0 .. 0.0 .. 0.0 0.0 0.0 0.0 0.0 .. 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 3.4 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.7 0.0 0.0 0.0 ..

Simple mean tariff

Weighted mean tariff

Simple mean tariff

Weighted mean tariff

14.1 4.9 15.6 14.6 17.7 5.8 9.9 2.4 20.0 5.6 21.7 .. 5.5 16.1 5.1 14.2 7.3 16.0 .. 26.3 .. 3.2 4.4 16.0 8.2 9.2 .. 0.0 7.6 13.9 14.8 2.4 17.5 12.8 11.1 1.2 3.8 10.7 6.6 5.2 6.2 18.0 8.2 5.1 4.1 15.6 1.4 5.9 14.0 11.8 1.8 4.4 14.2 0.5 11.5 15.2 5.8 3.8 6.8 ..

9.9 2.1 13.9 10.0 5.9 4.1 8.1 1.1 7.3 2.0 12.5 .. 2.2 8.6 1.6 3.9 1.3 12.6 .. 12.7 .. 3.1 1.3 14.2 5.0 1.6 .. 0.0 6.0 4.2 8.6 2.1 18.4 7.9 9.2 0.3 1.3 11.5 3.6 5.4 5.2 8.9 4.4 2.7 2.1 11.0 0.4 3.0 10.7 9.1 1.0 3.3 6.4 0.6 8.4 3.3 0.8 1.3 5.1 ..

10.0 4.3 13.2 12.9 9.7 2.5 5.9 1.8 8.7 5.2 24.8 .. 5.4 8.3 2.1 8.9 4.0 11.7 .. 7.3 .. 4.2 3.5 8.4 5.2 9.5 .. 0.0 3.9 11.9 12.7 5.8 22.8 12.8 12.8 2.1 5.5 7.4 3.8 4.9 1.6 8.2 7.5 3.9 6.7 12.5 2.6 4.2 12.8 10.7 0.3 3.5 14.7 3.1 7.1 3.4 8.2 4.9 5.0 ..

8.4 3.1 10.2 9.7 7.3 5.9 5.4 0.8 8.0 3.5 21.1 .. 3.6 9.3 1.6 5.9 3.1 6.6 .. 5.0 .. 4.4 9.4 12.6 5.0 10.9 .. 0.0 2.4 10.4 6.5 3.6 22.6 8.7 11.0 1.6 2.1 4.6 2.7 4.9 2.4 5.7 4.3 3.4 1.6 16.5 2.1 2.2 7.6 10.8 0.2 3.2 12.1 3.2 7.2 2.2 4.8 3.0 4.6 ..

2011 World Development Indicators

349

6.8

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 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 & North Africa

South Asia Sub-Saharan Africa High income OECD Non-OECD

2009 2009 2010 2009 2010 2005a 2007 2004 2010 2008 2010 2009 2009 2007 2007 2009 2010 2010 2010 2010 2006 2010 2009 2010 2009 2008 2008 2009 2002 2010 2010 2009 2010 2010 2009 2009 2010 2008 2009 2009 2007b

Binding coverage

Simple mean bound rate

100.0 .. 100.0 100.0 100.0 .. .. 100.0 69.6 100.0 .. 96.1 38.1 97.9 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 77.8 w 42.2 86.6 84.7 88.3 73.9 67.2 100.0 90.0 99.9 81.5 61.7 87.9 99.0 73.1

16.0 .. 89.3 10.8 30.0 .. .. 47.4 7.0 78.7 .. 19.4 30.1 75.9 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 27.3 w 57.7 30.3 31.8 29.0 35.5 25.8 5.8 32.5 30.4 41.6 41.8 7.9 10.7 9.1

a. Includes Montenegro. b. Rates are most favored nation rates.

350

2011 World Development Indicators

Simple mean tariff

4.2 8.1 9.9 4.0 13.4 8.1 6.5 .. 0.0 9.9 .. 7.6 10.1 14.3 9.6 11.3 13.4 11.6 10.9 0.0 6.7 4.9 12.9 10.8 .. 12.8 10.8 8.7 21.9 2.4 5.4 12.1 4.5 4.3 2.9 9.6 11.8 16.8 13.1 8.0 .. 5.5 10.8 16.7 6.2 w 12.1 8.9 8.4 9.2 9.5 5.3 4.5 9.2 6.7 13.0 11.1 2.7 3.6 1.8

Weighted mean tariff

3.8 5.9 6.0 3.9 8.9 6.0 28.3 .. 0.0 17.3 .. 4.4 6.4 13.7 9.0 8.4 7.9 11.9 10.2 0.0 6.1 3.8 8.2 4.9 .. 14.2 7.3 10.0 16.0 2.3 2.9 8.2 2.8 3.7 1.8 3.6 6.9 15.0 10.6 5.2 .. 4.2 3.8 17.3 2.5 w 10.0 6.3 5.8 6.4 6.4 4.8 2.8 6.6 6.1 8.2 7.5 1.8 2.2 0.6

Share of tariff Share of tariff lines lines with international with specific rates peaks

0.2 24.6 31.4 0.0 50.5 17.8 12.8 .. 0.0 2.6 .. 17.9 42.7 43.1 39.9 44.4 25.4 36.2 26.2 0.0 27.6 0.1 39.9 19.3 .. 47.3 64.7 43.6 57.8 4.6 14.8 37.5 1.1 0.2 3.4 29.3 20.1 65.0 21.9 19.8 .. 1.4 51.2 38.8 10.8 w 40.6 16.0 15.4 16.3 18.5 5.4 1.1 15.7 27.6 37.4 33.6 3.5 4.0 3.2

0.0 0.0 0.0 0.0 0.0 0.0 0.0 .. 0.0 0.8 .. 0.0 0.0 0.0 0.0 0.2 0.0 0.0 0.0 0.0 0.0 0.7 0.0 0.0 .. 0.0 0.0 0.4 0.0 0.0 2.8 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 .. 0.0 0.0 0.0 0.0 w 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

Simple mean tariff

Weighted mean tariff

Simple mean tariff

Weighted mean tariff

5.0 7.7 11.5 3.3 14.1 10.9 14.0 .. 0.0 14.8 .. 5.4 15.3 16.5 12.7 15.1 15.9 18.3 9.7 0.0 6.5 5.4 17.5 14.0 .. 14.4 12.1 16.6 26.8 13.8 14.7 15.7 5.9 4.5 2.6 5.6 12.6 19.5 12.2 10.7 .. 7.1 9.2 17.4 6.6 w 14.4 8.6 9.7 7.9 9.8 6.8 5.9 8.6 6.5 17.1 12.1 4.2 5.3 2.5

4.0 4.4 6.4 2.8 7.7 4.5 50.5 .. 0.0 23.3 .. 1.9 8.4 13.5 4.9 7.8 7.7 15.0 1.3 0.0 6.1 2.1 8.7 2.7 .. 12.4 5.5 3.1 12.0 4.3 12.6 8.8 2.5 2.7 1.2 1.1 3.9 16.9 10.0 4.1 .. 3.8 3.1 20.4 2.4 w 9.4 5.4 4.8 5.6 5.7 5.1 2.5 6.2 6.1 7.3 5.9 1.9 2.3 0.7

4.1 8.2 9.7 4.1 13.2 7.8 4.8 .. 0.0 9.2 .. 7.8 9.4 13.7 9.1 10.5 13.0 10.4 11.1 0.0 6.5 4.9 12.4 10.2 .. 12.6 10.5 7.6 21.2 1.2 3.8 11.6 4.3 4.2 3.0 9.9 11.7 16.1 13.1 7.4 .. 5.2 10.9 16.1 6.1 w 11.8 8.9 8.2 9.3 9.4 5.0 4.3 9.2 6.5 12.3 10.9 2.5 3.3 1.6

3.8 6.2 5.9 4.2 10.2 6.8 6.4 .. 0.0 8.8 .. 5.6 5.2 13.7 12.2 8.6 7.9 10.4 15.9 0.0 5.7 5.3 8.0 5.9 .. 14.9 9.0 17.2 17.9 1.4 1.1 7.9 3.0 4.2 2.0 5.2 7.3 14.2 10.7 5.7 .. 4.6 4.1 14.7 2.5 w 10.2 6.5 6.2 6.6 6.6 4.6 3.0 6.7 5.7 8.4 8.1 1.8 2.1 0.6

About the data

6.8

global links

Tariff barriers Definitions

Poor people in developing countries work primarily

trade and reduce the weights applied to these tariffs.

• Binding coverage is the percentage of product

in agriculture and labor–intensive manufactures,

Bound rates result from trade negotiations incorpo-

lines with an agreed bound rate. • Simple mean

sectors that confront the greatest trade barriers.

rated into a country’s schedule of concessions and

bound rate is the unweighted average of all the lines

Removing barriers to merchandise trade could

are thus enforceable.

in the tariff schedule in which bound rates have been

increase growth in these countries—even more if trade in services.

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-

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-

specific 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 data are from

and quarantine measures. Because of the difficulty

the United Nations Conference on Trade and Devel-

of combining nontariff barriers into an aggregate indi-

opment’s (UNCTAD) Trade Analysis and Information

cator, they are not included in the table.

System (TRAINS) database and the World Trade

Unless specified as most favored nation rates, the

Organization’s (WTO) Integrated Data Base (IDB)

tariff rates used in calculating the indicators in the

and Consolidated Tariff Schedules (CTS) database.

table are effectively applied rates. Effectively applied

Tariff line data were matched to Standard Interna-

rates are those in effect for partners in preferen-

tional Trade Classification (SITC) revision 2 codes to

tial trade arrangements such as the North Ameri-

define commodity groups and import weights. Import

can Free Trade Agreement. The difference between

weights were calculated using the United Nations

most favored nation and applied rates can be sub-

Statistics Division’s Commodity Trade (Comtrade)

stantial. Because more countries now report their

database. The table shows tariff rates for three com-

free trade agreements, suspensions of tariffs, and

modity groups: all products, primary products, and

other special preferences, this year’s World Develop-

manufactured products. Effectively applied rates at

ment Indicators includes effectively applied rates for

the six- and eight-digit product level are averaged for

most countries. All estimates are calculated using

products in each commodity group. When an effec-

the most recent information, which is not necessarily

tively applied rate is not available, the most favored

revised every year. As a result, data for the same year

nation rate is used instead.

may differ from data in last year’s edition.

Data are shown only for the last year for which com-

Data sources

Three measures of average tariffs are shown: sim-

plete data are available and for all economies with

ple bound rates and the simple and the weighted

populations of 1 million or more and for economies

All indicators in the table were calculated by World

tariffs. Bound rates are based on all products in a

with populations of less than 1 million when avail-

Bank staff using the World Integrated Trade Solu-

country’s tariff schedule, while the most favored

able. EU member countries apply a common tariff

tion system, available at http://wits.worldbank.

nation or applied rates are calculated using all traded

schedule that is listed under European Union and

org. Data on tariffs were provided by UNCTAD’s

items. Weighted mean tariffs are weighted by the

are thus not listed separately.

TRAINS database and the WTO’s IDB and CTS

value of the country’s trade with each trading part-

database. Data on global imports are from the

ner. Simple averages are often a better indicator of

United Nations Statistics Division’s Comtrade

tariff protection than weighted averages, which are

database.

biased downward because higher tariffs discourage

2011 World Development Indicators

351

6.9

Trade facilitation Logistics Performance Index

1–5 (worst to best) 2009

352

Burden of customs procedures

1–7 (worst to best) 2009–10 b

2.24 2.46 2.36 2.25 3.10 2.52 3.84 3.76 2.64 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 3.51 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 2.78

2011 World Development Indicators

.. 4.0 3.2 2.8 2.7 2.6 5.0 5.3 3.5 3.4 .. 4.6 4.2 2.7 3.6 4.7 3.3 3.5 4.4 3.0 3.5 3.8 4.9 .. 2.7 5.7 4.5 6.5 4.1 .. .. 4.0 3.8 4.1 .. 4.6 5.6 4.7 3.5 4.5 4.2 .. 5.3 3.6 5.7 4.9 .. 5.4 4.7 5.1 3.8 4.1 4.2 .. .. .. 4.2

Lead time

Documents

days

number

To export 2009

2.0 1.7 4.6 6.0 3.7 .. 2.6 2.0 7.0 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 .. 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 2.4

To import 2009

4.0 2.0 7.1 8.0 3.8 .. 2.8 3.7 3.0 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 .. 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 3.2

To export June 2010

12 7 8 11 9 3 6 4 9 6 8 4 7 8 5 6 8 5 10 9 10 11 3 9 6 6 7 4 6 8 11 6 10 7 .. 4 4 6 9 6 8 9 3 8 4 2 7 6 4 4 6 5 10 7 6 8 6

To import June 2010

11 9 9 8 7 6 5 5 14 8 8 5 7 7 7 9 7 7 10 10 10 12 4 17 10 7 5 4 8 9 10 7 9 8 .. 7 3 7 7 6 8 13 4 8 5 2 8 8 4 5 7 6 10 9 6 10 10

Liner Quality of Freight Shipping port costs to the Connectivity infrastructure United Index States

0–100 (low to high) 2010

.. 4.3 31.4 10.7 27.6 .. 28.1 .. .. 7.5 .. 84.0 11.5 .. .. .. 31.7 5.5 .. .. 4.5 11.3 42.4 .. .. 22.1 143.6 113.6 26.1 5.2 10.5 12.8 17.5 9.0 6.6 0.4 26.8 22.2 18.7 47.5 9.6 0.0 5.7 .. 8.4 74.9 8.5 5.4 4.0 90.9 17.3 34.3 13.3 6.3 3.5 7.6 9.1

1–7 (worst to best) 2009–10 b

1 kilogram DHL nondocument air packagea $ 2011

.. 3.5 3.2 2.1 3.8 2.9c 4.9 4.8c 4.2c 3.4 .. 6.4 4.0 2.9c 1.6 3.8 c 2.9 3.8 3.9c 3.0 c 3.9 3.3 5.7 .. 2.6c 5.5 4.3 6.8 3.5 .. .. 2.7 5.0 4.0 .. 4.6c 6.1 4.3 3.7 4.2 4.1 .. 5.6 4.4 c 6.4 5.9 .. 5.1 4.0 6.4 4.5 4.0 4.5 .. .. .. 5.3

143.10 155.85 157.10 157.10 90.75 143.10 98.00 129.45 155.85 98.00 155.85 112.50 157.10 90.75 155.85 157.10 90.75 155.85 157.10 157.10 95.70 157.10 72.20 157.10 157.10 90.75 84.55 90.45 90.75 157.10 157.10 90.75 157.10 155.85 75.05 155.85 129.45 75.05 90.75 143.10 90.75 157.10 155.85 157.10 129.45 112.50 157.10 157.10 155.85 112.50 157.10 129.45 90.75 157.10 157.10 75.05 90.75

Logistics Performance Index

1–5 (worst to best) 2009

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) 2009–10 b

4.3 4.0 3.9 3.5 .. 5.2 4.3 4.2 3.8 4.6 4.5 3.5 3.3 .. 4.5 .. 4.1 3.0 .. 4.1 3.5 3.8 .. 3.5 4.8 4.3 3.9 3.9 4.8 4.1 4.5 4.6 3.9 3.4 3.3 4.3 3.7 .. 4.2 3.4 5.2 5.8 3.6 .. 3.1 5.2 5.2 3.6 4.4 .. 3.8 4.5 3.0 4.3 4.9 4.7 4.9

Lead time

Documents

days

number

To export 2009

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

To import 2009

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

To export June 2010

5 8 5 7 10 4 5 4 6 4 7 10 8 .. 3 8 8 7 9 5 5 6 10 .. 6 6 4 11 7 7 11 5 5 6 8 7 7 .. 11 9 4 7 5 8 10 4 9 9 3 7 8 6 8 5 4 7 5

To import June 2010

7 9 6 8 10 4 4 4 6 5 7 12 7 .. 3 8 10 7 10 6 7 8 9 .. 6 6 9 10 7 10 11 6 4 7 8 10 10 .. 9 10 5 5 5 10 9 4 9 8 4 9 10 8 8 5 5 10 7

6.9

global links

Trade facilitation

Liner Quality of Freight Shipping port costs to the Connectivity infrastructure United Index States

0–100 (low to high) 2010

.. 41.4 25.6 30.7 4.2 7.6 33.2 59.6 33.1 67.4 17.8 .. 13.1 .. 82.6 .. 8.3 .. .. 6.0 30.3 .. 5.9 5.4 9.5 .. 7.4 .. 88.1 .. 5.6 16.7 36.3 .. .. 49.4 8.2 3.7 14.4 .. 90.0 18.4 8.7 .. 18.3 7.9 48.5 29.5 41.1 6.4 0.0 21.8 15.2 26.2 38.1 .. 7.7

1–7 (worst to best) 2009–10 b

1 kilogram DHL nondocument air packagea $ 2011

4.0 c 3.9 3.6 3.9 .. 4.4 4.6 3.9 5.3 5.2 4.4 3.3c 3.8 .. 5.5 .. 4.4 1.4 c .. 4.7 4.5 3.1c .. 3.2 4.7 3.7c 3.4 3.6c 5.6 3.7c 3.6 4.5 3.7 2.9 3.3c 4.4 3.5 .. 5.6 2.9 c 6.6 5.4 2.9 .. 3.0 5.7 5.3 4.0 6.0 .. 3.4c 3.3 2.8 3.3 4.9 5.4 5.4

155.85 98.00 98.00 143.10 143.10 112.00 143.10 112.50 75.05 120.80 143.10 155.85 157.10 95.70 98.00 .. 143.10 155.85 95.70 155.85 143.10 157.10 157.10 157.10 155.85 155.85 157.10 157.10 98.00 157.10 157.10 157.10 58.80 155.85 95.70 157.10 157.10 95.70 157.10 95.70 112.50 98.00 90.75 157.10 157.10 129.45 143.10 143.10 90.75 95.70 90.75 90.75 98.00 155.85 129.45 .. 143.10

2011 World Development Indicators

353

6.9

Trade facilitation Logistics Performance Index

1–5 (worst to best) 2009

Romania Russian Federation Rwanda Saudi Arabia Senegal Serbia Sierra Leone Singapore Slovak Republic Slovenia Somalia South Africa 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) 2009–10 b

2.84 2.61 2.04 3.22 2.86 2.69 d 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 2.87 e u 2.38 2.69 2.62 2.75 2.59 2.73 2.68 2.74 2.60 2.49 2.42 3.54 3.57

3.9 2.9 4.8 4.9 4.7 3.6 .. 6.3 4.4 5.2 .. 4.4 4.6 4.2 .. 3.5 5.8 5.1 2.8 3.6 3.4 4.1 3.6 .. 3.1 4.7 3.8 .. 4.1 3.0 5.8 4.8 4.5 4.0 .. 2.2 3.6 .. .. 4.2 3.6 4.2 u 3.8 3.8 3.7 3.9 3.8 3.8 3.7 3.8 3.8 3.7 3.9 4.9 4.9

Lead time

Documents

days

number

To export 2009

To import 2009

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 3.8 e u 6.8 3.8 4.4 3.1 4.6 3.6 2.9 3.9 2.7 1.9 8.1 2.1 2.2

2.0 2.9 .. 6.3 2.7 3.0d 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 4.6 e u 7.2 5.0 5.1 4.9 5.6 4.9 3.1 5.5 7.2 3.3 7.0 2.7 2.9

To export June 2010

To import June 2010

5 8 8 5 6 6 7 4 6 6 .. 8 6 8 6 9 3 4 8 10 5 4 6 6 5 4 7 .. 6 6 4 4 4 10 7 8 6 6 6 6 7 7u 8 7 7 7 7 7 7 7 7 9 8 5 4

6 13 8 5 5 6 7 4 8 8 .. 9 7 6 6 10 3 5 9 9 7 3 7 8 6 7 8 .. 8 8 5 4 5 10 9 9 8 6 9 8 9 7u 9 8 8 8 8 7 8 7 8 9 9 5 5

Liner Quality of Freight Shipping port costs to the Connectivity infrastructure United Index States

0–100 (low to high) 2010

15.5 20.9 .. 50.4 13.0 3.0d 5.8 103.8 .. 20.6 4.2 32.5 74.3 40.2 10.1 .. 30.6 2.6 15.2 .. 10.6 43.8 .. 14.2 15.8 6.5 36.1 .. .. 21.1 63.4 87.5 83.8 24.5 .. 18.6 31.4 .. 12.5 .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. ..

1–7 (worst to best) 2009–10 b

1 kilogram DHL nondocument air packagea $ 2011

3.0 3.7 2.8 5.2 4.7 2.8 .. 6.8 4.0c 5.3 .. 4.7 5.6 4.9 .. 4.2 6.2 5.2c 3.1 1.9c 3.0 5.0 2.5 .. 4.3 5.0 4.1 .. 3.5c 3.6 6.2 5.5 5.5 5.2 .. 2.4 3.6 .. .. 3.6c 4.4 c 4.3 u 3.5 3.8 3.8 3.9 3.7 3.8 3.3 3.9 4.0 3.8 3.8 5.3 5.3

155.85 155.85 157.10 143.10 157.10 155.85 157.10 90.45 155.85 155.85 157.10 157.10 129.45 98.00 157.10 157.10 129.45 129.45 143.10 155.85 157.10 98.00 95.70 157.10 75.05 157.10 143.10 155.85 157.10 155.85 143.10 112.50 .. 90.75 155.85 90.75 98.00 .. 143.10 157.10 157.10 .. .. .. .. .. .. .. .. .. .. .. .. .. ..

a. Transportation charges only; excludes fuel, assessorial/surcharges, duties and taxes. b. Average of the 2009 and 2010 survey ratings. c. Landlocked country. d. Includes Montenegro. e. Aggregates are computed according to the World Bank classification of economies as of July 1, 2010 and may differ from data published in the original source.

354

2011 World Development Indicators

About the data

6.9

global links

Trade facilitation Definitions

Broadly defined, trade facilitation encompasses

include the value of time to import or export and the

• Logistics Performance Index reflects percep-

customs efficiency and other physical and regulatory

risk of delay or loss of shipments. Long lead times

tions of a country’s logistics based on efficiency of

environments where trade takes place, harmoniza-

and burdensome regulatory procedures may lower

customs clearance process, quality of trade- and

tion of standards and conformance to international

competitiveness. Data on lead time are from the LPI

transport-related infrastructure, ease of arranging

regulations, and the logistics of moving goods and

survey. Respondents provided separate values for

competitively priced shipments, quality of logistics

associated documentation through countries and

the best case (10 percent of shipments) and the

services, ability to track and trace consignments, and

ports. Though collection of trade facilitation data

median case (50 percent of shipments). The data

frequency with which shipments reach the consignee

has improved over the last decade, data that allow

are exponentiated averages of the logarithm of sin-

within the scheduled time. The index ranges from 1

meaningful evaluation, especially for developing

gle value responses and of midpoint values of range

to 5, with a higher score representing better perfor-

economies, are lacking. Data on trade facilitation

responses for the median case.

mance. • Burden of customs procedure measures

are drawn from research by private and international

Data on the number of documents needed to export

business executives’ perceptions of their country’s

agencies. Most data are perception-based evalua-

or import are from the World Bank’s Doing Business

efficiency of customs procedures. Values range from

tions by business executives and professionals.

surveys, which compile procedural requirements for

1 to 7, with a higher rating indicating greater effi-

Because of different backgrounds, values, and per-

exporting and importing a standardized cargo of goods

ciency. • Lead time to export is the median time (the

sonalities, those surveyed may evaluate the same

by ocean transport from local freight forwarders, ship-

value for 50 percent of shipments) from shipment

situation quite differently. Caution should thus be

ping lines, customs brokers, port officials, and banks.

used when interpreting perception-based indicators.

To make the data comparable across economies, sev-

Nevertheless, they convey much needed information

eral assumptions about the business and the traded

on trade facilitation.

goods are used (see www.doingbusiness.org).

point to port of loading. • Lead time to import is the median time (the value for 50 percent of shipments) from port of discharge to arrival at the consignee. • Documents to export and documents to import are

The table presents data from Logistics Performance

Access to global shipping and air freight networks

Surveys conducted by the World Bank in partnership

and the quality and accessibility of ports and roads

with academic and international institutions and

affect logistics performance. The table shows two

private companies and individuals engaged in inter-

indicators related to trade and transport service infra-

national logistics. The Logistics Performance Index

structure: the Liner Shipping Connectivity Index and

assesses logistics performance across six aspects

the quality of port infrastructure rating. The Liner Ship-

of the logistics environment (see Definitions), based

ping Connectivity Index captures how well countries

on more than 5,000 country assessments by nearly

are connected to global shipping networks. It is com-

1,000 international freight forwarders. Respondents

puted by the United Nations Conference on Trade and

evaluate eight markets on six core dimensions on

Development (UNCTAD) based on five components of

a scale from 1 (worst) to 5 (best). The markets are

the maritime transport sector: number of ships, their

chosen based on the most important export and

container-carrying capacity, maximum vessel size,

import markets of the respondent’s country, random

number of services, and number of companies that

selection, and, for landlocked countries, neighboring

deploy container ships in a country’s ports. For each

countries that connect them with international mar-

component a country’s value is divided by the maxi-

kets. Scores for the six areas are averaged across all

mum value of each component in 2004, the five com-

respondents and aggregated to a single score. Details

ponents are averaged for each country, and the aver-

express rate for a 1 kilogram nondocument air pack-

of the survey methodology and index construction

age is divided by the maximum average for 2004 and

age. Fuel, assessorial/surcharges, duties, and taxes

methodology are in Arvis and others (2010).

multiplied by 100. The index generates a value of 100

are excluded.

Data on the burden of customs procedures are

for the country with the highest average index in 2004.

from the World Economic Forum’s Executive Opinion

The quality of port infrastructure measures busi-

all documents required per shipment by government ministries, customs authorities, port and container terminals, health and technical control agencies, and banks to export or import goods. Documents renewed annually and not requiring renewal per shipment are excluded. • Liner Shipping Connectivity Index indicates how well countries are connected to global shipping networks based on the status of their maritime transport sector. The highest value in 2004 is 100. • Quality of port infrastructure measures business executives’ perceptions of their country’s port facilities. Values range from 1 to 7, with a higher rating indicating better development of port infrastructure. • Freight costs to the United States is the DHL international U.S. inbound worldwide priority

Data sources

Survey. The 2010 round included more than 15,000

ness executives’ perception of their country’s port

Data on the Logistics Performance Index and lead

respondents from 139 countries. Sampling follows

facilities. Values range from 1 (port infrastructure

time to export and import are from Arvis and others’

a dual stratification based on company size and the

considered extremely underdeveloped) to 7 (port

Connecting to Compete: Trade Logistics in the Global

sector of activity. Data are collected online or through

infrastructure considered efficient by international

Economy 2010. Data on the burden of customs

in-person interviews. Responses are aggregated using

standards). Respondents in landlocked countries

procedure and quality of port infrastructure ratings

sector-weighted averaging. The data for the latest

were asked: “How accessible are port facilities (1 =

are from the World Economic Forum’s Global Com-

year are combined with the data for the previous year

extremely inaccessible; 7 = extremely accessible.)”

petitiveness Report 2010–2011. Data on number of

to create a two-year moving average. Respondents

The costs of transport services are a crucial deter-

documents to export and import are from the World

evaluated the efficiency of customs procedures in

minant of export competitiveness. The proxy indica-

Bank’s Doing Business project (www.doingbusiness.

their country. The lowest value (1) rates the customs

tor in the table is the shipping rates to the United

org). Data on the Liner Shipping Connectivity Index are

procedure as extremely inefficient, and the highest

States of an international freight moving business.

from UNCTAD’s Review of Maritime Transport 2010.

score (7) as extremely efficient. The direct costs of cross-border trade include

Freight costs to the United States are based on DHL’s “DHL Express Standard Rate Guideline 2011” (2011).

freight, customs, and storage fees. Indirect costs

2011 World Development Indicators

355

6.10

External debt Total external debt

Long-term debt

$ millions

356

1995

2009

1995

.. 456 33,053 11,500 98,465 371 .. .. 321 15,726 1,694 .. 1,398 5,272 .. 717 160,469 10,379 1,271 1,162 2,284 10,950 .. 946 843 22,038 118,090 .. 25,044 13,239 5,887 3,766 18,899 .. .. .. .. 4,447 13,877 33,475 2,509 37 .. 10,322 .. .. 4,361 426 1,240 .. 5,495 .. 3,282 3,248 895 821 4,851

2,328 4,719 5,345 16,715 120,183 4,935 .. .. 4,865 23,820 17,158 .. 1,073 5,745 9,583 1,617 276,932 40,582 1,835 518 4,364 2,941 .. 396 1,743 71,646 428,442 .. 52,223 12,183 5,041 8,070 11,701 .. .. .. .. 11,003 12,930 33,257 11,384 1,019 .. 5,025 .. .. 2,130 520 4,231 .. 5,720 .. 13,801 2,926 1,111 1,244 3,675

.. 330 31,314 9,543 54,913 298 .. .. 206 14,905 1,301 .. 1,267 4,459 .. 707 98,260 8,808 1,140 1,099 2,110 9,620 .. 854 777 7,178 94,674 .. 13,946 9,636 4,867 3,097 11,902 .. .. .. .. 3,653 11,951 30,687 1,979 37 .. 9,788 .. .. 3,977 385 1,039 .. 4,200 .. 2,328 2,991 794 766 4,247

2011 World Development Indicators

$ millions Public and publicly guaranteed IBRD loans Total and IDA credits 2009 1995 2009

2,203 2,829 2,871 13,722 72,923 2,376 .. .. 3,403 21,206 4,758 .. 990 2,545 3,569 1,388 87,317 4,772 1,725 420 4,099 2,128 .. 250 1,711 9,282 93,125 .. 35,364 10,788 4,785 3,190 10,979 .. .. .. .. 7,714 6,910 30,622 6,131 1,013 .. 4,812 .. .. 2,022 449 2,596 .. 4,126 .. 4,931 2,827 950 1,078 2,446

.. 109 2,049 81 4,913 96 .. .. 30 5,692 116 .. 498 865 472 108 6,038 444 608 591 65 1,082 .. 414 379 1,383 14,248 .. 2,559 1,413 279 303 2,386 .. .. .. .. 300 1,108 2,356 327 24 .. 1,470 .. .. 110 162 84 .. 2,434 .. 158 847 210 389 828

471 874 10 385 5,305 1,214 .. .. 939 10,746 256 .. 309 316 1,520 5 10,065 1,509 721 147 566 303 .. 9 896 216 22,226 .. 6,571 2,497 298 58 1,823 .. .. .. .. 756 542 3,250 578 477 .. 1,422 .. .. 18 64 1,253 .. 1,581 .. 1,112 1,269 304 39 502

Private nonguaranteed 1995 2009

.. 0 0 0 16,066 0 .. .. 0 0 0 .. 0 239 .. 0 30,830 342 0 0 0 288 .. 0 0 11,429 1,090 .. 5,553 0 0 214 2,660 .. .. .. .. 19 440 313 5 0 .. 0 .. .. 0 0 0 .. 27 .. 142 0 0 0 123

0 983 982 0 27,723 1,461 .. .. 590 0 1,504 .. 0 2,647 4,051 0 149,826 17,232 0 0 0 615 .. 0 0 44,888 94,808 .. 12,749 0 0 2,538 271 .. .. .. .. 843 4,600 74 3,139 0 .. 0 .. .. 0 0 518 .. 0 .. 7,644 0 0 0 880

Short-term debt

Use of IMF credit

$ millions 1995 2009

.. 20 62 835 261 1,492 1,958 2,634 21,355 19,537 2 512 .. .. .. .. 14 810 199 1,939 110 8,024 .. .. 47 45 307 554 .. 1,677 10 229 31,238 39,789 512 18,578 56 0 15 7 102 265 991 23 .. .. 57 67 17 4 3,431 17,476 22,325 240,509 .. .. 5,545 4,110 3,118 596 213 1,002 430 2,341 3,910 99 .. .. .. .. .. .. .. .. 616 1,679 1,312 1,419 2,372 2,561 525 2,114 0 6 .. .. 460 45 .. .. .. .. 287 108 15 42 85 330 .. .. 620 1,323 .. .. 811 1,226 164 40 95 151 27 0 382 317

.. 65 1,478 0 6,131 70 .. .. 101 622 283 .. 84 268 48 0 142 717 75 48 72 51 .. 35 49 0 0 .. 0 485 19 24 427 .. .. .. .. 160 173 103 0 0 .. 73 .. .. 97 26 116 .. 648 .. 0 94 6 29 99

106 71 0 359 0 587 .. .. 62 675 2,871 .. 39 0 286 0 0 0 110 91 0 175 .. 78 29 0 0 .. 0 800 43 0 352 .. .. .. .. 767 0 0 0 0 .. 168 .. .. 0 29 786 .. 271 .. 0 59 10 166 32

Total external debt

Long-term debt

$ millions

1995

2009

1995

.. 95,174 124,413 21,565 .. .. .. .. 4,581 .. 7,661 3,750 7,309 .. .. .. .. 609 2,155 .. 2,974 684 2,466 .. 769 1,277 4,302 2,238 34,343 2,958 2,396 1,416 165,379 695 531 23,771 7,458 5,771 .. 2,410 .. .. 10,396 1,604 34,092 .. .. 30,169 6,098 2,506 2,574 30,833 39,379 .. .. .. ..

.. 237,692 157,517 13,435 .. .. .. .. 10,959 .. 6,615 109,873 8,005 .. .. 359 .. 2,900 5,539 .. 24,864 705 1,660 .. 31,717 5,589 2,213 1,093 66,390 2,667 2,029 742 192,008 3,457 2,212 23,752 4,168 8,186 .. 3,683 .. .. 4,420 991 7,846 .. .. 53,710 12,418 1,555 4,323 29,593 62,911 .. .. .. ..

.. 81,091 65,323 15,116 .. .. .. .. 3,721 .. 6,624 2,834 5,857 .. .. .. .. 472 2,091 .. 1,559 642 1,153 .. 430 788 3,687 2,078 16,023 2,739 2,127 1,148 93,902 450 472 23,190 5,209 5,378 .. 2,339 .. .. 8,572 1,347 28,140 .. .. 23,727 3,781 1,668 1,453 18,931 28,525 .. .. .. ..

$ millions Public and publicly guaranteed IBRD loans Total and IDA credits 2009 1995 2009

.. 76,531 86,020 7,524 .. .. .. .. 6,664 .. 5,445 2,487 6,543 .. .. 359 .. 2,320 2,923 .. 20,979 681 677 .. 9,059 1,874 1,846 899 21,364 2,592 1,851 661 99,374 783 1,817 19,219 3,354 6,320 .. 3,563 .. .. 2,461 909 4,157 .. .. 41,484 11,282 1,037 2,308 20,791 41,738 .. .. .. ..

.. 27,348 13,259 316 .. .. .. .. 595 .. 806 295 2,412 .. .. .. .. 141 285 .. 113 207 269 .. 62 181 1,121 1,306 1,059 863 347 157 13,823 152 59 3,999 890 777 .. 1,023 .. .. 341 598 3,489 .. .. 6,403 175 407 189 1,729 5,185 .. .. .. ..

.. 34,028 10,111 836 .. .. .. .. 398 .. 1,109 547 3,156 .. .. 359 .. 656 680 .. 318 313 69 .. 23 653 1,105 213 39 698 282 212 10,143 443 392 2,557 1,356 777 .. 1,483 .. .. 418 266 2,852 .. .. 11,844 435 231 296 2,846 2,669 .. .. .. ..

Private nonguaranteed 1995 2009

.. 6,618 33,123 0 .. .. .. .. 128 .. 0 103 445 .. .. .. .. 0 0 .. 50 0 0 .. 29 289 0 0 11,046 0 0 267 18,348 9 0 331 1,769 0 .. 0 .. .. 0 133 301 .. .. 1,593 0 711 338 1,288 4,847 .. .. .. ..

.. 118,211 52,834 0 .. .. .. .. 3,241 .. 0 98,710 0 .. .. 0 .. 332 2,601 .. 670 0 0 .. 16,708 1,816 4 0 21,332 0 0 81 69,299 1,203 141 2,354 0 0 .. 0 .. .. 1,093 7 175 .. .. 3,265 1,136 397 1,263 4,073 17,171 .. .. .. ..

6.10

global links

External debt

Short-term debt

Use of IMF credit

$ millions 1995 2009

.. 5,049 25,966 6,449 .. .. .. .. 492 .. 785 381 634 .. .. .. .. 13 0 .. 1,365 4 978 .. 49 143 542 44 7,274 72 169 1 37,300 6 12 198 279 393 .. 23 .. .. 1,785 72 5,651 .. .. 3,235 2,207 78 784 9,659 5,279 .. .. .. ..

.. 42,950 18,662 5,911 .. .. .. .. 1,054 .. 1,158 8,676 1,011 .. .. 0 .. 81 0 .. 3,096 0 92 .. 5,949 1,900 262 67 23,695 32 163 0 23,335 1,318 72 2,179 643 1,866 .. 44 .. .. 716 18 3,514 .. .. 1,466 0 121 752 4,730 4,002 .. .. .. ..

.. 2,416 0 0 .. .. .. .. 240 .. 251 432 374 .. .. .. .. 124 64 .. 0 38 336 .. 262 57 73 116 0 147 100 0 15,828 230 47 52 202 0 .. 48 .. .. 39 52 0 .. .. 1,613 111 50 0 955 728 .. .. .. ..

2011 World Development Indicators

.. 0 0 0 .. .. .. .. 0 .. 12 0 451 .. .. 0 .. 167 16 .. 119 24 891 .. 0 0 101 127 0 44 16 0 0 154 182 0 171 0 .. 76 .. .. 150 57 0 .. .. 7,495 0 0 0 0 0 .. .. .. ..

357

6.10

External debt Total external debt

Long-term debt

$ millions 1995

Romania Russian Federation Rwanda Saudi Arabia Senegal Serbia Sierra Leone Singapore Slovak Republic Slovenia Somalia South Africa 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

6,832 121,401 1,029 .. 3,916 10,785a 1,220 .. .. .. 2,678 25,358 .. 8,395 17,603 249 .. .. 21,897 634 7,365 100,039 .. 1,476 .. 10,818 73,781 402 3,609 8,429 .. .. .. 5,318 1,799 35,744 25,428 .. 6,251 6,958 4,989 .. s 130,267 1,729,983 841,940 888,043 1,860,250 455,544 246,178 608,666 161,737 152,282 235,842 .. ..

2009

117,511 3,957 17,904 381,339 101,582 99,990 747 971 725 .. .. .. 3,503 3,266 2,961 33,402 6,788 a 8,725 444 1,028 371 .. .. .. .. .. .. .. .. .. 2,973 1,961 1,987 42,101 9,837 15,063 .. .. .. 17,208 7,175 13,647 20,139 9,779 12,998 418 238 391 .. .. .. .. .. .. 5,236 16,955 4,480 2,514 590 1,603 7,325 6,204 4,637 58,755 16,826 11,185 .. .. .. 1,640 1,286 1,502 .. .. .. 21,709 9,022 14,837 251,372 50,317 84,875 576 385 463 2,490 3,089 2,245 93,153 6,581 10,449 .. .. .. .. .. .. .. .. .. 12,159 3,833 10,955 4,109 1,415 3,238 54,503 28,428 35,184 28,674 21,778 23,403 .. .. .. 6,356 5,562 5,861 3,049 5,291 1,210 5,015 3,462 3,742 .. s .. s .. s 135,593 109,551 110,863 3,409,521 1,151,625 1,296,127 1,417,085 578,607 597,241 1,992,436 573,018 698,886 3,545,114 1,261,176 1,406,990 825,602 255,399 293,956 1,126,252 189,044 269,524 912,980 371,875 432,115 141,321 140,298 112,569 339,983 129,636 159,965 198,976 174,924 138,861 .. .. .. .. .. ..

a. Includes Montenegro.

358

1995

$ millions Public and publicly guaranteed IBRD loans Total and IDA credits 2009 1995 2009

2011 World Development Indicators

Private nonguaranteed 1995 2009

Short-term debt

Use of IMF credit

$ millions 1995 2009

844 2,995 534 69,031 1,303 21,032 1,524 3,211 0 250,725 10,201 30,624 512 254 0 0 32 6 .. .. .. .. .. .. 1,160 921 44 357 260 18 1,252a 2,459 1,773a 19,076 2,139a 4,000 234 124 0 0 27 0 .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. 432 448 0 0 551 810 0 21 4,935 13,764 9,673 13,274 .. .. .. .. .. .. 1,512 2,487 90 967 535 1,873 1,279 1,306 496 0 6,368 6,739 25 10 0 0 11 27 .. .. .. .. .. .. .. .. .. .. .. .. 471 16 0 0 4,942 756 0 373 0 855 43 15 2,269 2,598 0 1,016 964 1,342 1,906 133 39,117 19,689 44,095 27,881 .. .. .. .. .. .. 541 586 0 0 85 47 .. .. .. .. .. .. 1,766 1,405 193 2,070 1,310 4,801 5,069 9,816 7,079 118,814 15,701 39,725 1 13 0 38 17 75 1,792 1,379 0 0 103 235 491 3,294 84 51,857 223 19,873 .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. 513 1,099 127 80 1,336 1,124 157 368 15 727 212 144 1,639 0 2,013 3,310 3,063 16,009 231 6,270 0 0 3,272 5,186 .. .. .. .. .. .. 827 2,187 0 0 689 442 1,434 407 13 1,020 415 474 896 985 381 89 685 1,068 .. s .. s .. s .. s .. s .. s 33,428 39,578 2,818 5,946 11,139 12,833 144,453 185,309 205,673 1,346,264 319,724 720,903 97,821 123,481 94,497 394,555 156,647 402,423 46,632 61,827 111,176 951,710 163,077 318,479 177,881 224,887 208,491 1,352,210 330,863 733,736 37,604 44,253 89,982 208,994 108,826 322,361 11,522 33,110 10,256 656,239 31,250 165,385 38,485 41,907 87,303 340,984 122,856 138,637 12,751 11,847 887 6,150 18,375 22,402 42,036 61,257 8,301 122,442 9,051 48,495 35,483 32,512 11,760 17,399 40,504 36,456 .. .. .. .. .. .. .. .. .. .. .. ..

1,038 9,544 9,617 0 26 15 .. .. 347 167 84 a 1,601 165 73 .. .. .. .. .. .. 166 176 913 0 .. .. 595 721 960 403 0 0 .. .. .. .. 0 0 0 41 197 329 0 0 .. .. 105 91 .. .. 293 0 685 7,958 0 0 417 9 1,542 10,974 .. .. .. .. .. .. 21 0 157 0 2,239 0 377 84 .. .. 0 53 1,239 345 461 116 .. s .. s 6,760 5,951 52,961 46,227 12,188 22,866 40,772 23,361 59,721 52,179 1,337 291 15,628 35,103 26,632 1,243 2,177 200 5,293 9,081 8,654 6,261 .. .. .. ..

About the data

6.10

Definitions

External indebtedness affects a country’s credit-

Variations in reporting rescheduled debt also affect

• Total external debt is debt owed to nonresident

worthiness and investor perceptions. Data on exter-

cross-country comparability. For example, reschedul-

creditors and repayable in foreign currencies, goods,

nal debt are gathered through the World Bank’s

ing of official Paris Club creditors may be subject to

or services by public and private entities in the coun-

Debtor Reporting System. Indebtedness is calculated

lags between completion of the general rescheduling

try. It is the sum of long-term external debt, short-

using loan-by-loan reports submitted by countries on

agreement and completion of the speciﬁc bilateral

term debt, and use of IMF credit. Debt repayable

long-term public and publicly guaranteed borrowing

agreements that define the terms of the rescheduled

in domestic currency is excluded. • Long-term debt

and information on short-term debt collected by the

debt. Other areas of inconsistency include country

is debt that has an original or extended maturity of

countries or from creditors through the reporting sys-

treatment of arrears and of nonresident national

more than one year. It has three components: pub-

tems of the Bank for International Settlements (BIS).

deposits denominated in foreign currency.

lic, publicly guaranteed, and private nonguaranteed

These data are supplemented by information from

Aggregate data on long-term private nonguaran-

debt. • Public and publicly guaranteed debt com-

major multilateral banks and official lending agen-

teed debt are reported annually. DRS countries rec-

prises the long-term external obligations of public

cies in major creditor countries and by estimates by

ognize the importance of monitoring borrowing by

debtors, including the national government and politi-

World Bank and International Monetary Fund (IMF)

their private sector, particularly when it accounts for

cal subdivisions (or an agency of either) and autono-

staff. The table includes data on long-term private

a significant share of total external debt, but many

mous public bodies, and the external obligations of

nonguaranteed debt reported to the World Bank or

find doing so difficult. Detailed data are available

private debtors that are guaranteed for repayment

estimated by its staff.

only from countries with registration requirements

by a public entity. • IBRD loans and IDA credits are

Data coverage, quality, and timeliness vary by coun-

for private nonguaranteed debt, most commonly in

extended by the World Bank. The International Bank

try. Coverage varies for debt instruments and borrow-

connection with exchange controls. Where formal

for Reconstruction and Development (IBRD) lends

ers. The widening spectrum of debt instruments and

registration of private nonguaranteed debt is not

at market rates. The International Development

investors alongside the expansion of private nonguar-

mandatory, compilers must rely on balance of pay-

Association (IDA) provides credits at concessional

anteed borrowing makes comprehensive coverage

ments data and financial surveys. The data on private

rates. • Private nonguaranteed debt consists of the

of external debt more complex. Reporting countries

nonguaranteed debt in the table are as reported or

long-term external obligations of private debtors that

differ in their capacity to monitor debt, especially

estimated for countries where this type of external

are not guaranteed for repayment by a public entity.

private nonguaranteed debt. Even data on public

debt is known to be significant. Estimates are based

• Short-term debt is debt owed to nonresidents hav-

and publicly guaranteed debt are affected by cover-

on national data on quarterly external debt statistics.

ing an original maturity of one year or less and inter-

age and reporting accuracy—because of monitoring

The DRS encourages debtor countries to volun-

est in arrears on long-term debt and on the use of

capacity and sometimes because of unwillingness to

tarily provide information on their short-term external

IMF credit. • Use of IMF credit denotes members’

provide information. A key part often underreported

obligations. By its nature, short-term external debt

drawings on the IMF other than those drawn against

is military debt. Currently, 128 developing countries

is difficult to monitor: loan-by-loan registration is

the country’s reserve tranche position and includes

report to the Debtor Reporting System (DRS). Nonre-

normally impractical, and monitoring systems typi-

purchases and drawings under the Extended Credit

porting countries might have outstanding debt with

cally rely on information requested periodically by

Facility, Standby Credit Facility, Rapid Credit Facility,

the World Bank, other international financial institu-

the central bank from the banking sector. The World

Stand-By Arrangements, Flexible Credit Line, and the

tions, and private creditors.

Bank regards the debtor country as the authorita-

Extended Fund Facility.

Debt data, normally reported in the currency of

tive source of information on its short-term debt.

repayment, are converted into U.S. dollars to pro-

Where such information is not available from the

duce summary tables. Stock figures (amount of

debtor country, data from creditor sources may be

debt outstanding) are converted using end-of-period

used as an indication of the magnitude of a coun-

exchange rates, as published in the IMF’s Interna-

try’s short-term external debt. These data are derived

tional Financial Statistics (line ae). Flow figures are

from BIS data on international bank lending based

converted at annual average exchange rates (line

on time remaining to original maturity. The data are

rf). Projected debt service is converted using end-

reported based on residual maturity, but an estimate

of-period exchange rates. Debt repayable in multiple

of short-term external liabilities by original maturity

currencies, goods, or services and debt with a provi-

can be derived by deducting from claims due in one

sion for maintenance of the value of the currency of

year those that, 12 months earlier, had a maturity

repayment are shown at book value.

of between one and two years. However, not all com-

Data on external debt are mainly from reports to

Because ﬂow data are converted at annual aver-

mercial banks report to the BIS in a way that allows

the World Bank through its Debtor Reporting Sys-

age exchange rates and stock data at end-of-period

the full maturity distribution to be determined, and

tem from member countries that have received

exchange rates, year-to-year changes in debt out-

the BIS data include liabilities only to banks within

IBRD loans or IDA credits, with additional infor-

standing and disbursed are sometimes not equal to

the BIS reporting area. The results should thus be

mation from the files of the World Bank, the IMF,

net flows (disbursements less principal repayments);

interpreted with caution.

the African Development Bank and African Devel-

Data sources

similarly, changes in debt outstanding, including

Data related to the operations of the IMF are pro-

opment Fund, the Asian Development Bank and

undisbursed debt, differ from commitments less

vided by the IMF Treasurer’s Department. They are

Asian Development Fund, and the Inter-American

repayments. Discrepancies are particularly notable

converted from special drawing rights into U.S. dol-

Development Bank. Summary tables of the exter-

when exchange rates have moved sharply during

lars using end-of-period exchange rates for stocks

nal debt of developing countries are published

the year. Cancellations and reschedulings of other

and average-over-the-period exchange rates for flows.

annually in the World Bank’s Global Development

liabilities into long-term public debt also contribute

The IMF’s loan instruments have changed over time to

Finance, Global Development Finance CD‑ROM,

to the differences.

address the specific circumstances of its members.

and Global Development Finance database.

2011 World Development Indicators

359

global links

External debt

6.11 Afghanistan Albania Algeria Angola Argentina Armenia Australia Austria Azerbaijan 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 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 Honduras

360

Ratios for external debt Total external debt

Total debt service

Multilateral debt service

% of GNI 1995 2009

% of exports of goods and services and incomea 1995 2009

% of public and publicly guaranteed debt service 1995 2009

.. 18.5 83.5 311.9 38.9 25.3 .. .. 10.6 40.2 12.2 .. 71.2 81.2 .. 15.1 21.2 81.9 53.6 117.6 67.6 133.4 .. 85.9 58.5 32.1 16.5 .. 27.5 271.4 479.3 32.8 188.7 .. .. .. .. 28.5 72.0 55.8 26.4 6.3 .. 136.8 .. .. 101.6 113.0 48.2 .. 86.9 .. 22.6 90.0 379.4 .. 132.9

.. 40.3 3.8 28.2 40.1 55.3 .. .. 12.1 24.0 35.6 .. 16.1 34.5 54.6 14.1 17.9 90.4 22.9 38.9 45.0 13.6 .. 20.0 28.6 46.7 8.7 .. 23.6 121.4 83.8 28.1 53.0 .. .. .. .. 24.6 23.3 17.6 54.3 .. .. 17.6 .. .. 22.3 75.3 40.0 .. 37.3 .. 38.8 48.3 253.2 .. 25.9

2011 World Development Indicators

.. 2.8 .. 12.0 30.2 3.2 .. .. 1.3 16.1 3.4 .. 7.5 29.5 .. 3.1 38.5 16.5 .. 27.6 0.7 21.0 .. .. .. 24.5 9.9 .. 33.5 .. 13.5 14.2 23.1 .. .. .. .. 7.0 26.6 16.0 13.4 0.1 .. 18.5 .. .. 15.3 15.5 .. .. 24.2 .. 12.5 24.9 52.4 51.0 34.7

0.4 6.9 .. 8.4 17.3 20.9 .. .. 1.7 5.6 5.0 .. .. 14.4 10.5 1.2 23.4 21.3 .. .. 0.8 7.4 .. .. 2.8 22.6 2.9 .. 22.4 .. .. 9.6 9.5 .. .. .. .. 12.1 40.8 6.5 25.2 .. .. 3.1 .. .. 8.1 .. 7.3 .. 2.9 .. 18.4 .. .. 4.6 6.8

.. 11.4 17.7 0.6 21.6 69.8 .. .. 21.8 28.0 55.4 .. 54.6 75.5 .. 76.0 18.5 10.5 76.7 70.6 11.9 61.0 .. 100.0 86.1 76.2 7.6 .. 32.7 .. 21.1 50.6 59.3 .. .. .. .. 39.8 32.0 26.3 55.1 100.0 .. 41.9 .. .. 17.9 49.1 0.4 .. 48.4 .. 47.5 30.5 86.3 92.2 55.9

73.4 43.1 0.4 0.3 44.7 55.6 .. .. 28.4 70.2 3.1 .. 74.5 84.0 72.7 59.6 28.3 55.1 65.0 95.8 75.1 37.3 .. 65.0 85.3 3.7 27.2 .. 36.6 35.5 18.4 25.4 96.0 .. .. .. .. 25.2 12.9 30.2 67.9 56.1 .. 45.8 .. .. 16.9 51.4 47.4 .. 18.7 .. 74.9 62.8 100.0 81.0 44.0

Short-term debt

% of total debt 1995 2009

.. 13.7 0.8 17.0 21.7 0.6 .. .. 4.4 1.3 6.5 .. 3.4 5.8 .. 1.4 19.5 4.9 4.4 1.3 4.5 9.0 .. 6.0 2.0 15.6 18.9 .. 22.1 23.6 17.0 11.4 20.7 .. .. .. .. 13.8 9.5 7.1 20.9 0.0 .. 4.5 .. .. 6.6 3.5 6.9 .. 11.3 .. 24.7 5.0 10.6 3.2 7.9

0.9 17.7 27.9 15.8 16.3 10.4 .. .. 16.6 8.1 46.8 .. 4.2 9.6 17.5 14.2 14.4 45.8 0.0 1.4 6.1 0.8 .. 17.0 0.2 24.4 56.1 .. 7.9 4.9 4.2 29.0 0.8 .. .. .. .. 15.3 11.0 7.7 18.6 0.6 .. 0.9 .. .. 5.1 8.1 7.8 .. 23.1 .. 8.9 1.4 13.6 0.0 8.6

Present value of debt

% of total reserves 1995 2009

.. 23.5 6.3 919.7 133.6 1.9 .. .. 11.6 8.4 29.2 .. 23.7 30.5 .. 0.2 60.7 31.3 16.1 6.9 53.1 6,444.5 .. 24.0 11.6 23.1 27.8 .. 65.6 1,980.9 1,575.1 40.5 739.1 .. .. .. .. 165.3 73.4 13.9 55.9 0.0 .. 56.5 .. .. 187.8 14.0 43.0 .. 77.1 .. 103.6 188.9 469.2 13.4 141.7

.. 35.2 1.0 19.3 40.7 25.5 .. .. 15.1 18.8 142.3 .. 3.6 6.5 51.7 2.6 16.7 100.3 0.0 2.3 8.1 0.6 .. 31.9 0.6 69.1 9.8 .. 16.4 36.9 5.6 57.5 3.0 .. .. .. .. 57.8 37.4 7.3 67.7 .. .. 2.5 .. .. 5.4 18.9 15.7 .. .. .. 23.6 .. 89.5 0.0 ..

% of GNIa 2009

% of exports of goods, services, and incomea 2009

5 31 3 24 41 36 .. .. 10 17 30 .. 12b 16b 45 8 17 85 17b 13b 38 4b .. 12b 22b 43 9 .. 20 24b 20 b 27 46b .. .. .. .. 22 23 16 49 34b .. 12b .. .. 19 30 b 28 .. 27b .. 33 44b 203b 15b 13b

25 96 5 21 156 148 .. .. 14 90 51 .. 62b 34b 106 16 125 132 154b 143b 60 12b .. 75b 41b 84 25 .. 111 71b 18b 50 88b .. .. .. .. 73 59 53 162 811b .. 89 b .. .. 18 81b 80 .. 60 b .. 126 152b 647b 113b 25b

Total external debt

Total debt service

Multilateral debt service

% of GNI 1995 2009

% of exports of goods and services and incomea 1995 2009

% of public and publicly guaranteed debt service 1995 2009

% of total debt 1995 2009

.. 34.4 30.3 29.7 .. .. .. .. 18.8 .. 16.7 3.9 25.3 .. .. .. .. 13.3 6.1 .. .. 6.1 .. .. 1.3 .. 7.7 24.9 7.0 16.1 23.1 8.7 28.1 7.9 10.2 40.4 34.5 18.9 .. 7.9 .. .. 43.1 17.1 14.7 .. .. 30.9 3.4 20.8 5.8 17.3 16.3 .. .. .. ..

.. 24.2 28.4 1.3 .. .. .. .. 40.6 .. 33.5 7.8 32.5 .. .. .. .. 59.0 37.4 .. 13.5 60.3 .. .. 31.8 99.9 74.3 51.4 15.5 45.5 49.6 34.5 19.5 79.1 2.8 30.3 17.4 15.0 .. 54.2 .. .. 30.3 95.5 45.4 .. .. 43.2 52.7 31.7 48.0 49.9 29.2 .. .. .. ..

.. 5.3 20.9 29.9 .. .. .. .. 10.7 .. 10.2 10.2 8.7 .. .. .. .. 2.1 0.0 .. 45.9 0.6 39.6 .. 6.4 11.2 12.6 1.9 21.2 2.4 7.1 0.1 22.6 0.9 2.2 0.8 3.7 6.8 .. 0.9 .. .. 17.2 4.5 16.6 .. .. 10.7 36.2 3.1 30.4 31.3 13.4 .. .. .. ..

.. 27.0 63.4 23.9 .. .. .. .. 82.3 .. 118.8 18.5 83.8 .. .. .. .. 37.5 122.6 .. 24.4 55.8 .. .. 9.8 29.0 143.3 165.8 40.6 122.3 175.3 35.2 60.5 40.3 44.2 75.1 360.6 .. .. 54.7 .. .. 368.6 87.6 131.7 .. .. 49.4 80.9 57.3 31.5 60.3 51.7 .. .. .. ..

.. 18.2 30.2 4.1 .. .. .. .. 77.8 .. 28.3 113.0 26.5 .. .. 6.4 .. 65.8 95.5 .. 70.7 33.2 257.5 .. 85.3 62.2 .. 24.7 35.8 29.6 66.6 8.4 22.3 59.7 55.8 26.4 43.0 .. .. 28.7 .. .. 76.2 18.8 5.1 .. .. 31.3 52.5 19.9 29.5 24.8 39.2 .. .. .. ..

.. 5.9 18.4 .. .. .. .. .. 33.9 .. 4.8 80.2 5.0 .. .. 20.8 .. 14.0 .. .. 18.0 3.0 .. .. 31.0 14.8 2.3 .. 5.2 .. .. 2.7 16.0 14.9 4.8 12.5 1.6 .. .. 10.4 .. .. 17.2 4.5 0.8 .. .. 15.0 5.5 11.7 6.1 11.8 18.5 .. .. .. ..

.. 31.9 25.3 4.2 .. .. .. .. 17.6 .. 50.7 45.7 40.7 .. .. 100.0 .. 78.1 79.7 .. 5.7 81.7 30.3 .. 8.9 63.9 61.9 33.4 1.9 57.5 58.1 35.6 9.3 43.1 29.9 49.8 71.4 8.4 .. 77.6 .. .. 49.8 92.0 61.0 .. .. 50.5 22.5 58.0 52.6 33.2 13.5 .. .. .. ..

Short-term debt

.. 18.1 11.8 44.0 .. .. .. .. 9.6 .. 17.5 7.9 12.6 .. .. 0.0 .. 2.8 0.0 .. 12.5 0.0 5.5 .. 18.8 34.0 11.8 6.1 35.7 1.2 8.0 0.0 12.2 38.1 3.3 9.2 15.4 22.8 .. 1.2 .. .. 16.2 1.9 44.8 .. .. 2.7 0.0 7.8 17.4 16.0 6.4 .. .. .. ..

Present value of debt

% of total reserves 1995 2009

.. 22.1 174.2 .. .. .. .. .. 72.2 .. 34.4 23.0 164.9 .. .. .. .. 9.7 0.0 .. 16.9 0.9 3,481.0 .. 6.0 51.9 497.1 37.8 29.5 22.2 187.9 0.1 218.8 2.3 7.4 5.1 142.8 60.4 .. 3.5 .. .. 1,256.8 75.6 330.7 .. .. 128.0 282.4 29.1 70.8 111.6 67.8 .. .. .. ..

6.11

.. 15.1 28.2 .. .. .. .. .. 50.8 .. 9.5 37.4 26.3 .. .. 0.0 .. 5.1 0.0 .. 7.9 .. .. .. 89.4 83.0 23.0 41.0 24.5 2.0 68.4 0.0 23.4 89.0 5.4 9.2 .. .. .. .. .. .. 45.5 2.8 7.7 .. .. 10.8 0.0 4.6 19.5 14.2 9.1 .. .. .. ..

% of GNIa 2009

.. 17 30 4 .. .. .. .. 82 .. 27 96 19 .. .. 4 .. 36b 78 .. 80 19 316 b .. 72 59 17b 16b 31 14b 83b 7 18 55 35 23 18b .. .. 23 .. .. 36b 13b 4 .. .. 24 54 18 26 23 35 .. .. .. ..

2011 World Development Indicators

% of exports of goods, services, and incomea 2009

.. 71 99 .. .. .. .. .. 178 .. 46 157 72 .. .. 25 .. 62b 233 .. 105 27 347b .. 120 100 59b 65b 27 51b 153b 11 61 109 57 65 53b .. .. 154 .. .. 68b 67b 8 .. .. 157 66 21 48 78 90 .. .. .. ..

361

global links

Ratios for external debt

6.11 Romania Russian Federation Rwanda Saudi Arabia Senegal Serbia Sierra Leone Singapore Slovak Republic Slovenia Somalia South Africa 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

Ratios for external debt Total external debt

Total debt service

Multilateral debt service

% of GNI 1995 2009

% of exports of goods and services and incomea 1995 2009

% of public and publicly guaranteed debt service 1995 2009

19.4 31.0 79.2 .. 82.9 .. 149.0 .. .. .. .. 17.1 .. 65.3 136.3 14.0 .. .. 188.9 53.6 143.5 60.5 .. 116.7 .. 63.0 44.4 16.1 63.3 17.8 .. .. .. 28.0 13.5 49.0 124.0 .. 169.9 215.1 73.5 .. w 88.4 36.8 40.4 33.9 38.8 35.5 32.7 35.8 59.2 32.2 76.1 .. ..

71.6 31.9 14.9 .. 27.1 79.7 23.4 .. .. .. .. 15.1 .. 41.5 40.5 15.4 .. .. 10.3 51.2 34.0 23.3 .. 57.5 .. 58.2 41.2 3.0 16.2 83.8 .. .. .. 34.5 12.5 16.7 32.3 .. 25.5 26.8 .. .. w 30.9 21.8 15.6 30.3 22.1 13.2 44.7 23.7 15.4 20.7 22.9 .. ..

10.5 6.3 20.5 .. 17.8 .. 63.6 .. .. .. .. 9.5 .. 9.3 10.1 1.5 .. .. 4.5 .. 17.4 11.6 .. 6.2 .. 18.3 30.1 .. 19.8 6.6 .. .. .. 22.1 .. 22.9 .. .. 4.6 .. .. .. w .. 18.0 17.2 18.6 18.0 12.7 10.9 27.3 21.1 29.7 16.2 .. ..

31.4 17.7 4.7 .. .. 37.1 2.2 .. .. .. .. 9.3 .. 15.6 5.8 2.1 .. .. .. 38.4 3.5 6.8 .. .. .. 10.1 41.6 .. 2.0 36.2 .. .. .. 21.0 .. 6.4 1.8 .. .. 3.8 .. .. w 3.9 11.6 6.2 19.5 11.3 4.8 26.9 17.9 .. 6.8 5.9 .. ..

21.3 9.7 99.0 .. 62.2 100.0 c 8.4 .. .. .. .. 0.0 .. 14.0 100.0 64.0 .. .. 55.3 .. 66.7 20.9 .. 75.5 .. 45.2 20.7 1.9 69.7 13.6 .. .. .. 27.3 1.9 11.6 2.9 .. 78.3 50.6 33.6 .. w 40.2 22.5 25.5 20.0 23.0 18.2 16.6 26.2 19.7 27.4 35.0 .. ..

44.1 4.7 70.4 .. 59.1 51.9 62.9 .. .. .. .. 2.5 .. 20.0 22.3 82.8 .. .. 30.2 39.8 69.6 4.8 .. 98.3 .. 41.7 13.4 2.2 66.0 16.9 .. .. .. 22.3 21.6 13.4 18.3 .. 58.7 48.6 0.0 .. w 57.1 20.2 27.9 15.4 21.0 18.4 13.3 23.3 23.3 38.4 25.1 .. ..

Short-term debt

% of total debt 1995 2009

19.1 8.4 3.1 .. 6.6 19.8 c 2.2 .. .. .. 20.6 38.1 .. 6.4 36.2 4.5 .. .. 22.6 6.8 13.1 44.1 .. 5.8 .. 12.1 21.3 4.3 2.8 2.6 .. .. .. 25.1 11.8 8.6 12.9 .. 11.0 6.0 13.7 .. w 8.6 18.5 18.6 18.4 17.8 23.9 12.7 20.2 11.4 5.9 17.2 .. ..

Present value of debt

% of total reserves 1995 2009

17.9 49.7 8.0 56.6 0.8 32.3 .. .. 0.5 95.6 12.0 .. 0.0 77.8 .. .. .. .. .. .. 27.2 .. 31.5 216.7 .. .. 10.9 25.3 33.5 3,898.2 6.5 3.7 .. .. .. .. 14.4 1,102.7 0.6 .. 18.3 356.6 47.5 119.4 .. .. 2.9 65.1 .. .. 22.1 77.6 15.8 113.0 13.0 1.5 9.4 22.4 21.3 20.9 .. .. .. .. .. .. 9.2 73.7 3.5 .. 29.4 28.6 18.1 247.2 .. .. 7.0 107.9 15.6 186.2 21.3 77.2 .. w .. w 9.5 96.0 21.1 72.7 28.4 70.4 16.0 74.8 20.7 73.3 39.0 64.9 14.7 67.6 15.2 88.6 15.9 31.1 14.3 29.5 18.3 193.5 .. .. .. ..

47.4 7.0 0.9 .. 0.8 26.3 0.0 .. .. .. .. 33.5 .. 35.0 615.9 2.8 .. .. 4.1 .. 38.7 20.1 .. 6.7 .. 42.5 53.0 .. 7.9 75.0 .. .. .. 14.0 .. 46.6 31.5 .. 6.3 25.1 .. .. w 16.2 14.9 12.2 21.0 15.0 11.3 24.4 25.0 .. 15.4 21.3 .. ..

% of GNIa 2009

53 26 8b .. 20 b 71 20 b .. .. .. .. 15 .. 35 73b 13 .. .. 9 39 13b 22 .. 50 b .. 54 35 3 8b 62 .. .. .. 37 12 19 27 .. 17 10 b .. .. .. .. .. .. .. .. .. .. .. .. .. .. ..

% of exports of goods, services, and incomea 2009

166 74 64b .. 73b 223 104b .. .. .. .. 44 .. 136 352b 16 .. .. 24 114 57b 28 .. 136b .. 80 144 4 34b 123 .. .. .. 121 29 66 34 .. 47 24b 335 .. .. .. .. .. .. .. .. .. .. .. .. .. ..

a. The numerator refers to 2009, whereas the denominator is a three-year average of 2007â&#x20AC;&#x201C;09 data. b. Data are from debt sustainability analyses for low-income countries. Present value estimates for these countries are for public and publicly guaranteed debt only. c. Includes Montenegro.

362

2011 World Development Indicators

About the data

6.11

Definitions

A country’s external debt burden, both debt outstand-

value of external debt provides a measure of future

• Total external debt is debt owed to nonresidents

ing and debt service, affects its creditworthiness

debt service obligations.

and comprises public, publicly guaranteed, and pri-

and vulnerability. The table shows total external debt

The present value of external debt is calculated by

vate nonguaranteed long-term debt, short-term debt,

relative to a country’s size—gross national income

discounting the debt service (interest plus amortiza-

and use of IMF credit. It is presented as a share of

(GNI). Total debt service is contrasted with countries’

tion) due on long-term external debt over the life of

GNI. • Total debt service is the sum of principal

ability to obtain foreign exchange through exports of

existing loans. Short-term debt is included at face

repayments and interest actually paid in foreign cur-

goods, services, income, and workers’ remittances.

value. The data on debt are in U.S. dollars converted

rency, goods, or services on long-term debt; inter-

Multilateral debt service (shown as a share of the

at official exchange rates (see About the data for

est paid on short-term debt; and repayments (repur-

country’s total public and publicly guaranteed debt

table 6.10). The discount rate on long-term debt

chases and charges) to the IMF. • Exports of goods,

service) are obligations to international financial

depends on the currency of repayment and is based

services, and income are the total value of exports

institutions, such as the World Bank, the Interna-

on commercial interest reference rates established

of goods and services, receipts of compensation of

tional Monetary Fund (IMF), and regional develop-

by the Organisation for Economic Co-operation and

nonresident workers, and investment income from

ment banks. Multilateral debt service takes priority

Development. Loans from the International Bank

abroad. • Multilateral debt service is the repayment

over private and bilateral debt service, and borrowers

for Reconstruction and Development (IBRD), cred-

of principal and interest to the World Bank, regional

must stay current with multilateral debts to remain

its from the International Development Association

development banks, and other multilateral and inter-

creditworthy. While bilateral and private creditors

(IDA), and obligations to the IMF are discounted using

governmental agencies. • Short-term debt includes

often write off debts, international financial institu-

a special drawing rights reference rate. When the

all debt having an original maturity of one year or less

tion bylaws prohibit granting debt relief or canceling

discount rate is greater than the loan interest rate,

and interest in arrears on long-term debt. • Total

debts directly. However, the recent decrease in multi-

the present value is less than the nominal sum of

reserves comprise holdings of monetary gold, spe-

lateral debt service ratios for some countries reflects

future debt service obligations.

cial drawing rights, reserves of IMF members held

debt relief from special programs, such as the Heav-

Debt ratios are used to assess the sustainability of

by the IMF, and holdings of foreign exchange under

ily Indebted Poor Countries (HIPC) Debt Initiative and

a country’s debt service obligations, but no absolute

the control of monetary authorities. • Present value

the Multilateral Debt Relief Initiative (MDRI) (see

rules determine what values are too high. Empirical

of debt is the sum of short-term external debt plus

table 1.4.) Other countries have accelerated repay-

analysis of developing countries’ experience and

the discounted sum of total debt service payments

ment of debt outstanding. Indebted countries may

debt service performance shows that debt service

due on public, publicly guaranteed, and private non-

also apply to the Paris and London Clubs to renegoti-

difficulties become increasingly likely when the pres-

guaranteed long-term external debt over the life of

ate obligations to public and private creditors.

ent value of debt reaches 200 percent of exports.

existing loans.

Because short-term debt poses an immediate

Still, what constitutes a sustainable debt burden var-

burden and is particularly important for monitoring

ies by country. Countries with fast-growing econo-

vulnerability, it is compared with the total debt and

mies and exports are likely to be able to sustain

foreign exchange reserves that are instrumental in

higher debt levels.

providing coverage for such obligations. The present Data sources

Ratio of debt services to exports for middle-income economies have sharply increased in 2009 as export revenues declined

6.11a

Data on external debt are mainly from reports to the World Bank through its Debtor Reporting Sys-

Total debt service (% of exports of goods, services, and income) 30

tem from member countries that have received IBRD loans or IDA credits, with additional information from the files of the World Bank, the IMF,

the African Development Bank and African DevelMiddle-income economies

opment Fund, the Asian Development Bank and Asian Development Fund, and the Inter-American Development Bank. Data on GNI, exports of goods

10 Low-income economies

and services, and total reserves are from the World Bank’s national accounts files and the IMF’s Balance of Payments and International Financial

0 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009

Statistics databases. Summary tables of the exter-

Due to global financial crisis, export revenues in 2009 declined by 20 percent for middle-income economies, and

nal debt of developing countries are published

by 8 percent for low-income economies. Reduction in export revenues caused sharp raise in the ratio of debt

annually in the World Bank’s Global Development

service to exports, which has been declining since 2000 thanks to debt reduction efforts and export growth.

Finance, Global Development Finance CD‑ROM,

Source: Global Development Finance data files.

and Global Development Finance database.

2011 World Development Indicators

363

global links

Ratios for external debt

6.12

Global private financial flows Equity flows

Debt flows

$ millions Foreign direct investment 1995

364

0 70 .. 472 5,609 25 12,026 1,901 330 2 15 10,689a 13 393 .. 70 4,859 90 10 2 151 7 9,319 6 33 2,957 35,849 .. 968 –22 125 337 211 108 .. 2,568 4,139 414 452 598 38 .. 201 14 1,044 23,736 –315 8 .. 11,985 107 1,053 75 1 0 7 50

2011 World Development Indicators

2009

185 978 2,847 2,205 3,902 777 22,572 8,714 473 674 1,884 –38,860 93 423 235 252 25,949 4,595 171 0 530 340 19,898 42 462 12,702 78,193 52,395 7,207 951 2,083 1,347 381 2,951 .. 2,666 2,905 2,067 316 6,712 431 0 1,751 221 60 59,989 33 39 658 39,153 1,685 2,419 600 50 14 38 500

Portfolio equity 1995

.. 0 .. 0 1,552 .. 2,585 1,262 .. –15 .. 6,505a 0 0 .. 6 2,775 0 .. 0 .. 0 –3,077 .. .. –249 0 .. 165 0 0 0 1 4 .. 1,236 .. .. 13 0 0 .. 10 .. 2,027 6,823 .. .. .. –1,513 0 0 .. .. .. .. 0

$ millions Commercial bank and other lending

Bonds

2009

1995

2009

1995

2009

.. 4 .. 0 –212 1 .. 498 0 –154 1 –3,242 .. 0 .. 18 37,071 8 .. .. 0 0 23,349 .. .. 316 28,161 9,492 67 .. .. 0 –9 23 .. -311 8,152 0 2 393 0 .. –131 0 –273 68,285 .. 0 13 11,806 0 764 0 0 .. 0 0

.. 0 –278 0 3,705 0 .. .. 0 0 0 .. 0 0 .. 0 2,636 –6 0 0 0 0 .. 0 0 489 317 .. 1,008 0 0 –4 0 .. .. .. .. 0 0 0 0 0 .. 0 .. .. 0 0 0 .. 0 .. 44 0 0 0 –13

0 0 0 0 –1,114 0 .. .. 0 0 0 .. 0 –10 0 0 19,111 –372 0 0 0 0 .. 0 0 1,900 –39 .. 6,768 0 0 –225 0 .. .. .. .. –125 –2,987 0 0 0 .. 0 .. .. –44 0 0 .. 0 .. –50 0 0 0 50

.. 0 788 123 754 0 .. .. 0 –20 103 .. 0 41 .. -6 8,283 –93 0 –1 13 –65 .. 0 0 1,773 4,696 .. 1,250 0 –53 –20 14 .. .. .. .. –31 59 –311 –31 0 .. –48 .. .. –75 0 0 .. 38 .. –34 –15 0 0 38

0 451 –607 156 –1,849 42 .. .. 400 –13 –31 .. 0 –156 –40 –1 4,731 304 –3 0 0 –12 .. 0 0 2,572 –12,050 .. –1,018 –61 –1 538 –143 .. .. .. .. –213 –997 –33 175 0 .. 1,019 .. .. 74 0 135 .. 224 .. -574 4 0 0 222

Equity flows

Debt flows

$ millions Foreign direct investment

Portfolio equity

$ millions Commercial bank and other lending

Bonds

1995

2009

1995

2009

1995

2009

1995

2009

4,804 2,144 4,346 17 2 1,447 1,350 4,842 147 39 13 964 42 .. 1,776 .. 7 96 95 180 .. 275 5 –88 73 9 10 6 4,178 111 7 19 9,526 26 10 92 45 280 153 .. 12,206 3,316 89 7 1,079 2,393 46 723 223 455 103 2,557 1,478 3,659 685 .. ..

2,783 34,577 4,877 3,016 1,070 25,233 3,894 28,976 541 11,834 2,382 13,619 141 .. 1,506 406 145 189 319 94 4,804 63 218 1,711 230 248 543 60 1,387 109 –38 257 14,462 128 624 1,970 881 323 490 38 33,287 –1,259 434 739 5,787 11,271 2,210 2,387 1,773 423 205 4,760 1,948 13,796 2,808 .. ..

–62 1,590 1,493 0 .. 0 991 5,358 0 50,597 0 .. 5 .. 4,219 .. 0 .. 0 0 .. .. .. .. 6 .. .. .. 0 .. 0 22 519 –1 0 20 0 .. 46 0 –743 .. 0 .. 0 636 0 10 0 .. 0 171 0 219 –179 .. ..

954 21,112 787 .. .. 29,184 2,122 20,915 0 12,432 -30 46 3 .. 25,661 0 0 1 0 –8 929 .. 0 0 –2 –14 .. .. –449 .. .. –33 4,169 2 4 –4 0 .. 4 .. 19,256 967 0 .. 522 2,470 326 –37 0 .. 0 47 –1,096 1,579 1,616 .. ..

.. 285 2,248 0 .. .. .. .. 13 .. 0 0 0 .. .. .. .. 0 0 .. 350 0 0 .. 0 0 0 0 2,440 0 0 150 3,758 0 0 0 0 0 .. 0 .. .. 0 0 0 .. .. 0 0 –32 0 0 1,110 .. .. .. ..

.. 1,822 5,112 0 .. .. .. .. 740 .. -2 -2,108 0 .. .. 0 .. 0 0 .. 789 0 0 .. 2,488 244 0 0 143 0 0 0 7,499 –6 0 0 0 0 .. 0 .. .. 0 0 0 .. .. –500 1,323 0 0 2,828 3,527 .. .. .. ..

.. 955 60 –37 .. .. .. .. 15 .. –201 240 –163 .. .. .. .. 0 0 .. 333 12 0 .. 55 0 –4 –23 1,231 0 0 126 1,401 24 –14 158 24 36 .. –5 .. .. –81 –24 –448 .. .. 317 –12 –311 –16 43 –215 .. .. .. ..

.. 8,343 5,872 –1,417 .. .. .. .. –62 .. –3 6,554 24 .. .. 0 .. 29 387 .. –41 –1 –32 .. –1,971 244 0 0 –1,592 1 –1 29 –9,314 –18 46 –61 20 0 .. –1 .. .. –75 –7 –55 .. .. 26 70 25 425 –258 –783 .. .. .. ..

2011 World Development Indicators

365

global links

6.12

Global private financial flows

6.12

Global private financial flows Equity flows

Debt flows

$ millions Foreign direct investment 1995

Romania Russian Federation Rwanda Saudi Arabia Senegal Serbia Sierra Leone Singapore Slovak Republic Slovenia Somalia South Africa 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

419 2,065 2 –1,875 32 45b 7 11,535 236 150 1 1,248 8,086 56 12 52 14,939 4,158 100 10 120 2,068 .. 26 299 264 885 233 121 267 .. 21,731 57,800 157 –24 985 1,780 123 –218 97 118 340,573 s 1,540 93,318 54,045 39,273 94,858 50,797 5,599 30,212 907 2,931 4,411 245,715 89,322

a. Includes Luxembourg. b. Includes Montenegro.

366

2011 World Development Indicators

2009

6,310 36,751 119 10,499 208 1,921 74 16,809 –31 –579 108 5,354 6,451 404 2,682 66 11,538 27,588 1,434 16 415 4,976 .. 50 709 1,595 8,403 1,355 604 4,816 .. 72,924 134,710 1,262 750 –3,105 7,600 .. 129 699 60 1,163,874 s 10,950 348,451 177,583 170,868 359,401 101,428 86,067 76,629 27,766 38,414 29,096 804,473 371,020

Portfolio equity 1995

0 47 0 0 4 .. 0 –159 –16 .. .. 2,914 4,216 .. 0 1 1,853 5,851 0 .. 0 2,253 .. 0 17 12 195 .. 0 .. .. 8,070 16,523 0 .. 270 .. 0 .. .. .. 127,074 s –10 13,835 5,397 8,438 13,824 3,746 248 5,216 32 1,585 2,998 113,249 23,747

2009

7 3,369 0 .. .. 23 6 2,058 182 31 .. 9,364 9,378 –382 0 –7 1,400 9,241 .. 0 3 1,334 .. .. .. –89 2,827 .. 122 105 .. 78,845 160,534 -12 .. 121 128 .. 0 –13 .. 744,295 s –33 108,577 50,913 57,663 108,544 28,868 6,386 41,570 1,200 20,539 9,981 635,751 296,975

$ millions Commercial bank and other lending

Bonds 1995

2009

1995

0 –810 0 .. 0 0 0 .. .. .. 0 731 .. 0 0 0 .. .. 0 0 0 2,123 .. 0 .. 588 627 0 0 –200 .. .. .. 144 0 –468 0 .. 0 0 –30 .. s –30 20,954 6,470 14,484 20,924 8,206 –389 11,311 660 285 851 .. ..

32 –1,968 0 .. 200 0 0 .. .. .. 0 1,750 .. 400 0 0 .. .. 0 0 0 –341 .. 0 .. –313 1,152 0 0 –1,115 .. .. .. –420 0 4,992 –20 .. 0 0 0 .. s 0 51,121 8,555 42,566 51,121 8,383 –1,653 40,290 473 1,722 1,906 .. ..

413 444 0 .. –25 0 –28 .. .. .. 0 748 .. 103 0 0 .. .. –1 0 18 3,702 .. 0 .. –96 174 20 –9 –19 .. .. .. 39 201 -216 356 .. –2 –37 140 .. s –107 26,661 8,991 17,670 26,554 9,554 1,563 13,240 632 1,350 214 .. ..

2009

7,022 7,328 0 .. 157 104 0 .. .. .. 0 2,291 .. 238 0 0 .. .. 0 –54 84 –1,134 .. 0 .. 30 –12,036 –24 0 –1,605 .. .. .. –19 –118 –322 –1 .. –1 –36 0 .. s 1,601 88 –2,246 2,335 1,689 –9,217 6,921 –6,172 –2,132 8,575 3,715 .. ..

About the data

6.12

Definitions

Private financial flows—equity and debt—account for

International Development Association. The reports

• Foreign direct investment is net inflows of invest-

the bulk of development finance. Equity flows com-

are cross-checked with data from market sources

ment to acquire a lasting interest in or management

prise foreign direct investment (FDI) and portfolio

that include transactions data. Information on private

control over an enterprise operating in an economy

equity. Debt flows are financing raised through bond

nonguaranteed bonds and bank lending is collected

other than that of the investor. It is the sum of equity

issuance, bank lending, and supplier credits. Data

from market sources when data are not reported by

capital, reinvested earnings, other long-term capi-

on equity flows are based on balance of payments

countries to the Debtor Reporting System.

tal, and short-term capital, as shown in the balance

data reported by the International Monetary Fund

Data on equity flows are shown for all countries

of payments. Net inflows refer to new investments

(IMF). FDI data are supplemented by staff estimates

for which data are available. Debt flows are shown

made during the reporting period netted against dis-

using data from the United Nations Conference on

only for 128 developing countries that report to the

investments. • Portfolio equity includes net inflows

Trade and Development and official national sources.

Debtor Reporting System; nonreporting countries

from equity securities other than those recorded

may also receive debt flows.

as direct investment and including shares, stocks,

The internationally accepted definition of FDI (from the fifth edition of the IMF’s Balance of Payments

The volume of global private financial flows

depository receipts, and direct purchases of shares

Manual [1993]), includes three components: equity

reported by the World Bank generally differs from

in local stock markets by foreign investors • Bonds

investment, reinvested earnings, and short- and

that reported by other sources because of differ-

are securities issued with a fixed rate of interest for a

long-term loans between parent firms and foreign

ences in sources, classification of economies, and

period of more than one year. They include net flows

affiliates. Distinguished from other kinds of interna-

method used to adjust and disaggregate reported

through cross–border public and publicly guaranteed

tional investment, FDI is made to establish a lasting

information. In addition, particularly for debt financ-

and private nonguaranteed bond issues. • Commer-

interest in or effective management control over an

ing, differences may also reflect how some install-

cial bank and other lending includes net commercial

enterprise in another country. A lasting interest in

ments of the transactions and certain offshore issu-

bank lending (public and publicly guaranteed and pri-

investment enterprise typically involves establish-

ances are treated.

vate nonguaranteed) and other private credits.

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

World Debt Tables 1993–94 (vol. 1, chap. 3).

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 of

International Financial Statistics and Balance of

public and publicly guaranteed debt and private non-

Payments databases, and Dealogic. These data

guaranteed debt. Data on public and publicly guar-

are also published annually in the World Bank’s

anteed debt are reported through the Debtor Report-

Global Development Finance, Global Develop-

ing System by World Bank member economies that

ment Finance CD‑ROM, and Global Development

have received loans from the International Bank for

Finance database.

Reconstruction and Development or credits from the

2011 World Development Indicators

367

global links

Global private financial flows

6.13

Net official financial flows Total

International financial institutions

$ millions

From From bilateral multilateral sourcesa,b,c sources 2009 2009

Afghanistan 1.0 Albania 26.3 Algeria –84.8 Angola 786.6 Argentina 282.5 Armenia 610.9 Australia Austria Azerbaijan –17.5 Bangladesh –146.1 Belarus 975.7 Belgium Benin 25.3 Bolivia 61.9 Bosnia and Herzegovina 33.8 Botswana –5.1 Brazil 2,998.3 Bulgaria –5.2 Burkina Faso 13.6 Burundi 0.0 Cambodia 116.0 Cameroon –38.9 Canada Central African Republic –3.4 Chad –1.9 Chile –20.8 China –339.4 Hong Kong SAR, China .. Colombia –113.6 Congo, Dem. Rep. –168.9 Congo, Rep. –62.6 Costa Rica 74.8 Côte d’Ivoire –15.4 Croatia .. Cuba .. Czech Republic .. Denmark Dominican Republic 203.2 Ecuador –175.1 Egypt, Arab Rep. –907.4 El Salvador –38.8 Eritrea 41.6 Estonia .. Ethiopia 335.1 Finland France Gabon –99.4 Gambia, The 2.9 Georgia 23.8 Germany Ghana 99.2 Greece .. Guatemala –10.8 Guinea 3.9 Guinea-Bissau 0.0 Haiti 109.1 Honduras 12.7

368

World Banka IDA 2009

IMF

IBRD 2009

Conces sional 2009

Non- concessional 2009

United Nationsb,c

Regional development banksb Conces Non- Other sional concessional institutions 2009 2009 2009

$ millions UNICEF 2009

UNRWA 2009

UNTA 2009

Others 2009

194.1 130.8 7.2 386.4 1,437.1 758.9

26.7 25.5 0.0 13.5 0.0 128.5

0.0 6.9 –0.5 0.0 235.6 48.6

17.4 –12.1 0.0 0.0 0.0 –23.4

0.0 1.9 0.0 353.3 0.0 465.7

73.9 0.0 0.0 1.6 0.0 119.1

0.0 21.1 0.0 –0.4 914.8 1.3

7.4 83.0 –0.8 0.8 282.3 11.4

39.5 1.0 1.0 8.5 0.8 0.8

0.0 0.0 0.0 0.0 0.0 0.0

1.0 0.4 0.9 0.8 1.0 1.6

28.2 3.1 6.6 8.3 2.6 5.3

304.9 1,004.7 3,040.3

36.1 62.8 0.0

121.6 0.0 213.5

–15.6 –23.4 0.0

–2.9 0.0 2,825.2

15.1 149.8 0.0

93.8 701.9 –2.1

47.1 38.7 0.0

1.0 22.2 0.7

0.0 0.0 0.0

0.6 0.8 0.5

8.1 51.9 2.5

134.2 168.3 483.8 982.5 441.6 259.3 270.6 63.5 96.5 225.1

51.4 32.3 18.0 –0.5 0.0 0.0 89.7 8.6 16.4 46.3

0.0 0.0 –24.7 0.0 –597.9 285.0 0.0 0.0 0.0 –5.4

15.7 0.0 0.0 0.0 0.0 0.0 54.2 13.4 0.0 147.3

0.0 0.0 281.7 0.0 0.0 0.0 0.0 0.0 0.0 0.0

25.5 95.7 0.0 –3.6 0.0 0.0 78.3 4.2 47.6 23.6

0.0 –36.1 129.5 971.7 1,018.9 –13.6 0.0 0.0 0.0 –21.9

22.9 67.9 69.4 8.3 12.1 –12.1 4.1 2.6 4.5 12.1

4.9 1.5 0.8 1.2 1.1 .. 17.7 9.9 7.3 6.8

0.0 0.0 0.0 0.0 0.0 .. 0.0 0.0 0.0 0.0

0.8 0.6 0.8 0.4 1.5 .. 1.1 0.6 0.8 1.0

13.0 6.4 8.3 5.0 5.9 .. 25.5 24.2 19.9 15.3

30.1 25.3 58.6 1,098.7 .. 1,633.2 264.6 2.8 143.9 –289.9 .. .. ..

2.1 –14.6 –0.7 –329.8 .. –0.7 78.1 0.8 –0.2 –27.3 0.0 .. 0.0

0.0 0.0 14.9 298.5 .. 1,115.6 0.0 0.0 16.7 –73.3 39.8 .. 0.0

20.3 –12.7 0.0 0.0 .. 0.0 131.7 3.7 0.0 282.9 .. .. ..

0.0 0.0 0.0 0.0 .. 0.0 0.0 0.0 0.0 –125.4 .. .. ..

–1.8 0.4 0.0 0.0 .. –2.6 14.3 –0.4 –9.2 –4.2 .. .. ..

0.0 0.0 40.7 1,069.1 .. 534.9 –43.1 –8.5 3.8 –369.4 .. .. ..

–2.0 9.4 0.0 17.7 .. –23.6 –13.1 –3.6 128.7 –4.3 .. .. ..

4.5 13.4 0.8 10.5 .. 1.3 55.4 1.3 0.8 8.4 0.3 1.0 ..

0.0 0.0 0.0 0.0 .. 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ..

0.5 0.5 0.9 2.2 .. 0.8 1.3 0.2 0.7 1.1 0.6 1.4 ..

6.5 28.9 2.0 30.5 .. 7.5 40.0 9.3 2.6 21.6 2.9 3.4 ..

977.0 61.4 858.9 402.1 23.0 .. 977.1

–0.7 –1.1 –50.5 –0.8 0.8 0.0 549.2

298.6 –80.7 595.4 169.3 0.0 –6.7 0.0

0.0 0.0 0.0 0.0 0.0 .. 165.0

261.2 0.0 0.0 0.0 0.0 .. 0.0

–21.3 –26.4 –5.0 –22.9 3.4 .. 163.0

373.5 125.6 145.0 233.3 0.0 .. –6.7

62.3 38.5 160.8 17.6 0.6 .. 21.6

0.8 0.8 3.5 1.3 2.7 .. 35.9

0.0 0.0 0.0 0.0 0.0 .. 0.0

0.8 0.8 1.4 0.7 1.1 .. 1.1

1.8 3.9 8.3 3.6 14.4 .. 48.0

20.1 46.5 655.4

0.0 2.0 155.2

–2.3 0.0 100.0

0.0 15.8 –27.7

0.0 0.0 340.6

–0.2 7.4 111.4

33.3 0.0 –5.9

–16.0 12.9 –27.6

0.7 1.4 0.8

0.0 0.0 0.0

0.4 0.3 0.8

4.2 6.7 7.8

476.8 .. 554.1 –29.3 9.6 159.2 87.4

239.7 0.0 0.0 –27.2 0.0 –11.0 49.4

0.0 0.0 306.3 0.0 0.0 0.0 0.0

104.3 .. 0.0 –12.8 –1.6 57.4 0.0

0.0 .. 0.0 0.0 2.7 0.0 0.0

99.6 .. –6.9 2.9 –1.2 75.6 32.7

–2.0 .. 255.5 –5.6 0.0 0.0 –19.8

2.2 .. –5.4 –12.3 0.0 11.8 16.2

8.2 .. 0.8 7.6 2.7 2.4 0.7

0.0 .. 0.0 0.0 0.0 0.0 0.0

0.9 .. 0.6 0.5 0.2 0.8 1.1

23.9 .. 3.2 17.6 6.8 22.2 7.1

2011 World Development Indicators

Total

International financial institutions

$ millions

From From bilateral multilateral sourcesa,b,c sources 2009

2009

IMF

World Banka IDA 2009

IBRD 2009

Conces sional 2009

Non- concessional 2009

global links

6.13

Net official financial flows

United Nationsb,c

Regional development banksb Conces Non- Other sional concessional institutions 2009 2009 2009

$ millions UNICEF 2009

UNRWA 2009

UNTA 2009

Others 2009

.. –152.3 –1,099.1 –247.4 ..

–23.1 2,079.7 1,131.2 81.8 ..

0.0 455.3 212.8 0.0 ..

–23.1 671.5 908.6 74.7 ..

.. 0.0 0.0 0.0 ..

.. 0.0 88.6 0.0 ..

.. 857.9 –99.4 0.0 ..

.. 12.0 0.0 0.0 ..

.. 42.0 6.3 1.7 2.0

.. 0.0 0.0 0.0 0.0

.. 0.3 1.1 0.6 0.4

.. 40.7 13.2 4.8 7.8

–61.3

0.0

71.1

0.0

–4.6

81.6

34.7

1.1

0.0

0.3

1.2

–65.1 –13.3 59.8 ..

185.4 .. 548.3 604.2 385.0 ..

–2.6 0.0 82.9 ..

240.0 83.8 0.0 ..

0.0 0.0 191.2 ..

–15.9 0.0 0.0 ..

0.0 –0.2 54.0 ..

0.0 532.6 –5.0 ..

190.1 –16.1 11.1 ..

0.8 1.0 11.8 5.5

133.5 0.0 0.0 0.0

0.9 0.3 1.9 1.4

1.5 2.8 37.1 7.6

0.0 .. 332.0 114.9 .. –95.9 12.8 0.0 .. –2.3 6.4 34.8 12.2 –912.1 84.3 33.3 –24.8 466.6 –22.8 57.2 606.8 193.6 –7.9 .. –10.7

–199.4 .. 17.8 44.0 .. 106.3 3.9 37.6 .. 1,000.6 20.5 92.5 84.8 –89.6 383.8 204.6 107.1 6,463.4 11.9 262.9 1,301.8 484.7 34.7 .. 35.6

0.0 .. –4.1 –9.6 0.0 0.0 5.9 –3.3 .. 0.0 –7.0 30.4 24.2 0.0 159.2 37.9 –0.6 0.0 18.0 51.0 –1.4 197.1 0.0 .. –33.5

–207.7 .. 0.0 0.0 273.2 –49.8 –0.7 0.0 .. –3.1 33.3 0.0 0.0 –46.7 0.0 0.0 101.0 4,213.3 –17.6 0.0 2.7 0.0 0.0 .. 0.0

0.0 .. –0.3 –5.6 .. 0.0 –5.9 17.6 .. 0.0 0.0 0.0 0.0 0.0 3.1 0.0 0.0 0.0 –8.6 –6.5 0.0 153.3 0.0 .. –2.2

0.0 .. 0.0 0.0 .. 0.0 0.0 0.0 .. 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 –6.4 165.5 0.0 0.0 0.0 .. 0.0

0.0 .. 12.2 8.1 .. 0.0 –4.6 –1.0 .. 0.0 0.0 27.1 18.3 0.0 58.8 24.8 –0.2 0.0 0.0 43.3 –1.1 68.8 0.0 .. 14.9

0.0 .. –4.6 0.5 .. 0.0 0.0 0.0 .. –8.0 –4.9 0.0 –2.0 –40.3 0.0 –8.0 13.6 2,247.5 –3.5 0.0 545.2 0.0 0.0 .. 0.0

0.0 .. 4.7 33.9 .. 29.1 0.4 0.0 .. 1,011.7 –5.4 –0.9 7.9 –7.2 132.1 133.0 –10.0 0.0 19.2 1.6 751.1 20.6 –0.8 .. 16.1

1.5 .. 1.4 2.7 .. 0.8 1.4 5.7 0.0 .. 0.9 12.7 9.3 0.7 14.7 2.1 0.0 1.0 0.9 0.8 1.5 16.3 17.0 1.1 7.4

0.0 .. 0.0 0.0 .. 123.0 0.0 0.0 0.0 .. 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

0.0 .. 1.3 0.7 .. 1.0 0.6 0.4 0.4 .. 1.0 1.3 1.0 0.6 0.7 0.7 0.6 1.0 1.5 1.2 0.9 0.8 1.1 0.7 1.1

6.8 .. 7.2 13.3 .. 2.2 6.8 18.2 2.5 .. 2.6 21.9 26.1 3.3 15.2 14.1 2.7 0.6 8.4 6.0 2.9 27.8 17.4 5.3 31.8

–11.3 6.3 –72.1

66.7 15.8 475.6

0.0 0.0 –96.1

36.7 5.1 0.0

0.0 0.0 0.0

106.6 19.5 15.4

25.8 0.0 –91.3

20.7 35.2 4.3

1.3 18.2 48.8

0.0 0.0 0.0

1.4 0.7 1.0

6.6 16.0 28.5

.. 887.5 15.2 –20.1 –12.9 –961.9 –425.8 ..

265.8 110.5 386.2 .. .. 4,639.2 292.0 6.5 60.3 1,689.9 1,067.9 ..

0.0 988.8 0.0 10.5 –1.5 0.0 –7.0 0.0

0.0 –163.3 164.3 –9.0 68.1 134.4 –32.7 2,658.3

.. –223.3 0.0 0.0 0.0 0.0 0.0 ..

.. 3,307.1 0.0 0.0 0.0 0.0 0.0 ..

.. 223.6 –6.1 –9.2 –15.7 –3.5 –38.9 ..

.. 419.4 131.6 9.5 –2.1 1,380.8 1,116.4 ..

.. 18.1 2.0 –3.9 7.7 171.0 13.0 ..

0.0 19.8 0.7 1.5 0.8 0.9 3.1 ..

0.0 0.0 0.0 0.0 0.0 0.0 0.0 ..

0.1 1.9 0.5 0.0 0.5 0.8 0.8 ..

0.4 47.1 –1.0 7.1 2.5 5.5 13.2 ..

.. ..

2011 World Development Indicators

369

6.13

Net official financial flows Total

International financial institutions

$ millions

From From bilateral multilateral sourcesa,b,c sources 2009

Romania –14.4 Russian Federation –296.3 Rwanda 12.0 Saudi Arabia .. Senegal 127.2 Serbia 477.1 Sierra Leone –1.5 Singapore .. Slovak Republic .. Slovenia .. Somalia 0.0 South Africa 0.0 Spain Sri Lanka 341.6 Sudan 551.3 Swaziland 9.0 Sweden Switzerland Syrian Arab Republic –324.9 Tajikistan 88.0 Tanzania 4.8 Thailand –334.6 Timor-Leste .. Togo 22.2 Trinidad and Tobago .. Tunisia 40.3 Turkey 405.0 Turkmenistan –87.2 Uganda 9.8 Ukraine –154.6 United Arab Emirates .. United Kingdom United States Uruguay –21.2 Uzbekistan 100.9 Venezuela, RB 151.3 Vietnam 922.2 West Bank and Gaza .. Yemen, Rep. 66.4 Zambia –5.0 12.9 Zimbabwe World .. s Low income 1,421.6 Middle income 4,206.7 Lower middle income 114.0 Upper middle income 4,092.7 Low & middle income 5,628.3 East Asia & Pacific –1,882.0 Europe & Central Asia 2,478.3 Latin America & Carib. 2,975.6 Middle East & N. Africa –997.0 South Asia 1,006.3 Sub-Saharan Africa 2,047.0 High income .. Euro area ..

2009

World Banka IDA 2009

IMF

IBRD 2009

Conces sional 2009

Non- concessional 2009

United Nationsb,c

Regional development banksb Conces Non- Other sional concessional institutions 2009 2009 2009

$ millions UNICEF 2009

UNRWA 2009

UNTA 2009

Others 2009

12,394.0 –764.1 115.1 .. 324.2 1,916.3 79.5 .. .. .. 38.9 –25.2

0.0 0.0 10.5 .. 134.5 16.6 15.1 .. 0.0 0.0 0.0 0.0

441.6 –634.9 0.0 .. 0.0 55.7 0.0 .. –43.4 –6.1 0.0 –5.5

0.0 0.0 3.6 .. 99.8 0.0 18.8 .. .. .. 0.0 0.0

9,390.6 0.0 0.0 .. 0.0 1,575.1 0.0 .. .. .. 0.0 0.0

0.0 0.0 21.7 .. 38.5 0.0 16.8 .. .. .. 0.0 0.0

–26.0 –130.5 0.0 .. –12.8 109.1 0.0 .. .. .. 0.0 –32.1

2,587.8 1.3 46.4 .. 43.1 151.2 2.9 .. .. .. 0.0 0.0

.. .. 9.6 .. 6.3 0.6 8.4 .. .. .. 10.0 4.0

.. .. 0.0 .. 0.0 0.0 0.0 .. .. .. 0.0 0.0

.. .. 0.8 .. 1.3 1.0 1.1 .. .. .. 0.0 0.5

.. .. 22.5 .. 13.5 7.0 16.4 .. .. .. 28.9 7.9

827.9 99.2 1.9

90.8 0.0 –0.3

0.0 0.0 –6.6

–11.8 0.0 0.0

552.6 –10.6 0.0

60.4 0.0 –1.4

88.5 –2.7 –5.0

21.9 57.6 9.0

3.4 13.8 0.9

0.0 0.0 0.0

1.2 0.8 0.6

20.9 40.3 4.7

181.8 125.3 1,256.8 –46.6 .. 17.4 .. 443.7 1,984.6 –0.2 508.9 6,992.3 ..

–1.5 4.9 607.6 –3.4 .. –21.8 0.0 –2.1 –5.9 0.0 363.3 0.0 ..

0.0 0.0 0.0 9.3 .. 0.0 –7.7 31.7 1,619.0 –1.3 0.0 274.5 ..

0.0 25.1 306.8 0.0 .. 41.3 .. 0.0 0.0 0.0 0.0 0.0 ..

0.0 0.0 0.0 0.0 .. 0.0 .. 0.0 –706.5 0.0 0.0 6,081.6 ..

0.0 62.7 222.9 –46.2 .. –1.9 .. 0.0 0.0 0.0 73.5 0.0 ..

0.0 1.8 –1.0 –4.4 .. 0.0 .. 149.0 0.0 0.0 –0.9 549.7 ..

108.3 16.2 41.2 –11.3 .. –13.5 .. 260.6 1,067.5 –1.5 –1.0 78.3 ..

0.8 3.4 21.4 0.9 1.1 4.5 0.0 0.9 1.3 0.9 22.1 0.8 ..

60.1 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ..

1.3 0.8 1.1 1.2 0.5 0.5 0.1 0.8 0.6 0.0 1.1 1.8 ..

12.8 10.4 56.8 7.3 6.0 8.3 0.7 2.8 8.6 1.7 50.8 5.6 ..

704.6 0.0 364.7 0.0 0.0 157.1 27.6 –27.3 0.0 0.0 443.9 0.0 0.0 0.0 0.0 2,218.4 1,158.6 0.0 –38.3 0.0 .. .. .. .. .. 121.1 58.8 0.0 –41.0 –2.8 312.7 32.5 0.0 243.6 0.0 25.3 0.0 0.0 –0.1 0.0 .. s .. s .. s .. s .. s 8,153.0 2,579.9 0.0 1,552.6 1.0 65,736.3 3,871.5 11,287.2 198.1 24,750.4 30,161.2 3,782.9 2,998.2 193.2 11,114.1 35,590.5 88.5 8,289.0 4.9 13,636.3 75,594.8 6,451.3 11,287.1 1,750.7 24,751.5 5,945.1 1,099.7 1,126.6 –41.5 165.5 29,917.9 417.2 2,357.9 –62.6 20,246.6 16,487.2 136.2 6,489.2 126.7 269.6 4,246.7 6.4 894.2 –42.9 –18.7 8,845.7 1,614.0 508.2 –240.2 3,861.3 8,987.7 3,177.8 –88.9 2,011.1 227.1 .. .. .. .. .. .. .. .. .. ..

–2.1 18.7 0.0 392.4 .. 0.0 32.4 0.0 .. 1,469.8 1,257.5 1,293.0 –35.6 2,727.3 482.7 338.9 251.2 10.5 545.5 1,098.5 .. ..

318.0 78.0 143.6 647.3 .. 0.0 –5.4 0.0 .. s 619.9 14,527.3 7,063.5 7,463.8 15,147.1 2,699.7 1,315.9 7,784.3 839.2 2,075.8 432.3 .. ..

20.2 0.8 48.2 3.5 292.0 1.4 26.2 3.7 .. 4.9 68.8 9.2 –15.5 9.0 0.0 6.6 .. s 1,086.2 s 579.9 456.0 8,079.3 268.3 2,251.4 236.9 5,827.9 31.5 8,659.3 1,085.1 70.8 66.8 5,162.0 21.7 1,174.5 28.4 1,584.6 29.2 110.4 137.5 557.0 454.7 .. 1.1 .. ..

0.0 0.0 0.0 0.0 455.3 0.0 0.0 0.0 771.8 s 0.0 771.8 648.8 123.0 771.8 0.0 0.0 0.0 771.8 0.0 0.0 0.0 ..

0.6 2.4 0.4 8.0 0.5 6.4 1.5 27.0 0.1 8.5 0.9 27.2 1.6 14.5 0.5 18.3 645.3 s 2,561.1 s 33.1 860.8 111.8 613.1 39.3 539.9 25.0 136.2 644.3 2,319.3 98.3 176.5 13.7 106.6 67.8 159.3 71.9 100.5 6.4 226.8 156.0 962.1 1.0 6.2 .. ..

a. Aggregates include amounts for economies that do not report to the World Bank’s Debtor Reporting System and may differ from aggregates published in Global Development Finance 2011. b. Aggregates include amounts for economies not specified elsewhere. c. World and income group aggregates include flows not allocated by country or region.

370

2011 World Development Indicators

About the data

6.13

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, the

cipally on a country’s per capita income and eligibility

of public or publicly guaranteed loans and credits,

major international financial institutions, and UN

under IDA. Nonconcessional lending from the IMF

less repayments of principal. • IDA is the Interna-

agencies. The international financial institutions

is provided mainly through Stand-by Arrangements,

tional Development Association, the concessional

fund nonconcessional lending operations primarily

the Flexible Credit Line, and the Extended Fund Facil-

arm of the World Bank Group. • IBRD is the Inter-

by selling low-interest, highly rated bonds backed

ity. The IMF’s loan instruments have changed over

national Bank for Reconstruction and Development,

by prudent lending and financial policies and the

time to address the specific circumstances of its

the founding and largest member of the World Bank

strong financial support of their members. Funds

members.

Group. • IMF is the International Monetary Fund, which

are then on-lent to developing countries at slightly

Regional development banks also maintain conces-

provides concessional lending through its Extended

higher interest rates with 15- to 20-year maturities.

sional windows. Their loans are recorded in the table

Credit Facility, Standby Credit Facility, and Rapid Credit

Lending terms vary with market conditions and insti-

according to each institution’s classification and not

Facility and nonconcessional lending through credit

tutional policies.

according to the DAC definition.

to members, mainly for balance of payments needs.

Concessional flows from international financial

Data for flows from international financial institu-

• Regional development banks are the African Devel-

institutions are credits provided through conces-

tions are available for 128 countries that report to

opment Bank, which serves Africa, including North

sional lending facilities. Subsidies from donors or

the World Bank’s Debtor Reporting System. World

Africa; the Asian Development Bank, which serves

other resources reduce the cost of these loans.

Bank flows for nonreporting countries were collected

South and Central Asia and East Asia and Pacific;

Grants are not included in net flows. The Organisa-

from its operational records. Nonreporting countries

the European Bank for Reconstruction and Develop-

tion for Economic Co-operation and Development’s

may have net flows from other international financial

ment, which serves Europe and Central Asia; and

(OECD) Development Assistance Committee (DAC)

institutions.

the Inter-American Development Bank, which serves

defines concessional flows from bilateral donors as

Official flows from the United Nations are mainly

the Americas. • Concessional financial flows are dis-

flows with a grant element of at least 25 percent, eval-

concessional flows classified as official develop-

bursements through concessional lending facilities.

uated assuming a 10 percent nominal discount rate.

ment assistance but may include nonconcessional

• Nonconcessional financial flows are all disburse-

World Bank concessional lending is done by the

flows classified as other official flows in OECD DAC

ments that are not concessional. • Other institutions,

databases.

a residual category, include such institutions as the

International Development Association (IDA) based on gross national income (GNI) per capita and per-

Caribbean Development Fund, Council of Europe,

formance standards assessed by World Bank staff.

European Development Fund, Islamic Development

The cutoff for IDA eligibility, set at the beginning of

Bank, and Nordic Development Fund. • United Nations

the World Bank’s fiscal year, has been $1,165 since

includes the United Nations Children’s Fund (UNICEF),

July 1, 2010, measured in 2009 U.S. dollars using

United Nations Relief and Works Agency for Palestine

the Atlas method (see Users Guide). In exceptional

Refugees in the Near East (UNRWA), United Nations

circumstances IDA extends temporary eligibility to

Regular Programme for Technical Assistance (UNTA),

countries above the cutoff that are undertaking

and other UN agencies, such as the International

major adjustments but are not creditworthy for Inter-

Atomic Energy Agency, International Fund for Agricul-

national Bank for Reconstruction and Development

tural Development, Joint United Nations Programme

(IBRD) lending. Exceptions are also made for small

on HIV/AIDS, United Nations Development Programme,

island economies. The IBRD lends to creditworthy

United Nations Economic Commission for Europe,

countries at a variable base rate of six-month LIBOR

United Nations Population Fund, United Nations Refu-

plus a spread, either variable or fixed, for the life of

gee Agency, World Food Programme, and World Health

the loan. The lending rate is reset every six months

Organization.

and applies to the interest period beginning on that date. Although some outstanding IBRD loans have a

Data sources

low enough interest rate to be classified as conces-

Data on net financial flows from international finan-

sional under the DAC definition, all IBRD loans in the

cial institutions are from the World Bank’s Debtor

table are classified as nonconcessional. Lending by

Reporting System and published in the World

the International Finance Corporation, Multilateral

Bank’s Global Development Finance: External Debt

Investment Guarantee Agency, and the International

of Developing Countries and electronically in Global

Centre for the Settlement of Investment Disputes

Development Finance database. Data on official

is excluded.

flows from UN agencies are from the OECD DAC

The International Monetary Fund (IMF) makes con-

annual Development Co-operation Report and are

cessional funds available through its Extended Credit

available electronically on the OECD DAC Interna-

Facility (which replaced the Poverty Reduction and

tional Development Statistics CD-ROM and at www.

Growth Facility in 2010), the Standby Credit Facility,

oecd.org/dac/stats/idsonline.

2011 World Development Indicators

371

global links

Net official financial flows

6.14

Financial flows from Development Assistance Committee members

Net disbursements Total net flowsa

$ millions Australia Austria Belgium Canada Denmark Finland France Germany Greece Ireland Italy Japan Korea, Rep Luxembourg Netherlands New Zealand Norway Portugal Spain Sweden Switzerland United Kingdom United States Total

2009

3,188 3,273 3,224 7,340 3,757 3,185 38,418 26,003 850 4,188 5,569 49,405 6,442 428 6,045 387 4,089 1,209 12,809 7,164 9,106 68,936 115,276 380,290

Official development assistancea

Other official flowsa

Total

Bilateral grants

Bilateral loans

Contributions to multilateral institutions

2009

2,224 513 1,594 3,182 1,914 765 5,814 6,747 297 693 871 5,327 366 266 4,914 226 3,125 225 4,098 2,919 1,734 6,994 25,992 80,800

88 –6 –9 –41 –8 26 1,205 350 0 0 4 674 214 0 –116 0 43 52 375 90 16 663 –819 2,802

2,762 1,142 2,610 4,000 2,810 1,290 12,600 12,079 607 1,006 3,297 9,469 816 415 6,426 309 4,086 513 6,584 4,548 2,310 11,491 28,831 120,000

450 635 1,025 859 904 499 5,581 4,983 310 313 2,423 3,467 235 149 1,628 83 918 236 2,111 1,539 559 3,834 3,658 36,398

Private flowsa

Total 2009

426 –44 90 –1,138 233 137 294 187 0 0 –72 8,216 452 0 0 8 4 0 0 68 0 –13 988 9,836

2009

0 2,035 147 3,140 599 1,741 25,524 12,367 241 3,000 2,181 31,187 5,018 0 –923 24 0 692 6,225 2,473 6,438 57,129 69,168 228,407

Net grants by NGOsa

Bilateral Multilateral Foreign portfolio portfolio direct investment investment investment 2009

0 2,551 3 6,604 599 791 16,300 9,726 241 0 129 19,440 5,018 0 540 24 0 –2 6,294 885 5,570 55,947 28,275 158,934

2009

0 46 0 –37 0 950 9,434 58 0 3,000 1,590 10,981 0 0 –2,853 0 0 –63 0 0 0 –2,143 27,223 48,185

2009

0 0 0 0 0 0 0 1,242 0 0 0 1,987 0 0 989 0 0 0 0 0 1,462 0 13,160 18,839

Private export credits 2009

0 –562 144 –3,427 0 0 –210 1,341 0 0 463 –1,220 0 0 401 0 0 757 –70 1,588 –593 3,326 510 2,449

2009

0 140 377 1,338 116 17 0 1,369 2 182 162 533 156 13 542 46 0 4 0 74 357 329 16,288 22,047

Official development assistance Commitmentsb

$ millions 2000 2009

Australia Austria Belgium Canada Denmark Finland France Germany Greece Ireland Italy Japan Korea, Rep Luxembourg Netherlands New Zealand Norway Portugal Spain Sweden Switzerland United Kingdom United States DAC Countries, Total

2,251 1,026 1,558 3,412 2,994 618 8,699 9,825 451 450 3,115 16,257 399 257 6,580 229 2,481 822 2,940 2,287 1,536 6,723 15,431 90,339

2,963 1,252 3,068 4,925 2,938 1,639 14,928 16,924 618 1,083 3,918 16,429 2,206 435 6,490 358 5,902 633 6,724 5,230 2,753 17,757 33,018 152,192

Gross disbursementsb

$ millions 2000 2009

1,939 792 1,558 3,023 3,193 661 9,276 9,973 451 450 3,082 15,485 282 257 6,171 216 2,800 822 2,940 2,861 1,513 6,723 13,293 87,757

2,912 1,188 2,750 4,372 2,960 1,323 15,933 13,693 618 1,083 3,514 14,848 949 435 6,841 333 4,650 565 7,213 5,090 2,286 13,400 29,286 136,242

Note: Components may not sum to totals because of gaps in reporting. a. At current prices and exchange rates. b. At 2008 prices and exchange rates.

372

2011 World Development Indicators

Net disbursements

$ millionsb 2000 2009

1,939 787 1,517 2,981 3,159 649 7,616 8,641 451 450 2,653 12,833 260 257 5,995 216 2,787 535 2,531 2,861 1,509 6,649 12,182 79,456

2,912 1,174 2,670 4,328 2,923 1,323 12,920 12,397 618 1,083 3,334 8,545 910 435 6,676 333 4,650 528 6,800 5,085 2,276 13,162 28,469 123,551

Per capita $b

% of general government disbursementsa

2000

2009

% of GNIa 2000 2009

2000

2009

101 97 148 97 592 125 129 105 41 119 46 101 6 583 376 56 621 52 63 323 210 113 44 89

136 141 250 130 530 248 207 151 55 250 56 67 19 889 405 78 969 51 147 549 296 216 94 131

0.27 0.23 0.36 0.25 1.06 0.31 0.30 0.27 0.20 0.29 0.13 0.28 0.04 0.70 0.84 0.25 0.76 0.26 0.22 0.80 0.34 0.32 0.10 0.22

0.71 0.44 0.72 0.59 1.94 0.63 0.60 0.59 0.39 0.77 0.27 0.74 0.18 1.61 1.84 0.56 1.77 0.56 0.53 1.32 1.01 0.83 0.30 0.56

0.87 0.57 1.02 0.68 1.54 0.97 0.85 0.76 0.36 0.93 0.30 0.45 0.31 1.86 1.57 0.61 2.32 0.45 0.98 2.03 1.39 1.03 0.48 0.69

0.29 0.30 0.55 0.30 0.88 0.54 0.47 0.35 0.19 0.54 0.16 0.18 0.10 1.04 0.82 0.28 1.06 0.23 0.46 1.12 0.45 0.52 0.21 0.31

6.14

About the data

The flows of official and private financial resources

to accommodate changes in respect of Kosovo and

discount rate). • Contributions to multilateral insti-

from the members of the Development Assistance

the Former Yugoslav Republic of Macedonia. In the

tutions are concessional funding received by multi-

Committee (DAC) of the Organisation for Economic

past DAC distinguished aid going to Part I and Part

lateral institutions from DAC members as grants

Co-operation and Development (OECD) to developing

II countries. Part I countries, the recipients of ODA,

or capital subscriptions. • Other official flows

economies are compiled by DAC, based principally on

comprised many of the countries classified by the

are transactions by the official sector whose main

reporting by DAC members using standard question-

World Bank as low- and middle-income economies.

objective is other than development or whose grant

naires issued by the DAC Secretariat.

Part II countries, whose assistance was designated

element is less than 25 percent. • Private flows are

The table shows data reported by DAC member

official aid, included the more advanced countries

flows at market terms financed from private sector

economies and does not include aid provided by the

of Central and Eastern Europe, countries of the for-

resources in donor countries. They include changes

European Union Institutions—a multilateral member

mer Soviet Union, and certain advanced developing

in holdings of private long-term assets by reporting

of DAC.

countries and territories. This distinction has been

country residents. • Foreign direct investment is

dropped with the 2005 aid flows.

investment by residents of DAC member countries

DAC exists to help its members coordinate their development assistance and to encourage the

Flows are transfers of resources, either in cash or

to acquire a lasting management interest (at least

expansion and improve the effectiveness of the

in the form of commodities or services measured on

10 percent of voting stock) in an enterprise operating

aggregate resources flowing to recipient economies.

a cash basis. Short-term capital transactions (with

in the recipient country. The data reflect changes in

In this capacity DAC monitors the flow of all financial

one year or less maturity) are not counted. Repay-

the net worth of subsidiaries in recipient countries

resources, but its main concern is official develop-

ments of the principal (but not interest) of ODA loans

whose parent company is in the DAC source coun-

ment assistance (ODA). Grants or loans to countries

are recorded as negative flows. Proceeds from offi-

try. • Bilateral portfolio investment is bank lending

and territories on the DAC list of aid recipients have

cial equity investments in a developing country are

and the purchase of bonds, shares, and real estate

to meet three criteria to be counted as ODA. They

reported as ODA, while proceeds from their later sale

by residents of DAC member countries in recipi-

are provided by official agencies, including state and

are recorded as negative flows.

ent countries. • Multilateral portfolio investment

local governments, or by their executive agencies.

The table is based on donor country reports and

is transactions of private banks and nonbanks in

They promote economic development and welfare as

does not provide a complete picture of the resources

DAC member countries in the securities issued by

the main objective. And they are provided on conces-

received by developing economies for two reasons.

multilateral institutions. • Private export credits are

sional financial terms (loans must have a grant ele-

First, flows from DAC members are only part of the

loans extended to recipient countries by the private

ment of at least 25 percent, calculated at a discount

aggregate resource flows to these economies. Sec-

sector in DAC member countries to promote trade;

rate of 10 percent). The DAC Statistical Reporting

ond, the data that record contributions to multilateral

they may be supported by an official guarantee. • Net

Directives provide the most detailed explanation of

institutions measure the flow of resources made

grants by nongovernmental organizations (NGOs)

this definition and all ODA-related rules.

available to those institutions by DAC members, not

are private grants by NGOs, net of subsidies from

the flow of resources from those institutions to devel-

the official sector. • Commitments are obligations,

oping economies.

expressed in writing and backed by funds, under-

This definition excludes nonconcessional flows from official creditors, which are classified as “other official flows,” and aid for military and anti-terrorism

Aid as a share of gross national income (GNI), aid

taken by an official donor to provide specified assis-

purposes. Transfer payments to private individuals,

per capita, and ODA as a share of the general gov-

tance to a recipient country or multilateral organiza-

such as pensions, reparations, and insurance pay-

ernment disbursements of the donor are calculated

tion. • Gross disbursements are the international

outs, are in general not counted. In addition to finan-

by the OECD. The denominators used in calculating

transfer of financial resources, goods, and services,

cial flows, ODA includes technical cooperation, most

these ratios may differ from corresponding values

valued at the cost to the donor.

expenditures for peacekeeping under UN mandates

elsewhere in this book because of differences in tim-

and assistance to refugees, contributions to multi-

ing or definitions.

lateral institutions such as the United Nations and its specialized agencies, and concessional funding to multilateral development banks.

Definitions • Net disbursements are gross disbursements of

The DAC list of aid recipients shows all countries

grants and loans minus repayments of principal

and territories eligible to receive ODA. These con-

on earlier loans. • Total net flows are ODA flows,

sist of all low- and middle-income countries, except

other official flows, private flows, and net grants by

Data on financial flows are compiled by OECD DAC

members of the Group of Eight or the European Union

nongovernmental organizations. • Official develop-

and published in its annual statistical report, Geo-

(including countries with a firm date for EU acces-

ment assistance refers to flows that meet the DAC

graphical Distribution of Financial Flows to Devel-

sion). The DAC revises the list every three years.

definition of ODA and are made to countries and ter-

oping Countries, and its annual Development

Countries that have exceeded the high-income

ritories on the DAC list of aid recipients. • Bilateral

Co‑operation Report. Data are available electroni-

threshold for three consecutive years at the time

grants are transfers of money or in kind for which no

cally on the OECD DAC International Development

of the review are removed. In line with this review

repayment is required. • Bilateral loans are loans

Statistics CD‑ROM and at www.oecd.org/dac/

process, the DAC last revised the list in September

extended by governments or official agencies with a

stats/idsonline.

2008. A further update took place in August 2009

grant element of at least 25 percent (at a 10 percent

Data sources

2011 World Development Indicators

373

global links

Financial flows from Development Assistance Committee members

6.15 6.15a

Allocation of bilateral aid from Development Assistance Committee members

Aid by purpose Net disbursements

$ millionsa 2000 2009

Australia Austria Belgium Canada Denmark Finland France Germany Greece Ireland Italy Japan Korea, Rep. Luxembourg Netherlands New Zealand Norway Portugal Spain Sweden Switzerland United Kingdom United States Total

758 273 477 1,160 1,024 217 2,829 2,687 99 154 377 9,768 131 99 2,243 85 934 179 720 1,242 627 2,710 7,405 36,195

2,312 507 1,585 3,141 1,905 791 7,019 7,097 297 693 875 6,001 581 266 4,798 226 3,168 277 4,473 3,009 1,751 7,657 25,174 83,602

Share of bilateral ODA net disbursements Development projects, programs, and other resource provisions 2000 2009

27.8 28.7 33.6 39.6 65.8 40.8 25.4 16.8 69.6 79.1 10.2 60.4 77.8 84.4 41.1 39.7 57.9 30.4 69.3 60.9 58.6 47.7 14.6 40.6

32.7 23.6 40.9 15.4 71.9 31.1 10.9 24.6 14.8 75.9 49.4 59.7 66.8 74.6 71.3 54.3 56.2 49.6 60.1 64.1 42.3 75.9 70.5 54.6

% Technical cooperationb 2000 2009

55.1 41.8 46.9 43.0 25.3 41.4 50.6 63.8 23.8 0.4 8.1 24.9 15.7 3.2 33.7 48.1 23.0 50.4 17.9 13.6 19.4 25.5 64.4 39.3

49.2 49.7 39.2 64.2 11.0 45.6 42.7 65.0 72.2 3.6 10.9 38.4 25.5 3.6 14.5 28.3 27.9 53.4 23.5 15.9 30.1 8.9 6.0 25.2

Debt-related aid 2000 2009

1.1 20.4 6.6 1.1 1.0 0.0 17.0 6.6 0.0 0.0 57.5 4.2 0.0 0.8 6.8 0.0 1.0 14.6 2.3 3.1 0.9 5.7 1.7 5.3

0.1 11.6 6.6 1.5 1.9 0.0 39.5 1.2 0.0 0.0 19.9 –14.5 0.0 0.0 0.9 0.0 0.5 –10.0 2.2 0.7 9.3 0.6 0.7 3.5

Humanitarian assistance 2000 2009

9.7 2.7 5.4 5.0 0.0 10.5 0.4 4.1 6.4 15.5 18.3 0.9 0.4 10.4 9.1 3.4 11.3 1.9 3.7 14.6 20.2 12.7 9.6 6.1

13.3 7.2 7.4 10.3 6.8 13.1 0.6 5.2 5.1 14.1 13.0 4.4 2.9 14.4 6.3 6.9 8.6 0.4 9.9 12.0 9.1 9.5 17.4 10.3

Administrative costs 2000 2009

6.2 6.4 7.5 11.4 8.0 7.2 6.7 8.7 0.2 5.1 5.9 9.5 6.1 1.2 9.4 8.8 6.9 2.7 6.8 7.7 0.9 8.4 9.7 8.6

4.7 7.9 6.0 8.6 8.5 10.1 6.3 4.1 7.9 6.5 6.8 12.1 4.8 7.3 6.9 10.6 6.8 6.6 4.2 7.3 9.3 5.2 5.4 6.3

a. At current exchange rates and prices. b. Includes aid for promoting development awareness and aid provided to refugees in the donor economy.

About the data Aid can be used in many ways. The sector to which

provide debt relief on liabilities that recipient coun-

human resources from donors or action directed to

aid goes, the form it takes, and the procurement

tries have difficulty servicing. Thus, this type of aid

human resources (such as training or advice). Also

restrictions attached to it are important influences

may not provide a full value of new resource flows

included are aid for promoting development aware-

on aid effectiveness. The data on allocation of offi-

for development, in particular for heavily indebted

ness and aid provided to refugees in the donor econ-

cial development assistance (ODA) in the table are

poor countries. Humanitarian assistance provides

omy. Assistance specifically to facilitate a capital

based principally on reporting by members of the

relief following sudden disasters and supports food

project is not included. • Debt-related aid groups

Organisation for Economic Co-operation and Devel-

programs in emergency situations. This type of aid

all actions relating to debt, including forgiveness,

opment (OECD) Development Assistance Committee

does not generally contribute to financing long-term

swaps, buybacks, rescheduling, and refinancing.

(DAC). For more detailed explanation of ODA, see

development.

• Humanitarian assistance is emergency and dis-

About the data for table 6.14. The form in which an ODA contribution reaches

tress relief (including aid to refugees and assistance Definitions

for disaster preparedness). • Administrative costs

the benefiting sector or the economy is important. A

• Net disbursements are gross disbursements of

are the total current budget outlays of institutions

distinction is made between resource provision and

grants and loans minus repayments of principal on

responsible for the formulation and implementation

technical cooperation. Resource provision involves

earlier loans • Development projects, programs, and

of donor’s aid programs and other administrative

mainly cash or in-kind transfers and financing of

other resource provisions are aid provided as cash

costs incurred by donors in aid delivery.

capital projects, with the deliverables being finan-

transfers, aid in kind, development food aid, and the

cial support and the provision of commodities and

financing of capital projects, intended to increase

supplies. Technical cooperation includes grants to

or improve the recipient’s stock of physical capital

nationals of aid-recipient countries receiving educa-

and to support recipient’s development plans and

Data on aid flows are published by OECD DAC in

tion or training at home or abroad, and payments

other activities with finance and commodity supply.

its annual statistical report, Geographical Distri-

to consultants, advisers, and similar personnel and

• Technical cooperation is the provision of resources

bution of Financial Flows to Developing Countries,

to teachers and administrators serving in recipient

whose main aim is to augment the stock of human

and its annual Development Co‑operation Report.

countries. Technical cooperation is spent mostly in

intellectual capital, such as the level of knowledge,

Data are available electronically on the OECD DAC

the donor economy.

skills, and technical know-how in the recipient coun-

International Development Statistics CD-ROM and

Two other types of aid are presented because they

try (including the cost of associated equipment).

at www.oecd.org/dac/stats/idsonline.

serve distinctive purposes. Debt-related aid aims to

Contributions take the form mainly of the supply of

374

2011 World Development Indicators

Data sources

6.15b

6.15

Aid by sector

Share of bilateral ODA commitments (%) Australia Austria Belgium Canada Denmark Finland France Germany Greece Ireland Italy Japan Korea, Rep. Luxembourg Netherlands New Zealand Norway Portugal Spain Sweden Switzerland United Kingdom United States Total

Total sectorallocable aid

Social infrastructure and services

Economic infrastructure, Multi services, and production sector sector or crossTransport Government Water and comand civil supply and cutting

2009

Total 2009

Education 2009

Health 2009

77.7 67.5 73.2 72.7 68.8 72.9 68.8 87.6 74.5 70.8 64.1 74.5 96.2 68.1 71.1 61.9 64.0 70.3 70.8 54.3 42.9 72.4 73.2 72.8

48.7 45.8 39.5 52.4 42.2 32.6 36.3 49.6 62.8 58.0 35.6 28.9 27.6 47.2 26.8 45.5 40.6 56.9 44.6 33.4 21.6 41.7 53.5 42.7

11.9 23.8 13.1 15.4 5.1 6.7 19.2 19.1 32.4 13.0 10.3 5.3 9.7 12.5 4.2 21.0 8.6 24.1 7.1 3.1 3.1 8.9 4.0 8.8

7.5 5.9 9.2 15.2 7.6 3.3 1.5 3.6 4.6 13.5 9.4 2.0 10.4 12.7 2.7 5.5 6.2 2.9 6.2 3.5 3.1 7.8 3.7 4.6

Population 2009

2.2 0.4 1.6 2.5 2.8 0.5 0.3 1.9 2.6 4.0 0.9 0.4 0.2 4.6 2.1 2.3 2.0 0.1 4.3 1.9 0.2 5.0 19.0 6.7

sanitation 2009

society 2009

Total 2009

1.9 4.3 3.2 2.0 8.5 4.3 8.8 8.7 1.0 2.5 4.9 18.9 4.7 8.4 3.7 1.1 1.3 0.1 12.7 2.5 2.4 1.4 1.6 6.2

22.6 9.1 9.7 16.2 16.3 14.1 1.6 14.7 16.0 16.4 6.5 1.2 1.7 5.1 10.7 14.4 19.9 22.3 9.9 19.8 11.8 14.7 18.6 12.5

11.8 15.2 28.0 12.2 18.0 28.1 16.3 27.8 6.1 9.5 23.9 41.3 64.5 10.7 12.7 14.6 14.3 10.2 20.4 12.3 11.5 20.1 15.1 21.3

munication Agriculture 2009 2009

5.3 1.8 4.7 0.4 3.4 6.2 6.8 2.6 2.4 0.1 3.9 26.7 52.1 0.2 1.0 7.2 0.2 8.3 2.5 1.2 0.8 2.6 4.5 7.4

4.6 2.0 7.7 6.8 5.8 7.8 5.3 3.7 1.3 8.0 16.7 4.9 3.5 5.0 3.3 3.7 6.8 1.5 3.8 2.5 3.5 1.7 5.0 4.7

Untied aida

2009

17.2 6.6 5.7 8.1 8.5 12.2 16.2 10.1 5.6 3.3 4.6 4.2 4.0 10.2 31.6 1.9 9.2 3.2 5.8 8.6 9.7 10.6 4.6 8.8

90.8 55.2 95.5 98.3 96.6 90.3 89.2 97.1 49.8b 100.0 b 56.2 94.7 48.3 100.0 b 80.8 90.1 100.0 27.9 76.6 99.9 99.2 100.0 69.8 84.5

a. Excludes technical cooperation and administrative costs. b. Gross disbursements.

About the data

Definitions

The Development Assistance Committee (DAC)

• Bilateral official development assistance (ODA)

administrative apparatus and planning and activities

records the sector classification of aid using a

commitments are firm obligations, expressed in writ-

promoting good governance and civil society. • Eco-

three-level hierarchy. The top level is grouped by

ing and backed by the necessary funds, undertaken

nomic infrastructure, services, and production sec-

themes, such as social infrastructure and services;

by official bilateral donors to provide specified assis-

tor group assistance for networks, utilities, services

economic infrastructure, services, and production;

tance to a recipient country or a multilateral organi-

that facilitate economic activity, and contributions

and multisector or cross-cutting areas. The second

zation. Bilateral commitments are recorded in the

to all directly productive sectors. • Transport and

level is more specific. Education and health and

full amount of expected transfer, irrespective of the

communication refer to road, rail, water, and air

transport and storage are examples. The third level

time required for completing disbursements. • Total

transport; post and telecommunications; and televi-

comprises subsectors such as basic education and

sector-allocable aid is the sum of aid that can be

sion and print media. • Agriculture refers to sector

basic health. Some contributions are reported as

assigned to specific sectors or multisector activi-

policy, development, and inputs; crop and livestock

non-sector-allocable aid.

ties. • Social infrastructure and services refer to

production; and agricultural credit, cooperatives, and

Reporting on the sectoral destination and the

efforts to develop the human resources potential and

research. • Multisector or cross-cutting refers to

form of aid by donors may not be complete. Also,

improve the living conditions of aid recipients. • Edu-

support for projects that straddle several sectors.

measures of aid allocation may differ from the per-

cation refers to general teaching and instruction at

• Untied aid is ODA not subject to restrictions by

spectives of donors and recipients because of dif-

all levels, as well as construction to improve or adapt

donors on procurement sources.

ference in classification, available information, and

educational establishments. Training in a particular

recording time.

field is reported for the sector concerned. • Health

Data sources

The proportion of untied aid is reported because

refers to assistance to hospitals, clinics, other medi-

Data on aid flows are published annually by the

tying arrangements may prevent recipients from

cal and dental services, public health administra-

Organisation for Economic Co-operation and

obtaining the best value for their money. Tying

tion, and medical insurance programs. • Population

Development (OECD) DAC in Geographical Distri-

requires recipients to purchase goods and services

refers to all activities related to family planning and

bution of Financial Flows to Developing Countries

from the donor country or from a specified group of

research into population problems. • Water supply

and Development Co‑operation Report. Data are

countries. Such arrangements prevent a recipient

and sanitation refer to assistance for water supply

available electronically on the OECD DAC Interna-

from misappropriating or mismanaging aid receipts,

and use, sanitation, and water resources develop-

tional Development Statistics CD-ROM and at www.

but they may also be motivated by a desire to benefit

ment (including rivers). • Government and civil soci-

oecd.org/dac/stats/idsonline.

donor country suppliers.

ety refer to assistance to strengthen government 2011 World Development Indicators

375

global links

Allocation of bilateral aid from Development Assistance Committee members

6.16

Aid dependency Net official development assistance (ODA)

376

Aid dependency ratios

Net ODA as % of imports of goods, services, and income 2000 2009

Net ODA as % of central government expense 2000 2009

Total $ millions 2000 2009

Per capita $ 2000 2009

Net ODA as % of GNI 2000 2009

Net ODA as % of gross capital formation 2000 2009

136 317 200 302 52 216

6,070 358 319 239 128 528

6 103 7 21 1 70

204 113 9 13 3 171

.. 8.4 0.4 4.1 0.0 11.0

.. 3.0 0.2 0.4 0.0 5.9

.. 34.8 1.5 22.0 0.1 60.6

.. 10.3 0.6 2.1 0.2 19.3

.. 21.0 .. 4.1 0.1 21.2

.. 5.1 .. 0.5 0.2 12.5

.. .. .. .. .. ..

112.5 .. 0.9 .. .. 25.6

139 1,172 .. .. 243 482 737 31 231 .. 180 93 396 377

232 1,227 98 .. 683 726 415 280 338 .. 1,084 549 722 649

17 8 .. .. 37 58 199 18 1 .. 15 14 31 24

26 8 10 .. 76 74 110 143 2 .. 69 66 49 33

2.8 2.4 .. .. 10.9 5.9 12.1 0.6 0.0 .. 6.9 12.9 10.9 4.0

0.6 1.3 0.2 .. 10.3 4.4 2.4 2.5 0.0 .. 13.5 41.2 7.7 2.9

12.8 10.8 .. .. 57.0 31.6 65.1 1.7 0.2 .. 41.1 213.8 60.3 22.4

2.5 5.6 0.5 .. 41.1 24.7 11.0 9.8 0.1 .. .. .. 34.3 ..

5.8 11.7 .. .. 32.7 19.7 17.4 1.0 0.2 .. 26.0 56.5 16.1 12.7

1.7 5.0 0.3 .. .. 12.0 4.3 4.7 0.2 .. .. 102.0 9.7 9.4

.. .. .. .. .. .. .. .. 0.2 .. .. .. .. ..

.. 12.2 0.6 .. 68.2 .. 5.9 .. 0.1 .. 102.5 .. 62.9 ..

75 130 49 1,712 .. 186 177 32 10 351 66 44 ..

237 561 80 1,132 .. 1,060 2,354 283 109 2,366 169 116 ..

20 15 3 1 .. 5 3 11 2 20 15 4 ..

54 50 5 1 .. 23 36 77 24 112 38 10 ..

8.0 9.5 0.1 0.1 .. 0.2 4.5 1.4 0.1 3.6 0.3 0.1 ..

11.9 9.2 0.1 0.0 .. 0.5 23.9 4.1 0.4 10.6 0.3 .. ..

82.4 40.4 0.3 0.4 .. 1.2 119.1 4.4 0.4 31.2 1.6 1.2 ..

111.2 24.2 0.3 0.0 .. 2.0 74.6 12.0 1.9 90.4 1.0 .. ..

.. .. 0.2 0.6 .. 1.0 .. 1.6 0.1 7.9 0.6 .. ..

.. .. 0.1 0.1 .. 2.2 .. .. 0.8 23.9 0.6 .. ..

.. .. 0.3 .. .. .. 15.2 5.0 .. .. 0.8 .. ..

.. .. 0.2 .. .. 2.3 .. .. 1.4 57.6 0.7 .. ..

56 146 1,327 180 176 .. 686

120 209 925 277 145 .. 3,820

6 12 19 30 48 .. 10

12 15 11 45 29 .. 46

0.2 1.0 1.3 1.4 27.7 .. 8.4

0.3 0.4 0.5 1.4 7.8 .. 13.4

1.0 4.6 6.8 8.1 116.6 .. 41.4

1.7 1.1 2.5 10.0 .. .. 59.7

0.5 2.3 5.6 3.0 34.4 .. 41.0

0.7 1.1 1.6 3.1 .. .. 42.0

.. .. .. .. .. .. ..

.. .. 1.6 53.2 .. .. ..

12 50 169 .. 598

78 128 908 .. 1,583

9 38 36 .. 31

53 75 213 .. 66

0.3 12.4 5.3 .. 12.4

0.8 18.5 8.6 .. 6.1

1.1 67.8 20.8 .. 50.0

2.5 67.3 69.7 .. 30.9

0.5 .. 13.6 .. 17.2

.. 35.3 15.6 .. 14.1

.. .. 47.9 .. ..

.. .. 27.3 .. 33.8

263 153 81 208 448

376 215 146 1,120 457

23 18 62 24 72

27 21 90 112 61

1.4 5.0 39.9 .. 6.4

1.0 5.8 17.6 .. 3.3

7.6 24.9 333.0 20.7 22.3

7.7 24.2 .. 63.1 16.3

4.4 15.7 .. 15.1 8.9

2.7 13.6 .. 39.5 5.0

12.5 .. .. .. ..

8.0 .. .. .. 13.3

2011 World Development Indicators

Net official development assistance (ODA)

Total $ millions 2000 2009

Aid dependency ratios

Per capita $ 2000 2009

Net ODA as % of GNI 2000 2009

Net ODA as % of gross capital formation 2000 2009

Net ODA as % of imports of goods, services, and income 2000 2009

Net ODA as % of central government expense 2000 2009

.. 1,373 1,651 130 100

.. 2,393 1,049 93 2,791

.. 1 8 2 4

.. 2 5 1 89

.. 0.3 1.1 0.1 ..

.. 0.2 0.2 0.0 4.5

.. 1.2 4.5 0.4 ..

.. 0.5 0.6 .. ..

.. 1.7 2.5 0.7 ..

.. 0.7 0.8 .. ..

.. 1.9 .. 0.2 ..

.. 1.1 1.2 0.1 ..

9 .. 552 189 509 73

150 .. 761 298 1,778 67

3 .. 115 13 16 3

55 .. 128 19 45 3

0.1 .. 6.5 1.1 4.1 ..

1.3 .. 3.0 0.3 6.1 ..

.. .. 29.2 5.6 23.0 ..

5.8 .. 20.5 0.8 29.0 ..

0.2 .. 8.7 1.8 12.9 ..

2.1 .. 4.5 0.6 15.4 ..

.. .. 24.1 7.5 23.9 ..

3.0 .. 10.6 1.5 27.9 ..

1 .. 215 281 .. 199 37 67 .. .. 250 320 446 45 288 221 20 –58 123 217 419 906 106 152 386

788 .. 315 420 .. 641 123 505 39 .. 193 445 772 144 985 287 156 185 245 372 912 2,013 357 326 855

1 .. 44 52 .. 53 19 24 .. .. 124 21 38 2 27 85 17 –1 30 91 15 50 2 84 16

437 .. 59 66 .. 152 60 128 6 .. 95 23 51 5 76 87 122 2 68 139 28 88 7 150 29

.. .. 16.7 16.9 .. 1.1 3.8 17.4 .. .. 7.1 8.4 26.1 0.1 12.0 20.2 0.4 0.0 9.4 20.0 1.2 22.6 .. 3.9 7.0

14.0 .. 7.1 7.2 .. 1.8 6.4 78.3 0.1 .. 2.2 5.2 16.6 0.1 11.0 9.4 1.8 0.0 4.3 9.4 1.0 20.8 .. 3.6 6.7

.. .. 78.3 57.2 .. 5.7 11.1 .. .. .. 31.3 54.9 188.6 0.2 48.4 105.5 1.7 0.0 39.7 68.6 4.4 68.9 .. 22.8 28.9

52.7 .. 31.2 .. .. 6.2 24.8 .. .. .. 8.6 15.9 65.6 0.5 .. 37.7 8.5 0.1 16.7 17.6 2.8 98.2 .. 13.0 23.0

.. .. 28.5 44.0 .. .. 4.4 .. .. .. 10.5 20.2 65.6 0.0 27.5 .. 0.7 0.0 11.2 27.4 3.1 51.4 4.0 8.2 21.1

.. .. 8.1 25.2 .. 1.9 6.7 27.3 0.1 .. 3.2 .. .. 0.1 .. .. 2.8 0.1 5.7 13.1 2.3 44.1 .. 5.9 16.6

.. .. 99.2 .. .. 3.8 .. .. .. .. .. 77.8 .. 0.3 102.4 .. .. –0.1 32.9 85.2 .. .. .. 13.7 ..

.. .. 35.7 62.5 .. 6.3 .. .. .. .. .. .. .. 0.3 74.9 .. 8.4 .. 11.8 30.7 3.6 .. .. .. ..

560 208 174

774 470 1,659

110 19 1

135 31 11

15.0 11.7 0.4

13.1 8.9 1.0

47.2 101.4 ..

53.7 .. ..

23.5 43.0 1.1

16.4 .. 2.8

86.4 .. ..

60.2 .. ..

45 700 15 275 82 397 572 ..

212 2,781 66 414 148 442 310 ..

19 5 5 51 15 15 7 ..

75 16 19 61 23 15 3 ..

0.2 1.0 0.1 8.3 1.1 0.8 0.8 ..

.. 1.7 0.3 5.3 1.1 0.4 0.2 ..

1.9 5.5 0.5 35.7 6.1 3.7 3.6 ..

.. 9.1 1.1 26.3 6.7 1.5 1.3 ..

0.6 4.8 0.1 13.7 2.3 3.4 1.1 ..

0.8 7.1 0.4 7.6 1.8 1.3 0.5 ..

0.9 5.7 0.6 26.2 6.6 4.2 4.3 ..

.. 10.6 .. .. 6.1 2.0 1.0 ..

.. ..

2011 World Development Indicators

377

global links

6.16

Aid dependency

6.16

Aid dependency Net official development assistance (ODA)

Total $ millions 2000 2009

Romania Russian Federation Rwanda Saudi Arabia Senegal Serbia Sierra Leone Singapore Slovak Republic Slovenia Somalia South Africa 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

Aid dependency ratios

Per capita $ 2000 2009

Net ODA as % of GNI 2000 2009

.. .. 321 22 429 1,134 a 181 .. .. 61 101 486

.. .. 934 .. 1,018 608 437 .. .. .. 662 1,075

.. .. 40 1 43 151a 43 .. .. 31 14 11

.. .. 93 .. 81 83 77 .. .. .. 72 22

.. .. 18.7 0.0 9.3 18.6a 29.3 .. .. 0.3 .. 0.4

275 220 13

704 2,289 58

15 6 12

35 54 49

1.7 1.9 0.9

1.7 4.6 2.0

158 124 1,063 697 231 70 –2 222 327 31 853 .. ..

245 409 2,934 –77 217 499 7 474 1,362 40 1,786 668 ..

10 20 31 11 284 13 –1 23 5 7 35 .. ..

12 59 67 –1 191 75 5 45 18 8 55 15 ..

0.9 15.0 10.6 0.6 71.6 5.4 0.0 1.2 0.1 1.2 14.0 .. ..

5 8 3 22 212 14 76 14 8w 18 6 5 6 10 5 11 9 16 3 19 0 ..

15 7 2 43 748 21 98 59 19 w 47 11 10 11 22 5 20 16 41 9 53 0 ..

0.1 1.4 0.1 5.5 13.3 3.0 25.8 2.8 0.2 w 7.0 0.5 0.7 0.2 0.9 0.5 0.6 0.2 1.0 0.7 4.0 0.0 ..

17 51 186 190 76 67 1,681 3,744 637 3,026 263 500 795 1,269 176 737 49,527 s 127,527 s 12,349 39,834 25,127 50,840 18,635 39,070 5,777 10,762 49,234 127,093 8,563 10,278 4,462 8,101 4,847 9,104 4,472 13,589 4,114 14,332 13,067 44,510 294 433 .. ..

.. .. 18.0 .. 8.0 1.4 23.0 .. .. .. .. 0.4

Net ODA as % of gross capital formation 2000 2009

.. .. 101.2 0.1 44.7 212.2a 413.2 .. .. 1.1 .. 2.3

Net ODA as % of imports of goods, services, and income 2000 2009

Net ODA as % of central government expense 2000 2009

.. .. 82.3 .. 28.4 5.9 148.8 .. .. .. .. 1.9

.. .. 71.2 0.0 22.3 .. 68.8 .. .. 0.5 .. 1.3

.. .. 61.0 .. .. 3.1 64.8 .. .. .. .. 1.2

.. .. .. .. 71.9 .. 98.8 .. .. 187.8 .. 1.3

.. .. .. .. .. 3.8 101.9 .. .. .. .. 1.1

6.0 9.7 5.1

6.8 16.6 11.4

3.2 8.5 0.9

5.7 16.8 2.1

7.3 .. 3.9

.. .. ..

0.5 8.3 13.7 0.0 .. 17.5 0.0 1.3 0.2 0.2 11.4 0.6 ..

4.7 152.5 62.0 2.5 285.9 29.4 –0.1 4.2 0.6 3.1 70.7 .. ..

2.9 37.9 46.1 –0.1 .. .. .. 4.5 1.5 1.8 46.8 3.4 ..

2.4 .. 47.6 0.9 .. 10.5 0.0 2.1 0.5 .. 54.2 .. ..

.. 13.0 37.2 0.0 .. .. .. 2.0 0.8 .. 32.0 1.1 ..

.. 160.3 .. .. .. .. .. 4.1 .. .. 96.5 .. ..

.. .. .. –0.1 .. 100.6 .. 4.0 0.8 .. 86.9 1.4 ..

0.2 0.6 0.0 4.4 .. 2.0 11.1 14.1 0.2 w 9.2 0.3 0.4 0.2 0.8 0.2 0.3 0.2 1.1 0.8 4.9 0.0 ..

0.5 8.3 0.3 18.2 47.4 14.3 140.8 19.6 0.7 w 35.3 1.8 2.5 0.9 3.5 1.6 3.2 1.2 4.0 2.9 23.0 0.0 ..

0.3 .. 0.3 9.3 19.2 6.2 53.1 .. 0.5 w 22.8 1.5 2.4 0.6 2.8 1.4 1.8 0.9 3.3 3.5 10.9 0.0 ..

0.6 .. 0.1 4.9 .. 4.4 23.1 .. 0.7 w 24.9 1.1 1.5 0.5 2.7 0.6 0.9 1.0 3.9 3.3 12.0 0.0 ..

0.3 .. 0.3 .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. ..

0.5 .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. ..

0.9 2.3 0.1 10.9 .. .. 44.7 581.9 1.0 w 38.7 1.0 1.1 0.7 2.5 0.4 1.6 1.2 .. 2.5 25.0 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. a. Includes Montenegro.

378

2011 World Development Indicators

6.16

About the data

conclusions. For foreign policy reasons some coun-

The nominal values used here may overstate the

tance (ODA; see About the data for table 6.14) for

tries have traditionally received large amounts of

real value of aid to recipients. Changes in interna-

aid-receiving countries. The data cover loans and

aid. Thus aid dependency ratios may reveal as much

tional prices and exchange rates can reduce the pur-

grants from Development Assistance Committee

about a donor’s interests as about a recipient’s

chasing power of aid. Tying aid, still prevalent though

(DAC) member countries, multilateral organizations,

needs. Ratios are generally much higher in Sub-Saha-

declining in importance, also tends to reduce its pur-

and non-DAC donors. They do not reflect aid given by

ran Africa than in other regions, and they increased

chasing power (see About the data for table 6.15).

recipient countries to other developing countries. As

in the 1980s. High ratios are due only in part to aid

The aggregates refer to World Bank classifications

a result, some countries that are net donors (such as

flows. Many African countries saw severe erosion

of economies and therefore may differ from those

Saudi Arabia) are shown in the table as aid recipients

in their terms of trade in the 1980s, which, along

of the Organisation for Economic Co-operation and

(see table 6.16a).

with weak policies, contributed to falling incomes,

Development (OECD).

The table shows data for official development assis-

The table does not distinguish types of aid (pro-

imports, and investment. Thus the increase in aid

gram, project, or food aid; emergency assistance;

dependency ratios reflects events affecting both the

postconflict peacekeeping assistance; or technical

numerator (aid) and the denominator (GNI).

Definitions • Net official development assistance is flows (net

cooperation), which may have different effects on the

Because the table relies on information from

of repayment of principal) that meet the Development

economy. Expenditures on technical cooperation do

donors, it is not necessarily consistent with infor-

Assistance Committee (DAC) definition of ODA and

not always directly benefit the economy to the extent

mation recorded by recipients in the balance of pay-

are made to countries and territories on the DAC list

that they defray costs incurred outside the country

ments, which often excludes all or some technical

of aid recipients. See About the data for table 6.14.

on salaries and benefits of technical experts and

assistance—particularly payments to expatriates

• Net official development assistance per capita is

overhead costs of firms supplying technical services.

made directly by the donor. Similarly, grant com-

net ODA divided by midyear population. • Aid depen-

Ratios of aid to gross national income (GNI), gross

modity aid may not always be recorded in trade

dency ratios are calculated using values in U.S. dol-

capital formation, imports, and government spending

data or in the balance of payments. Moreover, DAC

lars converted at official exchange rates. Imports of

provide measures of recipient country dependency

statistics exclude aid for military and antiterrorism

goods, services, and income refer to international

on aid. But care must be taken in drawing policy

purposes.

transactions involving a change in ownership of general merchandise, goods sent for processing

6.16a

Official development assistance from non-DAC donors, 2005–09

of employee compensation for nonresident workers,

Net disbursements ($ millions) 2005

2006

2007

2008

2009

Czech Republic

135

161

179

249

215

Hungary

117

OECD members (non-DAC) 100

149

103

107

Iceland

Israela

111

138

124

Poland

205

297

363

372

375

Slovak Republic

Slovenia

601

714

602

780

707

Turkey

and repairs, nonmonetary gold, services, receipts and investment income. For definitions of GNI, gross capital formation, and central government expense, see Definitions for tables 1.1, 4.8, and 4.10.

Arab countries Kuwait Saudi Arabia United Arab Emirates

158

110

283

221

2,025

1,551

4,979

3,134

Data on financial flows are compiled by OECD DAC

141

219

429

834

and published in its annual statistical report, Geo-

483

513

514

435

411

oping Countries, and in its annual Development

178

Co‑operation Report. Data are available electroni-

Other donors Taiwan, China Thailand Othersb Total

Data sources

218 1,026

graphical Distribution of Financial Flows to Devel-

134

275

313

cally on the OECD DAC International Development

3,175

4,617

4,333

8,094

6,672

Statistics CD-ROM and at www.oecd.org/dac/

Note: The table does not reflect aid provided by several major emerging non–Organisation for Economic Co-operation and Development (OECD) donors because information on their aid has not been disclosed. a. The statistical data for Israel 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. The figures include $49.2 million in 2005, $45.5 million in 2006, $42.9 million in 2007, $43.6 million in 2008, and $35.4 million in 2009 for first-year sustenance expenses for people arriving from developing countries (many of which are experiencing civil war or severe unrest) or people who have left their country for humanitarian or political reasons. b. Includes Cyprus, Estonia, Latvia, Liechstenstein, Lithuania, Malta, and Romania. Source: Organisation for Economic Co-operation and Development.

stats/idsonline. Data on population, GNI, gross capital formation, imports of goods and services, and central government expense used in computing the ratios are from World Bank and International Monetary Fund databases.

2011 World Development Indicators

379

global links

Aid dependency

6.17

Distribution of net aid by Development Assistance Committee members Ten major DAC donors

$ millions Total $ millions 2009

Afghanistan Albania Algeria Angola Argentina Armenia Australia Austria Azerbaijan 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 CÔ te d'Ivoire Croatia Cuba 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 Honduras

380

United States 2009

EU Institutions 2009

United Kingdom 2009

Germany 2009

France 2009

Japan 2009

Netherlands 2009

Spain 2009

Norway 2009

Canada 2009

Other DAC donors $ millions 2009

5,319.2 314.9 282.9 170.4 100.2 273.8

2,979.9 33.0 8.1 41.5 2.6 78.5

395.4 69.3 82.8 38.9 21.3 38.8

324.4 2.2 3.6 4.4 1.0 1.0

337.3 58.8 13.1 8.4 22.7 31.0

49.8 4.2 94.5 4.2 12.3 5.7

170.5 -2.0 1.9 6.8 9.0 98.7

147.9 8.2 0.0 -3.3 0.2 3.0

98.9 14.3 54.4 20.3 24.1 0.4

115.9 1.0 0.9 17.8 0.1 3.1

232.6 0.1 2.8 0.9 2.1 0.7

466.5 125.7 20.9 30.4 4.8 13.0

136.5 849.5 72.3

40.4 63.8 12.2

12.5 131.9 11.1

1.4 250.1 0.6

42.7 67.3 21.7

27.9 -3.6 4.5

-2.0 14.1 0.6

0.0 70.4 0.0

0.7 6.0 0.8

4.0 14.6 2.6

0.3 52.5 0.0

8.7 182.5 18.3

472.3 562.8 349.0 255.7 328.0 .. 618.3 391.9 516.8 326.9

58.9 101.6 31.1 214.4 8.1 .. 51.1 47.6 68.6 31.4

146.6 77.8 72.6 32.3 18.8 .. 165.4 131.1 43.1 59.2

0.0 0.5 9.6 0.9 13.1 .. 0.2 14.4 32.3 2.3

43.1 45.7 27.6 2.1 196.1 .. 47.5 27.9 37.9 91.0

50.4 10.0 4.7 1.0 47.1 .. 77.4 12.9 29.8 90.6

25.8 31.8 5.0 -2.6 -93.2 .. 49.8 20.4 127.5 8.1

42.0 45.6 21.8 0.0 0.6 .. 66.0 18.3 0.1 0.1

3.5 97.6 36.9 0.1 64.9 .. 10.2 5.7 29.1 4.0

0.0 6.4 15.9 1.8 29.5 .. 0.5 25.1 3.2 0.4

7.0 24.3 4.1 1.3 10.8 .. 23.5 6.1 10.9 7.1

94.9 121.6 120.0 4.4 32.2 .. 126.6 82.5 134.4 32.7

153.3 474.5 70.5 1,199.8 .. 1,044.4 1,332.0 252.3 105.5 1,794.4 160.7 103.5 ..

30.5 169.6 1.8 52.9 .. 652.3 238.7 9.3 -0.6 230.7 3.7 20.0 ..

54.7 119.0 10.8 42.9 .. 45.9 232.8 26.2 6.8 71.9 129.9 16.9 ..

2.4 5.6 0.6 116.0 .. 7.8 225.5 0.0 2.6 0.2 1.9 1.0 ..

6.6 27.9 11.5 340.9 .. 45.2 79.4 25.8 15.0 15.1 12.6 2.5 ..

25.9 41.0 9.6 364.4 .. 22.5 30.3 93.2 4.7 1,200.6 4.0 2.7 ..

6.1 14.0 7.9 142.0 .. -6.7 65.7 0.4 58.3 10.4 -0.7 3.6 ..

2.8 8.4 0.2 5.3 .. 32.5 43.4 0.0 3.8 36.5 0.2 0.1 ..

4.3 13.2 9.6 45.8 .. 148.6 42.7 44.4 9.3 50.8 0.7 37.7 ..

0.6 2.2 13.3 21.7 .. 11.6 28.1 0.1 0.7 1.6 3.6 0.8 ..

3.8 12.1 2.0 11.1 .. 25.3 44.9 7.6 2.1 43.7 0.1 7.7 ..

15.6 61.6 3.2 57.1 .. 59.4 300.7 45.4 2.8 133.1 4.6 10.7 ..

118.3 209.9 784.7 284.6 86.3 .. 2,019.0

14.1 52.1 185.1 82.1 3.6 .. 726.0

66.1 62.6 204.7 24.9 42.9 .. 202.5

0.1 -0.2 35.6 0.0 6.5 .. 342.9

–2.2 24.7 138.8 18.1 1.4 .. 79.8

3.4 1.2 111.6 2.4 0.5 .. 38.3

0.2 -11.8 -18.8 -3.8 8.8 .. 97.8

0.0 1.6 17.8 0.4 3.7 .. 85.9

29.2 48.7 20.6 125.7 1.8 .. 94.0

0.3 1.6 0.7 0.5 9.6 .. 37.8

2.6 3.2 17.0 3.2 0.6 .. 87.2

4.4 26.0 71.7 31.1 7.0 .. 226.9

61.8 37.1 603.6

1.2 5.0 279.1

9.2 15.2 167.7

0.0 3.7 7.3

-3.3 0.3 67.0

54.0 0.3 14.0

0.1 11.4 12.3

0.0 0.7 5.1

0.4 3.0 0.9

0.0 0.1 11.0

1.0 1.2 0.8

–0.8 –3.9 38.5

987.2

150.5

166.9

153.9

61.2

49.7

64.8

98.3

24.1

2.5

99.8

115.5

369.3 212.2 110.7 806.8 344.5

83.9 34.9 1.1 319.6 128.8

28.0 41.2 60.1 102.7 39.8

0.7 0.9 0.1 8.0 0.1

16.1 19.5 0.4 16.9 15.9

2.9 82.1 6.1 49.0 1.4

26.0 18.2 9.4 24.8 41.7

28.4 0.0 0.0 0.2 0.8

113.4 5.0 13.1 144.9 58.4

7.7 0.0 0.0 4.3 1.4

7.1 5.2 1.2 119.7 24.1

55.2 5.4 19.2 16.8 32.2

2011 World Development Indicators

global links

6.17

Distribution of net aid by Development Assistance Committee members Ten major DAC donors

$ millions Total $ millions 2009

United States 2009

EU Institutions 2009

United Kingdom 2009

Germany 2009

France 2009

Japan 2009

Netherlands 2009

Spain 2009

Norway 2009

Canada 2009

Other DAC donors $ millions 2009

.. 1,567.6 446.0 67.7 2,686.0

.. 48.1 121.3 0.7 2,346.3

.. 98.9 113.1 1.9 57.3

.. 521.1 68.8 0.7 48.6

.. 263.4 -34.8 46.1 38.2

.. –29.0 187.1 14.6 9.3

.. 517.0 -512.8 -17.4 28.1

.. 7.2 81.1 4.5 7.3

.. 25.3 3.4 5.2 2.4

.. 16.1 12.9 0.8 11.6

.. 11.5 20.0 3.3 12.1

.. 87.8 385.9 7.3 124.9

112.3

–2.1

105.9

8.3

–6.9

–0.8

–5.3

–4.3

1.2

0.1

5.9

10.2

571.7 185.5 1,308.3 49.8

394.6 97.3 590.2 13.5

85.4 13.3 84.3 3.4

1.5 7.0 131.2 0.1

39.8 17.5 85.7 2.7

58.9 2.9 44.8 0.3

–57.4 37.1 33.7 0.0

0.6 0.6 25.4 1.2

10.2 -0.4 50.7 2.0

0.8 3.1 15.5 4.8

11.0 0.1 31.7 3.6

26.3 7.0 215.1 18.2

744.6 .. 168.4 285.9 .. 463.3 86.8 400.3 34.4 .. 186.5 297.2 519.3 133.0 676.4 157.9 156.8 164.8 202.4 212.6 987.1 1,492.3 310.8 279.1 548.8

207.4 .. 52.5 7.4 .. 136.9 24.7 96.9 5.7 .. 29.9 76.6 111.4 16.3 111.3 10.2 0.1 129.4 32.2 34.9 31.6 255.6 35.2 90.3 73.5

315.9 .. 28.7 25.9 .. 74.3 16.1 59.5 2.2 .. 53.2 55.6 84.1 0.1 101.7 35.7 93.2 6.1 106.2 5.4 282.4 204.7 76.8 32.6 44.0

11.8 .. 8.9 0.3 .. 5.4 8.2 33.4 1.9 .. 2.0 1.3 111.7 4.2 0.1 0.8 20.8 11.6 3.2 0.7 4.8 54.9 53.1 0.7 103.2

32.6 .. 24.0 27.4 .. 31.6 5.4 28.1 3.6 .. 18.8 17.8 30.2 11.0 46.9 11.6 0.5 40.8 9.0 25.4 81.7 113.8 9.7 36.7 59.6

1.0 .. 0.9 19.1 .. 102.5 -1.5 0.3 19.1 .. 3.0 97.5 0.3 –0.1 74.7 35.0 43.2 13.1 7.0 2.1 238.1 14.7 2.1 50.1 –3.4

0.2 .. 17.8 92.4 .. 3.5 2.6 14.7 0.1 .. 24.2 19.0 35.8 91.8 35.5 9.6 –2.1 –30.7 3.1 74.7 97.9 60.7 48.3 39.8 45.3

0.5 .. 0.1 0.0 .. 0.7 0.0 0.0 0.0 .. 18.3 0.3 0.9 0.1 77.3 0.0 0.0 –0.3 2.1 9.6 1.7 99.3 5.8 1.9 3.1

0.9 .. 1.3 1.7 .. 24.2 9.8 5.8 0.0 .. 1.8 4.1 9.8 0.1 24.3 44.7 0.0 –14.5 0.4 –1.3 190.7 68.8 1.1 12.0 49.6

21.2 .. 3.4 3.2 .. 9.8 1.0 15.4 0.0 .. 7.0 8.4 63.6 0.7 12.6 0.7 0.4 0.0 3.7 1.3 0.0 80.4 18.9 –6.7 45.3

0.0 .. 0.1 1.8 .. 13.9 1.0 2.2 0.1 .. 0.0 2.1 19.5 0.1 83.5 1.3 0.3 4.4 0.0 2.7 8.4 75.2 2.5 0.7 5.5

153.3 .. 30.7 106.8 .. 60.7 19.7 144.1 1.8 .. 28.6 14.5 52.0 8.8 108.5 8.3 0.4 4.8 35.5 57.1 49.9 464.4 57.4 21.0 123.2

519.0 319.8 769.4

89.3 37.1 354.0

46.1 64.4 81.9

7.1 6.2 188.9

28.8 22.0 26.7

1.1 57.4 9.1

17.4 35.1 28.9

31.0 0.1 4.5

142.4 22.2 7.0

17.5 1.6 9.2

13.6 9.8 17.5

124.8 63.9 41.8

8.4 1,428.3 60.8 354.5 152.9 412.5 294.8 ..

5.3 613.0 16.7 2.8 26.5 104.4 89.5 ..

0.0 97.6 2.2 32.4 31.5 73.8 50.4 ..

0.6 217.5 0.1 1.0 0.0 1.1 4.4 ..

0.7 107.5 1.7 2.5 6.2 79.8 40.1 ..

0.7 8.8 0.1 0.1 0.6 9.0 –7.3 ..

0.7 131.4 33.5 –4.2 37.3 –36.8 –8.4 ..

0.3 38.9 0.0 0.0 0.0 0.3 2.2 ..

0.0 13.7 6.3 0.9 38.9 100.2 -31.4 ..

0.0 46.6 0.0 1.7 0.9 –7.3 1.8 ..

0.0 41.9 0.8 0.2 2.2 17.9 17.0 ..

0.1 111.4 -0.5 317.1 8.6 70.1 136.3 ..

.. ..

2011 World Development Indicators

381

6.17

Distribution of net aid by Development Assistance Committee members Ten major DAC donors

$ millions

Romania Russian Federation Rwanda Saudi Arabia Senegal Serbia Sierra Leone Singapore Slovak Republic Slovenia Somalia South Africa 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 $ millions 2009

United States 2009

.. .. 624.3 .. 648.8 565.4 305.3 .. .. .. 607.5 1,014.6

.. .. 145.9 .. 67.7 46.5 17.0 .. .. .. 194.9 523.7

433.2 2,136.8 33.7

116.0 177.6 1,547.2 –71.2 193.3 408.2 6.0 457.6 1,345.1 17.4 1,141.3 574.0 ..

EU Institutions 2009

United Kingdom 2009

Germany 2009

France 2009

.. .. 104.5 .. 134.5 292.9 108.9 .. .. .. 108.0 153.3

.. .. 89.9 .. 6.5 7.7 80.3 .. .. .. 43.8 67.3

.. .. 44.0 .. 22.2 114.5 15.8 .. .. .. 20.9 86.9

.. .. 3.5 .. 140.9 12.7 0.3 .. .. .. 4.7 –15.6

32.3 954.6 15.6

59.2 225.8 15.1

18.2 292.4 –3.8

–5.6 47.2 –0.2

18.6 40.5 283.7 23.6 29.1 3.8 0.5 –5.3 –6.5 10.8 366.9 103.0 ..

54.8 37.3 138.4 21.3 10.3 46.4 1.6 108.1 787.0 4.0 128.0 177.0 ..

1.1 4.5 216.7 9.9 0.1 10.4 0.4 3.8 2.2 0.3 117.4 2.4 ..

0.0 1.8 2.2 93.8 94.9 35.9 73.5 109.9 7,657.0 s 2,622.5 2,083.3 1,852.0 225.9 7,653.4 389.5 74.4 158.6 247.5 1,438.0 2,708.4 3.6 ..

44.2 1.0 11.8 83.6 9.9 6.1 50.2 11.7 3.4 2,127.8 78.1 51.9 2,275.9 844.3 538.3 276.0 26.2 23.6 852.9 231.9 152.4 700.1 249.7 79.7 96,623.9 s 25,173.7 s 13,021.4 s 27,536.0 7,955.5 3,842.7 39,141.0 10,578.1 6,543.1 28,607.7 8,235.8 3,859.8 9,649.3 2,276.5 2,327.5 96,408.4 25,163.7 12,879.0 7,305.6 823.5 526.8 6,418.5 1,340.6 2,240.2 7,714.7 2,030.9 1,117.6 9,508.6 4,082.5 1,623.7 10,370.5 3,906.6 845.4 30,845.3 7,436.4 4,816.7 215.5 9.9 142.4 .. .. ..

Japan 2009

Netherlands 2009

Spain 2009

Norway 2009

Canada 2009

.. .. 21.3 .. 46.7 3.7 37.4 .. .. .. 22.6 4.7

.. .. 54.2 .. 45.7 2.6 1.5 .. .. .. 14.8 48.9

.. .. 25.0 .. 59.3 4.0 3.4 .. .. .. 52.8 5.3

.. .. 3.6 .. 0.5 19.9 3.1 .. .. .. 33.3 36.1

.. .. 13.7 .. 54.5 4.8 8.9 .. .. .. 25.7 13.0

.. .. 118.8 .. 70.5 56.0 28.8 .. .. .. 85.9 90.9

12.7 10.4 0.2

91.6 111.0 1.2

2.7 97.3 0.0

18.6 26.0 1.2

35.3 92.1 3.2

25.0 105.0 0.9

143.2 174.9 0.3

37.8 26.1 87.1 1.9 5.6 24.0 0.2 30.8 6.7 1.9 60.1 121.6 ..

25.7 4.7 7.9 –11.7 0.1 40.5 1.1 170.0 154.6 0.2 14.6 19.5 ..

–54.5 26.2 120.5 –150.3 11.9 34.1 0.1 14.4 210.8 –1.2 54.1 61.9 ..

0.1 0.3 62.6 3.6 0.0 0.9 0.0 –0.8 –0.3 0.0 45.0 0.0 ..

6.3 6.3 25.1 4.5 10.8 3.8 0.1 124.1 135.3 0.0 5.9 3.8 ..

0.0 3.1 116.4 0.7 8.5 0.1 0.0 0.0 0.2 0.6 67.3 3.1 ..

0.9 2.5 94.0 2.8 2.0 2.5 1.8 2.1 –2.3 0.0 16.9 18.0 ..

25.3 26.0 394.9 22.5 114.8 241.7 0.2 10.3 57.5 0.8 265.3 63.7 ..

–0.3 32.1 8.7 112.5 98.7 82.9 55.5 34.7 7,096.7 s 1,702.3 3,199.0 2,103.0 965.2 7,083.1 604.5 714.3 917.4 703.5 844.6 1,781.1 13.5 ..

1.4 2.9 7.1 142.9 79.2 5.9 7.4 4.6 7,019.4 s 993.6 4,510.2 3,157.9 1,254.3 7,010.9 899.2 273.7 231.7 1,000.9 35.4 3,396.9 8.5 ..

2.4 20.4 2.1 1,191.4 76.7 37.2 36.6 12.4 6,001.2 s 1,553.4 2,729.5 2,329.6 398.9 6,001.0 1,228.5 522.3 142.5 142.1 1,013.7 1,374.3 0.3 ..

0.0 0.0 0.1 45.4 46.2 30.9 64.8 22.3 4,798.0 s 1,067.9 899.5 609.1 255.7 4,797.4 157.0 66.2 262.0 108.9 274.8 1,197.6 0.5 ..

12.2 0.2 1.3 14.1 0.7 0.3 0.0 9.4 12.9 0.0 0.5 1.5 32.7 15.9 35.3 327.9 99.4 100.1 41.2 256.9 3.9 0.7 2.5 26.3 11.8 62.7 13.0 143.4 8.2 28.9 28.3 121.4 4,473.1 s 3,168.2 s 3,141.0 s 15,074.3 s 927.6 836.8 1,152.7 4,881.1 2,240.8 655.9 775.7 4,925.8 1,461.4 484.1 610.8 3,904.0 738.7 150.8 144.1 911.8 4,450.5 3,164.6 3,135.4 15,069.4 100.4 97.1 146.1 2,333.0 209.7 110.5 29.4 837.4 1,501.4 138.1 452.6 761.9 589.7 137.1 125.4 747.3 212.4 280.3 372.5 1,146.7 1,127.2 902.4 1,308.7 4,795.8 22.6 3.6 5.6 4.9 .. .. .. ..

Note: Regional aggregates include data for economies not specified elsewhere. World and income group totals include aid not allocated by country or region.

382

2011 World Development Indicators

Other DAC donors $ millions 2009

About the data

6.17

Definitions

The table shows net bilateral aid to low- and middle-

on behalf of DAC members are classified as bilateral

• Net aid refers to net bilateral official development

income economies from members of the Develop-

aid (since it is the donor country that effectively con-

assistance that meets the DAC definition of official

ment Assistance Committee (DAC) of the Organisation

trols the use of the funds) and are included in the

development assistance and is made to countries

for Economic Co-operation and Development (OECD).

data reported in this table.

and territories on the DAC list of aid recipients. See

DAC has 24 members, of which 23 are economies

The data include aid to some countries and terri-

About the data for table 6.14. • Other DAC donors

and 1 is a multilateral institution (the European Union

tories not shown in the table and aid to unspecified

are Australia, Austria, Belgium, Denmark, Finland,

Institutions). Previous editions of the table included

economies recorded only at the regional or global

Greece, Ireland, Italy, the Republic of Korea, Lux-

only DAC member economies; this year’s edition

level. Aid to countries and territories not shown in

embourg, New Zealand, Portugal, Sweden, and

includes data for the European Union Institutions.

the table has been assigned to regional totals based

Switzerland.

The table is based on donor country reports of

on the World Bank’s regional classification system.

bilateral programs, which may differ from reports by r

Aid to unspecified economies is included in regional

ecipient countries. Recipients may lack access to

totals and, when possible, income group totals. Aid

information on such aid expenditures as develop-

not allocated by country or region—including admin-

ment-oriented research, stipends and tuition costs

istrative costs, research on development, and aid to

for aid-financed students in donor countries, and

nongovernmental organizations—is included in the

payment of experts hired by donor countries. More-

world total. Thus regional and income group totals

over, a full accounting would include donor country

do not sum to the world total.

contributions to multilateral institutions, the flow

Some of the aid recipients shown in table are also

of resources from multilateral institutions to recipi-

aid donors. Development cooperation activities by

ent countries, and flows from countries that are not

non-DAC members have increased in recent years

members of DAC.

and in some cases surpass those of individual DAC

Data in this table exclude DAC members’ multilat-

members. Some non-DAC donors report their devel-

eral aid (contributions to the regular budgets of the

opment cooperation activities to DAC on a voluntary

multilateral institutions). These are included in data

basis. Many others do not yet report their aid flows

reported in table 6.14. Projects executed by multi-

to DAC. See table 6.16a for a summary of ODA from

lateral institutions or nongovernmental organizations

non-DAC countries.

Beyond the DAC: The role of other providers of development assistance

6.17a

Development assistance flows from non-DAC donor countries ($ millions) Country

Year

Source

8,094

2008

OECD/DAC Statistics

Brazil

437

2007

DAC Development Co-operation Report, estimates by Brazilian officials

China

1,800–3,000

2008

Fiscal Yearbook, Ministry of Finance, China. Upper estimate: Brautigam 2009

India

610

2008/09

Annual Reports, Ministry of Foreign Affairs, India

Russian Federation

200

2008

20 countries reporting to DAC (see table 6.16a)

South Africa

Estimate

109

2008/09

Russian Federation statement at DAC Senior Level Meeting, April 2010 Estimates of Public Expenditures 2009, Foreign Affairs, National Treasury of South Africa

Many countries that are not members of the OECD DAC have provided development assistance for decades. The past 10 years have seen their numbers rise fast, and in some cases their levels of development assistance now surpass those of individual DAC members. DAC estimates total net development assistance flows from non-DAC donors at $12–$14 billion in 2008, or 9–10 percent of global official development assistance (ODA) flows (assuming that the flows were consistent with the definition of ODA). Estimating overall aid volumes from non-DAC donors is challenging. Twenty countries, mostly emerging donors and Arab donors, voluntarily report aid volumes to DAC annually (see table 6.16a). Many others, including most major providers of aid from developing countries to developing countries (such as Brazil, China, India, and South Africa), do not. Estimates of aid volumes of countries that do not report to DAC must be treated with caution. Official figures often omit important cooperation activities, such as contributions to international organizations focused on development, leading to underestimates, and they often include expenditures that would not qualify as ODA, such as security-related or culturally motivated spending, or insufficiently concessional loans, leading to overestimates. Source: Smith, Fordelone, and Zimmermann 2010 and OECD 2010.

Data sources Data on financial flows are compiled by DAC and published in its annual statistical report, Geographical Distribution of Financial Flows to Aid Recipients, and its annual Development Cooperation Report. Data are available electronically on the OECD DAC International Development Statistics CD-ROM and at www.oecd.org/dac/stats/idsonline.

2011 World Development Indicators

383

global links

Distribution of net aid by Development Assistance Committee members

6.18

Movement of people across borders Net migration

International migrant stock

thousands 1990–95 2005–10

thousands 1995 2010

Afghanistan 3,266 Albania –423 Algeria –50 Angola 143 Argentina 120 Armenia –500 Australia 371 Austria 234 Azerbaijan –116 Bangladesh –500 Belarus 0 Belgium 85 Benin 105 Bolivia –100 Bosnia and Herzegovina –1,025 Botswana 14 Brazil –184 Bulgaria –349 Burkina Faso –128 Burundi –250 Cambodia 150 Cameroon –5 Canada 643 Central African Republic 37 Chad –10 Chile 90 China –829b Hong Kong SAR, China 300 Colombia –250 Congo, Dem. Rep. 1,208 Congo, Rep. –14 Costa Rica 62 Côte d’Ivoire 375 Croatia 153 Cuba –120 Czech Republic 8 Denmark 58 Dominican Republic –129 Ecuador –50 Egypt, Arab Rep. –498 El Salvador –249 Eritrea –359 Estonia –108 Ethiopia 768 Finland 43 France 239 Gabon 20 Gambia, The 45 Georgia –544 Germany 2,649 Ghana 40 Greece 470 Guatemala –360 Guinea 350 Guinea-Bissau 20 Haiti –133 Honduras –120

384

1,000 –75 –140 80 30 –75 500 160 –50 –570 0 200 50 –100 –10 15 –229 –50 –65 323 –5 –19 1,050 5 –75 30 –1,731b 113 –120 –100 –50 30 –145 10 –194 226 30 –140 –350 –340 –280 55 0 –300 55 500 5 15 –250 550 –51 150 –200 –300 –12 –140 –100

2011 World Development Indicators

70 71 299 38 1,588 682 3,854 989 525 1,006 1,185 916 146 70 73 39 731 47 464 295 116 246 5,047 67 78 136 437b 2,431 109 1,919 131 228 1,985 721 25 454 297 322 88 174 28 12 309 795 103 6,085 164 148 250 8,992 1,038 549 46 814 32 22 31

91 89 242 65 1,449 324 4,711 1,310 264 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 15 453 484 434 394 245 40 16 182 548 226 6,685 284 290 167 10,758 1,852 1,133 59 395 19 35 24

Refugees

Workers’ remittances and compensation of employees

thousands By country of origin By country of asylum 1995 2009 1995 2009

$ millions Received Paid 1995 2009 1995 2009

2,679.1 5.8 1.5 246.7 0.3 201.4 0.0 0.0 200.5 57.0 0.1 0.0 0.1 .. 769.8 0.0 0.1 4.2 0.1 350.6 61.2 2.0 0.0 0.2 59.7 14.3 124.7c 0.2 1.9 89.7 0.2 0.2 0.2 245.6 24.9 2.0 0.0 0.0 0.2 0.9 23.5 286.7 0.4 101.0 0.0 0.0 0.0 0.2 0.3 0.4 13.6 0.2 42.9 0.4 0.8 13.9 1.2

2,887.1 15.7 8.2 141.0 0.6 18.0 0.0 0.0 16.9 10.4 5.5 0.1 0.4 .. 70.0 0.0 1.0 2.7 1.0 94.2 17.0 14.8 0.1 159.6 55.0 1.3 200.6c 0.0 389.8 455.9 20.5 0.3 23.2 76.5 7.5 1.1 0.0 0.2 1.0 7.0 5.1 209.2 0.2 62.9 0.0 0.1 0.1 2.0 15.0 0.2 14.9 0.1 5.8 10.9 1.1 24.1 1.2

19.6 4.7 192.5 10.9 10.3 219.0 62.1 34.4 233.7 51.1 29.0 31.7 23.8 0.7 40.0 0.3 2.1 1.3 29.8 173.0 0.0 45.8 152.1 33.9 0.1 0.3 288.3 1.5 0.2 1,433.8 19.4 24.2 297.9 198.6 1.8 2.7 64.8 1.0 0.2 5.4 0.2 1.1 .. 393.5 10.2 155.2 0.8 6.6 0.1 1,267.9 83.2 4.4 1.5 672.3 15.4 .. 0.1

0.0 0.1 94.1 14.7 3.2 3.6 22.5 38.9 1.6 228.6 0.6 15.5 7.2 0.7 7.1 3.0 4.2 5.4 0.5 25.0 0.1 100.0 169.4 27.0 338.5 1.5 301.0 0.1 0.2 185.8 111.4 19.1 24.6 1.2 0.5 2.3 20.4 .. 116.6 94.4 0.0 4.8 0.0 121.9 7.4 196.4 8.8 10.1 0.9 593.8 13.7 1.7 0.1 15.3 7.9 0.0 0.0

.. 427 1,120a 5 64 65 1,651 1,012 3 1,202 29 4,937 100 7 .. 59 3,315 42 78 a .. 12 11 .. 0 1 .. 878a .. 815 .. 4 123 151 544 .. 191 523 839 386 3,226 1,064a .. 1 27 74 4,640 4 19a 284 4,523 17 3,286 358 1 2a .. 124

.. 1,317 2,059a 82 658 769 4,089a 3,286 1,274 10,523 358 10,437 243a 1,069 2,081 88 4,234 1,558 99a 28 338 148 .. .. .. 4 48,729a 348 4,180 .. 14a 513 185 1,476 .. 1,201 894 3,467 2,502 7,150 3,482 .. 325 262 859 15,551 10a 80 714 10,879 114 2,020 4,019 64 47 1,376 2,520

.. .. .. 210 195 17 700 346 9 1 12 3,252 26 9 .. 200 347 34 50a 5 52 22 .. 27 15 13a 86a .. 150 .. 27 36 457 16 .. 101 209 7 4 223 1a .. 3 0 54 4,935 99 .. 12 11,348 5 300 8 10 3 .. 8

.. 10 .. 716 702 145 3,000a 3,377 652 8 112 4,136 88 103 61 102 1,003 101 100 1 215 94 .. .. .. 6 4,444 413 92 .. 102 239 756 99 .. 2,562 3,413 29 81 255 21 .. 81 27 454 5,224 186 8 32 15,924 6 1,843 22 45 17a 135 12

Net migration

International migrant stock

thousands 1990–95 2005–10

104 –960 –725 –1,164 –154 –1 484 294 –113 474 509 –1,509 222 0 –627 .. –598 –273 –30 –134 230 –84 –523 10 –99 –27 –7 –920 287 –260 –15 –7 –1,364 –121 –173 –450 650 –126 –13 –101 191 143 –114 –3 –96 42 23 –2,611 8 0 –30 –300 –900 –77 0 –4 14

6.18

global links

Movement of people across borders Refugees

Workers’ remittances and compensation of employees

thousands 1995 2010

thousands By country of origin By country of asylum 1995 2009 1995 2009

$ millions Received Paid 1995 2009 1995 2009

293 7,022 219 3,016 134 264 1,919 1,723 22 1,363 1,608 3,295 528 35 584 .. 1,090 482 23 527 656 6 199 506 272 115 44 325 1,193 174 118 18 458 473 7 55 246 114 118 625 1,387 594 27 171 582 237 582 4,077 73 31 183 51 210 964 528 339 406

2.3 5.0 9.8 112.4 718.7 0.0 0.9 0.1 0.0 0.0 0.5 0.1 9.3 0.0 0.0 .. 0.8 0.0 58.2 0.2 13.5 0.0 744.6 0.6 0.1 12.9 0.1 0.0 0.1 77.2 84.3 0.0 0.4 0.5 0.0 0.3 125.6 152.3 0.0 0.0 0.1 .. 23.9 10.3 1.9 0.0 0.0 5.3 0.2 2.0 0.1 5.9 0.5 19.7 0.0 0.0 0.0

152 6,223 651 1,600a .. 347 701 2,364 653 1,151 1,441 116 298 a .. 1,080 .. .. 1 22 41 1,225a 411 .. .. 1 68 14 1 116 112 5 132a 4,368 1 .. 1,970 59 81 16 57 1,359 1,652 75 8 804 a 239 39 1,712 112 16 287 599 5,360 724 3,953 .. ..

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

1.5 19.5 18.2 72.8 1,785.2 0.0 1.3 0.0 0.9 0.2 2.1 3.7 9.6 0.9 0.6 .. 0.9 2.6 8.4 0.8 16.3 0.0 71.6 2.2 0.5 7.9 0.3 0.1 0.5 2.9 39.1 0.0 6.4 5.9 1.5 2.3 0.1 406.7 0.9 5.1 0.0 0.0 1.5 0.8 15.6 0.0 0.1 35.1 0.1 0.1 0.1 6.3 1.0 2.1 0.0 .. 0.1

11.4 227.5 0.0 2,072.0 116.7 0.4 .. 74.3 0.0 5.4 1,288.9d 15.6 234.7 .. 0.0 .. 3.3 13.4 .. .. 348.0d 0.1 120.1 4.0 0.0 9.0 0.1 1.0 5.3 17.9 34.4 .. 38.7 .. .. 0.1 0.1 .. 1.7 124.8 80.0 3.8 0.6 27.6 8.1 47.6 .. 1,202.5 0.9 9.6 0.1 0.6 0.8 0.6 0.2 .. ..

6.0 185.3 0.8 1,070.5 35.2 9.6 17.7 55.0 0.0 2.3 2,434.5d 4.3 358.9 .. 0.3 .. 0.2 0.4 .. 0.0 476.1d .. 7.0 9.0 0.8 1.5 .. 5.4 66.1 13.5 26.8 .. 1.2 0.1 0.0 0.8 3.5 .. 7.2 108.5 76.0 3.3 0.1 0.3 9.1 37.8 0.0 1,740.7 16.9 9.7 0.1 1.1 0.1 15.3 0.4 .. 0.0

2,130 49,468 6,793 1,045a 71 576 1,267 2,683 1,912 1,776 3,597 124 1,686a .. 2,522 .. .. 992a 38 591 7,558 414 54 a 14 a 1,169 381 10a 1a 1,131 405a 2a 211a 21,953 1,211 200 6,270 111 137a 14 2,986 3,691 628 768 89 9,585a 631 39 8,717 175 12 609 2,378 19,766 8,126 3,585 .. ..

146 419 .. .. .. 173 1,407 1,824 74 1,820 107 503 9 .. 635 .. 1,354 41 9 1 .. 75 .. 222 1 1 11 1 1,329 42 14 1 .. 1 .. 20 21 .. 11 9 2,802 427 .. 29 5 603 1,537 4 20 16 .. 34 151 262 527 .. ..

2011 World Development Indicators

1,223 2,893 2,702 .. 31a 1,988 3,283 12,986 314 4,069 502 3,138 61 .. 3,120 .. 9,912 188 22 46 5,749 35 1 1,361 620 26 21 0a 6,529 105 .. 12 .. 104 83 61 63 32a 16 12 14,212 977 .. 22a 66 4,174 5,313 8 229 323 .. 85 58 1,330 1,460 .. ..

385

6.18

Movement of people across borders Net migration

International migrant stock

thousands 1990–95 2005–10

Romania –529 –200 Russian Federation 2,220 250 Rwanda –1,681 15 Saudi Arabia –500 150 Senegal –100 –100 Serbia 451 0 Sierra Leone –450 60 Singapore 250 500 Slovak Republic –3 20 Slovenia 38 22 Somalia –893 –250 South Africa 900 700 Spain 324 1,750 Sri Lanka –256 –300 Sudan –168 135 Swaziland –38 –6 Sweden 151 150 Switzerland 227 100 Syrian Arab Republic –70 800 Tajikistan –296 –200 Tanzania 591 –300 Thailand –39 300 Timor-Leste 0 10 Togo –122 –5 Trinidad and Tobago –24 –20 Tunisia –43 –20 Turkey –70 –44 Turkmenistan 50 –25 Uganda 120 –135 Ukraine 100 –80 United Arab Emirates 340 343 United Kingdom 167 948 United States 6,565 5,052 Uruguay –20 –50 Uzbekistan –340 –400 Venezuela, RB 40 40 Vietnam –840 –200 West Bank and Gaza 1 –10 Yemen, Rep. 650 –135 Zambia –11 –85 Zimbabwe –192 –700 ..f s World ..f s Low income 287 –2,737 Middle income –13,401 –13,203 Lower middle income –9,961 –9,231 Upper middle income –3,441 –3,972 Low & middle income –13,114 –15,941 East Asia & Pacific –3,285 –3,781 Europe & Central Asia –3,386 –1,671 Latin America & Carib. –3,388 –5,214 Middle East & N. Africa –1,044 –1,089 South Asia –1,262 –2,376 Sub-Saharan Africa –749 –1,810 High income 13,097 15,894 Euro area 4,604 5,607

Refugees

Workers’ remittances and compensation of employees

thousands 1995 2010

thousands By country of origin By country of asylum 1995 2009 1995 2009

$ millions Received Paid 1995 2009 1995 2009

135 133 11,707 12,270 337 465 4,611 7,289 291 210 874 525 101 107 992 1,967 114 131 200 164 19 23 1,098 1,863 1,041 6,378 426 340 1,111 753 35 40 906 1,306 1,471 1,763 817 2,206 305 284 1,134 659 549 1,157 10 14 169 185 46 34 38 34 1,212 1,411 260 208 661 647 6,172 5,258 1,716 3,293 4,191 6,452 28,522 42,813 93 80 1,474 1,176 1,019 1,007 39 69 1,201 1,924 378 518 271 233 433 372 165,674g s 213,450g s 13,555 13,368 63,453 67,824 31,848 34,166 31,605 33,657 77,009 81,192 3,048 5,434 29,607 27,346 5,454 6,569 8,985 11,957 13,257 12,175 16,659 17,710 88,665 132,259 23,080 36,135

17.0 4.4 0.2 1.1 207.0 109.5 246.7 4.9 1,819.4 129.1 7.8 54.0 0.3 0.6 13.2 0.6 17.6 16.3 66.8 22.2 86.1e 195.6 650.7e 86.4 379.5 15.4 4.7 9.1 0.0 0.1 0.1 0.0 0.0 0.3 2.3 0.4 12.9 0.0 22.3 0.3 638.7 678.3 0.6 1.8 0.5 0.4 101.4 48.0 0.0 0.0 5.9 4.0 107.6 145.7 0.0 0.3 445.3 368.2 674.1 186.3 0.0 0.0 0.7 0.8 0.0 0.0 199.2 81.4 0.0 0.0 82.9 46.2 1,526.6d 8.0 17.9 373.5d 59.0 0.6 0.6 2.7 0.1 1.2 829.7 118.7 0.2 0.5 106.6 105.3 .. 0.0 .. 0.0 93.2 18.4 10.9 8.5 0.0 0.2 .. 0.0 0.3 2.3 0.2 0.1 44.9 146.4 12.8 10.4 0.0 0.7 23.3 0.1 24.2 7.6 229.4 127.3 1.7 24.5 5.2 7.3 0.0 0.4 0.4 0.3 0.1 0.2 90.9 269.4 0.2 2.4 623.3 275.5 0.3 0.2 0.1 0.2 0.1 6.7 2.6 0.6 0.5 6.2 1.6 201.3 543.5 339.3 34.4 2.4 1,885.2d 72.8 95.2 1,201.0 d 0.4 1.9 53.5 170.9 0.0 0.2 130.0 56.8 0.0 22.4 0.5 4.0 18,068.7s,d h 15,163.2s,d h 18,068.7d s 15,163.2d s 7,990.4 5,427.5 4,727.2 1,893.8 4,260.8 4,558.6 10,086.9 11,285.2 2,733.4 3,451.1 6,322.0 9,104.7 1,527.3 1,107.4 3,764.8 2,180.5 12,251.1 9,986.1 14,814.0 13,179.1 952.9 996.7 447.0 485.5 1,611.6 655.6 1,221.3 163.8 155.5 462.0 93.9 367.4 948.0 2,014.0 5,683.0 7,809.4 2,958.7 3,192.1 1,625.5 2,263.4 5,624.4 2,665.8 5,743.4 2,089.5 287.1 90.8 3,254.7 1,984.1 13.9 1.0 1,690.4 1,011.4

9 2,502 21 .. 146 1,295 24 .. 26 272 .. 105 3,237 809 346 83 288 1,473 339 .. 1 1,695 .. 15 32 680 3,327 4 .. 6 .. 2,469 2,179 .. .. 2 .. 582 1,081 .. 44 101,254 s 2,189 53,012 31,182 21,830 55,202 8,925 6,482 13,322 13,275 10,005 3,193 46,052 30,827

4,929 5,359 93 217 1,365 5,406a 47 .. 1,671 279 .. 902 9,904 3,363 2,993a 93 652 2,524 1,332a 1,748 23 1,637 .. 307a 99a 1,964 970 .. 750 5,073 .. 7,252 2,947 101 .. 131 6,626a 1,261a 1,160 41 .. 416,158 s 22,706 284,357 206,323 78,033 307,063 85,788 35,433 56,590 33,442 75,061 20,749 109,095 67,529

2 3,938 1 16,594 76 .. 0 .. 3 31 .. 629 868 16 1 4 336 10,114 15 .. 1 .. .. 5 14 36 .. 7 .. 1 .. 2,581 22,181 .. .. 203 .. 19 61 59 7 100,950 s 357 10,230 2,147 8,084 10,587 1,703 4,507 1,138 704 476 2,060 90,363 28,741

310 18,548 71 25,969 144a 91 3 .. 134 191 .. 1,158 12,646 435 2a 11 787 19,562 212a 124 81 .. .. 58a .. 13 141 .. 463 25 .. 3,400 48,308 6 .. 581 .. 9a 337 66 .. 289,122 s 2,047 57,377 15,095 42,283 59,425 14,459 24,427 3,788 8,536 3,471 4,743 229,697 85,677

a. World Bank estimate. 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. Includes Montenegro. f. 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. g. 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. h. 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.

386

2011 World Development Indicators

About the data

6.18

Definitions

Movement of people, most often through migration, is

granted refugee or refugee-like status or temporary

• Net migration is the net total of migrants during

a significant part of global integration. Migrants con-

protection. Asylum seekers and internally displaced

the period. It is the total number of immigrants less

tribute to the economies of both their host country

people—who are often confused with refugees—are

the total number of emigrants, including both citi-

and their country of origin. Yet reliable statistics on

not included. Unlike refugees, internally displaced

zens and noncitizens. Data are five-year estimates.

migration are difficult to collect and are often incom-

people remain under the protection of their own gov-

• International migrant stock is the number of people

plete, making international comparisons a challenge.

ernment, even if their reason for fleeing was similar

born in a country other than that in which they live.

to that of refugees.

It includes refugees. • Refugees are people who are

The United Nations Population Division provides data on net migration and migrant stock. Net migra-

Registrations, together with other sources—includ-

recognized as refugees under the 1951 Convention

tion is the total number of immigrants minus the

ing estimates and surveys—are the main sources

Relating to the Status of Refugees or its 1967 Proto-

total number of emigrants. However, data on emi-

of refugee data. But there are difficulties in collect-

col, the 1969 Organization of African Unity Convention

grant stock are not collected because it is difficult

ing accurate statistics. Although refugees are often

Governing the Specific Aspects of Refugee Problems

for countries to gather information on people who are

registered individually, the accuracy of registrations

in Africa, people recognized as refugees in accordance

not within their borders. To derive estimates of net

varies greatly. Many refugees may not be aware of

with the UNHCR statute, people granted refugee-like

migration, the migration history of a country or area,

the need to register or may choose not to do so. And

humanitarian status, and people provided temporary

the migration policy of a country, and the influx of

administrative records tend to overestimate the num-

protection. Asylum seekers—people who have applied

refugees in recent periods are taken into account.

ber of refugees because it is easier to register than to

for asylum or refugee status and who have not yet

The data to calculate these official estimates come

de-register. The UN Refugee Agency (UNHCR) collects

received a decision or who are registered as asylum

from a variety of sources, including border statistics,

and maintains data on refugees, except for Palestin-

seekers—are excluded. Palestinian refugees are

administrative records, surveys, and censuses. When

ian refugees residing in areas under the mandate

people (and their descendants) whose residence was

no official estimates can be made because of insuf-

of the United Nations Relief and Works Agency for

Palestine between June 1946 and May 1948 and who

ficient data, net migration is derived through the bal-

Palestine Refugees in the Near East (UNRWA). The

lost their homes and means of livelihood as a result

ance equation, which is the difference between overall

UNRWA provides services to Palestinian refugees who

of the 1948 Arab-Israeli conflict. • Country of origin

population growth and the natural increase during the

live in certain areas and who register with the agency.

refers to the nationality or country of citizenship of a

1990–2000 intercensal period.

Registration is voluntary, and estimates by the UNRWA

claimant. • Country of asylum is the country where an

The data used to estimate the international migrant

are not an accurate count of the Palestinian refugee

asylum claim was filed and granted. • Workers’ remit-

stock at a particular time are obtained mainly from

population. The table shows estimates of refugees

tances and compensation of employees received and

population censuses. The estimates are derived from

collected by the UNHCR, complemented by estimates

paid comprise current transfers by migrant workers

the data on foreign-born population—people who have

of Palestinian refugees under the UNRWA mandate.

and wages and salaries earned by nonresident work-

residence in one country but were born in another

Thus, the aggregates differ from those published by

ers. Remittances are classified as current private

country. When data on the foreign-born population

the UNHCR.

transfers from migrant workers resident in the host

are not available, data on foreign population— that

Workers’ remittances and compensation of employ-

country for more than a year, irrespective of their

is, people who are citizens of a country other than the

ees are World Bank staff estimates based on data

immigration status, to recipients in their country of

country in which they reside—are used as estimates.

from the International Monetary Fund’s (IMF) Bal-

origin. Migrants’ transfers are defined as the net worth

After the breakup of the Soviet Union in 1991 people

ance of Payments Statistics Yearbook. The IMF data

of migrants who are expected to remain in the host

living in one of the newly independent countries who

are supplemented by World Bank staff estimates for

country for more than one year that is transferred to

were born in another were classified as international

missing data for countries where workers’ remittances

another country at the time of migration. Compensa-

migrants. Estimates of migrant stock in the newly

are important. The data reported here are the sum of

tion of employees is the income of migrants who have

independent states from 1990 on are based on the

three items defined in the fifth edition of the IMF’s

lived in the host country for less than a year.

1989 census of the Soviet Union.

Balance of Payments Manual: workers’ remittances,

For countries with information on the international

compensation of employees, and migrants’ transfers.

migrant stock for at least two points in time, inter-

The distinction among these three items is not

polation or extrapolation was used to estimate the

always consistent in the data reported by countries to

Data on net migration are from the United Nations

international migrant stock on July 1 of the reference

the IMF. In some cases countries compile data on the

Population Division’s World Population Prospects:

years. For countries with only one observation, esti-

basis of the citizenship of migrant workers rather than

The 2008 Revision. Data on migration stock are

mates for the reference years were derived using rates

their residency status. Some countries also report

from the United Nations Population Division’s

of change in the migrant stock in the years preceding

remittances entirely as workers’ remittances or com-

Trends in Total Migrant Stock: The 2008 Revision.

or following the single observation available. A model

pensation of employees. Following the fifth edition of

Data on refugees are from the UNHCR’s Statisti-

was used to estimate migrants for countries that had

the Balance of Payments Manual in 1993, migrants’

cal Yearbook 2009, complemented by statistics

no data.

transfers are considered a capital transaction, but

on Palestinian refugees under the mandate of

The table shows data on refugees because they

previous editions regarded them as current transfers.

the UNRWA as published on its website. Data on

are an important part of migrant stock. Refugee fig-

For these reasons the figures presented in the table

remittances are World Bank staff estimates based

ures shown here refer to people who have crossed an

take all three items into account.

on IMF balance of payments data.

Data sources

international border to find sanctuary and have been

2011 World Development Indicators

387

global links

Movement of people across borders

6.19

Travel and tourism International tourists

Inbound tourism expenditure

thousands Inbound Outbound 1995 2009 1995 2009

388

.. 304 a,b 520a,c 9 2,289 12 3,726a 17,173e .. 156 161 5,560e 138 284 115e 521 1,991 3,466 124g 34 c .. 100 g 16,932 26b 19g 1,540 20,034 7,137 1,399a 35b 37g 785 188 1,485e 742b 3,381e 2,124 e 1,776b,c 440a,h 2,871 235 315a,c 530 103b 2,644 60,033 125b 45 85a 14,847e 286c 10,130 563a 12b .. 145 271

.. .. 1,856a,b 12 1,912a,c 1,090 366 3 4,329 3,815 575 .. 5,584 a 2,519 21,355e 3,713 1,409 432 267 830 95 626 6,815e 5,645 190 .. 671 249 311e .. 1,553 .. 4,802 2,600 5,739 3,524 269g .. 201c 36 2,046 31 185g .. 15,737 18,206 52b .. 25g .. 2,750 1,070 50,875 4,520 16,926 .. 2,147a 1,057 53b 50 85g .. 1,923 273 .. .. 9,335e .. 2,405b 72 6,032e .. 4,503e 5,035 3,992b,c 168 968 a,h 271 11,914 2,683 1,091 348 79a,c .. 1,900 1,764 330 c 120 3,423 5,147 76,800 18,686 358 203 142 .. 1,500a 228 24,220e 55,800 803c .. 14,915 .. 1,777a 333 30 b .. 30 .. 304 .. 870 149

2011 World Development Indicators

.. 3,404 1,677 .. 4,975 526 6,285 10,121 2,162 2,254 316 11,123 .. 628 .. .. 4,952 4,993 .. .. 340 .. 27,037 11 .. 2,895 47,656 81,958 2,122 .. .. 579 .. 2,497 206 6,618 6,347 415 814 4,531 1,012 .. 752 .. 5,832 23,347 .. 307 1,980 72,300 .. .. 1,326 .. .. .. 395

$ millions 1995 2009

.. 70 32d 27 2,550 14 11,915 14,529 87 25f 28 4,548f 85f 92 257 176 1,085 662 .. 2 71 75 9,176 4d 43d 1,186 8,730 f 9,604 d,f 887 .. 15 763 103 1,349 f 1,100d 2,880 f 3,691f 1,571f 315 2,954 152 58d 452 177 2,383 31,295 94 28f 75 24,052 30 4,182 216 1 3 90 f 85

.. 2,012 330 d 554 4,478 374 27,864 21,239 516 76f 562 11,144 236 306 761 454f 5,635 4,273 82 2 1,312 222 15,555 6 .. 2,270 42,632 20,884 d 2,671 .. 54 1,985 113f 9,224 2,106 7,396 6,686f 4,051f 674 11,757 549 26 1,444 1,119 4,141 58,480 .. 64 531 47,505 1,049 14,796 820 f 5 38 315 611

% of exports 1995 2009

.. 23.2 .. 0.7 10.2 4.7 17.1 16.2 11.1 0.6 0.5 2.4 13.8 7.5 22.9 7.3 2.1 9.8 .. 1.9 7.3 3.7 4.2 .. .. 6.1 5.9 3.5 7.2 .. 1.1 17.1 2.4 19.3 .. 10.2 5.6 27.4 6.1 22.3 7.5 43.1 17.6 23.1 5.0 8.6 3.2 16.0 13.1 4.0 1.9 26.9 7.7 0.1 5.5 46.8 5.2

.. 58.2 .. 1.3 6.7 27.9 11.9 11.2 2.3 0.4 2.3 3.3 14.5 5.6 13.9 10.9 3.1 18.4 11.0 1.5 22.1 4.2 4.1 .. .. 3.6 3.2 5.1 7.0 .. 0.9 15.8 1.0 40.8 .. 5.6 3.6 38.7 4.3 26.4 11.7 .. 10.7 32.6 4.6 9.5 .. 23.0 16.6 3.5 13.4 25.0 8.9 0.4 22.2 33.8 10.1

Outbound tourism expenditure

$ millions 1995 2009

.. 19 186d 113 4,013 12 7,260 11,686 165 234f 101 8,115f 48 72 97 153 3,982 312 .. 25f 22 140 12,658 43d 38d 934 3,688f 10,497d,f 1,162 .. 69 336 312 422f .. 1,635f 4,288f 267 331 1,371 99 .. 121 30 2,853 20,699 182 16 171 66,527 74 1,495 167 29 6 35f 99

.. 1,692 470 d 270 5,759 379 21,459 12,771 456 651 702 19,673 102 388 284 231f 12,897 1,955 110 71 162 549 30,232 61 .. 1,956 47,108 15,960 d,f 2,302 .. 168 463 345f 1,034 .. 4,157 9,678f 514f 806 2,941 253 .. 697 139 f 5,205 45,938 .. 9 311 92,738 848 3,401 680 28 46 443 355

% of imports 1995 2009

.. 2.3 .. 3.2 15.4 1.7 9.7 12.7 12.8 3.1 1.8 4.5 5.4 4.6 2.4 7.5 6.3 4.8 .. 9.7 1.6 8.7 6.3 .. .. 5.1 2.7 6.5 7.3 .. 5.1 7.1 8.2 4.6 .. 5.4 7.4 4.4 5.8 8.0 2.7 .. 4.2 2.1 7.6 6.2 10.6 7.0 12.1 11.3 3.5 6.0 4.5 2.9 6.5 4.4 5.3

.. 26.0 .. 0.6 11.8 10.3 10.0 7.3 4.6 2.8 2.3 6.0 4.3 7.5 3.0 4.5 7.4 7.2 3.8 13.7 2.3 8.4 7.4 .. .. 4.0 4.2 4.1 6.0 .. 2.6 3.8 3.9 4.2 .. 3.4 5.5 3.6 4.8 5.5 3.2 .. 5.6 1.5 6.2 6.9 .. 2.6 5.9 7.7 7.9 4.0 5.3 2.0 16.2 15.7 4.1

International tourists

Inbound tourism expenditure

thousands Inbound Outbound 1995 2009 1995 2009

.. 9,058 5,109h 2,124h 4,324 6,324 489 2,034 .. 61a 4,818 7,189 2,321h 2,215h 31,052 43,239 1,831b,c 1,147b,c 6,790a,h 3,345a,h 3,789 c 1,075h .. 3,118 918 1,392 .. .. 7,818a,c 3,753a,c .. .. 297g 72g 36 2,435 60 1,239 539 1,323 450 1,844 87 320 .. .. .. 34 650 1,341 259e 147e 163b 75b 192 755 7,469 23,646 160 b,g 42b,g .. .. 422 871 21,454 c 20,241c 32 7 108 411 8,341c 2,602c .. 2,224 117 243 272 931 363 510 9,921e 6,574 e 1,475 2,422 281 932 35 73 656 1,313 2,880 4,288 1,273g 279g 378 823 345 1,200 42 114 439h 438h 479 2,140 3,017c 1,760 c 19,215 11,890 12,321c 9,511h 3,551b 3,131b 1,405g 309g

13,083 3,056 .. 1,000 .. 2,547 2,259 18,173 .. 15,298 1,128 523 .. .. 3,819 .. 878 42 .. 1,812 .. .. .. 484 1,925 .. 39 .. 20,642 .. .. 107 8,450 71 .. 1,317 .. .. .. 100 12,313 920 255 10 .. 590 .. .. 185 51 427 508 1,615 36,387 .. 1,237 ..

16,906 11,067 5,053 .. .. 7,047 4,007 29,060 .. 15,446 2,368 5,243 .. .. 9,494 .. 2,649 1,521 .. 3,268 .. .. .. .. 1,288 .. .. .. .. .. .. 196 13,942 93 .. 2,293 .. .. .. 589 18,408 1,917 858 .. .. 3,395 .. .. 336 .. 280 1,958 3,066 50,243 20,989 1,319 ..

$ millions 1995 2009

2,938 2,582f 5,229 f 205 18f 2,698 3,491 30,426 1,199 4,894 973 155 785 .. 6,670 .. 307 5f 52 37 710 29 .. 4 102 19 f 106 22 5,044 26 11f 616 6,847 71 33 1,469 49 169 278f 232 10,611 2,318f 51 7f 47 2,730 193 582 372 25f 162 521 1,141 6,927 5,646 1,828d ..

6,740 11,509 6,773 2,196 555 8,187 4,332 41,872 2,070 12,537 3,468 1,184 1,095 .. 12,927 .. 553 506 271f 1,013 7,157 40 f 123f 159 1,183 232 518 48 17,231 286 .. 1,390 12,309 235 253f 7,978 217 59 469 397 17,876 4,396f 346f 86 791 4,444 1,108 903 2,279 1 247 2,471 2,837 9,853 12,329 3,473d 874d

% of exports 1995 2009

14.9 6.8 9.9 1.1 .. 5.5 12.7 10.3 35.3 1.0 28.0 2.6 22.3 .. 4.5 .. 2.2 1.1 12.8 1.8 .. 14.6 .. 0.1 3.2 2.7 14.2 4.7 6.1 4.9 2.2 26.2 7.7 8.0 6.5 16.2 10.2 12.9 16.0 22.5 4.4 13.0 7.7 2.2 0.4 4.9 2.5 5.7 4.9 0.8 3.4 7.9 4.3 19.4 17.5 .. ..

6.7 4.4 5.1 .. 1.4 4.1 6.4 8.2 51.3 1.9 31.8 2.5 14.8 .. 3.0 .. 0.9 19.8 18.8 9.0 33.1 5.1 27.1 0.4 5.8 6.5 .. .. 9.2 11.2 .. 33.2 5.0 11.7 11.0 30.2 8.8 1.2 11.6 26.6 3.5 13.2 12.1 8.2 1.3 2.8 3.8 4.1 13.7 0.0 3.4 8.1 6.0 5.8 18.3 .. ..

6.19

Outbound tourism expenditure

$ millions 1995 2009

1,501 996f 2,172f 247 117f 2,034f 2,626 17,219 173 46,966 719 296 230 .. 6,947 .. 2,514 7f 34 62 .. 17 .. 493 107 27f 79 53 2,722 74 30 184 3,587 73 22 356 68 18f 90 f 167 13,151 1,259 f 56 26 938 4,481 349 f 654 181 58f 173 428 551 5,865 2,539 1,155d ..

4,117 11,507 9,579 9,482 705 8,887 3,869 34,329 259 34,788 1,202 1,320 234f .. 14,648 .. 8,244 391 91f 906 4,928 22 51 1,683 1,140 150 123f 84 7,196 228 .. 384 8,628 307 242 1,712 249 40 109 511 21,076 2,559 f 224 98 5,308 12,366f 1,277 1,098 503 48 288 1,379 2,989 7,842 4,604 1,613d 3,751d

% of imports 1995 2009

7.5 2.1 4.0 1.6 .. 4.8 7.4 6.9 4.6 11.2 14.7 4.9 3.9 .. 4.5 .. 19.9 1.0 4.5 2.8 .. 1.6 .. 8.6 2.7 1.7 8.0 8.0 3.1 7.5 5.9 7.5 4.4 7.3 4.2 3.2 6.6 0.9 4.3 10.3 6.1 7.3 4.9 5.7 7.3 9.6 6.3 4.6 2.3 3.0 3.3 4.5 1.7 17.3 6.4 .. ..

2011 World Development Indicators

4.4 3.5 8.5 .. 3.3 5.3 6.1 6.6 4.1 5.3 7.4 3.4 2.1 .. 3.7 .. 26.9 10.6 5.8 7.9 16.3 1.2 3.0 6.2 5.5 2.6 .. .. 5.0 6.1 .. 7.5 3.3 7.7 9.2 4.6 5.8 1.4 2.1 10.0 4.6 8.0 5.0 5.0 11.1 11.8 5.9 3.1 3.3 1.0 3.9 5.3 5.4 4.6 5.5 .. ..

389

global links

Travel and tourism

6.19

Travel and tourism International tourists

thousands Inbound Outbound 1995 2009 1995 2009

Romania Russian Federation Rwanda Saudi Arabia Senegal Serbia Sierra Leone Singapore Slovak Republic Slovenia Somalia South Africa 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

5,445a 10,290a .. 3,325 .. .. 38 b 6,070 903e 732e .. 4,488 34,920 403h 29 300i 2,310e 6,946g 815e .. 285 6,952c .. 53g 260 b 4,120h 7,083 218 160 3,716 2,315c,i 21,719 43,490 2,022 92 700 1,351a 220g 61g 163 1,416a 537,385 t 6,379 139,405 58,101 82,221 147,674 43,654 33,946 39,151 13,555 3,819 12,978 384,359 203,060

7,575a 23,676a 699 10,897 875 645 36 b 7,489 1,298 e 1,824 e .. 9,934 52,231 448h 420 908g 4,678e 8,294g 6,092e .. 714 14,150 .. 150 g 413b 6,901h 25,506 8 817 20,798 .. 28,199 54,884 2,056 1,069 615 3,747a 396g 434g 710 1,956a 894,012 t 18,801 329,738 160,100 171,751 352,280 107,674 106,987 60,093 44,880 7,949 31,497 535,465 280,972

Inbound tourism expenditure

$ millions 1995 2009

5,737 11,723 689 1,669 21,329 36,538 4,312f 12,300 .. .. 4 218f .. 6,032 .. 6,678d .. .. 168 637 .. .. .. 986 6 73 57f 25f 9,200 f 2,867 6,961 7,611f 218 19,917 630 2,539 .. 2,586 1,128 2,733 .. .. .. .. 2,520 4,424 2,654 8,683 3,648 12,844 27,369 58,586 504 963 367 754 299 f 195 .. 8f .. 1,245 54 40 10,127 11,699 4,390 12,114 11,148 11,147 11,354 16,335 1,746 5,215 1,258f 5,152 .. .. .. 20 1,192 157 .. 502 f 1,820 4,535 9,257 19,421 .. .. .. .. .. .. 13f 44 261 .. 232 557 1,778 2,623 1,838 3,526 24,556 3,981 10,493 4,957f 21 38 13 .. 683 148 337 78 f 4,349 6,552 15,334 191f .. .. 632 7,162d 41,345 58,614 27,577 38,545 51,285 61,419 93,700 147,554 562 826 725 1,408 246 1,150 15 64d 534 1,651 995 853 .. .. .. 3,050 d .. .. 255f 269 f .. .. 50 f 496f .. .. 29 98 256 593 145 294d 555,382 t 961,575 t 487,033 t 1,022,301 t .. .. 3,253 11,845 129,489 327,671 82,794 270,868 35,678 134,442 40,251 135,230 86,296 .. 42,566 135,451 141,222 363,391 85,892 281,994 33,153 .. 31,197 94,687 47,292 106,450 12,014 58,244 21,841 41,194 21,838 49,773 13,407 25,352 9,771 43,050 5,151 17,100 4,016 14,339 .. .. 6,928 22,170 374,257 564,431 401,084 740,277 141,785 235,326 164,475 310,544

% of exports 1995 2009

7.3 4.6 5.4 .. 11.2 .. 44.4 4.8 5.7 10.9 .. 7.7 20.4 7.9 1.2 5.3 4.6 9.2 21.9 .. 39.7 13.2 .. 2.8 8.3 23.0 13.6 0.7 11.7 1.1 .. 8.6 11.8 20.7 .. 4.8 .. 33.4 2.3 2.4 .. 7.6 w 12.2 7.7 8.5 7.1 7.8 7.8 6.3 7.6 13.0 6.8 7.8 7.6 7.8

Outbound tourism expenditure

$ millions 1995 2009

3.3 749 3.6 11,599 f 40.8 13 3.3 .. 18.2 154 8.3 .. 7.7 51 2.5 4,663f 4.1 338 9.6 606 .. .. 11.1 2,414 16.9 5,826 8.4 279 3.6 43f 2.2 45 6.2 6,816 5.8 9,478 16.4 498f 1.6 .. 22.8 360 f 10.8 4,791 .. .. 3.9 40 2.8 91 17.7 294 17.2 911f .. 74 17.3 80 f 8.0 210 f .. .. 6.5 30,749 9.4 60,924 16.5 332 .. .. 1.4 1,852 4.9 .. 23.0 162f 7.0 76f 2.1 83 .. 106d 6.4 w 458,869 t 12.9 2,591 6.2 60,850 5.3 20,926 7.3 39,917 6.3 63,336 4.8 14,770 7.3 16,380 6.0 18,774 20.5 4,844 4.6 2,393 7.5 6,810 6.4 394,726 6.9 155,113

1,769 23,529 115 20,964 d 276 1,076 16 15,808f 2,249 1,533 .. 6,420 21,482 735 868f 98 13,432 12,552 910 f 6f 806 5,659 .. 68 102 492 4,627 .. 336 3,751 13,288d 61,130 105,202 436 .. 2,234 1,100 544f 277 83 .. 923,915 t 7,641 214,809 110,735 104,850 222,402 75,780 48,211 41,573 19,825 14,787 25,420 703,266 280,349

% of imports 1995 2009

6.6 14.0 3.5 .. 8.5 .. 19.4 3.2 3.2 5.6 .. 7.2 4.3 4.7 3.5 3.5 8.4 8.7 9.0 .. 16.8 5.8 .. 6.0 4.3 3.3 2.3 4.1 5.4 1.1 .. 9.4 6.8 9.3 .. 11.0 .. 5.8 3.1 6.2 .. 7.4 w 5.1 5.4 4.0 6.7 5.4 3.5 8.1 6.5 5.7 3.0 6.8 7.9 7.8

2.9 9.3 7.8 13.1 3.9 5.8 2.5 4.9 3.6 5.5 .. 7.9 5.7 6.3 7.7 4.2 8.1 5.1 4.7 0.2 10.7 3.6 .. 4.1 1.0 2.3 3.1 .. 6.4 6.7 .. 9.4 5.4 5.6 .. 4.6 1.5 11.0 2.8 2.0 .. 5.9 w 5.0 5.1 4.5 5.9 5.1 4.4 6.2 5.3 6.7 3.6 6.4 6.3 6.5

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. Data are from national sources. e. Arrivals in all types of accommodation establishments. f. Refers to expenditure of travel-related items only; excludes passenger transport items. g.Â Arrivals in hotels and similar establishments. h. Arrivals in hotels only. i.Â Arrivals by air only.

390

2011 World Development Indicators

About the data

6.19

Definitions

Tourism is defined as the activities of people trav-

For some countries number of arrivals is limited to

• International inbound tourists (overnight visitors)

eling to and staying in places outside their usual

arrivals by air and for others to arrivals staying in

are the number of tourists who travel to a country

environment for no more than one year for leisure,

hotels. Some countries include arrivals of nationals

other than that in which they usually reside, and out-

business, and other purposes not related to an activ-

residing abroad while others do not. Caution should

side their usual environment, for a period not exceed-

ity remunerated from within the place visited. The

thus be used in comparing arrivals across countries.

ing 12 months and whose main purpose in visiting

social and economic phenomenon of tourism has

The World Tourism Organization is improving its

is other than an activity remunerated from within the

coverage of tourism expenditure data, using balance

country visited. When data on number of tourists are

Statistical information on tourism is based mainly

of payments data from the International Monetary

not available, the number of visitors, which includes

on data on arrivals and overnight stays along with

Fund (IMF) supplemented by data from individual

tourists, same–day visitors, cruise passengers, and

balance of payments information. These data do not

countries. These data, shown in the table, include

crew members, is shown instead. • International out-

completely capture the economic phenomenon of

travel and passenger transport items as defined in

bound tourists are the number of departures that

tourism or provide the information needed for effec-

the IMF’s (1993) Balance of Payments Manual. When

people make from their country of usual residence

tive public policies and efficient business operations.

the IMF does not report data on passenger transport

to any other country for any purpose other than an

Data are needed on the scale and significance of

items, expenditure data for travel items are shown.

activity remunerated in the country visited. • Inbound

tourism. Information on the role of tourism in national

Tourism expenditure does not include all types of

tourism expenditure is expenditures by international

economies is particularly deficient. Although the

payments that visitors might make. It excludes pay-

inbound visitors, including payments to national carri-

World Tourism Organization reports progress in har-

ments not for consumption of goods and services,

ers for international transport. These receipts include

monizing definitions and measurement, differences

such as taxes and duties that are not part of the

any other prepayment made for goods or services

in national practices still prevent full comparability.

purchase prices of the products acquired by the visi-

received in the destination country. They may include

The usual environment of an individual is a key

tor; purchase of financial and nonfinancial assets

receipts from same–day visitors, except when these

concept in tourism statistics and is defined as the

including land and real estate; purchase of goods for

are important enough to justify separate classifica-

geographical area within which an individual con-

resale; and donations to charities or other individu-

tion. For some countries they do not include receipts

ducts regular life routines. This concept excludes as

als. The timing of tourism expenditure is also impor-

for passenger transport items. Their share in exports

visitors travelers who commute regularly between

tant because transportation and accommodation are

is calculated as a ratio to exports of goods and ser-

their place of usual residence and place of work or

often booked and paid for before being consumed.

vices (all transactions between residents of a coun-

study or who frequently visit places within their cur-

Payment might also happen after consumption of

try and the rest of the world involving a change of

rent life routine—for instance, homes of friends or

such services, such as when a visitor pays off a

ownership from residents to nonresidents of general

relatives; shopping centers, and religious, health-

credit card or a special loan drawn for travel pur-

merchandise, goods sent for processing and repairs,

care, or other facilities a substantial distance away

poses. Tourism expenditure should be reported for

nonmonetary gold, and services). • Outbound tour-

or in a different administrative area that are regularly

the period when the services are actually consumed

ism expenditure is expenditures of international out-

and frequently visited.

and goods are actually acquired, regardless of when

bound visitors in other countries, including payments

Tourism can be either domestic or international.

payment was made. Finally, the valuation of tour-

to foreign carriers for international transport. These

The table shows data relevant to international tour-

ism expenditure depends on the form of acquisition

expenditures may include those by residents travel-

ism, where the traveler’s country of residence dif-

of the goods and services concerned. In a market

ing abroad as same–day visitors, except when these

fers from the visiting country. International tourism

transaction expenditure should be valued using the

are important enough to justify separate classifica-

consists of inbound and outbound tourism. The data

purchaser price—value paid by the visitor. This price

tion. For some countries they do not include expen-

are from the World Tourism Organization, a United

should include all taxes and voluntary and compul-

ditures for passenger transport items. Their share in

Nations agency. The data on inbound and outbound

sory tips prevalent in the accommodation and food

imports is calculated as a ratio to imports of goods

tourists refer to the number of arrivals and depar-

services sectors. Discounts and rebates of sales

and services (all transactions between residents of a

tures, not to the number of people traveling. Thus a

tax or value added tax to nonresidents should be

country and the rest of the world involving a change of

person who makes several trips to a country during

taken into account, even if refunded at the border.

ownership from nonresidents to residents of general

a given period is counted each time as a new arrival.

However, following these recommendations for tour-

merchandise, goods sent for processing and repairs,

Unless otherwise indicated in the footnotes, the data

ism statistics may not be easy for countries. Tourism

nonmonetary gold, and services).

on inbound tourism show the arrivals of nonresident

expenditures reported in the table may not be fully

tourists (overnight visitors) at national borders.

comparable, so caution should be used when making

When data on international tourists are unavailable

cross-country comparisons.

grown substantially over the past quarter century.

Data sources Data on visitors and tourism expenditure are

or incomplete, the table shows the arrivals of inter-

The aggregates are calculated using the World

from the World Tourism Organization’s Yearbook

national visitors, which include tourists, same-day

Bank’s weighted aggregation methodology (see Sta-

of Tourism Statistics and Compendium of Tourism

visitors, cruise passengers, and crew members.

tistical methods) and differ from the World Tourism

Statistics 2011. Data in the table are updated

Organization’s aggregates.

from electronic files provided by the World Tour-

Sources and collection methods for arrivals differ

global links

Travel and tourism

across countries. In some cases data are from bor-

ism Organization. Data on exports and imports

der statistics (police, immigration, and the like) and

are from the IMF’s Balance of Payments Statistics

supplemented by border surveys. In other cases data

Yearbook and data files.

are from tourism accommodation establishments.

2011 World Development Indicators

391 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 increasing. Timely and reliable statistics are key to the broad development strategy often referred to as “managing for results.” Monitoring and reporting on publicly agreed indicators are central to implementing poverty reduction strategies and lie at the heart of the Millennium Development Goals and the Results Measurement System adopted for the 14th replenishment of the International Development Association. A global action plan to improve national and international statistics was agreed on during the Second Roundtable on Managing for Development Results in February 2004 in Marrakech, Morocco. The plan, now referred to as the Marrakech Action Plan for Statistics, or MAPS, has been widely endorsed and forms the overarching framework for statistical capacity building. The third roundtable conference, held in February 2007 in Hanoi, Vietnam, reaffirmed MAPS as the guiding strategy for improving the capacity of the national and international statistical systems. See www.mfdr.org/RT3 for reports from the conference.

2011 World Development Indicators

393

primary data documentation Currency

National accounts

SNA System of price Reference National Accounts valuation year

Base year

Afghanistan Albania Algeria American Samoa Andorra Angola Antigua and Barbuda Argentina Armenia Aruba Australia Austria Azerbaijan Bahamas, The Bahrain Bangladesh Barbados Belarus Belgium Belize Benin

Afghan afghani Albanian lek Algerian dinar U.S. dollar Euro Angolan kwanza East Caribbean dollar Argentine peso Armenian dram Aruban florin Australian dollar Euro New Azeri manat Bahamian dollar Bahraini dinar Bangladeshi taka Barbados dollar Belarusian rubel Euro Belize dollar CFA franc

Bermuda

Bermuda dollar

2002/03 a

394

Alternative conversion factor

VAB VAB VAB

Government IMF data finance dissem ination standard

Balance of Payments Manual in use

External debt

System of trade

Accounting concept

2005

BPM5 BPM4

Actual Actual Actual

G S S

C C B

G G G

2005

BPM5 BPM5 BPM5 BPM5 BPM5 BPM5 BPM5 BPM5 BPM5 BPM5 BPM5 BPM5 BPM5 BPM5 BPM5 BPM5

C C

G 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

PPP survey year

G 1997 1990 1993

b a

1996

2007

VAP VAB VAB VAB

1991–96

2000 a

2003

b b

2006 1985 1995/96 1974

2000

b b

2000 2000 1985

1996

VAB VAB VAB VAB VAP VAB VAB VAB VAB VAB VAP

Actual Actual

BPM5 BPM5

Actual Actual Actual

G S S

C C C

G G

2005 2005 2005 2005

BPM5 BPM5

Actual Actual

B C

BPM5

Actual

S S S S

G S G S

2005 2005 2005 2005 2005 2005

BPM4 BPM5 BPM5 BPM5 BPM5 BPM5

Estimate Actual Actual Actual

G G G S G S

B C C B C C

2005 2005

BPM4 BPM4

Preliminary Actual

G G

2005 2005 2005 2005 2005 2005 2005 2005

BPM5 BPM5 BPM5 BPM5 BPM5

Actual Preliminary

S G G G G G

C B C C B

S G S G S

C C C C C

G G S G S

C C C

S S S

1971–84 1990–95

2005 2005

1992–95

2005 2005 2005 2005 2005

1990–95

2005 2005

1992

2005

1960–85

2005 2005 2005

VAB b b

1996

b b

2002

b b

2007

b b b b

2000 2000 1995

b b

VAB VAB VAB VAB VAB VAP VAB VAB VAB VAB VAB VAB VAP VAB VAB VAB VAB VAP VAB VAB VAB VAP VAB VAP VAB VAP VAB VAB VAB VAB VAB VAB

Actual G S G G G S G G G G G G S G G

1995

CFA franc 1999 Burundi franc 1980 Cambodian riel 2000 CFA franc 2000 Canadian dollar 2000 Cape Verde escudo 1980 Cayman Islands dollar CFA franc 2000 CFA franc 1995 Pound sterling 2003, 2007 Chilean peso 2003 Chinese yuan 2000 Hong Kong dollar 2008 Macao pataca 2002 Colombian peso 2005 Comorian franc 1990 Congolese franc 1987 CFA franc 1978 Costa Rican colon 1991 CFA franc 1996 a Croatian kuna Cuban peso 1990 a Euro Czech koruna 2000 Danish krone 2000 Djibouti franc 1990

2011 World Development Indicators

1980

Bhutan Bhutanese ngultrum 2000 Bolivia Bolivian Boliviano 1990 a Bosnia and Herzegovina Bosnia and Herzegovina convertible mark Botswana Botswana pula 1993/94 Brazil Brazilian real 2000 Brunei Darussalam Brunei dollar 2000 a Bulgaria Bulgarian lev Burkina Faso Burundi Cambodia Cameroon Canada Cape Verde Cayman Islands Central African Republic Chad Channel Islands Chile China Hong Kong SAR, China Macao SAR, China Colombia Comoros Congo, Dem. Rep. Congo, Rep. Costa Rica Côte d’Ivoire Croatia Cuba Cyprus Czech Republic Denmark Djibouti

1996

Balance of payments and trade

Actual Actual

Actual

Actual Actual

BPM5

1978–89, 1991–92 1992–93

1978–93

1992–94 1999–2001 1993

2005 2005 2005 2005 2005 2005

BPM4 BPM5 BPM5 BPM5 BPM5

Actual

Actual Preliminary Estimate Preliminary Actual Actual

BPM5 BPM5 BPM5 Actual

S S S S G S G

G G S G

primary data documentation Latest population census

1979 2001 2008 2010

Latest demographic, education, or health household survey

Source of most recent income and expenditure data

MICS, 2003 DHS, 2008/09 MICS, 2006

IHS, 2008 LSMS, 2008 IHS, 1995

Yes

1998 2001

2009 2009 2009

Yes Yes

1970 2001 2010 2001 2010 2006 2001 2009 2010 2010 2001 2010 2009 2001 2010 2002

Vital Latest Latest registration agricultural industrial complete census data

MICS, 2001; MIS, 2006/07

IHS, 2000

DHS, 2005

IHS, 2009 IHS, 2009

DHS, 2006

ES/BS, 1994 IS, 2000 ES/BS, 2008

DHS, 2007

IHS, 2005

MICS, 2005 MICS, 2006 DHS, 2006

ES/BS, 2009 IHS, 2000 ES/BS, 1999 CWIQ, 2003

DHS, 2008 MICS, 2006

IHS, 2003 IHS, 2007 LSMS, 2007 ES/BS, 2003 LFS, 2008

1964–65 Yes Yes Yes Yes Yes Yes Yes

2002

2001 1999–2000

Yes

Bermuda

2010

Bhutan Bolivia Bosnia and Herzegovina

2005 2001 1991

Botswana Brazil Brunei Darussalam Bulgaria

2001 2010 2001 2001

MICS, 2000 DHS, 1996

Burkina Faso Burundi Cambodia Cameroon Canada Cape Verde Cayman Islands Central African Republic Chad Channel Islands Chile China Hong Kong SAR, China Macao SAR, China Colombia Comoros Congo, Dem. Rep. Congo, Rep. Costa Rica Côte d’Ivoire Croatia Cuba Cyprus Czech Republic Denmark Djibouti

2006 2008 2008 2005 2006 2010 2010 2003 2009 2001 2002 2010 2006 2006 2006 2003 1984 2007 2000 1998 2001 2002 2001 2001 2001 2009

MICS, 2006 MICS, 2005 DHS, 2005 MICS, 2006

2005 Yes Yes Yes

1994 1999–2000d 1992

2009 2009 2009 2009 2008 2009 2008 2006 1995 2009 2005 2009 2009 2008 2005

Yes

ES/BS, 2007

DHS, 2005

CWIQ, 2003 CWIQ, 2007 IHS, 2007 PS, 2007 LFS, 2000 ES/BS, 2007

MICS, 2006 DHS, 2004

PS, 2008 PS, 2002/03

NSS, 2007

IHS, 2009 IHS, 2005

IHS, 2009 IHS, 2004 1-2-3, 2005/06 CWIQ/PS, 2005 LFS, 2009 IHS, 2008 ES/BS, 2008

MICS, 2006 RHS, 1993 MICS, 2006

IS, 1996 ITR, 1997 PS, 2002

2000 2000 2005 2003 2000 2000 2000 2000 2001 2000 2000

1993 1996

2009 2009 2006 2009

2009 2010 2006 2009

2000 2000

2006 2005 2009 2007 2007 2009

2009 2009 2008 2006 2009 2009

2000 2000 2000 2000 2000

1985

2006 2008

2005 1995

2000 2000

1997 1997

2009 2009 2008 2007 2009 2009 2009 2009 2009 2009 2009 2008 2008 2009 2009 2007

2009 2009 2009 2009 2009 2007 1986 2005 2009 2009 2009 2006 2009 2009 2009 2009

2000 2000

1984 1996/2001 2004

2001

Yes Yes Yes Yes Yes

2000 1990 2000 2000

2009 2009 2010

1993

Yes

2000 2000 2000

2009

Yes Yes DHS, 2005 MICS, 2000 MICS, 2010 DHS, 2005; AIS, 2009 RHS, 1993 MICS, 2006

2009 2008 2008 2009 2006 1991 2007 2009 2009 2009 2009 2009 2009 2009 2007 2007 2009 2009 2009 2008 2006

2009 2009 2009

Yes Yes

Yes

Latest water withdrawal data

2000 1984–88 Yes

Yes Yes Yes

Latest trade data

1990 1985–86 1973 2001 2003

2000 1999–2000

2011 World Development Indicators

2000

2000 2000 2002 2000

2000 2000 2000 2000 2000

395

primary data documentation Currency

National accounts

SNA System of price Reference National Accounts valuation year

Base year

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

East Caribbean dollar 1990 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 2000 a Euro CFP franc CFA franc 1991 Gambian dalasi 1987 a Georgian lari Euro 2000 New Ghanaian cedi 2006 Gibraltar pound a Euro Danish krone East Caribbean dollar 1990 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 2000 Indian rupee 2004/05

Indonesia Iran, Islamic Rep. Iraq Ireland Isle of Man Israel Italy Jamaica Japan Jordan Kazakhstan Kenya Kiribati Korea, Dem. Rep.

Indonesian rupiah Iranian rial Iraqi dinar Euro Pound sterling Israeli new shekel Euro Jamaican dollar Japanese yen Jordanian dinar Kazakh tenge Kenyan shilling Australian dollar Democratic People’s Republic of Korean won Korean won Euro Kuwaiti dinar Kyrgyz som Lao kip Latvian lats Lebanese pound Lesotho loti

Korea, Rep. Kosovo Kuwait Kyrgyz Republic Lao PDR Latvia Lebanon Lesotho

396

2011 World Development Indicators

2000 1997/98 1997 2000 2005 2005 2000 2003 2000 1994

b b

2000

1996

b b

2000

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

Balance of Payments Manual in use

External debt

System of trade

Accounting concept

BPM5 BPM5 BPM5 BPM5 BPM5

Actual Actual Actual Actual Actual

S G S G G

C B C C

G G S S S

Actual S G

B C C

G S S

2005 2005 2005 2005 2005

BPM5 BPM5 BPM5 BPM5 BPM5

Preliminary Estimate Actual Actual

G G G G S S S S G G S G

C B

2005 2005 2005

BPM4 BPM5 BPM5 BPM5 BPM5 BPM5 BPM5

C C C B

G G S S G

2005

BPM5

Actual

S G S

Actual Estimate Estimate Actual Actual Actual

G S

B B

G G G

2005 2005 2005

BPM5 BPM5 BPM5 BPM5 BPM5 BPM5 BPM5 BPM5 BPM5

2005 2005 2005 2005

BPM5 BPM4 BPM5 BPM5

2005 2005

BPM5 BPM5 BPM5 BPM5 BPM5 BPM5 BPM5

PPP survey year

2005 2005 1965–84

2005

1987–95

2005 2005

1993 1990–95 1973–87

VAB VAB b

2000

VAB VAB VAB VAB VAB VAB VAB VAB VAB VAP VAB VAB VAB

2005 2005 1991 1988–89

1980–2002 1997, 2004

Government IMF data finance dissem ination standard

Actual Actual

S G G S S S

Actual

S S S G

C C C C

Actual Actual

G S

B C

S S G G S G G S

C C C C B C B

S S G S S S G G

S G

B B B C B C

G S

S G S

2003 b b

2000

2001 2006

2000

VAP VAB VAB VAB VAB VAB VAB VAB

1987–95

2005 2005 2005 2005

Actual Actual Actual Actual

BPM4 VAB

2005

BPM5 Actual

1995 a

1990 2000 1997 1995

1995

VAP VAB VAB VAB VAB VAB

1990–95 1987–95

2005 2005 2005 2005 2005 2005

BPM5 BPM5 BPM5 BPM5 BPM5 BPM5

Actual Preliminary Actual Actual

S S G

S G G

primary data documentation Latest population census

2001 2010 2010 2006 2007 2002 1984 2000 2007

Latest demographic, education, or health household survey

Source of most recent income and expenditure data

DHS, 2007 RHS, 2004 DHS, 2008 RHS, 2008

IHS, 2007 LFS, 2009 ES/BS, 2004/05 IHS, 2008

Yes

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

2010 2001 2001 2010 2001 2010 2002 1996 2009 2002 2003 2001 2001

Yes Yes

2001 2001–02

2009 2009 2009 2009 2009 2009 2009 2009 2009

1999–2000 1999–2000

2009 2009 2009

1974–75 2001–02 2004 1999–2000 1984

2009 2009 2009 2009 2009

1999–2000

2009

1971 1999–2000 1999–2000 1970–71

DHS, 2002

2007 2010 2006e 2007 2003 2003 2002

Vital Latest Latest registration agricultural industrial complete census data

DHS, 2008

CWIQ/IHS, 2005 IHS, 2003 IHS, 2008 IHS, 2000 LSMS, 2006 IHS, 2000

RHS, 2002 DHS, 2005 MICS, 2010 MICS, 2006 DHS, 2005/06 DHS, 2005/06

LSMS, 2006 CWIQ, 2007 CWIQ, 2002 IHS, 1998 IHS, 2001 IHS, 2007 ES/BS, 2007

2001

DHS, 2005/06

IHS, 2004/05

Indonesia Iran, Islamic Rep. Iraq Ireland Isle of Man Israel Italy Jamaica Japan Jordan Kazakhstan Kenya Kiribati Korea, Dem. Rep.

2010 2006 1997 2006 2006 2009 2001 2001 2010 2004 2009 2009 2005 2009

DHS, 2007 DHS, 2000 MICS, 2006

IHS, 2007 ES/BS, 2005 IHS, 2007 IHS, 2000

Korea, Rep. Kosovo Kuwait Kyrgyz Republic Lao PDR Latvia Lebanon Lesotho

2005 1981 2010 2009 2005 2000 1970 2006

MICS, 2005 DHS, 2009 MICS, 2006 SPA, 2004; DHS, 2008/09

ES/BS, 2001 ES/BS, 2000 LSMS, 2007 IS, 1993 ES/BS, 2006 ES/BS, 2007 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 Yes

1971 1993 2000 1995–96/ 2000–01 2003 2003 1981 2000 1981 2000 1996 2000 1997

Yes 1977–79

2008 2009 2009 2008 2009 2003 2009 2009 2009 2009 2009 2009 2010 2006 2009 2008 2009 2008

2000 2000 2000 2000 2000 2005 2000 2000

2009 2008 2008 2009

2009 2008 2005 2009 1997 2009 2009 2009 2009

2000 2000 2000 2000 2000 2000 2000 2000 2000

2009 2007 2002 2009

2009 2006 2008 2009

2000 2004 2000 2000

2009 2009 2008 2009 2009 2009 2009

2009 2009 2009 2009 2009 2009 2009 2009

2004 2000 2000 2000 2005 2000 2003

2009 2009 2002 2009

2000 ES/BS, 1998 IHS, 2006

MICS, 2000 DHS, 2009/10

2000 2000 2000 2000 2000 2004 2000 2002

2000

MICS, 2010

FHS, 1996 MICS, 2005/06 MICS, 2006

Latest water withdrawal data

2009 2007 2009

2009 2003 2000–01 1988

Latest trade data

ES/BS, 2007 ES/BS, 2008 IHS, 2008 ES/BS, 2002/03

Yes

2000

Yes Yes

1970 2002 1998–99 2001 1998–99 1999–2000

Yes Yes

2009 2009 2003 2008 2008 2009 2009 2009

2009

2000

2009 2009 1975 2009 2009 2004

2002 2000 2000 2000 2005 2000

2011 World Development Indicators

397

primary data documentation Currency

National accounts

Base year

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

Liberian dollar 1992 Libyan dinar 1999 Swiss franc Lithuanian litas 2000 Euro Macedonian denar 1997 Malagasy ariary 1984 Malawi kwacha 1994 Malaysian ringgit 2000 Maldivian rufiyaa 1995 CFA franc 1987 Euro 2005 U.S. dollar 1991 Mauritanian ouguiya 1998 Mauritian rupee 2006 Euro Mexican peso 2003 U.S. dollar 1998 a Moldovan leu Euro Mongolian tugrik 2005 Euro 2000 Moroccan dirham 1998 New Mozambican 2003 metical Myanmar Myanmar kyat 1985/86 Namibia Namibian dollar 2004/05 Nepal Nepalese rupee 2000/01 Netherlands Antilles Netherlands Antilles guilder a Netherlands Euro New Caledonia CFP franc New Zealand New Zealand dollar 2000/01 Nicaragua Nicaraguan gold cordoba 1994 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 1985 a Poland Polish zloty Portugal Euro 2000 Puerto Rico U.S. dollar 1954 Qatar Qatari riyal 2001 a Romania New Romanian leu Russian Federation Rwanda Samoa San Marino

398

Russian ruble Rwandan franc Samoan tala Euro

2011 World Development Indicators

2000 1995 2002 1995

SNA System of price Reference National Accounts valuation year

2000 1995

1996

b b

2000

2002

b b

2005

2000

VAP VAB VAB VAB VAB VAB VAB VAB VAP VAB VAB VAB VAB VAB VAB VAB VAB VAB VAB VAB VAB VAB

Balance of payments and trade

Alternative conversion factor

VAB VAP VAB VAB

External debt

BPM5 BPM5

Estimate

2005 2005 2005 2005 2005 2005 2005 2005 2005

BPM5 BPM5 BPM5 BPM5 BPM5 BPM5 BPM5 BPM4 BPM5

Actual

2005 2005

BPM4 BPM5

Actual Preliminary

2005

BPM5

Actual

1990–95

2005

BPM5

Actual

1992–95

2005 2005 2005 2005

BPM5 BPM5 BPM5 BPM5

Actual Actual Actual Actual

BPM5 BPM5 BPM5 BPM5

Estimate

2005 2005

1990–95

VAB

VAB VAP VAB VAB VAB VAB VAP VAB VAP VAB VAB VAP VAP VAB

Balance of Payments Manual in use

2005 1986

VAP VAB VAB

VAB VAB VAP VAB

PPP survey year

1965–95 1993 1971–98

1987–89, 1992 1987–95 1994

S S S S G G G G G G G G G G

G G

C C

S S G G G S

C B C B C

G S G G

G S G

S G

C B C

G G

Actual Actual Actual

S S G S G G

C B B B

G G G

Actual

G G G

C B B

S G G

C B B C B C C

B C

G S

C C

S G

G Actual S

BPM5

2005 2005 2005

BPM5 BPM5 BPM4 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

Actual

S G G S G S S G S S

BPM5 BPM5 BPM5

Preliminary Estimate Actual

G G G

2005 2005

Accounting concept

2005

1989 1985–90

Actual Actual Actual Estimate Actual Preliminary

System of trade

Government IMF data finance dissem ination standard

G S S S S

primary data documentation Latest population census

Latest demographic, education, or health household survey

Source of most recent income and expenditure data

CWIQ, 2007

2008 2006 2010 2001 2001 2002 1993 2008 2010 2006 2009 2005 1999 2000 2000 2007 2010 2000 2004 2008 2010 2003 2004 2007

DHS, 2007; MIS, 2009 MICS, 2000

Myanmar Namibia Nepal Netherlands Antilles

1983 2001 2001 2001

Netherlands New Caledonia New Zealand Nicaragua Niger Nigeria Northern Mariana Islands Norway Oman Pakistan Palau Panama Papua New Guinea Paraguay Peru Philippines Poland Portugal Puerto Rico Qatar Romania

2001 2009 2006 2005 2001 2006 2010 2001 2010 1998 2010 2010 2000 2002 2007 2010 2002 2001 2010 2010 2002

Russian Federation Rwanda Samoa San Marino

2010 2002 2006 2010

2001 ES/BS, 2008

MICS, 2005 DHS, 2008/09 MICS, 2006 DHS, 2009 DHS, 2006

Vital Latest Latest registration agricultural industrial complete census data

ES/BS, 2008 PS, 2005 LSMS, 2004/05 ES/BS, 2009 IHS, 2004 IHS, 2006

Yes Yes Yes Yes

Yes Yes Yes

MICS, 2007

2003 1999–2000d 1994 2004 1993

IHS, 2000

1984 2001 1984–85

DHS, 2005

LFS, 2008 IHS, 2000 ES/BS, 2008

MICS, 2005 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 DHS, 2006

ES/BS, 1993/94 LSMS, 2003/04

Latest water withdrawal data

2008 2008

1985 2004

2000 2000

2008 2009 2009 2009 2009 2009 2009 2007 2009 1999 2009 2009

2009 2009 2009 2009 2010 2009 2008 2008 2009

2000

2009 2009

2009

2000

2007

2000

1996 1999–2000

2009 2009 2009 2009

2009 2009

2000 2000

2003 1996–97 2002

2009 2009

2001 2008 2009 2008

2000 2000 2000 2000

2009 1997 2009 2009 2003 2006

2009 2008 2010 2009 2008 2009

2000 2000 2000 2000

2009 2004 2009 2007 2009 2009 2009 2009 2009 2009 2009 2001

2010 2009 2009

2000 2003 2000

2009 2004 2009 2009 2009 2009 2009

2000 2000 2000 2000 2000 2000 2000

2009

2008 2009

2005 2000

2009 2009 2009

2000 2000

1991 Yes Yes Yes Yes

RHS, 2006/07 DHS, 2006 DHS, 2008

IS, 1997 LSMS, 2005 CWIQ/PS, 2005 IHS, 2003/04 IS, 2000

FHS, 1995 DHS, 2006/07

Yes Yes Yes

Yes

IHS, 2006

1999–2000d 2002 2001 1980 1960 1999 1978–79 2000

Yes LSMS, 2003 DHS, 1996 RHS, 2004 DHS, 2007/08 DHS, 2008

LFS, 2009 IHS, 1996 IHS, 2008 IHS, 2009 ES/BS, 2009 ES/BS, 2008 IS, 1997

RHS, 1995/96 RHS, 1999

LFS, 2008

RHS, 1996 DHS, 2007/08 DHS, 2009

IHS, 2008 IHS, 2005

2001

Yes Yes Yes Yes Yes Yes Yes

2000 2000 2000 2003

Yes IHS, 1999

2000 2000 2000

2008 2009 2009 2009

Yes Yes ENPF, 1995

Latest trade data

1991 1994 2002 1996/2002 1999 1997/2002 2000–01 2002 1994–95 1984 1999

2000

Yes

2011 World Development Indicators

399

primary data documentation Currency

National accounts

SNA System of price Reference National Accounts valuation year

Base year

São Tomé & Príncipe

São Tomé & Príncipe dobra

2001

Saudi Arabia Senegal Serbia Seychelles Sierra Leone Singapore Slovak Republic Slovenia Solomon Islands Somalia South Africa Spain Sri Lanka St. Kitts and Nevis St. Lucia St. Vincent & Grenadines Sudan Suriname Swaziland Sweden Switzerland Syrian Arab Republic Tajikistan Tanzania

Saudi Arabian riyal CFA franc Serbian dinar Seychelles rupee Sierra Leonean leone Singapore dollar Euro Euro Solomon Islands dollar Somali shilling South African rand Euro Sri Lankan rupee East Caribbean dollar East Caribbean dollar East Caribbean dollar Sudanese pound Suriname dollar Swaziland lilangeni Swedish krona Swiss franc Syrian pound Tajik somoni Tanzanian shilling

1999 1999

Thailand Timor-Leste Togo Tonga Trinidad and Tobago

Thai baht U.S. dollar CFA franc Tongan pa’anga Trinidad and Tobago dollar

Tunisia Turkey Turkmenistan

Tunisian dinar New Turkish lira 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

U.S. dollar Australian dollar Ugandan shilling 2001/02 a Ukrainian hryvnia U.A.E. dirham 1995 Pound sterling 2000 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

400

2011 World Development Indicators

1986 1990 2000 2000

1987 2002

b b

1990 1985 2005 2000 2002 1990 1990 1990 1981/82f 1990 2000

1995 2000

b b

1996 b

2000

2000 2001

2000 2000 a

1988 2000 1978 2000/01 2000

1990 1998 a

2007

Balance of payments and trade

Alternative conversion factor

PPP survey year

VAP

2005

VAP VAB VAB VAP VAB VAB VAB VAB VAB VAB VAB VAB VAP VAB VAB VAB VAB VAB VAB VAB VAB VAB VAB VAB

2005 2005 2005 2005 2005 2005 2005

Balance of Payments Manual in use

BPM5 BPM5 BPM5 BPM5 BPM5 BPM5 BPM5 BPM5 BPM5

1977–90 2005 2005 2005

External debt

System of trade

Preliminary

Actual Actual Actual Preliminary

Actual Estimate Preliminary

2005 2005 2005 2005 2005 2005

BPM5 BPM5 BPM5 BPM5 BPM5 BPM5 BPM5 BPM5 BPM5 BPM5 BPM5 BPM5 BPM4 BPM5

VAP VAP VAP VAB VAB

2005

BPM5

Actual

2005

BPM5 BPM5 BPM5

Actual Actual

VAP VAB VAB

2005 2005

BPM5 BPM5 BPM4

Actual Actual Estimate

2005

1970–2008 1990–95

1987–95, 1997–2007

Actual Preliminary Actual Actual Actual Actual

Actual Actual Actual

G G G G G S S S G S G S G S G G G S S S

Government IMF data finance dissem ination standard

Accounting concept

B C C B C C C

C C B C B B B C C C C

G G G G G S S S

S S G G G G G G G S S G G G

S G S G S

B C

G G G

G S

C B

S S

G G S G G S

B C B C C C

G S G S S S

G G S G S S S G

C C

G G G

B B B C

G G G G

2003

2000 1997

VAB VAB VAB VAB VAB VAB VAB VAP VAB VAP VAB VAP VAB VAB

1991

2005 2005

BPM5 BPM5 BPM4 BPM5 BPM5 BPM5 BPM4 BPM5 BPM5 BPM4

Actual Actual Actual Actual Preliminary

1990–96 1990–92 1991, 1998

2005 2005 2005

BPM5 BPM5 BPM5 BPM4

Actual Preliminary Actual

1987–95

2005 2005 2005 2005 2005

1990–95

Actual Actual

primary data documentation Latest population census

Latest demographic, education, or health household survey

Source of most recent income and expenditure data

PS, 2000/01

São Tomé & Príncipe

2001

DHS, 2008/09

2010 2002 2002 2010 2004 2010 2001 2002 2009 1987 2001 2001 2001 2001 2010 2001 2008 2004 2007

Demographic survey, 2007 DHS, 2005; MIS, 2008/09 MICS, 2005/06

Thailand Timor-Leste Togo Tonga Trinidad and Tobago

2010 2010 2010 2006 2000

MICS, 2005/06 DHS, 2009 MICS, 2006 MICS, 2006

IHS, 1992

Tunisia Turkey Turkmenistan

2004 2000 1995

MICS, 2006 DHS, 2003 MICS, 2006

IHS, 2000 LFS, 2008 LSMS, 1998

2001 2002 2002 2001 2010 2001 2010 2004 1989 2009 2001 2009 2010 2007 2004

DHS, 2008 General household, 2005

IS, 1996 ES/BS, 2004 MICS, 2006 DHS, 2003 DHS, 2006/07

2000 2002

ES/BS, 2005 IHS, 2000 ES/BS, 2007 IHS, 1995

MICS/PAPFAM, 2006 MICS, 2006 DHS, 2006/07

2010 2004 2010 2002

PS, 2005 IHS, 2008 IHS, 2007 IHS, 2003

MICS, 2006 MICS, 2005 DHS, 2004/05; AIS, 2007/08

ES/BS, 1999 ES/BS, 2000/01 IS, 2000 ES/BS, 2000 ES/BS, 2004 LSMS, 2004 ES/BS, 2007

Vital Latest Latest registration agricultural industrial complete census data

Yes Yes Yes Yes Yes

IHS, 2009 LSMS, 2007 CWIQ, 2006

2009

1999 1998–99

2009 2009

1998 1984–85

2009 2003 2009 2009 2008 2009 1990 2009 2009 2009 2009 2009 2009 2009 2008 2009 2009 2009 2009 2009 2009

2009 2009 2008 2008 2002 2009 2009 2009 2007 1982 2009 2009 2009 2008 2008 2009 2009 2008 2007 2009 2009 2008 2000 2009

2009 2000 2005 2009 2009

2009 2005 2007 2007 2009

2000

2009 2009 2009

2009 2009 2000

2000 2003 2000

2001 2000

2000 1999 2002

2003 1999–2000 2000 1981 1994 2002–03 2003

Yes Yes

1996 2001 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, 2005 ES/BS, 2008

1991

Yes Yes Yes Yes

IHS, 2009 IHS, 2008

Yes Yes Yes

IHS, 2009 ES/BS, 2005 IHS, 2004/05 IHS, 2003

2009 2008 2008 2009 2009 2009 2009 2009

2006 2002 2003 2000

2003 2000 2000 2000

2000 2000 2000 2000 2000 2003 2000 2002

2002 2000

1997 2001

2009 2009 2007 2009 2009 2009 2009 2008 2005 2009

2007 2009 2008

2000

1971 2002 1990 1960

2003 2009 2009

2008 2009 2009 2009

2000 2000 2002

Yes

IS, 1999 LFS, 2000 IHS, 2009 ES/BS, 2003

Latest water withdrawal data

2005

Yes Yes Yes

Latest trade data

1998 1999–2000d 1997/2002 2000

2000 2005 2000 2000 2000 2000

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. Conducted annually. e. Rolling census. f. Reporting period switch from fiscal year to calendar year from 1996. Pre-1996 data converted to calendar year.

2011 World Development Indicators

401

Primary data documentation notes • Base year is the base or pricing period used for

estimates. • System of trade refers to the United

age and sex, as well as the detailed definition of count-

constant price calculations in the country’s national

Nations general trade system (G) or special trade sys-

ing, coverage, and completeness. Countries that hold

accounts. Price indexes derived from national accounts

tem (S). Under the general trade system goods entering

aggregates, such as the implicit deflator for gross

directly for domestic consumption and goods entered

tables every 5 or 10 years. Germany’s 2001 census is

domestic product (GDP), express the price level relative

into customs storage are recorded as imports at

a register-based test census using a sample of 1.2

to base year prices. • Reference year is the year in

arrival. Under the special trade system goods are

percent of the population. A rare case, France has been

which the local currency, constant price series of a

recorded as imports when declared for domestic con-

conducting a rolling census every year since 2004; the

country is valued. The reference year is usually the

sumption whether at time of entry or on withdrawal

1999 general population census was the last to cover

same as the base year used to report the constant

from customs storage. Exports under the general sys-

the entire population simultaneously (www.insee.fr/

price series. However, when the constant price data

tem comprise outward-moving goods: (a) national

en/recensement/page_accueil_rp.htm). • Latest

are chain linked, the base year is changed annually, so

goods wholly or partly produced in the country; (b)

demographic, education, or health household survey

the data are rescaled to a specific reference year to

foreign goods, neither transformed nor declared for

indicates the household surveys used to compile the

provide a consistent time series. When the country has

domestic consumption in the country, that move out-

demographic, education, and health data in section 2.

not rescaled following a change in base year, World

ward from customs storage; and (c) nationalized goods

AIS is HIV/AIDS Indicator Survey, CPS is Current Popu-

Bank staff rescale the data to maintain a longer histori-

that have been declared for domestic consumption and

lation Survey, DGHS is Demographic and General

cal series. To allow for cross-country comparison and

move outward without being transformed. Under the

Health Survey, DHS is Demographic and Health Survey,

data aggregation, constant price data reported in World

special system of trade, exports are categories a and

ENPF is National Family Planning Survey (Encuesta

Development Indicators are rescaled to a common ref-

c. In some compilations categories b and c are classi-

Nacional de Planificacion Familiar), FHS is Family

erence year (2000) and currency (U.S. dollars). • Sys-

fied as re-exports. Direct transit trade—goods entering

Health Survey, LSMS is Living Standards Measurement

tem of National Accounts identifies countries that use

or leaving for transport only—is excluded from both

Survey, MICS is Multiple Indicator Cluster Survey, MIS

the 1993 System of National Accounts (1993 SNA),

import and export statistics. See About the data for

is Malaria Indicator Survey, NSS is National Sample

the terminology applied in World Development Indica-

tables 4.4, 4.5, and 6.2 for further discussion. • Gov-

Survey on Population Change, PAPFAM is Pan Arab

tors since 2001, to compile national accounts.

ernment finance accounting concept is the account-

Project for Family Health, RHS is Reproductive Health

Although more countries are adopting the 1993 SNA,

ing basis for reporting central government financial

Survey, and SPA is Service Provision Assessments.

many still follow the 1968 SNA, and some low-income

data. For most countries government finance data have

Detailed information for AIS, DHS, MIS, and SPA are

countries use concepts from the 1953 SNA. • SNA

been consolidated (C) into one set of accounts captur-

available at www.measuredhs.com/aboutsurveys; for

price valuation shows whether value added in the

ing all central government fiscal activities. Budgetary

MICS at www.childinfo.org; and for RHS at www.cdc.

national accounts is reported at basic prices (VAB) or

central government accounts (B) exclude some central

gov/reproductivehealth/surveys. • Source of most

producer prices (VAP). Producer prices include taxes

government units. See About the data for tables 4.12,

recent income and expenditure data shows household

paid by producers and thus tend to overstate the actual

4.13, and 4.14 for further details. • IMF data dissemi-

surveys that collect income and expenditure data.

value added in production. However, VAB can be higher

nation standard shows the countries that subscribe to

Names and detailed information on household surveys

than VAP in countries with high agricultural subsidies.

the IMF’s Special Data Dissemination Standard (SDDS)

can be found on the website of the International House-

See About the data for tables 4.1 and 4.2 for further

or General Data Dissemination System (GDDS). S

hold Survey Network (www.surveynetwork.org). Core

discussion of national accounts valuation. • Alterna-

refers to countries that subscribe to the SDDS and

Welfare Indicator Questionnaire Surveys (CWIQ), devel-

tive conversion factor identifies the countries and

have posted data on the Dissemination Standards Bul-

oped by the World Bank, measure changes in key social

years for which a World Bank–estimated conversion

letin Board at http://dsbb.imf.org. G refers to countries

indicators for different population groups—specifically

factor has been used in place of the official exchange

that subscribe to the GDDS. The SDDS was estab-

indicators of access, utilization, and satisfaction with

rate (line rf in the International Monetary Fund’s [IMF]

lished for member countries that have or might seek

core social and economic services. Expenditure sur-

International Financial Statistics). See Statistical meth-

access to international capital markets to guide them

vey/budget surveys (ES/BS) collect detailed informa-

ods for further discussion of alternative conversion

in providing their economic and financial data to the

tion on household consumption as well as on general

factors. • Purchasing power parity (PPP) survey year

public. The GDDS helps countries disseminate com-

demographic, social, and economic characteristics.

is the latest available survey year for the International

prehensive, timely, accessible, and reliable economic,

Integrated household surveys (IHS) collect detailed

Comparison Program’s estimates of PPPs. See About

financial, and sociodemographic statistics. IMF mem-

information on a wide variety of topics, including

the data for table 1.1 for a more detailed description

ber countries elect to participate in either the SDDS or

health, education, economic activities, housing, and

of PPPs. • Balance of Payments Manual in use refers

the GDDS. Both standards enhance the availability of

utilities. Income surveys (IS) collect information on the

to the classification system used to compile and report

timely and comprehensive data and therefore contrib-

income and wealth of households as well as various

data on balance of payments items in table 4.17. BPM4

ute to the pursuit of sound macroeconomic policies.

social and economic characteristics. Labor force sur-

refers to the 4th edition of the IMF’s Balance of Pay-

The SDDS is also expected to improve the functioning

veys (LFS) collect information on employment, unem-

ments Manual (1977), and BPM5 to the 5th edition

of financial markets. • Latest population census

ployment, hours of work, income, and wages. Living

(1993). • External debt shows debt reporting status

shows the most recent year in which a census was

Standards Measurement Studies (LSMS), developed

for 2009 data. Actual indicates that data are as

conducted and in which at least preliminary results

by the World Bank, provide a comprehensive picture of

reported, preliminary that data are based on reported

have been released. The preliminary results from the

household welfare and the factors that affect it; they

or collected information but include an element of staff

very recent censuses could be reflected in timely revi-

typically incorporate data collection at the individual,

estimation, and estimate that data are World Bank staff

sions if basic data are available, such as population by

household, and community levels. Priority surveys (PS)

402

2010 World Development Indicators

Primary data documentation notes are a light monitoring survey, designed by the World

Economies with exceptional reporting periods

Bank, for collecting data from a large number of house-

Nations Statistics Division. The new base year is 1990, and the SNA price valuation has been changed

Economy

Fiscal year end

Reporting period for national accounts data

Afghanistan

Mar. 20

Australia

Jun. 30

Bangladesh

Jun. 30

Botswana

Jun. 30

Canada

Mar. 31

Egypt, Arab Rep.

Jun. 30

Jul. 7

Gambia, The

Jun. 30

Haiti

Sep. 30

India

Mar. 31

Indonesia

Mar. 31

Iran, Islamic Rep.

Mar. 20

Japan

Mar. 31

Kenya

Jun. 30

Kuwait

Jun. 30

Lesotho

Mar. 31

Malawi

Mar. 31

Myanmar

Mar. 31

Namibia

Mar. 31

Nepal

Jul. 14

New Zealand

Mar. 31

Pakistan

Jun. 30

Puerto Rico

Jun. 30

Sierra Leone

Jun. 30

Singapore

Mar. 31

withdrawal data show the most recent year for which

South Africa

Mar. 31

upward adjustment to estimates of output, particularly

data on freshwater withdrawals have been compiled

Swaziland

Mar. 31

in mining, services, and manufacturing. The constant

from a variety of sources. See About the data for table

Sweden

Jun. 30

price series were rebased from 1995 to 2004 prices.

3.5 for more information.

Thailand

Sep. 30

GDP in current prices average 14 percent higher than

Uganda

Jun. 30

previous estimates. • South Africa. The base year

Exceptional reporting periods

United States

Sep. 30

has been changed from 2000 to 2005. Data are

In most economies the fiscal year is concurrent with

Zimbabwe

Jun. 30

revised from 2000 onward with official government

holds cost-effectively and quickly. Income tax registers (ITR) provide 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 which report to have at least 90 percent complete registries of vital (birth and death) statistics to the United Nations Statistics Division and reported in Population and Vital Statistics Reports. Countries with complete vital statistics registries may have more accurate and more timely demographic indicators than other countries. • Latest agricultural census shows the most recent year in which an agricultural census was conducted and reported to the Food and Agriculture Organization of the United Nations. • Latest industrial data show the most recent year for which manufacturing value added data at the three-digit level of the International 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 Statistics Division’s Commodity Trade (Comtrade) database are available. • Latest water

Ethiopia

to basic prices. • Fiji. The new base year is 2005. Data are revised from 2005 onward based on official government data. • Ghana. The Ghana Statistical Service revised Ghana’s national accounts series from 1993 to 2006. New GDP data are about 60 percent higher than previously reported and incorporate improved data sources and methodology. • Guinea-Bissau. National accounts data for 2003– 09 are revised. The new data have broader coverage of all sectors of the economy, and the new base year is 2005. GDP in current prices average 89 percent higher than previous estimates. • Guyana. The Bureau of Statistics has introduced a new series of GDP rebased to year 2006. Current price GDP average 63 percent higher than previous estimates. • India. The base year has been changed from 1999 to 2004. Data are revised from 2004 onward with official government data. GDP at current prices average 4 percent higher than previous estimates. • Kazakhstan. National accounts data have been revised by the National Statistical Office. The new base year is 2000. • Kiribati. The base year has been changed from 2005 to 2006. Data are revised from 2000 onward with official government data. • Namibia. The Central Bureau of Statistics has revised national accounts data for 2000–07. An expanded data survey has resulted in a substantial

the calendar year. Exceptions are shown in the table

Revisions to national accounts data

data. • Tonga. Data are revised from 1995 onward

at right. The ending date reported here is for the fis-

National accounts data are revised by national sta-

with official government data. GDP in current prices

cal year of the central government. Fiscal years for

tistical offices when methodologies change or data

average 20 percent higher than previous estimates.

other levels of government and reporting years for

sources improve. National accounts data in World

• Vanuatu. The base year has been changed from

statistical surveys may differ.

Development Indicators are also revised when data

1983 to 2006. Data are revised from 1998 onward

The reporting period for national accounts data

sources change. The following notes, while not com-

with official government data. GDP in current prices

is designated as either calendar year basis (CY) or

prehensive, provide information on revisions from

average 11 percent higher than previous estimates.

fiscal year basis (FY). Most economies report their

previous data. • Bulgaria. The National Statistical

national accounts and balance of payments data

Office has revised national accounts data from 1995

Changes to national currencies

using calendar years, but some use fiscal years. In

onward. GDP in current prices are about 4 percent

• Malta. On January 1, 2008, the euro replaced

World Development Indicators fiscal year data are

higher than previous estimates. • Colombia. The

the Maltese liri as Malta’s currency. • Zimbabwe.

assigned to the calendar year that contains the larger

base year has been changed from 2000 to 2005,

As of January 2009, multiple hard currencies, such

share of the fiscal year. If a country’s fiscal year ends

and data from 2000 onward are new. GDP in cur-

as rand, pound sterling, euro and U.S. dollar are in

before June 30, data are shown in the first year of

rent prices average 2.8 percent higher than previous

use. Data are reported in U.S. dollars, the most-

the fiscal period; if the fiscal year ends on or after

estimates. • Croatia. The Statistical Bureau revised

used currency.

June 30, data are shown in the second year of the

national accounts for 1995–2007. The new base

period. Balance of payments data are reported in

year is 2000. • Cuba. National accounts data for

World Development Indicators by calendar year.

1970–2008 are revised with data from the United

2011 World Development Indicators

403

Statistical methods This section describes some of the statistical procedures used in preparing World

• Aggregates of ratios are denoted by a w when calculated as weighted averages

Development Indicators. It covers the methods employed for calculating regional

of the ratios (using the value of the denominator or, in some cases, another

and income group aggregates and for calculating growth rates, and it describes the

indicator as a weight) and denoted by a u when calculated as unweighted

World Bank Atlas method for deriving the conversion factor used to estimate gross

averages. The aggregate ratios are based on available data, including data

national income (GNI) and GNI per capita in U.S. dollars. Other statistical procedures

for economies not shown in the main tables. Missing values are assumed

and calculations are described in the About the data sections following each table.

to have the same average value as the available data. No aggregate is calculated if missing data account for more than a third of the value of weights

Aggregation rules

in the benchmark year. In a few cases the aggregate ratio may be computed

Aggregates based on the World Bank’s regional and income classifications of

as the ratio of group totals after imputing values for missing data according

economies appear at the end of most tables. The countries included in these

to the above rules for computing totals.

classifications are shown on the flaps on the front and back covers of the book.

• Aggregate growth rates are denoted by a w when calculated as a weighted

Most tables also include the aggregate euro area. This aggregate includes the

average of growth rates. In a few cases growth rates may be computed from

member states of the Economic and Monetary Union (EMU) of the European Union

time series of group totals. Growth rates are not calculated if more than half

that have adopted the euro as their currency: Austria, Belgium, Cyprus, Finland,

the observations in a period are missing. For further discussion of methods

France, Germany, Greece, Ireland, Italy, Luxembourg, Malta, Netherlands, Por-

of computing growth rates see below.

tugal, Slovak Republic, Slovenia, and Spain. Other classifications, such as the

• Aggregates denoted by an m are medians of the values shown in the table.

European Union and regional trade blocs, are documented in About the data for

No value is shown if more than half the observations for countries with a

the tables in which they appear.

population of more than 1 million are missing.

Because of missing data, aggregates for groups of economies should be

Exceptions to the rules occur throughout the book. Depending on the judg-

treated as approximations of unknown totals or average values. Regional and

ment of World Bank analysts, the aggregates may be based on as little as 50

income group aggregates are based on the largest available set of data, includ-

percent of the available data. In other cases, where missing or excluded values

ing values for the 155 economies shown in the main tables, other economies

are judged to be small or irrelevant, aggregates are based only on the data

shown in table 1.6, and Taiwan, China. The aggregation rules are intended to

shown in the tables.

yield estimates for a consistent set of economies from one period to the next and for all indicators. Small differences between sums of subgroup aggregates and

Growth rates

overall totals and averages may occur because of the approximations used. In

Growth rates are calculated as annual averages and represented as percentages.

addition, compilation errors and data reporting practices may cause discrepan-

Except where noted, growth rates of values are computed from constant price

cies in theoretically identical aggregates such as world exports and world imports.

series. Three principal methods are used to calculate growth rates: least squares,

Five methods of aggregation are used in World Development Indicators:

exponential endpoint, and geometric endpoint. Rates of change from one period

• For group and world totals denoted in the tables by a t, missing data are

to the next are calculated as proportional changes from the earlier period.

imputed based on the relationship of the sum of available data to the total in the year of the previous estimate. The imputation process works forward

Least squares growth rate. Least squares growth rates are used wherever

and backward from 2000. Missing values in 2000 are imputed using one of

there is a sufficiently long time series to permit a reliable calculation. No growth

several proxy variables for which complete data are available in that year. The

rate is calculated if more than half the observations in a period are missing.

imputed value is calculated so that it (or its proxy) bears the same relation-

The least squares growth rate, r, is estimated by fitting a linear regression trend

ship to the total of available data. Imputed values are usually not calculated

line to the logarithmic annual values of the variable in the relevant period. The

if missing data account for more than a third of the total in the benchmark

regression equation takes the form

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

ln Xt = a + bt

in agriculture, industry, manufacturing, and services in U.S. dollars. • 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 the series or a proxy for the series are missing in a given year.

404

2011 World Development Indicators

which is the logarithmic transformation of the compound growth equation, Xt = Xo (1 + r ) t.

In this equation X is the variable, t is time, and a = ln Xo and b = ln (1 + r) are

States, and the euro area. A country’s inflation rate is measured by the change

parameters to be estimated. If b* is the least-squares estimate of b, then the

in its GDP deflator.

average annual growth rate, r, is obtained as [exp(b*) – 1] and is multiplied by

The inflation rate for Japan, the United Kingdom, the United States, and the

100 for expression as a percentage. The calculated growth rate is an average

euro area, representing international inflation, is measured by the change in the

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 growth rate over n periods is calculated as r = exp[ln(pn/p 0)/n] – 1.

Yt$ = (Yt/Nt)/et* 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)

Like the exponential growth rate, it does not take into account intermediate

for year t, pt is the GDP deflator for year t, ptS$ is the SDR deflator in U.S. dollar

values of the series.

terms for year t, Yt$ is the Atlas GNI per capita in U.S. dollars in year t, Yt is current GNI (local currency) for year t, and Nt is the midyear population for year t.

World Bank Atlas method In calculating GNI and GNI per capita in U.S. dollars for certain operational

Alternative conversion factors

purposes, the World Bank uses the Atlas conversion factor. The purpose of the

The World Bank systematically assesses the appropriateness of official exchange

Atlas conversion factor is to reduce the impact of exchange rate fluctuations in

rates as conversion factors. An alternative conversion factor is used when the

the cross-country comparison of national incomes.

official exchange rate is judged to diverge by an exceptionally large margin from the

The Atlas conversion factor for any year is the average of a country’s

rate effectively applied to domestic transactions of foreign currencies and traded

exchange rate (or alternative conversion factor) for that year and its exchange

products. This applies to only a small number of countries, as shown in Primary data

rates for the two preceding years, adjusted for the difference between the rate

documentation. Alternative conversion factors are used in the Atlas methodology

of inflation in the country and that in Japan, the United Kingdom, the United

and elsewhere in World Development Indicators as single-year conversion factors.

2011 World Development Indicators

405

Credits 1. World view

resources to the book, for which the team is very grateful. Other contributors were

Section 1 was prepared by a team led by Eric Swanson. Eric Swanson

Brian Blankespoor, Lopamudra Chakraborti, Susmita Dasgupta, Olivier Dupriez,

wrote the introduction with input from Uranbileg Batjargal and Neil Fan-

Kirk Hamilton, Esther Grace Lee, Craig Meisner, Kiran Pandey, Giovanni Ruta,

tom. Bala Bhaskar Naidu Kalimili coordinated tables 1.1 and 1.6. Shota

and Akiko Saesaka.

Hatakeyama, Mehdi Akhlaghi, Buyant Khaltarkhuu, and Masako Hiraga prepared tables 1.2, 1.3, and 1.5. Uranbileg Batjargal prepared table 1.4, with

4. Economy

input from Azita Amjadi. Signe Zeikate of the World Bank’s Economic Policy

Section 4 was prepared by Bala Bhaskar Naidu Kalimili, Mahyar Eshragh-Tabary,

and Debt Department provided the estimates of debt relief for the Heavily

and Soong Sup Lee in close collaboration with the Sustainable Development and

Indebted Poor Countries Debt Initiative and Multilateral Debt Relief Initiative.

Economic Data Team of the World Bank’s Development Data Group. Soong Sup Lee wrote the introduction with valuable suggestions from Eric Swanson and the

2. People

IMF’s Financial Institutions Division, Statistics Department. Contributions to the

Section 2 was prepared by Masako Hiraga and Shota Hatakeyama, in partner-

section were provided by Azita Amjadi, Lopamudra Chakraborti, Kirk Hamilton,

ship with the World Bank’s Human Developmebnt Network and the Development

Barbro Hexeberg, Esther Grace Lee, Giovanni Ruta, and from Justin Thyme Matz

Research Group in the Development Economics Vice Presidency. The introduc-

and Yutong Li of the IMF’s Statistical Information Management Division, Statistics

tion was written by Sulekha Patel and Masako Hiraga, with valuable inputs and

Department. The national accounts data for low- and middle-income economies

comments from Eric Swanson. The poverty estimates at national poverty lines

were gathered by the World Bank’s regional staff through the annual Unified Sur-

were compliled by the Global Poverty Working Group: a team of poverty experts

vey. Maja Bresslauer, Mahyar Eshragh-Tabary, Bala Bhaskar Naidu Kalimili, and

from the Poverty Reduction and Equality Network, the Development Research

Buyant Khaltarkhuu worked on updating, estimating, and validating the databases

Group, and the Development Data Group. The poverty estimates at international

for national accounts. The team is grateful to Eurostat, the International Monetary

poverty lines were prepared by Shaohua Chen and Prem Sangraula of the World

Fund, Organisation for Economic Co-operation and Development, United Nations

Bank’s Development Research Group. The data on children at work were prepared

Industrial Development Organization, and World Trade Organization for access

by Lorenzo Guarcello and Furio Rosati from the Understanding Children’s Work

to their databases.

project. Other contributions were provided by Emi Suzuki (population, health, and nutrition); Montserrat Pallares-Miralles and Carolina Romero Robayo (vulnerability

5. States and markets

and security); Sara Elder of the International Labour Organization (labor force);

Section 5 was prepared by David Cieslikowski and Buyant Khaltarkhuu, in partner-

Amelie Gagnon, Said Ould Voffal, and Weixin Lu of the United Nations Educa-

ship with the World Bank’s Financial and Private Sector Development Network,

tional, Scientific, and Cultural Organization Institute for Statistics (education and

Poverty Reduction and Economic Management Network, Sustainable Develop-

literacy); the World Health Organization’s Chandika Indikadahena (health expen-

ment Network, the International Finance Corporation, and external partners.

diture), Charu Garg (national health account), Monika Bloessner and Mercedes

David Cieslikowski wrote the introduction to the section with input from Eric

de Onis (malnutrition and overweight), Neeru Gupta and Teena Kunjument (health

Swanson. Other contributors include Ada Karina Izaguirre (privatization and

workers), Jessica Ho (hospital beds), Rifat Hossain (water and sanitation), and

infrastructure projects); Leora Klapper and Inessa Love (business registration);

Hazim Timimi (tuberculosis); Delice Gan of the International Diabetes Federation

Federica Saliola and Joshua Wimpey (Enterprise Surveys); Sylvia Solf and Carolin

(diabetes); and Nyein Nyein Lwin of the United Nations Children’s Fund (health).

Geginat (Doing Business); Alka Banerjee and Michael Orzano (Standard & Poor’s

Eric Swanson provided valuable comments and suggestions on the introduction

global stock market indexes); Oya Pinar Ardic Alper (financial access); Satish

and at all stages of production.

Mannan (public policies and institutions); Henry Boyd and James Hackett of the International Institute for Strategic Studies (military personnel); Sam Perlo-

3. Environment

Freeman and Siemon Wezeman of the Stockholm International Peace Research

Section 3 was prepared by Mehdi Akhlaghi in partnership with the World Bank’s

Institute (military expenditures and arms transfers); Kacem Iaych of the Interna-

Sustainable Development Network. The introdcution was prepared by Soong Sup

tional Road Federation, Narjess Teyssier and Zubair Anwar of the International

Lee and Neil Fantom. The guidance of Glenn-Marie Lange is gratefully acknowl-

Civil Aviation Organization, and Hélène Stephan (transport); Jane Degerlund of

edged. Carola Fabi and Edward Gillin of the Food and Agriculture Organization

Containerisation International (ports); Vanessa Grey, Esperanza Magpantay, and

of the United Nations; Ricardo Quercioli and Karen Treanton of the International

Susan Teltscher of the International Telecommunication Union; Georges Boade of

Energy Agency; Laura Battlebury of the World Conservation Monitoring Centre;

the United Nations Educational, Scientific, and Cultural Organization Institute for

and Gerhard Metchies and Armin Wagner of German International Cooperation

Statistics (research and development, researchers, and technicians); and Ryan

(GIZ). The World Bank’s Environment Department devoted substantial staff

Lamb of the World Intellectual Property Organization (patents and trademarks).

406

2011 World Development Indicators

6. Global links

Client services

Section 6 was prepared by Uranbileg Batjargal in partnership with the Finan-

The Development Data Group’s Client Services and Communications Team (Azita

cial Data Team of the World Bank’s Development Data Group, Development

Amjadi, Buyant Erdene Khaltarkhuu, Alison Kwong, Beatriz Prieto-Oramas, Jomo

Research Group (trade), Development Prospects Group (commodity prices and

Tariku, and Vera Wen) contributed to the design and planning and helped coordi-

remittances), International Trade Department (trade facilitation), and external

nate work with the Office of the Publisher.

partners. Uranbileg Batjargal wrote the introduction, with substantial input from Ingo Borchert (Services Policy Restrictiveness Database), Caglar Ozden (bilateral

Administrative assistance, office technology, and systems support

migration matrix), and Evis Rucaj (public sector debt). Eric Swanson provided

Awatif Abuzeid, Elysee Kiti, Premi Ratham Raj and Estela Zamora provided admin-

valuable comments. Substantial input for the data and tables came from Azita

istrative assistance. Jean-Pierre Djomalieu, Gytis Kanchas, and Nacer Megherbi

Amjadi (trade and tariffs) and Yasue Sakuramoto (external debt and financial

provided information technology support. Ramvel Chandrasekaran, Ugendran

data). Other contributors include Frederic Docquier (emigration rates); Flavine

Machakkalai, Atsushi Shimo, and Malarvizhi Veerappan provided systems support

Creppy and Yumiko Mochizuki of the United Nations Conference on Trade and

on the Development Data Platform application.

Development (trade); Betty Dow (commodity prices); Thierry Geiger of the World Economic Forum (trade facilitation); Jeff Reynolds and Joseph Siegel of DHL

Publishing and dissemination

(freight costs); Yasmin Ahmad and Elena Bernaldo of the Organisation for Eco-

The Office of the Publisher, under the direction of Carlos Rossel, provided valu-

nomic Co-operation and Development (aid); Hiroko Maeda and Ibrahim Levent

able assistance throughout the production process. Denise Bergeron, Nazim

(external debt); Henrik Pilgaard of the United Nations Refugee Agency (refugees);

Aziz Gokdemir, Stephen McGroarty, and Nora Ridolfi coordinated printing and

Costanza Giovannelli and Bela Hovy of the United Nations Population Division

supervised marketing and distribution. Merrell Tuck-Primdahl of the Develop-

(migration); Sanket Mohapatra and Ani Rudra Silwal (remittances); and Teresa

ment Economics Vice President’s Office managed the communications strategy.

Ciller of the World Tourism Organization (tourism). Ramgopal Erabelly, Shelley Lai Fu, and William Prince provided valuable technical assistance.

World Development Indicators CD-ROM Software preparation and testing was managed by Vilas Mandlekar with the assis-

Other parts of the book

tance of Ramgopal Erabelly, Buyant Erdene Khaltarkhuu, Parastoo Oloumi, and

Jeff Lecksell of the World Bank’s Map Design Unit coordinated preparation of the

William Prince. Systems development was undertaken by the Data and Informa-

maps on the inside covers. William Prince prepared Users guide. Eric Swanson

tion Systems Team led by Reza Farivari. William Prince coordinated user interface

wrote Statistical methods. Maja Bresslauer, Buyant Khaltarkhuu, and William

design and overall production and provided quality assurance, with assistance

Prince prepared Primary data documentation. Alison Kwong prepared Partners

from Jomo Tariku. Photo credits belong to the World Bank photo library.

and Index of indicators.

Open Data and Online Access Database management

Coordination of the Open Data website (data.worldbank.org/) was provided by

William Prince coordinated management of the World Development Indicators

Neil Fantom and Nicole Frost. Design, programming, and testing were carried

database. Operation of the database management system was made possible

out by Reza Farivari and his team: Azita Amjadi, Ramvel Chandrasekaran, Shelley

by Ramgopal Erabelly, Shelley Fu, and Shahin Outadi in the Data and Information

Fu, Buyant Erdene Khaltarkhuu, Ugendran Machakkalai, Shanmugam Natarajan,

Systems Team under the leadership of Reza Farivari.

Atsushi Shimo, Lakshmikanthan Subramanian, Jomo Tariku, Malarvizhi Veerappan, and Vera Wen. William Prince coordinated production and provided quality

Design, production, and editing

assurance. Support from the Corporate Communications Unit in External Affairs

Azita Amjadi, Alison Kwong, and Jomo Tariku coordinated all stages of production.

was provided by a team including Livia Barton, George Gongadze and Jeffrey

Jomo Tariku prepared the cover. Deborah Arroyo, Jomo Tariku, and Elaine Wilson

Mccoy. The multilingual web team was led by Valerie Hufbauer.

typeset the book. Communications Development Incorporated provided overall design direction and editing, led by Meta de Coquereaumont, Bruce Ross-Larson,

Client feedback

and Christopher Trott. Katrina Van Duyn proofread of the book. Staff from External

The team is grateful to the many people who have taken the time to provide

Affairs Office of the Publisher oversaw printing and dissemination of the book.

feedback and suggestions, which have helped improve this year’s edition. Please contact us at data@worldbank.org.

2011 World Development Indicators

407

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Index of indicators References are to table numbers.

Agriculture

4.1

as share of GDP

4.2

Aid

agricultural raw materials commodity prices

by recipient 6.6

exports as share of total exports

4.4

from high-income economies as share of total exports

6.4

imports

aid dependency ratios

6.16

per capita

6.16

total 6.16 net flows from bilateral sources

6.13

as share of total imports

4.5

from international financial institutions

6.13

by high-income economies as share of total imports

6.4

from multilateral sources

6.13

6.4

from UN agencies

6.13

tariff rates applied by high-income countries cereal

official development assistance by DAC members

area under production

3.2

exports from high-income economies as share of total exports

6.4

imports, by high-income economies as share of total imports

6.4

tariff rates applied by high-income countries

6.4

yield 3.3 employment, as share of total

3.2

fertilizer

administrative costs, as share of net bilateral ODA disbursements 6.15a bilateral aid

6.15a, 6.15b, 6.17

by purpose

6.15a

by sector commitments

6.15b 6.14, 6.15b

debt-related aid, as share of net bilateral ODA disbursements

commodity prices

6.6

consumption, per hectare of arable land

3.2

food

6.15a

development projects, programs, and other resource provisions, asÂ share of net bilateral ODA disbursements

6.15a

disbursements net

commodity prices

6.6

exports 4.4 exports from high-income economies as share of total exports

6.4

imports 4.5

gross disbursements

6.14

humanitarian assistance, as share of net bilateral ODA disbursements 6.15a net disbursements

imports by high-income economies as share of total imports

6.4

as share of general government disbursements

tariff rates applied by high-income countries

6.4

as share of donor GNI

3.5

from major donors, by recipient

6.17

per capita of donor country

6.14

freshwater withdrawals for, as share of total land

6.14 1.4, 6.14

agricultural, as share of land area

3.2

arable, as share of land area

3.1

gross 6.14

arable, per 100 people

3.1

tfor basic social services, as share of sector-allocable

area under cereal production

3.2

Irrigated 3.2 permanent cropland, as share of land area

3.1

machinery tractors per 100 square kilometers of arable land

total

bilateral ODA commitments net disbursements

3.2

6.15a

net disbursements

6.15a

total sector allocable, as share of bilateral ODA commitments

food 3.3

untied aid 6.15b

livestock 3.3

AIDSâ&#x20AC;&#x201D;see HIV, prevalence

2011 World Development Indicators

1.4

humanitarian assistance, as share of bilateral ODA

crop 3.3

value added

6.14, 6.15a

technical cooperation, as share of bilateral ODA

production indexes

418

annual growth

6.15b

Air pollution—see Pollution

Bonds—see Debt flows; Private financial flows

Air transport

Brain drain—see Emigration of people with tertiary education to OECD

air freight

5.10

passengers carried

5.10

registered carrier departures worldwide

5.10

Asylum seekers—see Migration; Refugees

countries Breastfeeding, exclusive

2.20

Broad Money

4.15

Business environment businesses registered

Balance of payments

entry density

current account balance

4.17

as share of GDP

4.a

exports and imports of goods and services

4.17

net current transfers

4.17

net income

4.17

total reserves

4.a, 4.17

See also Exports; Imports; Investment; Private financial flows; Trade

Battle-related deaths

total 5.1 closing a business time to resolve insolvency

5.3

corruption informal payments to public officials

5.2

crime

Base metal commodity prices and price index

5.1

new 5.1

losses due to theft, robbery, vandalism, and arson 6.6 5.8

5.2, 5.8

dealing with construction permits to build a warehouse number of procedures

5.3

time required

5.3

enforcing contracts Beverages commodity prices

6.6

number of procedures

5.3

time required

5.3

finance firms using banks to finance investment

Biodiversity—see Biological diversity

5.2

gender female participation in ownership

Biological diversity assessment, date prepared, by country

3.15

GEF benefits index

3.4

threatened species

3.4

firms formally registered when operations started

5.2, 5.8

infrastructure value lost due to electrical outages

birds

3.4

fish

3.4

innovation

higher plants

3.4

internationally recognized quality certification ownership

mammals

3.4

permits and licenses

treaty 3.15

5.2

informality

time required to obtain operating license

5.2 5.2 5.2

protecting investors Birth rate, crude

2.1

Births attended by skilled health staff

disclosure, index

5.3

registering property