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
11
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
11
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
4
R
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)
4b
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
4c
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
4d
Average difference in GDP (percent) 3
2
1
0
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.
4e
Ghana’s revised GDP was 60 percent higher in the new base year, 2006 GDP ($ billions)
World Development Indicators 2010
World Development Indicators 2011
30
20
10
0
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
Source: World Development Indicators data files.
4f
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
4g
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
average annual % growth 2009 2010a
average annual % growth 2009 2010a
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
average annual % growth 2009 2010a
average annual % growth 2009 2010a
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
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
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
% 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
Hungary India Indonesiaa Iran, Islamic Rep. Iraq Ireland Israela Italy Jamaica Japan Jordan Kazakhstan Kenya Korea, Dem. Rep. Korea, Rep. Kosovo Kuwait a Kyrgyz Republic Lao PDR Latvia Lebanon Lesotho Liberia Libya Lithuania Macedonia, FYR Madagascar Malawi Malaysiaa Mali Mauritania Mauritius Mexico Moldova Mongolia Morocco Mozambique Myanmar a Namibia Nepal Netherlands New Zealand Nicaragua Niger a Nigeria Norway Omana Pakistan Panama Papua New Guinea Paraguaya Peru Philippinesa Poland Portugal Puerto Ricoa Qatar
Industry
% of GDP
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
% 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
% of total 1998 2007
% of total 1998 2007
% of total 1998 2007
% 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 and transport equipment
Chemicals
Other manufacturinga
% of total 1998 2007
% of total 1998 2007
% of total 1998 2007
% of total 1998 2007
% 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 and transport equipment
Chemicals
Other manufacturinga
% of total 1998 2007
% of total 1998 2007
% of total 1998 2007
% of total 1998 2007
% 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
% of total 1995 2009
% of total 1995 2009
% of total 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
Hungary India Indonesia Iran, Islamic Rep. Iraq Ireland Israel Italy Jamaica Japan Jordan Kazakhstan Kenya Korea, Dem. Rep. Korea, Rep. Kosovo Kuwait Kyrgyz Republic Lao PDR Latvia Lebanon Lesotho Liberia Libya Lithuania Macedonia, FYR Madagascar Malawi Malaysia Mali Mauritania Mauritius Mexico Moldova Mongolia Morocco Mozambique Myanmar Namibia Nepal Netherlands New Zealand Nicaragua Niger Nigeria Norway Oman Pakistan Panama Papua New Guinea Paraguay Peru Philippines Poland Portugal Puerto Rico Qatar
4.4
economy
Structure of merchandise exports Merchandise exports
Food
Agricultural raw materials
Fuels
Ores and metals
Manufactures
$ millions 1995 2009
% of total 1995 2009
% of total 1995 2009
% of total 1995 2009
% of total 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 raw materials
Fuels
Ores and metals
Manufactures
$ millions 1995 2009
% of total 1995 2009
% of total 1995 2009
% of total 1995 2009
% of total 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
% of total 1995 2009
% of total 1995 2009
% of total 1995 2009
% 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
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
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 raw materials
Fuels
Ores and metals
Manufactures
% of total 1995 2009
% of total 1995 2009
% of total 1995 2009
% of total 1995 2009
% 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 raw materials
Fuels
Ores and metals
Manufactures
% of total 1995 2009
% of total 1995 2009
% of total 1995 2009
% of total 1995 2009
% 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
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
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
% 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
Hungary India Indonesia Iran, Islamic Rep. Iraq Ireland Israel Italy Jamaica Japan Jordan Kazakhstan Kenya Korea, Dem. Rep. Korea, Rep. Kosovo Kuwait Kyrgyz Republic Lao PDR Latvia Lebanon Lesotho Liberia Libya Lithuania Macedonia, FYR Madagascar Malawi Malaysia Mali Mauritania Mauritius Mexico Moldova Mongolia Morocco Mozambique Myanmar Namibia Nepal Netherlands New Zealand Nicaragua Niger Nigeria Norway Oman Pakistan Panama Papua New Guinea Paraguay Peru Philippines Poland Portugal Puerto Rico Qatar
5,086 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
90
60
30
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
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
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
% 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
Hungary India Indonesia Iran, Islamic Rep. Iraq Ireland Israel Italy Jamaica Japan Jordan Kazakhstan Kenya Korea, Dem. Rep. Korea, Rep. Kosovo Kuwait Kyrgyz Republic Lao PDR Latvia Lebanon Lesotho Liberia Libya Lithuania Macedonia, FYR Madagascar Malawi Malaysia Mali Mauritania Mauritius Mexico Moldova Mongolia Morocco Mozambique Myanmar Namibia Nepal Netherlands New Zealand Nicaragua Niger Nigeria Norway Oman Pakistan Panama Papua New Guinea Paraguay Peru Philippines Poland Portugal Puerto Rico Qatar
3,765 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
% 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 –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
% of GDP 1995 2009
% of GDP 1995 2009
% of GDP 1995 2009
% of GDP 1995 2009
% 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
Hungary India Indonesia Iran, Islamic Rep. Iraq Ireland Israel Italy Jamaica Japan Jordan Kazakhstan Kenya Korea, Dem. Rep. Korea, Rep. Kosovo Kuwait Kyrgyz Republic Lao PDR Latvia Lebanon Lesotho Liberia Libya Lithuania Macedonia, FYR Madagascar Malawi Malaysia Mali Mauritania Mauritius Mexico Moldova Mongolia Morocco Mozambique Myanmar Namibia Nepal Netherlands New Zealand Nicaragua Niger Nigeria Norway Oman Pakistan Panama Papua New Guinea Paraguay Peru Philippines Poland Portugal Puerto Rico Qatar
4.8
economy
Structure of demand Household final consumption expenditure
General government final consumption expenditure
Gross capital formation
Exports of goods and services
Imports of goods and services
Gross savings
% of GDP 1995 2009
% of GDP 1995 2009
% of GDP 1995 2009
% of GDP 1995 2009
% of GDP 1995 2009
% 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
% of GDP 1995 2009
% of GDP 1995 2009
% of GDP 1995 2009
% of GDP 1995 2009
% 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 .. –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
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
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
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
Hungary India Indonesia Iran, Islamic Rep. Iraq Ireland Israel Italy Jamaica Japan Jordan Kazakhstan Kenya Korea, Dem. Rep. Korea, Rep. Kosovo Kuwait Kyrgyz Republic Lao PDR Latvia Lebanon Lesotho Liberia Libya Lithuania Macedonia, FYR Madagascar Malawi Malaysia Mali Mauritania Mauritius Mexico Moldova Mongolia Morocco Mozambique Myanmar Namibia Nepal Netherlands New Zealand Nicaragua Niger Nigeria Norway Oman Pakistan Panama Papua New Guinea Paraguay Peru Philippines Poland Portugal Puerto Rico Qatar
–0.1 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
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
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
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
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
% growth 2000–2009
% 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
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
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
% growth 2000–2009
% 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 .. ..
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
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
% growth 2000–2009
% growth 2000–2009
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
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
% of GNI 2009
% of GNI 2009
% of GNI 2009
% of GNI 2009
% of GNI 2009
% of GNI 2009
% of GNI 2009
% 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
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
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
Hungary India Indonesia Iran, Islamic Rep. Iraq Ireland Israel Italy Jamaica Japan Jordan Kazakhstan Kenya Korea, Dem. Rep. Korea, Rep. Kosovo Kuwait Kyrgyz Republic Lao PDR Latvia Lebanon Lesotho Liberia Libya Lithuania Macedonia, FYR Madagascar Malawi Malaysia Mali Mauritania Mauritius Mexico Moldova Mongolia Morocco Mozambique Myanmar Namibia Nepal Netherlands New Zealand Nicaragua Niger Nigeria Norway Oman Pakistan Panama Papua New Guinea Paraguay Peru Philippines Poland Portugal Puerto Rico Qatar
4.11
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
% of GNI 2009
% of GNI 2009
% of GNI 2009
% of GNI 2009
% of GNI 2009
% of GNI 2009
% of GNI 2009
% 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 .. ..
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 .. ..
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
% of GNI 2009
% of GNI 2009
% of GNI 2009
% of GNI 2009
% of GNI 2009
% of GNI 2009
% 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
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
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
% of GDP 1995 2009
% 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
% 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
2009
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.
80
Data sources Data on central government finances are from the
40
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
0
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
% of expense 1995 2009
% of expense 1995 2009
% of expense 1995 2009
% 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
% of expense 1995 2009
% of expense 1995 2009
% of expense 1995 2009
% 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
% of expense 1995 2009
% of expense 1995 2009
% of expense 1995 2009
% 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
10
Data on central government expenses are from the s
M
au
rit
iu
p.
a
Re
Eg
yp
t,
Ar
ab
a bi
an Gh
m
az il Br
lo Co
ke y Tu r
in es Se yc he lle s
h
di a
pp
Ph
ili
In
es
ka an
la d ng
iL
Ba
& s itt
.K St
Sr
n
Ne vis
n no
is ta Pa k
ba Le
Ja
m
ai
ca
0
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
% of revenue 1995 2009
% of revenue 1995 2009
% of revenue 1995 2009
% 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
% of revenue 1995 2009
% of revenue 1995 2009
% of revenue 1995 2009
% 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
% of revenue 1995 2009
% of revenue 1995 2009
% of revenue 1995 2009
% 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
60
50
40
30
Data sources
20
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
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
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
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
$ millions 1995 2009
$ millions 1995 2009
$ 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
Hungary India Indonesia Iran, Islamic Rep. Iraq Ireland Israel Italy Jamaica Japan Jordan Kazakhstan Kenya Korea, Dem. Rep. Korea, Rep. Kosovo Kuwait Kyrgyz Republic Lao PDR Latvia Lebanon Lesotho Liberia Libya Lithuania Macedonia, FYR Madagascar Malawi Malaysia Mali Mauritania Mauritius Mexico Moldova Mongolia Morocco Mozambique Myanmar Namibia Nepal Netherlands New Zealand Nicaragua Niger Nigeria Norway Oman Pakistan Panama Papua New Guinea Paraguay Peru Philippines Poland Portugal Puerto Rico Qatar
$ millions
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
$ millions 1995 2009
$ millions 1995 2009
$ 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
$ millions 1995 2009
$ millions 1995 2009
$ 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
74
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
N
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.
5
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
5a
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
5b
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
5c
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
5d
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
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
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
2009
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
Hungary India Indonesia Iran, Islamic Rep. Iraq Ireland Israel Italy Jamaica Japan Jordan Kazakhstan Kenya Korea, Dem. Rep. Korea, Rep. Kosovo Kuwait Kyrgyz Republic Lao PDR Latvia Lebanon Lesotho Liberia Libya Lithuania Macedonia, FYR Madagascar Malawi Malaysia Mali Mauritania Mauritius Mexico Moldova Mongolia Morocco Mozambique Myanmar Namibia Nepal Netherlands New Zealand Nicaragua Niger Nigeria Norway Oman Pakistan Panama Papua New Guinea Paraguay Peru Philippines Poland Portugal Puerto Rico Qatar
Energy
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
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
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
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
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
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 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
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 Inter 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
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
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 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
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 Inter 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
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
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
Hungary India Indonesia Iran, Islamic Rep. Iraq Ireland Israel Italy Jamaica Japan Jordan Kazakhstan Kenya Korea, Dem. Rep. Korea, Rep. Kosovo Kuwait Kyrgyz Republic Lao PDR Latvia Lebanon Lesotho Liberia Libya Lithuania Macedonia, FYR Madagascar Malawi Malaysia Mali Mauritania Mauritius Mexico Moldova Mongolia Morocco Mozambique Myanmar Namibia Nepal Netherlands New Zealand Nicaragua Niger Nigeria Norway Oman Pakistan Panama Papua New Guinea Paraguay Peru Philippines Poland Portugal Puerto Rico Qatar
4 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 0–10 (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 0–10 (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
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
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
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
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
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
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
2009
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
2009
2009
2009
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
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
Hungary India Indonesia Iran, Islamic Rep. Iraq Ireland Israel Italy Jamaica Japan Jordan Kazakhstan Kenya Korea, Dem. Rep. Korea, Rep. Kosovo Kuwait Kyrgyz Republic Lao PDR Latvia Lebanon Lesotho Liberia Libya Lithuania Macedonia, FYR Madagascar Malawi Malaysia Mali Mauritania Mauritius Mexico Moldova Mongolia Morocco Mozambique Myanmar Namibia Nepal Netherlands New Zealand Nicaragua Niger Nigeria Norway Oman Pakistan Panama Papua New Guinea Paraguay Peru Philippines Poland Portugal Puerto Rico Qatar
7 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
2009
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
2009
2009
2009
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
2009
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
2009
2009
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
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
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
June 2010
June 2010
June 2010
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
Hungary India Indonesia Iran, Islamic Rep. Iraq Ireland Israel Italy Jamaica Japan Jordan Kazakhstan Kenya Korea, Dem. Rep. Korea, Rep. Kosovo Kuwait Kyrgyz Republic Lao PDR Latvia Lebanon Lesotho Liberia Libya Lithuania Macedonia, FYR Madagascar Malawi Malaysia Mali Mauritania Mauritius Mexico Moldova Mongolia Morocco Mozambique Myanmar Namibia Nepal Netherlands New Zealand Nicaragua Niger Nigeria Norway Oman Pakistan Panama Papua New Guinea Paraguay Peru Philippines Poland Portugal Puerto Rico Qatar
5.6
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
June 2010
June 2010
June 2010
June 2010
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
June 2010
June 2010
June 2010
June 2010
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 Fund’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
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
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
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
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
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
% 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
2009
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)
1–6 (low to high)
Macroeconomic management
Fiscal policy
Debt policy
Average
2009
2009
2009
2009
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
2009
2009
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)
1–6 (low to high)
Macroeconomic management
Fiscal policy
Debt policy
Average
2009
2009
2009
2009
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
2009
2009
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)
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
2009
2009
2009
2009
2009
2009
2009
2009
2009
2009
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
Lao PDR Lesotho Liberia Madagascar Malawi Maldives Mali Mauritania Moldova Mongolia Mozambique Nepal Nicaragua Niger Nigeria Pakistan Papua New Guinea Rwanda Samoa São Tomé and Príncipe Senegal Sierra Leone Solomon Islands Sri Lanka St. Lucia St. Vincent & Grenadines Sudan Tajikistan Tanzania Timor-Leste Togo Tonga Uganda Uzbekistan Vanuatu Vietnam Yemen, Rep. Zambia Zimbabwe
5.9
Policies for social inclusion and equity
Public sector management and institutions
1–6 (low to high)
1–6 (low to high)
Policies and institutions for Social protection environmental and labor sustainability Average
Quality of budgetary and Property financial rights and rule-based management governance
states and markets
Public policies and institutions
Transparency, accountability, and corruption Quality Efficiency in the public of public of revenue sector Average mobilization administration
Gender equality
Equity of public resource use
Building human resources
2009
2009
2009
2009
2009
2009
2009
2009
2009
2009
2009
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
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
302
Total road network km
Paved roads %
Passengers carried million passengerkm
2000–08a
2000–08a
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
2009
2009
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
Hungary India Indonesia Iran, Islamic Rep. Iraq Ireland Israel Italy Jamaica Japan Jordan Kazakhstan Kenya Korea, Dem. Rep. Korea, Rep. Kosovo Kuwait Kyrgyz Republic Lao PDR Latvia Lebanon Lesotho Liberia Libya Lithuania Macedonia, FYR Madagascar Malawi Malaysia Mali Mauritania Mauritius Mexico Moldova Mongolia Morocco Mozambique Myanmar Namibia Nepal Netherlands New Zealand Nicaragua Niger Nigeria Norway Oman Pakistan Panama Papua New Guinea Paraguay Peru Philippines Poland Portugal Puerto Rico Qatar
Total road network km
Paved roads %
Passengers carried million passengerkm
2000–08a
2000–08a
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
2009
2009
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
2000–08a
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
2009
2009
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
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
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
2009
2008
2008
2008
2009
2009
2008
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
Hungary India Indonesia Iran, Islamic Rep. Iraq Ireland Israel Italy Jamaica Japan Jordan Kazakhstan Kenya Korea, Dem. Rep. Korea, Rep. Kosovo Kuwait Kyrgyz Republic Lao PDR Latvia Lebanon Lesotho Liberia Libya Lithuania Macedonia, FYR Madagascar Malawi Malaysia Mali Mauritania Mauritius Mexico Moldova Mongolia Morocco Mozambique Myanmar Namibia Nepal Netherlands New Zealand Nicaragua Niger Nigeria Norway Oman Pakistan Panama Papua New Guinea Paraguay Peru Philippines Poland Portugal Puerto Rico Qatar
3,989 566 591 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
2009
2008
2008
2008
2009
2009
2008
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
2008
2008
2009
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
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
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
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
2008
2009
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
Hungary India Indonesia Iran, Islamic Rep. Iraq Ireland Israel Italy Jamaica Japan Jordan Kazakhstan Kenya Korea, Dem. Rep. Korea, Rep. Kosovo Kuwait Kyrgyz Republic Lao PDR Latvia Lebanon Lesotho Liberia Libya Lithuania Macedonia, FYR Madagascar Malawi Malaysia Mali Mauritania Mauritius Mexico Moldova Mongolia Morocco Mozambique Myanmar Namibia Nepal Netherlands New Zealand Nicaragua Niger Nigeria Norway Oman Pakistan Panama Papua New Guinea Paraguay Peru Philippines Poland Portugal Puerto Rico Qatar
per 1,000 people
%
2000–05b
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
2008
2009
2009
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
2000–05b
2008
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
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 Union’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
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
314
per million people
per million people
2000–08 d
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
2009
2009
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
2009
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
Hungary India Indonesia Iran, Islamic Rep. Iraq Ireland Israel Italy Jamaica Japan Jordan Kazakhstan Kenya Korea, Dem. Rep. Korea, Rep. Kosovo Kuwait Kyrgyz Republic Lao PDR Latvia Lebanon Lesotho Liberia Libya Lithuania Macedonia, FYR Madagascar Malawi Malaysia Mali Mauritania Mauritius Mexico Moldova Mongolia Morocco Mozambique Myanmar Namibia Nepal Netherlands New Zealand Nicaragua Niger Nigeria Norway Oman Pakistan Panama Papua New Guinea Paraguay Peru Philippines Poland Portugal Puerto Rico Qatar
per million people
per million people
2000–08 d
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
2009
2009
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
2009
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
per million people
2000–08 d
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
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
2009
2009
2009
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
T
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.
6
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
6a
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 Co‑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 “International Trade and balance of Payments� 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 workers’ 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 $127 billion (30 percent of total inflows; figure 6e). Large amounts of remittance flows are sent through private and informal channels that are not officially recorded. No comprehensive dataset is available on the bilateral flow of remittances. Bilateral remittance flows estimated through approximation and allocation methods using the proportions of migrant stocks in destination and sending countries or the incomes of destination and sending countries are available at www.worldbank.org/prospects/migrationandremittances (Ratha and Shaw 2007). The data shed light on patterns of remittance flows, but the estimates are sensitive to the assumptions and allocation method chosen.
Bilateral migration stocks Because migration data come mostly from destination countries, the quality of global migration
Discrepancies persist in measures of FDI net flows
6c
Global FDI net flows ($ 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 immigrants’ nationality, while others collect data based on the immigrants’ 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
6d
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
6e
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
6f
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
40
High-income to high-income
20
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
6g
Central government debt (percent of GDP) 80 Brazil Pakistan
60 40
Mauritius
Kenya
Turkey Mexico
20
Honduras Lithuania
0
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
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
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
2009
2009
2009
2009
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
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
Trade
International finance
Financing through international capital markets % of GDP Gross Merchandise Services inflows
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
2009
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
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
2009
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 .. .. ..
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
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
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
2009
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
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
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
average annual % growth
average annual % growth
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
average annual % growth
average annual % growth
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
average annual % growth
average annual % growth
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
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
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
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 Africaa World
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
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 Africaa World
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
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
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
1990 2009
1990 2009
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 d’Ivoire Ethiopia Gabon Gambia, The Ghana Guinea Guinea-Bissau Kenya Liberia Madagascar Malawi Mali Mauritania Mauritius Mozambique Niger Nigeria Rwanda Senegal Sierra Leone Somalia South Africa Sudan Tanzania Togo Uganda Zambia Zimbabwe
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
75
• Imports from developing economies outside region are the sum of merchandise imports by the
50
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
75
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
29
81
103
120
100
110
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 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
% 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 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
% 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
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
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
Hungary India Indonesia Iran, Islamic Rep. Iraq Ireland Israel Italy Jamaica Japan Jordan Kazakhstan Kenya Korea, Dem. Rep. Korea, Rep. Kosovo Kuwait Kyrgyz Republic Lao PDR Latvia Lebanon Lesotho Liberia Libya Lithuania Macedonia, FYR Madagascar Malawi Malaysia Mali Mauritania Mauritius Mexico Moldova Mongolia Morocco Mozambique Myanmar Namibia Nepal Netherlands New Zealand Nicaragua Niger Nigeria Norway Oman Pakistan Panama Papua New Guinea Paraguay Peru Philippines Poland Portugal Puerto Rico Qatar
2.99 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
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
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
$ 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
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
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
$ 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
$ 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
Hungary India Indonesia Iran, Islamic Rep. Iraq Ireland Israel Italy Jamaica Japan Jordan Kazakhstan Kenya Korea, Dem. Rep. Korea, Rep. Kosovo Kuwait Kyrgyz Republic Lao PDR Latvia Lebanon Lesotho Liberia Libya Lithuania Macedonia, FYR Madagascar Malawi Malaysia Mali Mauritania Mauritius Mexico Moldova Mongolia Morocco Mozambique Myanmar Namibia Nepal Netherlands New Zealand Nicaragua Niger Nigeria Norway Oman Pakistan Panama Papua New Guinea Paraguay Peru Philippines Poland Portugal Puerto Rico Qatar
Total 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 2007–09 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,
20
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
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
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
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
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
$ 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
$ millions
From From bilateral multilateral sourcesa,b,c sources 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
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 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
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
$ 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
2009
2009
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
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)
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
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
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
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
27
41
48
48
34
Israela
95
90
111
138
124
Poland
205
297
363
372
375
Slovak Republic
56
55
67
92
75
Slovenia
35
44
54
68
71
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
88
834
and published in its annual statistical report, Geo-
483
513
514
435
411
oping Countries, and in its annual Development
..
74
67
178
40
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-
51
77
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
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
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
Hungary India Indonesia Iran, Islamic Rep. Iraq Ireland Israel Italy Jamaica Japan Jordan Kazakhstan Kenya Korea, Dem. Rep. Korea, Rep. Kosovo Kuwait Kyrgyz Republic Lao PDR Latvia Lebanon Lesotho Liberia Libya Lithuania Macedonia, FYR Madagascar Malawi Malaysia Mali Mauritania Mauritius Mexico Moldova Mongolia Morocco Mozambique Myanmar Namibia Nepal Netherlands New Zealand Nicaragua Niger Nigeria Norway Oman Pakistan Panama Papua New Guinea Paraguay Peru Philippines Poland Portugal Puerto Rico Qatar
Net 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
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
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
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
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
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
.. 9,058 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. Arrivals in hotels and similar establishments. h. Arrivals in hotels only. i. 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
b
a
2007
b
VAP VAB VAB VAB
1991–96
b
2000 a
2003
b b
2006 1985 1995/96 1974
b
a
2000
b b
2000 2000 1985
b
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
G
2005 2005
BPM4 BPM4
Preliminary Actual
S
B
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 b
2002
b
b b
b
2007
b b b b
b
b
b
2000 2000 1995
b
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
b
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
C
G G S G
primary data documentation Latest population census
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
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
c
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
b b
b
2000
1996
b
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
C
S
BPM5
Actual
S G S
B
G
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
b
2000
b
b
b
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
G
C
S S G G S G G S
C C C C B C B
S S G S S S G G
G
C
S
S G
B B B C B C
G S
S G S
2003 b b
a
2000
b
2001 2006
b
2000
b
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
b
b
b
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
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
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
c
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
c
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
c
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
b
2000 1995
b
b
1996
b
b b
b
2000
b
b
2000
b
b
b
2002
b b
2005
b
b
2000
b
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
B
G G
C C
S S G G G S
C B C B C
G S G G
C
S
G S G
C
S
C
G
S G
C
S G
C B C
G G
C
S
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
G
B C
G S
C C
S G
C
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
C
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
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
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
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
a
1986 1990 2000 2000
1987 2002
b b
b b
a
1990 1985 2005 2000 2002 1990 1990 1990 1981/82f 1990 2000
1995 2000
b b
b b
b
1996 b
a
2000
a
2000 2001
2000 2000 a
1988 2000 1978 2000/01 2000
b
b
1990 1998 a
2007
b
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
S
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
G
B C C B C C C
C C B C B B B C C C C
G
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
C
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
G
2003
b
b
2000 1997
b
b
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
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
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
Turks and Caicos Islands Tuvalu Uganda Ukraine United Arab Emirates United Kingdom United States Uruguay Uzbekistan Vanuatu Venezuela, R.B. Vietnam Virgin Islands (U.S.) West Bank and Gaza Yemen, Rep. Zambia Zimbabwe
2001 2002 2002 2001 2010 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
c
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
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
register-based censuses produce similar census
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
FY
Australia
Jun. 30
FY
Bangladesh
Jun. 30
FY
Botswana
Jun. 30
FY
Canada
Mar. 31
CY
Egypt, Arab Rep.
Jun. 30
FY
Jul. 7
FY
Gambia, The
Jun. 30
CY
Haiti
Sep. 30
FY
India
Mar. 31
FY
Indonesia
Mar. 31
CY
Iran, Islamic Rep.
Mar. 20
FY
Japan
Mar. 31
CY
Kenya
Jun. 30
CY
Kuwait
Jun. 30
CY
Lesotho
Mar. 31
CY
Malawi
Mar. 31
CY
Myanmar
Mar. 31
FY
Namibia
Mar. 31
CY
Nepal
Jul. 14
FY
New Zealand
Mar. 31
FY
Pakistan
Jun. 30
FY
Puerto Rico
Jun. 30
FY
Sierra Leone
Jun. 30
CY
Singapore
Mar. 31
CY
withdrawal data show the most recent year for which
South Africa
Mar. 31
CY
upward adjustment to estimates of output, particularly
data on freshwater withdrawals have been compiled
Swaziland
Mar. 31
CY
in mining, services, and manufacturing. The constant
from a variety of sources. See About the data for table
Sweden
Jun. 30
CY
price series were rebased from 1995 to 2004 prices.
3.5 for more information.
Thailand
Sep. 30
CY
GDP in current prices average 14 percent higher than
Uganda
Jun. 30
FY
previous estimates. • South Africa. The base year
Exceptional reporting periods
United States
Sep. 30
CY
has been changed from 2000 to 2005. Data are
In most economies the fiscal year is concurrent with
Zimbabwe
Jun. 30
CY
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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———. 2010. Progress on Sanitation and Drinking Water. Geneva: World Health Organization and United Nations Children’s Fund. ———. 2011. Global Atlas of the Health Workforce. Geneva: World Health Organization and United Nations Children’s Fund. ———. Various years. WHO-UNICEF estimates of national immunization coverage database. Online database. [www.who.int/immunization_monitoring/routine/ immunization_coverage/en/index4.html]. Geneva. WHO (World Health Organization), UNICEF (United Nations Children’s Fund), UNFPA (United Nations Population Fund), and World Bank. 2010. Trends in Maternal Mortality: 1990–2008 Estimates Developed by WHO, UNICEF, UNFPA, and the World Bank. Geneva: World Health Organization. WIPO (World Intellectual Property Organization). 2010. WIPO Patent Report: Statistics on Worldwide Patent Activity. Geneva: World Intellectual Property Organization. World Bank. 1990. World Development Report 1990: Poverty. Washington, D.C.: World Bank. ———. 2000. Trade Blocs. New York: Oxford University Press. ———. 2001. World Development Report 2000/2001: Attacking Poverty. New York: Oxford University Press. ———. 2002. Global Economic Prospects 2002: Making Trade Work for the World’s Poor. Washington, D.C.: World Bank. ———. 2007. Healthy Development: The World Bank Strategy for Health, Nutrition, and Population Results. Washington, D.C.: World Bank. ———. 2008a. “Brazil Country Partnership Strategy 2008–2011.” World Bank, Latin America and the Caribbean Region, Washington, D.C. ———. 2008b. “Improving Trade and Transport for Landlocked Developing Coun-
database. [www.worldbank.org/ida]. Washington, D.C. ———. n.d. PovcalNet oOnline database. [http://iresearch.worldbank.org/PovcalNet]. Washington, D.C. ———. n.d. Private Participation in Infrastructure Database. Online database. [http://ppi.worldbank.org/]. Washington, D.C. ———.n.d. Quarterly Public Sector Debt. Online database. [http://databank.worldbank.org/ddp/home.do?Step=12&id=4&CNO=3009]. Washington, D.C. ———. n.d. World Trade Indicators. Online database. [www.worldbank.org/wti]. Washington, D.C. ———. Various issues. Commodity Market Review. Washington, D.C.: World Bank, Development Prospects Group. ———. Various issues. Food price watch. Washington, D.C.: World Bank, Poverty Reduction and equity Group. ———. Various issues. Commodity Price Data. Washington, D.C.: World Bank, Development Prospects Group. ———. Various issues. Migration and Development Briefs. Washington, D.C.: World Bank, Development Prospects Group. ———. Various years. Global Development Finance: External Debt of Developing Countries. Washington, D.C.: World Bank. ———. Various years. Global Development Finance: Volumes I and II. Washington, D.C.: World Bank. ———. Various years. World Debt Tables. Washington, D.C.: World Bank. ———. Various years. World Development Indicators. Washington, D.C.: World Bank. World Bank and Dartmouth College. n.d. Global Preferential Trade Agreements Database. Online database. [http://wits.worldbank.org/gptad/]. Washington, D.C.
tries: World Bank Contributions to Implementing the Almaty Programme of
World Bank and IFPRI (International Food Policy Research Institute). 2006.
Action: A Report for the Mid-Term Review October 2008.” World Bank, Interna-
Agriculture and Achieving the Millennium Development Goals. Report 32729-GLB.
tional Trade Department, Washington, D.C.
Washington, D.C.: World Bank.
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Bibliography World Bank and IMF (International Monetary Fund). 2011. Global Monitoring Report 2011: Improving the Odds of Achieving the MDGs: Heterogeneity, Gaps, and Challenges. Washington, DC: World Bank. World Economic Forum. 2010. The Global Competitiveness Report 2010–2011. Geneva: World Economic Forum. World Tourism Organization. Various years. Compendium of Tourism Statistics. Madrid: World Tourism Organization. ———. Various years. Yearbook of Tourism Statistics. Vols. 1 and 2. Madrid: World Tourism Organization. WTO (World Trade Organization). n.d. Regional Trade Agreements Gateway. [www. wto.org/english/tratop_e/region_e/region_e.htm]. Geneva. ———. n.d. Regional Trade Agreements Information System. Online database. [http://rtais.wto.org/]. Geneva. ———. Various years. Annual Report. Geneva.
416
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2011 World Development Indicators
417
Index of indicators References are to table numbers.
A
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, as 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
AIDS—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
B
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
See also Fertility rate 2.19
number of procedures
5.3
time to register
5.3
regulation and tax Birthweight, low
2.20
average number of times firms spend meeting with tax officials
5.2
time dealing with officials
5.2 2011 World Development Indicators
419
index of indicators starting a business
fixed capital
cost to start a business
5.3
number of start-up procedures
5.3
time to start a business
5.3
trade
4.10, 4.11
government, general final expenditure annual growth as share of GDP
4.9 4.8
household final expenditure
average time to clear direct exports
5.2
multilateral, as share of public and publicly
workforce firms offering formal training
guaranteed debt service 5.2
average annual growth per capita
C
as share of GDP
6.11 4.9 4.9 4.8
See also Purchasing power parity (PPP)
Carbon dioxide damage, as share of GNI
4.11
Contraceptives condom use, male and female
emissions
2.21
3.8
prevalence rate
1.3, 2.19
per capita
1.3, 3.8
unmet need for
2.19
total
1.6, 3.8
per unit of GDP
intensity 3.8 Children at work by economic activity
2.6
male and female
2.6
study and work
2.6
status in employment total
2.6 2.6, 5.8
work only
2.6
Contract enforcement number of procedures
5.3
time required for
5.3
Corruption, informal payments to public officials
5.2
Country Policy and Institutional Assessment (CPIA)—see Economic management; Social inclusion and equity policies; Public sector management and institutions; Structural policies Credit
Cities—see Urban environment;
getting credit Closing a business—see Business environment Commercial bank and other lending
6.12
See also Debt flows; Private financial flows Commodity prices and price indexes
6.6
Communications—see Internet; Newspapers, daily; Telephones; Television,
depth of credit information index
5.5
strength of legal rights index
5.5
provided by banking sector
5.5
to private sector
5.1
Crime intentional homicide rate
5.8
losses due to
5.2
households with Current account balance of central government employees
4.13 Customs
See also Remittances Computers (personal) per 100 people Consumption distribution—see Income distribution
420
2011 World Development Indicators
4.17
See also Balance of payments
Compensation
5.12
average time to clear
5.2
burden of procedures
6.9
D
E
Economic management (Country Policy and Institutional Assessment)
DAC (Development Assistance Committee)—see Aid Death rate, crude
2.1
See also Mortality rate
6.11
service 6.11
children out of school 2.12 2.15
cohort survival rate to grade 5, male and female
2.13
6.10
to last grade of primary education, male and female
2.13
completion rate, primary 6.10
male and female
2.14, 2.15
poorest and richest wealth quintiles
public and publicly guaranteed IBRD loans and IDA credits
5.9
6.11
long-term private nonguaranteed
5.9
macroeconomic management
poorest and richest wealth quintile
multilateral, as share of public and publicly guaranteed debt total, as share of exports of goods and services and income
5.9
fiscal policy
male and female
debt service
IMF credit, use of
5.9
economic management cluster average
Education
Debt, external as share of GNI
debt policy
6.10
total 6.10
total enrollment ratio girls to boys enrollment in primary and secondary education
present value as share of GNI
6.11
as share of exports of goods and services and income
6.11 6.11
as share of total reserves
6.11
1.2
gross
short-term as share of total debt
2.15 1.2, 2.14
by level
2.12
primary
5.8
secondary
2.4
net
total 6.10 total 6.10
by level
2.12
primary, adjusted
2.12
intake ratio, gross first grade of primary education
2.13
bonds 6.12
grade 1
2.15
commercial banks and other lending
primary participation rate, gross
2.15
Debt flows 6.12
public expenditure on
See also Private financial flows
as share of GDP Deforestation, average annual Demand—see Comsumption; Imports; Exports; Savings Density—see Population, density Dependency ratio—See Population, age dependency ratio
3.4
2.11
as share of GNI
4.11
as share of total government expenditure
2.11
per student, as share of GDP per capita, by level
2.11
pupil–teacher ratio, primary
2.11
repeaters, primary, male and female
2.13
teachers, primary, trained
2.11
transition to secondary school, male and female
2.13
unemployment by level of educational attainment Development assistance—see Aid Disease—see Health risks
years of schooling, average Electricity consumption
Distribution of income or consumption—see Income distribution
2.5 2.15
5.11
production 2011 World Development Indicators
421
index of indicators total
3.10
combustible renewables and waste, as share of total
sources
3.10
fossil fuel consumption, as share of total
3.7
5.11
GDP per unit of energy use
3.8
per capita
3.7
total
3.7
transmission and distribution losses value lost due to outages
5.2
3.7
See also Electricity; Fuels Emissions carbon dioxide average annual growth intensity per capita per unit of GDP total
Enforcing contracts—see Business environment 3.9 3.8
Enrollment—see Education
1.3, 3.8 3.8
Entry regulations for business—see Business environment
1.6, 3.8
methane
Environmental strategy or action plans, year adopted 3.15
agricultural, as share of total
3.9
from energy processes, as share of total
3.9
Equity flows
total 3.9
foreign direct investment
6.12
nitrous oxide
portfolio equity
6.12
agricultural, as share of total
3.9
energy and industry, as share of total
3.9
total
3.9
other greenhouse gases
3.9
Employment
See also Private financial flows European Commission distribution of net aid from
6.17
Exchange rates
children in employment
2.6, 5.8
in agriculture as share of total employment female male
3.2 1.5, 2.3 2.3
in industry, male and female
2.3
in services, male and female
2.3
to population ratio
2.4
vulnerable
official, local currency units to U.S. dollar
4.16
purchasing power parity conversion factor
4.16
ratio of PPP conversion factor to official exchange rate
4.16
real effective
4.16
See also Purchasing power parity (PPP) Export credits private, from DAC members
6.14
1.2, 2.4
See also Labor force; Unemployment
Exports
Endangered species—see Biological diversity; Plants, higher
arms
5.7
documents required for
6.9
goods and services Energy
as share of GDP
commodity price index
6.6
consumption, road sector
3.13
depletion, as share of GNI
4.11
emissions—see Pollution
total
3.8
production
3.7
use
total information and communications technology lead time
alternative and nuclear energy
3.7
average annual growth
3.7
2011 World Development Indicators
4.8 4.a, 4.9 4.17
high-technology share of manufactured exports
imports, net
422
average annual growth
5.13 5.13 5.12 6.9
merchandise annual growth
6.2, 6.3
from high-income countries, by product
6.4
from developing countries, by recipient
6.5
private 6.14
by regional trade blocs
6.7
total 6.14
direction of trade
6.3
See also Aid
structure
4.4
total
4.4
value, average annual growth
6.2
volume, average annual growth
6.2
services
6.14
Financing through international capital markets
6.1
See also Private financial flows Food—see Agriculture, production indexes; Commodity prices and price
structure
4.6
total
4.6
travel
other official flows
4.6, 6.19
indexes Foreign direct investment, net—see Investment; Private financial flows
See also Trade Forest
F
area
Female-headed households Female participation in ownership
2.10 5.2
3.4
deforestation, average annual
3.4
net depletion, as share of GNI
4.11
consumption
adolescent 2.19
road sector
2.1
3.13
exports
desired 2.19 total
3.1
total
Fuels
Fertility rate crude birth rate
as share of total land area
as share of total merchandise exports
2.18, 2.21
4.5
crude petroleum, from high-income economies, Finance, firms using banks to finance investment
as share of total exports
5.2
6.4
from high-income economies, as share of total exports Financial access, stability, and efficiency automated teller machines
6.4
petroleum products, from high-income economies, as share of total exports
5.5
6.4
imports
bank capital to asset ratio
5.5
bank nonperforming loans, ratio to total gross loans
5.5
as share of total imports
commercial bank branches
5.5
crude petroleum, by high-income economies, as share of total
deposit accounts at commercial banks
5.5
loan accounts at commercial banks
5.5
by high-income economies, as share of total imports
point-of-sale terminals
5.5
petroleum products, by high-income economies, as share of total
4.4
imports 6.4 6.4
imports 6.4 prices 3.13
Financial flows, net from DAC members from bilateral sources
6.13
from international financial institutions
6.13
from multilateral sources
6.13
from UN agencies
6.13
total 6.13 official development assistance net grants by NGOs
tariff rates applied by high-income countries
6.14
official
G
GEF benefits index for biodiversity
6.4
3.4
Gender differences
6.14
in children in employment
6.14
in condom use
2.6, 5.8 2.21 2011 World Development Indicators
423
index of indicators in education
1.2, 2.12, 2.13, 2.14
in employment by economic activity
Gross domestic product (GDP)
2.3
annual growth
2.3
contribution of natural resources
unemployment
3.16
implicit deflator—see Prices
total
2.5
youth
2.10
nonagricultural wage employment
1.5
unpaid family workers
1.5
vulnerable employment
2.4
in HIV prevalence
1.1, 1.6, 4.1, 4.10
2.21
in labor force participation
2.2
in life expectancy at birth
1.5
per capita, annual growth
1.1, 1.6
total
4.2, 4.10
Gross enrollment—see Education Gross national income (GNI) adjusted net national income
in literacy
annual growth
4.10
total
4.10
adult 2.14
annual growth
youth 2.14
per capita
in mortality
4.10
PPP dollars
adult 2.22
rank
child 2.22
U.S. dollars
1.1, 1.6 1.1 1.1, 1.6
in ownership of firms
5.2
in parliaments
1.5
PPP dollars
1.1
in smoking
2.21
U.S. dollars
1.1
in survival to age 65
2.22
Gini index
rank
total
2.9
Government, central cash surplus or deficit
4.12
debt
PPP dollars
1.1, 1.6
U.S. dollars
1.1, 1.6, 4.10
H
Health care
as share of GDP
4.12
children sleeping under treated nets
2.18
interest, as share of revenue
4.12
children with acute respiratory infection taken to health provider
2.18
expense
children with diarrhea who received oral rehydration and
as share of GDP
4.12
by economic type
4.13
children with fever receiving antimalarial drugs
4.12
HIV, prevalence
net incurrence of liabilities, as share of GDP, domestic and foreign revenue
continued feeding
2.18 2.18 1.3
hospital beds per 1,000 people
2.16
as share of GDP
4.12
immunization rate, child
2.18
grants and other
4.14
nurses and midwives per 1,000 people
2.16
social contributions
4.14
outpatient visits per capita
2.16
physicians per 1,000 people
2.16
taxes as share of GDP by source, as share of revenue
5.6 4.14
reproductive anemia, prevalence of, pregnant women births attended by skilled health staff
Greenhouse gases—see Emissions
contraceptive prevalence rate
2.20 2.19 1.3, 2.19
fertility rate Gross capital formation
adolescent 2.19
annual growth
4.9
as share of GDP
4.8
424
2011 World Development Indicators
total 2.19 low-birthweight babies
2.20
maternal mortality ratio lifetime risk of maternal death pregnant women receiving prenatal care unmet need for contraception
1.3, 2.19, 5.8
prevalence
2.19
female
1.5, 2.19 2.19
total
tuberculosis incidence treatment success rate
2.21
population ages 15–24, male and female
2.21 1.3, 2.21
prevention 1.3, 2.20
condom use, male and female
2.21
2.18 Homicide rate, intentional
5.8
Health expenditure as share of GDP
2.16
external resources
2.16
out of pocket
2.16
per capita
2.16
durable dwelling units
3.12
public
2.16
home ownership
3.12
household size
3.12
multiunit dwellings
3.12
Hospital beds—see Health care Housing conditions, national and urban
Health information census, year last completed
2.17
overcrowding 3.12
completeness of vital registration
vacancy rate
birth registration
2.17
infant death
2.17
total death
2.17
health survey, year last completed
2.17
national health account number completed
2.17
year last completed
2.17
Health risks
3.12
Hunger, depth
5.8
I Immunization rate, child DPT, share of children ages 12–23 months
2.18
measles, share of children ages 12–23 months
2.18
anemia, prevalence of children under age 5
2.20
pregnant women
2.20
Imports arms 5.7
child malnutrition, prevalence
1.2, 2.20
documents required for
6.9
condom use, male and female
2.21
energy, net, as share of total energy use
3.8
2.21
goods and services
diabetes, prevalence HIV, prevalence
1.3, 2.21
as share of GDP
4.8
low-birthweight babies
2.20
average annual growth
4.9
overweight children, prevalence
2.20
total 4.17
smoking, prevalence, male and female tuberculosis, incidence undernourishment, prevalence
2.21 1.3, 2.21 2.20
information and communications technology goods lead time
6.9
merchandise annual growth
Heavily indebted poor countries (HIPCs)
HIV
5.12
6.3
by high-income countries, by product
6.4 6.5
assistance
1.4
by developing countries, by partner
completion point
1.4
structure 4.5
decision point
1.4
tariffs
Multilateral Debt Relief Initiative (MDRI) assistance
1.4
total 4.5
6.4, 6.8
value, average annual growth
6.2
volume, average annual growth
6.2
2011 World Development Indicators
425
index of indicators services structure 4.7
net financial flows from
6.13
use of IMF credit
6.10
total 4.7 travel
4.7, 6.18
Internet
See also Trade
broadband subscribers fixed broadband access tariff
Income distribution Gini index percentage of
international Internet bandwidth 2.9
secure servers
1.2, 2.9
Industry
5.12 5.12 5.12, 6.1 5.12
users 5.12 Investment
annual growth
4.1
foreign direct, net inflows
as share of GDP
4.2
as share of GDP
employment, male and female
2.3
from DAC members
freshwater withdrawals for output
3.5
6.1 6.14
total 6.12 foreign direct, net outflows as share of GDP
Inflation—see Prices
6.1
infrastructure, private participation in Informal economy, firms formally registered when operations started
energy 5.1
5.2
telecommunications 5.1 Information and communications technology trade
transport 5.1
5.11
water and sanitation Innovation, internationally recognized certification ownership
5.2
Integration, global economic, indicators
6.1
5.1
See also Gross capital formation; Private financial flows Iodized salt, consumption of
2.20
L
Interest payments—see Government, central, debt
Labor force
Interest rates deposit 4.15
annual growth
lending 4.15
armed forces
5.7
real 4.15
children at work
2.6
risk premium on lending
female 2.2
5.5
spread 5.5
nonagricultural part-time
Internally displaced persons
5.8
participation of population ages 15 and older, male and female
2.2
1.5 1.5 2.2
total 2.2 See also Employment; Migration; Unemployment
International Bank for Reconstruction and Development (IBRD) IBRD loans and IDA credits
6.10
net financial flows from
6.13
Land area arable—see Agriculture, land; Land use See also Protected areas; Surface area
International Development Association (IDA) IBRD loans and IDA credits
6.10
net concessional flows from
6.13
Resource Allocation Index International Monetary Fund (IMF)
426
2011 World Development Indicators
5.8, 5.9
Land use arable land, as share of total land
3.1
per 100 people
3.1
area under cereal production
3.2
by type
3.1
clothing 1.4
forest area, as share of total land
3.1
textiles 1.4
irrigated land
3.2
permanent cropland, as share of total land
3.1
total area
3.1
Merchandise exports agricultural raw materials
Life expectancy at birth male and female total
1.5 1.6, 2.22
6.5
cereals
6.4
chemical products
6.4 6.4
food
adult total
6.7
from developing countries, by partner
crude petroleum
Literacy male and female
4.4
from regional trade blocs
2.14 1.6
4.4, 6.4
footwear
6.4
fuels
4.4 6.4
mathematics, PISA mean score
2.14
furniture
youth, male and female
2.14
information and communications technology goods
5.12
information and communications technology services
5.12
Logistics Performance Index
M
Malnutrition, in children under age 5
6.9
1.2, 2.20
Malaria
iron and steel
6.4
machinery and transport equipment
6.4
manufactures
4.4
ores and metals
4.4
ores and nonferrous materials
6.4
petroleum products
6.4
structure
4.4 6.4 4.4
children sleeping under treated bednets
2.18
textiles
children with fever receiving antimalarial drugs
2.18
total
to low-income economies from high-income economies, by product 6.4 Management time dealing with officials
5.2
to middle-income economies from high-income economies, by product
Manufacturing annual growth as share of GDP 4
4.1 .2
6.4
value, average annual growth
6.2
volume, average annual growth
6.2
within regional trade blocs
6.7
imports
value added chemicals
4.3
agricultural raw materials
4.5
food, beverages, and tobacco
4.3
by developing countries, by partner
6.5
machinery and transport equipment
4.3
cereals 6.4
other
4.3
chemicals 6.4
structure
4.3
crude petroleum
textiles and clothing
4.3
food 4.5
total
4.3
footwear 6.4
6.4
to low-income economies by high-income economies, by product 6.4
See also Merchandise
to middle-income economies by high-income economies, by product 6.4 fuels 4.5
Market access to high-income countries goods admitted free of tariffs
1.4
furniture 6.4
support to agriculture
1.4
information and communications technology goods
tariffs on exports from least developed countries agricultural products
1.4
5.12
iron and steel
6.4
machinery and transport equipment
6.4
2011 World Development Indicators
427
index of indicators manufactures 4.5
average tariff imposed by developed countries on exports of
ores and metals
4.5
ores and nonferrous materials
6.4
births attended by skilled health staff
least developed countries
petroleum products
6.4
carbon dioxide emissions per capita
textiles 6.4
children sleeping under treated bednets
total 4.5
contraceptive prevalence rate
value, average annual growth
6.2
employment to population ratio
volume, average annual growth
6.2
enrollment ratio, net, primary
trade
1.4 2.19 1.3, 3.8 2.18 1.3, 2.19 2.4 2.12
female to male enrollments, primary and secondary
1.2
as share of GDP
6.1
fertility rate, adolescent
by developing countries, by partner
6.5
goods admitted free of tariffs from least developed countries
direction 6.3
2.19 1.4
heavily indebted poor countries (HIPCs)
growth 6.3
assistance
1.4
regional trade blocs
completion point
1.4
decision point
1.4
6.7
Multilateral Debt Relief Initiative (MDRI) assistance
Metals and minerals commodity prices and price index
6.6
nominal debt service relief committed
1.4
immunization rate, child DPT 2.18
Methane emissions agricultural as share of total
3.9
industrial as share of total
3.9
total 3.9
measles 2.18 income or consumption, national share of poorest quintile
1.2, 2.9
infant mortality rate
2.22
Internet users per 100 people labor productivity, GDP per person employed
Migration emigration of people with tertiary education to OECD countries
6.1
literacy rate of 15- to 24-year-olds
6.1
malaria
as share of total population
total 6.18 net
2.4 2.14
malnutrition, prevalence
international migrant stock
6.1, 6.18
1.2, 2.20
children under age 5 sleeping under insecticide treated bednets 2.18 children under age 5 with fever who are treated with appropriate antimalarial drugs
See also Refugees; Remittances
maternal mortality ratio
2.18 1.3, 2.19, 5.8
national parliament seats held by women
Military
Mobile cellular subscriptions per 100 people
armed forces personnel as share of labor force
5.7
total 5.7
official development assistance for basic social services as share of total sector allocable
exports 5.7
net disbursements, as share of donor GNI
imports 5.7
untied commitments poverty gap
military expenditure as share of central government expenditure as share of GDP
5.7 5.7, 5.8
pregnant women receiving prenatal care share of cohort reaching last grade of primary education support to agriculture telephone lines, fixed, per 100 people
Millennium Development Goals, indicators for access to improved sanitation facilities access to improved water source
1.3, 2.18, 3.11, 5.8 2.18, 3.5, 5.8
1.4, 6.14 6.15b 2.7, 2.8 1.5, 2.19 2.13 1.4 5.11
tuberculosis case detection rate incidence
2011 World Development Indicators
1.5 5.11
ODAÂ commitments 1.4
arms transfers
428
1.3, 5.12
2.18 1.3, 2.21
treatment success rate under-five mortality rate
2.18
undernourishment, prevalence
2.20
unmet need for contraception
2.19
vulnerable employment women in wage employment in the nonagricultural sector Minerals depletion, as share of GNI
total 3.9
1.2, 2.22, 5.8 Nutrition anemia, prevalence of
1.2, 2.4 1.5 4.11
children ages under 5
2.20
pregnant women
2.20
breastfeeding, exclusive
2.20
iodized salt consumption
2.20
malnutrition, child
1.2, 2.20
overweight children, prevalence
2.20
broad money
4.15
undernourishment, prevalence
2.20
claims central government
4.15
vitamin A supplementation
2.20
other claims on domestic economy
4.15
Monetary indicators
Mortality rate adult, male and female child, male and female children under age 5 crude death rate
2.22 2.22
maternal lifetime risk of maternal death survival to age 65
Official development assistance—see Aid
1.2, 2.22, 5.8 2.1
infant 2.22 life expectancy at birth
O
2.22 1.3, 2.19, 5.8 2.19
Official flows—see Aid; Financial flows, net
P
Passenger cars per 1,000 people
3.13
2.22 Particulate matter
Motor vehicles passenger cars
3.13
per 1,000 people
3.13
per kilometer of road
3.13
road density
3.13
See also Roads; Traffic MUV G-5 index
selected cities
3.14
urban-population-weighted PM10
3.13
Patent applications filed
5.13
Peacebuilding and peacekeeping operations 6.6
N
operation name
5.8
troops, police, and military observers
5.8
Pension average, as share of per capita income
2.10
contributors
Natural resource depletion, as share of GNI
4.10
Net enrollment—see Education Newspapers, daily
5.12
as share of labor force
2.10
as share of working age population
2.10
public expenditure on, as share of GDP
2.10
Permits and licenses, time required to obtain operating license
5.2
Physicians—see Health care
Nitrous oxide emissions agricultural as share of total
3.9
industrial as share of total
3.9
Plants, higher
2011 World Development Indicators
429
index of indicators threatened species
3.4
in selected cities
3.14
in urban agglomerations
3.11
total 3.11
Pollution
See also Migration
carbon dioxide damage, as share of GNI
4.11
emissions
Portfolio—see Equity flows; Private financial flows
average annual growth
3.9
intensity
3.8
per unit of GDP
3.8
container traffic in
5.9
per capita 1.3,
3.8
quality of infrastructure
6.9
total
Ports
1.6, 3.8
local damage 4.11
Poverty
methane emissions
international poverty line
agricultural, as share of total
3.9
local currency
from energy processes, as share of total
3.9
population living below
total nitrogen dioxide, selected cities
3.9 3.14
nitrous oxide emissions
2.8
$1.25 a day
2.8
$2 a day
2.8
national poverty line
agricultural, as share of total
3.9
population living below, national, rural, and urban
2.7
energy and industry, as share of total
3.9
poverty gap, national, rural, and urban
2.7
total
3.9
organic water pollutants, emissions
Power—see Electricity, production
by industry
3.6
per day
3.6
per worker
3.6
particulate matter concentration selected cities
1.5, 2.19
Prices 3.14
total 3.13 sulfur dioxide, selected cities
Prenatal care, pregnant women receiving
3.14
commodity prices and price indexes consumer, annual growth
fuel 3.8 GDP implicit deflator, annual growth
Population age dependency ratio, young and old
2.1
average annual growth
2.1
by age group, as share of total
6.6 4.16 4.16
net barter terms of trade
6.2
wholesale, annual growth
4.16
Primary education—see Education
0–14 2.11 5–64 2.1 65 and older density
2.1 1.1, 1.6
female, as share of total
1.5
rural
debt flows bonds 6.12 commercial bank and other lending
6.12
equity flows
annual growth
3.1
foreign direct investment, net inflows
6.12
as share of total
3.1
portfolio equity
6.12
total
1.1, 1.6, 2.1
urban
430
Private financial flows
financing through international capital markets, as share of GDP from DAC members
as share of total
3.11
average annual growth
3.11
in largest city
3.11
2011 World Development Indicators
See also Investment Productivity
6.1 6.14
agricultural
3.3
labor
2.4
management time dealing with officials
5.2
water
3.5
meeting with tax officials, number of times
5.2
Protected areas
Regulation and tax administration
Relative prices (PPP)—see Purchasing power parity (PPP)
marine as share of total surface area
3.4
Remittances
total 3.4
workers’ remittances and compensation of employees
terrestrial
as share of GDP
6.1
as share of total surface area
3.4
paid 6.18
total
3.4
received 6.18
Protecting investors disclosure index
5.3
Research and development
Public sector management and institutions (Country Policy and Institutional Assessment)
expenditures
5.13
researchers
5.13
technicians
5.13
efficiency of revenue mobilization
5.9
property rights and rule-based governance
5.9
public sector management and institutions cluster average
5.9
quality of budgetary and financial management
5.9
quality of public administration
5.9
goods hauled by
5.10
transparency, accountability, and corruption in the public sector
5.9
passengers carried
5.10
Reserves, gross international—see Balance of payments Roads
Purchasing power parity (PPP) conversion factor gross national income
4.16
paved, as share of total
5.10
sectoral energy consumption
3.13
total network
5.10
1.1, 1.6 Royalty and license fees
R
payments 5.13 receipts 5.13
Railways Rural environment
goods hauled by
5.10
lines, total
5.10
access to improved sanitation facilities
passengers carried
5.10
access to an improved water source
3.11 3.5
population Refugees by country of asylum
5.8, 6.18
by country of origin
5.8, 6.18
internally displaced persons Regional development banks, net financial flows from
5.8 6.13
annual growth
3.1
as share of total
3.1
S S&P/Global Equity Indices
5.4
Regional trade agreements—see Trade blocs, regional Sanitation, access to improved facilities, population with Registering property
total
number of procedures
5.3
time to register
5.3
1.3, 2.18, 5.8
urban and rural
3.11
Savings 2011 World Development Indicators
431
index of indicators adjusted net
4.11
gross, as share of GDP
4.8
gross, as share of GNI
4.11
Stock markets listed domestic companies as share of GDP
5.4
total 5.4
Schooling—see Education Science and technology scientific and technical journal articles
5.4
market capitalization
5.13
market liquidity
5.4
S&P/Global Equity Indices
5.4
turnover ratio
5.4
See also Research and development Steel products, commodity prices and price index
6.6
Secondary education—see Education Structural policies (Country Policy and Institutional Assessment) Services employment, male and female
2.3
exports
5.9
financial sector
5.9
structural policies cluster average
5.9
trade 5.9
computer, information and communications, and other commercial services
business regulating environment
4.6
insurance and financial services
4.6
structure
4.6
total commercial
4.6
transport
4.6
travel
4.6
imports
Sulfur dioxide emissions—see Pollution Surface area
1.1, 1.6
See also Land use Survival to age 65, male and female
2.22
computer, information and communications, and other commercial services
4.7
insurance and financial services
4.7
structure
4.7
total commercial
4.7
transport
4.7
travel
4.7
trade, as share of GDP
6.1
value added
Suspended particulate matter—see Pollution
T
Tariffs all products binding coverage
6.8
simple mean bound rate
6.8
annual growth
4.1
simple mean tariff
6.8
as share of GDP
4.2
weighted mean tariff
6.8
applied rates on imports from low- and middle-income economies Smoking, prevalence, male and female
2.21
Social inclusion and equity policies (Country Policy and Institutional Assessment)
6.4
manufactured products simple mean tariff
6.8
weighted mean tariff
6.8
building human resources
5.9
on exports of least developed countries
equity of public resource use
5.9
primary products
gender equity
5.9
simple mean tariff
6.8
policy and institutions for environmental sustainability
5.9
weighted mean tariff
6.8
social inclusion and equity cluster average
5.9
share of tariff lines with international peaks
6.8
social protection and labor
5.9
share of tariff lines with specific rates
6.9
Starting a business—see Business environment Taxes and tax policies
432
2011 World Development Indicators
1.4
business taxes
tourists
average number of times firms meet with tax officials
5.2
inbound
6.19
labor tax, as share of commercial profits
5.6
outbound
6.19
number of payments
5.6
other taxes, as share of commercial profits
5.6
profit tax, as share of commercial profits
5.6
arms 5.7
time to prepare, file, and pay
5.6
barriers 6.8
total tax rate, as share of commercial profits
5.6
facilitation
Trade
goods and services taxes, domestic
4.14
burden of customs procedures
income, profit, and capital gains taxes
4.14
documents
international trade taxes
4.14
to export
6.9
other taxes
4.14
to import
6.9
social contributions
4.14
tax revenue collected by central government, as share of GDP
5.6
Technology—see Computers; Exports, high-technology; Internet; Research and development; Science and technology
per 100 people
5.11
residential tariff
5.11 5.11, 6.1
mobile cellular per 100 people
to export
6.9
to import
6.9
liner shipping connectivity index
6.9
logistics performance index
6.9
information and communications technology
fixed line
1.3, 5.11
6.9
lead time
quality of port infrastructure
Telephones
international voice traffic
freight costs to the United States
6.9
6.9 5.12
merchandise as share of GDP
6.1
direction of, by developing countries
6.5
direction of, by region
6.3
high-income economy with low- and middle-income economies,
population covered
5.11
by product
6.4
prepaid tariff
5.11
nominal growth, by region
6.3
mobile cellular and fixed-line subscribers per employee
5.11
regional trading blocs
total revenue
5.11
structure
4.4, 4.5
total
4.4, 4.5
Television, households with
5.12
6.7
services as share of GDP
Terms of trade index, net barter
6.2
6.1
structure
4.6, 4.7
total
4.6, 4.7
See also Balance of payments; Exports; Imports; Manufacturing;
Tertiary education—see Education
Merchandise; Terms of trade; Trade blocs Threatened species—see Animal species; Biological diversity; Plants, higher Trade blocs, regional exports within bloc
6.7
tourism expenditure
total exports, by bloc
6.7
inbound
type of agreement
6.7
Tourism, international
as share of exports
6.19
year of creation
6.7
total
6.19
year of entry into force of the most recent agreement
6.7
outbound as share of imports
6.19
total
6.19
Trademark applications filed
5.13
Trade policies—see Tariffs 2011 World Development Indicators
433
index of indicators Traffic—see Fuels; Motor vehicles; Roads
sulfur dioxide
3.14
housing conditions Transport—see Air transport; Ports; Railways; Roads Travel—see Tourism, international Treaties, participation in biological diversity
3.15
durable dwelling units
3.12
home ownership
3.12
household size
3.12
multiunit dwellings
3.12
overcrowding
3.12
vacancy rate
3.12
population
CFC control
3.15
climate change
3.15
as share of total
3.11
Convention on International Trade on Endangered Species (CITES)
3.15
average annual growth
3.11
Convention to Combat Desertification (CCD)
3.15
in largest city
3.11
Kyoto Protocol
3.15
in selected cities
3.14
Law of the Sea
3.15
in urban agglomerations
3.11
Ozone layer
3.15
total
3.11
Stockholm Convention on Persistent Organic Pollutants
3.15
Tuberculosis case detection rate incidence treatment rate
U
2.18 1.3, 2.21 2.18
See also Pollution; Population; Sanitation; Water
V
Value added as share of GDP in agriculture
4.2
in industry
4.2
in manufacturing
UN agencies, net official financial flows from
in services
6.13
4.2 4.1, 4.2
per worker Undernourishment, prevalence of Unemployment incidence of long-term, total, male, and female
2.5
by level of educational attainment, primary, secondary, tertiary
2.5
total, male, and female
2.5
youth, male, and female
in agriculture
2.20
1.3, 2.10
W
Water access to improved source of, population with total urban and rural
UNICEF, net official financial flows from
6.13
3.3
2.18, 5.8 3.5
freshwater annual withdrawals
UNTA, net official financial flows from
6.13
UNRWA net official financial flows from
6.13
access to an improved water source
3.11 3.5
nitrogen dioxide
3.14
particulate matter
3.14
2011 World Development Indicators
3.5
for domestic use
3.5
for industry
3.5
total
3.5
flows
3.5
per capita
3.5
pollution—see Pollution, organic water pollutants
emissions, selected cities
434
3.5
for agriculture
internal renewable resources
Urban environment access to improved sanitation facilities
as share of internal resources
productivity
3.5
Women in development female-headed households
2.10
female population, as share of total
1.5
life expectancy at birth pregnant women receiving prenatal care
1.5 1.5, 2.19
teenage mothers
1.5
unpaid family workers
1.5
vulnerable employment
2.4
women in nonagricultural sector
1.5
women in parliaments
1.5
Workforce, firms offering formal training World Bank, net financial flows from
5.2 6.13
See also International Bank for Reconstruction and Development; International Development Association
2011 World Development Indicators
435
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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
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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
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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
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The world by region
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South Asia Afghanistan Bangladesh Bhutan India Maldives Nepal Pakistan Sri Lanka Sub-Saharan Africa Angola Benin Botswana Burkina Faso Burundi Cameroon
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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
11
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
11
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.