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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

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.


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.


Designed and edited by Communications Development Incorporated, Washington, D.C., with Peter Grundy Art & Design, London


2011

WORLD DEVELOPMENT INDICATORS


Copyright 2011 by the International Bank for Reconstruction and Development/the world bank 1818 H Street NW, Washington, D.C. 20433 USA All rights reserved Manufactured in the United States of America First printing April 2011 This volume is a product of the staff of the Development Data Group of the World Bank’s Development Economics Vice Presidency, and the judgments herein do not necessarily reflect the views of the World Bank’s Board of Executive Directors or the countries they represent. The World Bank does not guarantee the accuracy of the data included in this publication and accepts no responsibility whatsoever for any consequence of their use. The boundaries, colors, denominations, and other information shown on any map in this volume do not imply on the part of the World Bank any judgment on the legal status of any territory or the endorsement or acceptance of such boundaries. This publication uses the Robinson projection for maps, which represents both area and shape reasonably well for most of the earth’s surface. Nevertheless, some distortions of area, shape, distance, and direction remain. The material in this publication is copyrighted. Requests for permission to reproduce portions of it should be sent to the Office of the Publisher at the address in the copyright notice above. The World Bank encourages dissemination of its work and will normally give permission promptly and, when reproduction is for noncommercial purposes, without asking a fee. Permission to photocopy portions for classroom use is granted through the Copyright Center, Inc., Suite 910, 222 Rosewood Drive, Danvers, MA 01923 USA. Photo credits: Front cover, Curt Carnemark/World Bank; page xxiv, Curt Carnemark/World Bank; page 30, Trevor Samson/World Bank; page 122, Curt Carnemark/World Bank; page 188, Curt Carnemark/World Bank; page 262, Ray Witlin/World Bank; page 318, Curt Carnemark/World Bank. If you have questions or comments about this product, please contact: Development Data Group The World Bank 1818 H Street NW, Room MC2-812, Washington, D.C. 20433 USA Hotline: 800 590 1906 or 202 473 7824; fax 202 522 1498 Email: data@worldbank.org Web site: www.worldbank.org or data.worldbank.org ISBN 978-0-8213-8709-2 ECO-AUDIT Environmental Benefits Statement The World Bank is committed to preserving endangered forests and natural resources. The Office of the Publisher has chosen to print World Development Indicators 2011 on recycled paper with 50 percent post-consumer fiber in accordance with the recommended standards for paper usage set by the Green Press Initiative, a nonprofit program supporting publishers in using fiber that is not sourced from endangered forests. For more information, visit www. greenpressinitiative.org. Saved: 91 trees 29 million Btu of total energy 8,609 pounds of net greenhouse gases 41,465 gallons of waste water 2,518 pounds of solid waste


2011

WORLD DEVELOPMENT INDICATORS


Preface World Development Indicators 2011, the 15th edition in its current format, aims to provide relevant, high-quality, internationally comparable statistics about development and the quality of people’s lives around the globe. This latest printed volume is one of a group of products; others include an online dataset, accessible at http://data.worldbank. org; the popular Little Data Book series; and DataFinder, a data query and charting application for mobile devices. Fifteen years ago, World Development Indicators was overhauled and redesigned, organizing the data to present an integrated view of development, with the goal of putting these data in the hands of policymakers, development specialists, students, and the public in a way that makes the data easy to use. Although there have been small changes, the format has stood the test of time, and this edition employs the same sections as the first one: world view, people, environment, economy, states and markets, and global links. Technical innovation and the rise of connected computing devices have gradually changed the way users obtain and consume the data in the World Development Indicators database. Last year saw a more abrupt change: the decision in April 2010 to make the dataset freely available resulted in a large, immediate increase in the use of the on-line resources. Perhaps more important has been the shift in how the data are used. Software developers are now free to use the data in applications they develop—and they are doing just that. We applaud and encourage all efforts to use the World Bank’s databases in creative ways to solve the world’s most pressing development challenges. This edition of World Development Indicators focuses on the impact of the decision to make data freely available under an open license and with better online tools. To help those who wish to use and reuse the data in these new ways, the section introductions discuss key issues in measuring the economic and social phenomena described in the tables and charts and introduce new sources of data. World Development Indicators is possible only through the excellent collaboration of many partners who provide the data that form part of this collection, and we thank them all: the United Nations family, the International Monetary Fund, the World Trade Organization, the Organisation for Economic Co-operation and Development, the statistical offices of more than 200 economies, and countless others who make this unique product possible. As always, we welcome your ideas for making the data in World Development Indicators useful and relevant for improving the lives of people around the world.

Shaida Badiee Director Development Economics Data Group

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Acknowledgments This book was prepared by a team led by Soong Sup Lee under the management of Neil Fantom and comprising Awatif Abuzeid, Mehdi Akhlaghi, Azita Amjadi, Uranbileg Batjargal, Maja Bresslauer, David Cieslikowski, Mahyar EshraghTabary, Shota Hatakeyama, Masako Hiraga, Bala Bhaskar Naidu Kalimili, Buyant Khaltarkhuu, Elysee Kiti, Alison Kwong, Ibrahim Levent, Johan Mistiaen, Sulekha Patel, William Prince, Premi Rathan Raj, Evis Rucaj, Eric Swanson, Jomo Tariku, and Estela Zamora, working closely with other teams in the Development Economics Vice Presidency’s Development Data Group. World Development Indicators electronic products were prepared by a team led by Reza Farivari, consisting of Ramvel Chandrasekaran, Ying Chi, Jean‑Pierre Djomalieu, Ramgopal Erabelly, Shelley Fu, Gytis Kanchas, Ugendran Makhachkala, Vilas Mandlekar, Nacer Megherbi, Parastoo Oloumi, Malarvizhi Veerappan, and Vera Wen. The work was carried out under the direction of Shaida Badiee. Valuable advice was provided by Shahrokh Fardoust. The choice of indicators and text content was shaped through close consultation with and substantial contributions from staff in the World Bank’s four thematic networks—Sustainable Development, Human Development, Poverty Reduction and Economic Management, and Financial and Private Sector Development—and staff of the International Finance Corporation and the Multilateral Investment Guarantee Agency. Most important, the team received substantial help, guidance, and data from external partners. For individual acknowledgments of contributions to the book’s content, please see Credits. For a listing of our key partners, see Partners. Communications Development Incorporated (CDI) provided editorial services, led by Meta de Coquereaumont, Bruce Ross-Larson, and Christopher Trott. Jomo Tariku designed the cover, Deborah Arroyo and Elaine Wilson typeset the book, and Katrina Van Duyn provided proofreading. Azita Amjadi and Alison Kwong oversaw the production process. Staff from External Affairs Office of the Publisher oversaw printing and dissemination of the book.

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table of contents front

2. people

Preface v Acknowledgments vii Partners xii Users guide xxii

1. world view Introduction 1

Tables Size of the economy 10 Millennium Development Goals: eradicating poverty and saving lives 14 Millennium Development Goals: protecting our common 1.3 environment 18 Millennium Development Goals: overcoming obstacles 22 1.4 Women in development 24 1.5 Key indicators for other economies 28 1.6

1.1 1.2

1a 1b 1c 1d 1e 1f 1g 1h 1i 1j 1k 1l 1.2a 1.3a 1.4a

Text figures, tables, and boxes Use of World Bank data has risen with the launch of the Open Data Initiative Terms of use for World Bank data Access to information at the World Bank Progress toward eradicating poverty Progress toward universal primary education completion Progress toward gender parity Progress toward reducing child mortality Progress toward improving maternal health HIV incidence is remaining stable or decreasing in many developing countries, but many lack data Progress on access to an improved water source Progress on access to improved sanitation Official development assistance provided by Development Assistance Committee members Location of indicators for Millennium Development Goals 1–4 Location of indicators for Millennium Development Goals 5–7 Location of indicators for Millennium Development Goal 8

1 2 3 4 4 4 5 5 5 6 6 7 17 21 23

Tables Population dynamics 36 Labor force structure 40 Employment by economic activity 44 Decent work and productive employment 48 Unemployment 52 Children at work 56 Poverty rates at national poverty lines 60 Poverty rates at international poverty lines 63 Distribution of income or consumption 68 Assessing vulnerability and security 72 Education inputs 76 Participation in education 80 Education efficiency 84 Education completion and outcomes 88 Education gaps by income and gender 92 Health systems 94 Health information 98 Disease prevention coverage and quality 102 Reproductive health 106 Nutrition 110 Health risk factors and future challenges 114 Mortality 118

2.1 2.2 2.3 2.4 2.5 2.6 2.7 2.8 2.9 2.10 2.11 2.12 2.13 2.14 2.15 2.16 2.17 2.18 2.19 2.20 2.21 2.22

2a 2b 2c 2d 2e 2f 2g 2h 2i 2.6a 2.8a 2.8b 2.8c 2.13a 2.17a

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2011 World Development Indicators

Introduction 31

Text figures, tables, and boxes Maternal mortality ratios have declined in all developing country regions since 1990 31 Maternal mortality ratios have declined fastest among low- and lower middle-income countries but remain high 31 The births of many children in Asia and Africa go unregistered 32 In Nigeria, children’s births are more likely to be unregistered in rural areas . . . 33 . . . in poor households . . . 33 . . . and where the mother has a lower education level 33 Most people live in countries with low-quality cause of death statistics34 More countries used surveys for mortality statistics, but civil registration did not expand 34 Estimates of infant mortality in the Philippines differ by source 35 The largest sector for child labor remains agriculture, and the majority of children work as unpaid family members 59 While the number of people living on less than $1.25 a day has fallen, the number living on $1.25–$2.00 a day has increased 65 Poverty rates have begun to fall 65 Regional poverty estimates 66 There are more overage children among the poor in primary school in Zambia 87 South Asia has the highest number of unregistered births 101


3. environment

Introduction 123

3.4a

Tables Rural population and land use 126 Agricultural inputs 130 Agricultural output and productivity 134 Deforestation and biodiversity 138 Freshwater 142 Water pollution 146 Energy production and use 150 Energy dependency and efficiency and carbon dioxide emissions 154 Trends in greenhouse gas emissions 158 Sources of electricity 162 Urbanization 166 Urban housing conditions 170 Traffic and congestion 174 Air pollution 178 Government commitment 180 Contribution of natural resources to gross domestic product 184

3.5a

3.1 3.2 3.3 3.4 3.5 3.6 3.7 3.8 3.9 3.10 3.11 3.12 3.13 3.14 3.15 3.16

3a 3b 3.1a 3.2a 3.2b 3.3a 3.3b

Text figures, tables, and boxes The 10 countries with the highest natural resource rents are primarily oil and gas producers  Countries with negative adjusted net savings are depleting natural capital without replacing it and are becoming poorer What is rural? Urban? Nearly 40 percent of land globally is devoted to agriculture Rainfed agriculture plays a significant role in Sub-Saharan agriculture where about 95 percent of cropland depends on precipitation, 2008 The food production index has increased steadily since early 1960, and the index for low-income economies has been higher than the world average since early 2000 Cereal yield in Sub-Saharan Africa increased between 1990 and 2009 but still is the lowest among the regions

124 124 129 133

3.5b 3.6a 3.7a 3.7b 3.8a 3.9a 3.9b 3.10a 3.10b 3.11a 3.11b 3.12a 3.13a

133

3.13b 3.16a

137

3.16b

At least 33 percent of assessed species are estimated to be threatened 141 Agriculture is still the largest user of water, accounting for some 70 percent of global withdrawals . . . 145 . . . and approaching 90 percent in some developing regions 145 Emissions of organic water pollutants vary among countries from 1990 to 2007 149 A person in a high-income economy uses more than 14 times as much energy on average as a person in a low-income economy in 2008 153 Fossil fuels are still the primary global energy source in 2008 153 High-income economies depend on imported energy 157 The six largest contributors to methane emissions account for about 50 percent of emissions 161 The five largest contributors to nitrous oxide emissions account for about 50 percent of emissions 161 More than 50 percent of electricity in Latin America is produced by hydropower 165 Lower middle-income countries produce the majority of their power from coal 165 Urban population is increasing in developing economies, especially in low and lower middle-income economies 169 Latin America and Caribbean has the greatest share of urban population, even greater than the high-income economies in 2009 169 Selected housing indicators for smaller economies 173 Biogasoline consumption as a share of total consumption is highest in Brazil . . . 177 . . . but the United States consumes the most biogasoline 177 Oil dominates the contribution of natural resources in the Middle East and North Africa 187 Upper middle-income countries have the highest contribution of natural resources to GDP 187

137

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table of contents 4. economy

Tables Recent economic performance Growth of output Structure of output Structure of manufacturing Structure of merchandise exports Structure of merchandise imports Structure of service exports Structure of service imports Structure of demand Growth of consumption and investment Toward a broader measure of national income Toward a broader measure of saving Central government finances Central government expenses Central government revenues Monetary indicators Exchange rates and prices Balance of payments current account

4.a 4.1 4.2 4.3 4.4 4.5 4.6 4.7 4.8 4.9 4.10 4.11 4.12 4.13 4.14 4.15 4.16 4.17

4a 4b 4c 4d 4e 4f 4g 4.3a 4.4a 4.5a 4.6a 4.7a 4.9a 4.10a 4.12a 4.13a 4.14a 4.17a

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5. states and markets

Introduction 189 192 194 198 202 206 210 214 218 222 226 230 234 238 242 246 250 254 258

Text figures, tables, and boxes Differences in GDP growth among developing country regions 189 Developing countries are contributing more to global growth 189 Economies—both developing and high income—rebounded in 2010 190 Revisions to GDP decline over time, and GDP data become more stable on average 190 Ghana’s revised GDP was 60 percent higher in the new base year, 2006190 Revised data for Ghana show a larger share of services in GDP 190 Commission on the Measurement of Economic and Social Progress191 Manufacturing continues to show strong growth in East Asia and Pacific through 2009 205 Developing economies’ share of world merchandise exports continues to expand 209 Top 10 developing economy exporters of merchandise goods in 2009 213 Top 10 developing economy exporters of commercial services in 2009 217 The mix of commercial service imports by developing economies is changing 221 GDP per capita is still lagging in some regions 229 GDP and adjusted net national income in Sub-Saharan Africa, 2000–09 233 Twenty selected economies had a central government debt to GDP ratio of 65 percent or higher 241 Interest payments are a large part of government expenses for some developing economies 245 Rich economies rely more on direct taxes 249 Top 15 economies with the largest reserves in 2009 261

2011 World Development Indicators

Introduction 263

Tables Private sector in the economy Business environment: Enterprise Surveys Business environment: Doing Business indicators Stock markets Financial access, stability, and efficiency Tax policies Military expenditures and arms transfers Fragile situations Public policies and institutions Transport services Power and communications The information age Science and technology

5.1 5.2 5.3 5.4 5.5 5.6 5.7 5.8 5.9 5.10 5.11 5.12 5.13

5a 5b 5c 5d

Text figures, tables, and boxes The average business in Latin America and the Caribbean spends about 400 hours a year in preparing, filing, and paying business taxes, 2009 Firms in East Asia and the Pacific have the lowest business tax rate, 2010 Two approaches to collecting business environment data: Doing Business and Enterprise Surveys People living in developing countries of East Asia and Pacific have more commercial bank accounts than those in other developing country regions, 2009

266 270 274 278 282 286 290 294 298 302 306 310 314

264 264 265

265


6. global links

Introduction 319

Tables Integration with the global economy 324 Growth of merchandise trade 328 Direction and growth of merchandise trade 332 High-income economy trade with low- and middle-income economies 335 Direction of trade of developing economies 338 6.5 Primary commodity prices 341 6.6 Regional trade blocs 344 6.7 Tariff barriers 348 6.8 Trade facilitation 352 6.9 External debt 356 6.10 Ratios for external debt 360 6.11 Global private financial flows 364 6.12 Net official financial flows 368 6.13 Financial flows from Development Assistance Committee 6.14 members 372 Allocation of bilateral aid from Development Assistance 6.15 Committee members 374 Aid dependency 376 6.16 Distribution of net aid by Development Assistance 6.17 Committee members 380 Movement of people across borders 384 6.18 Travel and tourism 388 6.19

6.1 6.2 6.3 6.4

6a 6b 6c 6d 6e 6f 6g 6.3a 6.4a 6.5a 6.6a 6.7a 6.11a 6.16a 6.17a

Text figures, tables, and boxes Source of data for bilateral trade flows 320 Trade in professional services faces the highest barriers 320 Discrepancies persist in measures of FDI net flows 321 Source of data on FDI 322 At least 30 percent of remittance inflows go unrecorded by the sending economies 323 Migrants originating from low- and middle-income economies and residing in high-income economies rose fivefold over 1960–2000323 The ratio of central government debt to GDP has increased for most economies, 2007–10 323 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 334 Low-income economies have a small market share in the global market of various commodities 337 Developing economies are trading more with other developing economies 340 Primary commodity prices soared again in 2010 343 Global Preferential Trade Agreements Database 347 Ratio of debt services to exports for middle-income economies have sharply increased in 2009 as export revenues declined 363 Official development assistance from non-DAC donors, 2005–09 379 Beyond the DAC: The role of other providers of development assistance 383

back

Primary data documentation 393 Statistical methods 404 Credits 406 Bibliography 408 Index of indicators 418

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Partners Defining, gathering, and disseminating international statistics is a collective effort of many people and organizations. The indicators presented in World Development Indicators are the fruit of decades of work at many levels, from the field workers who administer censuses and household surveys to the committees and working parties of the national and international statistical agencies that develop the nomenclature, classifications, and standards fundamental to an international statistical system. Nongovernmental organizations and the private sector have also made important contributions, both in gathering primary data and in organizing and publishing their results. And academic researchers have played a crucial role in developing statistical methods and carrying on a continuing dialogue about the quality and interpretation of statistical indicators. All these contributors have a strong belief that available, accurate data will improve the quality of public and private decisionmaking. The organizations listed here have made World Development Indicators possible by sharing their data and their expertise with us. More important, their collaboration contributes to the World Bank’s efforts, and to those of many others, to improve the quality of life of the world’s people. We acknowledge our debt and gratitude to all who have helped to build a base of comprehensive, quantitative information about the world and its people. For easy reference, Web addresses are included for each listed organization. The addresses shown were active on March 1, 2011. Information about the World Bank is also provided.

International and government agencies Carbon Dioxide Information Analysis Center The Carbon Dioxide Information Analysis Center (CDIAC) is the primary global climate change data and information analysis center of the U.S. Department of Energy. The CDIAC’s scope includes anything that would potentially be of value to those concerned with the greenhouse effect and global climate change, including concentrations of carbon dioxide and other radiatively active gases in the atmosphere, the role of the terrestrial biosphere and the oceans in the biogeochemical cycles of greenhouse gases, emissions of carbon dioxide to the atmosphere, long-term climate trends, the effects of elevated carbon dioxide on vegetation, and the vulnerability of coastal areas to rising sea levels. For more information, see http://cdiac.esd.ornl.gov/.

Deutsche Gesellschaft für Internationale Zusammenarbeit The Deutsche Gesellschaft für Internationale Zusammenarbeit (GIZ) GmbH is a German government-owned corporation for international cooperation with worldwide operations. GIZ’s aim is to positively shape political, economic, ecological, and social development in partner countries, thereby improving people’s living conditions and prospects. For more information, see www.giz.de/.

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Food and Agriculture Organization The Food and Agriculture Organization, a specialized agency of the United Nations, was founded in October 1945 with a mandate to raise nutrition levels and living standards, to increase agricultural productivity, and to better the condition of rural populations. The organization provides direct development assistance; collects, analyzes, and disseminates information; offers policy and planning advice to governments; and serves as an international forum for debate on food and agricultural issues. For more information, see www.fao.org/.

Internal Displacement Monitoring Centre The Internal Displacement Monitoring Centre was established in 1998 by the Norwegian Refugee Council and is the leading international body monitoring conflict-induced internal displacement worldwide. The center contributes to improving national and international capacities to protect and assist the millions of people around the globe who have been displaced within their own country as a result of conflicts or human rights violations. For more information, see www.internal-displacement.org/.

International Civil Aviation Organization The International Civil Aviation Organization (ICAO), a specialized agency of the United Nations, is responsible for establishing international standards and recommended practices and procedures for the technical, economic, and legal aspects of international civil aviation operations. ICAO’s strategic objectives include enhancing global aviation safety and security and the efficiency of aviation operations, minimizing the adverse effect of global civil aviation on the environment, maintaining the continuity of aviation operations, and strengthening laws governing international civil aviation. For more information, see www.icao.int/.

International Energy Agency The International Energy Agency (IEA) was founded in 1973/74 with a mandate to facilitate cooperation among the IEA member countries to increase energy efficiency, promoting use of clean energy and technology, and diversify their energy sources while protecting the environment. IEA publishes annual and quarterly statistical publications covering both OECD and non-OECD countries’ statistics on oil, gas, coal, electricity and renewable sources of energy, energy supply and consumption, and energy prices and taxes. IEA also contributes in analysis of all aspects of sustainable development globally and provides policy recommendations. For more information, see www.iea.org/.

International Labour Organization The International Labour Organization (ILO), a specialized agency of the United Nations, seeks the promotion of social justice and internationally recognized human and labor rights. ILO helps advance the creation of decent jobs and the kinds of economic and working conditions that give working people and business people

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Partners a stake in lasting peace, prosperity, and progress. As part of its mandate, the ILO maintains an extensive statistical publication program. For more information, see www.ilo.org/.

International Monetary Fund The International Monetary Fund (IMF) is an international organization of 187 member countries established to promote international monetary cooperation, a stable system of exchange rates, and the balanced expansion of international trade and to foster economic growth and high levels of employment. The IMF reviews national, regional, and global economic and financial developments; provides policy advice to member countries; and serves as a forum where they can discuss the national, regional, and global consequences of their policies. The IMF also makes financing temporarily available to member countries to help them address balance of payments problems. Among the IMF’s core missions are the collection and dissemination of high-quality macroeconomic and financial statistics as an essential prerequisite for formulating appropriate policies. The IMF provides technical assistance and training to member countries in areas of its core expertise, including the development of economic and financial data in accordance with international standards. For more information, see www.imf.org/.

International Telecommunication Union The International Telecommunication Union (ITU) is the leading UN agency for information and communication technologies. ITU’s mission is to enable the growth and sustained development of telecommunications and information networks and to facilitate universal access so that people everywhere can participate in, and benefit from, the emerging information society and global economy. A key priority lies in bridging the so-called Digital Divide by building information and communication infrastructure, promoting adequate capacity building, and developing confidence in the use of cyberspace through enhanced online security. ITU also concentrates on strengthening emergency communications for disaster prevention and mitigation. For more information, see www.itu.int/.

National Science Foundation The National Science Foundation (NSF) is an independent U.S. government agency whose mission is to promote the progress of science; to advance the national health, prosperity, and welfare; and to secure the national defense. NSF’s goals—discovery, learning, research infrastructure, and stewardship—provide an integrated strategy to advance the frontiers of knowledge, cultivate a world-class, broadly inclusive science and engineering workforce, expand the scientific literacy of all citizens, build the nation’s research capability through investments in advanced instrumentation and facilities, and support excellence in science and engineering research and education through a capable and responsive organization. For more information, see www.nsf.gov/.

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Organisation for Economic Co-operation and Development The Organisation for Economic Co-operation and Development (OECD) includes 34 member countries sharing a commitment to democratic government and the market economy to support sustainable economic growth, boost employment, raise living standards, maintain financial stability, assist other countries’ economic development, and contribute to growth in world trade. With active relationships with some 100 other countries, it has a global reach. It is best known for its publications and statistics, which cover economic and social issues from macroeconomics to trade, education, development, and science and innovation. The Development Assistance Committee (DAC, www.oecd.org/dac/) is one of the principal bodies through which the OECD deals with issues related to cooperation with developing countries. The DAC is a key forum of major bilateral donors, who work together to increase the effectiveness of their common efforts to support sustainable development. The DAC concentrates on two key areas: the contribution of international development to the capacity of developing countries to participate in the global economy and the capacity of people to overcome poverty and participate fully in their societies. For more information, see www.oecd.org/.

Stockholm International Peace Research Institute The Stockholm International Peace Research Institute (SIPRI) conducts research on questions of conflict and cooperation of importance for international peace and security, with the aim of contributing to an understanding of the conditions for peaceful solutions to international conflicts and for a stable peace. SIPRI’s main publication, SIPRI Yearbook, is an authoritive and independent source on armaments and arms control and other conflict and security issues. For more information, see www.sipri.org/.

Understanding Children’s Work As part of broader efforts to develop effective and long-term solutions to child labor, the International Labour Organization, the United Nations Children’s Fund (UNICEF), and the World Bank initiated the joint interagency research program “Understanding Children’s Work and Its Impact” in December 2000. The Understanding Children’s Work (UCW) project was located at UNICEF’s Innocenti Research Centre in Florence, Italy, until June 2004, when it moved to the Centre for International Studies on Economic Growth in Rome. The UCW project addresses the crucial need for more and better data on child labor. UCW’s online database contains data by country on child labor and the status of children. For more information, see www.ucw-project.org/.

United Nations The United Nations currently has 192 member states. The purposes of the United Nations, as set forth in its charter, are to maintain international peace and security; to develop friendly relations among nations; to cooperate in solving international economic, social, cultural, and humanitarian problems and in promoting respect for human rights and fundamental freedoms; and to be a center for harmonizing the actions of nations in attaining these ends. For more information, see www.un.org/.

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Partners United Nations Centre for Human Settlements, Global Urban Observatory The Urban Indicators Programme of the United Nations Human Settlements Programme was established to address the urgent global need to improve the urban knowledge base by helping countries and cities design, collect, and apply policy-oriented indicators related to development at the city level. With the Urban Indicators and Best Practices programs, the Global Urban Observatory is establishing a worldwide information, assessment, and capacity-building network to help governments, local authorities, the private sector, and nongovernmental and other civil society organizations. For more information, see www.unhabitat.org/.

United Nations Children’s Fund The United Nations Children’s Fund (UNICEF) works with other UN bodies and with governments and non­ governmental organizations to improve children’s lives in more than 190 countries through various programs in education and health. UNICEF focuses primarily on five areas: child survival and development, basic education and gender equality (including girls’ education), child protection, HIV/AIDS, and policy advocacy and partnerships. For more information, see www.unicef.org/.

United Nations Conference on Trade and Development The United Nations Conference on Trade and Development (UNCTAD) is the principal organ of the United Nations General Assembly in the field of trade and development. Its mandate is to accelerate economic growth and development, particularly in developing countries. UNCTAD discharges its mandate through policy analysis; intergovernmental deliberations, consensus building, and negotiation; monitoring, implementation, and follow-up; and technical cooperation. For more information, see www.unctad.org/.

United Nations Department of Peacekeeping Operations The United Nations Department of Peacekeeping Operations contributes to the most important function of the United Nations—maintaining international peace and security. The department helps countries torn by conflict to create the conditions for lasting peace. The first peacekeeping mission was established in 1948 and has evolved to meet the demands of different conflicts and a changing political landscape. Today’s peacekeepers undertake a wide variety of complex tasks, from helping build sustainable institutions of governance, to monitoring human rights, to assisting in security sector reform, to disarmaming, demobilizing, and reintegrating former combatants. For more information, see www.un.org/en/peacekeeping/.

United Nations Educational, Scientific, and Cultural Organization, Institute for Statistics The United Nations Educational, Scientific, and Cultural Organization (UNESCO) is a specialized agency of the United Nations that promotes international cooperation among member states and associate members in education, science, culture, and communications. The UNESCO Institute for Statistics is the organization’s

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statistical branch, established in July 1999 to meet the growing needs of UNESCO member states and the international community for a wider range of policy-relevant, timely, and reliable statistics on these topics. For more information, see www.uis.unesco.org/.

United Nations Environment Programme The mandate of the United Nations Environment Programme is to provide leadership and encourage partnership in caring for the environment by inspiring, informing, and enabling nations and people to improve their quality of life without compromising that of future generations. For more information, see www.unep.org/.

United Nations Industrial Development Organization The United Nations Industrial Development Organization was established to act as the central coordinating body for industrial activities and to promote industrial development and cooperation at the global, regional, national, and sectoral levels. Its mandate is to help develop scientific and technological plans and programs for industrialization in the public, cooperative, and private sectors. For more information, see www.unido.org/.

United Nations Office on Drugs and Crime The United Nations Office on Drugs and Crime was established in 1977 and is a global leader in the fight against illicit drugs and international crime. The office assists member states in their struggle against illicit drugs, crime, and terrorism by helping build capacity, conducting research and analytical work, and assisting in the ratification and implementation of relevant international treaties and domestic legislation related to drugs, crime, and terrorism. For more information, see www.unodc.org/.

The UN Refugee Agency The UN Refugee Agency (UNHCR) is mandated to lead and coordinate international action to protect refugees and resolve refugee problems worldwide. Its primary purpose is to safeguard the rights and well-being of refugees. UNHCR also collects and disseminates statistics on refugees. For more information, see www.unhcr.org/.

Upsalla Conflict Data Program The Upsalla Conflict Data Program has collected information on armed violence since 1946 and is one of the most accurate and well used data sources on global armed conflicts. Its definition of armed conflict is becoming a standard in how conflicts are systematically defined and studied. In addition to data collection on armed violence, its researchers conduct theoretically and empirically based analyses of the causes, escalation, spread, prevention, and resolution of armed conflict. For more information, see www.pcr.uu.se/research/UCDP/.

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Partners World Bank The World Bank is a vital source of financial and technical assistance for developing countries. The World Bank is made up of two unique development institutions owned by 187 member countries—the International Bank for Reconstruction and Development (IBRD)  and the International Development Association (IDA). These institutions play different but collaborative roles to advance the vision of an inclusive and sustainable globalization. The IBRD focuses on middle-income and creditworthy poor countries, while IDA focuses on the poorest countries. Together they provide low-interest loans, interest-free credits, and grants to developing countries for a wide array of purposes, including investments in education, health, public administration, infrastructure, financial and private sector development, agriculture, and environmental and natural resource management. The World Bank’s work focuses on achieving the Millennium Development Goals by working with partners to alleviate poverty. For more information, see http://data.worldbank.org/.

World Health Organization The objective of the World Health Organization (WHO), a specialized agency of the United Nations, is the attainment by all people of the highest possible level of health. It is responsible for providing leadership on global health matters, shaping the health research agenda, setting norms and standards, articulating evidence-based policy options, providing technical support to countries, and monitoring and assessing health trends. For more information, see www.who.int/.

World Intellectual Property Organization The World Intellectual Property Organization (WIPO) is a specialized agency of the United Nations dedicated to developing a balanced and accessible international intellectual property (IP) system, which rewards creativity, stimulates innovation, and contributes to economic development while safeguarding the public interest. WIPO carries out a wide variety of tasks related to the protection of IP rights. These include developing international IP laws and standards, delivering global IP protection services, encouraging the use of IP for economic development, promoting better understanding of IP, and providing a forum for debate. For more information, see www.wipo.int/.

World Tourism Organization The World Tourism Organization is an intergovernmental body entrusted by the United Nations with promoting and developing tourism. It serves as a global forum for tourism policy issues and a source of tourism know-how. For more information, see www.unwto.org/.

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World Trade Organization The World Trade Organization (WTO) is the only international organization dealing with the global rules of trade between nations. Its main function is to ensure that trade flows as smoothly, predictably, and freely as possible. It does this by administering trade agreements, acting as a forum for trade negotiations, settling trade disputes, reviewing national trade policies, assisting developing countries in trade policy issues—through technical assistance and training programs—and cooperating with other international organizations. At the heart of the system—known as the multilateral trading system—are the WTO’s agreements, negotiated and signed by a large majority of the world’s trading nations and ratified by their parliaments. For more information, see www.wto.org/.

Private and nongovernmental organizations Containerisation International Containerisation International Yearbook is one of the most authoritative reference books on the container industry. The information can be accessed on the Containerisation International Web site, which also provides a comprehensive online daily business news and information service for the container industry. For more information, see www.ci-online.co.uk/.

DHL DHL provides shipping and customized transportation solutions for customers in more than 220 countries and territories. It offers expertise in express, air, and ocean freight; overland transport; contract logistics solutions; and international mail services. For more information, see www.dhl.com/.

International Institute for Strategic Studies The International Institute for Strategic Studies (IISS) provides information and analysis on strategic trends and facilitates contacts between government leaders, business people, and analysts that could lead to better public policy in international security and international relations. The IISS is a primary source of accurate, objective information on international strategic issues. For more information, see www.iiss.org/.

International Road Federation The International Road Federation (IRF) is a nongovernmental, not-for-profit organization whose mission is to encourage and promote development and maintenance of better, safer, and more sustainable roads and road networks. Working together with its members and associates, the IRF promotes social and economic benefits that flow from well planned and environmentally sound road transport networks. It helps put in place technological solutions and management practices that provide maximum economic and social returns from national road investments. The IRF works in all aspects of road policy and development worldwide with governments and financial institutions, members, and the community of road professionals. For more information, see www.irfnet.org/.

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Partners Netcraft Netcraft provides Internet security services such as antifraud and antiphishing services, application testing, code reviews, and automated penetration testing. Netcraft also provides research data and analysis on many aspects of the Internet and is a respected authority on the market share of web servers, operating systems, hosting providers, Internet service providers, encrypted transactions, electronic commerce, scripting languages, and content technologies on the Internet. For more information, see http://news.netcraft.com/.

PricewaterhouseCoopers PricewaterhouseCoopers provides industry-focused services in the fields of assurance, tax, human resources, transactions, performance improvement, and crisis management services to help address client and stakeholder issues. For more information, see www.pwc.com/.

Standard & Poor’s Standard & Poor’s is the world’s foremost provider of independent credit ratings, indexes, risk evaluation, investment research, and data. S&P’s Global Stock Markets Factbook draws on data from S&P’s Emerging Markets Database (EMDB) and other sources covering data on more than 100 markets with comprehensive market profiles for 82 countries. Drawing a sample of stocks in each EMDB market, Standard & Poor’s calculates indexes to serve as benchmarks that are consistent across national boundaries. For more information, see www.standardandpoors.com/.

World Conservation Monitoring Centre The World Conservation Monitoring Centre provides information on the conservation and sustainable use of the world’s living resources and helps others to develop information systems of their own. It works in close collaboration with a wide range of people and organizations to increase access to the information needed for wise management of the world’s living resources. For more information, see www.unep-wcmc.org/.

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World Economic Forum The World Economic Forum (WEF) is an independent international organization committed to improving the state of the world by engaging leaders in partnerships to shape global, regional, and industry agendas. Economic research at the WEF—led by the Global Competitiveness Programme—focuses on identifying the impediments to growth so that strategies to achieve sustainable economic progress, reduce poverty, and increase prosperity can be developed. The WEF’s competitiveness reports range from global coverage, such as Global Competitiveness Report, to regional and topical coverage, such as Africa Competitiveness Report, The Lisbon Review, and Global Information Technology Report. For more information, see www.weforum.org/.

World Resources Institute The World Resources Institute is an independent center for policy research and technical assistance on global environmental and development issues. The institute provides—and helps other institutions provide—­ objective information and practical proposals for policy and institutional change that will foster environmentally sound, socially equitable development. The institute’s current areas of work include trade, forests, energy, economics, technology, biodiversity, human health, climate change, sustainable agriculture, resource and environmental information, and national strategies for environmental and resource management. For more information, see www.wri.org/.

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Users guide Tables

gap-filled estimates for missing data and by an s, for

complex technical and conceptual problems that can-

The tables are numbered by section and display the

simple totals, where they do not), median values (m),

not be resolved unequivocally. Data coverage may

identifying icon of the section. Countries and econo-

weighted averages (w), or simple averages (u). Gap

not be complete because of special circumstances

mies are listed alphabetically (except for Hong Kong

filling of amounts not allocated to countries may result

affecting the collection and reporting of data, such

SAR, China, which appears after China). Data are

in discrepancies between subgroup aggregates and

as problems stemming from conflicts.

shown for 155 economies with populations of more

overall totals. For further discussion of aggregation

than 1 million, as well as for Taiwan, China, in selected

methods, see Statistical methods.

tables. Table 1.6 presents selected indicators for 58

For these reasons, although data are drawn from sources thought to be the most authoritative, they should be construed only as indicating trends and

other economies—small economies with populations

Aggregate measures for regions

characterizing major differences among economies

between 30,000 and 1 million and smaller econo-

The aggregate measures for regions include only

rather than as offering precise quantitative mea-

mies if they are members of the International Bank

low- and middle-income economies including econo-

sures of those differences. Discrepancies in data

for Reconstruction and Development (IBRD) or, as it

mies with populations of less than 1 million listed

presented in different editions of World Development

is commonly known, the World Bank. Data for these

in table 1.6.

Indicators reflect updates by countries as well as

economies are included on the World Development

The country composition of regions is based on the

revisions to historical series and changes in meth-

Indicators CD-ROM and the World Bank’s Open Data

World Bank’s analytical regions and may differ from

odology. Thus readers are advised not to compare

website at data.worldbank.org/.

common geographic usage. For regional classifica-

data series between editions of World Development

The term country, used interchangeably with

tions, see the map on the inside back cover and the

Indicators or between different World Bank publica-

economy, does not imply political independence, but

list on the back cover flap. For further discussion of

tions. Consistent time-series data for 1960–2009

refers to any territory for which authorities report

aggregation methods, see Statistical methods.

are available on the World Development Indicators

separate social or economic statistics. When avail-

CD-ROM and at data.worldbank.org/.

able, aggregate measures for income and regional

Statistics

groups appear at the end of each table.

Except where otherwise noted, growth rates are

Data are shown for economies as they were con-

in real terms. (See Statistical methods for information

Indicators are shown for the most recent year or

stituted in 2009, and historical data are revised to

on the methods used to calculate growth rates.) Data

period for which data are available and, in most tables,

reflect current political arrangements. Exceptions are

for some economic indicators for some economies

for an earlier year or period (usually 1990 or 1995 in

noted throughout the tables.

are presented in fiscal years rather than calendar

this edition). Time-series data for all 213 economies

Additional information about the data is provided

years; see Primary data documentation. All dollar fig-

are available on the World Development Indicators CD-

in Primary data documentation. That section sum-

ures are current U.S. dollars unless otherwise stated.

ROM and at data.worldbank.org/.

marizes national and international efforts to improve

The methods used for converting national currencies

Known deviations from standard definitions or

basic data collection and gives country-level informa-

are described in Statistical methods.

breaks in comparability over time or across countries

tion on primary sources, census years, fiscal years,

are either footnoted in the tables or noted in About

statistical methods and concepts used, and other

Country notes

the data. When available data are deemed to be

background information. Statistical methods provides

• Unless otherwise noted, data for China do not

too weak to provide reliable measures of levels and

technical information on some of the general calcula-

include data for Hong Kong SAR, China; Macao

trends or do not adequately adhere to international

tions and formulas used throughout the book.

SAR, China; or Taiwan, China. • Data for Indonesia include Timor-Leste through

standards, the data are not shown. Data consistency, reliability, and comparability

1999 unless otherwise noted.

Aggregate measures for income groups

Considerable effort has been made to standardize

• Montenegro declared independence from Serbia

The aggregate measures for income groups include

the data, but full comparability cannot be assured,

and Montenegro on June 3, 2006. Where avail-

213 economies (the economies listed in the main

and care must be taken in interpreting the indicators.

able, data for each country are shown separately.

tables plus those in table 1.6) whenever data are

Many factors affect data availability, comparability,

However, for the Serbia listing, some indicators

available. To maintain consistency in the aggregate

and reliability: statistical systems in many develop-

continue to include data for Montenegro through

measures over time and between tables, missing

ing economies are still weak; statistical methods,

2005; these data are footnoted in the tables.

data are imputed where possible. The aggregates

coverage, practices, and definitions differ widely; and

Moreover, data from 1999 onward for Serbia for

are totals (designated by a t if the aggregates include

cross-country and intertemporal comparisons involve

most indicators exclude data for Kosovo, 1999

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being the year when Kosovo became a territory

more. The 17 participating member countries of the

under international administration pursuant to

Euro area are presented as a subgroup under high-

UN Security Council Resolution 1244 (1999); any

income economies. Estonia joined the Euro area on

exceptions are noted. Kosovo became a World

January 1, 2011.

Bank member on June 29, 2009; available data are shown separately for Kosovo in the main tables. • Netherlands Antilles ceased to exist on October

Symbols ..

10, 2010. Curaçao and St. Maarten became

means that data are not available or that aggregates

countries within the Kingdom of the Netherlands.

cannot be calculated because of missing data in the

Bonaire, St. Eustatius, and Saba became special

years shown.

municipalities of the Netherlands. 0 or 0.0

Classification of economies

means zero or small enough that the number would

For operational and analytical purposes the World

round to zero at the displayed number of decimal

Bank’s main criterion for classifying economies is

places.

gross national income (GNI) per capita (calculated by the World Bank Atlas method). Every economy

/

is classified as low income, middle income (subdi-

in dates, as in 2003/04, means that the period of

vided into lower middle and upper middle), or high

time, usually 12 months, straddles two calendar

income. For income classifications see the map on

years and refers to a crop year, a survey year, or a

the inside front cover and the list on the front cover

fiscal year.

flap. Low- and middle-income economies are sometimes referred to as developing economies. The term

$

is used for convenience; it is not intended to imply

means current U.S. dollars unless otherwise noted.

that all economies in the group are experiencing similar development or that other economies have

>

reached a preferred or final stage of development.

means more than.

Note that classification by income does not necessarily reflect development status. Because GNI per

<

capita changes over time, the country composition

means less than.

of income groups may change from one edition of World Development Indicators to the next. Once the

Data presentation conventions

classification is fixed for an edition, based on GNI

• A blank means not applicable or, for an aggre-

per capita in the most recent year for which data are

gate, not analytically meaningful.

available (2009 in this edition), all historical data

• A billion is 1,000 million.

presented are based on the same country grouping.

• A trillion is 1,000 billion.

Low-income economies are those with a GNI per

• Figures in italics refer to years or periods other

capita of $995 or less in 2009. Middle-income econ-

than those specified or to growth rates calculated

omies are those with a GNI per capita of more than $995 but less than $12,196. Lower middle-income and upper middle-income economies are separated

for less than the full period specified. • Data for years that are more than three years from the range shown are footnoted.

at a GNI per capita of $3,945. High-income economies are those with a GNI per capita of $12,196 or

The cutoff date for data is February 1, 2011.

2011 World Development Indicators

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World View


Introduction

“Our aim is for open data, open knowledge, and open solutions.” —Robert Zoellick, Georgetown University, September 2010

W

orld Development Indicators provides a comprehensive selection of national and international data that focus attention on critical development issues, facilitate research, encourage debate and analysis of policy options, and monitor progress toward development goals. Organized around six themes—world view, people, environment, economy, states and markets, and global links—the book contains more than 800 indicators for 155 economies with a population of 1 million people or more, together with relevant aggregates. The online database includes more than 1,100 indicators for 213 economies, with many time series extending back to 1960. In 2010, to improve the impact of the indicators and to provide a platform for others to use the data to solve pressing development challenges, the World Development Indicators database and many other public databases maintained by the World Bank were made available as open data: free of charge, in accessible nonproprietary formats on the World Wide Web. This year, the first part of the introduction to the World View section provides an overview of the initiative, the impact of moving to an open data platform, a brief survey of the global open data movement, and an examination of its relevance to development. The second part reviews progress toward the Millennium Development Goals—whose target date of 2015 is now just four years away.

The World Bank Open Data Initiative The Open Data Initiative is a new strategy for reaching data users and a major change in the Bank’s business model for data, which had previously been a subscription-based model for licensing data access and use, using a network of university libraries, development agencies, and private firms, and free access provided through the World Bank’s Public Information Centers and depository libraries. At the time of the open data announcement there were around 140,000 regular users of the subscription database annually—a substantial number for a highly specialized data product. But providing free and easier access to the databases has had an immediate and lasting impact on data use. Since April 2010 the new data website—http://data.worldbank.

1

org—has recorded well over 20 million page views. And at the time of printing this edition of World Development Indicators, it provides data to more than 100,000 unique visitors each week, three times as many as before (figure 1a). Making the World Development Indicators and other databases free was only the first step in creating an open data environment. Open data should mean that users can access and search public datasets at no cost, combine data from different sources, add data and select data records to include or exclude in derived works, change the format or structure of the data, and give away or sell any products they create. For the World Bank, this required designing new user interfaces and developing new search tools to more easily find and report the data. It also required a new license defining the terms of Use of World Bank data has risen with the launch of the Open Data Initiative

1a

Weekly unique visitors to http://data.worldbank.org (thousands) 125

April 2010 Launch of the Open Data Initiative

100 75

Recess period for US and European academic teaching institutions

50 25 0 January 2010

April 2010

July 2010

October 2010

January 2011

Source: World Bank staff calculations from Omniture data.

2011 World Development Indicators

1


1b

Terms of use for World Bank data

Why do open data need to be licensed? Because a license conveys certain rights to the licensee—in this case, the data user—while protecting the interests of the licensor. If there is no explicit license attached to a dataset, users may be uncertain of their rights. Can they republish these data? Can they include them in a new dataset along with data from other sources? Can they give them away or resell them? Intellectual property laws differ by country. In an international environment where data are published on the World Wide Web, it may not be clear what law applies. Lacking a license, a cautious data user would assume that he or she should seek permission of the dataset owner or publisher, creating a real or imagined impediment to using the data. A license can help encourage data use by making clear exactly what is permitted, true even for free data. Use of data in the World Bank’s Data Catalog is governed by the Terms of Use of Datasets posted at http://data.worldbank.org. The terms follow the general model of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0) and the Open Data Commons Attribution License (www.opendatacommons.org/licenses/by/1.0). These licenses require users to acknowledge the original source when they publish or reuse the data, particularly important for World Development Indicators, where many datasets are obtained from sources such as specialized UN and international agencies. The terms of use impose some further limitations, still within the spirit of an open data license: users may not claim endorsement by the World Bank or use its name or logos without permission. Acknowledging data sources is good practice, regardless of the terms of a license. Identifying sources makes it possible for others to locate the same or similar data. And credit to data producers or publisher recognizes their effort and encourages them to continue. The World Bank’s Terms of Use for Datasets provide a suggested form of attribution: The World Bank: Dataset name: Data source. The information for completing this form of attribution is available in the metadata supplied with data downloaded from http://data.worldbank.org.

use for data (box 1b). And it required new thinking to promote the use and reuse of data. To reach out to new audiences and communities of data users, the World Bank organized a global “Apps for Development” competition—one of the first of its kind—inviting developers to create new applications for desktop computers or mobile devices using World Bank datasets, including World Development Indicators data. Open data and open government Advocates of greater transparency in public agencies—the open government movement— have been among the most vocal proponents of open data. Likewise, those seeking databases to build new applications have supported freedom of information laws and unrestricted access to data created by public agencies. Opening public databases empowers people because data are essential for monitoring the performance of governments and the impact of public policies on citizens. For advocates of open data, governments are vast repositories of statistical and nonstatistical information with unrealized potential for creative applications. The political, philosophical, and economic impulses for open data and open government are often linked. One advocate of open data writes, “The term ‘Open Data’ refers to the philosophical and methodological approach to the democratization of data

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2011 World Development Indicators

enabling citizens to access and create value through the reuse of public sector information” (Rahemtulla 2011). The Sunlight Foundation, a U.S.-based civil society organization, describes its goals as “improving access to government information by making it available online, indeed redefining ‘public’ information as meaning ‘online,’ and . . . creating new tools and websites to enable individuals and communities to better access that information and put it to use. . . . We want to catalyze greater government transparency by engaging individual citizens and communities— technologists, policy wonks, open government advocates, and ordinary citizens­— demanding policies that will enable all of us to hold government accountable” (http://sunlightfoundation. com/about/). Digital information and communication technologies permitting dissemination of large amounts of data at little or no cost have strengthened the argument for providing free access to public sector information. Pollock (2010) estimates the direct benefit to the U.K. public of providing free access to public sector information that was previously sold to be £1.6–£6 billion, 4–15 times the forgone sales revenues of £400 million. Additional indirect benefits come from new products and services using open datasets or complementary products and services and from reducing the transaction costs to data users and reusers. Open data and open government initiatives have progressed farther in rich countries than in developing ones. This may reflect a lack of political will or popular demand, but it often reflects a lack of technical capacity and resources to make data available in accessible formats. A study commissioned by the Transparency and Accountability Initiative (Hogge 2010) identified three drivers behind the success of the U.K. and U.S. data.gov initiatives: • Civil society, particularly a small and motivated group of “civic hackers” responsible for developing grassroots political engagement websites. • An engaged and well resourced “middle layer” of skilled government bureaucrats. • A top-level mandate, motivated by an outside force (in the United Kingdom) or a refreshed political administration hungry for change (in the United States). Statistical offices exemplify the “middle layer” of a government bureaucracy, uniquely


world view

skilled in collecting and organizing large datasets. But even they may lack the motivation or resources to make their products freely available to the public unless they enjoy full support from the top. In developing countries aid donors can act as fourth driver by providing technical assistance and funding for open data projects and by modeling transparency in their own practices. The International Aid Transparency Initiative— the World Bank is a founding member—aims to create a global repository of information on aid flows, starting from the commitment of funding from donors and continuing through its disbursement to recipient countries, the allocation of aid money in national budgets, the procurement of goods and services, and the measurement of results. To fulfill the initiative’s goal of providing a complete accounting of aid to the citizens of donor and developing countries will require cooperation among donors and recipients. Terminology and coding systems must be standardized and agreements reached on everything from the timing of reports to the mechanisms for posting and accessing the datasets. In many cases donor governments and international agencies will have to change their rules on access to information to provide full transparency to their aid programs (box 1c). For more information on the initiative, see www. aidtransparency.net. Mapping for results—making data not just accessible but useful The new Access to Information Policy and the Open Data Initiative provide much greater access to the World Bank Group’s knowledge resources than before. But accessible information is not the same as usable information. Project documents contain a wealth of data about planned activities—for instance, on their location. But it may be difficult for many interested parties, such as project beneficiaries, citizen groups, and civil society organizations, to extract and visualize relevant data from long texts or tables. To help solve this problem, the World Bank, on a pilot basis, has started to provide geolocation codes along with data and information about the projects that it supports. The objective is to improve aid effectiveness through enhanced transparency and accountability of project activities. Location information makes

1c

Access to information at the World Bank

Opening the World Bank’s databases is part of a broader effort to introduce greater transparency in the World Bank’s operations, and a new policy on information disclosure went into effect on July 1, 2010. Besides formalizing the Open Data Initiative, the Access to Information Policy (www.worldbank.org/wbaccess) establishes the principle that the World Bank will disclose any information in its possession that is not on a specific list of exceptions. In the past, only documents selected for disclosure were available to the public. The new policy reverses the process and presumes that most information is disclosable. Exceptions include personal information and staff records, internal deliberations and administrative matters, and information received in confidence from clients and third parties. Some documents with restricted access are subject to a declassification schedule, ensuring that they will become available to the public in due course. A process for requesting documents has also been established that allows users to search for documents by country and topic in seven languages.

the data become “local” and much more accessible and relevant to project stakeholders. The data are open and available directly to software developers though an application programming interface and through an interactive web-based application called Mapping for Results (http:// maps.worldbank.org). In keeping with the philosophy of the Open Data Initiative, the Mapping for Results application uses the dataset of geo-located project activities and combines the data with subnational human and social development indicators, such as child mortality rates, poverty incidence, malnutrition, and population measures. But even more value may lie in what other researchers and software developers might do with the data, combining them with their own data or with data from other sources, performing their own analysis, or providing applications that help citizens and beneficiaries connect directly with the project during implementation, through feedback or other mechanisms.

Countdown to the Millennium Development Goals in 2015 There are four years to the target date for the Millennium Development Goals (MDGs). The MDGs have focused the world’s attention on the living conditions of billions of people who live in poor and developing countries and on the need to improve the quality, frequency, and timeliness of the statistics used to track their progress. Progress toward the MDGs has been marked by slow changes in outcome indicators and by improvements in data availability. World Development Indicators has monitored global and regional trends in poverty reduction, education, health, and the environment since 1997. After the UN Millennium Summit in 2000, World Development Indicators began closely tracking the progress of countries 2011 World Development Indicators

3


Progress toward eradicating poverty Share of countries making progress toward reducing extreme poverty by half (percent) 100

1d Reached target Off track Seriously off track

On track Insufficient data

50

0

50

100

2004 140 countries

2011 144 countries

Source: World Bank staff estimates.

Progress toward universal primary education completion Share of countries making progress toward full completion of primary education (percent)

1e

Reached target Off track Seriously off track

On track Insufficient data

100

50

0

50

100

2004 140 countries

2011 144 countries

Source: World Bank staff estimates.

Progress toward gender parity Share of countries making progress toward gender parity in primary and secondary education (percent) 100

1f Reached On track target Off track Seriously off track Insufficient data

50

0

50

100

2004 140 countries

Source: World Bank staff estimates.

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2011 World Development Indicators

2011 144 countries

against the targets selected for the MDGs. The MDGs highlight important outcomes, but the focus on this limited set of indicators should not obscure the fact that development is a complex process whose course is determined in part by geographic location, historical circumstances, institutional capacity, and uncontrollable events such as weather and natural disasters. Success or failure, while not arbitrary or entirely accidental, still has a large component of chance. This review employs the same assessment method that World Development Indicators has used since 2004 to track progress of countries toward the time-bound and quantified targets of the MDGs. Countries are “on track” if their past progress equals or exceeds the rate of change necessary to reach an MDG target. A few countries have already reached their targets. They are counted as having achieved the goal, although some may slip back. Countries making less than necessary progress are “off track,” or “seriously off track” if their past rate progress would not allow them to reach the target even in another 25 years. The remaining countries do not have sufficient data to evaluate their progress—in some cases because there are no data for the benchmark period of 1990–99 and in others because more recent data are missing. But the situation is improving: starting from the earliest World Development Indicators progress assessments in 2004 (based on data for 1990–2002), the number of countries with insufficient data has fallen, enhancing our picture of progress toward the MDGs. For more information on the work of the World Bank and its partners to achieve the MDGs, see www.worldbank.org/mdgs, which includes a link to the World Bank’s MDG eAtlas. Goal 1. Eradicate extreme poverty and hunger The number of people living on less than $1.25 a day fell from 1.8 billion in 1990 to 1.4 billion in 2005. New global and regional estimates, to become available later in 2011, are likely to show a continuation of past trends, although the financial crisis of 2008 and the recent surge in food prices will have slowed progress in some countries. Because household income and expenditure surveys are expensive and time consuming, they are not conducted frequently and there are often difficulties in making reliable comparisons over time or across countries.


world view

For 140 developing countries, figure 1d compares the progress assessments in 2005 and in 2011, based on available data. Forty-three countries are on track or have reached the target of cutting the extreme poverty rate in half, twice as many as in 2005. They include China, Brazil, and the Russian Federation. India, with more than 400 million people living in poverty lags behind, but with faster economic growth may well reach the 2015 target. Goal 2. Achieve universal primary education The goal of providing universal primary education has proved surprisingly hard to achieve. Completion rates measure the proportion of children enrolled in the final year of primary education after adjusting for repetition. In 2011, 49 countries had achieved or were on track to achieve 100 percent primary completion rates, only three more than in 2004, and the number of countries seriously off track has increased, especially in Sub-Saharan Africa (figure 1e). There are more and better data, but the goal remains elusive. Goal 3. Promote gender equality Gender equality and empowering women foster progress toward all the Millennium Development Goals. Equality of educational opportunities, measured by the ratio of girls’ to boys’ enrollments in primary and secondary education, is a starting point. Since the 2004 assessment, the number of countries on track to reach the target has increased steadily, driven by rising enrollments of girls, and the number of countries without sufficient data to measure progress has dropped (figure 1f). Goal 4. Reduce child mortality Of 144 countries with data in February 2011, 11 had achieved a two-thirds reduction in their under-five child mortality rate, and another 25 were on track to do so (figure 1g). This is remarkable progress since 2004, but more than 100 countries remain off track, and only a few of them are likely to reach the MDG target by 2015. Measuring child mortality is the product of a successful collaboration of international statisticians. By bringing together the most reliable data from multiple sources and applying appropriate estimation methods, consistent time series comparable across countries are available for monitoring this important indicator. More information about data sources

Progress toward reducing child mortality

1g

Share of countries making progress toward reducing under-five child mortality by two-thirds (percent)

Reached On track target Off track Seriously off track Insufficient data

100

50

0

50

100

2004 140 countries

2011 144 countries

Source: World Bank staff estimates.

Progress toward improving maternal health

1h

Share of countries making progress toward providing skilled attendants at births (percent)

Reached On track target Off track Seriously off track Insufficient data

100

50

0

50

100

2004 140 countries

2011 144 countries

Source: World Bank staff estimates.

HIV incidence is remaining stable or decreasing in many developing countries, but many lack data

1i

Change in HIV incidence rate, 2001–09 (number of developing countries) 100

75

50

25

0

Incidence increased by more than 25%

Stable

Incidence decreased by more than 25%

No data

Source: Joint United Nations Programme on HIV/AIDS.

2011 World Development Indicators

5


Progress on access to an improved water source Share of countries reducing proportion of population without access to an improved water source by half (percent)

1j Reached On track target Off track Seriously off track Insufficient data

100

50

0

50

100

2004 140 countries

2011 144 countries

Source: World Bank staff estimates.

Progress on access to improved sanitation Share of countries making progress toward improved sanitation (percent)

1k Reached target Off track Seriously off track

On track Insufficient data

100

50

0

50

100

2004 140 countries

2011 144 countries

Source: World Bank staff estimates.

and estimation methods is available at www. childmortality.org. Goal 5. Improve maternal health Reliable measurements of maternal mortality are difficult to obtain. Many national estimates are not comparable over time or across countries because of differences in methods and estimation techniques. Consistently modeled estimates that became available only recently show that 30 countries are on track to achieve a three-quarter reduction in their maternal mortality ratio and that 94 are off track or seriously off track. Figure 1h compares the availability of skilled birth attendants, a critical factor for reducing maternal and infant deaths, using data from the 2004 and 2011 World Development

6

2011 World Development Indicators

Indicators. While the number of countries seriously off track has increased, the number without adequate data has decreased, and the number providing skilled attendants at birth has risen 35 percent. Goal 6. Combat HIV/AIDS, malaria, and other diseases When the MDGs were formulated, the HIV/AIDS epidemic was spreading rapidly, engulfing many poor countries in Southern Africa. Data on the extent of the epidemic were derived from sentinel sites and limited reporting through health systems. The goal refers to halting and reversing the spread of HIV/AIDS. Under the circumstances it was impossible to set time-bound quantified targets. Now the statistical record is beginning to improve. UNAIDS, in its 2010 Report on the Global AIDS Epidemic, estimates that the annual number of new HIV infections has fallen 21 percent since its peak in 1997 (figure 1i). But reliable estimates of incidence are available for only 60 developing countries and do not include Brazil, China, and the Russian Federation. Goal 7. Ensure environmental sustainability Reversing environmental losses and ensuring a sustainable flow of services from the Earthâ&#x20AC;&#x2122;s resources have many dimensions: preserving forests, protecting plant and animal species, reducing carbon emissions, and limiting and adapting to the effects of climate change. Improving the built environment is also important. The MDGs set targets for reducing the proportion of people without access to safe water and sanitation by half. The ability to measure progress toward both targets has improved significantly since 2004, and almost half the developing countries with sufficient data are on track to meet the water target (figure 1j). Progress in providing access to sanitation has been slower: almost half the countries are seriously off track (figure 1k). Goal 8. Develop a global partnership for development Partnership between high-income and developing economies, fundamental to achieving the MDGs, rests on four pillars: reducing external debt of developing countries, increasing their access to markets in OECD countries, realizing the benefits of new technologies and essential drugs, and providing financing for development programs in the poorest countries. Following


world view

the adoption of the MDGs, the International Conference on Financing for Development in 2002 urged developed countries “to make concrete efforts toward the target of 0.7 percent of gross national income [GNI] as official development assistance to developing countries.” Since then many countries have increased their official development assistance, but few have reached the target of 0.7 percent (figure 1l). In 2009, five countries provided more than 0.7 percent of their GNI as aid, but their share of total aid was only 15 percent. The largest share of total aid was provided by 10 donors that gave 0.3–0.7 percent of their GNI. The largest single donor, the United States, provided 0.21 percent of its GNI as official development assistance.

Official development assistance provided by Development Assistance Committee members Official development assistance provided, by share of GNI (2009 $ billions)

1l

0.7% GNI or more 0.3% to <0.7% GNI 0.2% to <0.3% GNI <0.2% GNI

150

100

50

0

2000

2009

Source: World Bank staff estimates.

2011 World Development Indicators

7


Millennium Development Goals Goals and targets from the Millennium Declaration

Goal 1 Eradicate extreme poverty and hunger Target 1.A Halve, between 1990 and 2015, the proportion of people whose income is less than $1 a day

Target 1.B Achieve full and productive employment and decent work for all, including women and young people

Target 1.C Halve, between 1990 and 2015, the proportion of people who suffer from hunger Goal 2 Achieve universal primary education Target 2.A Ensure that by 2015 children everywhere, boys and girls alike, will be able to complete a full course of primary schooling Goal 3 Promote gender equality and empower women Target 3.A Eliminate gender disparity in primary and secondary education, preferably by 2005, and in all levels of education no later than 2015

Goal 4 Reduce child mortality Target 4.A Reduce by two-thirds, between 1990 and 2015, the under-five mortality rate

Goal 5 Improve maternal health Target 5.A Reduce by three-quarters, between 1990 and 2015, the maternal mortality ratio Target 5.B Achieve by 2015 universal access to reproductive health

Goal 6 Combat HIV/AIDS, malaria, and other diseases Target 6.A Have halted by 2015 and begun to reverse the spread of HIV/AIDS

Target 6.B Achieve by 2010 universal access to treatment for HIV/AIDS for all those who need it Target 6.C Have halted by 2015 and begun to reverse the incidence of malaria and other major diseases

Indicators for monitoring progress 1.1 Proportion of population below $1 purchasing power parity (PPP) a day1 1.2 Poverty gap ratio [incidence × depth of poverty] 1.3 Share of poorest quintile in national consumption 1.4 Growth rate of GDP per person employed 1.5 Employment to population ratio 1.6 Proportion of employed people living below $1 (PPP) a day 1.7 Proportion of own-account and contributing family workers in total employment 1.8 Prevalence of underweight children under five years of age 1.9 Proportion of population below minimum level of dietary energy consumption 2.1 Net enrollment ratio in primary education 2.2 Proportion of pupils starting grade 1 who reach last grade of primary education 2.3 Literacy rate of 15- to 24-year-olds, women and men 3.1 Ratios of girls to boys in primary, secondary, and tertiary education 3.2 Share of women in wage employment in the nonagricultural sector 3.3 Proportion of seats held by women in national parliament 4.1 Under-five mortality rate 4.2 Infant mortality rate 4.3 Proportion of one-year-old children immunized against measles 5.1 5.2 5.3 5.4 5.5

Maternal mortality ratio Proportion of births attended by skilled health personnel Contraceptive prevalence rate Adolescent birth rate Antenatal care coverage (at least one visit and at least four visits) 5.6 Unmet need for family planning 6.1 HIV prevalence among population ages 15–24 years 6.2 Condom use at last high-risk sex 6.3 Proportion of population ages 15–24 years with comprehensive, correct knowledge of HIV/AIDS 6.4 Ratio of school attendance of orphans to school attendance of nonorphans ages 10–14 years 6.5 Proportion of population with advanced HIV infection with access to antiretroviral drugs 6.6 Incidence and death rates associated with malaria 6.7 Proportion of children under age five sleeping under insecticide-treated bednets 6.8 Proportion of children under age five with fever who are treated with appropriate antimalarial drugs 6.9 Incidence, prevalence, and death rates associated with tuberculosis 6.10 Proportion of tuberculosis cases detected and cured under directly observed treatment short course

The Millennium Development Goals and targets come from the Millennium Declaration, signed by 189 countries, including 147 heads of state and government, in September 2000 (www. un.org/millennium/declaration/ares552e.htm) as updated by the 60th UN General Assembly in September 2005. The revised Millennium Development Goal (MDG) monitoring framework shown here, including new targets and indicators, was presented to the 62nd General Assembly, with new numbering as recommended by the Inter-agency and Expert Group on MDG Indicators at its 12th meeting on 14 November 2007. The goals and targets are interrelated and should be seen as a whole. They represent a partnership between the developed countries and the developing countries “to create an environment—at the national and global levels alike—which is conducive to development and the elimination of poverty.” All indicators should be disaggregated by sex and urban-rural location as far as possible.

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2011 World Development Indicators


Goals and targets from the Millennium Declaration Goal 7 Ensure environmental sustainability Target 7.A Integrate the principles of sustainable development into country policies and programs and reverse the loss of environmental resources Target 7.B Reduce biodiversity loss, achieving, by 2010, a significant reduction in the rate of loss

Target 7.C Halve by 2015 the proportion of people without sustainable access to safe drinking water and basic sanitation Target 7.D Achieve by 2020 a significant improvement in the lives of at least 100 million slum dwellers Goal 8 Develop a global partnership for development Target 8.A Develop further an open, rule-based, predictable, nondiscriminatory trading and financial system

Indicators for monitoring progress 7.1 Proportion of land area covered by forest 7.2 Carbon dioxide emissions, total, per capita and per $1 GDP (PPP) 7.3 Consumption of ozone-depleting substances 7.4 Proportion of fish stocks within safe biological limits 7.5 Proportion of total water resources used 7.6 Proportion of terrestrial and marine areas protected 7.7 Proportion of species threatened with extinction 7.8 Proportion of population using an improved drinking water source 7.9 Proportion of population using an improved sanitation facility 7.10 Proportion of urban population living in slums2

Some of the indicators listed below are monitored separately for the least developed countries (LDCs), Africa, landlocked developing countries, and small island developing states.

(Includes a commitment to good governance, development, and poverty reductionâ&#x20AC;&#x201D;both nationally and internationally.)

Official development assistance (ODA) 8.1 Net ODA, total and to the least developed countries, as percentage of OECD/DAC donorsâ&#x20AC;&#x2122; gross national income 8.2 Proportion of total bilateral, sector-allocable ODA of OECD/DAC donors to basic social services (basic education, primary health care, nutrition, safe water, and Target 8.B Address the special needs of the least developed sanitation) countries 8.3 Proportion of bilateral official development assistance of OECD/DAC donors that is untied (Includes tariff and quota-free access for the least 8.4 ODA received in landlocked developing countries as a developed countriesâ&#x20AC;&#x2122; exports; enhanced program of proportion of their gross national incomes debt relief for heavily indebted poor countries (HIPC) and cancellation of official bilateral debt; and more 8.5 ODA received in small island developing states as a proportion of their gross national incomes generous ODA for countries committed to poverty reduction.) Market access Target 8.C Address the special needs of landlocked 8.6 Proportion of total developed country imports (by value developing countries and small island developing and excluding arms) from developing countries and least states (through the Programme of Action for developed countries, admitted free of duty the Sustainable Development of Small Island 8.7 Average tariffs imposed by developed countries on Developing States and the outcome of the 22nd agricultural products and textiles and clothing from special session of the General Assembly) developing countries 8.8 Agricultural support estimate for OECD countries as a percentage of their GDP 8.9 Proportion of ODA provided to help build trade capacity Target 8.D Deal comprehensively with the debt problems of developing countries through national and Debt sustainability international measures in order to make debt 8.10 Total number of countries that have reached their HIPC sustainable in the long term decision points and number that have reached their HIPC completion points (cumulative) 8.11 Debt relief committed under HIPC Initiative and Multilateral Debt Relief Initiative (MDRI) 8.12 Debt service as a percentage of exports of goods and services Target 8.E In cooperation with pharmaceutical companies, 8.13 Proportion of population with access to affordable provide access to affordable essential drugs in essential drugs on a sustainable basis developing countries Target 8.F In cooperation with the private sector, make 8.14 Telephone lines per 100 population available the benefits of new technologies, 8.15 Cellular subscribers per 100 population especially information and communications 8.16 Internet users per 100 population 1. Where available, indicators based on national poverty lines should be used for monitoring country poverty trends. 2. T he proportion of people living in slums is measured by a proxy, represented by the urban population living in households with at least one of these characteristics: lack of access to improved water supply, lack of access to improved sanitation, overcrowding (3 or more persons per room), and dwellings made of nondurable material. 2011 World Development Indicators

9


1.1

Size of the economy Population

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 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

10

30 3 35 18 40 3 22 8 9 162 10 11 9 10 4 2 194 8 16 8 15 20 34 4 11 17 1,331 7 46 66 4 5 21 4 11 10 6 10 14 83 6 5 1 83 5 63c 1 2 4 82 24 11 14 10 2 10 7

Surface area

Population density

thousand sq. km 2009

people per sq. km 2009

$ billions 2009

Rank 2009

$ 2009

Rank 2009

$ billions 2009

Per capita $ 2009

46 115 15 15 15 108 3 101 106 1,246 48 356 81 9 74 3 23 70 58 323 84 41 4 7 9 23 143 6,721 41 29 11 90 66 79 105 136 130 209 55 83 297 50 32 83 18 114 c 6 171 61 235 105 88 131 41 57 364 67

9.1 12.6 154.2 69.4 304.1 9.5 957.5 388.5 42.5 93.5 53.7 488.4 6.7 16.1 17.7 12.2 1,564.2 46.0 8.0 1.2 9.7 23.2 1,416.4 2.0 6.7 160.7 4,856.2 221.1 227.8 10.6 7.7 28.7 22.5 61.0 62.2 181.6 326.5 45.9 54.1 172.1 20.8 1.6 18.9 27.2 245.3 2,750.9 10.9 0.7 11.1d 3,476.1 28.4 327.7 37.2 3.8 0.8 .. 13.5

125 114 49 63 29 124 15 25 76 57 68 19 138 105 103 117 8 73 133 186 123 93 10 177 139 48 3 37 36 121 135 86 95 66 65 43 28 74 67 45 100 180 102 89 33 5 120 196 118 4 87 27 81 162 194

310 4,000 4,420 3,750 7,550 3,100 43,770 46,450 4,840 580 5,560 45,270 750 1,630 4,700 6,260 8,070 6,060 510 150 650 1,190 41,980 450 600 9,470 3,650 31,570 4,990 160 2,080 6,260 1,070 13,770 5,550 17,310 59,060 4,550 3,970 b 2,070 3,370 320 14,060 330 45,940 42,620 7,370 440 2,530 d 42,450 1,190e 29,040 2,650 370 510 ..f 1,800

207 116 112 123 85 131 23 17 106 189 100 20 182 155 107 92 83 95 190 213 185 162 28 195 187 75 125 40 103 211 147 92 168 65 98 57 9 110 118 148 127 207 63 206 19 25 86 196 140 26 162 42 138 202 190

25.1a 27.3 283.2a 96.1 567.5 16.7 842.3 321.3 79.2 250.6 123.1 395.0 13.5 41.9 33.0 25.0 1,968.0 100.6 18.4 3.3 27.0 42.8 1,257.7 3.3 13.0 227.7 9,170.1 311.9 392.5 19.6 11.2 50.0a 34.5 85.1 .. 251.1 214.4 81.9a 110.4 471.2 39.6a 2.9a 25.6 77.3 188.3 2,191.2 18.4 2.3 20.6d 3,017.3 36.6 325.0 64.1a 9.5 1.7 .. 27.7a

860a 8,640 8,110a 5,190 14,090 5,410 38,510 38,410 9,020 1,550 12,740 36,610 1,510 4,250 8,770 12,840 10,160 13,260 1,170 390 1,820 2,190 37,280 750 1,160 13,420 6,890 44,540 8,600 300 3,040 10,930a 1,640 19,200 .. 23,940 38,780 8,110a 8,100 5,680 6,420a 580a 19,120 930 35,280 33,950 12,450 1,330 4,700 d 36,850 1,530 28,800 4,570a 940 1,060 .. 3,710a

652 29 2,382 1,247 2,780 30 7,741 84 87 144 208 31 113 1,099 51 582 8,515 111 274 28 181 475 9,985 623 1,284 756 9,600 1 1,142 2,345 342 51 322 57 110 79 43 49 256 1,001 21 118 45 1,104 338 549c 268 11 70 357 239 132 109 246 36 28 112

2011 World Development Indicators

Gross national income, Atlas method

111

Gross national income per capita, Atlas method

153

Purchasing power parity gross national income

Gross domestic product

Rank 2009

% growth 2008–09

Per capita % growth 2008–09

201 106 110 131 76 128 24 25 101 181 88 32 183 146 105 87 98 84 193 211 176 169 29 207 194 81 119 18 107 212 157 95 179 65

40.8 2.5 2.1 0.7 0.9 –14.4 1.3 –3.9 9.3 5.7 1.4 –2.8 3.8 3.4 –2.9 –3.7 –0.6 –4.9 3.5 3.5 –1.9 2.0 –2.5 2.4 –1.6 –1.5 9.1 –2.8 0.8 2.7 7.6 –1.5 3.6 –5.8 4.3 –4.2 –4.9 3.5 0.4 4.6 –3.5 3.6 –14.1 8.7 –8.0 –2.6 –1.0 4.6 –3.9d –4.7 4.7 –2.0 0.6 –0.3 3.0 2.9 –1.9

37.1 2.1 0.6 –1.9 –0.1 –14.6 –0.8 –4.2 8.0 4.3 1.6 –3.5 0.6 1.6 –2.7 –5.1 –1.5 –4.5 0.1 0.6 –3.5 –0.3 –3.7 0.5 –4.2 –2.5 8.5 –3.1 –0.6 0.0 5.6 –2.8 1.2 –5.8 4.3 –4.8 –5.5 2.0 –0.7 2.8 –4.0 0.6 –14.1 5.9 –8.4 –3.2 –2.7 1.8 –4.0 d –4.5 2.5 –2.4 –1.9 –2.6 0.7 1.3 –3.8

59 23 110 112 126 121 210 66 200 34 36 89 186 137 31 182 46 139 199 196 148


Population

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

10 1,155 230 73 31 4 7 60 3 128 6 16 40 24 49 2 3 5 6 2 4 2 4 6 3 2 20 15 27 13 3 1 107 4 3 32 23 50 2 29 17 4 6 15 155 5 3 170 3 7 6 29 92 38 11 4 1

Surface area

Population density

thousand sq. km 2009

people per sq. km 2009

93 3,287 1,905 1,745 438 70 22 301 11 378 89 2,725 580 121 100 11 18 200 237 65 10 30 111 1,760 65 26 587 118 331 1,240 1,031 2 1,964 34 1,564 447 799 677 824 147 42 268 130 1,267 924 324 310 796 75 463 407 1,285 300 313 92 9 12

112 389 127 45 72 65 344 205 249 350 67 6 70 199 503 166 157 28 27 36 413 68 41 4 53 81 34 162 84 11 3 628 55 110 2 72 29 77 3 205 490 16 48 12 170 16 9 220 46 15 16 23 308 125 116 447 122

Gross national income, Atlas method

Gross national income per capita, Atlas method

Purchasing power parity gross national income

$ billions 2009

Rank 2009

$ 2009

Rank 2009

$ billions 2009

Per capita $ 2009

130.1 1,405.7 471.0 330.6 69.7 197.1 192.0 2,114.5 12.4 4,857.2 23.7 110.0 30.3 .. 966.6 5.9 117.0 4.6 5.6 27.9 34.1 2.0 0.7 77.2 38.1 9.0 8.5 4.4 201.8 8.9 3.3 9.2 962.1 5.6g 4.4 89.9h 10.0 .. 9.3 13.0 801.1 124.3 5.7 5.2 184.7 408.5 49.8 169.8 22.7 7.9 14.3 122.4 164.6 467.6 232.9 .. ..

51 11 20 26 62 39 40 7 116 2 92 55 84

12,980 1,220 2,050 4,530 2,210 44,280 25,790 35,110 4,590 38,080 3,980 b 6,920 760 ..f 19,830 3,240 43,930 870 880 12,390 8,060 980 b 160 12,020 11,410 4,400 430 290 7,350 680 990 7,250 8,960 1,560 g 1,630 2,770 h 440 ..f 4,270 440 48,460 28,810 1,000 340 1,190 84,640 17,890 1,000 6,570 1,180 2,250 4,200 1,790 12,260 21,910 ..i ..i

66 160 149 111 146 22 46 35 109 32 117 89 181

191.3 3,786.3 855.0 836.5 105.0 147.0 201.0 1,919.2 19.5a 4,265.3 34.1 164.0 62.5 .. 1,328.0 .. 143.5 11.7 13.9 39.7 56.6 3.7 1.2 105.3a 57.8 22.2 19.5 11.9 376.6 15.4 6.4 16.9 1,506.3 10.7g 8.9 143.1h 20.1 .. 13.8 34.7 657.0 120.0 14.6a 10.3 321.0 267.5 68.3 454.7 42.1a 15.2a 28.1 236.7 325.6 697.9 256.1 .. ..

19,090 3,280 3,720 11,470 3,330 33,040 27,010 31,870 7,230a 33,440 5,730 10,320 1,570 .. 27,240 .. 53,890 2,200 2,200 17,610 13,400 1,800 290 16,400a 17,310 10,880 990 780 13,710 1,190 1,940 13,270 14,020 3,010 g 3,330 4,400h 880 .. 6,350 1,180 39,740 27,790 2,540a 680 2,070 55,420 24,530 2,680 12,180a 2,260a 4,430 8,120 3,540 18,290 24,080 .. ..

13 143 50 153 146 88 82 175 197 61 80 128 131 156 38 129 166 127 14 145 157 58 122 126 113 16 53 144 148 42 24 69 46 94 134 108 54 47 21 35

54 129 10 179 178 68 84 175 211 71 72 113 200 210 87 184 174 88 78 157 155 136 196 114 196 15 43 171 204 162 3 56 171 91 165 145 115 154 69 51

world view

1.1

Size of the economy

Gross domestic product

Rank 2009

% growth 2008–09

Per capita % growth 2008–09

67 154 147 94 151 38 52 41 117 37 125 97 180

–6.3 9.1 4.5 1.8 4.2 –7.1 0.8 –5.0 –3.0 –5.2 2.3 1.2 2.6 .. 0.2 4.0 4.4 2.3 6.4 –18.0 9.0 0.9 4.6 2.1 –15.0 –0.7 –3.7 7.6 –1.7 4.3 –1.1 2.1 –6.5 –6.5g –1.6 4.9h 6.3 .. –0.8 4.7 –4.0 –0.4 –5.6 1.0 5.6 –1.6 12.8 3.6 2.4 4.5 –3.8 0.9 1.1 1.7 –2.6 .. 8.6

–6.2 7.7 3.4 0.5 1.6 –7.6 –1.0 –5.7 –3.5 –5.1 –0.1 –0.2 –0.1 .. –0.1 3.4 1.9 1.5 4.5 –17.6 8.2 0.0 0.3 0.1 –14.6 –0.8 –6.2 4.7 –3.3 1.9 –3.3 1.6 –7.5 –6.4g –2.7 3.6h 4.0 .. –2.7 2.8 –4.5 –1.5 –6.9 –2.9 3.2 –2.8 10.4 1.4 0.8 2.1 –5.5 –0.3 –0.7 1.6 –2.7 .. –1.3

51 6 167 167 71 82 178 213 74 72 96 197 206 78 189 173 83 77 158 151 143 201 122 191 22 48 163 209 170 8 54 162 91 166 142 109 149 69 57

2011 World Development Indicators

11


1.1

Size of the economy Population

millions 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

21 142 10 25 13 7 6 5 5 2 9 49 46 20 42 1 9 8 21 7 44 68 1 7 1 10 75 5 33 46 5 62 307 3 28 28 87 4 24 13 13 6,775 s 846 4,813 3,811 1,002 5,659 1,944 404 572 331 1,568 840 1,117 327

Surface area

Population density

thousand sq. km 2009

people per sq. km 2009

238 17,098 26 2,000j 197 88 72 1 49 20 638 1,219 505 66 2,506 17 450 41 185 143 947 513 15 57 5 164 784 488 241 604 84 244 9,832 176 447 912 331 6 528 753 391 134,123 s 17,838 80,558 31,898 48,659 98,396 16,302 23,549 20,394 8,778 5,131 24,242 35,727 2,583

93 9 405 13 65 83 80 7,125 113 101 15 41 92 324 18 69 23 193 115 50 49 133 76 122 261 67 97 11 166 79 55 256 34 19 65 32 281 672 45 17 32 52 w 49 61 124 21 59 123 18 28 38 329 36 33 128

Gross national income, Atlas method

Gross national income per capita, Atlas method

Purchasing power parity gross national income

$ billions 2009

Rank 2009

$ 2009

Rank 2009

$ billions 2009

178.9 1,324.4 4.9 436.9 13.1 43.9 1.9 185.7 87.4 48.1 .. 284.3 1,476.2 40.4 51.5 2.9 454.4 505.8 50.9 4.8 21.4k 254.7 2.7 2.9 22.4 38.9 652.4 17.5 15.2 128.9 .. 2,558.1 14,233.5 30.2 30.6 286.4 87.7 .. 25.0 12.5 4.6 59,162.8 t 431.0 16,346.7 8,845.9 7,515.1 16,792.6 6,148.6 2,745.8 4,011.3 1,190.2 1,735.4 944.2 42,417.7 12,723.2

44 12 150 23 112 75 178 41 60 72

8,330 9,340 490 17,210 1,040 6,000 340 37,220 16,130 23,520 ..f 5,760 32,120 1,990 1,220 2,470 48,840 65,430 2,410 700 500k 3,760 2,460 440 16,700 3,720 8,720 3,420 460 2,800 ..i 41,370 46,360 9,010 1,100 10,090 1,000 b ..l 1,060 960 360 8,732 w 509 3,397 2,321 7,502 2,968 3,163 6,793 7,007 3,597 1,107 1,125 37,990 38,872

81 76 193 58 170 96 204 33 60 49

312.4 2,599.4 11.3 609.8 22.7 85.6 4.5 248.3 119.8 54.1 .. 495.6 1,447.2 95.8 84.1 5.7 353.9 364.1 97.3 13.5 57.9k 517.5 5.2a 5.6 33.4a 81.4 1,009.8 35.7a 39.0 284.4 .. 2,217.4 14,011.0 43.1 80.9a 346.9 243.6 .. 55.0 16.5 .. 71,774.4 t 1,032.5 30,653.8 18,229.1 12,461.9 31,684.3 11,712.8 5,097.0 5,888.7 2,617.6 4,658.7 1,722.2 40,433.9 11,127.6

31 9 77 70 167 22 18 71 151 97 32 169 168 96 78 17 104 106 52 6 1 85 83 30 59 90 115 154

97 39 151 160 143 14 8 144 183 192 122 141 196 59 124 79 126 194 135 29 18 77 167 74 171 169 176 203

Per capita $ 2009

14,540 18,330 1,130 24,020 1,810 11,700 790 49,780 22,110 26,470 .. 10,050 31,490 4,720 1,990 4,790 38,050 47,100 4,620 1,950 1,360k 7,640 4,730a 850 24,970a 7,810 13,500 6,980a 1,190 6,180 .. 35,860 45,640 12,900 2,910a 12,220 2,790 .. 2,330 1,280 .. 10,594 w 1,220 6,370 4,784 12,440 5,599 6,026 12,609 10,286 7,911 2,972 2,051 36,213 33,997

Rank 2009

75 68 195 58 177 93 205 11 63 53 99 43 136 171 134 28 14 138 172 184 115 133 203 55 113 80 118 189 123 33 16 86 159 90 161 165 187

Gross domestic product

% growth 2008–09

–8.5 –7.9 4.1 0.6 2.2 –3.0 4.0 –1.3 –6.2 –7.8 .. –1.8 –3.6 3.5 4.5 1.2 –5.1 –1.9 4.0 3.4 6.0k –2.2 1.9 2.5 –3.0 3.1 –4.7 8.0 7.1 –15.1 –0.7 –4.9 –2.6 2.9 8.1 –3.3 5.3 .. 3.8 6.4 5.7 –1.9 w 4.6 2.6 7.1 –2.6 2.7 7.4 –5.8 –1.9 3.4 8.1 1.7 –3.3 –4.1

Per capita % growth 2008–09

–8.4 –7.8 1.2 –1.7 –0.4 –2.6 1.5 –4.2 –6.4 –8.8 .. –2.8 –4.5 2.8 2.2 –0.3 –6.0 –3.0 1.5 1.7 3.0k –2.8 –1.3 0.0 –3.4 2.1 –5.8 6.6 3.6 –14.6 –3.2 –5.6 –3.5 2.5 6.3 –4.8 4.0 .. 0.8 3.8 5.2 –3.0 w 2.4 1.5 5.9 –3.4 1.4 6.6 –6.1 –3.0 1.6 6.5 –0.7 –3.9 –4.5

a. Based on regression; others are extrapolated from the 2005 International Comparison Program benchmark estimates. b. Included in the aggregates for lower middle-income economies based on earlier data. c. Excludes the French overseas departments of French Guiana, Guadeloupe, Martinique, and Réunion. d. Excludes Abkhazia and South Ossetia. e. Included in the aggregates for low-income economies based on earlier data. f. Estimated to be low income ($995 or less). g. Excludes Transnistria. h. Includes Former Spanish Sahara. i. Estimated to be high income ($12,196 or more). j. Provisional estimate. k. Covers mainland Tanzania only. l. Estimated to be lower middle income ($996–$3,945).

12

2011 World Development Indicators


About the data

1.1

world view

Size of the economy Definitions

Population, land area, income, and output are basic

conventional price indexes allow comparison of real

• Population is based on the de facto definition of

measures of the size of an economy. They also

values over time.

population, which counts all residents regardless of

provide a broad indication of actual and potential

PPP rates are calculated by simultaneously com-

legal status or citizenship—except for refugees not

resources. Population, land area, income (as mea-

paring the prices of similar goods and services

permanently settled in the country of asylum, who

sured by gross national income, GNI), and output

among a large number of countries. In the most

are generally considered part of the population of

(as measured by gross domestic product, GDP) are

recent round of price surveys conducted by the Inter-

their country of origin. The values shown are midyear

therefore used throughout World Development Indica-

national Comparison Program (ICP), 146 countries

estimates. See also table 2.1. •  Surface area is

tors to normalize other indicators.

and territories participated in the data collection,

a country’s total area, including areas under inland

Population estimates are generally based on

including China for the first time, India for the first

bodies of water and some coastal waterways. • Pop-

extrapolations from the most recent national cen-

time since 1985, and almost all African countries.

ulation density is midyear population divided by land

sus. For further discussion of the measurement of

The PPP conversion factors presented in the table

area in square kilometers. • Gross national income

population and population growth, see About the data

come from three sources. For 45 high- and upper

(GNI) is the sum of value added by all resident pro-

middle-income countries conversion factors are

ducers plus any product taxes (less subsidies) not

The surface area of an economy includes inland

provided by Eurostat and the Organisation for Eco-

included in the valuation of output plus net receipts

bodies of water and some coastal waterways. Sur-

nomic Co-operation and Development (OECD), with

of primary income (compensation of employees and

face area thus differs from land area, which excludes

PPP estimates for 34 European countries incorpo-

property income) from abroad. Data are in current

bodies of water, and from gross area, which may

rating new price data collected since 2005. For the

U.S. dollars converted using the World Bank Atlas

include offshore territorial waters. Land area is par-

remaining 2005 ICP countries the PPP estimates are

method (see Statistical methods). • GNI per capita is

ticularly important for understanding an economy’s

extrapolated from the 2005 ICP benchmark results,

GNI divided by midyear population. GNI per capita in

agricultural capacity and the environmental effects

which account for relative price changes between

U.S. dollars is converted using the World Bank Atlas

of human activity. (For measures of land area and

each economy and the United States. For countries

method. • Purchasing power parity (PPP) GNI is GNI

data on rural population density, land use, and agri-

that did not participate in the 2005 ICP round, the

converted to international dollars using PPP rates. An

cultural productivity, see tables 3.1–3.3.) Innova-

PPP estimates are imputed using a statistical model.

international dollar has the same purchasing power

tions in satellite mapping and computer databases

More information on the results of the 2005 ICP

over GNI that a U.S. dollar has in the United States.

for table 2.1.

have resulted in more precise measurements of land and water areas.

is available at www.worldbank.org/data/icp.

• Gross domestic product (GDP) is the sum of value

All 213 economies shown in World Development

added by all resident producers plus any product

GNI measures total domestic and foreign value

Indicators are ranked by size, including those that

taxes (less subsidies) not included in the valuation

added claimed by residents. GNI comprises GDP

appear in table 1.6. The ranks are shown only in

of output. Growth is calculated from constant price

plus net receipts of primary income (compensation

table 1.1. No rank is shown for economies for which

GDP data in local currency. • GDP per capita is GDP

of employees and property income) from nonresident

numerical estimates of GNI per capita are not pub-

divided by midyear population.

sources. The World Bank uses GNI per capita in U.S.

lished. Economies with missing data are included in

dollars to classify countries for analytical purposes

the ranking at their approximate level, so that the rel-

and to determine borrowing eligibility. For definitions

ative order of other economies remains consistent.

of the income groups in World Development Indicators, see Users guide. For discussion of the usefulness of national income and output as measures of productivity or welfare, see About the data for tables 4.1 and 4.2.

Data sources

When calculating GNI in U.S. dollars from GNI

Population estimates are prepared by World Bank

reported in national currencies, the World Bank fol-

staff from a variety of sources (see Data sources

lows the World Bank Atlas conversion method, using

for table 2.1). Data on surface and land area are

a three-year average of exchange rates to smooth

from the Food and Agriculture Organization (see

the effects of transitory fluctuations in exchange

Data sources for table 3.1). GNI, GNI per capita,

rates. (For further discussion of the World Bank Atlas

GDP growth, and GDP per capita growth are esti-

method, see Statistical methods.)

mated by World Bank staff based on national

Because exchange rates do not always reflect

accounts data collected by World Bank staff during

differences in price levels between countries,

economic missions or reported by national statis-

the table also converts GNI and GNI per capita

tical offices to other international organizations

estimates into international dollars using purchas-

such as the OECD. PPP conversion factors are

ing power parity (PPP) rates. PPP rates provide

estimates by Eurostat/OECD and by World Bank

a standard measure allowing comparison of real

staff based on data collected by the ICP.

levels of expenditure between countries, just as

2011 World Development Indicators

13


1.2

Millennium Development Goals: eradicating poverty and saving lives Eradicate extreme poverty and hunger Share of poorest quintile Vulnerable in national employment consumption Unpaid family workers and or income own-account workers % % of total employment 1995– 2009a,b 1990 2008

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

14

9.0 8.1 6.9 2.0d 4.1d 8.8 .. 8.6 8.0 9.4 9.2 8.5 6.9 2.8 6.7 .. 3.3 5.0 7.0 9.0 6.6 5.6 7.2 5.2 6.3 8.6 5.7 5.3 2.5 5.5 5.0 4.2 5.6 8.1 .. 10.2 8.3 4.4 4.2 9.0 4.3 .. 6.8 9.3 9.6 7.2 6.1 4.8 5.3 8.5 5.2 6.7 3.4 6.4 7.2 2.5 2.0

2011 World Development Indicators

.. .. .. .. .. .. 10 .. .. .. .. 16 .. 40 e .. .. 29e .. .. .. .. .. .. .. 94 .. .. 6 28e .. .. 25 .. .. .. 7 7 39 36e 28e 35 .. 2e .. .. 11 48 .. .. .. .. 40 e .. .. .. .. 49e

.. .. .. .. 20e .. 9 9 53 .. .. 10 .. .. .. .. 27 9 .. .. .. .. 10e .. .. 25 .. 7e 41 .. .. 20 .. 22f .. 13 5 42 34e 25 36 .. 6e 52e 9 6 .. .. 62 7 .. 27 .. .. .. .. ..

Prevalence of malnutrition Underweight % of children under age 5 1990

2004–09a

.. .. 9.2 .. .. .. .. .. .. 61.5 .. .. .. 9.7 .. .. .. .. 29.6 30.2 .. 18.0 .. .. .. .. 12.6 .. 8.8 .. 21.1 2.5 .. .. .. 0.9 .. 8.4 .. 10.5 11.1 36.9 .. .. .. .. .. .. .. .. 24.1 .. 27.8 .. .. 23.7 15.8

32.9 6.6 3.7 .. 2.3 4.2 .. .. 8.4 41.3 1.3 .. 20.2 4.5 1.6 .. 2.2 1.6 26.0 .. 28.8 16.6 .. .. 33.9 0.5 4.5 .. 5.1 28.2 11.8 .. 16.7 1.0 .. .. .. 3.4 6.2 6.8 .. .. .. 34.6 .. .. .. 15.8 2.3 1.1 14.3 .. .. 20.8 17.4 18.9 8.6

Achieve universal primary education

Promote gender equality

Reduce child mortality

Primary completion rate %

Ratio of girls to boys enrollments in primary and secondary education %

Under-five mortality rate per 1,000

1991

2009c

1991

2009c

1990

2009

28 .. 80 33 .. .. .. .. 95 41 94 79 22 71 .. 90 93 90 20 46 .. 53 .. 28 18 .. 107 102 73 48 54 79 42 .. 99 92 98 .. .. .. 65 .. .. 23 97 106 62 45 .. 100 64 99 .. 17 .. 27 64

.. 90 91 .. 102 98 .. 99 92 61 96 86 62 99 .. 95 .. 90 43 52 83 73 .. 38 33 95 .. 93 115 56 74 96 46 100 98 95 101 90 103 95 89 48 100 55 98 .. .. 79 107 104 83 101 80 62 .. .. 90

54 96 83 .. .. .. 100 95 100 75 .. 101 .. .. .. 109 .. 99 .. 82 .. 83 99 61 41 100 86 .. 108 70 89 101 .. 103 106 98 101 .. 100 81 101 82 103 68 109 102 96 65 98 99 78 99 87 45 55 .. 104

62 100 .. .. 105 103 97 97 102 108 101 98 .. 99 102 100 103 97 86 93 90 86 .. 69 64 99 105 102 105 77 .. 102 .. 102 99 101 102 97 103 .. 98 77 101 88 102 100 .. 102 96 98 95 97 94 77 .. .. 107

250 51 61 258 28 56 9 9 98 148 24 10 184 122 23 60 56 18 201 189 117 148 8 175 201 22 46 .. 35 199 104 18 152 13 14 12 9 62 53 90 62 150 17 210 7 9 93 153 47 9 120 11 76 231 240 152 55

199 15 32 161 14 22 5 4 34 52 12 5 118 51 14 57 21 10 166 166 88 154 6 171 209 9 19 .. 19 199 128 11 119 5 6 4 4 32 24 21 17 55 6 104 3 4 69 103 29 4 69 3 40 142 193 87 30


Eradicate extreme poverty and hunger Share of poorest quintile Vulnerable in national employment consumption Unpaid family workers and or income own-account workers % % of total employment 1995â&#x20AC;&#x201C; 2009a,b 1990 2008

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

8.4 8.1 7.6 6.4 .. 7.4 5.7 6.5 5.2 .. 7.2 8.7 4.7 .. 7.9 .. .. 8.8 7.6 6.8 .. 3.0 6.4 .. 6.6 5.4 6.2 7.0 4.5 6.5 6.2 .. 3.9 6.8 7.1 6.5 5.2 .. .. 6.1 7.6 6.4 3.8 8.3 5.1 9.6 .. 9.0 3.6 4.5 3.8 3.9 5.6 7.6 5.8 .. 3.9

7e .. .. .. .. 20 .. 27 42 19 .. .. .. .. .. .. .. .. .. .. .. 38 .. .. .. .. 84 .. 29 .. .. 12 26 .. .. .. .. .. .. .. 8 13 .. .. .. .. .. .. 34 .. 23e 36e .. 28e 25e .. ..

7 .. 63 43 .. 12 7 19 35 11 .. .. .. .. 25 .. .. 47 .. 7 .. .. .. .. 9 22 .. .. 22 .. .. 17 30 32 .. 51 .. .. .. .. 9 12 45 .. .. 6 .. 62 28 .. 47 40e 45e 19 19 .. ..

Prevalence of malnutrition Underweight % of children under age 5 1990

2004â&#x20AC;&#x201C;09a

2.3 59.5 31.0 .. 10.4 .. .. .. 4.0 .. 4.8 .. 20.1 .. .. .. .. .. 39.8 .. .. 13.8 .. .. .. .. 35.5 24.4 22.1 29.0 43.3 .. 13.9 .. 10.8 8.1 .. 28.8 21.5 .. .. .. 9.6 41.0 35.1 .. 21.4 39.0 .. .. 2.8 8.8 29.8 .. .. .. ..

.. 43.5 17.5g .. 7.1 .. .. .. 2.2 .. 1.9 4.9 16.4 20.6 .. .. 1.7 2.7 31.6 .. 4.2 16.6 20.4 5.6 .. 1.8 36.8 15.5 .. 27.9 16.7 .. 3.4 3.2 5.3 9.9 .. .. 17.5 38.8 .. .. 4.3 39.9 26.7 .. .. .. .. 18.1 .. 5.4 .. .. .. .. ..

1.2

world view

Millennium Development Goals: eradicating poverty and saving lives Achieve universal primary education

Promote gender equality

Reduce child mortality

Primary completion rate %

Ratio of girls to boys enrollments in primary and secondary education %

Under-five mortality rate per 1,000

1991

2009c

1991

2009c

1990

2009

82 .. 93 88 58 103 .. 98 94 102 101 .. .. .. 99 .. 57 .. 41 .. .. 59 .. .. .. 98 36 31 91 .. 33 115 88 .. .. 48 26 .. 74 51 .. .. 42 17 .. 100 74 .. 86 46 68 .. 88 96 .. .. 71

95 95 109 101 64 99 99 104 89 101 100 106 .. .. 99 .. 93 94 75 95 85 70 58 .. 92 92 79 59 97 59 64 89 104 93 93 80 57 99 87 .. .. .. 75 40 79 98 80 61 102 .. 94 101 94 96 .. .. 108

100 73 93 85 79 104 105 100 103 101 101 .. .. .. 99 .. 100 102 77 101 101 124 .. .. 96 99 96 82 101 58 71 102 97 105 109 70 71 95 106 59 97 100 119 53 77 102 89 48 99 80 98 96 99 101 103 .. 98

98 92 98 97 81 103 101 99 100 100 102 99 95 .. 97 .. 101 101 87 100 104 107 .. .. 100 98 97 100 103 78 103 101 102 101 103 88 88 100 104 .. 98 103 102 75 85 99 97 82 101 .. 100 99 102 99 100 102 120

17 118 86 73 53 9 11 10 33 6 39 60 99 45 9 .. 17 75 157 16 40 93 247 36 15 36 167 218 18 250 129 24 45 37 101 89 232 118 73 142 8 11 68 305 212 9 48 130 31 91 42 78 59 17 15 .. 19

6 66 39 31 44 4 4 4 31 3 25 29 84 33 5 .. 10 37 59 8 12 84 112 19 6 11 58 110 6 191 117 17 17 17 29 38 142 71 48 48 4 6 26 160 138 3 12 87 23 68 23 21 33 7 4 .. 11

2011 World Development Indicators

15


1.2

Millennium Development Goals: eradicating poverty and saving lives Eradicate extreme poverty and hunger Share of poorest quintile Vulnerable in national employment consumption Unpaid family workers and or income own-account workers % % of total employment 1995â&#x20AC;&#x201C; 2009a,b 1990 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

8.1 6.0 4.2 .. 6.2 9.1 6.1 5.0 8.8 8.2 .. 3.1 7.0 6.9 .. 4.5 9.1 7.6 7.7 9.3 6.8 3.9 9.0 5.4 .. 5.9 5.7 6.0 5.8 9.4 .. 6.1 5.4 5.6 7.1 4.9 7.3 .. 7.2 3.6 4.6

27e 1 .. .. 83 .. .. 8 .. 12e .. .. 22e .. .. .. .. 9 .. .. .. 70 .. .. 22 .. .. .. .. .. .. 10 .. .. .. .. .. .. .. 65 .. .. w .. .. .. .. .. .. .. .. .. .. .. .. ..

31 6 .. .. .. 23 .. 10 11 11 .. 3 12 41e .. .. 7 10 .. .. 88e 53 .. .. .. .. 35 .. .. .. .. 11 .. 25e .. 30 .. 36 .. .. .. .. w .. .. .. 26 .. .. 19 30 37 .. .. 12 11

Prevalence of malnutrition Underweight % of children under age 5 1990

5.0 .. 24.3 .. 19.0 .. 25.4 .. .. .. .. .. .. 29.3 31.8 .. .. .. 11.5 .. 25.1 16.3 .. 21.2 4.7 8.5 8.7 .. 19.7 .. .. .. .. 6.5 .. 6.7 40.7 .. 29.6 21.2 8.0 .. w .. 31.7 33.5 .. 32.5 18.0 .. .. .. 57.2 .. .. ..

2004â&#x20AC;&#x201C;09a

.. .. 18.0 5.3 14.5 1.8 21.3 .. .. .. 32.8 .. .. 21.6 31.7 6.1 .. .. 10.0 14.9 16.7 7.0 .. 22.3 .. 3.3 3.5 .. 16.4 .. .. .. 1.3 6.0 4.4 3.7 20.2 2.2 .. 14.9 14.0 21.3 w 27.7 20.8 24.0 .. 22.4 8.8 .. 3.8 6.8 42.5 24.7 .. ..

Achieve universal primary education

Promote gender equality

Reduce child mortality

Primary completion rate %

Ratio of girls to boys enrollments in primary and secondary education %

Under-five mortality rate per 1,000

1991

2009c

1991

2009c

1990

2009

96 .. 50 .. 39 .. .. .. 95 95 .. 76 104 101 .. 61 96 53 89 .. 55 .. .. 35 102 74 90 .. .. 92 103 .. .. 94 .. 81 .. .. .. .. 97 79 w 44 83 82 88 78 101 92 84 .. 62 51 .. 101

96 95 54 93 57 96 88 .. 96 96 .. 93 100 97 57 72 94 94 112 98 102 .. 80 61 93 93 93 .. 72 95 99 .. 95 106 92 95 .. 82 61 87 .. 88 w 63 92 90 100 87 99 96 101 95 79 64 98 ..

99 105 95 .. 69 .. 64 .. 102 103 .. 104 104 102 78 .. 102 97 85 .. 97 99 .. 59 101 86 81 .. 77 102 104 102 100 .. .. 105 .. .. .. .. 92 87 w 80 85 81 98 84 89 98 99 80 69 82 100 ..

99 98 100 91 95 101 84 .. 100 99 53 99 103 .. 89 92 99 97 97 91 96 103 .. 75 101 103 93 .. 99 99 100 101 100 104 99 102 .. 104 .. 96 97 96 w 91 97 95 101 96 102 97 102 96 91 88 99 ..

32 27 171 43 151 29 285 8 15 10 180 62 9 28 124 92 7 8 36 117 162 32 184 150 34 50 84 99 184 21 17 10 11 24 74 32 55 43 125 179 81 92 w 171 85 93 51 100 55 52 52 76 125 181 12 9

12 12 111 21 93 7 192 3 7 3 180 62 4 15 108 73 3 4 16 61 108 14 56 98 35 21 20 45 128 15 7 6 8 13 36 18 24 30 66 141 90 61 w 118 51 57 22 66 26 21 23 33 71 130 7 4

a. Data are for the most recent year available. b. See table 2.9 for survey year and whether share is based on income or consumption expenditure. c. Provisional data. d. Covers urban areas only. e. Limited coverage. f. Data are for 2009. g. Data are for 2010.

16

2011 World Development Indicators


About the data

1.2

world view

Millennium Development Goals: eradicating poverty and saving lives Definitions

Tables 1.2–1.4 present indicators for 17 of the 21

nutrients, and undernourished mothers who give

• Share of poorest quintile in national consump-

targets specified by the Millennium Development

birth to underweight children.

tion or income is the share of the poorest 20 per-

Goals. Each of the eight goals includes one or more

Progress toward universal primary education is

cent of the population in consumption or, in some

targets, and each target has several associated

measured by the primary completion rate. Because

cases, income. • Vulnerable employment is the sum

indicators for monitoring progress toward the target.

many school systems do not record school comple-

of unpaid family workers and own-account workers

Most of the targets are set as a value of a specific

tion on a consistent basis, it is estimated from the

as a percentage of total employment. • Prevalence

indicator to be attained by a certain date. In some

gross enrollment rate in the final grade of primary

of malnutrition is the percentage of children under

cases the target value is set relative to a level in

education, adjusted for repetition. Official enroll-

age 5 whose weight for age is more than two stan-

1990. In others it is set at an absolute level. Some

ments sometimes differ significantly from atten-

dard deviations below the median for the interna-

of the targets for goals 7 and 8 have not yet been

dance, and even school systems with high average

tional reference population ages 0–59 months. The

quantified.

enrollment ratios may have poor completion rates.

data are based on the new international child growth

The indicators in this table relate to goals 1–4.

Eliminating gender disparities in education would

standards for infants and young children, called the

Goal 1 has three targets between 1990 and 2015:

help increase the status and capabilities of women.

Child Growth Standards, released in 2006 by the

to halve the proportion of people whose income is

The ratio of female to male enrollments in primary

World Health Organization. • Primary completion

less than $1.25 a day, to achieve full and productive

and secondary education provides an imperfect mea-

rate is the percentage of students completing the

employment and decent work for all, and to halve the

sure of the relative accessibility of schooling for girls.

last year of primary education. It is calculated as

proportion of people who suffer from hunger. Esti-

The targets for reducing under-five mortality rates

the total number of students in the last grade of

mates of poverty rates are in tables 2.7 and 2.8.

are among the most challenging. Under-five mortal-

primary education, minus the number of repeaters

The indicator shown here, the share of the poorest

ity rates are harmonized estimates produced by a

in that grade, divided by the total number of children

quintile in national consumption or income, is a dis-

weighted least squares regression model and are

of official graduation age. • Ratio of girls to boys

tributional measure. Countries with more unequal

available at regular intervals for most countries.

enrollments in primary and secondary education

distributions of consumption (or income) have a

Most of the 60 indicators relating to the Millennium

is the ratio of the female to male gross enrollment

higher rate of poverty for a given average income.

Development Goals can be found in World Develop-

rate in primary and secondary education. • Under-

Vulnerable employment measures the portion of the

ment Indicators. Table 1.2a shows where to find the

five mortality rate is the probability that a newborn

labor force that receives the lowest wages and least

indicators for the first four goals. For more informa-

baby will die before reaching age five, if subject to

security in employment. No single indicator captures

tion about data collection methods and limitations,

current age-specific mortality rates. The probability

the concept of suffering from hunger. Child malnutri-

see About the data for the tables listed there. For

is expressed as a rate per 1,000.

tion is a symptom of inadequate food supply, lack

information about the indicators for goals 5–8, see

of essential nutrients, illnesses that deplete these

About the data for tables 1.3 and 1.4.

1.2a

Location of indicators for Millennium Development Goals 1–4 Goal 1. Eradicate extreme poverty and hunger

Table

1.1 Proportion of population below $1.25 a day

2.8

1.2 Poverty gap ratio

2.7, 2.8

1.3 Share of poorest quintile in national consumption

1.2, 2.9

1.4 Growth rate of GDP per person employed

2.4

1.5 Employment to population ratio

2.4

1.6 Proportion of employed people living below $1 per day

1.7 Proportion of own-account and unpaid family workers in total employment

1.2, 2.4

1.8 Prevalence of underweight in children under age five

1.2, 2.20

1.9 Proportion of population below minimum level of dietary energy consumption

2.20

Data sources

Goal 2. Achieve universal primary education 2.1 Net enrollment ratio in primary education

2.12

2.2 Proportion of pupils starting grade 1 who reach last grade of primary

2.13

2.3 Literacy rate of 15- to 24-year-olds

2.14

The indicators here and throughout this book have been compiled by World Bank staff from primary and secondary sources. Efforts have been made

Goal 3. Promote gender equality and empower women 3.1 Ratio of girls to boys in primary, secondary, and tertiary education

1.2, 2.12*

to harmonize the data series used to compile this

3.2 Share of women in wage employment in the nonagricultural sector

1.5, 2.3*

3.3 Proportion of seats held by women in national parliament

1.5

table with those published on the United Nations

Goal 4. Reduce child mortality

Millennium Development Goals Web site (www.

4.1 Under-five mortality rate

1.2, 2.22

un.org/millenniumgoals), but some differences in

4.2 Infant mortality rate

2.22

timing, sources, and definitions remain. For more

4.3 Proportion of one-year-old children immunized against measles

2.18

information see the data sources for the indica-

— No data are available in the World Development Indicators database. * Table shows information on related indicators.

tors listed in table 1.2a.

2011 World Development Indicators

17


1.3

Millennium Development Goals: protecting our common environment Improve maternal health Maternal mortality ratio Modeled estimate per 100,000 live births 2008

Afghanistan Albania Algeria Angola Argentina Armenia Australia Austria Azerbaijan 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

18

1,400 31 120 610 70 29 8 5 38 340 15 5 410 180 9 190 58 13 560 970 290 600 12 850 1,200 26 38 .. 85 670 580 44 470 14 53 8 5 100 140 82 110 280 12 470 8 8 260 400 48 7 350 2 110 680 1,000 300 110

Combat HIV/AIDS and other diseases

Contraceptive prevalence rate % of married women ages 15–49 1990 2004–09b

.. .. 47 .. .. .. .. .. .. 40 .. 78 .. 30 .. 33 59 .. .. .. .. 16 .. .. .. 56 85 86 66 8 .. .. .. .. .. 78 78 56 53 47 47 .. .. 4 77 81 .. 12 .. 75 13 .. .. .. .. 10 47

2011 World Development Indicators

15 69 61 .. 78 53 .. .. 51 53 73 75 17 61 36 53 81 .. 17 9 40 29 .. 19 3 58 85 .. 78 21 44 80 13 .. 78 .. .. 73 73 60 73 .. .. 15 .. 71 .. .. 47 .. 24 .. 54 9 10 32 65

Ensure environmental sustainability

HIV Incidence prevalence of tuberculosis Carbon dioxide emissions % of per capita population per 100,000 metric tons people ages 15–49 2009 2009 1990 2007

.. .. 0.1 2.0 0.5 0.1 0.1 0.3 0.1 <0.1 0.3 0.2 1.2 0.2 .. 24.8 .. 0.1 1.2 3.3 0.5 5.3 0.2 4.7 3.4 0.4 0.1c .. 0.5 .. 3.4 0.3 3.4 <0.1 0.1 <0.1 0.2 0.9 0.4 <0.1 0.8 0.8 1.2 .. 0.1 0.4 5.2 2.0 0.1 0.1 1.8 0.1 0.8 1.3 2.5 1.9 0.8

189 15 59 298 28 73 6 11 110 225 39 9 93 140 50 694 45 41 215 348 442 182 5 327 283 11 96 82 35 372 382 10 399 25 6 9 7 70 68 19 30 99 30 359 9 6 501 269 107 5 201 5 62 318 229 238 58

0.1 2.3 3.1 0.4 3.5 1.1 17.2 7.9 6.0 0.1 9.6 10.8 0.1 0.8 1.2 1.6 1.4 8.8 0.1 0.1 0.0 0.1 16.2 0.1 0.0 2.6 2.2 4.8 1.7 0.1 0.5 1.0 0.5 3.8 3.1 13.5 9.8 1.3 1.6 1.3 0.5 .. 16.3 0.1 10.2 7.0 6.6 0.2 2.9 12.0 0.3 7.2 0.6 0.2 0.2 0.1 0.5

0.0 1.4 4.1 1.4 4.6 1.6 17.7 8.3 3.7 0.3 6.9 9.7 0.5 1.4 7.7 2.6 1.9 6.8 0.1 0.0 0.3 0.3 16.9 0.1 0.0 4.3 5.0 5.8 1.4 0.0 0.4 1.8 0.3 5.6 2.4 12.1 9.1 2.1 2.2 2.3 1.1 0.1 15.2 0.1 12.1 6.0 1.4 0.2 1.4 9.6 0.4 8.8 1.0 0.1 0.2 0.2 1.2

Proportion of species threatened with extinction % 2008

0.7 1.5 2.1 1.4 1.9 0.9 4.7 1.9 0.8 1.9 0.7 1.3 1.5 0.8 13.1 0.5 1.3 1.1 1.0 1.5 29.8 5.4 1.8 0.6 1.0 2.4 2.4 13.2 1.2 2.5 1.0 1.9 3.9 1.8 4.2 1.5 1.6 2.1 10.4 4.1 1.8 15.0 0.6 1.3 1.3 2.5 2.1 2.2 1.0 2.2 3.7 2.1 2.4 2.2 2.4 2.3 3.5

Develop a global partnership for development

Access to improved sanitation facilities % of population 1990 2008

.. .. 88 25 90 .. 100 100 .. 39 .. 100 5 19 .. 36 69 99 6 44 9 47 100 11 6 84 41 .. 68 9 .. 93 20 .. 80 100 100 73 69 72 75 9 .. 4 100 100 .. .. 96 100 7 97 65 9 .. 26 44

37 98 95 57 90 90 100 100 45 53 93 100 12 25 95 60 80 100 11 46 29 47 100 34 9 96 55 .. 74 23 30 95 23 99 91 98 100 83 92 94 87 14 95 12 100 100 33 67 95 100 13 98 81 19 21 17 71

Internet users per 100 peoplea 2009

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


Improve maternal health Maternal mortality ratio Modeled estimate per 100,000 live births 2008

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

13 230 240 30 75 3 7 5 89 6 59 45 530 250 18 .. 9 81 580 20 26 530 990 64 13 9 440 510 31 830 550 36 85 32 65 110 550 240 180 380 9 14 100 820 840 7 20 260 71 250 95 98 94 6 7 18 8

Combat HIV/AIDS and other diseases

Contraceptive prevalence rate % of married women ages 15–49 1990 2004–09b

.. 43 50 49 14 60 68 .. 55 58 40 .. 27 62 79 .. .. .. .. .. .. 23 .. .. .. .. 17 13 50 .. 3 75 .. .. .. 42 .. 17 29 23 76 .. .. 4 6 74 9 15 .. .. 48 59 36 49 .. .. ..

.. 54 57 79 50 89 .. .. .. 54 59 51 46 .. 80 .. .. 48 38 .. 58 47 11 .. .. 14 40 41 .. 8 9 .. 73 68 55 63 16 41 55 48 69 .. 72 11 15 88 .. 30 .. 32 79 73 51 .. 67 .. ..

Ensure environmental sustainability

HIV Incidence prevalence of tuberculosis Carbon dioxide emissions % of per capita population per 100,000 metric tons people ages 15–49 2009 2009 1990 2007

<0.1 0.3 0.2 0.2 .. 0.2 0.2 0.3 1.7 <0.1 .. 0.1 6.3 .. <0.1 .. .. 0.3 0.2 0.7 0.1 23.6 1.5 .. 0.1 .. 0.2 11.0 0.5 1.0 0.7 1.0 0.3 0.4 <0.1 0.1 11.5 0.6 13.1 0.4 0.2 0.1 0.2 0.8 3.6 0.1 0.1 0.1 0.9 0.9 0.3 0.4 <0.1 0.1 0.6 .. 0.1

16 168 189 19 64 9 5 6 7 21 6 163 305 345 90 .. 35 159 89 45 15 634 288 40 71 23 261 304 83 324 330 22 17 178 224 92 409 404 727 163 8 8 44 181 295 6 13 231 48 250 47 113 280 24 30 2 49

1.3

6.1 0.8 0.8 4.2 2.8 8.6 7.2 7.5 3.3 9.3 3.3 15.9 0.2 12.1 5.6 .. 19.2 2.4 0.1 5.1 3.1 .. 0.2 9.2 6.0 5.6 0.1 0.1 3.1 0.0 1.3 1.4 4.3 4.8 4.5 0.9 0.1 0.1 0.0 0.0 11.0 6.9 0.6 0.1 0.5 7.4 5.6 0.6 1.3 0.5 0.5 1.0 0.7 9.1 4.5 .. 25.2

5.6 1.4 1.8 7.0 3.3 10.2 9.3 7.7 5.2 9.8 3.8 14.7 0.3 3.0 10.4 .. 32.3 1.2 0.3 3.4 3.2 .. 0.2 9.3 4.5 5.5 0.1 0.1 7.3 0.0 0.6 3.1 4.5 1.3 4.0 1.5 0.1 0.3 1.5 0.1 10.6 7.7 0.8 0.1 0.6 9.1 13.7 1.0 2.2 0.5 0.7 1.5 0.8 8.3 5.5 .. 55.4

Proportion of species threatened with extinction % 2008

1.8 3.3 3.4 1.0 11.0 1.8 4.3 2.2 7.7 4.9 3.4 1.1 3.9 1.3 1.7 .. 6.3 0.8 1.2 1.4 1.2 0.6 3.8 1.6 0.9 0.9 6.4 3.3 6.9 1.0 2.9 24.3 3.2 1.3 1.1 1.9 2.9 2.7 2.1 1.1 1.3 5.1 1.3 1.0 4.3 1.5 4.2 1.7 2.9 3.6 0.5 2.8 6.6 1.2 2.8 3.6 ..

Develop a global partnership for development

Access to improved sanitation facilities % of population 1990 2008

100 18 33 83 .. 99 100 .. 83 100 .. 96 26 .. 100 .. 100 .. .. .. .. 32 11 97 .. .. 8 42 84 26 16 91 66 .. .. 53 11 .. 25 11 100 .. 43 5 37 100 85 28 58 47 37 54 58 .. 92 .. 100

world view

Millennium Development Goals: protecting our common environment

100 31 52 .. 73 99 100 .. 83 100 98 97 31 .. 100 .. 100 93 53 78 .. 29 17 97 .. 89 11 56 96 36 26 91 85 79 50 69 17 81 33 31 100 .. 52 9 32 100 .. 45 69 45 70 68 76 90 100 .. 100

2011 World Development Indicators

Internet users per 100 peoplea 2009

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

19


1.3

Millennium Development Goals: protecting our common environment Improve maternal health Maternal mortality ratio Modeled estimate per 100,000 live births 2008

Romania Russian Federation Rwanda Saudi Arabia Senegal Serbia Sierra Leone Singapore Slovak Republic Slovenia Somalia South Africa Spain Sri Lanka Sudan Swaziland Sweden Switzerland Syrian Arab Republic Tajikistan Tanzania Thailand Timor-Leste Togo Trinidad and Tobago Tunisia Turkey Turkmenistan Uganda Ukraine United Arab Emirates United Kingdom United States Uruguay Uzbekistan Venezuela, RB Vietnam West Bank and Gaza Yemen, Rep. Zambia Zimbabwe World Low income Middle income Lower middle income Upper middle income Low & middle income East Asia & Pacific Europe & Central Asia Latin America & Carib. Middle East & N. Africa South Asia Sub-Saharan Africa High income Euro area

27 39 540 24 410 8 970 9 6 18 1,200 410 6 39 750 420 5 10 46 64 790 48 370 350 55 60 23 77 430 26 10 12 24 27 30 68 56 .. 210 470 790 260 w 580 200 230 82 290 89 32 86 88 290 650 15 7

Combat HIV/AIDS and other diseases

Contraceptive prevalence rate % of married women ages 15–49 1990 2004–09b

.. 34 21 .. .. .. .. 65 74 .. 1 57 .. .. 9 20 .. .. .. .. 10 .. .. 34 .. 50 63 .. 5 .. .. .. 71 .. .. .. 53 .. 10 15 43 57 w 23 58 60 52 54 75 .. .. 42 40 15 70 ..

70 80 36 24 12 41 8 .. .. .. 15 .. 66 68 8 51 .. .. 58 37 26 77 22d 17 43 60 73 48 24 67 .. .. .. 78 65 .. 80 50 28 41 65 61 w 33 66 63 75 61 77 69 75 62 51 21 .. ..

Ensure environmental sustainability

HIV Incidence prevalence of tuberculosis Carbon dioxide emissions % of per capita population per 100,000 metric tons people ages 15–49 2009 2009 1990 2007

0.1 1.0 2.9 .. 0.9 0.1 1.6 0.1 <0.1 <0.1 0.7 17.8 0.4 <0.1 1.1 25.9 0.1 0.4 .. 0.2 5.6 1.3 .. 3.2 1.5 <0.1 <0.1 .. 6.5 1.1 .. 0.2 0.6 0.5 0.1 .. 0.4 .. .. 13.5 14.3 0.8 w 2.7 0.6 0.4 1.4 0.9 0.2 0.6 0.5 0.1 0.3 5.4 0.3 0.3

125 106 376 18 282 21 644 36 9 12 285 971 17 66 119 1,257 6 5 21 202 183 137 498 446 23 24 29 67 293 101 4 12 4 22 128 33 200 19 54 433 742 137 w 294 138 147 101 161 136 89 45 39 180 342 14 9

6.8 13.9 0.1 13.2 0.4 .. 0.1 15.4 8.6 6.2 0.0 9.5 5.9 0.2 0.2 0.5 6.0 6.4 2.9 3.9 0.1 1.7 .. 0.2 13.9 1.6 2.7 7.2 0.0 11.7 29.3 10.0 19.5 1.3 5.3 6.2 0.3 .. 0.8 0.3 1.5 4.3e w 0.7 2.6 1.6 6.1 2.4 1.9 10.7 2.3 2.5 0.7 0.9 11.9 8.6

4.4 10.8 0.1 16.6 0.5 .. 0.2 11.8 6.8 7.5 0.1 9.0 8.0 0.6 0.3 0.9 5.4 5.0 3.5 1.1 0.1 4.1 0.2 0.2 27.9 2.3 4.0 9.2 0.1 6.8 31.0 8.8 19.3 1.9 4.3 6.0 1.3 0.6 1.0 0.2 0.8 4.6e w 0.3 3.3 2.8 5.3 2.9 4.0 7.2 2.7 3.7 1.2 0.8 12.5 8.2

Proportion of species threatened with extinction % 2008

1.6 1.3 1.6 3.8 2.2 .. 3.2 9.7 1.1 2.1 3.2 1.6 3.8 14.0 2.4 0.8 1.4 1.4 2.0 0.8 5.1 3.4 .. 1.2 1.7 2.1 1.4 10.7 2.5 1.1 14.1 2.8 5.7 2.6 1.0 1.1 3.5 .. 12.6 0.7 0.9                        

Develop a global partnership for development

Access to improved sanitation facilities % of population 1990 2008

71 87 23 .. 38 .. .. 99 100 100 .. 69 100 70 34 .. 100 100 83 .. 24 80 .. 13 93 74 84 98 39 95 97 100 100 94 84 82 35 .. 18 46 43 52 w 23 45 37 78 43 42 87 69 73 22 27 100 100

72 87 54 .. 51 92 13 100 100 100 23 77 100 91 34 55 100 100 96 94 24 96 50 12 92 85 90 98 48 95 97 100 100 100 100 .. 75 89 52 49 44 61 w 35 57 50 84 54 59 89 79 84 36 31 99 100

Internet users per 100 peoplea 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

a. Data are from the International Telecommunication Union’s (ITU) World Telecommunication Development Report database. Please cite ITU for third-party use of these data. b. Data are for the most recent year available. c. Includes Hong Kong SAR, China. d. Data are for 2010. e. Includes emissions not allocated to specific countries.

20

2011 World Development Indicators


About the data

The Millennium Development Goals address concerns common to all economies. Diseases and environmental degradation do not respect national boundaries. Epidemic diseases, wherever they occur, pose a threat to people everywhere. And environmental damage in one location may affect the well-being of plants, animals, and humans far away. The indicators in the table relate to goals 5, 6, and 7 and the targets of goal 8 that address access to new technologies. For the other targets of goal 8, see table 1.4. The target of achieving universal access to reproductive health has been added to goal 5 to address the importance of family planning and health services in improving maternal health and preventing maternal death. Women with multiple pregnancies are more likely to die in childbirth. Access to contraception is an important way to limit and space births. Measuring disease prevalence or incidence can be difficult. Most developing economies lack reporting systems for monitoring diseases. Estimates are often derived from survey data and report data from sentinel sites, extrapolated to the general population. Tracking diseases such as HIV/AIDS, which has a long latency

1.3

world view

Millennium Development Goals: protecting our common environment Definitions

between contraction of the virus and the appearance of symptoms, or malaria, which has periods of dormancy, can be particularly difficult. The table shows the estimated prevalence of HIV among adults ages 15–49. Prevalence among older populations can be affected by life-prolonging treatment. The incidence of tuberculosis is based on case notifications and estimates of cases detected in the population. Carbon dioxide emissions are the primary source of greenhouse gases, which contribute to global warming, threatening human and natural habitats. In recognition of the vulnerability of animal and plant species, a new target of reducing biodiversity loss has been added to goal 7. Access to reliable supplies of safe drinking water and sanitary disposal of excreta are two of the most important means of improving human health and protecting the environment. Improved sanitation facilities prevent human, animal, and insect contact with excreta. Internet use includes narrowband and broadband Internet. Narrowband is often limited to basic applications; broadband is essential to promote e-business, e-learning, e-government, and e-health.

• Maternal mortality ratio is the number of women who die from pregnancy-related causes during pregnancy and childbirth, per 100,000 live births. Data are from various years and adjusted to a common 2008 base year. The values are modeled estimates (see About the data for table 2.19). • Contraceptive prevalence rate is the percentage of women ages 15–49 married or in union who are practicing, or whose sexual partners are practicing, any form of contraception. • HIV prevalence is the percentage of people ages 15–49 who are infected with HIV. • Incidence of tuberculosis is the estimated number of new tuberculosis cases (pulmonary, smear positive, and extrapulmonary). • Carbon dioxide emissions are those stemming from the burning of fossil fuels and the manufacture of cement. They include emissions produced during consumption of solid, liquid, and gas fuels and gas flaring (see table 3.8). • Proportion of species threatened with extinction is the total number of threatened mammal (excluding whales and porpoises), bird, and higher native, vascular plant species as a percentage of the total number of known species of the same categories.

1.3a

Location of indicators for Millennium Development Goals 5–7 Goal 5. Improve maternal health 5.1 Maternal mortality ratio 5.2 Proportion of births attended by skilled health personnel 5.3 Contraceptive prevalence rate 5.4 Adolescent fertility rate 5.5 Antenatal care coverage 5.6 Unmet need for family planning Goal 6. Combat HIV/AIDS, malaria, and other diseases 6.1 HIV prevalence among pregnant women ages 15–24 6.2 Condom use at last high-risk sex 6.3 Proportion of population ages 15–24 with comprehensive, correct knowledge of HIV/AIDS 6.4 Ratio of school attendance of orphans to school attendance of nonorphans ages 10–14 6.5 Proportion of population with advanced HIV infection with access to antiretroviral drugs 6.6 Incidence and death rates associated with malaria 6.7 Proportion of children under age 5 sleeping under insecticide-treated bednets 6.8 Proportion of children under age 5 with fever who are treated with appropriate antimalarial drugs 6.9 Incidence, prevalence, and death rates associated with tuberculosis 6.10 Proportion of tuberculosis cases detected and cured under directly observed treatment short course Goal 7. Ensure environmental sustainability 7.1 Proportion of land area covered by forest 7.2 Carbon dioxide emissions, total, per capita and per $1 purchasing power parity GDP 7.3 Consumption of ozone-depleting substances 7.4 Proportion of fish stocks within safe biological limits 7.5 Proportion of total water resources used 7.6 Proportion of terrestrial and marine areas protected 7.7 Proportion of species threatened with extinction 7.8 Proportion of population using an improved drinking water source 7.9 Proportion of population using an improved sanitation facility Proportion of urban population living in slums

Table 1.3, 2.19 2.19 1.3, 2.19 2.19 1.5, 2.19 2.19

•  Access to improved sanitation facilities is the percentage of the population with at least adequate access to excreta disposal facilities (private or shared, but not public) that can effectively prevent human, animal, and insect contact with excreta (facilities do not have to include treatment to render sewage outflows innocuous). Improved facilities range from simple but protected pit latrines to flush toilets with a sewerage connection. To be effective,

1.3*, 2.21* 2.21* —

facilities must be correctly constructed and properly maintained. • Internet users are people with access to the worldwide network.

— — — 2.18 2.18 1.3, 2.21 2.18

Data sources

3.1

The indicators here and throughout this book have

3.8 3.9* — 3.5 — 1.3 1.3, 2.18, 3.5 1.3, 2.18, 3.11 —

— No data are available in the World Development Indicators database. * Table shows information on related indicators.

been compiled by World Bank staff from primary and secondary sources. Efforts have been made to harmonize the data series used to compile this table with those published on the United Nations Millennium Development Goals Web site (www. un.org/millenniumgoals), but some differences in timing, sources, and definitions remain. For more information see the data sources for the indicators listed in tables 1.3a and 1.4a.

2011 World Development Indicators

21


1.4

Millennium Development Goals: overcoming obstacles

Development Assistance Committee members Official development assistance (ODA) by donor For basic social services a Net disbursements % of total sector% of donor allocable ODA GNI commitments 2009 2009

Australia Canada European Union Austria Belgium Denmark Finland France Germany Greece Ireland Italy Luxembourg Netherlands Portugal Spain Sweden United Kingdom Japan Korea, Rep.c New Zealandc Norway Switzerland United States

0.29 0.30

14.5 25.5

0.30 0.55 0.88 0.54 0.46 0.35 0.19 0.54 0.16 1.04 0.82 0.23 0.46 1.12 0.52 0.18 0.10 0.28 1.06 0.45 0.21

6.3 12.7 21.3 5.8 8.8 8.7 11.2 32.1 12.9 35.4 11.9 3.6 24.2 10.8 21.4 18.6 6.7 27.7 21.9 9.5 31.7

Least developed countries’ access to high-income markets Goods (excluding arms) admitted free of tariffs % of exports from least developed countries 2002 2008

Support to agriculture

Average tariff on exports of least developed countries % Agricultural products 2002 2008

Textiles

Clothing

2002

2008

2002

2008

% of GDP 2009b

95.9 67.2 97.0

100.0 100.0 98.7

0.2 0.3 1.8

0.0 0.1 0.9

5.1 5.7 0.1

0.0 0.2 0.1

19.7 17.9 1.2

0.0 1.7 1.2

0.15 0.75 0.84

33.2 14.6 98.0 97.9 93.4 61.7

99.6 57.7 98.2 99.9 100.0 83.8

4.8 26.1 3.1 3.8 5.1 6.3

1.4 28.5 0.0 18.0 0.1 5.8

2.8 11.4 0.3 3.1 0.0 6.6

2.6 4.0 0.0 0.0 0.0 5.7

0.1 12.5 0.3 1.3 0.0 12.5

0.1 3.7 0.0 1.0 0.0 11.3

1.11 2.44 0.20 1.07 1.37 0.87

Heavily indebted poor countries (HIPCs) HIPC HIPC HIPC decision completion Initiative pointd pointd assistance

Afghanistan Benin Boliviae Burkina Fasoe,f Burundi Cameroon Central African Republic Chad Comoros Congo, Dem. Rep. Congo, Rep. Côte d’Ivoire Ethiopiaf Gambia, The Ghana Guinea Guinea-Bissau Guyanae

Jul. 2007 Jul. 2000 Feb. 2000 Jul. 2000 Aug. 2005 Oct. 2000 Sep. 2007 May 2001 Jun. 2010 Jul. 2003 Mar. 2006 Mar. 2009 Nov. 2001 Dec. 2000 Feb. 2002 Dec. 2000 Dec. 2000 Nov. 2000

Jan. 2010 Mar. 2003 Jun. 2001 Apr. 2002 Jan. 2009 Apr. 2006 Jun. 2009 Floating Floating Jul. 2010 Jan. 2010 Floating Apr. 2004 Dec. 2007 Jul. 2004 Floating Dec. 2010 Dec. 2003

HIPC HIPC HIPC decision completion Initiative pointd pointd assistance

MDRI assistance

MDRI assistance

end-2009 net present value

end-2009 net present value

$ millions

$ millions

654 385 1,949 812 1,009 1,861 675 241 151 9,493 1,906 3,245 2,735 98 3,091 801 746 897

20 754 1,953 764 58 646 435 .. .. 515 120 .. 1,862 232 2,570 .. 77 493

Haiti Honduras Liberia Madagascar Malawif Malie Mauritania Mozambiquee Nicaragua Niger f Rwandaf São Tomé & Principef Senegal Sierra Leone Tanzania Togo Ugandae Zambia

Nov. 2006 Jul. 2000 Mar. 2008 Dec. 2000 Dec. 2000 Sep. 2000 Feb. 2000 Apr. 2000 Dec. 2000 Dec. 2000 Dec. 2000 Dec. 2000 Jun. 2000 Mar. 2002 Apr. 2000 Nov. 2008 Feb. 2000 Dec. 2000

Jun. 2009 Apr. 2005 Jun. 2010 Oct. 2004 Aug. 2006 Mar. 2003 Jun. 2002 Sep. 2001 Jan. 2004 Apr. 2004 Apr. 2005 Mar. 2007 Apr. 2004 Dec. 2006 Nov. 2001 Dec. 2010 May 2000 Apr. 2005

164 816 2,958 1,228 1,379 792 913 3,147 4,861 947 956 172 717 919 2,977 305 1,509 3,672

665 1,893 243 1,598 898 1,308 558 1,322 1,191 651 283 34 1,661 465 2,517 463 2,245 1,962

a. Includes primary education, basic life skills for youth, adult and early childhood education, basic health care, basic health infrastructure, basic nutrition, infectious disease control, health education, health personnel development, population policy and administrative management, reproductive health care, family planning, sexually transmitted disease control including HIV/AIDS, personnel development for population and reproductive health, basic drinking water supply and basic sanitation, and multisector aid for basic social services. b. Provisional data. c. Calculated by World Bank staff using the World Integrated Trade Solution based on the United Nations Conference on Trade and Development’s Trade Analysis and Information Systems database. d. Refers to the Enhanced HIPC Initiative. e. Also reached completion point under the original HIPC Initiative. The assistance includes original debt relief. f. Assistance includes topping up at completion point.

22

2011 World Development Indicators


About the data

1.4

world view

Millennium Development Goals: overcoming obstacles Definitions

Achieving the Millennium Development Goals

lines with “international peaks”). The averages in

•  Official development assistance (ODA) net dis-

requires an open, rule-based global economy in

the table include ad valorem duties and equivalents.

bursements are grants and loans (net of repayments of

which all countries, rich and poor, participate. Many

Subsidies to agricultural producers and exporters

principal) that meet the DAC definition of ODA and are

poor countries, lacking the resources to finance

in OECD countries are another barrier to developing

made to countries on the DAC list of recipients. • ODA

development, burdened by unsustainable debt, and

economies’ exports. Agricultural subsidies in OECD

unable to compete globally, need assistance from

economies are estimated at $384 billion in 2009.

rich countries. For goal 8—develop a global partner-

The Debt Initiative for Heavily Indebted Poor Coun-

ship for development—many indicators therefore

tries (HIPCs), an important step in placing debt relief

monitor the actions of members of the Organisa-

within the framework of poverty reduction, is the first

tion for Economic Co-operation and Development’s

comprehensive approach to reducing the external

(OECD) Development Assistance Committee (DAC).

debt of the world’s poorest, most heavily indebted

for basic social services is aid commitments by DAC donors for basic education, primary health care, nutrition, population policies and programs, reproductive health, and water and sanitation services. • Goods admitted free of tariffs are exports of goods (excluding arms) from least developed countries admitted without tariff. • Average tariff is the unweighted average of the effectively applied rates for all products subject to

Official development assistance (ODA) has risen

countries. A 1999 review led to an enhancement of

tariffs. • Agricultural products are plant and animal

in recent years as a share of donor countries’ gross

the framework. In 2005, to further reduce the debt

products, including tree crops but excluding timber and

national income (GNI), but the poorest economies

of HIPCs and provide resources for meeting the Mil-

fish products. • Textiles and clothing are natural and

need additional assistance to achieve the Millen-

lennium Development Goals, the Multilateral Debt

synthetic fibers and fabrics and articles of clothing

nium Development Goals. In 2009 total net ODA from

Relief Initiative (MDRI), proposed by the Group of

made from them. • Support to agriculture is the value

OECD DAC members rose 0.7 percent in real terms

Eight countries, was launched.

of gross transfers from taxpayers and consumers aris-

to $119.6 billion, representing 0.31 percent of DAC members’ combined gross national income.

Under the MDRI four multilateral institutions—the

ing from policy measures, net of associated budgetary

International Development Association (IDA), Inter-

receipts, regardless of their objectives and impacts on farm production and income or consumption of farm

One important action that high-income economies

national Monetary Fund (IMF), African Development

can take is to reduce barriers to exports from low-

Fund (AfDF), and Inter-American Development Bank

and middle- income economies. The European Union

(IDB)—provide 100 percent debt relief on eligible

has begun to eliminate tariffs on exports of “every-

debts due to them from countries having completed

thing but arms” from least developed countries, and

the HIPC Initiative process. Data in the table refer

the United States offers special concessions to Sub-

to status as of March 2011 and might not show

Saharan African exports. However, these programs

countries that have since reached the decision or

still have many restrictions.

completion point. Debt relief under the HIPC Initia-

fully completes the key structural reforms agreed on

Average tariffs in the table reflect high-income

tive has reduced future debt payments by $59 bil-

at the decision point, including implementing a poverty

OECD member tariff schedules for exports of coun-

lion (in end-2009 net present value terms) for 36

reduction strategy. The country then receives full debt

tries designated least developed countries by the

countries that have reached the decision point. And

relief under the HIPC Initiative without further policy

United Nations. Although average tariffs have been

32 countries that have reached the completion point

conditions. • HIPC Initiative assistance is the debt

falling, averages may disguise high tariffs on specific

have received additional assistance of $30 billion (in

relief committed as of the decision point (assuming full

goods (see table 6.8 for each country’s share of tariff

end-2009 net present value terms) under the MDRI.

participation of creditors). Topping-up assistance and

products. • HIPC decision point is the date when a heavily indebted poor country with an established track record of good performance under adjustment programs supported by the IMF and the World Bank commits to additional reforms and a poverty reduction strategy and starts receiving debt relief. • HIPC completion point is the date when a country success-

assistance provided under the original HIPC Initiative

1.4a

Location of indicators for Millennium Development Goal 8 Goal 8.1 8.2 8.3 8.4 8.5 8.6

8. Develop a global partnership for development Net ODA as a percentage of DAC donors’ gross national income Proportion of ODA for basic social services Proportion of ODA that is untied Proportion of ODA received in landlocked countries as a percentage of GNI Proportion of ODA received in small island developing states as a percentage of GNI Proportion of total developed country imports (by value, excluding arms) from least developed countries admitted free of duty 8.7 Average tariffs imposed by developed countries on agricultural products and textiles and clothing from least developed countries 8.8 Agricultural support estimate for OECD countries as a percentage of GDP 8.9 Proportion of ODA provided to help build trade capacity 8.10 Number of countries reaching HIPC decision and completion points 8.11 Debt relief committed under new HIPC initiative 8.12 Debt services as a percentage of exports of goods and services 8.13 Proportion of population with access to affordable, essential drugs on a sustainable basis 8.14 Telephone lines per 100 people 8.15 Cellular subscribers per 100 people 8.16 Internet users per 100 people

Table 1.4, 6.14 1.4 6.15b — — 1.4 1.4, 6.8* 1.4 — 1.4 1.4 6.11* — 1.3*, 5.11 1.3*, 5.11 5.12

— No data are available in the World Development Indicators database. * Table shows information on related indicators.

were committed in net present value terms as of the decision point and are converted to end-2009 terms. • MDRI assistance is 100 percent debt relief on eligible debt from IDA, IMF, AfDF, and IDB, delivered in full to countries having reached the HIPC completion point.

Data sources Data on ODA are from the OECD. Data on goods admitted free of tariffs and average tariffs are from the World Trade Organization, in collaboration with the United Nations Conference on Trade and Development and the International Trade Centre. These data are available at www.mdg-trade. org. Data on subsidies to agriculture are from the OECD’s Producer and Consumer Support Estimates, OECD Database 1986–2009. Data on the HIPC Initiative and MDRI are from the World Bank’s Economic Policy and Debt Department.

2011 World Development Indicators

23


1.5

Women in development Female population

% of total 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

48.2 50.6 49.5 50.7 51.0 53.4 50.3 51.2 51.1 49.4 53.5 51.0 49.5 50.1 51.9 50.0 50.8 51.7 50.1 51.0 51.1 50.0 50.5 50.9 50.3 50.5 48.1c 52.6 50.8 50.4 50.1 49.2 49.1

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

24

Life expectancy at birth

years Male Female 2009 2009

Pregnant women receiving prenatal care

% 2004–09a

Teenage mothers

% of women ages 15–19 2004–09a

44 74 71 46 72 71 79 77 68 66 65 78 61 64 73 55 69 70 52 49 60 51 79 46 48 76 72c 80 70 46 53 77 57

44 80 74 50 79 77 84 83 73 68 76 84 63 68 78 55 76 77 55 52 63 52 84 49 50 82 75c 86 77 49 55 82 59

36 97 89 80 99 93 .. .. 77 51 99 .. 84 86 99 94 97 .. 85 92 83b 82 .. 69 39 .. 91 .. 94 85 86 90 85

.. .. .. 29 .. 5 .. .. 6 33 .. .. 21 .. .. .. .. .. .. .. 8 28 .. .. 37 .. .. .. 21 24 27 .. ..

51.8

73

80

100 b

4

49.9 50.9 50.4 49.8 49.9 49.7 52.8 50.8 53.9 50.3 51.0 51.4 50.0 50.4 53.0 51.0 49.3 50.4 51.3 49.5 50.5 50.6 50.0

77 74 77 70 72 69 67 58 70 54 77 78 60 55 68 77 56 78 67 56 47 60 70

81 80 81 76 78 72 76 62 80 57 83 85 62 58 75 83 58 83 74 60 50 63 75

100 .. .. 99 84 74 94 .. .. 28 .. .. .. 98 94 .. 90 .. .. 88 78 85 92

2011 World Development Indicators

.. .. .. 21 19 10 .. .. .. 17 .. .. .. .. 10 .. 13 .. .. 32 .. 14 22

Women in wage employment in nonagricultural sector % of nonagricultural wage employment 2008

Unpaid family workers

Male Female % of male % of female employment employment 2008 2008

Female part-time employment

Ratio Women in of female parliaments to male wages in manufacturing

%

% of total 2004–09a

2004–09a

% of total seats 1990 2010

.. .. 13 .. 45 45 47 47 44 .. 56 47 .. 38 36 43 42 51 .. .. .. .. 50 .. .. 36 .. 49 48 .. .. 42 ..

.. .. .. .. 0.7b .. 0.2 2.0 0.0 .. .. 0.4 .. .. 2.0 .. 4.6 0.6 .. .. .. .. 0.1 .. .. 0.9 .. 0.1b 3.2 .. .. 1.3 ..

.. .. .. .. 1.6 b .. 0.4 2.7 0.0 .. .. 2.2 .. .. 8.9 .. 8.1 1.5 .. .. .. .. 0.2 .. .. 2.8 .. 1.1b 6.1 .. .. 2.8 ..

.. .. .. .. 61b .. 71b 81 .. .. .. 81 .. .. .. .. .. 54 .. .. .. .. 68b .. .. 56 .. .. .. .. .. .. ..

.. .. .. .. .. .. 90 .. .. .. .. 86 .. .. .. 66 .. 69 .. .. .. .. .. .. .. .. .. 59 60 .. .. 70 ..

4 29 2 15 6 36 6 12 .. 10 .. 9 3 9 .. 5 5 21 .. .. .. 14 13 4 .. .. 21 .. 5 5 14 11 6

45d

0.9d

3.9d

59

77

..

24

43 46 49 39 39 19 48 .. 52 47 51 49 .. .. 46 47 .. 42 43 .. .. .. 34

.. 0.3 0.3 2.9 4.4b 8.6 8.8 .. 0.0 b 7.8b 0.6 0.3 .. .. .. 0.4 .. 3.4 .. .. .. .. ..

.. 1.0 0.5 3.4 11.1b 32.6 9.9 .. 0.0 b 12.7b 0.4 0.9 .. .. .. 1.5 .. 9.8 .. .. .. .. ..

.. 69 62 .. .. .. .. .. 68 56b 64 80 .. .. 56 80 .. 68 .. .. .. .. ..

.. .. 87 .. .. 76 85 .. .. .. 84 82 .. .. 61 74 .. .. .. .. .. .. ..

34 .. 31 8 5 4 12 .. .. .. 32 7 13 8 .. .. .. 7 7 .. 20 .. 10

43 22 38 21 32 2 19 22 23 28 40 19 15 8 7 33 8 17 12 19 10 4 18

28 16 8 39 39 9 25 28 11 19 35 39 11 25 19 8 9 21 15 32 21 14 22 10 5 14 21 .. 8 8 7 39 9


Female population

% of total 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

52.5 48.3 50.1 49.2 49.4 49.9 50.4 51.4 51.1 51.3 48.7 52.4 50.0 50.6 50.5 .. 40.5 50.7 50.1 53.9 51.0 52.8 50.3 48.3 53.2 50.1 50.2 50.3 49.2 50.6 49.3 50.4 50.8 52.5 50.5 50.9 51.4 51.2 50.7 50.3 50.4 50.6 50.5 49.9 49.9 50.3 43.6 48.5 49.6 49.2 49.5 49.9 49.6 51.8 51.6 52.0 24.6

Life expectancy at birth

years Male Female 2009 2009

70 63 69 70 65 77 80 79 69 80 71 64 54 65 77 68 76 62 64 68 70 45 57 72 68 72 59 53 72 48 55 69 73 65 64 69 47 60 61 66 79 78 70 51 48 79 75 67 73 59 70 71 70 72 76 75 75

78 66 73 73 72 82 84 84 75 86 75 74 55 70 84 72 80 72 67 78 74 46 60 77 79 77 62 55 77 50 59 76 78 72 70 74 49 64 62 68 83 82 77 53 49 83 78 67 79 64 74 76 74 80 82 83 77

Pregnant women receiving prenatal care

% 2004–09a

.. 75 93 98 84 .. .. .. 91 .. 99 100 92 .. .. .. .. 97 35 .. 96 92 79 .. .. 94 86 92 79 70 75 .. 94 98 100 68 89 80 95 44 .. .. 90 46 58 .. .. 61 .. 79 96 94 91 .. .. .. ..

Teenage mothers

% of women ages 15–19 2004–09a

.. 16 9 .. .. .. .. .. .. .. 4 7 .. .. .. .. .. .. 17 .. .. 20 38 .. .. .. 34 34 .. 36 .. .. .. 6 .. 7 .. .. 15 19 .. .. 25 39 23 .. .. 9 .. .. 13 26 10 .. .. .. ..

Women in wage employment in nonagricultural sector % of nonagricultural wage employment 2008

48 .. 32 .. 12 49 49 44 48 42 16 50 .. .. 42 .. .. 51 .. 53 .. .. .. .. 53 42 .. .. 39 .. .. 37 39 54 51 21 .. .. .. .. 48 48 38 36 .. 49 22 13 42 .. 40 38 42 47 48 42 13

Unpaid family workers

Female part-time employment

Male Female % of male % of female employment employment 2008 2008

0.3 .. 7.8 5.4 .. 0.6 0.1 1.2 0.5 1.1 .. .. .. .. 1.2 .. .. 8.8 26.4 1.4 .. .. .. .. 1.0 7.0 .. .. 2.7 .. .. 0.9 4.9 1.3 .. 16.5 .. .. 0.9 .. 0.2 0.8 12.2 .. .. 0.2 .. 18.6 2.3 .. 10.8 4.7b 9.0 b 2.7 0.7 0.0 ..

0.5 .. 33.6 32.7 .. 0.8 0.4 2.5 2.2 7.3 .. .. .. .. 12.7 .. .. 19.3 64.2 1.2 .. .. .. .. 2.0 14.9 .. .. 8.8 .. .. 4.7 10.0 3.4 .. 51.8 .. .. 1.1 .. 0.8 1.5 9.1 .. .. 0.4 .. 61.9 4.0 .. 8.9 9.9 b 18.0 b 5.9 1.2 0.0 ..

% of total 2004–09a

65 .. .. .. .. 77 73 78 .. 70 .. .. .. .. 59 .. .. .. .. 59 .. .. .. .. 60 47 .. .. .. .. .. 44 65 .. .. .. .. .. .. .. 75 72b .. .. .. 71 .. .. 47 .. .. .. .. 68 68 .. ..

1.5

world view

Women in development

Women in Ratio parliaments of female to male wages in manufacturing

% 2004–09a

77 .. .. .. .. .. .. .. .. 60 61 70 .. .. 57 .. .. .. .. 77 .. .. .. .. 71 .. .. .. .. .. .. .. 70 .. 77 .. .. 89 .. .. 82 82 .. .. .. 89 .. .. 95 .. .. .. 91 .. 69 .. 142

2011 World Development Indicators

% of total seats 1990 2010

21 5 12 2 11 8 7 13 5 1 0 .. 1 21 2 .. .. .. 6 .. 0 .. .. .. .. .. 7 10 5 .. .. 7 12 .. 25 0 16 .. 7 6 21 14 15 5 .. 36 .. 10 8 0 6 6 9 14 8 .. ..

9 11 18 3 25 14 18 21 13 11 6 18 10 16 15 .. 8 26 25 22 3 24 13 8 19 33 8 21 10 10 22 19 26 24 4 11 39 .. 24 33 41 34 21 12 7 40 0 22 9 1 13 28 21 20 27 .. 0

25


1.5

Women in development Female population

% of total 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

51.4 53.8 51.6 44.8 50.4 50.5 51.3 49.8 51.5 51.2 50.4 50.7 50.7 50.8 49.6 51.1 50.4 51.2 49.5 50.6 50.1 50.8 49.1 50.5 51.4 49.7 49.8 50.7 49.9 53.9 32.7 50.9 50.7 51.7 50.3 49.8 50.6 49.1 49.4 50.1 51.7 49.6 w 50.1 49.3 48.8 50.9 49.4 48.8 52.2 50.6 49.6 48.5 50.2 50.6 51.1

Life expectancy at birth

years Male Female 2009 2009

70 63 49 73 54 71 47 79 71 76 49 50 79 71 57 47 79 80 73 64 56 66 61 61 66 73 70 61 53 64 77 78 76 73 65 71 73 72 62 46 45 67 w 56 67 66 69 65 71 66 71 69 63 51 77 78

77 75 52 74 57 76 49 84 79 82 52 53 85 78 60 46 83 84 76 70 57 72 63 65 73 77 75 69 54 75 79 82 81 80 71 77 77 75 65 47 46 71 w 59 71 70 75 69 74 75 77 73 66 54 83 83

Pregnant women receiving prenatal care

% 2004–09a

94 .. 96 .. 94 98 87 .. .. .. 26 .. .. 99 64 85 .. .. 84 80 76 98 .. 84 96 96 95 99 94 99 .. .. .. 96 99 .. 91 99 47 94 93 82 w 67 85 83 95 82 91 .. 95 83 70 71 .. ..

Teenage mothers

% of women ages 15–19 2004–09a

.. .. 4 .. 18 .. 34 .. .. .. .. .. .. .. .. 23 .. .. .. .. 26 .. .. .. .. .. .. .. 25 4 .. .. .. .. .. .. .. .. .. 28 21                             

Women in wage employment in nonagricultural sector % of nonagricultural wage employment 2008

46 51 .. 15 .. 44 .. 46 48 47 .. 44 45 31 .. .. 50 48 16 37 31 45 .. .. .. .. 22 .. .. 55 20 52 48 46 39 42 .. 18 6 .. .. .. w ..  .. .. 43 .. .. 48 41 .. .. .. 46 47

Unpaid family workers

Male Female % of male % of female employment employment 2008 2008

6.0 0.1 .. .. .. 3.1 .. 0.4b 0.1 3.2 .. 0.3 0.8 4.4b .. .. 0.2 1.7b .. .. 9.7 14.0 .. .. .. .. 5.3 .. .. 0.4 .. 0.2 0.1 0.9 b .. 0.6 .. 6.6 .. .. .. .. w .. .. .. 3.3 .. .. 1.9 4.0 .. .. .. 0.6 0.8

a. Data are for the most recent year available. b. Limited coverage. c. Includes Taiwan, China. d. Data are for 2009.

26

2011 World Development Indicators

18.9 0.1 .. .. .. 11.9 .. 1.3b 0.2 5.4 .. 0.6 1.4 21.7b .. .. 0.3 3.2b .. .. 13.0 29.9 .. .. .. .. 37.7 .. .. 0.3 .. 0.5 0.1 3.0 b .. 1.6 .. 31.5 .. .. .. .. w .. .. .. 7.2 .. .. 5.3 7.5 .. .. .. 2.4 1.8

Female part-time employment

% of total 2004–09a

49 62 .. .. .. .. .. .. 59 57 .. .. 79 .. .. .. 64 81 .. .. .. .. .. .. .. .. 58 .. .. .. .. 76 67b 59b .. .. .. .. .. .. .. .. w .. .. .. .. .. .. .. .. .. .. .. 71 78

Women in Ratio parliaments of female to male wages in manufacturing

% 2004–09a

74 .. .. .. .. .. .. 65 .. .. .. .. .. 93 .. .. 90 77 .. .. .. .. .. .. .. .. .. .. .. 71 .. 80 .. .. .. .. .. 53 .. .. .. 71 m 89 71 85 70 71 91 71 70 53 93 66 71 73

% of total seats 1990 2010

34 .. 17 .. 13 .. .. 5 .. .. 4 3 15 5 .. 4 38 14 9 .. .. 3 .. 5 17 4 1 26 12 .. 0 6 7 6 .. 10 18 .. 4 7 11 13 w .. 13 13 12 13 17 .. 12 4 6 .. 12 12

11 14 56 0 23 22 13 23 15 14 7 45 37 5 26 14 45 29 12 19 31 13 29 11 29 28 9 17 32 8 23 22 17 15 22 19 26 .. 0 14 15 19 w 19 18 17 19 18 19 15 24 9 19 20 23 26


About the data

1.5

world view

Women in development Definitions

Despite much progress in recent decades, gender

in non-agricultural wage employment. The indicator

• Female population is the percentage of the popu-

inequalities remain pervasive in many dimensions of

does not reveal any differences in the quality of the

lation that is female. • Life expectancy at birth is

life—worldwide. But while disparities exist through-

different types of non-agricultural wage employment,

the number of years a newborn infant would live if

out the world, they are most prevalent in developing

regarding earnings, conditions of work, or the legal

prevailing patterns of mortality at the time of its birth

countries. Gender inequalities in the allocation of

and social protection, which they offer. The indica-

were to stay the same throughout its life. • Pregnant

such resources as education, health care, nutrition,

tor cannot reflect whether women are able to reap

women receiving prenatal care are the percentage

and political voice matter because of the strong

the economic benefits of such employment, either.

of women attended at least once during pregnancy

association with well-being, productivity, and eco-

Finally it should be noted that the female employ-

by skilled health personnel for reasons related to

nomic growth. These patterns of inequality begin at

ment of any kind tends to be underreported in all

pregnancy. • Teenage mothers are the percentage of

an early age, with boys routinely receiving a larger

kinds of surveys. In addition, the employment share

women ages 15–19 who already have children or are

share of education and health spending than do girls,

of the agricultural sector, for both men and women,

currently pregnant. • Women in wage employment

for example.

is severely underreported.

in nonagricultural sector are female wage employ-

Because of biological differences girls are

Women’s wage work is important for economic

ees in the nonagricultural sector as a percentage

expected to experience lower infant and child mor-

growth and the well-being of families. But women

of total nonagricultural wage employment. • Unpaid

tality rates and to have a longer life expectancy than

often face such obstacles as restricted access to

family workers are those who work without pay in a

boys. This biological advantage may be overshad-

credit markets, capital, land, training, and educa-

market-oriented establishment or activity operated

owed, however, by gender inequalities in nutrition

tion, time constraints due to their traditional family

by a related person living in the same household.

and medical interventions and by inadequate care

responsibilities, and labor market bias and discrimi-

• Part-time employment, female is a female share

during pregnancy and delivery, so that female rates

nation. These obstacles force women to limit their

of total part-time workers. Part-time worker is an

of illness and death sometimes exceed male rates.

full participation in paid economic activities, and to

employed person whose normal hours of work are

These gender bias can be seen in the child mortal-

be less productive and to receive lower wages. More

less than those of comparable full-time workers. Defi-

ity rates (table 2.22) or life expectancy by gender.

women than men are found in unpaid family employ-

nition of part-time varies across countries. • Ratio of

Female child mortality rates that are as high as or

ment and part time employment. The gender wage

female to male wages in manufacturing is a ratio of

higher than male child mortality rates may indicate

gap in manufacturing remains an unfortunate reality

women’s wage to men’s in manufacturing. • Women

discrimination against girls.

of almost all countries of the world, even though the

in parliaments are the percentage of parliamentary

gap may not be attributed entirely to discrimination.

seats in a single or lower chamber held by women.

Having a child during the teenage years limits girls’ opportunities for better education, jobs, and income.

Women are vastly underrepresented in decision-

Pregnancy is more likely to be unintended during

making positions in government, although there is

the teenage years, and births are more likely to be

some evidence of recent improvement. Gender parity

Data on female population are from the United

premature and are associated with greater risks of

in parliamentary representation is still far from being

Nations Population Division’s World Population

complications during delivery and of death. In many

realized. In 2010 women accounted for 19 percent

Prospects: The 2008 Revision, and data on life

countries maternal mortality (tables 1.3 and 2.19) is

of parliamentarians worldwide, compared with 9 per-

expectancy for more than half the countries in the

a leading cause of death among women of reproduc-

cent in 1987. Without representation at this level, it

table (most of them developing countries) are from

tive age, although most of them are preventable.

is difficult for women to influence policy.

its World Population Prospects: The 2008 Revision,

Data sources

Women in wage employment in nonagricultural sec-

For information on other aspects of gender, see

with additional data from census reports, other

tor shows the extent that women have access to paid

tables 1.2 (Millennium Development Goals: eradicat-

statistical publications from national statistical

employment, which will affect their integration into

ing poverty and saving lives), 1.3 (Millennium Devel-

offices, Eurostat’s Demographic Statistics, the

the monetary economy. It also indicates the degree

opment Goals: protecting our common environment),

Secretariat of the Pacific Community’s Statistics

that labour markets are open to women in industry

2.3 (Employment by economic activity), 2.4 (Decent

and Demography Programme, and the U.S. Bureau

and services sectors which affects not only equal

work and productive employment), 2.5 (Unemploy-

of the Census International Data Base. Data on

employment opportunity for women, but also eco-

ment), 2.6 (Children at work), 2.10 (Assessing vulner-

pregnant women receiving prenatal care are from

nomic efficiency through flexibility of the labor market

ability and security), 2.13 (Education efficiency), 2.14

UNICEF’s The State of the World’s Children 2010

and the economy’s capacity to adapt to changes over

(Education completion and outcomes), 2.15 (Educa-

time. In many developing countries, non-agricultural

tion gaps by income and gender), 2.19 (Reproductive

based on household surveys including DemoData sources graphic and Health Surveys by Macro International

wage employment represents only a small portion

health), 2.21 (Health risk factors and future chal-

and Multiple Indicator Cluster Surveys by UNICEF.

of total employment. As a result the contribution of

lenges), and 2.22 (Mortality).

Data on teenage mothers are from Demographic

women to the national economy is underestimated

and Health Surveys by Macro International. Data

and therefore misrepresented. The indicator is dif-

on labor force, employment and wage are from the

ficult to interpret, unless additional information is

International Labour Organization’s Key Indicators

available on the share of women in total employ-

of the Labour Market, 6th edition. Data on women in

ment, which would allow an assessment to be made

parliaments are from the Inter-Parliamentary Union.

of whether women are under- or over-represented

2011 World Development Indicators

27


1.6

Key indicators for other economies Population Surface Population area density

Gross national income

thousand sq. km 2009

people per sq. km 2009

$ millions 2009

Per capita $ 2009

..b

American Samoa

67

0.2

336

..

Andorra

85

0.5

181

3,447

41,130

Antigua and Barbuda

88

0.4

199

1,062

12,130

592

..

Life Adult expectancy literacy at birth rate

Carbon dioxide emissions

Purchasing power parity

Atlas method thousands 2009

Gross domestic product

..d

$ millions 2009

Per capita $ 2009

% growth 2008–09

Per capita % growth 2008–09

years 2009

% ages 15 thousand and older metric tons 2005–09a 2007

..

..

..

..

..

..

..

..

..

3.6

1.6

..

..

539

–8.5

–9.5

..

99

436

..

..

75

98

2,396

1,548 c ..

17,670 c ..

Aruba

107

0.2

Bahamas, The

342

13.9

34

7,136

21,390

..

..

2.8

1.5

74

..

2,147

Bahrain

791

0.8

1,041

19,712

25,420

26,130

33,690

6.3

4.1

76

91

22,446 1,345

Barbados

256

0.4

595

..

Belize

333

23.0

15

1,205

64

0.1

1,288

..

Bhutan

697

38.4

18

1,405

Brunei Darussalam

400

5.8

76

..

Cape Verde

Bermuda

..d 3,740 ..d 2,020

.. 1,929c ..

.. 5,990 c ..

..

..

77

..

0.0

–3.4

77

..

425

–8.1

–8.4

79

..

513

3,692

5,290

7.4

5.8

67

53

579

..d 19,706

51,200

0.6

–1.3

78

95

7,599

1,783

3,530

2.8

1.4

71

84

308

..

..

..

..

..

99

539

506

4.0

125

1,520

Cayman Islands

55

0.3

229

..

Channel Islands

150

0.2

789

10,242

68,610

..

..

5.9

5.7

79

..

..

Comoros

659

1.9

354

531

810

779

1,180

1.8

–0.6

66

74

121

Cyprus

871

9.3

94

24,400e

30,290e

–1.0e

–1.9e

80

98

8,193

Djibouti

864

23.2

37

1,106

1,280

2,480

5.0

3.2

56

..

487

74

0.8

98

360

4,900

8,460 c

–0.8

–1.3

..

..

121

676

28.1

24

8,398

12,420

–5.4

–7.8

51

93

4,793

Dominica Equatorial Guinea

3,010 ..d

30,480e 24,250 e

..d

2,140 623c 13,069

19,330

49

1.4

35

..

..

..

..

..

80

..

696

Fiji

849

18.3

46

3,259

3,840 f

3,850

4,530

–3.0

–3.6

69

..

1,458

French Polynesia

806

Faeroe Islands

269

4.0

74

..

..d

..

..

..

..

75

..

Gibraltar

31

0.0

3,105

..

..d

..

..

..

..

..

..

407

Greenland

56

410.5

..

..

–5.4

–5.0

68

..

520

104

0.3

–6.8

–7.1

75

..

242

..

..

76

..

..

3.3

3.4

68

..

1,506

Grenada

0g 306

1,467

26,160

580

5,580 ..d

802c ..

7,710 c ..

Guam

178

0.5

329

..

Guyana

762

215.0

4

2,026

2,660

Iceland

319

103.0

3

13,858

43,430

10,478

32,840

–6.5

–7.0

81

..

2,338

80

0.6

141

3,972

49,310

..

..

7.5

7.4

..

..

..

Isle of Man

About the data

2,491c

3,270 c

Definitions

The table shows data for economies with populations

• Population is based on the de facto definition of

included in the valuation of output plus net receipts

between 30,000 and 1 million and for smaller econo-

population, which counts all residents regardless of

of primary income (compensation of employees

mies if they are members of the World Bank. Where

legal status or citizenship—except for refugees not

and property income) from abroad. Data are in cur-

data on gross national income (GNI) per capita are

permanently settled in the country of asylum, who

rent U.S. dollars converted using the World Bank

not available, the estimated range is given. For more

are generally considered part of the population of

Atlas method (see Statistical methods). • Purchasing

information on the calculation of GNI and purchasing

their country of origin. The values shown are midyear

power parity (PPP) GNI is GNI converted to interna-

power parity (PPP) conversion factors, see About the

estimates. For more information, see About the data

tional dollars using PPP rates. An international dollar

data for table 1.1. Additional data for the economies

for table 2.1. •  Surface area is a country’s total

has the same purchasing power over GNI that a U.S.

in the table are available on the World Development

area, including areas under inland bodies of water

dollar has in the United States. • GNI per capita is

Indicators CD-ROM or in WDI Online.

and some coastal waterways. • Population density

GNI divided by midyear population. • Gross domes-

is midyear population divided by land area in square

tic product (GDP) is the sum of value added by all

kilometers. •  Gross national income (GNI), Atlas

resident producers plus any product taxes (less sub-

method, is the sum of value added by all resident

sidies) not included in the valuation of output. Growth

producers plus any product taxes (less subsidies) not

is calculated from constant price GDP data in local

28

2011 World Development Indicators


Population Surface Population area density

Gross national income

thousand sq. km 2009

people per sq. km 2009

$ millions 2009

Life Adult expectancy literacy at birth rate

Carbon dioxide emissions

Purchasing power parity

Atlas method thousands 2009

Gross domestic product

1.6

world view

Key indicators for other economies

Per capita $ 2009

$ millions 2009

Per capita $ 2009

324 c

3,310 c

% growth 2008–09

Per capita % growth 2008–09

years 2009

% ages 15 thousand and older metric tons 2005–09a 2007

Kiribati

98

0.8

121

180

1,830

–0.7

–2.2

..

..

Liechtenstein

36

0.2

224

4,906

136,630

..

..

–1.2

–1.9

83

..

33 ..

Luxembourg

498

2.6

192

38,188

76,710

29,669

59,590

–4.1

–5.8

80

..

10,834

39,550

30,874

57,390

1.3

–0.9

81

93

1,554

1,625

5,250

–3.0

–4.4

72

98

898

80

92

2,722 99

Macao SAR, China

538

0.0

19,213

21,275

Maldives

309

0.3

1,031

1,229

Malta

415

0.3

1,297

7,621

18,360

9,616

23,170

–2.1

–2.8

3,060

..

..

0.0

–2.2

..

..

..

..

..

..

76

..

..

–1.5

–1.8

69

..

62

Marshall Islands

3,970h

61

0.2

339

186

Mayotte

197

0.4

531

..

Micronesia, Fed. Sts.

111

0.7

158

277

2,500

Monaco

..b

359 c

3,240 c

33

0.0

16,406

6,483

197,590

..

..

–2.6

–2.9

..

..

..

Montenegro

624

13.8

46

4,149

6,650

8,183

13,110

–5.7

–6.0

74

..

..

Netherlands Antilles

198

0.8

248

..

..d

..

..

..

..

76

96

6,232

New Caledonia

250

18.6

14

..

..d

..

..

..

..

77

96

2,847

Northern Mariana Islands

87

0.5

189

..

..d

..

..

..

..

..

..

..

Palau

20

0.5

44

127

6,220

..

..

–2.1

–2.7

..

..

213

179

2.8

63

508

2,840

–5.5

–5.5

72

99

161

31

0.1

524

1,572

50,670

..

..

1.9

0.4

83

..

..

163

1.0

170

185

1,130

301

1,850

4.0

2.4

66

88

128

Samoa San Marino Sao Tome and Principe Seychelles Solomon Islands St. Kitts and Nevis St. Lucia St. Vincent and the Grenadines Suriname Tonga Turks and Caicos Islands

4,270 c

88

0.5

191

746

8,480

1,477c

16,790 c

–7.6

–8.7

74

92

623

523

28.9

19

477

910

974 c

1,860 c

–2.2

–4.5

67

..

198

50

0.3

191

503

10,150

676c

13,640 c

–8.0

–8.8

..

..

249

8,860 c

–3.8

–4.9

..

..

381

172

0.6

282

894

5,190

1,525c

109

0.4

280

560

5,130

964 c

8,830 c

–2.8

–2.8

72

..

202

520

163.8

3

2,454

4,760

3,469c

6,730 c

5.1

4.2

69

95

2,437

104

0.8

144

339

3,260

475c

4,570 c

–0.4

–0.8

72

99

176

..

..

..

..

158

33

1.0

35

..

..d

..

0.0

..

..

..i

Vanuatu

240

12.2

20

627

2,620

Virgin Islands (U.S.)

110

0.4

314

..

Tuvalu

763c

..d

.. .. 1,028 c ..

.. .. 4,290 c ..

..

..

..

..

..

4.0

1.4

71

81

103

..

..

79

..

..

a. Data are for the most recent year available. b. Estimated to be upper middle income ($3,946–$12,195). c. Based on regression; others are extrapolated from the 2005 International Comparison Program benchmark estimates. d. Estimated to be high income ($12,196 or more). e. Data are for the area controlled by the government of the Republic of Cyprus. f. Included in the aggregates for upper middle-income economies based on earlier data. g. Less than 0.5. h. Included in the aggregates for lower middle-income economies based on earlier data. i. Estimated to be lower middle income ($996–$3,945).

currency. • GDP per capita is GDP divided by midyear population. • Life expectancy at birth is the number of years a newborn infant would live if prevailing patterns of mortality at the time of its birth were to stay the same throughout its life. • Adult literacy rate is the percentage of adults ages 15 and older who can,

Data sources

with understanding, read and write a short, simple

The indicators here and throughout the book

statement about their everyday life. • Carbon dioxide

are compiled by World Bank staff from primary

emissions are those stemming from the burning of

and secondary sources. More information about

fossil fuels and the manufacture of cement. They

the indicators and their sources can be found in

include carbon dioxide produced during consumption

the About the data, Definitions, and Data sources

of solid, liquid, and gas fuels and gas flaring.

entries that accompany each table in subsequent sections.

2011 World Development Indicators

29

Text figures, tables, and boxes


People


Introduction

S

ustainable development is about improving the quality of peoples’ lives and expanding their abilities to shape their futures. This generally calls for higher per capita incomes, but also for human capital development through improvements in health and education. Although developing countries have made large investments in human capital, good health and basic education remain elusive to many. This limits people’s ability to take advantage of employment opportunities and work their way out of poverty.

The tables in this section review the achievements countries have made in improving the welfare of their people. They show the levels of poverty prevalent in countries, the distribution of income, and the prevalence of child labour—which while it reduces household poverty, is always at the expense of children’s education and future human capital. The section also looks at investments in health and education and their impact on the worst aspects of nonincome poverty by reducing hunger and mal­nutrition, lowering mortality rates, and improving education outcomes. This year’s national and international poverty estimates were prepared by the World Bank’s Global Poverty Working Group, recently established by the Poverty Board. The results of their work are evident in tables 2.7–2.9. The baseline database, with estimates for 231 data points (country and year combinations) covering 104 countries, was updated to include estimates for 577 data points covering 115 countries. Because of space restrictions in the printed edition, this report cannot include estimates for all countries. Thus, it includes only countries

for which estimates are available since 2000. But the full range of these poverty estimates can be accessed through the Bank’s Open Data Initiative (data.worldbank.org), and the entire database of $1.25 and $2 a day purchasing power parity poverty rate and poverty gap estimates will also be available through PovcalNet. In addition, several new indicators have been added to existing tables. Data on children’s learning assessment, from the Programme for International Student Assessment, have been added to table 2.14, and the lifetime risk of maternal death has been added to table 2.19. The new maternal mortality ratio, estimated by the Inter-Agency group, is now available in a consistent time series for the first time, and data for 1990 and the most recent year are presented in table 2.19. The entire time series can be accessed through data.worldbank.org; regional and income group aggregates for maternal mortality ratios are in figures 2a and 2b. The next sections look at civil registration, highlighting the problems countries face in planning for

Maternal mortality ratios have declined in all developing country regions since 1990

2a

2

Maternal mortality ratios have declined fastest among low- and lower middle-income countries but remain high

Maternal mortality ratio by region Maternal mortality ratio, modeled estimates (per 100,000 live births)

Maternal mortality ratio by income group Maternal mortality ratio, modeled estimates (per 100,000 live births)

1,000

1,000

750 500

Sub-Saharan Africa

Latin America and Caribbean

250 0

750

South Asia

1990

2b

Low income

500 Middle East and North Africa

1995

East Asia and Pacific

2000

Lower middle income Europe and Central Asia 2005

2008

Source: WHO, UNICEF, UNFPA, World Bank. Trends in Maternal Mortality: 1990–2008.

250 Upper middle income 0

High income 1990

1995

2000

2005

2008

Source: WHO, UNICEF, UNFPA, World Bank. Trends in Maternal Mortality: 1990–2008.

2011 World Development Indicators

31


the welfare of their people. Countries need to know, at a minimum, how many people are born and die each year. In most developing countries this is not easy. The discussion highlights the obstacles countries must surmount in recording births and deaths and the interim measures they have adopted, and it indicates the way forward for countries and their development partners.

Civil registration, the missing pillar In 2009 the births of 50 million children went unrecorded. They entered the world with no proof of age, citizenship, or parentage. That same year 40 million people died unnoted except by family or friends. There are no records of where they died, when they died, and more importantly how they died. In most high-income countries these vital events (births and deaths) are recorded by civil registration systems, which also record marriages, adoptions, and divorces. But in many developing countries registration systems are incomplete or absent. In South Asia only 1 percent of the population is covered by complete vital registration records (at least 90 percent coverage for births and deaths), and in SubSaharan Africa only 2 percent (UN, Population and Vital Statistics Report, 2011). Lacking effective registration systems, countries must rely on infrequent and expensive censuses and surveys to estimate the vital statistics needed to support the core functions of government and to plan for the future. A state-of-the-art statistical system has three pillars: censuses and surveys, administrative records, and civil registration, each with an important and complementary role. Censuses give benchmark estimates that provide a base for and a check on vital statistics, and surveys 2c

The births of many children in Asia and Africa go unregistered Children under age 5 whose births are unregistered, 2007 (percent) 75

50

25

0

CEE/CISa

East Asia & Pacificb

Latin America & Caribbean

Middle East & North Africa

a. Central and Eastern Europe and Commonwealth of Independent States. b. Excludes China. Source: UNICEF Childinfo (www.childinfo.org/birth_registration_progress.html).

32

2011 World Development Indicators

South Asia

Sub-Saharan Africa

provide detailed characteristics of the population recorded by censuses and civil registration systems. Administrative records from health and education systems add further information to manage those services and—combined with census, survey, and vital statistics­— are used to plan for future needs. Civil registration has two functions: administrative­ — providing legal documentation that protects identities, citizenship, property, and other economic, social, and human rights—and statistical—providing regular, frequent, and timely information on the dynamics of population growth, size, and distribution and on records of births and deaths by age, sex, and cause at the national and subnational levels. Vital statistics from civil registration systems are essential for planning basic social services and infrastructure development and for understanding and monitoring health status and health issues in the country. A complete civil registration system has three strengths: it costs less than conducting a census or survey, data are based on a record of events rather than recall, and information can be made available at low cost. In a well functioning civil registration system a family member or caretaker reports births and deaths at the registration office in the local area and receives appropriate legal documentation. Medical certification of death from a health care provider identifies the cause of death. To be considered complete, civil registration systems must collect information on at least 90 percent of vital events. Systems in most developing country regions fall well short of that standard. So today, most people in Africa and South Asia are born and die without a trace in any legal record or official statistic (figure 2c), causing a vicious cycle. These are the regions where most premature deaths occur and where the need for robust information for planning is most critical. Roughly half the countries claim to have complete registration of births and deaths (UN, Population and Vital Statistics Report, 2011), leaving nearly 40 percent of births and 70 percent of deaths unregistered (WHO 2007). In many countries vital events are unreported or only partially reported for certain areas, ages, or populations for a variety of reasons. People may not know their responsibility to register events or where to register. They may choose not to register because of the distance to the registration offices or for cultural reasons.


people

Or they cannot afford the registration costs. Data from Nigeria show that most unregistered births are found among the rural poor, for whom a significant barrier may be the distance to the nearest registration facility, and among poorly educated mothers (figures 2d–2f). Where many infants die young, parents may be reluctant to go through the formalities of registration until they have some confidence in the child’s survival or need a birth certificate for administrative purposes. In many cultures, especially in Western Africa, a child’s death before age 2 is generally not registered. In Burkina Faso, for example, there are different words to express or describe death. The word for infant death among the Mossi is lebame, which translates literally to “s/he went back,” which is different from kiime, which is used for a teenager or adult who has died (private conversation). Reporting is lower for deaths than for births because people perceive death as a private, sad event and because there are fewer incentives associated with registering a death, especially where formal inheritance is rare. Such recording lapses have consequences for data quality. Even where there is complete registration, births and deaths may be recorded as need arises, rather than when they occur, reducing the timeliness and relevance of data. Not all administrative levels have the same capacity to maintain registers, resulting in omissions that may be difficult to quantify and therefore rectify, since underregistration cannot be assumed to be uniform across the population. Correct information on cause of death is critical for guiding policies and priorities for the health system. Routine data from civil registration in the United Kingdom helped identify the causal association between smoking and lung cancer in the 1950s. But even when deaths are recorded, age or cause of death may be misreported or miscoded. Correct reporting of cause of death is particularly difficult in developing countries, where many deaths occur at home without medical care or certification. In Myanmar only 10 percent of deaths occur in the hospital (Mahar 2010). More than two-thirds of people live in countries where cause of death statistics are partially reported and therefore of limited use or where deaths are not reported at all (table 2g; Mahapatra and others 2007). Because of the lack of reliable vital statistics from civil registration systems, the long-term social, economic, and demographic

impact of major diseases in developing countries can be estimated using only models or intuition and educated guesses rather than facts (Cooper and others 1998). Without data on the cause of death, verbal autopsy (an interview with caregivers or family members after a death to establish probable cause of death) can be used. In Tanzania several districts implemented sentinel demographic surveillance systems that provided routine monitoring of vital events and data for cause of death derived from a validated set of core verbal autopsy procedures. District councils used this information In Nigeria, children’s births are more likely to be unregistered in rural areas . . .

2d

Registered births, by area, Nigeria 2007 (percent) 50 40 30 20 10 0

Urban

Rural

Source: Multiple Indicator Cluster Survey 2007.

2e

. . . in poor households . . . Registered births, by wealth quintile, Nigeria 2007 (percent) 60

40

20

0

Poorest

Secondary

Middle

Fourth

Richest

Source: Multiple Indicator Cluster Survey 2007.

2f

. . . and where the mother has a lower education level Registered births, by mother’s education, Nigeria 2007 (percent) 60

40

20

0

Nonstandard curriculum

None

Primary

Secondary

Source: Multiple Indicator Cluster Survey 2007.

2011 World Development Indicators

33


Most people live in countries with low-quality cause of death statistics

2g

Classification of countries based on the quality of cause of death statistics reported to the World Health Organization, 2007 Quality

Number of countries

Percent of global population

High

31

13 15

Medium

50

Low

26

7

Limited use

17

41

No report Total

68

24

192

100

Source: Mahapatra and others 2007.

to identify disease burdens, set priorities, and allocate resources (Setel 2007). But verbal autopsy is often limited to small areas, such as sample vital registration and demographic surveillance systems, because it is expensive, and accuracy depends on family members��� knowledge of events leading to the death, the skill of interviewers, and the competence of physicians who do the diagnosis and coding.

Why civil registration fails to develop Good civil registration systems require longterm political commitment, a supportive legal framework, allocation of roles and responsibilities among stakeholders, mobilization of financial and human resources, and most critically, the trust of citizens (AbouZahr and others 2007). Although establishing civil registration systems takes time, there is no substitute in the long run. But when civil registration systems lack a sponsor or key stakeholder, or citizens lack incentives to participate, and when high initial costs deter investments, civil registration fails to take root. No single blueprint for establishing and maintaining civil registration systems ensures the availability of timely and sound vital statistics. Each country faces different challenges, and strategies must be tailored accordingly. Some obstacles to a viable civil registration system can be removed only through long-term social and economic development. These generally relate to geography and population distribution, with widely dispersed populations requiring transportation to registration centers. And a largely illiterate population may be unaware of the need to comply with the law or be unmotivated to do so.

34

2011 World Development Indicators

Other obstacles relate to the need for human and physical infrastructure to set up and maintain a civil registration system. While technical assistance and development grants can finance fixed costs and provide initial staff training, countries need to finance recurring costs to run a civil registration system efficiently. Because many developing countries have enormous economic and social development needs, this would claim low priority. A first and inexpensive step is adequate legislation. But while most countries have legislation requiring registration of vital events, many have not established organizational arrangements to direct, coordinate, and supervise the operation.

Interim approaches Because of the time and expense of building complete civil registration systems, many countries have adopted alternative approaches to measure and monitor vital events and related sociodemographic information. But as dependence on these measures (often intended as interim) grows, national authorities have fewer incentives to invest in complete civil registration systems (figure 2h; Setel and others 2007). These alternative approaches—notably censuses, demographic household surveys, sample registration systems with verbal autopsies, demographic surveillance sites, and facility­-based information—effectively fill data gaps with up-to-date information in many developing countries. Figure 2i illustrates the high underreporting of deaths in the civil registration system in the Philippines, based on calculations by the Inter-agency Group for Child Mortality Estimation, using surveys and other sources of mortality data. More countries used surveys for mortality statistics, but civil registration did not expand 2h Collection and reporting of data for mortality by sources in 57 low-income countries, 1980–2004 (number of countries) 50

Surveys

40 30 20 10 0

Civil register 1980–84

1985–89

1990–94

Source: Boerma and Stansfield 2007.

1995–99

2000–04


people

These interim approaches also produce supplemental information that is not collected through civil registration, such as socioeconomic information, risk factors, and health status. But these approaches are not a complete or permanent solution. Censuses and surveys are expensive, and developing countries often require international technical and financial assistance. They must be repeated regularly to yield useful data. And they must be supplemented or adjusted to produce satisfactory estimates. Burkina Faso, which has partial coverage of civil registration (birth registration coverage is 60 percent), has conducted four censuses (1975, 1985, 1996, 2006), five Demographic and Health Surveys (1991, 1993, 1998, 2003, 2010), two Multiple Indicator Cluster Surveys (1996, 2006), and a migration and urbanization survey (1993).

How to build a good civil registration system Over the years, international and development agencies have tried to identify the strengths and weaknesses of national civil registration systems and assess the quality of the data they produce. In 2001 the United Nations updated the Principles and Recommendations for a Vital Statistics System, first published in 1973, to offer best practice guidelines for establishing a civil registration system and producing timely, complete, and accurate statistics. Regional initiatives by the United Nations include the 1994 African Workshop on Strategies for Accelerating the Improvement of Civil Registration and Vital Statistics Systems. In 2005 the World Health Organization (WHO) established the Health Metrics Network, which recommends an integrated approach for developing health information systems, including civil registration. Some 85 countries have used the networkâ&#x20AC;&#x2122;s Framework and Standards for Country Health Information Systems, which

Estimates of infant mortality in the Philippines differ by source

2i

Infant mortality rate (per 1,000 live births) 80 70

Estimate by Inter-Agency Group for Child Mortality Estimation

60 50 40 30

World Health Organization vital registration

20 10 1960

1965

1970

1975

1980

1985

1990

1995

2000

2005

2009

Note: Dotted lines are Demographic and Health Surveys, World Fertility Surveys, and Family Planning Surveys for various years. Source: Inter-agency Group for Child Mortality Estimation (www.childmortality.org).

aims to ensure consistency and comparability of statistics across countries and over time. Used correctly, these principles and guidelines improve data quality, as in Chile and Tanzania (Setel and others 2007), but in reality few countries have pursued or attained most recommendations. The WHOâ&#x20AC;&#x2122;s International Classification of Diseases and Related Health Problems has improved the comparability of cause of death data. Still, there are substantial differences in interpretation and application of these codes. In 2007 only 31 of 192 WHO member countries (13 percent of the worldâ&#x20AC;&#x2122;s population) reported reliable cause-of-death statistics to the WHO, most of them high-income countries (WHO 2007).

International support The international community can continue its strong supportive rule by setting standards and guidelines for collecting and validating systems and data, publicizing the importance of civil registration, and providing comprehensive and integrated technical and financial assistance. Since no single UN agency has a clear mandate for guidance and technical support for civil registration, good coordination is key.

2011 World Development Indicators

35


Tables

2.1

Population dynamics Population

Average annual population growth

Population age composition

Dependency ratio

%

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 Franceb Gabon Gambia, The Georgia Germany Ghana Greece Guatemala Guinea Guinea-Bissau Haiti Honduras

36

1990

millions 2009

2015

18.6 3.3 25.3 10.7 32.5 3.5 17.1 7.7 7.2 115.6 10.2 10.0 4.8 6.7 4.3 1.4 149.6 8.7 8.8 5.7 9.7 12.2 27.8 2.9 6.1 13.2 1,135.2 5.7 33.2 37.0 2.4 3.1 12.6 4.8 10.6 10.4 5.1 7.4 10.3 57.8 5.3 3.2 1.6 48.3 5.0 56.7 0.9 0.9 5.5 79.4 15.0 10.2 8.9 6.1 1.0 7.1 4.9

29.8 3.2 34.9 18.5 40.3 3.1 21.9 8.4 8.8 162.2 9.7 10.8 8.9 9.9 3.8 1.9 193.7 7.6 15.8 8.3 14.8 19.5 33.7 4.4 11.2 17.0 1,331.5 7.0 45.7 66.0 3.7 4.6 21.1 4.4 11.2 10.5 5.5 10.1 13.6 83.0 6.2 5.1 1.3 82.8 5.3 62.6 1.5 1.7 4.3 81.9 23.8 11.3 14.0 10.1 1.6 10.0 7.5

35.0 3.3 38.1 21.7 42.4 3.1 23.4 8.4 9.4 176.3 9.4 11.0 10.6 10.8 3.7 2.1 202.4 7.3 19.0 9.4 16.4 22.2 35.7 4.9 13.1 17.9 1,377.7 7.3 49.3 77.4 4.2 4.9 24.2 4.4 11.2 10.6 5.6 10.8 14.6 91.7 6.4 6.0 1.3 96.2 5.4 63.9 1.6 2.0 4.1 80.6 26.6 11.4 16.2 11.8 1.8 10.7 8.4

2011 World Development Indicators

% 1990–2009 2009–15

2.5 –0.2 1.7 2.9 1.1 –0.7 1.3 0.4 1.1 1.8 –0.3 0.4 3.3 2.1 –0.7 1.9 1.4 –0.7 3.1 2.0 2.2 2.5 1.0 2.2 3.2 1.3 0.8 1.1 1.7 3.0 2.2 2.1 2.7 –0.4 0.3 0.1 0.4 1.7 1.5 1.9 0.8 2.5 –0.8 2.8 0.4 0.5 2.4 3.4 –1.3 0.2 2.4 0.6 2.4 2.6 2.4 1.8 2.2

2.7 0.5 1.4 2.6 0.9 0.2 1.2 0.1 1.1 1.4 –0.4 0.3 2.9 1.6 –0.2 1.3 0.7 –0.6 3.1 2.1 1.7 2.1 0.9 1.8 2.6 0.9 0.6 0.8 1.3 2.6 2.3 1.3 2.3 –0.2 0.0 0.2 0.2 1.1 1.1 1.7 0.6 2.8 –0.1 2.5 0.3 0.3 1.8 2.5 –0.7 –0.3 1.8 0.2 2.4 2.7 2.3 1.1 1.9

Ages 0–14 2009

Ages 15–64 2009

Ages 65+ 2009

46 24 27 45 25 20 19 15 24 31 15 17 43 36 15 33 26 13 46 38 33 41 17 41 46 23 20 a 12 29 47 40 26 41 15 18 14 18 31 31 32 32 42 15 44 17 18 36 42 17 14 38 14 42 43 43 36 37

52 67 68 53 64 68 67 68 69 65 72 66 54 59 71 63 67 69 52 59 63 56 70 55 51 68 72a 75 65 51 56 68 55 68 70 71 65 63 62 63 61 56 68 53 67 65 60 55 69 66 58 68 54 54 54 59 58

2 10 5 2 11 11 14 17 7 4 14 17 3 5 14 4 7 17 2 3 3 4 14 4 3 9 8a 13 5 3 4 6 4 17 12 15 16 6 7 5 7 2 17 3 17 17 4 3 14 20 4 18 4 3 3 4 4

% of working-age population Young Old 2009 2009

89 35 40 86 39 30 28 22 35 49 21 25 80 61 22 53 39 19 90 65 53 74 24 73 89 33 28a 16 45 92 73 38 73 22 25 20 28 50 50 51 53 74 22 82 25 28 61 77 24 20 66 21 78 79 79 61 64

4 14 7 5 16 16 20 26 10 6 19 26 6 8 20 6 10 25 4 5 6 6 20 7 6 13 11a 17 8 5 7 9 7 25 17 21 25 10 10 7 12 4 25 6 25 26 7 5 21 31 6 27 8 6 6 7 7

Crude death rate

Crude birth rate

per 1,000 people 2009

per 1,000 people 2009

19 6 5 16 8 9 6 9 6 6 14 10 9 7 10 12 6 14 13 14 8 14 7 17 16 5 7 6 6 17 13 4 11 12 7 10 10 6 5 6 7 8 12 12 9 9 10 11 12 10 11 10 6 11 17 9 5

46 15 21 42 17 15 14 9 17 21 12 12 39 27 9 24 16 11 47 34 25 36 11 35 45 15 12 12 20 44 34 16 34 10 10 11 11 22 20 24 20 36 12 38 11 13 27 36 12 8 32 11 32 39 41 27 27


Population

Average annual population growth

Population age composition

Dependency ratio

%

Hungary India Indonesia Iran, Islamic Rep. Iraq Ireland Israel Italy Jamaica Japan Jordan Kazakhstan Kenya Korea, Dem. Rep. Korea, Rep. Kosovo Kuwait Kyrgyz Republic Lao PDR Latvia Lebanon Lesotho Liberia Libya Lithuania Macedonia, FYR Madagascar Malawi Malaysia Mali Mauritania Mauritius Mexico Moldova Mongolia Morocco Mozambique Myanmar Namibia Nepal Netherlands New Zealand Nicaragua Niger Nigeria Norway Oman Pakistan Panama Papua New Guinea Paraguay Peru Philippines Poland Portugal Puerto Rico Qatar

1990

millions 2009

2015

10.4 849.5 177.4 54.4 18.9 3.5 4.7 56.7 2.4 123.5 3.2 16.3 23.4 20.1 42.9 1.9 2.1 4.4 4.2 2.7 3.0 1.6 2.2 4.4 3.7 1.9 11.3 9.5 18.1 8.7 2.0 1.1 83.2 4.4 2.2 24.8 13.5 40.8 1.4 19.1 15.0 3.4 4.1 7.9 97.3 4.2 1.8 108.0 2.4 4.1 4.2 21.8 62.4 38.1 9.9 3.5 0.5

10.0 1,155.3 230.0 72.9 31.5 4.5 7.4 60.2 2.7 127.6 6.0 15.9 39.8 23.9 48.7 1.8 2.8 5.3 6.3 2.3 4.2 2.1 4.0 6.4 3.3 2.0 19.6 15.3 27.5 13.0 3.3 1.3 107.4 3.6 2.7 32.0 22.9 50.0 2.2 29.3 16.5 4.3 5.7 15.3 154.7 4.8 2.8 169.7 3.5 6.7 6.3 29.2 92.0 38.1 10.6 4.0 1.4

9.9 1,246.9 247.5 78.6 36.3 4.8 8.2 60.8 2.8 125.3 6.8 16.9 46.4 24.4 49.3 1.9 3.2 5.7 7.0 2.2 4.4 2.2 4.8 7.2 3.2 2.0 22.8 18.0 30.0 15.4 3.7 1.3 113.1 3.5 2.9 34.3 25.9 53.0 2.4 32.5 16.8 4.6 6.3 19.1 178.7 5.1 3.2 193.5 3.8 7.7 7.0 31.2 102.7 38.0 10.7 4.0 1.6

% 1990–2009 2009–15

–0.2 1.6 1.4 1.5 2.7 1.3 2.5 0.3 0.6 0.2 3.3 –0.2 2.8 0.9 0.7 –0.2 1.4 1.0 2.1 –0.9 1.8 1.3 3.2 2.0 –0.5 0.4 2.9 2.5 2.2 2.1 2.7 1.0 1.3 –1.0 1.0 1.3 2.8 1.1 2.2 2.3 0.5 1.2 1.7 3.5 2.4 0.7 2.3 2.4 1.9 2.6 2.1 1.5 2.0 0.0 0.4 0.6 5.8 c

–0.2 1.3 1.2 1.2 2.4 1.1 1.6 0.1 0.4 –0.3 2.2 1.0 2.6 0.3 0.2 0.6 2.1 1.3 1.8 –0.5 0.8 0.8 3.2 1.8 –0.7 0.0 2.5 2.7 1.5 2.8 2.1 0.4 0.9 –0.7 1.1 1.2 2.1 1.0 1.7 1.7 0.3 1.0 1.4 3.7 2.4 0.8 1.9 2.2 1.5 2.2 1.6 1.1 1.8 –0.1 0.0 0.3 2.4

Ages 0–14 2009

Ages 15–64 2009

Ages 65+ 2009

15 31 27 24 41 21 28 14 29 13 34 24 43 22 17 .. 23 29 38 14 25 39 43 30 15 18 43 46 29 44 39 23 28 17 26 28 44 27 37 37 18 20 35 50 43 19 31 37 29 40 34 30 34 15 15 20 16

69 64 67 71 56 68 62 66 63 65 62 69 55 69 73 .. 74 65 59 69 67 56 54 66 69 70 54 51 66 54 58 70 65 72 70 66 53 68 60 59 67 67 60 48 54 66 66 59 64 58 61 64 62 72 67 66 83

16 5 6 5 3 11 10 20 8 22 4 7 3 10 11 .. 2 5 4 17 7 5 3 4 16 12 3 3 5 2 3 7 6 11 4 5 3 5 4 4 15 13 5 2 3 15 3 4 7 2 5 6 4 13 18 14 1

% of working-age population Young Old 2009 2009

22 49 40 34 74 30 45 22 47 21 56 34 78 32 23 .. 31 45 64 20 38 69 79 46 22 26 79 91 45 83 68 32 44 23 37 43 83 40 62 62 26 31 58 104 78 29 48 63 46 69 56 48 55 21 23 31 19

24 8 9 7 6 16 16 31 12 34 6 10 5 14 15 .. 3 8 6 25 11 8 6 6 23 17 6 6 7 4 5 10 10 15 6 8 6 8 6 7 22 19 7 4 6 22 5 7 10 4 8 9 7 19 26 21 1

people

2.1

Population dynamics

Crude death rate

Crude birth rate

per 1,000 people 2009

per 1,000 people 2009

13 7 6 6 6 7 5 10 7 9 4 9 11 10 5 7 2 7 7 13 7 17 10 4 13 9 9 12 5 15 10 7 5 13 7 6 16 10 8 6 8 7 5 15 16 9 3 7 5 8 6 5 5 10 10 8 2

10 22 18 19 31 17 22 10 16 9 25 22 38 14 10 19 17 25 27 10 16 29 38 23 11 11 35 40 20 42 33 12 18 12 19 20 38 20 27 25 11 15 24 53 39 13 22 30 20 31 24 21 24 11 9 12 12

2011 World Development Indicators

37


2.1

Population dynamics Population

Average annual population growth

Population age composition

Dependency ratio

%

1990

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

23.2 148.3 7.2 16.3 7.5 7.6 4.1 3.0 5.3 2.0 6.6 35.2 38.8 17.1 27.1 0.9 8.6 6.7 12.7 5.3 25.5 56.7 0.7 3.9 1.2 8.2 56.1 3.7 17.7 51.9 1.9 57.2 249.6 3.1 20.5 19.8 66.2 2.0 12.3 7.9 10.5 5,278.9 s 547.3 3,751.3 2,930.9 820.3 4,298.6 1,599.6 392.4 435.6 227.4 1,128.7 514.9 980.4 301.6

millions 2009

21.5 141.9 10.0 25.4 12.5 7.3 5.7 5.0 5.4 2.0 9.1 49.3 46.0 20.3 42.3 1.2 9.3 7.7 21.1 7.0 43.7 67.8 1.1 6.6 1.3 10.4 74.8 5.1 32.7 46.0 4.6 61.8 307.0 3.3 27.8 28.4 87.3 4.0 23.6 12.9 12.5 6,775.2 s 846.1 4,812.5 3,810.8 1,001.7 5,658.7 1,943.8 404.2 572.5 330.9 1,567.7 839.6 1,116.6 327.3

2015

21.0 139.0 11.7 28.6 14.5 7.2 6.6 5.4 5.4 2.1 10.7 51.1 47.9 21.2 47.7 1.3 9.6 7.9 24.1 7.8 52.1 69.9 1.4 7.6 1.4 11.1 79.9 5.5 39.7 44.4 5.2 63.8 323.5 3.4 30.2 31.0 92.8 4.8 27.8 15.0 14.0 7,241.9 s 962.6 5,131.2 4,084.9 1,046.3 6,093.8 2,035.8 409.0 606.9 366.1 1,706.5 969.5 1,148.0 332.3

% 1990–2009 2009–15

–0.4 –0.2 1.8 2.3 2.7 –0.2 1.8 2.6 0.1 0.1 1.7 1.8 0.9 0.9 2.3 1.7 0.4 0.7 2.7 1.4 2.8 0.9 2.2 2.7 0.5 1.3 1.5 1.7 3.2 –0.6 4.7 0.4 1.1 0.4 1.6 1.9 1.5 3.8 3.4 2.6 0.9 1.3 w 2.3 1.3 1.4 1.1 1.4 1.0 0.2 1.4 2.0 1.7 2.6 0.7 0.4

–0.4 –0.3 2.7 2.0 2.4 –0.3 2.3 1.2 0.1 0.3 2.7 0.6 0.7 0.7 2.0 1.4 0.5 0.4 2.2 1.8 2.9 0.5 3.3 2.3 0.3 1.1 1.1 1.2 3.2 –0.6 2.0 0.5 0.9 0.2 1.4 1.5 1.0 2.8 2.7 2.4 1.9 1.1 w 2.1 1.1 1.2 0.7 1.2 0.8 0.2 1.0 1.7 1.4 2.4 0.5 0.3

Ages 0–14 2009

15 15 42 32 44 18d 43 16 15 14 45 31 15 24 39 39 17 15 35 37 45 22 45 40 21 23 27 29 49 14 19 17 20 23 29 30 26 45 44 46 40 27 w 39 27 28 25 29 23 19 28 31 32 43 17 15

Ages 15–64 2009

70 72 55 65 54 68d 55 74 73 70 52 65 68 68 57 57 65 68 62 59 52 71 52 57 73 70 67 66 49 70 80 66 67 63 66 65 68 52 54 51 56 65 w 57 66 66 68 65 70 70 65 64 63 54 67 66

Ages 65+ 2009

15 13 2 3 2 14 d 2 10 12 16 3 4 17 7 4 3 18 17 3 4 3 8 3 4 7 7 6 4 3 16 1 16 13 14 4 5 6 3 2 3 4 8w 4 6 6 8 6 7 11 7 4 5 3 15 18

% of working-age population Young Old 2009 2009

22 21 77 50 81 26d 79 22 21 20 86 47 22 36 68 69 25 23 57 62 86 31 86 71 28 33 40 45 101 20 24 26 30 36 44 46 38 86 81 91 71 42 w 69 41 42 36 45 32 28 43 48 51 78 26 23

21 18 5 5 4 21d 3 13 17 23 5 7 25 11 6 6 28 25 5 6 6 11 6 6 9 10 9 6 5 22 1 25 19 22 7 8 9 6 4 6 7 12 w 6 10 9 11 9 11 16 10 7 7 6 23 27

Crude death rate

Crude birth rate

per 1,000 people 2009

per 1,000 people 2009

12 14 14 4 11 14 15 4 10 9 16 15 8 5 10 15 10 8 3 6 11 9 8 8 8 6 6 8 12 15 2 9 8 9 5 5 5 3 7 17 15 8w 11 8 8 8 8 7 11 6 6 7 14 8 9

10 12 41 24 38 10 40 10 11 11 44 22 11 19 31 30 12 10 27 28 41 14 40 32 15 18 18 22 46 11 14 13 14 15 22 21 17 35 36 42 30 20 w 34 19 20 17 21 14 15 18 24 24 38 12 10

a. Includes Taiwan, China. b. Excludes the French overseas departments of French Guiana, Guadeloupe, Martinique, and Réunion. c. Increase is due to a surge in the number of migrants since 2004. d. Includes Kosovo.

38

2011 World Development Indicators


About the data

2.1

people

Population dynamics Definitions

Population estimates are usually based on national

Dependency ratios capture variations in the propor-

• Population is based on the de facto definition of popu-

population censuses. Estimates for the years before

tions of children, elderly people, and working-age peo-

lation, which counts all residents regardless of legal sta-

and after the census are interpolations or extrapola-

ple in the population that imply the dependency burden

tus or citizenship—except for refugees not permanently

tions based on demographic models. Errors and under-

that the working-age population bears in relation to

settled in the country of asylum, who are generally con-

counting occur even in high income countries; in devel-

children and the elderly. But dependency ratios show

sidered part of the population of their country of origin.

oping countries errors may be substantial because

only the age composition of a population, not economic

The values shown are midyear estimates for 1990 and

of limits in the transport, communications, and other

dependency. Some children and elderly people are part

2009 and projections for 2015. • Average annual popu-

resources required to conduct and analyze a full census.

of the labor force, and many working-age people are not.

lation growth is the exponential change for the period

The quality and reliability of official demographic

Vital rates are based on data from birth and death

indicated. See Statistical methods for more information.

data are also affected by public trust in the govern-

registration systems, censuses, and sample surveys

• Population age composition is the percentage of the

ment, government commitment to full and accurate

by national statistical offices and other organiza-

total population that is in specific age groups. • Depen-

enumeration, confidentiality and protection against

tions, or on demographic analysis. Data for 2009

dency ratio is the ratio of dependents—people younger

misuse of census data, and census agencies’ inde-

for most high-income countries are provisional esti-

than 15 or older than 64—to the working age popula-

pendence from political influence. Moreover, compara-

mates based on vital registers. The estimates for

tion—those ages 15–64. • Crude death rate and crude

bility of population indicators is limited by differences

many countries are projections based on extrapo-

birth rate are the number of deaths and the number of

in the concepts, definitions, collection procedures,

lations of levels and trends from earlier years or

live births occurring during the year, per 1,000 people,

and estimation methods used by national statistical

interpolations of population estimates and projec-

estimated at midyear. Subtracting the crude death rate

agencies and other organizations that collect the data.

tions from the United Nations Population Division.

from the crude birth rate provides the rate of natural

Of the 155 economies in the table and the 55 econo-

Vital registers are the preferred source for these

increase, which is equal to the population growth rate in

mies in table 1.6, 180 (about 86 percent) conducted a

data, but in many developing countries systems for

census during the 2000 census round (1995–2004).

registering births and deaths are absent or incomplete

As of January 2011, 119 countries have completed

because of deficiencies in the coverage of events or

a census for the 2010 census round (2005–14).

geographic areas. Many developing countries carry out

The currentness of a census and the availability of

special household surveys that ask respondents about

complementary data from surveys or registration

recent births and deaths. Estimates derived in this

systems are objective ways to judge demographic

way are subject to sampling errors and recall errors.

data quality. Some European countries’ registration

The United Nations Statistics Division monitors the

systems offer complete information on population in

completeness of vital registration systems. Progress

the absence of a census. See table 2.17 and Primary

has been made over the past 60 years in some coun-

data documentation for the most recent census or

tries. But many countries still have deficiencies in civil

survey year and for the completeness of registration.

registration systems. For example, only 60 percent of

the absence of migration.

Current population estimates for developing coun-

countries and areas register at least 90 percent of

tries that lack recent census data and pre- and post-

births, and only 47 percent register at least 90 percent

census estimates for countries with census data are

of deaths. Some of the most populous developing coun-

Data sources

provided by the United Nations Population Division and

tries—Bangladesh, Brazil, India, Indonesia, Nigeria,

The World Bank’s population estimates are compiled

other agencies. The cohort component method—a

Pakistan—lack complete vital registration systems.

and produced by its Development Data Group in con-

standard method for estimating and projecting popu-

International migration is the only other factor

sultation with its Human Development Network, oper-

lation— requires fertility, mortality, and net migration

besides birth and death rates that directly deter-

ational staff, and country offices. The United Nations

data, often collected from sample surveys, which can

mines a country’s population growth. From 1990 to

Population Division’s World Population Prospects: The

be small or limited in coverage. Population estimates

2005 the number of migrants in high-income coun-

2008 Revision is a source of the demographic data for

are from demographic modeling and so are susceptible

tries rose 40 million. About 195 million people (3

more than half the countries, most of them developing

to biases and errors from shortcomings in the model

percent of the world population) live outside their

countries, and the source of data on age composi-

and in the data. Because the five-year age group is the

home country. Estimating migration is difficult. At

tion and dependency ratios for all countries. Other

cohort unit and five-year period data are used, interpo-

any time many people are located outside their

important sources are census reports and other sta-

lations to obtain annual data or single age structure

home country as tourists, workers, or refugees or

tistical publications from national statistical offices;

may not reflect actual events or age composition.

for other reasons. Standards for the duration and

household surveys conducted by national agencies,

The growth rate of the total population conceals

purpose of international moves that qualify as migra-

Macro International, and the U.S. Centers for Disease

age-group differences in growth rates. In many

tion vary, and estimates require information on flows

Control and Prevention; Eurostat’s Demographic Sta-

developing countries the once rapidly growing under-

into and out of countries that is difficult to collect.

tistics; Secretariat of the Pacific Community, Statistics

15 population is shrinking. Previously high fertility

and Demography Programme; and U.S. Bureau of the

rates and declining mortality rates are now reflected

Census, International Data Base.

in the larger share of the working-age population.

2011 World Development Indicators

39


2.2

Labor force structure Labor force participation rate

Labor force

% ages 15 and older Male

Afghanistan Albania Algeria Angola Argentina Armenia Australia Austria Azerbaijan 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

40

Female

1990

2009

84 74 75 90 78 78 76 70 74 89 75 61 89 82 67 82 85 63 91 90 84 83 76 87 81 77 85 80 78 85 84 84 88 69 73 71 75 85 78 74 83 84 77 91 72 65 83 86 78 73 73 67 88 90 81 81 88

85 70 80 88 78 75 72 68 67 83 67 61 78 82 68 81 82 61 91 88 86 81 73 87 78 73 80 69 78 86 83 80 82 60 67 68 71 80 78 75 77 83 69 90 65 62 81 85 74 67 75 65 88 89 84 83 80

2011 World Development Indicators

Ages 15 and older average annual % growth

Total millions

1990

2009

1990

2009

32 51 23 74 43 61 52 43 59 61 60 36 57 59 53 64 45 55 77 91 78 48 58 69 65 32 73 47 29 53 59 33 43 47 36 52 62 43 33 27 41 55 63 72 59 46 63 71 60 45 70 36 39 79 59 57 41

33 49 37 75 52 60 58 53 60 59 55 47 67 62 55 72 60 48 78 91 74 54 63 72 63 42 67 52 41 57 63 45 51 46 41 49 60 51 47 22 46 63 55 81 57 51 70 71 55 53 74 43 48 79 60 58 40

5.9 1.4 7.0 4.6 13.5 1.7 8.5 3.5 3.1 49.5 5.3 3.9 1.9 2.8 2.0 0.5 62.6 4.1 3.9 2.8 4.3 4.4 14.7 1.3 2.4 5.0 643.9 2.9 11.2 13.4 1.0 1.2 4.7 2.2 4.4 4.9 2.9 2.9 3.5 16.8 1.9 1.2 0.8 21.5 2.6 25.0 0.4 0.4 2.8 38.8 6.0 4.2 3.1 2.9 0.4 2.8 1.7

9.6 1.4 14.8 8.3 19.6 1.6 11.5 4.3 4.2 78.6 5.0 4.8 3.7 4.5 1.9 1.0 101.5 3.6 7.1 4.6 7.8 7.7 19.1 2.1 4.3 7.5 783.2 3.7 19.0 24.9 1.6 2.1 8.4 2.0 5.0 5.2 3.0 4.5 5.9 27.4 2.5 2.2 0.7 40.0 2.7 28.7 0.7 0.8 2.3 42.3 11.0 5.2 5.5 4.8 0.7 4.5 2.8

Female % of labor force

1990–2009

1990

2009

2.5 0.2 3.9 3.1 1.9 –0.2 1.6 1.0 1.5 2.4 –0.3 1.0 3.5 2.6 0.0 3.2 2.5 –0.7 3.2 2.6 3.1 3.0 1.4 2.5 3.1 2.1 1.0 1.4 2.8 3.3 2.6 3.2 3.1 –0.4 0.6 0.3 0.1 2.3 2.7 2.6 1.4 3.2 –1.0 3.3 0.2 0.7 3.1 3.4 –1.2 0.5 3.2 1.1 3.0 2.7 2.4 2.5 2.6

26.2 39.9 23.4 46.3 36.9 46.3 41.3 40.9 46.8 39.9 48.9 39.0 41.1 43.1 45.2 45.5 35.1 47.9 48.0 52.5 52.8 37.5 44.1 45.6 45.6 30.5 44.8 36.3 28.2 39.9 42.1 27.4 30.1 42.7 33.0 44.4 46.1 33.2 29.5 26.6 35.2 41.4 49.5 45.1 47.1 43.3 44.2 46.2 46.9 40.7 48.9 36.2 31.0 46.8 43.0 43.0 32.3

26.6 42.5 31.6 46.9 41.6 49.6 45.4 45.5 49.5 41.2 49.5 44.9 46.2 43.8 47.1 47.4 43.7 46.1 47.1 52.6 48.3 40.1 47.0 46.5 45.2 37.2 44.6 46.3 35.8 40.6 43.6 35.5 36.9 45.8 38.1 43.2 46.9 38.8 38.0 23.0 41.9 44.5 49.1 47.9 48.1 46.8 46.7 46.2 46.8 45.6 49.1 40.5 37.9 46.9 42.4 42.3 33.9


Labor force participation rate

Labor force

% ages 15 and older Male

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

65 84 81 80 73 71 64 66 80 77 71 78 90 80 73 .. 82 74 83 77 72 83 78 75 74 68 89 80 80 68 82 81 84 74 77 81 88 89 64 85 70 74 85 91 76 73 80 85 79 74 87 75 83 72 73 61 94

59 81 86 73 69 73 63 61 74 72 74 76 88 78 72 .. 83 79 79 70 72 78 76 79 62 65 89 79 79 67 81 75 81 53 78 80 87 85 63 80 73 76 78 88 73 71 77 85 81 74 87 76 79 62 69 58 93

Ages 15 and older average annual % growth

Total millions

Female

1990

2.2

people

Labor force structure

1990

2009

1990

2009

46 34 50 22 11 35 42 35 65 50 15 62 75 55 47 .. 36 58 80 63 20 68 65 15 59 46 83 76 43 37 53 38 34 61 63 25 85 71 48 52 43 54 39 27 36 57 19 14 39 71 47 49 48 55 49 31 40

43 33 52 32 14 54 52 38 56 48 23 66 76 55 50 .. 45 55 78 54 22 71 67 25 50 43 84 75 44 38 59 41 43 47 68 26 85 63 52 63 60 62 47 39 39 63 25 22 48 72 57 58 49 46 56 36 50

4.5 317.8 74.9 15.5 4.3 1.3 1.7 23.7 1.1 63.9 0.7 7.8 9.8 10.0 19.2 .. 0.9 1.8 1.9 1.4 0.9 0.7 0.8 1.2 1.9 0.8 5.4 3.9 7.0 2.5 0.7 0.4 29.9 2.1 0.9 7.8 6.3 20.7 0.4 7.5 6.9 1.7 1.4 2.3 29.4 2.2 0.6 31.0 0.9 1.8 1.7 8.3 24.1 18.1 4.7 1.2 0.3

4.3 457.5 115.6 29.2 7.7 2.2 3.1 25.4 1.2 65.8 1.9 8.6 18.7 12.4 24.7 .. 1.5 2.5 3.1 1.2 1.5 0.9 1.6 2.4 1.6 0.9 9.7 6.3 12.0 3.8 1.4 0.6 47.2 1.5 1.4 12.0 11.0 27.0 0.8 13.3 9.0 2.4 2.3 4.8 50.0 2.6 1.1 58.1 1.6 3.0 3.0 13.6 38.8 17.4 5.6 1.5 1.0

Female % of labor force

1990–2009

1990

2009

–0.3 1.9 2.3 3.3 3.0 2.7 3.1 0.4 0.5 0.2 5.0 0.5 3.4 1.1 1.3 .. 2.8 1.7 2.5 –1.0 2.8 1.9 3.4 3.7 –1.0 0.6 3.1 2.5 2.8 2.2 3.3 1.3 2.4 –1.8 2.5 2.2 3.0 1.4 3.0 3.0 1.4 1.7 2.8 3.8 2.8 0.9 3.4 3.3 2.8 2.8 3.1 2.6 2.5 –0.2 0.9 1.2 6.9

44.5 27.1 38.4 20.1 13.1 33.9 40.6 36.5 46.6 40.7 16.2 47.0 46.0 42.6 39.7 .. 22.4 46.1 49.8 49.6 23.3 51.7 46.7 14.8 48.1 40.7 48.4 50.7 34.5 36.1 39.8 32.1 30.0 48.7 45.6 23.7 53.2 45.3 44.9 38.0 38.8 43.0 32.3 24.7 33.0 44.7 13.7 12.7 32.4 46.9 34.9 39.7 36.5 45.4 42.4 35.8 13.5

45.1 27.6 38.1 29.8 16.7 43.0 46.5 40.5 44.9 41.6 23.0 49.8 46.7 42.7 41.9 .. 25.0 42.3 50.4 48.3 25.0 52.4 47.6 22.5 48.7 40.1 49.2 49.8 35.4 37.3 42.0 36.1 36.2 49.9 47.4 25.8 52.0 44.2 46.5 45.4 45.7 46.1 38.7 31.6 35.1 47.7 18.8 19.4 37.4 48.9 39.4 43.6 38.6 45.0 46.9 40.8 11.9

2011 World Development Indicators

41


2.2

Labor force structure Labor force participation rate

Labor force

% ages 15 and older Male 1990

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

42

73 76 89 80 90 .. 68 79 72 59 84 62 67 79 79 81 72 81 81 80 91 87 82 87 76 76 81 72 91 71 92 74 76 76 68 81 82 66 74 79 80 81 w 86 82 83 78 83 84 75 82 77 85 82 73 69

2011 World Development Indicators

Female 2009

60 69 85 74 89 .. 68 76 69 65 85 63 69 75 74 75 69 74 80 78 91 81 83 86 78 71 70 74 91 65 92 70 72 76 71 80 76 68 74 79 74 78 w 84 79 80 75 80 80 69 80 75 82 81 70 65

1990

60 60 87 15 62 .. 66 51 59 47 58 36 34 37 27 45 63 57 18 59 87 75 58 56 39 21 34 58 81 56 25 52 57 48 53 36 74 11 16 61 67 52 w 65 52 54 45 53 69 56 40 22 35 57 49 42

Ages 15 and older average annual % growth

Total millions 2009

45 58 87 17 65 .. 65 54 51 53 57 47 49 34 31 53 61 61 21 57 86 66 59 64 55 26 24 62 78 52 42 55 58 54 58 52 68 17 20 60 60 52 w 66 50 50 48 52 64 50 52 26 35 61 52 49

1990

11.8 76.8 3.2 5.0 3.0 .. 1.6 1.6 2.6 0.8 2.6 10.4 15.6 6.8 8.0 0.3 4.7 3.8 3.3 2.1 12.3 32.1 0.3 1.5 0.5 2.4 20.7 1.4 7.9 25.5 1.0 29.0 129.2 1.4 7.3 7.2 31.1 0.4 2.6 3.0 4.1 2,342.6 t 232.9 1,646.7 1,317.1 329.6 1,879.5 853.5 180.3 169.1 63.3 418.8 194.6 463.0 135.2

2009

9.5 75.9 5.0 8.6 5.4 .. 2.1 2.7 2.7 1.0 3.5 18.8 22.9 8.3 13.5 0.5 5.0 4.4 6.9 2.9 21.4 38.7 0.4 3.0 0.7 3.8 25.6 2.4 14.1 23.0 2.9 31.8 159.0 1.7 12.7 13.1 46.6 1.0 6.2 4.8 5.0 3,175.8 t 384.5 2,244.8 1,786.5 458.2 2,629.2 1,090.7 187.2 269.3 115.2 625.9 341.0 546.6 158.5

1990–2009

–1.1 –0.1 2.3 2.8 3.0 .. 1.6 2.9 0.3 1.2 1.6 3.1 2.0 1.1 2.7 2.7 0.3 0.7 4.0 1.8 2.9 1.0 1.8 3.5 2.3 2.4 1.1 2.9 3.0 –0.5 5.8 0.5 1.1 0.9 2.9 3.2 2.1 4.4 4.5 2.5 1.0 1.6 w 2.6 1.6 1.6 1.7 1.8 1.3 0.2 2.4 3.2 2.1 3.0 0.9 0.8

Female % of labor force 1990

2009

46.3 48.6 52.1 11.5 40.8 .. 50.9 39.1 46.8 46.8 41.8 37.5 34.8 31.8 26.0 41.2 47.7 42.9 18.3 43.3 49.8 47.0 40.4 40.1 35.0 21.6 29.7 46.1 47.7 49.2 9.8 43.2 44.4 40.8 45.5 30.5 50.7 13.8 18.0 44.3 46.3 39.4 w 43.8 38.1 38.2 37.6 38.8 44.2 45.8 33.8 22.0 27.8 42.0 41.6 39.8

45.0 50.1 52.8 14.9 43.3 .. 51.4 41.5 44.7 46.2 40.9 43.7 42.8 32.4 29.5 43.4 47.4 46.8 20.9 43.9 49.4 46.1 40.9 43.5 43.3 26.7 25.7 47.1 46.5 49.0 15.7 45.7 46.0 44.1 45.9 39.3 48.6 19.0 21.1 43.4 47.5 40.1 w 44.6 38.4 37.7 40.8 39.3 43.9 45.5 40.5 25.7 29.0 43.6 43.9 44.4


About the data

2.2

people

Labor force structure Definitions

The labor force is the supply of labor available for pro-

information on source, reference period, or defini-

• Labor force participation rate is the proportion

ducing goods and services in an economy. It includes

tion, consult the original source.

of the population ages 15 and older that engages

people who are currently employed and people who

The labor force participation rates in the table are

actively in the labor market, either by working or

are unemployed but seeking work as well as first-time

from the ILO’s Key Indicators of the Labour Market,

looking for work during a reference period. • Total

job-seekers. Not everyone who works is included,

6th edition, database. These harmonized estimates

labor force is people ages 15 and older who engage

however. Unpaid workers, family workers, and stu-

use strict data selection criteria and enhanced

actively in the labor market, either by working or look-

dents are often omitted, and some countries do not

methods to ensure comparability across countries

ing for work during a reference period. It includes

count members of the armed forces. Labor force size

and over time, including collection and tabulation

both the employed and the unemployed. • Average

tends to vary during the year as seasonal workers

methodologies and methods applied to such country-

annual percentage growth of the labor force is cal-

enter and leave.

specific factors as military service requirements.

culated using the exponential endpoint method (see

Data on the labor force are compiled by the Inter-

Estimates are based mainly on labor force surveys,

Statistical methods for more information). • Female

national Labour Organization (ILO) from labor force

with other sources (population censuses and nation-

labor force as a percentage of the labor force shows

surveys, censuses, establishment censuses and

ally reported estimates) used only when no survey

the extent to which women are active in the labor

surveys, and administrative records such as employ-

data are available.

force.

ment exchange registers and unemployment insur-

The labor force estimates in the table were calcu-

ance schemes. For some countries a combination

lated by applying labor force participation rates from

of these sources is used. Labor force surveys are

the ILO database to World Bank population estimates

the most comprehensive source for internationally

to create a series consistent with these population

comparable labor force data. They can cover all

estimates. This procedure sometimes results in

noninstitutionalized civilians, all branches and sec-

labor force estimates that differ slightly from those

tors of the economy, and all categories of workers,

in the ILO’s Yearbook of Labour Statistics and its

including people holding multiple jobs. By contrast,

database Key Indicators of the Labour Market.

labor force data from population censuses are often

Estimates of women in the labor force and employ-

based on a limited number of questions on the eco-

ment are generally lower than those of men and are

nomic characteristics of individuals, with little scope

not comparable internationally, reflecting that demo-

to probe. The resulting data often differ from labor

graphic, social, legal, and cultural trends and norms

force survey data and vary considerably by country,

determine whether women’s activities are regarded

depending on the census scope and coverage. Estab-

as economic. In many countries many women work

lishment censuses and surveys provide data only

on farms or in other family enterprises without pay,

on the employed population, not unemployed work-

and others work in or near their homes, mixing work

ers, workers in small establishments, or workers in

and family activities during the day.

the informal sector (ILO, Key Indicators of the Labour Market 2001–2002). The reference period of a census or survey is another important source of differences: in some countries data refer to people’s status on the day of the census or survey or during a specific period before the inquiry date, while in others data are recorded without reference to any period. In developing countries, where the household is often the basic unit of production and all members contribute to output, but some at low intensity or irregularly, the estimated labor force may be much smaller than the numbers actually working. Differing definitions of employment age also affect

Data sources

comparability. For most countries the working age is

Data on labor force participation rates are from

15 and older, but in some countries children younger

the ILO’s Key Indicators of the Labour Market, 6th

than 15 work full- or part-time and are included in the

edition, database. Labor force numbers were cal-

estimates. Similarly, some countries have an upper

culated by World Bank staff, applying labor force

age limit. As a result, calculations may systemati-

participation rates from the ILO database to popu-

cally over- or underestimate actual rates. For further

lation estimates.

2011 World Development Indicators

43


2.3

Employment by economic activity Agriculture

Male % of male employment 1990–92a 2005–08a

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

44

.. .. .. .. 0b,c .. 6 6 .. 54 .. 3 .. .. .. .. 31c .. .. .. .. .. 6c .. .. 24 .. 1c .. .. .. 32 .. .. .. .. 7 26 10 c 35 48 .. 23 .. 11 7 .. .. .. 4 66 20 .. .. .. 76 53c

.. .. .. .. 1c 46 4 6 40 42 15 2 .. .. .. 35 23 9 .. .. .. .. 3c .. .. 16 .. 0 b,c 27 .. .. 18 .. 13d 25 4 4 21 11c 28 29 .. 5 9c,d 6 4 .. .. 51 3 .. 11 44 .. .. .. 51c

2011 World Development Indicators

Industry

Female % of female employment 1990–92a

.. .. .. .. 0 b,c .. 4 8 .. 85 .. 2 .. .. .. .. 25c .. .. .. .. .. 2c .. .. 6 .. 0 b,c .. .. .. 5 .. .. .. .. 3 3 2c 52 15 .. 13 .. 6 5 .. .. .. 4 59 26 .. .. .. 50 6c

2005–08a

.. .. .. .. 0 b,c 46 2 6 38 68 9 1 .. .. .. 24 15 6 .. .. .. .. 2c .. .. 6 .. 0 b,c 6 .. .. 5 .. 15d 9 2 1 2 4c 43 5 .. 2 10 c,d 3 2 .. .. 57 2 .. 12 16 .. .. .. 13c

Male % of male employment 1990–92a

.. .. .. .. 40 c .. 32 47 .. 16 .. 41 .. .. .. .. 27c .. .. .. .. .. 31c .. .. 32 .. 37c .. .. .. 27 .. .. .. .. 37 23 29c 25 23 .. 42 .. 38 39 .. .. .. 50 10 29 .. .. .. 9 18 c

2005–08a

.. .. .. .. 33c 21 31 37 17 15 33 36 .. .. .. 19 28 42 .. .. .. .. 32c .. .. 31 .. 21c 22 .. .. 28 .. 39d 22 51 32 26 28 c 26 26 .. 48 25c,d 39 34 .. .. 17 41 .. 30 24 .. .. .. 20 c

Services

Female % of female employment 1990–92a

.. .. .. .. 18 c .. 12 20 .. 9 .. 16 .. .. .. .. 10 c .. .. .. .. .. 11c .. .. 15 .. 27c .. .. .. 25 .. .. .. .. 16 21 17c 10 23 .. 30 .. 15 17 .. .. .. 24 10 17 .. .. .. 9 25c

2005–08a

.. .. .. .. 11c 10 9 12 9 13 24 11 .. .. .. 11 13 29 .. .. .. .. 11c .. .. 11 .. 6c 16 .. .. 13 .. 15d 12 27 12 14 13c 6 19 .. 23 20 c,d 11 11 .. .. 4 16 .. 9 21 .. .. .. 23c

Male % of male employment 1990–92a

.. .. .. .. 59c .. 61 46 .. 25 .. 56 .. .. .. .. 43c .. .. .. .. .. 64 c .. .. 45 .. 63c .. .. .. 41 .. .. .. .. 56 52 62c 41 29 .. 36 .. 51 54 .. .. .. 46 23 51 .. .. .. 13 29c

2005–08a

.. .. .. .. 66c 33 64 57 44 43 37 61 .. .. .. 46 50 49 .. .. .. .. 65c .. .. 53 .. 78 c 51 .. .. 54 .. 48d 54 45 64 53 61c 46 45 .. 46 76c,d 54 61 .. .. 33 56 .. 59 32 .. .. .. 29c

Female % of female employment 1990–92a

.. .. .. .. 81c .. 84 72 .. 2 .. 81 .. .. .. .. 65c .. .. .. .. .. 87c .. .. 79 .. 73c .. .. .. 69 .. .. .. .. 82 76 81c 37 63 .. 57 .. 78 78 .. .. .. 73 32 57 .. .. .. 38 69c

2005–08a

.. .. .. .. 89c 45 89 82 53 19 64 88 .. .. .. 65 72 65 .. .. .. .. 88 c .. .. 84 .. 94 c 78 .. .. 82 .. 69d 79 71 86 84 83c 51 76 .. 75 64 c,d 86 86 .. .. 39 83 .. 79 63 .. .. .. 63c


Agriculture

Male % of male employment 1990–92a 2005–08a

Hungary India Indonesia Iran, Islamic Rep. Iraq Ireland Israel Italy Jamaica Japan Jordan Kazakhstan Kenya Korea, Dem. Rep. Korea, Rep. Kosovo Kuwait Kyrgyz Republic Lao PDR Latvia Lebanon Lesotho Liberia Libya Lithuania Macedonia, FYR Madagascar Malawi Malaysia Mali Mauritania Mauritius Mexico Moldova Mongolia Morocco Mozambique Myanmar Namibia Nepal Netherlands New Zealand Nicaragua Niger Nigeria Norway Oman Pakistan Panama Papua New Guinea Paraguay Peru Philippines Poland Portugal Puerto Rico Qatar

19 .. 54 .. .. 19 5 8 36 6 .. .. .. .. 14 .. .. .. .. .. .. .. .. .. .. .. .. .. 23 .. .. 15 34 .. .. .. .. .. 45 75 5 13c .. .. .. 7 .. 45 35 .. .. 1c 53c .. 10 5 ..

6 .. 41 21 .. 9 3 5 26 4 .. .. .. .. 7 .. .. 37 .. 10 .. .. .. .. 10 19 82 .. 18 .. .. 10 19 36 41 35 .. .. 23 .. 3 9 42 .. .. 4 .. 36 21 .. 33 12c 42d 15c 11 2 4

Industry

Female % of female employment 1990–92a

13 .. 57 .. .. 3 2 9 16 7 .. .. .. .. 18 .. .. .. .. .. .. .. .. .. .. .. .. .. 20 .. .. 13 11 .. .. .. .. .. 52 91 2 8c .. .. .. 3 .. 69 3 .. .. 0 b,c 32c .. 13 0b ..

Male % of male employment

people

2.3

Employment by economic activity

Services

Female % of female employment

Male % of male employment

Female % of female employment

2005–08a

1990–92a

2005–08a

1990–92a

2005–08a

1990–92a

2005–08a

1990–92a

2005–08a

2 .. 41 33 .. 2 1 3 8 4 .. .. .. .. 8 .. .. 35 .. 6 .. .. .. .. 6 17 83 .. 10 .. .. 8 4 30 35 60 .. .. 8 .. 2 5 8 .. .. 1 .. 72 3 .. 24 6c 23d 14 c 12 0b 0

43 .. 15 .. .. 33 38 41 25 40 .. .. .. .. 40 .. .. .. .. .. .. .. .. .. .. .. .. .. 31 .. .. 36 25 .. .. .. .. .. 21 4 33 31c .. .. .. 34 .. 20 20 .. .. 30 c 17c .. 39 27 ..

42 .. 21 33 .. 38 32 39 27 35 .. .. .. .. 33 .. .. 26 .. 40 .. .. .. .. 41 33 5 .. 32 .. .. 36 31 25 21 24 .. .. 24 .. 27 32 20 .. .. 33 .. 23 25 .. 24 41c 18d 41c 40 26 48

29 .. 13 .. .. 18 15 23 12 27 .. .. .. .. 28 .. .. .. .. .. .. .. .. .. .. .. .. .. 32 .. .. 48 19 .. .. .. .. .. 8 1 10 13c .. .. .. 10 .. 15 11 .. .. 13c 14 c .. 24 19 ..

21 .. 15 29 .. 10 11 16 5 17 .. .. .. .. 16 .. .. 11 .. 17 .. .. .. .. 19 29 2 .. 23 .. .. 26 18 12 15 15 .. .. 9 .. 8 10 18 .. .. 8 .. 13 10 .. 9 43c 10 d 18 c 17 10 4

38 .. 31 .. .. 48 57 52 39 54 .. .. .. .. 46 .. .. .. .. .. .. .. .. .. .. .. .. .. 46 .. .. 48 41 .. .. .. .. .. 34 20 60 56c .. .. .. 58 .. 35 45 .. .. 69c 29c .. 51 67 ..

52 .. 38 47 .. 53 65 57 47 59 .. .. .. .. 60 .. .. 37 .. 49 .. .. .. .. 49 48 13 .. 51 .. .. 54 50 39 39 41 .. .. 24 .. 63 58 38 .. .. 63 .. 41 54 .. 43 46c 41d 44 c 49 72 48

58 .. 31 .. .. 78 83 68 72 65 .. .. .. .. 54 .. .. .. .. .. .. .. .. .. .. .. .. .. 48 .. .. 39 70 .. .. .. .. .. 40 8 81 79c .. .. .. 86 .. 16 85 .. .. 87c 55c .. 63 80 ..

77 .. 44 38 .. 88 88 81 87 77 .. .. .. .. 76 .. .. 54 .. 77 .. .. .. .. 75 54 16 .. 67 .. .. 66 77 58 50 25 .. .. 63 .. 85 85 73 .. .. 90 .. 15 87 .. 68 51c 68d 68 c 71 89 96

2011 World Development Indicators

45


2.3

Employment by economic activity Agriculture

Male % of male employment 1990–92a 2005–08a

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

29 .. .. .. .. .. .. 1 .. .. .. .. 11 .. .. .. 5c 5 23 .. .. 59 .. .. 15 .. 33 .. .. .. .. 3 4 .. .. 17 .. .. 44 47 .. .. w .. .. .. .. .. .. .. .. .. .. .. 6 7

27 11 .. 5d 34 22 .. 2 6 10 c .. 5d 6 28 c .. .. 3c 5 .. .. 71 43 .. .. 6 .. 18d .. .. .. 6 2 2 16c .. 13 .. 11 .. .. .. .. w .. .. .. 17 .. .. 18 20 .. .. .. 4 5

Industry

Female % of female employment 1990–92a

2005–08a

38 .. .. .. .. .. .. 0b .. .. .. .. 8 .. .. .. 2c 4 54 .. .. 62 .. .. 6 .. 72 .. .. .. .. 1 1 .. .. 2 .. .. 83 56 .. .. w .. .. .. .. .. .. .. .. .. .. .. 5 6

30 7 .. 0 b,d 33 20 .. 1 2 10 c .. 3d 3 37c .. .. 1c 3 .. .. 78 40 .. .. 2 .. 42d .. .. .. 0b 1 1 5c .. 2 .. 36 .. .. .. . w. .. .. .. 12 .. .. 18 9 .. .. .. 3 3

Male % of male employment 1990–92a

44 .. .. .. .. .. .. 36 .. .. .. .. 41 .. .. .. 40 c 39 28 .. .. 17 .. .. 34 .. 26 .. .. .. .. 41 34 .. .. 32 .. .. 14 15 .. .. w .. .. .. .. .. .. .. .. .. .. .. 38 42

2005–08a

38 38 .. 23d 20 37 .. 26 52 44 c .. 31d 40 26c .. .. 33c 34 .. .. 7 22 .. .. 41 .. 21d .. .. .. 45 32 30 29 c .. 30 .. 27 .. .. .. .. w .. .. .. 32 .. .. 34 29 .. .. .. 34 38

Note: Data across sectors may not sum to 100 percent because of workers not classified by sector. a. Data are for the most recent year available. b. Less than 0.5. c. Limited coverage. d. Data are for 2009.

46

2011 World Development Indicators

Services

Female % of female employment 1990–92a

30 .. .. .. .. .. .. 32 .. .. .. .. 17 .. .. .. 12c 15 8 .. .. 13 .. .. 14 .. 11 .. .. .. .. 16 14 .. .. 16 .. .. 2 3 .. .. w .. .. .. .. .. .. .. .. .. .. .. 19 20

2005–08a

24 20 .. 2d 5 20 .. 18 24 23c .. 13d 11 27c .. .. 9c 12 .. .. 3 19 .. .. 16 .. 15d .. .. .. 6 9 9 13c .. 12 .. 10 .. .. .. .. w .. .. .. 20 .. .. 20 16 .. .. .. 13 13

Male % of male employment 1990–92a

28 .. .. .. .. .. .. 63 .. .. .. .. 49 .. .. .. 55c 57 49 .. .. 24 .. .. 51 .. 41 .. .. .. .. 55 62 .. .. 52 .. .. 38 22 .. .. w .. .. .. .. .. .. .. .. .. .. .. 55 50

2005–08a

35 51 .. 72d 33 42 .. 72 43 45c .. 57d 55 41c .. .. 64 c 62 .. .. 22 35 .. .. 52 .. 53d .. .. .. 49 66 68 56c .. 56 .. 61 .. .. .. .. w .. .. .. 50 .. .. 48 51 .. .. .. 61 57

Female % of female employment 1990–92a

33 .. .. .. .. .. .. 68 .. .. .. .. 75 .. .. .. 86c 81 38 .. .. 25 .. .. 80 .. 17 .. .. .. .. 82 85 .. .. 82 .. .. 13 18 .. .. w .. .. .. .. .. .. .. .. .. .. .. 76 73

2005–08a

46 73 .. 98d 42 61 .. 82 74 65c .. 79d 86 34 c .. .. 90 c 86 .. .. 19 41 .. .. 82 .. 43d .. .. .. 92 90 90 83c .. 86 .. 53 .. .. .. .. w .. .. .. 68 .. .. 63 75 .. .. .. 84 83


About the data

2.3

people

Employment by economic activity Definitions

The International Labour Organization (ILO) classi-

Such broad classification may obscure fundamental

• Agriculture corresponds to division 1 (ISIC revi-

fies economic activity using the International Stan-

shifts within countries’ industrial patterns. A slight

sion 2) or tabulation categories A and B (ISIC revi-

dard Industrial Classification (ISIC) of All Economic

majority of countries report economic activity accord-

sion 3) and includes hunting, forestry, and fishing.

Activities, revision 2 (1968) and revision 3 (1990).

ing to the ISIC revision 2 instead of revision 3. The

• Industry corresponds to divisions 2–5 (ISIC revi-

Because this classification is based on where work

use of one classification or the other should not have

sion 2) or tabulation categories C–F (ISIC revision

is performed (industry) rather than type of work per-

a significant impact on the information for the three

3) and includes mining and quarrying (including oil

formed (occupation), all of an enterprise’s employees

broad sectors presented in the table.

production), manufacturing, construction, and public

are classified under the same industry, regardless

The distribution of economic wealth in the world

utilities (electricity, gas, and water). • Services corre-

of their trade or occupation. The categories should

remains strongly correlated with employment by

spond to divisions 6–9 (ISIC revision 2) or tabulation

sum to 100 percent. Where they do not, the differ-

economic activity. The wealthier economies are

categories G–P (ISIC revision 3) and include whole-

ences are due to workers who cannot be classified

those with the largest share of total employment in

sale and retail trade and restaurants and hotels;

by economic activity.

services, whereas the poorer economies are largely

transport, storage, and communications; financing,

agriculture based.

insurance, real estate, and business services; and

Data on employment are drawn from labor force surveys, household surveys, official estimates, cen-

The distribution of economic activity by gender

suses and administrative records of social insurance

reveals some clear patterns. Men still make up the

schemes, and establishment surveys when no other

majority of people employed in all three sectors, but

information is available. The concept of employment

the gender gap is biggest in industry. Employment in

generally refers to people above a certain age who

agriculture is also male-dominated, although not as

worked, or who held a job, during a reference period.

much as industry. Segregating one sex in a narrow

Employment data include both full-time and part-time

range of occupations significantly reduces economic

workers.

efficiency by reducing labor market flexibility and thus

There are many differences in how countries define

the economy’s ability to adapt to change. This seg-

and measure employment status, particularly mem-

regation is particularly harmful for women, who have

bers of the armed forces, self-employed workers, and

a much narrower range of labor market choices and

unpaid family workers. Where members of the armed

lower levels of pay than men. But it is also detri-

forces are included, they are allocated to the service

mental to men when job losses are concentrated

sector, causing that sector to be somewhat over-

in industries dominated by men and job growth is

stated relative to the service sector in economies

centered in service occupations, where women have

where they are excluded. Where data are obtained

better chances, as has been the recent experience

from establishment surveys, data cover only employ-

in many countries.

ees; thus self-employed and unpaid family workers

There are several explanations for the rising impor-

are excluded. In such cases the employment share

tance of service jobs for women. Many service jobs—

of the agricultural sector is severely underreported.

such as nursing and social and clerical work—are

Caution should be also used where the data refer

considered “feminine” because of a perceived simi-

only to urban areas, which record little or no agricul-

larity to women’s traditional roles. Women often do

tural work. Moreover, the age group and area covered

not receive the training needed to take advantage of

could differ by country or change over time within a

changing employment opportunities. And the greater

country. For detailed information on breaks in series,

availability of part-time work in service industries

consult the original source.

may lure more women, although it is unclear whether

Countries also take different approaches to the

community, social, and personal services.

this is a cause or an effect.

treatment of unemployed people. In most countries unemployed people with previous job experience are classified according to their last job. But in some countries the unemployed and people seeking their first job are not classifiable by economic activity. Because of these differences, the size and distribution of employment by economic activity may not be fully comparable across countries. The ILO reports data by major divisions of the ISIC

Data sources

revision 2 or revision 3. In the table the reported

Data on employment are from the ILO’s Key Indica-

divisions or categories are aggregated into three

tors of the Labour Market, 6th edition, database.

broad groups: agriculture, industry, and services.

2011 World Development Indicators

47


2.4

Decent work and productive employment Employment to population ratio

Gross enrollment ratio, secondary

Vulnerable employment

Labor productivity

Unpaid family workers and own-account workers Total

Youth

% ages 15 and older

Afghanistan Albania Algeria Angola Argentina Armenia Australia Austria Azerbaijan 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

48

% ages 15–24

% of relevant age group

1991

2008

1991

2008

1991

2009a

54 49 39 77 53 38 56 52 57 74 58 44 70 61 42 47 56 45 82 85 77 59 58 73 67 51 75 62 52 68 66 56 63 50 52 58 59 44 52 43 59 66 61 71 57 47 58 73 57 54 68 44 55 82 66 56 59

55 46 49 76 57 38 59 55 60 68 52 47 72 71 42 46 64 46 82 84 75 59 61 73 70 50 71 57 62 67 65 57 60 46 54 54 60 53 61 43 54 66 55 81 55 48 58 72 54 52 65 48 62 81 67 55 56

45 37 25 71 42 24 58 61 38 66 40 31 64 48 17 34 54 27 77 74 66 37 57 59 51 34 71 54 38 60 49 48 52 27 40 48 65 28 39 22 42 60 43 64 45 28 37 59 28 58 40 31 50 75 57 37 49

47 36 31 69 36 25 64 53 39 56 35 27 59 49 18 27 53 27 74 73 68 33 61 58 50 24 55 38 43 62 46 43 45 29 32 29 61 34 40 23 39 54 29 74 44 29 33 55 22 44 40 28 52 73 63 47 43

16 89 60 12 74 .. 132 102 88 18 93 101 .. .. .. 49 .. 98 7 5 25 26 101 12 6 97 41 .. 53 21 46 45 .. 83 94 91 109 .. 55 69 38 11 100 14 116 100 40 19 95 98 35 94 23 11 5 .. 33

44 72 .. .. 85 93 149 100 99 42 95 108 .. 81 91 82 101 89 20 21 40 41 .. 14 24 90 78 82 95 37 .. 96 .. 90 90 95 119 77 81 .. 64 32 99 34 110 113 .. 51 108 102 57 102 57 37 .. .. 65

2011 World Development Indicators

Male % of male employment

Female % of female employment

1990

2008

1990

2008

.. .. .. .. .. .. 12 .. .. .. .. 17 .. 32b .. .. 29b .. .. .. .. .. .. .. .. .. .. .. 30 b .. .. 26 .. .. .. .. 7 42 33b .. .. .. 2b .. .. 11 .. .. .. .. .. .. .. .. .. .. 48b

.. .. .. .. 22b .. 11 9 41 .. .. 11 .. .. .. .. 30 10 .. .. .. .. 12b .. .. 25 .. 10 b 41 .. .. 20 .. 23c .. 15 7 49 29 b 20 29 .. 8b 48b 11 7 .. .. .. 7 .. 27 .. .. .. .. ..

.. .. .. .. .. .. 9 .. .. .. .. 15 .. 50 b .. .. 30 b .. .. .. .. .. .. .. .. .. .. .. 26 b .. .. 21 .. .. .. .. 6 30 41b .. .. .. 3b .. .. 10 .. .. .. .. .. .. .. .. .. .. 50 b

.. .. .. .. 17b .. 7 9 66 .. .. 9 .. .. .. .. 24 8 .. .. .. .. 9b .. .. 24 .. 4b 41 .. .. 20 .. 20 c .. 9 3 30 41b 44 44 .. 4b 56 b 7 5 .. .. .. 6 .. 27 .. .. .. .. ..

GDP per person employed % growth 1990–92

.. –17.5 –4.0 –5.0 9.0 –24.8 3.3 0.7 –12.6 1.9 –4.0 1.6 .. 2.6 –14.8 .. –0.3 3.1 1.3 .. 4.0 –6.7 0.8 .. .. 6.6 6.8 5.3 –0.7 –12.9 .. 2.4 –3.6 –7.7 .. –5.2 2.5 0.7 –0.1 2.1 .. .. –9.4 –8.4 1.4 1.4 .. .. –25.3 3.7 2.8 2.4 1.0 .. .. .. ..

2005–08

.. 6.1 –0.7 14.6 3.7 12.2 0.7 0.4 21.4 4.0 8.7 0.7 .. 1.8 1.6 .. 3.2 3.0 1.3 .. 6.5 1.0 0.2 .. .. 0.2 10.6 3.0 4.8 2.9 .. 1.9 –0.7 2.8 .. 3.4 –0.7 5.4 0.5 4.4 .. .. 2.4 7.4 1.5 0.6 .. .. 10.1 0.9 3.7 2.4 1.4 .. .. .. ..


Employment to population ratio

Gross enrollment ratio, secondary

Vulnerable employment

Labor productivity

Unpaid family workers and own-account workers Total

Youth

% ages 15 and older

Hungary India Indonesia Iran, Islamic Rep. Iraq Ireland Israel Italy Jamaica Japan Jordan Kazakhstan Kenya Korea, Dem. Rep. Korea, Rep. Kosovo Kuwait Kyrgyz Republic Lao PDR Latvia Lebanon Lesotho Liberia Libya Lithuania Macedonia, FYR Madagascar Malawi Malaysia Mali Mauritania Mauritius Mexico Moldova Mongolia Morocco Mozambique Myanmar Namibia Nepal Netherlands New Zealand Nicaragua Niger Nigeria Norway Oman Pakistan Panama Papua New Guinea Paraguay Peru Philippines Poland Portugal Puerto Rico Qatar

% ages 15–24

% of relevant age group

1991

2008

1991

2008

1991

2009a

48 58 63 46 37 44 45 43 61 61 36 63 73 62 59 .. 62 58 80 58 44 48 66 45 54 37 79 72 60 49 67 56 57 58 50 46 80 74 45 60 51 55 57 59 53 58 53 48 50 70 61 53 59 53 58 37 73

45 56 62 49 37 58 50 44 56 54 38 64 73 64 58 .. 65 58 78 55 46 54 66 49 50 35 83 72 61 47 47 54 57 45 52 46 78 74 43 62 59 63 58 60 52 62 51 52 59 70 73 69 60 48 56 41 77

37 46 46 33 27 38 25 30 40 43 25 46 62 46 36 .. 29 41 74 43 31 40 57 28 36 17 65 48 47 40 54 45 50 39 39 40 67 62 24 52 55 55 46 50 29 49 30 38 33 57 51 34 42 31 53 21 35

20 40 41 36 23 44 27 25 29 40 20 42 59 39 28 .. 30 40 64 35 29 40 57 27 18 13 71 49 45 35 23 37 42 17 35 35 66 53 14 46 67 56 48 52 24 56 29 44 40 54 58 53 39 27 35 29 47

86 46 46 53 40 100 92 79 70 97 82 98 .. .. 91 .. 53 100 21 92 61 24 .. .. 92 76 19 17 57 7 13 55 54 90 82 36 7 23 43 34 120 92 43 7 24 103 45 23 62 12 31 67 70 87 66 .. 84

97 60 79 83 51 115 90 101 91 101 88 99 59 .. 97 .. 90 84 44 98 82 45 .. .. 99 84 32 30 69 38 24 87 90 88 92 56 23 53 66 .. 121 119 68 12 30 112 91 33 73 .. 67 89 82 100 104 84 85

Male % of male employment

2.4

people

Decent work and productive employment

Female % of female employment

1990

2008

1990

2008

8b .. .. .. .. 25 .. 29 46 15 .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. 31 .. .. 13 29 .. .. .. .. .. .. .. 7 15 .. .. .. .. .. .. 44 .. 17b 30 b .. .. 22 .. ..

8 .. 60 40 .. 17 9 21 38 10 .. .. .. .. 23 .. .. 47 .. 8 .. .. .. .. 11 24 .. .. 23 .. .. 18 28 35 .. 46 .. .. .. .. 10 14 45 .. .. 8 .. 58 30 .. 45 33b 44b 20 18 .. ..

7b .. .. .. .. 9 .. 24 37 26 .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. 25 .. .. 7 15 .. .. .. .. .. .. .. 10 10 .. .. .. .. .. .. 19 .. 31b 46b .. .. 30 .. ..

6 .. 68 56 .. 5 5 15 31 12 .. .. .. .. 28 .. .. 47 .. 6 .. .. .. .. 8 20 .. .. 21 .. .. 15 32 30 .. 65 .. .. .. .. 8 10 46 .. .. 3 .. 75 24 .. 50 47b 47b 18 19 .. ..

GDP per person employed % growth 1990–92

0.3 1.0 6.2 6.5 –33.6 2.4 0.0 0.6 0.7 0.7 –5.5 –15.1 –3.9 .. 5.0 .. –0.2 –13.1 .. –19.6 .. .. .. .. –13.9 –5.6 –5.9 –1.9 6.0 0.4 .. .. 1.0 –22.0 .. –1.7 –3.0 2.0 .. .. 0.4 0.5 .. –5.7 –2.9 3.9 0.2 6.5 .. .. .. –0.8 –3.3 2.8 2.2 .. 0.1

2011 World Development Indicators

2005–08

2.0 5.9 3.8 1.8 1.9 0.7 1.3 –0.3 –2.2 1.2 2.5 4.8 2.5 .. 3.1 .. 3.2 4.3 .. 2.9 .. .. .. .. 5.2 1.2 2.2 5.6 3.1 1.9 .. .. 1.0 6.9 .. 2.8 5.5 5.8 .. .. 1.0 –0.3 .. 2.3 3.3 –1.1 3.7 2.5 .. .. .. 0.2 3.9 1.9 0.9 .. 13.3

49


2.4

Decent work and productive employment Employment to population ratio

Gross enrollment ratio, secondary

Vulnerable employment

Labor productivity

Unpaid family workers and own-account workers Total

Youth

% ages 15 and older 1991

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

56 57 87 50 67 49d 64 64 55 55 66 39 41 51 46 54 62 65 47 54 87 77 64 66 45 41 53 56 82 57 71 56 59 53 54 51 75 30 38 57 70 62 w 71 63 65 53 63 73 55 55 43 59 64 55 48

2008

48 57 80 48 66 44 d 65 62 53 54 67 41 49 55 47 50 58 61 45 55 78 72 67 65 61 41 42 58 83 54 76 56 59 56 58 61 69 30 39 61 65 60 w 70 61 62 56 62 69 53 61 45 57 64 55 50

% ages 15–24 1991

42 34 79 26 60 28d 38 56 43 38 59 19 36 31 29 34 59 69 38 36 79 70 51 58 33 29 48 35 73 37 43 66 56 42 36 35 75 19 23 40 48 52 w 60 52 55 41 53 67 38 46 29 48 50 47 41

% of relevant age group

2008

24 33 64 13 55 21d 42 38 30 32 58 15 37 36 23 26 45 63 32 38 70 46 58 53 46 22 31 34 75 34 46 56 51 39 39 40 51 15 22 46 50 45 w 58 42 44 38 45 51 33 45 29 42 49 43 37

a. Provisional data. b. Limited coverage. c. Data are for 2009. d. Includes Montenegro.

50

2011 World Development Indicators

1991

2009a

92 93 18 .. 15 .. 16 .. 88 89 .. 69 105 72 20 49 90 98 48 102 5 31 .. 20 82 45 48 .. 10 94 68 87 92 84 99 56 35 .. .. 21 49 50 w 26 47 42 67 44 41 85 57 54 37 22 91 ..

92 85 27 97 30 91 35 .. 92 97 8 94 120 .. 38 53 103 96 75 84 27 76 51 41 89 92 82 .. 27 94 95 99 94 88 104 82 .. 87 .. 49 .. 67 w 38 68 63 88 63 74 89 89 73 52 34 100 ..

Male % of male employment 1990

21 1 .. .. 77 .. .. 10 .. .. .. .. 20b .. .. .. .. 8 .. .. .. 67 .. .. 22 .. .. .. .. .. .. 13 .. .. .. .. .. .. .. 56 .. .. w .. .. .. .. .. .. .. .. .. .. .. .. ..

2008

31 6 .. .. .. 25 .. 12 14 12 .. 2 13 39 b .. .. 9 10 .. .. 82b 51 .. .. .. .. 30 .. .. .. .. 14 .. 26b .. 28 .. 34 .. .. .. .. w .. .. .. 26 .. .. 19 30 33 .. .. 13 12

Female % of female employment 1990

33 1 .. .. 91 .. .. 6 .. .. .. .. 24b .. .. .. .. 11 .. .. .. 74 .. .. 21 .. .. .. .. .. .. 6 .. .. .. .. .. .. .. 81 .. .. w .. .. .. .. .. .. .. .. .. .. .. .. ..

2008

32 6 .. .. .. 20 .. 7 6 10 .. 3 10 44b .. .. 4 11 .. .. 93b 56 .. .. .. .. 49 .. .. .. .. 7 .. 24b .. 33 .. 44 .. .. .. .. w .. .. .. 26 .. .. 19 30 52 .. .. 11 9

GDP per person employed % growth 1990–92

2005–08

–9.3 –7.9 .. 4.9 –1.0 .. .. 1.5 –0.8 –2.3 .. –4.5 2.4 5.5 –1.3 .. 1.9 –0.6 6.5 –20.4 –2.4 6.8 .. .. –3.5 2.6 1.0 –13.0 –1.1 –7.9 –3.9 2.0 1.7 5.2 –7.8 4.5 4.6 .. 0.9 –2.5 –4.7 0.7 w –3.2 1.3 3.2 –2.3 1.1 6.5 –9.1 1.8 1.4 3.1 –5.3 2.3 2.4

6.5 6.4 .. 0.7 0.9 .. .. –1.8 6.1 3.0 .. 3.7 0.7 9.3 7.5 .. 0.6 1.0 0.3 6.3 4.5 2.7 .. .. 5.4 2.7 2.6 7.9 6.1 5.9 0.7 2.2 1.4 4.9 5.9 4.3 5.6 .. –0.8 3.9 –7.7 3.1 w 4.4 6.2 7.4 3.6 6.1 8.7 5.8 2.6 2.2 5.5 4.1 1.2 0.7


About the data

2.4

people

Decent work and productive employment Definitions

Four targets were added to the UN Millennium Dec-

Data on employment by status are drawn from

• Employment to population ratio is the proportion

laration at the 2005 World Summit High-Level Ple-

labor force surveys and household surveys, supple-

of a country’s population that is employed. People

nary Meeting of the 60th Session of the UN General

mented by official estimates and censuses for a

ages 15 and older are generally considered the

Assembly. One was full and productive employment

small group of countries. The labor force survey is

working-age population. People ages 15–24 are

and decent work for all, which is seen as the main

the most comprehensive source for internationally

generally considered the youth population. • Gross

route for people to escape poverty. The four indi-

comparable employment, but there are still some

enrollment ratio, secondary, is the ratio of total

cators for this target have an economic focus, and

limitations for comparing data across countries and

enroll­ment in secondary education, regardless of

three of them are presented in the table.

over time even within a country. Information from

age, to the population of the age group that officially

The employment to population ratio indicates how

labor force surveys is not always consistent in what

corresponds to secondary educa­tion. • Vulnerable

efficiently an economy provides jobs for people who

is included in employment. For example, informa-

employment is unpaid family workers and own-

want to work. A high ratio means that a large pro-

tion provided by the Organisation for Economic

account workers as a percentage of total employ-

portion of the population is employed. But a lower

Co-operation and Development relates only to civil-

ment. •  Labor productiv­ity is the growth rate

employment to population ratio can be seen as a

ian employment, which can result in an underesti-

of gross domestic product (GDP) divided by the num-

positive sign, especially for young people, if it is

mation of “employees” and “workers not classified

ber of people engaged in the production of goods

caused by an increase in their education. This indi-

by status,” especially in countries with large armed

and services.

cator has a gender bias because women who do not

forces. While the categories of unpaid family work-

consider their work employment or who are not per-

ers and self-employed workers, which include own-

ceived as working tend to be undercounted. This bias

account workers, would not be affected, their relative

has different effects across countries and reflects

shares would be. Geographic coverage is another

demographic, social, legal, and cultural trends and

factor that can limit cross-country comparisons. The

norms.

employment by status data for many Latin Ameri-

Comparability of employment ratios across coun-

can countries covers urban areas only. Similarly, in

tries is also affected by variations in definitions of

some countries in Sub-Saharan Africa, where limited

employment and population (see About the data for

information is available anyway, the members of pro-

table 2.3). The biggest difference results from the

ducer cooperatives are usually excluded from the

age range used to define labor force activity. The

self-employed category. For detailed information on

population base for employment ratios can also vary

definitions and coverage, consult the original source.

(see table 2.1). Most countries use the resident,

Labor productivity is used to assess a country’s

noninstitutionalized population of working age living

economic ability to create and sustain decent

in private households, which excludes members of

employment opportunities with fair and equitable

the armed forces and individuals residing in men-

remuneration. Productivity increases obtained

tal, penal, or other types of institutions. But some

through investment, trade, technological progress, or

countries include members of the armed forces in

changes in work organization can increase social pro-

the population base of their employment ratio while

tection and reduce poverty, which in turn reduce vul-

excluding them from employment data (International

nerable employment and working poverty. Productiv-

Labour Organization, Key Indicators of the Labour

ity increases do not guarantee these improvements,

Market, 6th edition).

but without them—and the economic growth they

The proportion of unpaid family workers and

bring—improvements are highly unlikely. For compa-

own-account workers in total employment is derived

rability of individual sectors labor productivity is esti-

from information on status in employment. Each

mated according to national accounts conventions.

status group faces different economic risks, and

However, there are still significant limitations on the

unpaid family workers and own-account workers

availability of reliable data. Information on consis-

are the most vulnerable—and therefore the most

tent series of output in both national currencies and

likely to fall into poverty. They are the least likely to

purchasing power parity dollars is not easily avail-

have formal work arrangements, are the least likely

able, especially in developing countries, because the

Data on employment to population ratio, vulner-

to have social protection and safety nets to guard

definition, coverage, and methodology are not always

able employment, and labor productivity are from

against economic shocks, and often are incapable of

consistent across countries. For example, countries

the ILO’s Key Indicators of the Labour Market,

generating sufficient savings to offset these shocks.

employ different methodologies for estimating the

6th edition, database. Data on gross enrollment

A high proportion of unpaid family workers in a coun-

missing values for the nonmarket service sectors

ratios are from the United Nations Educational,

try indicates weak development, little job growth, and

and use different definitions of the informal sector.

Scientific, and Cultural Organization Institute for

often a large rural economy.

Data sources

Statistics.

2011 World Development Indicators

51


2.5

Unemployment Unemployment

Total % of total labor force 1990–92a 2006–09a

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

52

.. .. 23.0 .. 6.7b .. 10.8 3.6 .. 1.9 .. 6.7 1.5 5.5b 17.6 13.8 6.4b .. .. 0.5 .. .. 11.2b .. .. 4.4 2.3b 2.0 b 9.5b .. .. 4.1 6.7 11.1 .. 2.3 9.0 20.7 8.9b .. 7.9b .. 3.7b 1.3 11.6 10.2 .. .. .. 6.6 4.7 7.8 .. .. .. 12.7 3.2b

.. 12.7 11.3 .. 8.6b 28.6b 5.6b 4.8 6.1 .. .. 7.9 .. 5.2b 23.9 17.6b 8.3 6.8 .. .. .. 2.9 8.3b .. .. 9.7 4.3 5.2b 12.0 .. .. 4.9 .. 9.1 1.6 6.7 6.0 14.2 6.5 9.4 5.9 .. 13.7 20.5b 8.2 9.1 .. .. 16.5 7.7 .. 9.5 1.8 .. .. .. 2.9b

2011 World Development Indicators

Male % of male labor force 1990–92a 2006–09a

.. .. 24.2 .. 6.4b .. 11.4 3.5 .. 2.0 .. 4.8 2.2 5.5b 15.5 11.7 5.4b .. .. 0.7 .. .. 12.0 b .. .. 3.9 .. 2.0 b 6.8b .. .. 3.5 .. 11.1 .. 2.4 8.3 12.0 6.0 b .. 8.4b .. 3.9b 1.1 13.3 8.1 .. .. .. 5.3 3.7 4.9 .. .. .. 11.9 3.3b

.. .. 11.0 .. 7.8b 21.9b 5.7b 5.0 7.1 .. .. 7.7 .. 4.5b 21.8 15.3b 6.1 7.0 .. .. .. 2.5 9.4b .. .. 9.1 .. 6.0 b 9.3 .. .. 4.1 .. 8.0 1.4 5.8 6.5 8.5 5.2 5.2 7.5 .. 17.0 12.1b 8.9 8.9 .. .. 16.8 8.1 .. 6.9 1.5 .. .. .. 2.9b

Female % of female labor force 1990–92a 2006–09a

.. .. 20.3 .. 7.0 b .. 10.0 3.8 .. 1.9 .. 9.5 0.6 5.6b 21.6 17.2 7.9 b .. .. 0.3 .. .. 10.2b .. .. 5.3 .. 1.9b 13.0 b .. .. 5.4 .. 11.2 .. 2.1 9.9 35.2 13.2b .. 7.2b .. 3.5b 1.6 9.6 12.8 .. .. .. 8.4 5.5 12.9 .. .. .. 13.8 3.0 b

.. .. 10.1 .. 9.8b 35.0 b 5.4b 4.5 4.9 .. .. 8.1 .. 6.0 b 27.1 19.9 b 11.0 6.6 .. .. .. 3.3 7.0 b .. .. 10.7 .. 4.3b 15.8 .. .. 6.2 .. 10.2 2.0 7.7 5.4 22.8 8.4 22.9 3.6 .. 10.8 29.9b 7.5 9.3 .. .. 16.1 7.3 .. 13.1 2.4 .. .. .. 2.9b

Long-term unemployment

Unemployment by educational attainment

% of total unemployment Total Male Female 2006–09a 2006–09a 2006–09a

% of total unemployment Primary Secondary Tertiary 2006–09a 2006–09a 2006–09a

.. .. .. .. .. .. 14.7b 20.3 .. .. .. 44.2 .. .. .. .. .. 43.3 .. .. .. .. 7.8b .. .. .. .. .. .. .. .. .. .. 56.2 .. 31.2 9.1 .. .. .. .. .. 27.4 .. 16.6 35.4 .. .. .. 45.5 .. 40.8 .. .. .. .. ..

.. .. .. .. .. .. 15.0 b 19.7 .. .. .. 43.5 .. .. .. .. .. 40.7 .. .. .. .. 8.1b .. .. .. .. .. .. .. .. .. .. 50.8 .. 29.0 8.9 .. .. .. .. .. 26.8 .. 18.2 35.6 .. .. .. 44.4 .. 34.4 .. .. .. .. ..

.. .. .. .. .. .. 14.4b 21.0 .. .. .. 45.0 .. .. .. .. .. 46.4 .. .. .. .. 7.4b .. .. .. .. .. .. .. .. .. .. 61.0 .. 33.4 9.4 .. .. .. .. .. 28.4 .. 14.7 35.3 .. .. .. 47.0 .. 45.6 .. .. .. .. ..

.. .. .. .. 48.1b 5.2 48.0 37.9b 6.3 .. 10.8 42.1 .. .. 95.7 .. 51.6 41.8 .. .. .. .. 27.7b .. .. 17.8 .. 40.8 b 76.6 .. .. 65.2 .. 16.0 43.0 26.8 35.9 35.0 74.0 b .. .. .. 23.1b .. 35.5 39.9 .. .. 5.1b 33.1 .. 29.3b .. .. .. .. ..

.. .. .. .. 36.7b 83.0 34.1 52.1b 78.9 .. 38.6 38.2 .. .. .. .. 33.6 49.7 .. .. .. .. 41.1b .. .. 58.5 .. 41.4b .. .. .. 27.3 .. 70.4 52.4 68.8 35.1 44.5 .. .. .. .. 57.8b .. 45.9 39.6 .. .. 52.5b 56.3 .. 48.4b .. .. .. .. ..

.. .. .. .. 15.3b 11.9 17.9 10.0 b 14.9 .. 50.6 19.7 .. .. 4.0 .. 3.6 8.6 .. .. .. .. 31.2b .. .. 23.5 .. 16.6b 20.6 .. .. 6.4 .. 11.6 4.6 4.3 23.0 16.4 23.6b .. .. .. 16.6b .. 18.6 19.9 .. .. 42.3b 10.6 .. 21.8 b .. .. .. .. ..


Unemployment

Total % of total labor force 1990–92a 2006–09a

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

9.9 .. 2.8 11.1 .. 15.0 11.2 9.3 15.4 2.2 .. .. .. .. 2.5b .. .. .. .. .. .. .. .. .. .. .. .. .. 3.7 .. .. 3.3 3.1 .. .. 16.0 b .. 6.0 19.0 .. 5.6 10.6b 14.4 .. .. 5.9 .. 5.2 14.7 7.7 5.0 b 9.4b 8.6b 13.3 4.1b 16.9 ..

10.0 .. 7.9 10.5 17.5 11.7 7.6 7.8 11.4 5.0 12.9 6.6 .. .. 3.6b 45.4 .. 8.2 .. 17.1 9.0 .. 5.6 .. 13.7 32.2 .. .. 3.7 .. .. 7.3 5.2 6.4 .. 10.0 .. .. 37.6 .. 3.4 6.1b 5.0 .. .. 3.2 .. 5.0 5.9 .. 5.6 6.8b 7.5 8.2 9.5 13.4 0.5

Male % of male labor force 1990–92a 2006–09a

11.0 .. 2.7 9.5 .. 14.9 9.2 6.7 9.4 2.1 .. .. .. .. 2.8b .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. 3.2 2.7 .. .. 13.0 b .. 4.7 20.0 .. 4.0 11.4b 11.3 .. .. 6.6 .. 3.8 10.8 9.0 6.0 b 7.5b 7.9b 12.2 3.5b 19.1 ..

10.3 .. 7.5 9.1 16.2 14.7 7.6 6.8 8.5 5.3 10.3 5.6 .. .. 4.1b 40.7 .. 7.3 .. 20.4 8.6 .. 6.8 .. 17.1 31.7 .. .. 3.2 .. .. 4.4 5.4 7.8 .. 9.8 .. .. 32.5 .. 3.4 6.1b 4.9 .. .. 3.6 .. 4.0 4.6 .. 4.4 5.4b 7.5 7.8 8.9 14.9 0.2

Female % of female labor force 1990–92a 2006–09a

8.7 .. 3.0 24.4 .. 15.2 13.9 13.9 22.2 2.2 .. .. .. .. 2.1b .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. 3.6 4.0 .. .. 25.3b .. 8.8 19.0 .. 7.8 9.7b 19.5 .. .. 5.1 .. 14.0 22.3 5.9 3.7b 12.5b 9.9b 14.7 5.0 b 13.3 ..

9.7 .. 8.5 16.8 22.5 8.0 7.6 9.3 14.8 4.7 24.1 7.5 .. .. 3.0 b 56.4 .. 9.4 .. 14.0 10.1 .. 4.2 .. 10.4 33.0 .. .. 3.7 .. .. 12.3 4.8 4.9 .. 10.5 .. .. 43.0 .. 3.5 6.1b 5.1 .. .. 2.6 .. 8.7 7.9 .. 7.5 8.3b 7.4 8.7 10.1 11.6 2.6

2.5

people

Unemployment Long-term unemployment

Unemployment by educational attainment

% of total unemployment Total Male Female 2006–09a 2006–09a 2006–09a

% of total unemployment Primary Secondary Tertiary 2006–09a 2006–09a 2006–09a

42.6 .. .. .. .. 29.0 28.6 44.4 .. 28.5 .. .. .. .. 0.5 81.7 .. .. .. 26.7 .. .. .. .. 23.2 81.6 .. .. .. .. .. .. 1.9 .. .. .. .. .. .. .. 24.8 6.3b .. .. .. 7.7 .. .. .. .. .. .. .. 25.2 44.2 .. ..

42.4 .. .. .. .. 32.1 32.3 42.0 .. 34.8 .. .. .. .. 0.6 82.8 .. .. .. 27.1 .. .. .. .. 21.0 82.2 .. .. .. .. .. .. 1.8 .. .. .. .. .. .. .. 23.7 6.3b .. .. .. 7.5 .. .. .. .. .. .. .. 23.3 40.8 .. ..

42.8 .. .. .. .. 21.7 25.0 46.9 .. 18.8 .. .. .. .. 0.3 79.8 .. .. .. 26.0 .. .. .. .. 26.8 80.6 .. .. .. .. .. .. 2.1 .. .. .. .. .. .. .. 26.1 6.4b .. .. .. 8.0 .. .. .. .. .. .. .. 27.3 47.5 .. ..

33.1b .. 44.4 .. .. 39.8 12.2 46.5 9.7 67.2 .. .. .. .. 15.2 64.0 19.4 13.3 .. 24.3b .. .. .. .. 14.2b .. .. .. 13.3 .. .. 44.2 50.7 .. .. .. .. .. .. .. 41.3 30.6 72.8 .. .. 25.4 .. 14.3 36.0 .. 49.9 30.0 b 13.8 16.4b 68.1b .. ..

58.7b .. 40.7 .. .. 37.2 12.8 40.6 4.3 .. .. .. .. .. 49.7 46.0 41.4 77.1 .. 59.9b .. .. .. .. 70.4b .. .. .. 61.6 .. .. 48.5 24.5 .. .. .. .. .. .. .. 39.7 38.8 2.1 .. .. 49.2 .. 11.4 39.6 .. 38.0 31.9b 45.2 73.2b 15.4b .. ..

2011 World Development Indicators

8.1b .. 9.6 .. .. 18.2 72.5 11.3 8.4 32.8 .. .. .. .. 35.2 15.0 9.6 9.6 .. 14.6b .. .. .. .. 15.4b .. .. .. 25.1 .. .. 6.4 22.9 .. .. .. .. .. .. .. 17.0 26.9 18.0 .. .. 20.6 .. 26.0 24.0 .. 9.9 37.6b 41.1 10.4b 13.2b .. ..

53


2.5

Unemployment Unemployment

Total % of total labor force 1990–92a 2006–09a

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

.. 5.2 0.3 .. .. .. .. 2.7b .. 7.1 .. .. 18.1 14.2b .. .. 5.7 2.8 6.8 .. 3.6b 1.4 .. .. 19.6 .. 8.5 .. 1.0 .. .. 9.7 7.5b 9.0 b .. 7.7 .. .. .. 18.9 .. .. w .. .. .. 6.7 .. 2.5 .. 6.6 .. .. .. 7.5 9.1

6.9 8.2 .. 5.4 10.0 16.6 .. 5.9 12.1 5.9 .. 23.8 18.0 7.6 .. .. 8.3 4.1 8.4 .. 4.3 1.2 .. .. 5.3 14.2 14.0 .. .. 8.8 4.0 7.7 9.3b 7.3 .. 7.6 2.4 24.5 15.0 .. .. .. w .. .. .. 9.1 .. 4.6 9.2 7.9 10.6 .. .. 8.1 9.4

Male % of male labor force 1990–92a 2006–09a

.. 5.2 0.6 .. .. .. .. 2.7b .. 8.1 .. .. 13.9 .. .. .. 6.7 2.3 5.2 .. 2.8b 1.3 .. .. 17.0 .. 8.8 .. 1.3 .. .. 11.5 7.9b 6.8 b .. 8.2 .. .. .. 16.3 .. .. w .. .. .. 6.4 .. .. .. 5.4 .. .. .. 7.1 7.2

a. Data are for the most recent year available. b. Limited coverage.

54

2011 World Development Indicators

7.7 8.4 .. 3.5 7.9 15.3 .. 5.4 11.4 5.9 .. 22.0 17.7 7.2 .. .. 8.6 3.7 5.2 .. 2.8 1.2 .. .. 3.5 .. 13.9 .. .. 6.6 2.0 8.8 10.3b 5.3 .. 7.2 .. 17.7 11.5 .. .. .. w .. .. .. 8.5 .. .. 9.9 6.6 8.9 .. .. 8.4 9.2

Female % of female labor force 1990–92a 2006–09a

.. 5.2 0.2 .. .. .. .. 2.6b .. 6.0 .. .. 25.8 .. .. .. 4.6 3.5 14.0 .. 4.3b 1.5 .. .. 23.9 .. 7.8 .. 0.6 .. .. 7.3 7.0 b 11.8b .. 6.8 .. .. .. 22.4 .. .. w .. .. .. 7.4 .. .. .. 8.4 .. .. .. 8.0 11.9

5.8 7.9 .. 15.9 13.6 18.4 .. 6.5 12.9 5.8 .. 25.9 18.4 8.1 .. .. 8.0 4.5 25.7 .. 5.8 1.1 .. .. 6.2 .. 14.3 .. .. 6.1 12.0 6.4 8.1b 9.7 .. 8.1 .. 38.6 40.9 .. .. .. w .. .. .. 10.3 .. .. 8.6 9.8 16.7 .. .. 7.7 9.6

Long-term unemployment

Unemployment by educational attainment

% of total unemployment Total Male Female 2006–09a 2006–09a 2006–09a

% of total unemployment Primary Secondary Tertiary 2006–09a 2006–09a 2006–09a

31.6 35.7 .. .. .. 71.1 .. .. 50.9 30.1 .. 14.4 30.2 .. .. .. 12.8 30.0 .. .. .. .. .. .. .. .. 25.3 .. .. .. .. 24.6 16.3b .. .. .. .. .. .. .. .. .. w .. .. .. .. .. .. .. .. .. .. .. 24.8 38.2

32.2 33.3 .. .. .. 70.1 .. .. 47.8 28.3 .. .. 26.9 .. .. .. 13.1 26.4 .. .. .. .. .. .. .. .. 22.6 .. .. .. .. 26.5 16.4b .. .. .. .. .. .. .. .. .. w .. .. .. .. .. .. .. .. .. .. .. 25.3 36.7

30.6 38.4 .. .. .. 72.1 .. .. 54.4 32.1 .. .. 34.4 .. .. .. 12.4 33.6 .. .. .. .. .. .. .. .. 32.2 .. .. .. .. 21.5 16.1b .. .. .. .. .. .. .. .. .. w .. .. .. .. .. .. .. .. .. .. .. 23.8 39.8

25.8 13.7 .. 7.5 40.2 20.3 .. 31.0 29.2 25.0 b .. 36.2 54.8 b 45.4b .. .. 32.2b 28.8 .. 66.5 .. 40.5 .. .. .. .. 52.3 .. .. 8.5 .. 37.3 18.7 59.1b .. .. .. 54.3 .. .. .. .. w .. .. .. 43.4 .. .. 26.7 50.8 .. .. .. 33.9 41.3

66.3 54.2 .. 48.6 6.9 68.4 .. 25.6 65.3 60.4b .. 56.3 23.6b 22.0 b .. .. 46.0 b 53.2 .. 28.8 .. 45.5 .. .. .. .. 28.2 .. .. 52.2 .. 47.7 35.5 27.0 b .. .. .. 14.2 .. .. .. .. w .. .. .. 40.9 .. .. 50.2 34.9 .. .. .. 43.7 43.0

6.1 32.1 .. 43.6 2.5 11.2 .. 43.2 5.3 12.5b .. 4.5 20.4b 32.6b .. .. 17.1b 17.9 .. 4.6 .. 0.1 .. .. .. .. 12.7 .. .. 39.3 .. 14.3 45.7 13.8b .. .. .. 23.5 .. .. .. .. w .. .. .. 14.3 .. .. 24.1 12.3 .. .. .. 25.7 14.9


About the data

2.5

people

Unemployment Definitions

Unemployment and total employment are the broad-

generate statistics that are more comparable inter-

• Unemployment is the share of the labor force with-

est indicators of economic activity as reflected by

nationally. But the age group, geographic coverage,

out work but available for and seeking employment.

the labor market. The International Labour Organiza-

and collection methods could differ by country or

Definitions of labor force and unemployment may

tion (ILO) defines the unemployed as members of the

change over time within a country. For detailed infor-

differ by country (see About the data). • Long-term

economically active population who are without work

mation, consult the original source.

unemployment is the number of people with continu-

but available for and seeking work, including people

Women tend to be excluded from the unemploy-

ous periods of unemployment extending for a year or

who have lost their jobs or who have voluntarily left

ment count for various reasons. Women suffer more

longer, expressed as a percentage of the total unem-

work. Some unemployment is unavoidable. At any

from discrimination and from structural, social, and

ployed. • Unemployment by educational attainment

time some workers are temporarily unemployed—

cultural barriers that impede them from seeking

is the unemployed by level of educational attainment

between jobs as employers look for the right workers

work. Also, women are often responsible for the

as a percentage of the total unemployed. The levels

and workers search for better jobs. Such unemploy-

care of children and the elderly and for household

of educational attainment accord with the ISCED97

ment, often called frictional unemployment, results

affairs. They may not be available for work during

of the United Nations Educational, Scientific, and

from the normal operation of labor markets.

the short reference period, as they need to make

Cultural Organization.

Changes in unemployment over time may reflect

arrangements before starting work. Furthermore,

changes in the demand for and supply of labor; they

women are considered to be employed when they

may also reflect changes in reporting practices.

are working part-time or in temporary jobs, despite

Paradoxically, low unemployment rates can disguise

the instability of these jobs or their active search for

substantial poverty in a country, while high unemploy-

more secure employment.

ment rates can occur in countries with a high level of

Long-term unemployment is measured by the

economic development and low rates of poverty. In

length of time that an unemployed person has been

countries without unemployment or welfare benefits

without work and looking for a job. The data in the

people eke out a living in vulnerable employment. In

table are from labor force surveys. The underlying

countries with well developed safety nets workers

assumption is that shorter periods of joblessness

can afford to wait for suitable or desirable jobs. But

are of less concern, especially when the unem-

high and sustained unemployment indicates serious

ployed are covered by unemployment benefits or

inefficiencies in resource allocation.

similar forms of support. The length of time that a

The ILO definition of unemployment notwithstand-

person has been unemployed is difficult to measure,

ing, reference periods, the criteria for people consid-

because the ability to recall that time diminishes as

ered to be seeking work, and the treatment of people

the period of joblessness extends. Women’s long-

temporarily laid off or seeking work for the first time

term unemployment is likely to be lower in countries

vary across countries. In many developing countries

where women constitute a large share of the unpaid

it is especially difficult to measure employment and

family workforce.

unemployment in agriculture. The timing of a survey,

Unemployment by level of educational attainment

for example, can maximize the effects of seasonal

provides insights into the relation between the edu-

unemployment in agriculture. And informal sector

cational attainment of workers and unemployment

employment is difficult to quantify where informal

and may be used to draw inferences about changes

activities are not tracked.

in employment demand. Information on educational

Data on unemployment are drawn from labor force

attainment is the best available indicator of skill

sample surveys and general household sample

levels of the labor force. Besides the limitations to

surveys, censuses, and official estimates, which

comparability raised for measuring unemployment,

are generally based on information from different

the different ways of classifying the education level

sources and can be combined in many ways. Admin-

may also cause inconsistency. Education level is

istrative records, such as social insurance statistics

supposed to be classified according to Interna-

and employment office statistics, are not included

tional Standard Classification of Education 1997

in the table because of their limitations in cover-

(ISCED97). For more information on ISCED97, see

age. Labor force surveys generally yield the most

About the data for table 2.11.

comprehensive data because they include groups not covered in other unemployment statistics, particularly people seeking work for the first time. These

Data sources

surveys generally use a definition of unemployment

Data on unemployment are from the ILO’s Key Indi-

that follows the international recommendations more

cators of the Labour Market, 6th edition, database.

closely than that used by other sources and therefore

2011 World Development Indicators

55


2.6

Children at work Survey year

% of children ages 7–14 in employment

% of children ages 7–14

Afghanistan Albania Algeria Angolab Argentina Armenia Australia Austria Azerbaijan Bangladesh Belarus Belgium Benin Bolivia Bosnia and Herzegovina Botswana Brazil Bulgaria Burkina Faso Burundi Cambodiad Cameroon Canada Central African Republic Chad Chile China Hong Kong SAR, China Colombia Congo, Dem. Rep.d Congo, Rep Costa Ricad Côte d’Ivoire Croatia Cuba Czech Republic Denmark Dominican Republicd Ecuador Egypt, Arab Rep. El Salvador Eritrea Estonia Ethiopia Finland France Gabon Gambia, The Georgia Germany Ghana Greece Guatemala Guinea Guinea-Bissau Haiti Honduras

56

2005 2001 2004

2005 2006 2005 2006 2008 2006 2008 2006 2005 2003/04 2007 2000 2004 2003

2007 2000 2005 2004 2006

2005 2006 2005 2007

2005

2005 2006 2006 2006 1994 2006 2005 2007

Employment by economic activitya

Status in employmenta

% of children ages 7–14 in employment

% of children ages 7–14 in employment SelfUnpaid employed Wage family

Children in employment

Total

Male

Female

Work only

Study and work

.. 25.0

.. 18.8 .. 30.0 15.7 .. .. .. 5.8 25.7 12.1 .. 72.8 33.0 11.7 .. 6.9 .. 49.0 12.5 49.6 43.5 .. 66.5 64.4 5.1 .. .. 5.3 39.9 29.9 8.1 47.7 .. .. .. .. 9.0 16.9 11.5 10.1 .. .. 64.3 .. .. .. 33.9 33.6 .. 49.9 .. 24.5 47.2 52.8 37.3 13.3

.. 22.0 .. 30.1 9.8 .. .. .. 4.5 6.4 11.2 .. 76.1 31.1 9.5 .. 3.5 .. 34.5 11.0 48.1 43.4 .. 67.6 56.2 3.1 .. .. 2.3 39.8 30.2 3.5 43.6 .. .. .. .. 2.7 11.6 4.3 3.8 .. .. 47.1 .. .. .. 52.3 29.9 .. 48.0 .. 11.7 49.5 48.1 29.6 4.1

.. 6.7 .. 26.6 4.8 .. .. .. 6.3 37.8 0.0 .. 36.1 5.2 0.1 .. 4.8 .. 67.7 38.9 13.8 21.9 .. 54.9 49.1 3.2 .. .. 24.8 35.7 9.9 44.6 46.8 .. .. .. .. 6.2 21.0 21.0 24.9 .. .. 69.4 .. .. .. 32.1 1.0 .. 18.7 .. 28.4 98.6 36.4 17.7 45.1

.. 93.3 .. 73.4 95.2 .. .. .. 93.7 62.2 100.0 .. 63.9 94.8 99.9 .. 95.2 .. 32.3 61.1 86.2 78.1 .. 45.1 50.9 96.8 .. .. 75.2 64.3 90.1 55.4 53.2 .. .. .. .. 93.8 79.0 79.0 75.1 .. .. 30.6 .. .. .. 67.9 99.0 .. 81.3 .. 71.6 1.4 63.6 82.3 54.9

30.1 12.9 .. .. .. 5.2 16.2 11.7 .. 74.4 32.1 10.6 .. 5.2 .. 42.1 11.7 48.9 43.4 .. 67.0 60.4 4.1 .. .. 3.9 39.8 30.1 5.7 45.7 .. .. .. .. 5.8 14.3 7.9 7.1 .. .. 56.0 .. .. .. 43.5 31.8 .. 48.9 .. 18.2 48.3 50.5 33.4 8.7

2011 World Development Indicators

Agriculture Manufacturing

.. .. .. .. .. .. .. .. 91.7 .. .. .. .. 73.2 .. .. 54..7 .. 70.9 .. 82.3 88.5 .. .. .. 24.1 .. .. 41.2 .. .. 40.3 .. .. .. .. .. 18.5 69.3 .. 50.1 .. .. 94.6 .. .. .. .. .. .. .. .. 63.7 .. .. .. 61.6

.. .. .. .. .. .. .. .. 0.7 .. .. .. .. 6.1 .. .. 7.6 .. 1.4 .. 4.2 3.1 .. .. .. 6.9 .. .. 10.8 .. .. 9.5 .. .. .. .. .. 9.8 6.3 .. 13.3 .. .. 1.5 .. .. .. .. .. .. .. .. 9.7 .. .. .. 10.4

Services

.. .. .. .. .. .. .. .. 7.4 .. .. .. .. 19.2 .. .. 34.6 .. 24.9 .. 12.9 8.2 .. .. .. 66.9 .. .. 46.1 .. .. 49.0 .. .. .. .. .. 57.5 22.8 .. 35.2 .. .. 3.7 .. .. .. .. .. .. .. .. 24.7 .. .. .. 25.1

.. .. .. .. 34.2 .. .. .. 4.1 .. .. .. 0.9 .. .. 5.5 .. 1.9 .. 6.0 2.5 .. .. .. .. .. .. 22.7 .. .. 15.8 .. .. .. .. .. 23.8 3.6 2.2 .. .. 1.7 .. .. .. .. .. .. .. .. 2.0 .. .. .. 3.5

.. 1.4 .. 6.2 8.1 .. .. .. 3.8 17.0 9.2 .. .. 9.2 1.6 .. 24.7 .. 2.2 25.9 4.1 9.5 .. 2.0 1.8 .. .. .. 29.1 6.6 4.2 57.7 2.4 .. .. .. .. 19.5 15.2 11.4 23.6 .. .. 2.4 .. .. .. 1.1 4.3 .. 6.1 .. 18.8 .. 4.0 1.8 23.0

.. 94.5 .. 80.1 56.2 .. .. .. 92.1 77.8 78.8 .. .. 89.9 92.1 .. 69.8c .. 95.8 68.6 89.4 87.6 .. 56.4 77.2 .. .. .. 45.6 76.7 84.5 26.6 88.0 .. .. .. .. 56.2e 81.2 87.4 74.2 .. .. 95.8 .. .. .. 87.3 77.0 .. 76.2 .. 79.2 .. 87.7 79.4 73.5


Survey year

Children in employment

% of children ages 7–14 in employment

% of children ages 7–14

Hungary India Indonesia Iran, Islamic Rep. Iraq Ireland Israel Italy Jamaica Japan Jordan Kazakhstan Kenya Korea, Dem. Rep. Korea, Rep. Kosovo Kuwait Kyrgyz Republic Lao PDR Latvia Lebanon Lesotho Liberia Libya Lithuania Macedonia, FYR Madagascar Malawi Malaysia Mali Mauritania Mauritius Mexicof Moldova Mongolia Morocco Mozambiqued Myanmar Namibia Nepal Netherlands New Zealand Nicaragua Niger Nigeria Norway Oman Pakistan Panama Papua New Guinea Paraguayc Peru Philippines Poland Portugal Puerto Rico Qatar

2004/05 2000 2006

2005

2006 2000

2006

2002 2007

2005 2007 2006 2006

2009 2000 2006/07 1998/99 1996 1999 1999

2005 2006

2008 2005 2007 2001 2001

Total

Male

Female

.. 4.2 8.9 .. 14.7 .. .. .. 9.8 .. .. 3.6 37.7 .. .. .. .. 5.2 .. .. .. 2.6 37.4 .. .. 11.8 26.0 40.3 .. 49.5 .. .. 12.2 33.5 10.1 13.2 1.8 .. 15.4 47.2 .. .. 10.1 47.1 .. .. .. .. 8.9 .. 15.3 42.2 13.3 .. 3.6 .. ..

.. 4.2 8.8 .. 17.9 .. .. .. 11.3 .. .. 4.4 40.1 .. .. .. .. 5.8 .. .. .. 4.0 37.8 .. .. 14.8 27.7 41.3 .. 55.0 .. .. 16.5 34.1 11.4 13.5 1.9 .. 16.2 42.2 .. .. 16.2 49.2 .. .. .. .. 12.1 .. 22.6 44.8 16.3 .. 4.6 .. ..

.. 4.2 9.1 .. 11.3 .. .. .. 8.3 .. .. 2.8 35.2 .. .. .. .. 4.6 .. .. .. 1.3 37.1 .. .. 8.6 24.2 39.4 .. 44.1 .. .. 7.6 32.8 8.6 12.8 1.7 .. 14.7 52.4 .. .. 3.9 45.0 .. .. .. .. 5.4 .. 7.7 39.5 10.0 .. 2.6 .. ..

Work only

.. 84.9 24.9 .. 32.4 .. .. .. 2.5 .. .. 1.6 14.1 .. .. .. .. 7.9 .. .. .. 74.4 45.0 .. .. 2.8 40.9 10.5 .. 59.5 .. .. 22.6 3.8 16.4 93.2 100.0 .. 9.5 35.6 .. .. 30.8 66.5 .. .. .. .. 14.6 .. 24.2 4.0 14.8 .. 3.6 .. ..

Study and work

.. 15.2 75.1 .. 67.6 .. .. .. 97.5 .. .. 98.4 85.9 .. .. .. .. 92.1 .. .. .. 25.6 55.0 .. .. 97.2 59.1 89.5 .. 40.5 .. .. 77.4 96.2 83.6 6.8 0.0 .. 90.5 64.4 .. .. 69.2 33.5 .. .. .. .. 85.4 .. 75.7 96.0 85.2 .. 96.4 .. ..

2.6

people

Children at work Employment by economic activitya

Status in employmenta

% of children ages 7–14 in employment

% of children ages 7–14 in employment SelfUnpaid employed Wage family

Agriculture Manufacturing

.. 69.4 .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. 58.0 .. .. .. .. 87.6 .. .. .. .. .. 38.2 .. 91.3 60.6 .. .. 91.5 87.0 .. .. 70.5 .. .. .. .. .. 73.3 .. 60.8 62.6 64.3 .. 48.5 .. ..

.. 16.0 .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. 0.0 .. .. .. .. 2.9 .. .. .. .. .. 11.7 .. 0.3 8.3 .. .. 0.4 1.4 .. .. 9.7 .. .. .. .. .. 2.9 .. 6.2 5.0 4.1 .. 11.2 .. ..

Services

.. 12.4 .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. 10.4 .. .. .. .. 8.2 .. .. .. .. .. 47.0 .. 6.3 10.1 .. .. 8.0 11.1 .. .. 19.3 .. .. .. .. .. 22.9 .. 32.1 31.1 30.6 .. 33.3 .. ..

.. 7.1 .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. 3.7 .. .. .. .. 0.1 .. .. .. .. .. 2.7 .. 5.1 2.1 .. .. 0.1 4.2 .. .. 1.2 4.8 .. .. .. .. 12.6 .. 9.3 3.8 4.1 .. .. .. ..

.. 6.8 17.8 .. 7.0 .. .. .. 16.3 .. .. 4.0 .. .. .. .. .. 3.7 .. .. .. 36.6 1.7 .. .. 3.9 10.0 6.7 .. 1.6 .. .. 34.3 2.9 0.1 10.0 .. .. 4.5 3.3 .. .. 13.8 74.5 .. .. .. .. 11.3 .. 24.8 7.6 22.8 .. .. .. ..

2011 World Development Indicators

.. 59.3 75.8e .. 85.3 .. .. .. 74.9 .. .. 75.0 .. .. .. .. .. 81.9 .. .. .. 59.7c 79.3 .. .. 89.5 89.9 75.5 .. 80.4 .. .. 63.1 82.0 94.7 81.7 .. .. 95.0 92.4 .. .. 85.0c .. .. .. .. 76.1c .. 65.8 88.6 73.1 .. .. .. ..

57


2.6

Children at work Survey year

Children in employment

% of children ages 7–14 in employment

% of children ages 7–14

Romania Russian Federation Rwanda Saudi Arabia Senegal Serbia Sierra Leone Singapore Slovak Republic Slovenia Somalia South Africa Spain Sri Lanka Sudang Swaziland Sweden Switzerland Syrian Arab Republic Tajikistan Tanzaniah Thailand Timor-Leste Togo Trinidad and Tobago Tunisia Turkeyi Turkmenistan Uganda Ukraine United Arab Emirates United Kingdom United States Uruguay Uzbekistan Venezuela, RBd Vietnam West Bank and Gaza Yemen, Rep. Zambia Zimbabwe

2000 2008 2005 2005 2007

2006 1999 1999 2000 2000

2006 2005 2005/06 2005 2006 2000 2006 2005/06 2005

2005 2006 2006 2006 2008 1999

Total

Male

Female

Work only

Study and work

1.4 .. 7.5 .. 18.5 6.9 14.9 .. .. .. 43.5 27.7 .. 17.0 19.1 11.2 .. .. 6.6 8.9 31.1 15.1 .. 38.7 3.9 .. 2.6 .. 38.2 17.3 .. .. .. .. 5.1 5.1 21.3 .. 18.3 34.4 14.3

1.7 .. 8.0 .. 24.4 7.2 14.9 .. .. .. 45.5 29.0 .. 20.4 21.5 11.4 .. .. 8.8 8.7 35.0 15.7 .. 39.8 5.2 .. 3.3 .. 39.8 18.0 .. .. .. .. 5.3 6.9 21.0 .. 20.7 35.4 15.3

1.1 .. 7.0 .. 12.6 6.6 14.9 .. .. .. 41.5 26.4 .. 13.4 16.8 10.9 .. .. 4.3 9.1 27.1 14.4 .. 37.4 2.8 .. 1.8 .. 36.5 16.6 .. .. .. .. 4.9 3.3 21.6 .. 15.9 33.3 13.3

20.7 .. 18.5 .. 61.9 2.1 57.7 .. .. .. 53.5 5.1 .. 5.4 55.9 14.0 .. .. 34.6 9.0 28.2 4.2 .. 29.8 12.8 .. 38.8 .. 7.7 0.1 .. .. .. .. 1.0 19.8 11.9 .. 30.9 18.6 12.0

79.3 .. 81.5 .. 38.1 97.9 42.3 .. .. .. 46.5 94.9 .. 94.6 44.1 86.0 .. .. 65.4 91.0 71.8 95.8 .. 70.2 87.2 .. 61.2 .. 92.3 99.9 .. .. .. .. 99.0 80.2 88.1 .. 69.1 81.4 88.0

Employment by economic activitya

Status in employmenta

% of children ages 7–14 in employment

% of children ages 7–14 in employment SelfUnpaid employed Wage family

Agriculture Manufacturing

97.1 .. 85.5 .. 79.1 .. 83.8 .. .. .. .. .. .. 71.2 .. .. .. .. .. .. 85.3 .. .. 82.9 .. .. 57.1 .. 95.5 .. .. .. .. .. .. 32.3 .. .. .. 91.9 ..

0.0 .. 0.7 .. 5.0 .. 0.8 .. .. .. .. .. .. 13.1 .. .. .. .. .. .. 0.7 .. .. 1.3 .. .. 14.3 .. 1.4 .. .. .. .. .. .. 7.2 .. .. .. 0.7 ..

Services

2.3 .. 10.5 .. 14.0 .. 13.4 .. .. .. .. .. .. 15.0 .. .. .. .. .. .. 14.0 .. .. 15.1 .. .. 27.1 .. 3.0 .. .. .. .. .. .. 55.7 .. .. .. 7.0 ..

4.5 .. 14.8 .. 6.3 .. 9.7 .. .. .. .. 7.1 .. 2.9 .. .. .. .. .. .. 56.3 .. .. 5.0 .. .. 2.1 .. 1.4 .. .. .. .. .. .. 31.6 .. .. .. 2.9 3.4

.. .. 12.8 .. 4.4 5.2 0.9 .. .. .. 1.6 7.1 .. 8.3 7.3 10.4 .. .. 21.5 24.2 0.9 13.5 .. 1.6 29.8 .. 34.1 .. 1.5 3.1 .. .. .. .. 3.8 33.1 5.9 .. 6.1 3.9 28.4

92.9e .. 72.3 .. 84.1 89.4 87.8 .. .. .. 94.8 85.8 .. 88.0 81.3 85.9 .. .. 68.8 71.3 42.8 e 80.0 .. 93.4 64.9 63.8 .. 97.1 79.3 .. .. .. .. 78.6 35.3 91.2 .. 86.1 93.1 68.2

a. Shares may not sum to 100 percent because of a residual category not included in the table. b. Covers only Angola-secured territory. c. Refers to unpaid workers, regardless of whether they are family workers. d. Covers children ages 10–14. e. Refers to family workers, regardless of whether they are paid. f. Covers children ages 12–14. g. Northern Sudan only. h. Refers mainly to work on own shamba. i. Estimates are for children ages 6–14.

58

2011 World Development Indicators


About the data

2.6

people

Children at work Definitions

The data in the table refer to children’s work in the

data on children in employment and in the sampling

•  Survey year is the year in which the underlying

sense of “economic activity”—that is, children in

design underlying the surveys. Differences exist

data were collected. • Children in employment are

employment, a broader concept than child labor

not only across different household surveys in the

children involved in any economic activity for at least

(see ILO 2009a for details on this distinction).

same country but also across the same type of sur-

one hour in the reference week of the survey. • Work

In line with the definition of economic activity

vey carried out in different countries, so estimates

only refers to children who are employed and not

adopted by the 13th International Conference of

of working children are not fully comparable across

attending school. • Study and work refer to chil­dren

Labour Statisticians, the threshold for classifying a

countries.

attending school in combination with employment.

person as employed is to have been engaged at least

The table aggregates the distribution of children in

• Employment by economic activity is the distribu-

one hour in any activity during the reference period

employment by the industrial categories of the Inter-

tion of children in employment by the major industrial

relating to the production of goods and services

national Standard Industrial Classification (ISIC):

categories (ISIC revision 2 or revi­sion 3). • Agricul-

set by the 1993 UN System of National Accounts.

agriculture, manufacturing, and services. A residual

ture corresponds to division 1 (ISIC revision  2) or

Children seeking work are thus excluded. Economic

category—which includes mining and quarrying;

categories A and B (ISIC revision  3) and includes

activity covers all market production and certain non-

electricity, gas, and water; construction; extraterri-

agriculture and hunting, forestry and logging, and

market production, including production of goods for

torial organization; and other inadequately defined

fishing. • Manufacturing corresponds to division 3

own use. It excludes unpaid household services (com-

activities—is not presented. Both ISIC revision 2 and

(ISIC revision 2) or category D (ISIC revision 3). • Ser-

monly called “household chores”)—that is, the pro-

revision 3 are used, depending on the country’s codi-

vices correspond to divisions 6–9 (ISIC revision

duction of domestic and personal services by house-

fication for describing economic activity. This does

2) or categories G–P (ISIC revision  3) and include

hold members for own-household consumption.

not affect the definition of the groups in the table.

wholesale and retail trade, hotels and restaurants,

Data are from household surveys conducted by

The table also aggregates the distribution of

transport, financial intermediation, real estate, pub-

the International Labor Organization (ILO), the United

children in employment by status in employment,

lic administration, education, health and social work,

Nations Children’s Fund (UNICEF), the World Bank,

based on the International Classification of Status in

other community services, and private household

and national statistical offices. The surveys yield data

Employment (1993), which shows the distribution in

activity. • Self-employed workers are people whose

on education, employment, health, expenditure, and

employment by three major categories: selfemployed

remuneration depends directly on the profits derived

consumption indicators related to children’s work.

workers, wage workers (also known as employees),

from the goods and services they produce, with or

Household survey data generally include information

and unpaid family workers. A residual category—

without other employees, and include employers,

on work type—for example, whether a child is working

which includes those not classifiable by status—is

own-account workers, and members of produc-

for payment in cash or in kind or is involved in unpaid

not presented.

ers cooperatives. • Wage workers (also known as

work, working for someone who is not a member of the

In most countries more boys are involved in employ-

employees) are people who hold explicit (written or

household, or involved in any type of family work (on the

ment or the gender difference is small. However, girls

oral) or implicit employment contracts that provide

farm or in a business). Country surveys define the ages

are often more present in hidden or under-reported

basic remuneration that does not depend directly on

for child labor as 5–17. The data in the table have been

forms of employment such as domestic service, and

the revenue of the unit for which they work. • Unpaid

recalculated to present statistics for children ages 7–14.

in almost all societies girls bear greater responsibil-

family workers are people who work without pay in a

Although efforts are made to harmonize the defini-

ity for household chores in their own homes, work

market-oriented establishment operated by a related

tion of employment and the questions on employ-

that lies outside the System of National Accounts

person living in the same household.

ment in survey questionnaires, significant differ-

production boundary and is thus not considered in

ences remain in the survey instruments that collect

estimates of children’s employment.

The largest sector for child labor remains agriculture, and the majority of children work as unpaid family members Child labor by sector (% of children ages 5–17), 2004–08 Not defined Industry 7% 7%

Child labor by status in employment (% of children ages 5–17), 2004–08

Self-employment 5%

Data sources Data on children at work are estimates produced

2.6a

by the Understanding Children’s Work project based on household survey data sets made avail­ able by the ILO’s International Programme on the Elimination of Child Labour under its Statistical

Not defined 6%

Monitoring Programme on Child Labour, UNICEF under its Multiple Indicator Cluster Survey pro­ gram, the World Bank under its Living Standards

Service 26%

Agriculture 60%

Paid employment 21%

Measurement Study program, and national sta­ Unpaid family workers 68%

tistical offices. Information on how the data were collected and some indication of their reliability can be found at www.ilo.org/public/english/ standards/ipec/simpoc/, www.childinfo.org, and www.worldbank.org/lsms. Detailed country statis­

Source: Accelerating Action Against Child Labour, ILO, Geneva 2010.

tics can be found at www.ucw-project.org.

2011 World Development Indicators

59


2.7

Poverty rates at national poverty lines Population below national poverty linea

Survey year b

Afghanistanc Albaniac Angola Argentina Armeniac Azerbaijanc Bangladesh Belarus Benin Bhutan Bolivia Bosnia and Herzegovinac Botswana Brazil Bulgariac Burkina Faso Burundi Cambodiac Cameroon Cape Verde Central African Republic Chad Chile China Colombia Comoros Congo, Dem. Rep. Congo, Rep. Costa Rica Croatia Côte d’Ivoirec Dominican Republic Ecuador Egypt, Arab Rep. El Salvador Ethiopia Fiji Gabon Gambia, Thec Georgiac Ghana Guatemala Guinea Guinea-Bissau Haiti Honduras India Indonesia Iraq Jamaica Jordan Kazakhstanc Kenya Kosovoc Kyrgyz Republicc Lao PDRc Latviac

60

2005 2008e 2008 2001 2000 2008

2006e 2004 1993 2008 e 1997

2004

2006e 2004e 2008e

2008e 2002 2002 2005e 2008e 2005 2007e,f 1999 2003

1998 2000 e

2008 e,f 1994 2009 2006e 2002 2001 2005 2003 2003 2002

Rural %

.. 24.2 .. .. 22.9 42.5 52.3 .. .. .. 76.5 22.0 40.4 .. .. .. .. 37.8 .. .. .. .. 12.3 2.8 65.2 .. .. .. 22.2 .. 45.8 60.2 59.7 26.8 43.8 45.4 40.0 .. .. .. 49.6 74.5 .. .. .. 64.1 37.3 17.4 .. .. 18.7 23.2 .. 37.2 57.5 .. 11.6

2011 World Development Indicators

Urban %

National %

.. 11.2 .. 15.3 23.8 55.7 35.2 .. .. .. 50.3 11.3 24.7 .. .. .. .. 17.6 .. .. .. .. 13.9 .. 39.8 .. .. .. 19.5 .. 32.3 49.9 22.6 10.1 29.8 36.9 28.0 .. .. .. 19.4 27.1 .. .. .. 55.0 32.4 10.7 .. .. 12.9 13.0 .. 30.3 35.7 .. ..

.. 18.5 .. .. 23.5 49.6 48.9 6.1 .. .. 59.9 17.7 32.9 22.6 36.0 .. .. 34.7 .. .. .. .. 13.7 .. 46.0 .. .. .. 20.7 11.2 40.2 53.5 35.1 19.6 34.6 44.2 35.0 .. .. .. 39.5 56.2 .. .. .. 59.6 36.0 14.2 .. 14.3 14.2 17.6 .. 34.8 49.9 33.5 7.5

Survey year b

2008d 2008 2000 d 2009e 2009 2008 2005 2009 2003d 2007d 2007e 2007 2003 2009e 2001 2003d 2006d 2007 2007d 2007d 2008d 2003d 2009e 2005e 2009e 2004 d 2005 2005 2009e 2004 2008 2006e 2009e 2008 2008e,f 2004 2009 2005 2003d 2007 2006 2006e 2007d 2002 2001e 2009e,f 2005 2010 2007 2007e 2006 2002 2005d 2006 2005 2008 2004

Poverty gap at national poverty linea

Rural %

Urban %

37.5 14.6 .. .. 25.5 18.5 43.8 .. 46.0 30.9 77.3 17.8 44.8 .. .. 52.4 68.9 34.5 55.0 44.3 69.4 58.6 12.9 2.5 64.3 48.7 75.7 57.7 .. .. 54.2 57.1 57.5 30.0 49.0 39.3 43.3 44.6 67.8 29.7 39.2 70.5 63.0 69.1 88.0 64.4 28.3 16.6 39.3 .. 19.0 21.7 49.1 49.2 50.8 31.7 12.7

29.0 10.1 62.3 13.2 26.9 14.8 28.4 .. 29.0 1.7 50.9 8.2 19.4 .. .. 19.2 34.0 11.8 12.2 13.2 49.6 24.6 15.5 .. 39.6 34.5 61.5 .. .. .. 29.4 45.3 25.0 10.6 35.7 35.1 18.6 29.8 39.6 18.3 10.8 30.0 30.5 51.6 45.0 52.8 25.7 9.9 16.1 .. 12.0 10.2 33.7 37.4 29.8 17.4 ..

National %

36.0 12.4 .. .. 26.5 15.8 40.0 5.4 39.0 23.2 60.1 14.0 30.6 21.4 12.8 46.4 66.9 30.1 39.9 26.6 62.0 55.0 15.1 .. 45.5 44.8 71.3 50.1 21.7 11.1 42.7 49.4 36.0 22.0 40.0 38.9 31.0 32.7 58.0 23.6 28.5 51.0 53.0 64.7 77.0 58.8 27.5 13.3 22.9 9.9 13.0 15.4 45.9 45.0 43.1 27.6 5.9

Survey year b

2008d 2008

2009 2008 2005 2003d 2007d

2003 2001 2003d 2006d 2007 2007d 2007d 2008d 2003d

2004 d 2005 2005 2004 2008

2004 2009 2005 2003d 2007 2006 2007d 2002

2010 2007 2006 2002 2005d 2006 2005 2004

Rural %

Urban %

8.3 2.6 .. .. .. .. 9.8 .. 14.0 8.1 .. .. 18.4 .. .. 17.6 24.2 8.3 17.5 14.3 35.0 23.3 .. .. .. 17.8 34.9 20.6 .. .. 20.3 .. .. .. .. 8.5 14.8 16.0 30.5 9.2 13.5 .. 22.0 27.8 .. .. .. 2.8 9.0 .. .. 4.5 17.5 14.3 12.0 .. ..

6.2 1.9 .. .. .. .. 6.5 .. 8.0 0.4 .. .. 6.5 .. .. 5.1 10.3 2.8 2.8 3.3 29.8 7.4 .. .. .. 12.1 26.2 .. .. .. 9.5 .. .. .. .. 7.7 5.4 8.5 14.8 5.3 3.1 .. 7.7 16.9 .. .. .. 1.6 2.7 .. .. 2.0 11.4 11.3 7.0 .. ..

National %

7.9 2.3 .. .. 4.9 2.0 9.0 .. 12.0 6.1 .. .. 11.7 .. 4.2 15.3 23.4 7.2 12.3 8.1 33.1 21.6 .. .. .. 16.3 32.2 18.9 .. 2.6 15.3 .. .. .. .. 8.3 10.1 10.0 25.1 7.2 9.6 .. 17.6 25.0 .. .. .. 2.2 4.5 .. 2.8 3.1 16.3 13.3 10.0 .. 1.2


Population below national poverty linea

Lesothoc Liberiac Macedonia, FYRc Madagascar Malawi Malaysiac Mali Mauritania Mexico Moldovac Mongolia Montenegro Morocco Mozambique Namibia Nepal Nicaragua Niger Nigeria Pakistan Panama Paraguay Peru Philippines Polandc Romaniac Russian Federationc Rwanda São Tomé and Príncipe Senegalc Serbiac Sierra Leone South Africa Sri Lanka Swaziland Tajikistanc Tanzania Thailand Timor-Leste Togo Turkey Uganda Ukrainec Uruguay Venezuela, RB Vietnam West Bank and Gaza Yemen, Rep. Zambia Zimbabwe

Survey year b

Rural %

1994

68.9 .. 21.2 77.3 58.1 7.1 .. .. 54.7 .. .. 12.0 .. 55.3 .. 43.3 67.8 .. .. 28.1 62.7 48.8 59.8 .. .. 23.5 22.7 .. .. .. 13.9 .. .. 24.7 .. 54.4 38.6 11.5 .. .. 34.6 34.2 18.1 29.4 .. 20.4 .. 42.5 77.3 ..

2005 2004 1998 2007

2006e 2004 2007 2002 1996 2001e

2005 2003 2008e 2008 2006 2001 2005 2005

2006 2000 2002 2007 2000 2008 2001 2008 2005 2004 2007e 2008e 2006 2007 1998 2004

2.7

people

Poverty rates at national poverty lines

Poverty gap at national poverty linea

Urban %

National %

Survey year b

Rural %

Urban %

36.7 .. 19.8 53.7 18.5 2.0 .. .. 35.6 .. .. 5.5 .. 51.5 .. 21.6 30.1 .. .. 14.9 20.0 30.2 23.5 .. .. 8.1 8.1 .. .. .. 5.2 .. .. 7.9 .. 49.3 23.1 3.0 .. .. 9.4 13.7 12.0 25.5 .. 3.9 .. 32.3 29.1 ..

66.6 .. 20.4 72.1 54.1 3.6 .. .. 42.6 26.5 .. 8.0 .. 54.1 .. 41.8 45.8 .. .. 23.9 36.8 37.9 36.2 26.4 15.6 15.1 11.9 .. .. .. 9.0 .. 38.0 22.7 .. 53.1 35.6 9.0 39.7 .. 17.1 31.1 14.0 26.0 32.6 16.0 31.2 40.1 58.4 ..

2003 2007 2006 2005 2004 2009 2006d 2000 d 2008e 2005 2008d 2008 2001 2008 2003d 2004 2005e 2007d 2004 d 2006 2008 2009e 2009 2009 2002 2006 2006 2006d 2001 2005d 2007 2003d 2005 2007 2001d 2009 2007 2009 2007 2006 2009 2009 2005 2008e 2009e 2008 2009 2005 2006 2003d

60.5 67.7 21.3 73.5 55.9 8.2 57.6 61.2 60.8 .. 46.6 8.9 25.1 56.9 49.0 34.6 67.9 63.9 63.8 27.0 59.8 49.8 60.3 .. .. 22.3 21.2 64.2 64.9 61.9 9.8 78.5 .. 15.7 75.0 49.2 37.4 10.4 .. 74.3 38.7 27.2 11.3 22.2 .. 18.7 .. 40.1 76.8 ..

41.5 55.1 17.7 52.0 25.4 1.7 25.5 25.4 39.8 .. 26.9 2.4 7.6 49.6 17.0 9.6 29.1 36.7 43.1 13.1 17.7 24.7 21.1 .. .. 6.8 7.4 23.2 45.0 35.1 4.3 47.0 .. 6.7 49.0 41.8 21.8 3.0 .. 36.8 8.9 9.1 6.3 20.3 .. 3.3 .. 20.7 26.7 ..

National %

56.6 63.8 19.0 68.7 52.4 3.8 47.4 46.3 47.4 29.0 35.2 4.9 15.3 54.7 38.0 30.9 46.2 59.5 54.7 22.3 32.7 35.1 34.8 26.5 16.6 13.8 11.1 58.5 53.8 50.8 6.6 66.4 23.0 15.2 69.2 47.2 33.4 8.1 49.9 61.7 18.1 24.5 7.9 20.5 29.0 14.5 21.9 34.8 59.3 72.0

Survey year b

2007 2006 2005 2004 2009 2006d 2000 d

2008d 2008 2008 2003d 2004 2007d 2004 d

2009 2006 2006 2006d 2001 2005d 2007 2003d 2005 2007 2001d 2007

2006 2009 2005

2008 2009 2005 2006

Rural %

Urban %

.. 26.3 7.7 28.9 19.2 1.8 .. 24.1 .. .. 13.4 1.4 .. 22.2 16.0 8.5 .. 21.2 26.6 .. .. .. .. .. .. 5.3 5.5 26.0 24.7 21.5 2.0 34.6 .. 3.2 37.0 .. 11.0 .. .. 29.3 .. 7.6 2.3 .. .. 4.6 .. 10.6 38.8 ..

.. 20.2 6.9 19.3 7.1 0.3 .. 6.3 .. .. 7.7 0.6 .. 19.1 6.0 2.2 .. 11.3 16.2 .. .. .. .. .. .. 1.4 1.7 8.0 14.9 9.3 0.8 16.3 .. 1.3 20.0 .. 6.5 .. .. 10.3 .. 1.8 1.1 .. .. 0.5 .. 4.5 9.4 ..

National %

.. 24.4 7.2 26.8 17.8 0.8 16.7 17.0 .. .. 10.1 0.9 .. 21.2 13.0 7.5 .. 19.6 22.8 .. .. .. .. 2.7 .. 3.2 2.7 24.0 19.2 16.4 1.3 27.5 7.0 3.1 32.9 .. 9.9 .. .. 22.9 .. 6.8 1.5 .. .. 3.5 4.9 8.9 28.5 ..

a. Based on per capita consumption estimated from household survey data, unless otherwise noted. b. Refers to the year in which the underlying household survey data were collected; in cases for which the data collection period bridged two calender years, the year in which most of the data were collected is reported. c. World Bank estimates. d. Estimates based on survey data from earlier year(s) are available, but are not comparable with the most recent year reported here; these are available online at http://data.worldbank.org. e. Based on income per capita estimated from household survey data. f. Measured as a share of households.

2011 World Development Indicators

61


2.7

Poverty rates at national poverty lines

About the data

Definitions

Estimates of poverty rates and gaps at national pov-

As with any indicator measured from household

• Survey year is the year in which the underlying

erty lines are useful for comparing poverty across

surveys, data quality issues can affect the precision

household survey data were collected; when the data

time within but not across countries. Table 2.8 shows

of poverty estimates and their comparability over

collection period bridged two calendar years, the year

poverty indicators at international poverty lines that

time. These include selective survey nonresponse,

in which most of the data were collected is reported.

allow for comparisons across countries.

seasonality effects, differences in the number of

• Population below national poverty line is the per-

For countries with an active poverty monitoring pro-

income or consumption items in the questionnaire,

centage of the rural, urban, and national population

gram, the World Bank—in collaboration with national

and the time period over which respondents are

living below the corresponding rural, urban, national

institutions, other development agencies, and civil

asked to recall their expenditures.

poverty line, based on consumption estimated from

society—periodically prepares poverty assessments

household survey data, unless otherwise noted.

and other analytical reports to assess the extent

National poverty lines

• Poverty gap at national poverty line is the mean

and causes of poverty. These reports review levels

National poverty lines are the benchmark for esti-

shortfall from the rural, urban, or national poverty

and changes in poverty indicators over time and

mating poverty indicators that are consistent with

line (counting the nonpoor as having zero shortfall)

across regions within countries, assess the impact

the country’s specific economic and social circum-

as a percentage of the corresponding rural, urban,

of growth and public policy on poverty and inequal-

stances. National poverty lines reflect local percep-

or national poverty line, based on consumption esti-

ity, review the adequacy of monitoring and evalua-

tions of the level and composition of consumption or

mated from household survey data, unless otherwise

tion, and contain detailed technical overviews of

income needed to be nonpoor. The perceived bound-

noted. This measure reflects the depth of poverty as

the underlying household survey data and poverty

ary between poor and nonpoor typically rises with the

well as its incidence.

measurement methods used. The reports are a key

average income of a country and thus does not pro-

source of comprehensive information on poverty indi-

vide a uniform measure for comparing poverty rates

cators at national poverty lines and generally feed

across countries. While poverty rates at national

into country-owned processes to reduce poverty,

poverty lines should not be used for comparing pov-

build in-country capacity, and support joint work.

erty rates across countries, they are appropriate for

An increasing number of countries have their own national programs to monitor and disseminate

guiding and monitoring the results of country-specific national poverty reduction strategies.

official poverty estimates at national poverty lines

Almost all national poverty lines are anchored to

along with well documented household survey data

the cost of a food bundle—based on the prevailing

sources and estimation methodology. Estimates

national diet of the poor—that provides adequate

from national poverty monitoring programs and the

nutrition for good health and normal activity, plus

underlying methods used are periodically reviewed by

an allowance for nonfood spending. National poverty

the World Bank and included in the table.

lines must be adjusted for inflation between survey

The complete online database of poverty estimates

years to remain constant in real terms and thus allow

at national poverty lines (available at http://data.

for meaningful comparisons of poverty over time.

worldbank.org) is regularly updated and may con-

Because diets and consumption baskets change

tain more recent data or revisions not incorporated

over time, countries periodically recalculate the pov-

in the table. It is maintained by the Global Poverty

erty line based on new survey data. In such cases

Working Group, a team of poverty experts from the

the new poverty lines should be deflated to obtain

Poverty Reduction and Equity Network, the Develop-

comparable poverty estimates from earlier years.

ment Research Group, and the Development Data

The table reports indicators based on the two most

Group, which recently updated the database to cover

recent years for which survey data is available. Coun-

115 countries and more than 575 sets of poverty

tries for which the most recent indicators reported

Data sources

estimates at national poverty lines for 1974−2010.

are not comparable to those based on survey data

Poverty rates at national poverty lines are com-

from an earlier year are footnoted in the table.

piled by the Global Poverty Working Group, based

Data quality

on data from World Bank’s country poverty assess-

Poverty estimates at national poverty lines are com-

ments and analytical reports as well as country

puted from household survey data collected from

Poverty Reduction Strategies and official poverty

nationally representative samples of households.

estimates. Further documentation of the data,

These data must contain sufficiently detailed infor-

measurement methods and tools, and research,

mation to compute a comprehensive estimate of

as well as poverty assessments and analytical

total household income or consumption (including

reports, are available at http://data.worldbank.

consumption or income from own production), from

org, www.worldbank.org/poverty, and http://econ.

which it is possible to construct a correctly weighted

worldbank.org.

distribution of per capita consumption or income.

62

2011 World Development Indicators


Population below International poverty linea

International poverty line in local currency

Albania Algeria Angola Argentina Armenia Azerbaijan Bangladesh Belarus Belize Benin Bhutan Bolivia Bosnia and Herzegovina Botswana Brazil Bulgaria Burkina Faso Burundi Cambodia Cameroon Cape Verde Central African Republic Chad Chile China Colombia Comoros Congo, Dem. Rep. Congo, Rep. Costa Rica Croatia Czech Republic Côte d’Ivoire Djibouti Dominican Republic Ecuador Egypt, Arab Rep. El Salvador Estonia Ethiopia Gabon Gambia, The Georgia Ghana Guatemala Guinea Guinea-Bissau Guyana Haiti Honduras Hungary India Indonesia Iraq Jamaica Jordan Kazakhstan

$1.25 a day

$2 a day

2005

2005

75.5 48.4 c 88.1 1.7 245.2 2,170.9 31.9 949.5 1.8 c 344.0 23.1 3.2 1.1 4.2 2.0 0.9 303.0 558.8 2,019.1 368.1 97.7 384.3 409.5 484.2 5.1 g 1,489.7 368.0 395.3 469.5 348.7c 5.6 19.0 407.3 134.8 25.5c 0.6 2.5 6.0 c 11.0 3.4 554.7 12.9 1.0 5,594.8 5.7c 1,849.5 355.3 131.5c 24.2c 12.1c 171.9 19.5i 5,241.0i 799.8 54.2c 0.6 81.2

120.8 77.5c 141.0 2.7 392.4 3,473.5 51.0 1,519.2 2.9c 550.4 36.9 5.1 1.7 6.8 3.1 1.5 484.8 894.1 3,230.6 589.0 156.3 614.9 655.1 774.7 8.2g 2,383.5 588.8 632.5 751.1 557.9c 8.9 30.4 651.6 215.6 40.8 c 1.0 4.0 9.6c 17.7 5.5 887.5 20.7 1.6 8,951.6 9.1c 2,959.1 568.6 210.3c 38.7c 19.3c 275.0 31.2i 8,385.7i 1,279.7 86.7c 1.0 129.9

2.8

people

Poverty rates at international poverty lines

Survey year b

2005 1988   2006d,e 2003 2005 2000 f 2005 1995     2005e 2004 1986 2008 e 2003 1998 1998 2004 2001   1993   2006 e 2002h 2003e

2005e 2005 1993e 2002 1996 2006e 2007e 2000 2005e 2003 2000   1998 2005 1998 2002e 2003 1993 1993e   2006e 2004 1994h 2005h   2002 2003 2003

Population below $1.25 a day %

Poverty gap at $1.25 a day %

Population below $2 a day %

Poverty gap at $2 a day %

Population below $1.25 a day %

Survey year b

<2 6.6 ..  2.8 10.6 <2 57.8 <2 14.0 ..  ..  19.6 <2 35.6 4.3 <2 70.0 86.4 40.2 32.8 ..  82.8  .. <2 28.4 15.4  ..  ..  .. 2.4 <2 <2 23.3 4.8 4.0 4.7 <2 11.0 <2 55.6  .. 66.7 13.4 39.1 16.9 70.1 52.1 5.8  .. 18.2 <2 49.4 21.4  .. <2 <2 3.1

<0.5 1.8 ..  0.6 1.9 <0.5 17.3 <0.5 5.4 ..  ..  9.7 <0.5 13.8 1.4 <0.5 30.2 47.3 11.3 10.2 ..  57.0  .. <0.5 8.7 6.1  ..  ..  .. <0.5 <0.5 <0.5 6.8 1.6 0.7 1.2 <0.5 4.8 <0.5 16.2  .. 34.7 4.4 14.4 6.5 32.2 20.6 2.6  .. 8.2 <0.5 14.4 4.6  .. <0.5 <0.5 <0.5

7.9 23.8  .. 8.0 43.5 <2 85.4 <2 23.6  .. ..  30.4 <2 54.7 10.4 2.4 87.6 95.4 68.2 57.7 ..  90.8  .. 2.4 51.1 26.3  ..  ..  .. 8.6 <2 <2 46.8 15.1 13.5 12.8 19.4 20.5 2.7 86.4  .. 82.0 30.4 63.3 29.8 87.2 75.7 15.0  .. 29.7 <2 81.7 53.8  .. 8.7 11.0 17.2

1.5 6.6  .. 2.4 11.3 <0.5 38.8 <0.5 10.5 ..  ..  15.5 <0.5 25.8 3.6 0.9 49.1 64.1 28.0 23.7  .. 68.4 ..  <0.5 20.6 10.9 ..  ..  ..  2.3 <0.5 <0.5 17.6 4.5 3.7 4.0 3.5 8.9 0.9 37.9 ..  50.0 10.9 28.5 12.9 50.3 37.4 5.4 ..  14.2 <0.5 35.3 17.3 ..  1.6 2.1 3.9

2008 1995 2000 d 2009d,e 2008 2008 2005f 2008 1999e 2003 2003 2007e 2007 1994 2009e 2007 2003 2006 2007 2007 2001 2003 2003 2009e 2005h 2006e 2004 2006 2005 2009e 2008 1996e 2008 2002 2007e 2009e 2005 2008e 2004 2005 2005 2003 2008 2006 2006e 2007 2002 1998 e 2001e 2007e 2007 2005h 2009h 2007 2004 2006 2007

<2 6.8 54.3 <2 <2 <2 49.6 <2 12.1 47.3 26.2 14.0 <2 31.2 3.8 <2 56.5 81.3 28.3 9.6 20.6 62.4 61.9 <2 15.9 16.0 46.1 59.2 54.1 <2 <2 <2 23.8 18.8 4.3 5.1 <2 5.1 <2 39.0 4.8 34.3 14.7 30.0 12.7 43.8 48.8 7.7 54.9 23.2 <2 41.6 18.7 4.0 <2 <2 <2

Poverty gap at $1.25 a day %

<0.5 1.4 29.9 <0.5 <0.5 <0.5 13.1 <0.5 4.7 15.7 7.0 5.8 <0.5 11.0 1.1 <0.5 20.3 36.4 6.1 1.2 5.9 28.3 25.6 <0.5 4.0 5.7 20.8 25.3 22.8 <0.5 <0.5 <0.5 7.5 5.3 0.9 1.6 <0.5 1.1 <0.5 9.6 0.9 12.1 4.6 10.5 3.8 15.2 16.5 3.9 28.2 11.3 <0.5 10.8 3.6 0.6 <0.5 <0.5 <0.5

Population below $2 a day %

Poverty gap at $2 a day %

4.3 23.6 70.2 <2 12.4 7.8 81.3 <2 23.9 75.3 49.5 24.7 <2 49.4 9.9 7.3 81.2 93.5 56.5 30.8 40.3 81.9 83.3 <2 36.3 27.9 65.0 79.6 74.4 4.8 <2 <2 46.0 41.2 13.6 13.4 18.5 15.2 <2 77.6 19.6 56.7 32.6 53.6 25.7 70.0 77.9 16.8 72.2 35.6 <2 75.6 50.7 25.3 5.9 3.5 <2

0.9 6.5 42.4 <0.5 2.3 1.5 33.8 <0.5 9.7 33.5 18.8 10.9 <0.5 22.3 3.2 1.5 39.3 56.1 20.2 8.4 14.9 45.3 43.9 <0.5 12.2 11.9 34.2 42.4 38.8 0.9 <0.5 <0.5 17.9 14.6 3.9 4.4 3.5 4.5 <0.5 28.9 5.0 24.9 11.8 22.3 9.6 31.3 34.8 6.9 41.8 18.1 <0.5 30.4 15.5 5.6 0.9 0.6 <0.5

2011 World Development Indicators

63


2.8

Poverty rates at international poverty lines Population below International poverty linea

International poverty line in local currency

Kenya Kyrgyz Republic Lao PDR Latvia Lesotho Liberia Lithuania Macedonia, FYR Madagascar Malawi Malaysia Maldives Mali Mauritania Mexico Micronesia, Fed. Sts. Moldova Mongolia Montenegro Morocco Mozambique Namibia Nepal Nicaragua Niger Nigeria Pakistan Panama Papua New Guinea Paraguay Peru Philippines Poland Romania Russian Federation Rwanda São Tomé and Príncipe Senegal Serbia Seychelles Sierra Leone Slovak Republic Slovenia South Africa Sri Lanka St. Lucia Suriname Swaziland Syrian Arab Republic Tajikistan Tanzania Thailand Timor-Leste Togo Trinidad and Tobago Tunisia

64

$1.25 a day

$2 a day

2005

2005

40.9 16.2 4,677.0 0.4 4.3 0.6 2.1 29.5 945.5 71.2 2.6 12.2 362.1 157.1 9.6 0.8 c 6.0 653.1 0.6 6.9 14,532.1 6.3 33.1 9.1c 334.2 98.2 25.9 0.8 c 2.1c 2,659.7 2.1 30.2 2.7 2.1 16.7 295.9 7,953.9 372.8 42.9 5.6c 1,745.3 23.5 198.2 5.7 50.0 2.4 c 2.3c 4.7 30.8 1.2 603.1 21.8 0.6c 352.8 5.8 c 0.9

65.4 26.0 7,483.2 0.7 6.9 1.0 3.3 47.2 1,512.8 113.8 4.2 19.5 579.4 251.3 15.3 1.3c 9.7 1,045.0 1.0 11.0 23,251.4 10.1 52.9 14.6c 534.7 157.2 41.4 1.2c 3.4 c 4,255.6 3.3 48.4 4.3 3.4 26.8 473.5 12,726.3 596.5 68.6 9.0 c 2,792.4 37.7 317.2 9.1 80.1 3.8 c 3.7c 7.5 49.3 1.9 964.9 34.9 1.0 c 564.5 9.2c 1.4

2011 World Development Indicators

Survey year b

1997 2004 2002 2004 1995 2004 2003 2001 1998 2004 e   2001 1996 2006   2004   2001 2003   1996 2001e 2005 1996 2005 2006e   2007e 2006e 2003 2005 2005 2005 2000   2001   2000 1990 1992e 2002 1995 2002     1995   2003 2000 2004 2001   1988 e 1995

Population below $1.25 a day %

19.6 21.8 44.0 <2 47.6  .. <2 <2 76.3 83.1 <2  .. 61.2 23.4 <2  .. 8.1  ..  .. 6.3 74.7  .. 68.4 19.4 65.9 68.5 22.6 9.5  .. 6.5 7.9 22.0 <2 <2 <2 76.6  .. 44.2  .. <2 62.8 <2 <2 21.4 14.0  ..  .. 78.6  .. 36.3 88.5 <2 52.9  .. <2 6.5

Poverty gap at $1.25 a day %

Population below $2 a day %

Poverty gap at $2 a day %

Survey year b

4.6 4.4 12.1 <0.5 26.7 ..  <0.5 <0.5 41.4 46.0 <0.5 ..  25.8 7.1 <0.5 ..  1.7  .. ..  0.9 35.4 ..  26.7 6.7 28.1 32.1 4.4 3.1 ..  2.7 1.9 5.5 <0.5 <0.5 <0.5 38.2 ..  14.3 ..  <0.5 44.8 <0.5 <0.5 5.2 2.6 ..  ..  47.7 ..  10.3 46.8 <0.5 19.1 ..  <0.5 1.3

42.7 51.9 76.9 <2 61.1  .. <2 3.2 88.8 93.5 7.8  .. 82.0 48.3 4.8  .. 29.0  ..  .. 24.3 90.0  .. 88.1 37.5 85.6 86.4 60.3 17.9  .. 14.2 18.5 43.8 <2 3.4 <2 90.3  .. 71.3  .. <2 75.0 <2 <2 39.9 39.7  ..  .. 89.3  .. 68.8 96.6 11.5 77.5  .. 8.6 20.4

14.7 16.8 31.1 <0.5 37.3 ..  <0.5 0.7 57.2 62.3 1.4 ..  43.6 17.8 1.0 ..  7.9 ..  ..  6.3 53.6 ..  46.8 14.5 46.7 49.7 18.7 7.1 ..  5.5 6.0 16.0 <0.5 0.9 <0.5 55.7 ..  31.2 ..  <0.5 54.0 <0.5 <0.5 15.0 11.9 ..  ..  61.7 ..  26.7 64.4 2.0 37.1 ..  1.9 5.8

2005 2007 2008 2008 2003 2007 2008 2008 2005 2004 2009e 2004 2006 2000 2008 2000 2008 2002 2008 2007 2008 1993e 2004 2005e 2007 2004 2006 2009e 1996 2008e 2009e 2006 2008 2008 2008 2005 2001 2005 2008 2007 2003 1996e 2004 2000 2007 1995e 1999e 2001 2004 2004 2007 2009 2007 2006 1992e 2000

Population below $1.25 a day %

19.7 <2 33.9 <2 43.4 83.7 <2 <2 67.8 73.9 <2 <2 51.4 21.2 <2 31.1 <2 15.5 <2 2.5 60.0 49.1 55.1 15.8 43.1 64.4 22.6 2.4 35.8 5.1 5.9 22.6 <2 <2 <2 76.8 28.6 33.5 <2 <2 53.4 <2 <2 26.2 7.0 20.9 15.5 62.9 <2 21.5 67.9 12.8 37.4 38.7 4.2 2.6

Poverty gap at $1.25 a day %

6.1 <0.5 9.0 <0.5 20.8 40.8 <0.5 <0.5 26.5 32.3 <0.5 <0.5 18.8 5.7 <0.5 16.3 <0.5 3.6 <0.5 0.5 25.2 24.6 19.7 5.2 11.9 29.6 4.1 <0.5 12.3 1.5 1.4 5.5 <0.5 <0.5 <0.5 40.9 8.2 10.8 <0.5 <0.5 20.3 <0.5 <0.5 8.2 1.0 7.2 5.9 29.4 <0.5 5.1 28.1 2.4 8.9 11.4 1.1 <0.5

Population below $2 a day %

Poverty gap at $2 a day %

39.9 29.4 66.0 <2 62.3 94.8 <2 4.3 89.6 90.5 2.3 12.2 77.1 44.1 8.6 44.7 12.5 38.9 <2 14.0 81.6 62.2 77.6 31.9 75.9 83.9 61.0 9.5 57.4 13.2 14.7 45.0 <2 <2 <2 89.6 57.3 60.4 <2 <2 76.1 <2 <2 42.9 29.1 40.6 27.2 81.0 16.9 50.9 87.9 26.5 72.8 69.3 13.5 12.8

15.1 5.5 24.8 <0.5 33.1 59.5 <0.5 0.7 46.9 51.8 <0.5 2.5 36.5 15.9 2.0 24.5 2.6 12.3 <0.5 3.2 42.9 36.5 37.8 12.3 30.6 46.9 18.8 2.4 25.5 4.3 4.7 16.4 <0.5 0.5 <0.5 57.2 21.6 24.7 <0.5 <0.5 37.5 <0.5 <0.5 18.3 7.4 15.5 11.7 45.8 3.3 16.8 47.5 8.3 27.0 27.9 3.9 3.0


Population below International poverty linea

International poverty line in local currency

$1.25 a day

$2 a day

2005

2005

5,961.1c 930.8 2.1 19.1 470.1c 1,563.9 7,399.9 113.8 3,537.9

Turkmenistan Uganda Ukraine Uruguay Uzbekistan Venezuela, RB Vietnam Yemen, Rep. Zambia

9,537.7c 1,489.2 3.4 30.6 752.1c 2,502.2 11,839.8 182.1 5,660.7

people

2.8

Poverty rates at international poverty lines

Survey year b

1993e 2005 2005 2006e 2002 2005e 2006 1998 2003

Population below $1.25 a day %

63.5 51.5 <2 <2 42.3 10.0 21.5 12.9 64.6

Poverty gap at $1.25 a day %

Population below $2 a day %

25.8 19.1 <0.5 <0.5 12.4 4.5 4.6 3.0 27.1

85.7 75.6 <2 4.2 75.6 19.8 48.4 36.4 85.2

Poverty gap at $2 a day %

Survey year b

44.9 36.4 <0.5 0.6 30.6 8.4 16.2 11.1 45.8

1998 2009 2008 2009e 2003 2006e 2008 2005 2004

Population below $1.25 a day %

Poverty gap at $1.25 a day %

Population below $2 a day %

Poverty gap at $2 a day %

24.8 37.7 <2 <2 46.3 3.5 13.1 17.5 64.3

7.0 12.1 <0.5 <0.5 15.0 1.1 2.3 4.2 32.8

49.7 64.5 <2 <2 76.7 10.2 38.4 46.6 81.5

18.4 27.2 <0.5 <0.5 33.2 3.2 10.8 14.8 48.3

a. Based on nominal per capita consumption averages and distributions estimated from household survey data, unless otherwise noted. b. Refers to the year in which the underlying household survey data were collected; in cases for which the data collection period bridged two calender years, the year in which most of the data were collected is reported. c. Based on purchasing power parity (PPP) dollars imputed using regression. d. Urban areas only. e. Based on per capita income averages and distribution data estimated from household survey data. f. Adjusted by spatial consumer price index data. g. PPP conversion factor based on urban prices. h. Population-weighted average of urban and rural estimates. i. Based on benchmark national PPP estimate rescaled to account for cost-of-living differences in urban and rural areas.

Regional poverty estimates and progress toward

84 percent to 16 percent, leaving 620 million fewer

developing countries in 2005 was $2.00 a day. The

the Millennium Development Goals

people in poverty.

poverty rate for all developing countries measured

Global poverty measured at the $1.25 a day pov-

Over the same period the poverty rate in South Asia

at this line fell from nearly 70 percent in 1981 to 47

erty line has been decreasing since the 1980s. The

fell from 59 percent to 40 percent (table 2.8c). In con-

percent in 2005, but the number of people living on

share of population living on less than $1.25 a day

trast, the poverty rate fell only slightly in Sub- Saharan

less than $2.00 a day has remained nearly constant

fell 10 percentage points, to 42 percent, in 1990 and

Africa—from less than 54 percent in 1981 to more

at 2.5 billion. The largest decrease, both in number

then fell nearly 17 percentage points between 1990

than 58 percent in 1999 then down to 51 percent

and proportion, occurred in East Asia and Pacific, led

and 2005. The number of people living in extreme

in 2005. But the number of people living below the

by China. Elsewhere, the number of people living on

poverty fell from 1.9 billion in 1981 to 1.8 billion

poverty line has nearly doubled. Only East Asia and

less than $2.00 a day increased, and the number of

in 1990 to about 1.4 billion in 2005 (figure 2.8a).

Pacific is consistently on track to meet the Millennium

people living between $1.25 and $2.00 a day nearly

This substantial reduction in extreme poverty over

Development Goal target of reducing 1990 poverty

doubled, to 1.2 billion.

the past quarter century, however, disguises large

rates by half by 2015. A slight acceleration over his-

Once household survey data collected after 2005

regional differences.

torical growth rates could lift Latin America and the

in large countries—such as China and India, as well

The greatest reduction in poverty occurred in East

Caribbean and South Asia to the target. However, the

as some countries in Sub-Saharan Africa and the

Asia and Pacific, where the poverty rate declined

recent slowdown in the global economy may leave

Middle East and North Africa—become available,

from 78 percent in 1981 to 17 percent in 2005 and

these regions and many countries short of the target.

the World Bank’s Development Research Group will

the number of people living on less than $1.25 a day

Most of the people who have escaped extreme

update regional poverty estimates at international

dropped more than 750 million (figure 2.8b). Much

poverty remain very poor by the standards of mid-

poverty lines; see http://iresearch.worldbank.org/

of this decline was in China, where poverty fell from

dle- income economies. The median poverty line for

povcalnet/.

While the number of people living on less than $1.25 a day has fallen, the number living on $1.25–$2.00 a day has increased 2.8a

80

2.5

1.5 1.0 0.5

People living on less than $1.25 a day, other developing regions

People living on more than $1.25 and less than $2.00 a day, all developing regions

20

People living on less than $1.25 a day, South Asia

People living on less than $1.25 a day, Sub-Saharan Africa

0 1984

1987

Source: World Bank PovcalNet.

1990

Sub-Saharan Africa

60

40

People living on less than $1.25 a day, East Asia & Pacific

1981

2.8b

Share of population living on less than $1.25 a day, by region (percent)

People living in poverty (billions) 3.0

2.0

Poverty rates have begun to fall

1993

1996

1999

2002

2005

South Asia East Asia & Pacific

Europe & Central Asia Middle East & North Africa

Latin America & Caribbean

0 1981

1984

1987

1990

1993

1996

1999

2002

2005

Source: World Bank PovcalNet.

2011 World Development Indicators

65


2.8

Poverty rates at international poverty lines 2.8c

Regional poverty estimates Region or country

1981

1984

1987

1990

1993

1996

1999

2002

2005

People living on less than 2005 PPP $1.25 a day (millions) East Asia & Pacific China Europe & Central Asia

1,072

947

822

873

845

622

635

507

316

835

720

586

683

633

443

447

363

208

7

6

5

9

20

22

24

22

17 45

Latin America & Caribbean

47

59

57

50

47

53

55

57

Middle East & North Africa

14

12

12

10

10

11

12

10

11

548

548

569

579

559

594

589

616

596

South Asia India Sub-Saharan Africa Total

420

416

428

436

444

442

447

460

456

211

242

258

297

317

356

383

390

388

1,900

1,814

1,723

1,818

1,799

1,658

1,698

1,601

1,374

Share of people living on less than 2005 PPP $1.25 a day (percent) East Asia & Pacific China Europe & Central Asia Latin America & Caribbean Middle East & North Africa

77.7

65.5

54.2

54.7

50.8

36.0

35.5

27.6

16.8

84.0

69.4

54.0

60.2

53.7

36.4

35.6

28.4

15.9

1.7

1.3

1.1

2.0

4.3

4.6

5.1

4.6

3.7

12.9

15.3

13.7

11.3

10.1

10.9

10.9

10.7

8.2

7.9

6.1

5.7

4.3

4.1

4.1

4.2

3.6

3.6

59.4

55.6

54.2

51.7

46.9

47.1

44.1

43.8

40.3

59.8

55.5

53.6

51.3

49.4

46.6

44.8

43.9

41.6

Sub-Saharan Africa

53.4

55.8

54.5

57.6

56.9

58.8

58.4

55.0

50.9

Total

51.9

46.7

41.9

41.7

39.2

34.5

33.7

30.5

25.2

South Asia India

People living on less than 2005 PPP $2.00 a day (millions) East Asia & Pacific China

1,278

1,280

1,238

1,274

1,262

1,108

1,105

954

729

972

963

907

961

926

792

770

655

474

Europe & Central Asia

35

28

25

32

49

56

68

57

42

Latin America & Caribbean

90

110

103

96

96

107

111

114

94

Middle East & North Africa South Asia India Sub-Saharan Africa Total

46

44

47

44

48

52

52

51

51

799

836

881

926

950

1,009

1,031

1,084

1,092

609

635

669

702

735

757

783

813

828

294

328

351

393

423

471

509

536

556

2,542

2,625

2,646

2,765

2,828

2,803

2,875

2,795

2,564

Share of people living on less than 2005 PPP $2.00 a day (percent) East Asia & Pacific China Europe & Central Asia Latin America & Caribbean

92.6

88.5

81.6

79.8

75.8

64.1

61.8

51.9

38.7

97.8

92.9

83.7

84.6

78.6

65.1

61.4

51.2

36.3

8.3

6.5

5.6

6.9

10.3

11.9

14.3

12.0

8.9

24.6

28.1

24.9

21.9

20.7

22.0

21.8

21.6

17.1

Middle East & North Africa

26.7

23.1

22.7

19.7

19.8

20.2

19.0

17.6

16.9

South Asia

86.5

84.8

83.9

82.7

79.7

79.9

77.2

77.1

73.9

86.6

84.8

83.8

82.6

81.7

79.8

78.4

77.6

75.6

Sub-Saharan Africa

73.8

75.5

74.0

76.1

75.9

77.9

77.6

75.6

72.9

Total

69.4

67.7

64.3

63.4

61.6

58.3

57.1

53.3

47.0

India

Source: World Bank PovcalNet.

66

2011 World Development Indicators


2.8

people

Poverty rates at international poverty lines About the data The World Bank produced its first global poverty esti-

The statistics reported here are based on consump-

PPP rates were designed for comparing aggregates from

mates for developing countries for World Development

tion data or, when unavailable, on income surveys.

national accounts, not for making international poverty

Report 1990: Poverty using household survey data for

Analysis of some 20 countries for which income and

comparisons. As a result, there is no certainty that an

22 countries (Ravallion, Datt, and van de Walle 1991).

consumption expenditure data were both available from

international poverty line measures the same degree

Since then there has been considerable expansion in

the same surveys found income to yield a higher mean

of need or deprivation across countries. So-called pov-

the number of countries that field household income

than consumption but also higher inequality. When pov-

erty PPPs, designed to compare the consumption of

and expenditure surveys. The World Bank’s poverty

erty measures based on consumption and income were

the poorest people in the world, might provide a better

monitoring database now includes more than 600

compared, the two effects roughly cancelled each other

basis for comparison of poverty across countries. Work

surveys representing 115 developing countries. More

out: there was no significant statistical difference.

on these measures is ongoing.

than 1.2 million randomly sampled households were

Definitions

interviewed in these surveys, representing 96 percent

International poverty lines

of the population of developing countries.

International comparisons of poverty estimates entail

• International poverty line in local currency is the

both conceptual and practical problems. Countries have

international poverty lines of $1.25 and $2.00 a day in

Data availability

different definitions of poverty, and consistent compari-

2005 prices, converted to local currency using the PPP

The number of data sets within two years of any given

sons across countries can be difficult. Local poverty

conversion factors estimated by the International Com-

year rose dramatically, from 13 between 1978 and

lines tend to have higher purchasing power in rich coun-

parison Program. • Survey year is the year in which the

1982 to 158 between 2001 and 2006. Data cover-

tries, where more generous standards are used, than

underlying household survey data were collected; when

age is improving in all regions, but the Middle East

in poor countries.

the data collection period bridged two calendar years,

and North Africa and Sub-Saharan Africa continue to

Poverty measures based on an international poverty

the year in which most of the data were collected is

lag. A complete database of estimates, maintained

line attempt to hold the real value of the poverty line con-

reported. • Population below $1.25 a day and popula-

by a team in the World Bank’s Development Research

stant across countries, as is done when making com-

tion below $2 a day are the percentages of the popula-

Group, is updated annually as new survey data

parisons over time. Since World Development Report

tion living on less than $1.25 a day and $2.00 a day at

become available, and a major reassessment of prog-

1990 the World Bank has aimed to apply a common

2005 international prices based on nominal per capita

ress against poverty is made about every three years.

standard in measuring extreme poverty, anchored to

consumption averages and distributions estimated from

The most recent estimates and a complete overview

what poverty means in the world’s poorest countries.

household survey data, unless otherwise noted. As a

of data availability by year and country are available

The welfare of people living in different countries can

result of revisions in PPP exchange rates, poverty rates

at http://iresearch.worldbank.org/povcalnet/.

be measured on a common scale by adjusting for dif-

for individual countries cannot be compared with pov-

ferences in the purchasing power of currencies. The

erty rates reported in earlier editions. • Poverty gap

Data quality

commonly used $1 a day standard, measured in 1985

is the mean shortfall from the poverty line (counting

Besides the frequency and timeliness of survey data,

international prices and adjusted to local currency using

the nonpoor as having zero shortfall), expressed as a

other data quality issues arise in measuring household

purchasing power parities (PPPs), was chosen for World

percentage of the poverty line. This measure reflects

living standards. The surveys ask detailed questions on

Development Report 1990 because it was typical of the

the depth of poverty as well as its incidence.

sources of income and how it was spent, which must

poverty lines in low-income countries at the time. Data sources

be carefully recorded by trained personnel. Income is

Early editions of World Development Indicators used

generally more difficult to measure accurately, and

PPPs from the Penn World Tables to convert values in

The poverty measures are prepared by the World

consumption comes closer to the notion of living stan-

local currency to equivalent purchasing power measured

Bank’s Development Research Group. The interna-

dards. And income can vary over time even if living

in U.S dollars. Later editions used 1993 consumption

tional poverty lines are based on nationally repre-

standards do not. But consumption data are not always

PPP estimates produced by the World Bank. Interna-

sentative primary household surveys conducted by

available: the latest estimates reported here use con-

tional poverty lines were recently revised using the new

national statistical offices or by private agencies

sumption for about two-thirds of countries.

data on PPPs compiled in the 2005 round of the Inter-

under the supervision of government or interna-

However, even similar surveys may not be strictly

national Comparison Program, along with data from an

tional agencies and obtained from government

comparable because of differences in timing or in

expanded set of household income and expenditure

statistical offices and World Bank Group country

the quality and training of enumerators. Comparisons

surveys. The new extreme poverty line is set at $1.25

departments. The World Bank Group has pre-

of countries at different levels of development also

a day in 2005 PPP terms, which represents the mean

pared an annual review of its poverty work since

pose a potential problem because of differences in

of the poverty lines found in the poorest 15 countries

1993. For details on data sources and methods

the relative importance of the consumption of nonmar-

ranked by per capita consumption. The new poverty line

used to derive the World Bank’s latest estimates,

ket goods. The local market value of all consumption

maintains the same standard for extreme poverty—

further discussion of the results, and related

in kind (including own production, particularly impor-

the poverty line typical of the poorest countries in the

publications, see http://iresearch.worldbank.org/

tant in underdeveloped rural economies) should be

world—but updates it using the latest information on

povcalnet/ and Shaohua Chen and Martin Rav-

included in total consumption expenditure, but may

the cost of living in developing countries.

allion’s “The Developing World Is Poorer Than

not be. Most survey data now include valuations for

PPP exchange rates are used to estimate global pov-

consumption or income from own production, but valu-

erty, because they take into account the local prices

ation methods vary.

of goods and services not traded internationally. But

We Thought, but No Less Successful in the Fight against Poverty” (2008).

2011 World Development Indicators

67


2.9

Distribution of income or consumption Survey year

Gini index

2008b 2008b 1995b 2000 b 2009d 2008b 1994 d 2000d 2008b 2005b 2008b 2000 d 1999d 2003b 2007d 2007b 1994b 2009d 2007b 2003b 2006b 2007b 2001b 2000 d 2003b 2003b 2009d 2005d 1996d 2006d 2006b 2005b 2009d 2008b 2008b

29.4 34.5 35.3 58.6 45.8 30.9 35.2 29.1 33.7 31.0 27.2 33.0 54.4 38.6 57.3 36.2 61.0 53.9 45.3 39.6 33.3 44.4 44.6 32.6 43.6 39.8 22.6 41.5 43.4 58.5 44.4 47.3 50.3 41.5 33.7 .. 25.8 24.7 48.4 49.0 32.1 46.9 .. 36.0 29.8 26.9 32.7 41.5 47.3 41.3 28.3 42.8 34.3 53.7 39.4 35.5 59.5 57.7

Percentage share of income or consumptiona

Lowest 10%

Afghanistan Albania Algeria Angolac Argentinac Armenia Australia Austria Azerbaijan Bangladesh Belarus Belgium Belize 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

68

1996d 1997d 2007d 2009d 2005b 2007d 2004b 2005b 2000 d 1995d 2005b 2003b 2008b 2000 d 2006b 2000 d 2006d 2007b 2002b 2001d 2007d

2011 World Development Indicators

3.8 3.5 2.8 0.6 1.5 3.7 2.0 3.3 3.4 4.3 3.8 3.4 1.2 2.9 1.0 2.7 1.3 1.2 2.0 3.0 4.1 3.0 2.4 2.6 2.1 2.6 3.1 2.4 2.0 0.9 2.3 2.1 1.7 2.2 3.3 .. 4.3 2.6 1.7 1.6 3.9 1.6 .. 2.7 4.1 4.0 2.8 2.5 2.0 2.0 3.2 1.9 2.5 1.3 2.7 2.9 0.9 0.6

Lowest 20%

9.0 8.1 6.9 2.0 4.1 8.8 5.9 8.6 8.0 9.4 9.2 8.5 3.4 6.9 2.8 6.7 3.1 3.3 5.0 7.0 9.0 6.6 5.6 7.2 5.2 6.3 8.6 5.7 5.3 2.5 5.5 5.0 4.2 5.6 8.1 .. 10.2 8.3 4.4 4.2 9.0 4.3 .. 6.8 9.3 9.6 7.2 6.1 4.8 5.3 8.5 5.2 6.7 3.4 6.4 7.2 2.5 2.0

Second 20%

Third 20%

Fourth 20%

13.1 12.1 11.5 5.7 8.9 12.8 12.0 13.3 12.1 12.6 13.8 13.0 7.2 10.9 6.4 11.3 5.8 7.2 9.1 10.6 11.9 9.4 9.3 12.7 9.4 10.4 15.5 9.8 9.4 6.0 9.2 8.4 7.8 10.1 12.2 .. 14.3 14.7 8.4 8.3 12.6 9.0 .. 11.6 13.2 14.1 12.6 10.1 8.6 10.3 13.7 9.8 11.9 7.2 10.5 11.6 5.9 6.0

16.9 15.9 16.3 10.8 14.3 16.7 17.2 17.4 16.2 16.1 17.8 16.3 11.9 15.1 11.1 16.1 9.6 11.9 13.9 14.7 15.4 13.1 13.7 17.2 14.3 15.0 20.2 14.7 13.9 10.7 13.8 13.0 12.5 14.9 16.2 .. 17.5 18.2 13.1 13.2 16.1 13.9 .. 16.2 16.8 17.5 17.2 14.6 13.2 15.2 17.8 14.8 16.8 12.0 15.1 16.0 10.5 11.3

22.3 20.9 22.8 19.7 22.2 21.9 23.6 22.9 21.7 21.1 22.9 20.8 19.1 21.2 18.8 22.7 16.4 19.5 21.0 20.6 21.0 19.2 20.5 23.0 21.7 21.8 24.7 22.0 20.7 18.7 20.9 20.5 20.1 21.8 21.6 .. 21.7 22.9 20.5 20.4 20.9 20.9 .. 22.5 21.4 22.1 22.8 21.2 20.6 22.1 23.1 21.9 23.0 19.5 21.9 22.1 18.1 20.0

Highest 20%

38.7 43.0 42.4 61.9 50.5 39.8 41.3 37.8 42.1 40.8 36.4 41.4 58.5 45.9 61.0 43.2 65.0 58.1 51.0 47.1 42.8 51.7 50.9 39.9 49.4 46.6 30.9 47.8 50.7 62.1 50.6 53.1 55.4 47.6 42.0 .. 36.2 35.8 53.6 53.9 41.5 51.9 .. 43.0 39.4 36.7 40.2 47.9 52.8 47.2 36.9 48.3 41.5 57.8 46.2 43.0 63.0 60.8

Highest 10%

24.0 29.0 26.9 44.7 33.6 25.4 25.4 23.0 27.4 26.6 21.9 28.1 43.5 31.0 45.4 27.3 51.2 42.5 35.2 32.4 28.0 37.3 35.5 24.8 33.0 30.8 16.5 31.4 34.9 46.2 34.7 37.1 39.4 31.8 27.5 .. 22.7 21.3 37.8 38.3 27.6 36.3 .. 27.7 25.6 22.6 25.1 32.7 36.9 31.3 22.1 32.5 26.0 42.4 30.3 28.0 47.8 43.8


Survey year

Gini index

Percentage share of income or consumptiona

Lowest 10%

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 Maldives Mali Mauritania Mauritius Mexico Micronesia Moldova Mongolia Montenegro Morocco Mozambique Myanmar Namibia Nepal Netherlands New Zealand Nicaragua Niger Nigeria Norway Oman Pakistan Panama Papua New Guinea Paraguay Peru Philippines Poland

2007b 2005b 2009 b 2005b 2000 d 2001d 2000 d 2004b 1993d 2006b 2007b 2005b 1998 d

2007b 2008b 2008b 2003b 2007b 2008 b 2008 b 2005b 2004b 2009d 2004b 2006b 2000 b 2008 d 2000 b 2008b 2008b 2008b 2007b 2008b 1993d 2004b 1999d 1997d 2005d 2007b 2004b 2000 d 2006b 2009d 1996b 2008d 2009d 2006b 2008b

31.2 36.8 36.8 38.3 .. 34.3 39.2 36.0 45.5 24.9 37.7 30.9 47.7 .. 31.6 .. .. 33.4 36.7 35.7 .. 52.5 52.6 .. 37.6 44.2 47.2 39.0 46.2 37.4 39.0 39.0 .. 51.7 61.1 38.0 36.5 30.0 40.9 45.6 .. 74.3 47.3 30.9 36.2 52.3 34.0 42.9 25.8 .. 32.7 52.3 50.9 52.0 48.0 44.0 34.2

2.9

people

Distribution of income or consumption

3.5 3.6 3.3 2.6 .. 2.9 2.1 2.3 2.1 4.8 3.0 3.8 1.8 .. 2.9 .. .. 4.1 3.3 2.7 .. 1.0 2.4 .. 2.6 2.2 2.6 2.9 1.8 2.7 2.7 2.5 .. 1.5 0.4 2.9 3.0 3.6 2.7 1.9 .. 0.6 2.7 2.5 2.2 1.4 3.7 2.0 3.9 .. 4.0 1.3 1.9 1.4 1.4 2.4 3.2

Lowest 20%

8.4 8.1 7.6 6.4 .. 7.4 5.7 6.5 5.2 10.6 7.2 8.7 4.7 .. 7.9 .. .. 8.8 7.6 6.8 .. 3.0 6.4 .. 6.6 5.4 6.2 7.0 4.5 6.5 6.5 6.2 .. 3.9 1.6 6.8 7.1 8.5 6.5 5.2 .. 1.5 6.1 7.6 6.4 3.8 8.3 5.1 9.6 .. 9.0 3.6 4.5 3.8 3.9 5.6 7.6

Second 20%

Third 20%

Fourth 20%

12.9 11.3 11.3 10.9 .. 12.3 10.5 12.0 9.0 14.2 11.1 12.8 8.8 .. 13.6 .. .. 11.8 11.3 11.7 .. 7.2 11.4 .. 11.1 9.3 9.6 10.8 8.7 10.9 10.7 10.5 .. 7.9 5.2 10.9 11.2 13.1 10.5 9.5 .. 2.8 8.9 13.2 11.4 7.7 12.0 9.7 14.0 .. 12.4 7.4 7.7 7.7 8.4 9.1 12.0

16.9 14.9 15.1 15.6 .. 16.3 15.9 16.8 13.8 17.6 15.2 16.7 13.3 .. 18.0 .. .. 15.5 15.3 16.3 .. 12.5 15.7 .. 15.7 14.0 13.1 14.9 13.7 15.7 15.2 15.4 .. 12.5 10.2 15.4 15.6 17.2 14.5 13.7 .. 5.5 12.5 17.2 15.8 12.3 15.8 14.7 17.2 .. 15.8 12.2 12.1 12.4 13.6 13.7 16.3

22.0 20.4 21.1 22.2 .. 21.9 23.0 22.8 20.9 22.0 21.1 22.0 20.3 .. 23.1 .. .. 21.2 20.9 22.4 .. 21.0 21.6 .. 22.1 21.0 17.7 20.9 21.6 22.7 21.6 22.3 .. 19.4 19.1 21.7 22.1 22.4 20.6 20.1 .. 12.0 18.4 23.3 22.6 19.4 21.1 21.9 22.0 .. 20.7 20.1 19.3 19.7 21.5 21.2 22.0

Highest 20%

39.9 45.3 44.9 45.0 .. 42.0 44.9 42.0 51.2 35.7 45.4 39.9 53.0 .. 37.5 .. .. 42.8 44.8 42.9 .. 56.4 45.0 .. 44.4 50.3 53.5 46.4 51.5 44.2 46.0 45.7 .. 56.2 64.0 45.3 44.0 38.8 47.9 51.5 .. 78.3 54.2 38.7 43.8 56.9 42.8 48.6 37.2 .. 42.1 56.8 56.4 56.5 52.6 50.4 42.2

2011 World Development Indicators

Highest 10%

25.4 31.1 29.9 29.6 .. 27.2 28.8 26.8 35.6 21.7 30.7 25.2 37.8 .. 22.5 .. .. 27.9 30.3 27.6 .. 39.4 30.1 .. 29.1 34.5 41.5 31.7 34.7 28.0 30.5 29.6 .. 41.4 47.1 29.8 28.4 24.1 33.2 36.7 .. 65.0 40.4 22.9 27.8 41.8 28.5 32.4 23.4 .. 28.3 40.6 40.9 41.0 35.9 33.9 27.2

69


2.9

Distribution of income or consumption Survey year

Gini index

Percentage share of income or consumptiona

Lowest 10%

Portugal Puerto Rico Qatar Romania Russian Federation Rwanda São Tomé & Príncipe Saudi Arabia Senegal Serbia Seychelles 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

1997d 2007b 2008b 2008b 2005b 2000 b 2005b 2008b 2007b 2003b 1998 d 1996d 2004b 2000 b 2000 d 2007b 2001b 2000 d 2000 d 2004b 2007b 2007b 2009 b 2007b 2006b 1992d 2000 b 2008b 1998 b 2009b 2008b 1999d 2000 d 2009d 2003b 2006d 2008b 2005b 2004b 1995b

38.5 .. 41.1 31.2 42.3 53.1 50.8 .. 39.2 28.2 19.0 42.5 42.5 25.8 31.2 .. 57.8 34.7 40.3 .. 50.7 25.0 33.7 35.8 29.4 37.6 53.6 31.9 34.4 40.3 40.8 39.7 40.8 44.3 27.5 .. 36.0 40.8 42.4 36.7 43.5 37.6 .. 37.7 50.7 50.1

2.0 .. 1.3 3.3 2.6 1.7 2.2 .. 2.5 3.9 4.7 2.6 1.9 3.1 3.4 .. 1.3 2.6 3.1 .. 1.8 3.6 2.9 3.4 4.0 2.8 1.6 4.0 2.0 2.1 2.4 2.1 2.5 2.4 4.1 .. 2.1 1.9 2.3 2.9 1.9 3.2 .. 2.9 1.3 1.8

Lowest 20%

5.8 .. 3.9 8.1 6.0 4.2 5.2 .. 6.2 9.1 10.8 6.1 5.0 8.8 8.2 .. 3.1 7.0 6.9 .. 4.5 9.1 7.6 7.7 9.3 6.8 3.9 9.0 5.4 5.5 5.9 5.7 6.0 5.8 9.4 .. 6.1 5.4 5.6 7.1 4.9 7.3 .. 7.2 3.6 4.6

Second 20%

Third 20%

Fourth 20%

11.0 .. .. 12.8 9.8 7.7 8.5 .. 10.6 13.5 15.7 9.7 9.4 14.9 12.8 .. 5.6 12.1 10.4 .. 8.0 14.0 12.2 11.4 13.4 11.1 7.0 12.5 10.3 10.3 10.2 10.8 10.2 9.6 13.6 .. 11.4 10.7 9.8 11.5 9.6 10.9 .. 11.3 7.8 8.1

15.5 .. .. 17.1 14.3 11.7 12.2 .. 15.3 17.5 19.9 14.0 14.6 18.6 17.0 .. 9.9 16.4 14.4 .. 12.3 17.6 16.3 15.5 16.7 15.6 11.4 16.1 15.2 15.5 14.9 15.6 14.9 13.8 17.5 .. 16.0 15.7 14.5 15.7 14.7 15.1 .. 15.3 12.8 12.2

21.9 .. .. 22.7 20.9 18.2 17.7 .. 22.0 22.5 24.2 20.9 22.0 22.9 22.6 .. 18.8 22.5 20.5 .. 19.4 22.7 22.6 21.4 21.5 21.7 19.2 21.2 22.0 22.7 21.8 22.1 21.7 20.0 22.5 .. 22.5 22.4 21.4 21.5 21.8 21.3 .. 21.0 20.6 19.3

Highest 20%

45.9 .. 52.0 39.3 48.9 58.2 56.4 .. 45.9 37.4 29.4 49.3 49.0 34.8 39.4 .. 62.7 42.0 47.8 .. 55.9 36.6 41.3 43.9 39.0 44.8 58.6 41.3 47.1 45.9 47.2 45.8 47.2 50.7 37.1 .. 44.0 45.8 48.6 44.2 49.0 45.4 .. 45.3 55.2 55.7

Highest 10%

29.8 .. 35.9 24.5 33.5 44.0 43.6 .. 30.1 22.8 15.4 33.6 32.8 20.8 24.6 .. 44.9 26.6 32.9 .. 40.8 22.2 25.9 28.9 25.2 29.6 42.6 27.0 31.3 29.9 31.6 30.3 31.8 36.1 22.6 .. 28.5 29.9 32.9 29.5 33.0 30.2 .. 30.8 38.9 40.3

a. Percentage shares by quintile may not sum to 100 percent because of rounding. b. Refers to expenditure shares by percentiles of population, ranked by per capita expenditure. c. Covers urban areas only. d. Refers to income shares by percentiles of population, ranked by per capita income.

70

2011 World Development Indicators


About the data

2.9

people

Distribution of income or consumption Definitions

Inequality in the distribution of income is reflected

• Survey year is the year in which the underlying data

in the percentage shares of income or consumption

were collected. •  Gini index measures the extent

accruing to portions of the population ranked by

to which the distribution of income (or consump-

income or consumption levels. The portions ranked

tion expenditure) among individuals or households

lowest by personal income receive the smallest

within an economy deviates from a perfectly equal

shares of total income. The Gini index provides a con-

distribution. A Lorenz curve plots the cumulative

venient summary measure of the degree of inequal-

percentages of total income received against the

ity. Data on the distribution of income or consump-

cumulative number of recipients, starting with the

tion come from nationally representative household

poorest individual. The Gini index measures the area

surveys. Where the original data from the house-

between the Lorenz curve and a hypothetical line of

hold survey were available, they have been used to

absolute equality, expressed as a percentage of the

directly calculate the income or consumption shares

maximum area under the line. Thus a Gini index of

by quintile. Otherwise, shares have been estimated

0 represents perfect equality, while an index of 100

from the best available grouped data.

implies perfect inequality. •  Percentage share of

The distribution data have been adjusted for

income or consumption is the share of total income

household size, providing a more consistent measure

or consumption that accrues to subgroups of popula-

of per capita income or consumption. No adjustment

tion indicated by deciles or quintiles.

has been made for spatial differences in cost of living within countries, because the data needed for such calculations are generally unavailable. For further details on the estimation method for low- and middleincome economies, see Ravallion and Chen (1996). Because the underlying household surveys differ in method and type of data collected, the distribution data are not strictly comparable across countries. These problems are diminishing as survey methods improve and become more standardized, but achieving strict comparability is still impossible (see About the data for tables 2.7 and 2.8). Two sources of non-comparability should be noted in particular. First, the surveys can differ in many respects, including whether they use income or consumption expenditure as the living standard indicator. The distribution of income is typically more unequal than the distribution of consumption. In addition, the definitions of income used differ more often among surveys. Consumption is usually a much better welfare indicator, particularly in developing countries. Second, households differ in size (number of members) and in the extent of income sharing among members. And individuals differ in age and consumption needs. Differences among countries in these respects may bias comparisons of distribution. World Bank staff have made an effort to ensure that the data are as comparable as possible. Wher-

Data sources

ever possible, consumption has been used rather

Data on distribution are compiled by the World

than income. Income distribution and Gini indexes for

Bank’s Development Research Group using pri-

high-income economies are calculated directly from

mary household survey data obtained from govern-

the Luxembourg Income Study database, using an

ment statistical agencies and World Bank country

estimation method consistent with that applied for

departments. Data for high-income economies are

developing countries.

from the Luxembourg Income Study database.

2011 World Development Indicators

71


2.10

Assessing vulnerability and security Youth unemployment

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

72

Female-headed households

Pension contributors

Male % of male labor force ages 15–24

Female % of female labor force ages 15–24

% of total

2006–09a

2006–09a

2006–09a

Year

.. .. .. .. 19 b 47b 13b 10 19 .. .. 21 .. .. 45 .. 14 18 .. .. .. .. 18b .. .. 21 .. 15b 18 .. .. 10 .. 19 3 17 12 21 12b 17 13 .. 32 20 b 22 23 .. .. 32 12 .. 19 .. .. .. .. ..

.. .. .. .. 25b 69b 10 b 9 10 .. .. 22 .. .. 52 .. 23 14 .. .. .. .. 12b .. .. 24 .. 10 b 30 .. .. 13 .. 27 4 17 10 45 18b 48 8 .. 21 29 b 19 22 .. .. 41 10 .. 34 .. .. .. .. ..

.. .. .. 25 34 .. .. .. 25 13 .. .. 23 .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. 19 21 .. .. .. 24 46 .. .. 35 .. .. .. .. .. .. .. .. .. .. .. .. 34 .. .. .. .. 44 26

2005 2007 2002

2011 World Development Indicators

 

2008 2008 2005 2005 2007 2004 2008 2005  

2008 2009 2006 2008 2008 2004      

2007 2004  

2008 2007 2008 2008    

2004  

2010  

2007 2007 2008 2004 2009 2008  

2004  

2005 2005  

2006 2004 2005 2004 2005 2008 1993 2004  

2008

Public expenditure on pensions

% of labor force

% of workingage population

.. 51.1 36.7 .. 41.9 39.2 92.6 96.4 35.4 2.8 93.5 94.2 .. 11.4 70.2 9.0 53.8 72.7 1.2 .. .. .. 66.9 1.5 .. 53.8 19.3 .. 31.3 .. .. 55.3 .. 82.9 .. 84.5 94.4 21.0 31.6 57.0 23.9 .. 95.2 .. 88.7 89.9 .. 2.7 29.9 88.2 9.1 85.2 20.3 1.5 1.9 .. 18.7

2.2 34.7 22.1 .. 31.3 23.9 69.6 68.7 24.7 2.1 66.8 61.6 .. 8.9 28.7 7.3 41.7 49.6 1.0 .. .. .. 53.6 1.3 .. 36.2 15.9 55.6 20.0 .. .. 37.6 .. 52.6 .. 67.3 86.9 15.2 21.1 31.0 16.2 .. 68.6 .. 67.2 61.4 .. 2.2 22.7 65.5 7.1 58.5 14.7 1.8 1.5 .. 12.6

Year

2005 2009 2002  

2007 2008 2005 2005 2007 2006 2008 2005 2006 2000 2009  

2004 2007      

2001 2005 2004  

2001    

2008  

2004 2006  

2009  

2007 2005 2000 2002 2004 2006 2001 2007 2006 2005 2005    

2004 2005 2002 2005 2005  

2005    

% of GDP

0.5 6.1 3.2 .. 8.0 4.3 3.5 12.6 3.8 0.3 10.2 9.0 1.5 4.5 9.4 .. 12.6 9.8 .. .. .. 0.8 4.1 0.8 .. 2.9 .. .. 3.0 .. 0.9 2.4 .. 10.3 .. 8.5 5.4 0.8 2.5 4.1 1.9 0.3 10.9 0.3 8.4 12.4 .. .. 3.0 11.4 1.3 11.5 1.0 .. 2.1 .. ..

Year        

2000 2007    

2006  

2002            

2004              

2006              

2005  

2005            

2007          

2003                

Average pension % of average wage

.. .. .. .. 43.8 20.3 .. .. 24.3 .. 41.6 .. .. .. .. .. .. 42.9 .. .. .. .. .. .. .. 53.5 .. .. .. .. .. .. .. 32.4 .. 40.7 .. .. .. .. .. .. 35.4 .. .. .. .. .. 13.0 .. .. .. .. .. .. .. ..


Youth unemployment

Hungary India Indonesia Iran, Islamic Rep. Iraq Ireland Israel Italy Jamaica Japan Jordan Kazakhstan Kenya Korea, Dem. Rep. Korea, Rep. Kosovo Kuwait Kyrgyz Republic Lao PDR Latvia Lebanon Lesotho Liberia Libya Lithuania Macedonia, FYR Madagascar Malawi Malaysia Mali Mauritania Mauritius Mexico Moldova Mongolia Morocco Mozambique Myanmar Namibia Nepal Netherlands New Zealand Nicaragua Niger Nigeria Norway Oman Pakistan Panama Papua New Guinea Paraguay Peru Philippines Poland Portugal Puerto Rico Qatar

Female-headed households

Pension contributors

Male % of male labor force ages 15–24

Female % of female labor force ages 15–24

% of total

2006–09a

2006–09a

2006–09a

Year

28 .. 22 20 .. 31 16 23 22 10 23 7 .. .. 12b .. .. 14 .. 38 22 .. 6b .. 35 53 .. .. 10 .. .. 18 10 16 .. 23 .. .. .. .. 7 16b 8 .. .. 10 .. 7 12 .. 9 13b 16 20 19 29 b 1

24 .. 23 34 .. 17 14 29 33 8 46 8 .. .. 9b .. .. 16 .. 28 22 .. 4b .. 22 59 .. .. 12 .. .. 26 11 15 .. 19 .. .. .. .. 6 17b 10 .. .. 8 .. 10 21 .. 17 16b 19 21 22 22b 7

.. 14 13 .. 11 .. .. .. .. .. 10 .. .. .. .. .. .. 25 .. .. .. .. 31 .. .. 8 .. .. .. 12 .. .. .. .. 29 .. .. .. 44 23 .. .. .. 19 .. .. .. 10 .. .. .. 22 19 .. .. .. ..

2008 2006 2008 2001 2009 2005  

2005 2004 2005 2006 2004 2006  

2005    

2006  

2003 2003 2005  

2004 2007 2008    

2008    

2000 2008 2009 2005 2007      

2008 2005 2003 2008 2006 2004 2005  

2008    

2004 2008 2007 2005 2005    

2.10

Public expenditure on pensions

% of labor force

% of workingage population

92.0 10.3 11.7 35.1 16.8 88.0 .. 92.4 17.4 95.3 38.4 34.4 7.5 .. 49.5 .. .. 42.2 .. 92.4 33.1 5.7 .. 65.5 99.3 47.9 .. .. 49.0 .. .. 51.4 30.3 58.7 27.9 23.8 .. .. .. 3.4 90.7 92.7 21.7 1.9 1.9 93.2 .. 3.9 .. .. 11.6 19.1 25.0 83.8 92.0 .. ..

56.7 6.4 8.7 20.0 15.2 63.9 .. 58.4 12.6 75.0 19.9 26.5 6.5 .. 34.3 .. .. 28.9 .. 66.5 19.9 3.6 .. 38.1 68.7 30.4 .. .. 32.5 .. .. 33.6 20.6 32.1 21.3 13.6 .. .. .. 2.6 70.7 72.3 14.6 1.2 1.1 75.2 .. 2.2 .. .. 9.1 13.9 17.0 54.7 71.6 .. ..

Year

2008 2007  

2000 2009 2005  

2005  

2005 2001 2009 2003  

2005 2007 2010  

2009 2003    

2001 2009 2008            

2005 2009 2007 2003      

2006 2005 2005  

2006  

2005  

2004    

2001 2000  

2009 2005    

% of GDP

10.5 2.2 .. 1.1 3.9 3.4 .. 14.0 .. 8.7 2.2 3.2 1.1 .. 1.6 2.7c .. 2.7 .. 8.5 2.1 .. .. 2.1 8.9 9.4 .. .. .. .. .. .. 1.3 9.1 6.5d 1.9 .. .. .. 0.2 5.0 e 4.4 e .. 0.7 .. 4.8e .. 0.5 .. .. 1.2 2.6 .. 10.0 10.2e .. ..

Year

2005                    

2003          

2003  

2005        

2005 2006              

2003                                      

2007      

2011 World Development Indicators

Average pension % of average wage

39.8 .. .. .. .. .. .. .. .. .. .. 24.9 .. .. .. .. .. 27.5 .. 33.1 .. .. .. .. 30.9 55.0 .. .. .. .. .. .. .. 20.9 .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. 47.1 .. .. ..

73

people

Assessing vulnerability and security


2.10

Assessing vulnerability and security Youth unemployment

Romania Russian Federation Rwanda Saudi Arabia Senegal Serbia Sierra Leone Singapore Slovak Republic Slovenia Somalia South Africa Spain Sri Lanka Sudan Swaziland Sweden Switzerland Syrian Arab Republic Tajikistan Tanzania Thailand Timor-Leste Togo Trinidad and Tobago Tunisia Turkey Turkmenistan Uganda Ukraine United Arab Emirates United Kingdom United States Uruguay Uzbekistan Venezuela, RB Vietnam West Bank and Gaza Yemen, Rep. Zambia Zimbabwe World Low income Middle income Lower middle income Upper middle income Low & middle income East Asia & Pacific Europe & Central Asia Latin America & Carib. Middle East & N. Africa South Asia Sub-Saharan Africa High income Euro area

Female-headed households

Pension contributors

Male % of male labor force ages 15–24

Female % of female labor force ages 15–24

% of total

2006–09a

2006–09a

2006–09a

Year

.. .. .. .. .. 29 .. .. .. .. .. .. .. .. 19 48 .. .. .. .. .. 30 .. .. .. .. .. .. 30 49 .. .. .. .. .. .. .. .. .. 24 38

2007 2007 2004

21 18 .. 24 12 31 .. 10 28 14 .. 45 39 17 .. .. 26 8 13 .. 7 4 .. .. 9 .. 25 .. .. .. 8 22 20 b 16 .. 12 .. 39 .. .. .. .. w .. .. .. 19 .. .. 17 12 18 .. .. 19 21

20 19 .. 46 20 41 .. 17 27 13 .. 53 36 28 .. .. 24 9 49 .. 10 5 .. .. 13 .. 25 .. .. .. 22 16 15b 25 .. 16 .. 47 .. .. .. .. w .. .. .. 23 .. .. 18 18 37 .. .. 16 21

 

2003 2003 2004 2008 2003 2008  

2007 2005 2006    

2005 2005 2008  

2006 2008    

2008 2004 2007  

2004 2010  

2005 2005 2007 2005 2008 2008 2009 2006 2006  

 

                       

 

                       

Public expenditure on pensions

% of labor force

% of workingage population

54.8 67.0 4.6 .. 5.1 45.0 5.5 61.7 78.9 87.4 .. 6.5 69.4 24.1 .. .. 88.8 95.4 26.8 .. 4.3 23.0 .. .. 76.4 48.6 60.3 .. 10.3 65.3 .. 93.2 92.2 72.7 86.1 32.1 19.3 18.5 10.4 10.9 ..

36.4 50.0 4.1 .. 4.1 35.4 3.8 45.3 55.3 63.2 .. 3.7 48.7 14.9 .. .. 72.2 78.7 13.8 .. 4.0 18.6 .. .. 54.2 25.5 31.0 .. 9.2 52.3 .. 71.5 71.5 56.9 57.5 22.7 15.2 8.0 5.0 8.0 ..

 

                       

Year

2009 2007    

2003 2010    

2007 2007  

2006 2005 2007    

2005 2005 2004 2006        

2003 2008 2003 2010  

2005 2005 2007 2005 2001 2009 2008 2002

 

                     

 

                       

% of GDP

8.3 4.7 .. .. 1.3 14.0 .. .. 9.3e 12.7 .. 1.2 8.1e 2.0 .. .. 7.7e 6.8 e 1.3 .. 0.9 .. .. .. .. 4.3 6.2 .. 0.3 17.8 .. 5.7 6.0e 10.0e 6.5 2.7 .. 4.0 .. 1.0 2.3

Year

2005 2003

Average pension % of average wage

 

41.5 29.2 .. .. .. .. .. .. 44.7 44.3 .. .. 58.6 .. .. .. .. 40.0 .. 25.7 .. .. .. .. .. .. 61.3 .. .. 48.3 .. .. 29.2 .. 40.0 .. .. .. .. .. ..

 

 

 

                       

                       

           

2005 2005    

2006        

2000  

2003            

2007    

2007    

2006  

2005          

                       

a. Data are for the most recent year available. b. Limited coverage. c. Includes only expenditure on social pensions. d. Includes old-age, survivors, disability, military, work accident or disease pensions. e. Includes only expenditures on old-age and survivors’ benefits.

74

2011 World Development Indicators


About the data

2.10

people

Assessing vulnerability and security Definitions

As traditionally measured, poverty is a static con-

citizenship, residency, or income status. In contri-

• Youth unemployment is the share of the labor force

cept, and vulnerability a dynamic one. Vulnerabil-

bution-related schemes, however, eligibility is usually

ages 15–24 without work but available for and seek-

ity reflects a household’s resilience in the face of

restricted to individuals who have contributed for a

ing employment. • Female-headed households are

shocks and the likelihood that a shock will lead to a

minimum number of years. Definitional issues—relat-

the percentage of households with a female head.

decline in well-being. Thus, it depends primarily on

ing to the labor force, for example—may arise in

•  Pension contributors are the share of the labor

the household’s assets and insurance mechanisms.

comparing coverage by contribution-related schemes

force or working-age population (here defined as

Because poor people have fewer assets and less

over time and across countries (for country-specific

ages 15 and older) covered by a pension scheme.

diversified sources of income than do the better-off,

information, see Hinz and others 2011). The share

• Public expenditure on pensions is all government

fluctuations in income affect them more.

of the labor force covered by a pension scheme may

expenditures on cash transfers to the elderly, the

be overstated in countries that do not try to count

disabled, and survivors and the administrative costs

Enhancing security for poor people means reducing their vulnerability to such risks as ill health, pro-

informal sector workers as part of the labor force.

of these programs. • Average pension is the aver-

viding them the means to manage risk themselves,

Public interventions and institutions can provide

age pension payment of all pensioners of the main

and strengthening market or public institutions for

services directly to poor people, although whether

pension schemes (including old-age, survivors, dis-

managing risk. Tools include microfinance programs,

these interventions and institutions work well for the

ability, military, and work accident or disease pen-

public provision of education and basic health care,

poor is debated. State action is often ineffective,

sions) divided by the average wage of all formal sec-

and old age assistance (see tables 2.11 and 2.16).

in part because governments can influence only a

tor workers.

Poor households face many risks, and vulnerability

few of the many sources of well-being and in part

is thus multidimensional. The indicators in the table

because of difficulties in delivering goods and ser-

focus on individual risks—youth unemployment,

vices. The effectiveness of public provision is further

female-headed households, income insecurity in

constrained by the fiscal resources at governments’

old age—and the extent to which publicly provided

disposal and the fact that state institutions may not

services may be capable of mitigating some of these

be responsive to the needs of poor people.

risks. Poor people face labor market risks, often hav-

The data on public pension spending cover the

ing to take up precarious, low-quality jobs and to

pension programs of the social insurance schemes

increase their household’s labor market participa-

for which contributions had previously been made.

tion by sending their children to work (see tables

In many cases noncontributory pensions or social

2.4 and 2.6). Income security is a prime concern

assistance targeted to the elderly and disabled are

for the elderly.

also included. A country’s pattern of spending is cor-

Youth unemployment is an important policy issue for many economies. Experiencing unemployment

related with its demographic structure—spending increases as the population ages.

may permanently impair a young person’s productive potential and future employment opportunities. The table presents unemployment among youth ages 15–24, but the lower age limit for young people in a country could be determined by the minimum age for leaving school, so age groups could differ across countries. Also, since this age group is likely to include school leavers, the level of youth ­unemployment varies considerably over the year as a result of different school opening and closing dates. The youth unemployment rate shares similar limitations on comparability as the general unemployment rate. For further information, see About the data for table 2.5 and the original source.

Data sources Data on youth unemployment are from the ILO’s

The definition of female-headed household differs

Key Indicators of the Labour Market, 6th edition,

greatly across countries, making cross-country com-

database. Data on female-headed households are

parison difficult. In some cases it is assumed that a

from Macro International Demographic and Health

woman cannot be the head of any household with an

Surveys. Data on pension contributors and pen-

adult male, because of sex-biased stereotype. Cau-

sion spending are from Hinz and others’ Interna-

tion should be used in interpreting the data.

tional Patterns of Pension Provision II: Facts and

Pension scheme coverage may be broad or

Figures of the 2000s (2011).

even universal where eligibility is determined by

2011 World Development Indicators

75


2.11

Education inputs Public expenditure per student

Primary

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

76

1999

2009a

.. .. 12.0 .. 12.9 .. 16.4 25.1 6.9 .. .. 18.2 12.1 14.2 .. .. 10.8 15.5 .. 14.7 5.9 .. .. .. .. 14.4 .. 12.4 15.2 .. .. 15.5 14.8 .. 27.8 11.2 24.6 7.2 4.4 .. 8.6 15.0 20.9 .. 17.4 17.3 .. .. .. .. .. 11.7 6.7 .. .. .. ..

.. .. .. .. 14.7 11.0 16.4 23.3 .. 10.7 .. 20.5 .. .. .. 12.4 17.3 23.5 29.0 21.1 .. 7.4 .. 4.5 12.7 14.7 .. 13.8 15.9 .. .. 14.6 .. 21.8 44.7 13.0 24.5 7.3 .. .. 8.5 .. 20.0 12.4 17.5 17.7 .. .. 14.5 15.7 .. .. 10.5 7.1 .. .. ..

2011 World Development Indicators

% of GDP per capita Secondary 1999 2009a

.. .. .. .. 18.2 .. 15.0 30.2 17.0 12.5 .. 23.8 24.6 11.7 .. .. 9.5 18.8 .. .. 11.5 .. .. .. .. 14.8 11.5 17.7 16.1 .. .. 21.4 42.8 .. 41.2 21.7 38.1 .. 9.6 .. 7.5 37.3 27.2 .. 25.8 28.5 .. .. .. .. .. 15.5 4.3 .. .. .. ..

.. .. .. .. 21.9 18.8 14.5 26.7 .. 14.9 .. 33.3 .. .. .. 37.6 18.0 22.3 30.2 59.4 .. 30.7 .. 16.1 24.1 16.0 .. 16.7 15.4 .. .. 14.4 .. 25.2 51.9 22.0 32.2 7.4 .. .. 9.1 .. 23.9 8.9 30.8 26.4 .. .. 15.2 21.8 .. .. 6.2 6.3 .. .. ..

Public expenditure on education

Tertiary 1999

.. .. .. .. 17.7 .. 26.6 52.1 19.1 50.7 .. 38.3 212.7 44.1 .. .. 57.1 17.9 .. 1,051.5 43.6 .. 44.0 .. .. 19.4 90.0 .. 37.7 .. .. .. 146.3 35.8 86.2 33.7 65.9 .. .. .. 8.9 429.6 31.8 .. 40.4 29.7 .. .. .. .. .. 26.2 .. .. .. .. ..

2009a

.. .. .. .. 15.6 6.8 20.2 47.6 15.6 39.8 15.0 35.3 .. .. .. 251.5 29.6 20.1 307.1 520.4 .. 35.8 .. 124.1 217.8 12.1 .. 56.2 27.4 .. .. .. 119.1 26.2 58.8 30.5 53.8 .. .. .. 13.7 .. 20.8 642.9 31.7 34.8 .. .. 11.2 .. .. .. 19.0 102.3 .. .. ..

% of GDP 2009a

.. .. 4.3 .. 4.9 3.0 4.5 5.4 2.8 2.4 4.5 6.0 3.5 .. .. 8.9 5.1 4.1 4.6 8.3 2.1 3.7 4.9 1.3 3.2 4.0 .. 4.5 4.8 .. .. 6.3 4.6 4.6 13.6 4.2 7.8 2.3 .. 3.8 3.6 .. 4.8 5.5 5.9 5.6 .. .. 3.2 4.5 .. .. 3.2 2.4 .. .. ..

Trained Primary teachers school in primary pupil–teacher education ratio

% of total government expenditure

% of total

pupils per teacher

2009a

2009a

2009a

.. .. 20.3 .. 13.5 15.0 .. 11.1 9.1 14.0 10.6 12.4 15.9 .. .. 22.0 16.1 10.0 21.8 23.4 12.4 19.2 .. 11.7 12.6 18.2 .. 24.1 14.9 .. .. 37.7 24.6 10.4 17.5 9.9 15.4 12.0 .. 11.9 13.1 .. 13.9 23.3 12.5 10.7 .. .. 7.7 10.3 .. .. .. 19.2 .. .. ..

.. .. .. .. .. .. .. .. 99.9 58.4 99.9 .. 40.4 .. .. 97.4 .. .. 86.1 91.2 99.5 .. .. .. 34.6 .. .. 95.1 100.0 93.4 .. 87.6 100.0 100.0 100.0 .. .. 83.6 82.6 .. 93.2 92.2 .. 84.6 .. .. .. .. 94.6 .. 47.6 .. .. 73.1 .. .. 36.4

43 20 23 .. 16 19 .. 12 11 44 15 11 45 24 .. 25 23 16 49 51 49 46 .. 95 61 25 18 16 29 37 64 18 42 11 9 18 .. 25 17 27 31 38 12 58 14 19 .. 34 9 13 33 10 29 44 .. .. 33


Public expenditure per student

Primary

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

1999

2009a

18.0 11.9 .. 9.1 .. 11.0 20.5 24.0 13.4 21.1 13.7 .. 21.5 .. 18.4 .. 19.2 .. 2.3 19.5 .. 34.5 .. .. .. .. 5.7 14.0 12.5 14.3 11.4 9.3 11.7 .. .. 17.2 .. .. 21.4 9.1 15.2 20.2 .. .. .. 21.8 11.2 .. 13.7 .. 13.6 7.6 12.8 .. 19.5 .. ..

24.9 .. 11.0 15.1 .. 15.7 19.4 22.6 15.8 21.7 12.7 .. .. .. 17.0 .. 10.9 .. .. 23.3 .. 22.6 5.7 .. 15.8 .. 7.1 .. 14.3 13.0 .. 9.3 13.3 42.4 16.2 16.1 .. .. 15.6 17.6 16.9 17.6 .. 28.3 .. 18.5 .. .. 7.5 .. 10.8 8.1 9.0 24.3 .. .. 9.2

% of GDP per capita Secondary 1999 2009a

19.1 24.7 .. 9.9 .. 16.8 21.9 27.7 21.0 20.9 15.8 .. 14.5 .. 15.7 .. .. .. 4.5 23.7 .. 76.7 .. .. .. .. .. 10.0 21.7 56.1 35.9 14.2 14.2 .. .. 45.1 .. 6.9 35.2 13.1 22.2 24.1 .. .. .. 30.4 21.8 .. 19.1 .. 18.5 10.8 11.0 10.9 27.5 .. ..

23.1 .. 12.5 21.0 .. 23.2 19.0 25.2 26.8 22.4 16.3 .. .. .. 22.2 .. 14.9 .. .. 24.1 .. 50.8 8.4 .. 20.1 .. 10.5 .. 12.4 32.6 .. 15.1 13.4 40.3 .. 38.7 .. .. 15.8 11.3 24.5 19.6 .. 56.6 .. 26.5 .. .. 9.9 .. 16.3 9.9 9.1 22.0 .. .. 9.8

Public expenditure on education

Tertiary 1999

34.2 95.0 .. 34.8 .. 28.6 30.9 27.6 70.4 15.1 .. .. 209.0 .. 8.4 .. .. 24.3 68.6 27.9 13.9 875.4 .. 23.9 34.2 .. .. 2,613.3 81.1 241.3 79.0 25.4 47.8 .. .. 96.2 1,412.2 28.0 152.2 141.6 47.4 40.1 .. .. .. 45.8 .. .. 33.6 .. 58.9 21.2 15.4 21.1 28.1 .. ..

2009a

23.8 .. 16.2 22.2 .. 26.2 22.7 22.1 42.4 20.1 .. 7.9 .. .. 9.0 .. .. 17.3 .. 16.3 10.2 .. .. .. 17.1 .. 132.4 .. 34.0 117.7 .. 16.7 37.0 46.1 .. 71.1 .. .. .. 55.5 40.2 28.6 .. 429.3 .. 47.3 .. .. 21.6 .. 26.0 .. 9.6 16.6 .. .. 337.7

% of GDP 2009a

5.2 .. 2.8 4.7 .. 4.9 5.9 4.3 5.8 3.5 .. 2.8 .. .. 4.2 4.3 .. 5.9 2.3 5.0 1.8 12.4 2.8 .. 4.7 .. 3.0 .. 4.1 4.4 .. 3.2 4.8 9.6 5.6 5.6 .. .. 6.4 4.6 5.3 6.1 .. 4.5 .. 6.8 .. 2.7 3.8 .. 4.0 2.7 2.8 4.9 .. .. ..

2.11

people

Education inputs

Trained Primary teachers school in primary pupilâ&#x20AC;&#x201C;teacher education ratio

% of total government expenditure

% of total

pupils per teacher

2009a

2009a

2009a

10.4 .. 17.9 20.9 .. 13.8 13.1 9.0 .. 9.4 .. .. .. .. 14.8 17.4 .. 19.0 12.2 13.9 7.2 23.7 12.1 .. 13.4 .. 13.4 .. 17.2 22.3 .. 11.4 .. 21.0 14.6 25.7 .. .. 22.4 19.5 11.7 .. .. 19.3 .. 16.5 .. 11.2 .. .. 11.9 20.7 16.9 11.7 .. .. ..

.. .. .. 98.4 .. .. .. .. .. .. .. .. 96.8 .. .. .. 100.0 65.7 96.9 .. .. 57.6 40.2 .. .. .. .. .. .. 50.0 100.0 100.0 95.4 .. 100.0 100.0 71.2 98.9 95.6 66.4 .. .. 72.7 98.0 .. .. 100.0 85.2 91.5 .. .. .. .. .. .. 6.6 48.9

2011 World Development Indicators

10 .. 17 20 17 16 13 10 .. 18 .. 16 47 .. 24 .. 9 24 29 11 14 37 24 .. 13 17 48 .. 15 50 39 22 28 16 30 27 61 29 30 33 .. 15 29 39 46 .. 12 40 24 .. 26 21 34 10 11 12 11

77


2.11

Education inputs Public expenditure per student

Primary

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

1999

2009a

.. .. 11.0 .. 14.1 .. .. .. 10.2 26.3 .. 14.2 18.0 .. .. 8.5 22.5 22.7 11.2 .. .. 17.8 .. 8.5 11.5 15.6 9.8 .. .. .. 8.7 13.9 17.9 7.2 .. .. .. .. .. 7.2 12.7 .. m .. .. .. 12.0 .. .. .. 12.7 .. .. .. 18.0 17.4

20.0 .. 8.2 18.4 20.9 56.9 7.1 10.5 15.6 .. .. 15.1 19.4 .. .. 13.0 25.0 22.5 18.3 .. 22.1 24.0 27.6 13.0 9.0 .. .. .. 7.3 .. 4.9 23.0 22.0 .. .. 9.2 19.7 .. .. .. .. .. m .. .. .. 13.8 .. .. .. 12.2 .. .. .. 19.4 17.6

a. Provisional data.

78

2011 World Development Indicators

% of GDP per capita Secondary 1999 2009a

.. .. 41.9 .. .. .. .. .. 18.4 25.7 .. 20.0 24.4 .. .. 23.7 26.2 27.3 21.7 .. .. 15.9 .. 30.3 12.2 27.1 9.6 .. .. .. 11.6 23.8 22.5 9.9 .. .. .. .. .. 19.4 19.3 .. m .. .. .. 16.4 .. .. .. 13.7 .. 13.6 .. 22.5 25.1

16.6 .. 34.3 18.3 25.7 13.6 18.0 15.7 14.7 .. .. 17.7 24.1 .. .. 36.2 30.6 25.2 15.5 .. 18.8 9.1 .. 19.1 9.9 .. .. .. 21.2 .. 6.7 28.2 24.2 .. .. 8.2 17.3 .. .. .. .. .. m .. .. .. 17.0 .. .. .. 13.4 .. .. .. 23.9 24.8

Public expenditure on education

Tertiary 1999

32.6 10.9 1,206.8 .. .. .. .. .. 32.9 27.9 .. .. 19.6 .. .. 444.5 52.1 53.8 .. .. .. 36.0 .. .. 148.7 89.4 33.5 .. .. 36.5 41.4 25.6 27.0 .. .. .. .. .. .. 164.6 193.0 .. m .. .. .. .. .. 38.2 .. .. .. 90.8 .. 31.4 29.1

2009a

% of GDP 2009a

26.2 .. 222.8 .. 191.5 40.1 .. 27.3 19.5 .. .. .. 25.1 .. .. .. 38.3 46.7 .. 21.8 .. 22.3 92.7 155.2 .. 54.5 .. .. 105.4 25.1 15.5 24.4 21.7 .. .. .. 61.7 .. .. .. .. .. m .. .. .. .. .. .. .. .. .. .. .. 25.2 28.9

4.3 .. 4.1 5.6 5.8 4.7 4.3 3.0 3.6 .. .. 5.4 4.3 .. .. 7.8 6.6 5.2 4.9 3.5 6.8 4.1 16.8 4.6 .. 7.1 .. .. 3.2 5.3 1.2 5.5 5.5 .. .. 3.7 5.3 .. 5.2 1.3 .. 4.5 m 3.7 4.1 .. 4.5 .. 3.5 4.2 4.0 4.6 2.9 3.8 5.1 5.2

Trained Primary teachers school in primary pupilâ&#x20AC;&#x201C;teacher education ratio

% of total government expenditure

% of total

pupils per teacher

2009a

2009a

2009a

.. .. 93.9 91.5 .. 94.2 49.4 94.3 .. .. .. 87.4 .. .. 59.7 94.0 .. .. .. 88.3 100.0 .. .. 14.6 88.0 .. .. .. 89.4 99.9 100.0 .. .. .. 100.0 86.3 99.6 100.0 .. .. .. .. m 80.4 .. .. .. .. .. .. .. .. .. .. .. ..

16 17 68 11 35 16 44 19 17 17 36 31 12 23 38 32 10 .. 18 23 54 16 29 41 17 17 .. .. 49 16 16 18 14 15 17 16 20 28 .. 61 .. 24 w 46 23 23 21 26 18 17 24 23 .. 45 15 15

11.8 .. 20.4 19.3 19.0 9.3 18.1 11.6 10.5 .. .. 16.9 11.1 .. .. 21.6 12.7 16.1 16.7 18.7 27.5 20.3 15.5 17.6 .. 22.4 .. .. 15.0 20.2 23.4 11.7 14.1 .. .. .. 19.8 .. 16.0 .. .. .. m .. .. .. 13.5 .. 15.9 13.4 .. 18.0 .. .. 12.5 11.1


About the data

2.11

people

Education inputs Definitions

Data on education are collected by the United

The primary school pupil–teacher ratio reflects the

• Public expenditure per student is public current

Nations Educational, Scientific, and Cultural Organi-

average number of pupils per teacher at the specified

and capital spending on education divided by the

zation (UNESCO) Institute for Statistics from official

level of education. It differs from the average class

number of students by level as a percentage of gross

responses to its annual education survey. The data

size because of the different practices countries

domestic product (GDP) per capita. • Public expen-

are used for monitoring, policymaking, and resource

employ, such as part-time teachers, school shifts,

diture on education is current and capital expendi-

allocation. While international standards ensure

and multigrade classes. The comparability of pupil–

tures on education by local, regional, and national

comparable datasets, data collection methods may

teacher ratios across countries is affected by the

governments, including municipalities. • Trained

vary by country and within countries over time.

definition of teachers and by differences in class size

teachers in primary education are the percentage

For most countries the data on education spend-

by grade and in the number of hours taught, as well

of primary school teachers who have received the

ing in the table refer to public spending—total gov-

as the different practices mentioned above. More-

minimum organized teacher training (pre-service or

ernment spending on education at all levels plus

over, the underlying enrollment levels are subject to

in-service) required for teaching at the specified level

subsidies provided to households and other private

a variety of reporting errors (for further discussion of

of education in their country. • Primary school pupil–

entities—and generally exclude the part of foreign

enrollment data, see About the data for table 2.12).

teacher ratio is the number of pupils enrolled in pri-

aid for education that is not included in the govern-

While the pupil–teacher ratio is often used to com-

mary school divided by the number of primary school

ment budget. The data may also exclude spending

pare the quality of schooling across countries, it is

teachers (regardless of their teaching assignment).

by religious schools, which play a significant role in

often weakly related to student learning and quality

many developing countries. Data are gathered from

of education.

ministries of education and from other ministries or agencies involved in education spending.

All education data published by the UNESCO Institute for Statistics are mapped to the International

The share of public expenditure devoted to educa-

Standard Classification of Education 1997 (ISCED

tion allows an assessment of the priority a govern-

1997). This classification system ensures the com-

ment assigns to education relative to other public

parability of education programs at the international

investments, as well as a government’s commitment

level. UNESCO developed the ISCED to facilitate

to investing in human capital development. However,

comparisons of education statistics and indicators

returns on investment to education, especially pri-

of different countries on the basis of uniform and

mary and lower secondary education, cannot be

internationally agreed definitions. First developed in

understood simply by comparing current education

the 1970s, the current version was formally adopted

indicators with national income. It takes a long time

in November 1997.

before currently enrolled children can productively

The reference years shown in the table reflect the

contribute to the national economy (Hanushek

school year for which the data are presented. In

2002).

some countries the school year spans two calendar

High-quality data on education finance are scarce.

years (for example, from September 2009 to June

Improving the quality of education finance data is a

2010); in these cases the reference year refers to

priority of the UNESCO Institute for Statistics. Addi-

the year in which the school year ended (2010 in the

tional resources are being allocated for technical

previous example).

assistance to countries in need, especially those in Sub-Saharan Africa. Interagency partnerships and collaborations with national ministries in charge of education finance data are improving, and actual expenditure data are increasingly being collected. Tracking private educational spending is still a challenge for all countries. The share of trained teachers in primary education reveals a country’s commitment to invest in the development of its human capital engaged in teaching, but it does not take into account differences in teachers’ experiences and status, teaching methods, teaching materials, and classroom conditions—all factors that affect the quality of teaching

Data sources

and learning. Some teachers without this formal

Data on education inputs are from the UNESCO

training may have acquired equivalent pedagogical

Institute for Statistics (www.uis.unesco.org).

skills through professional experience.

2011 World Development Indicators

79


2.12

Participation in education Gross enrollment ratio

Preprimary 2009a

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

80

.. 58 23 40 69 33 82 95 24 10 102 122 14 47 15 17 65 81 3 10 19 26 71 5 1 55 47 121 51 4 13 70 4 60 105 111 96 37 131 16 60 13 95 4 65 110 .. 22 63 109 70 69 29 12 .. .. 40

% of relevant age group Primary Secondary 2009a

104 119 108 128 116 99 106 100 95 95 99 103 122 107 109 109 120 101 78 147 116 114 98 89 90 106 113 104 120 90 120 110 74 94 104 103 98 106 117 100 115 48 100 102 97 110 .. 86 108 105 105 101 114 90 .. .. 116

2011 World Development Indicators

Net enrollment rate

Adjusted net enrollment rate, primary

% of relevant age group Primary Secondary

Tertiary

2009a

2009a

1991

2009a

1999

2009a

44 72 .. .. 85 93 149 100 99 42 95 108 .. 81 91 82 90 89 20 21 40 41 .. 14 24 90 78 82 95 37 .. 96 .. 90 90 95 119 77 81 .. 64 32 99 34 110 113 .. 51 108 102 57 102 57 37 .. .. 65

4 .. 31 .. 68 50 77 55 19 8 77 63 .. 38 37 .. 38 51 3 3 10 9 .. 2 2 55 25 57 37 6 6 .. 8 51 118 58 78 .. 42 28 25 2 64 4 94 55 .. 5 25 .. 9 91 18 9 .. .. 19

28 .. 89 .. .. .. 98 90 89 64 .. 96 51 .. .. 89 .. .. 27 50 .. 69 98 53 .. .. 97 .. 71 56 .. 87 46 .. 94 .. 98 .. .. .. .. 20 .. 30 99 100 .. 50 .. 84 .. 95 .. 27 .. 21 88

.. 85 94 .. .. 84 97 .. 85 86 94 98 95 91 87 87 95 96 63 99 95 92 .. 67 .. 95 .. 94 90 .. .. .. 57 91 99 .. 95 87 97 94 94 36 94 83 96 98 .. 69 100 98 76 99 95 73 .. .. 97

.. 70 .. .. 76 86 90 .. 75 40 82 .. 18 68 .. 54 66 85 9 .. 15 .. 95 .. 7 .. .. 74 56 .. .. .. 19 81 73 81 88 38 46 71 47 17 84 12 95 94 .. 26 76 .. 33 82 24 12 10 .. ..

27 .. .. .. 79 87 88 .. 93 41 87 .. .. 69 .. 60 52 83 15 9 34 .. .. 10 .. 85 .. 75 74 .. .. .. .. .. 83 .. 90 61 59 .. 55 27 89 .. 96 98 .. 42 81 .. 46 91 40 29 .. .. ..

% of primary-schoolage children Male Female 2009a

2009a

.. 86 96 .. .. 92 97 .. 86 86 94 98 99 92 86 86 96 97 68 98 90 97 .. 77 .. 96 .. 97 93 .. .. .. 62 91 100 .. 94 96 .. 97 95 39 96 86 96 99 .. 69 96 .. 76 99 98 78 .. .. 96

.. 84 94 .. .. 94 98 .. 85 93 96 99 86 92 88 88 94 98 60 100 87 86 .. 57 .. 95 .. 100 93 .. .. .. 52 92 99 .. 97 89 .. 93 96 34 97 81 96 99 .. 74 93 .. 77 100 95 68 .. .. 96

Children out of school

thousand primary-schoolage children Male Female 2009a

.. 15 59 .. .. 5 33 .. 38 1,234 12 6 7 58 11 21 289 4 392 9 99 38 .. 78 .. 35 .. 6 155 .. .. .. 609 8 2 .. 12 23 .. 137 23 190 1 929 7 18 .. 40 6 .. 430 2 23 174 .. .. 22

2009a

.. 16 82 .. .. 3 22 .. 37 575 7 4 91 53 9 18 393 3 473 1 131 210 .. 149 .. 41 .. 0b 152 .. .. .. 774 8 2 .. 7 70 .. 324 15 202 1 1,255 7 15 .. 33 10 .. 398 0b 55 244 .. .. 9


Gross enrollment ratio

Preprimary 2009a

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

87 54 50 40 6 .. 97 100 86 89 36 52 51 .. 111 .. 76 18 22 89 77 .. 145 .. 72 23 10 .. 71 4 .. 98 114 74 59 57 .. 7 .. .. 100 94 56 3 16 95 38 .. 66 .. 109 72 49 62 81 154 53

% of relevant age group Primary Secondary 2009a

99 117 121 103 103 105 111 103 93 102 97 108 113 .. 105 .. 95 95 121 98 103 104 91 .. 96 88 160 119 95 95 104 100 114 94 110 107 114 116 112 .. 107 101 117 62 93 99 84 85 109 .. 102 109 110 97 115 91 106

2009a

97 60 79 83 51 115 90 101 91 101 88 99 59 .. 97 .. 90 84 44 98 82 45 .. .. 99 84 32 30 69 38 24 87 90 88 92 56 23 53 66 .. 121 119 68 12 30 112 91 33 73 .. 67 89 82 100 104 84 85

Net enrollment rate

% of relevant age group Primary Secondary

Tertiary 2009a

65 13 24 36 .. 58 60 67 24 58 41 41 4 .. 98 .. 29 51 13 69 53 .. .. .. 77 40 4 0 36 6 4 26 27 38 53 13 .. 11 9 .. 61 78 .. 1 .. 73 26 6 45 .. 29 .. 29 69 60 78 10

Adjusted net enrollment rate, primary

1991

2009a

1999

2009a

.. .. 95 97 76 90 .. .. 97 100 .. .. .. .. 99 .. 47 .. 59 .. .. 72 .. .. .. .. 72 .. .. .. .. 93 98 .. .. 56 42 .. 82 .. 95 100 70 23 .. 100 69 .. 92 65 94 86 96 .. 98 .. 89

90 91 95 99 88 97 97 98 80 100 89 89 83 .. 99 .. 88 84 93c .. 90 73 .. .. 92 86 98 91 94 73 76 94 98 88 90 90 91 .. 89 .. 99 99 92 54 61 99 77 66 97 .. 87 94 92 95 99 .. 93

82 .. 50 .. 30 84 86 88 83 99 79 87 33 .. 97 .. 89 .. 26 .. .. 17 20 .. 90 79 .. 29 65 .. 14 67 56 79 58 30 3 31 39 .. 91 90 35 6 .. 96 65 .. 59 .. 46 62 50 90 82 .. 74

91 .. 69 .. 43 88 86 95 77 98 82 89 50 .. 95 .. 80 79 36 .. 75 29 .. .. 92 .. 26 25 68 30 16 .. 72 80 82 .. 15 50 54 .. 88 .. 45 9 26 96 82 33 66 .. 59 71 61 94 88 .. 77

% of primary-schoolage children Male Female 2009a

2009a

95 91 .. .. 93 96 97 100 82 .. 93 89 83 .. 100 .. 94 91 84 .. 92 71 .. .. 96 91 99 89 94 84 74 93 99 91 99 92 93 .. 88 .. 99 99 93 60 66 99 82 72 98 .. 88 97 91 95 99 .. 98

95 88 .. .. 82 98 98 99 79 .. 94 90 84 .. 98 .. 93 91 81 .. 90 76 .. .. 96 92 100 94 94 70 79 95 100 90 99 88 88 .. 92 .. 99 100 94 48 60 99 81 60 97 .. 88 98 93 95 99 .. 98

2.12

people

Participation in education

Children out of school

thousand primary-schoolage children Male Female 2009a

9 5,543 .. .. 176 9 13 5 31 .. 30 52 532 .. 4 .. 6 19 65 .. 19 54 .. .. 3 6 16 152 97 165 66 4 39 8 1 154 149 .. 22 .. 4 1 29 511 4,023 3 33 3,108 4 .. 52 54 555 62 2 .. 1

2011 World Development Indicators

2009a

9 7,112 .. .. 415 5 9 15 35 .. 23 42 497 .. 31 .. 8 18 76 .. 21 45 .. .. 3 5 3 85 95 304 51 3 23 8 1 203 264 .. 14 .. 9 0b 24 637 4,626 3 34 4,191 6 .. 50 43 407 55 4 .. 1

81


2.12

Participation in education Gross enrollment ratio

Preprimary 2009a

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

73 90 17 11 12 51 5 .. 94 83 .. 64 126 .. 28 .. 102 102 9 9 33 92 .. 7 81 .. 18 .. 12 101 94 81 58 86 26 77 .. 34 .. .. .. 44 w 15 46 42 63 40 44 55 68 20 .. 17 77 110

% of relevant age group Primary Secondary 2009a

100 97 151 99 84 98 158 .. 103 97 33 101 107 97 74 108 95 103 122 102 105 91 113 115 104 107 99 .. 122 98 105 106 99 114 92 103 .. 79 85 113 .. 107 w 104 109 107 111 107 111 99 116 105 108 100 101 ..

a. Provisional data. b. Less than 0.5. c. Data are for 2010.

82

2011 World Development Indicators

2009a

92 85 27 97 30 91 35 .. 92 97 8 94 120 .. 38 53 103 96 75 84 27 76 51 41 89 92 82 .. 27 94 95 99 94 88 104 82 .. 87 .. 49 .. 67 w 38 68 63 88 63 74 89 89 73 52 34 100 ..

Net enrollment rate

% of relevant age group Primary Secondary

Tertiary 2009a

66 77 5 37 8 50 .. .. 54 87 .. .. 71 .. .. .. 71 49 .. 20 .. 45 15 5 .. 34 38 .. 4 79 30 57 83 65 10 79 .. 46 10 .. 3 26 w 6 24 19 42 21 .. 55 35 27 11 6 67 ..

Adjusted net enrollment rate, primary

1991

2009a

73 .. .. .. 45 .. .. .. .. .. .. 90 100 .. .. 74 100 84 91 .. 51 .. .. 65 90 94 89 .. .. .. 97 97 97 91 .. .. .. .. .. .. .. .. w .. .. .. .. .. 96 90 .. .. 68 .. 95 ..

90 .. 96 86 73 94 .. .. .. 97 .. 85 100 95 .. 83 95 94 .. 97 96 90 82 94 93 98 95 .. 92 89 90 100 92 99 87 92 .. 75 73 91 .. 88 w 80 88 87 93 87 .. 92 94 89 86 75 95 ..

1999

75 .. .. .. .. .. .. .. .. 90 .. 63 88 .. .. 32 96 84 36 63 5 .. 23 20 70 63 62 .. 8 91 69 95 88 .. .. 47 59 77 32 17 40 52 w .. .. .. 67 .. .. 79 59 60 .. .. 88 ..

2009a

73 .. .. 72 .. 90 25 .. .. 91 .. 72 95 .. .. 29 99 85 69 83 .. 71 .. .. 74 71 74 .. 22 85 83 93 88 70 92 71 .. 85 .. 46 .. 59 w .. .. .. 75 55 .. 81 73 64 .. .. 90 ..

% of primary-schoolage children Male Female

Children out of school

thousand primary-schoolage children Male Female

2009a

2009a

2009a

2009a

96 .. 95 88 74 96 .. .. .. 98 .. 89 100 95 .. 82 95 99 .. 99 96 91 84 98 97 99 96 .. 91 89 98 100 93 99 91 94 .. 78 80 91 .. 91 w 83 92 91 94 90 .. 94 95 92 92 78 95 ..

97 .. 97 85 76 96 .. .. .. 97 .. 91 100 96 .. 84 94 99 .. 96 97 89 82 89 94 100 94 .. 94 90 97 100 94 99 89 94 .. 77 66 94 .. 89 w 79 90 88 94 88 .. 94 95 89 88 75 96 ..

16 .. 38 205 262 5 .. .. .. 1 .. 385 1 45 .. 19 16 3 .. 2 160 281 15 10 2 6 147 .. 310 89 2 5 944 1 101 108 .. 57 395 112 ..

14 .. 22 244 232 6 .. .. .. 1 .. 331 3 36 .. 18 17 1 .. 15 107 305 17 56 4 0b 214 .. 213 81 4 1 770 2 119 96 .. 55 641 78 ..


About the data

2.12

Definitions

School enrollment data are reported to the United

children of primary age enrolled in preprimary edu-

• Gross enrollment ratio is the ratio of total enroll-

Nations Educational, Scientific, and Cultural Organi-

cation) are compiled from administrative data. Large

ment, regardless of age, to the population of the age

zation (UNESCO) Institute for Statistics by national

numbers of children out of school create pressure

group that officially corresponds to the level of educa-

education authorities and statistical offices. Enroll-

to enroll children and provide classrooms, teachers,

tion shown. • Preprimary education (ISCED O) refers

ment indicators help monitor whether a country is on

and educational materials, a task made difficult in

to programs at the initial stage of organized instruc-

track to achieve the Millennium Development Goal of

many countries by limited education budgets. How-

tion, designed primarily to introduce very young chil-

universal primary education by 2015, and whether

ever, getting children into school is a high priority for

dren, usually from age 3, to a school-type environment

an education system has the capacity to meet the

countries and crucial for achieving the Millennium

and to provide a bridge between the home and school.

needs of universal primary education.

Development Goal of universal primary education.

On completing these programs, children continue their

Enrollment indicators are based on annual school

In 2006 the UNESCO Institute for Statistics

education at the primary level. • Primary education

surveys but do not necessarily reflect actual atten-

changed its convention for citing the reference year.

(ISCED 1) refers to programs normally designed to

dance or dropout rates during the year. Also, the

For more information, see About the data for table

give students a sound basic education in reading,

length of primary education differs across coun-

2.11.

writing, and mathematics along with an elementary

tries and can influence enrollment rates and ratios,

understanding of other subjects such as history,

although the International Standard Classification of

geography, natural science, social science, art, and

Education (ISCED) tries to minimize the difference.

music. Religious instruction may also be featured. It

A shorter duration for primary education tends to

is sometimes called elementary education. • Sec-

increase the ratio; a longer one to decrease it (in

ondary education refers to programs of lower (ISCED

part because older children are more at risk of drop-

2) and upper (ISCED 3) secondary education. Lower

ping out).

secondary education continues the basic programs

Over- or under-age enrollments are frequent, par-

of the primary level, but the teaching is typically more

ticularly when parents prefer children to start school

subject focused, requiring more specialized teachers

at other than the official age. Age at enrollment may

for each subject area. In upper secondary educa-

be inaccurately estimated or misstated, especially

tion, instruction is often organized even more along

in communities where registration of births is not

subject lines, and teachers typically need a higher or

strictly enforced.

more subject-specific qualification. • Tertiary educa-

Population data used to calculate population-

tion refers to a wide range of programs with more

based indicators are drawn from the United Nations

advanced educational content. The first stage of ter-

Population Division. Using a single source for popula-

tiary education (ISECD 5) refers to theoretically based

tion data standardizes definitions, estimations, and

programs intended to provide sufficient qualifications

interpolation methods, ensuring a consistent meth-

to enter advanced research programs or professions

odology across countries and minimizing potential

with high-skill requirements and programs that are

enumeration problems in national censuses.

practical, technical, or occupationally specific. The

Gross enrollment ratios indicate the capacity of

second stage of tertiary education (ISCED 6) refers

each level of the education system, but a high ratio

to programs devoted to advanced study and original

may reflect a substantial number of over-age children

research and leading to the award of an advanced

enrolled in each grade because of repetition or late

research qualification. • Net enrollment rate is the

entry, rather than a successful education system.

ratio of total enrollment of children of official school

The net enrollment rate excludes over- and under-

age to the population of the age group that offi-

age students and more accurately captures the sys-

cially corresponds to the level of education shown.

tem’s coverage and internal efficiency. Differences

• Adjusted net enrollment rate, primary, is the ratio

between the gross enrollment ratio and net enroll-

of total enrollment of children of official school age

ment rate show the incidence of over- and under-age

for primary education who are enrolled in primary or

enrollments.

secondary education to the total primary school-age

The adjusted net enrollment rate in primary educa-

population. • Children out of school are the number

tion captures primary-school-age children who have

of primary-school-age children not enrolled in primary

progressed to secondary education faster than their

or secondary school.

peers and who would not be counted in the traditional net enrollment rate.

Data sources

Data on children out of school (primary-school-

Data on participation in education are from

age children not enrolled in primary or secondary

the UNESCO Institute for Statistics, www.uis.

school—dropouts, children never enrolled, and

unesco.org.

2011 World Development Indicators

83

people

Participation in education


2.13

Education efficiency Gross intake ratio in first grade of primary education

Cohort survival rate

Repeaters in primary education

Transition rate to secondary education

% of grade 1 students Reaching grade 5

% of relevant age group

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

84

Male 2009a

Female 2009a

129 89 101 .. 111 86 .. 104 95 101 97 97 161 114 89 114 .. 107 90 152 158 134 .. 110 131 101 94 117 118 119 115 98 77 95 100 109 98 109 119 98 123 45 102 158 100 .. .. 91 107 100 109 102 123 106 .. .. 126

93 82 99 .. 111 89 .. 100 94 105 102 98 152 113 92 112 .. 108 83 146 157 117 .. 86 98 98 98 124 114 106 112 96 67 94 102 107 99 90 124 96 119 39 102 141 98 .. .. 96 112 99 111 103 121 96 .. .. 122

2011 World Development Indicators

Male 1991

2008a

89 .. 82 .. .. .. 98 .. .. .. .. 87 30 57 .. 73 .. .. 61 66 .. 67 .. 52 43 .. .. .. 53 66 66 70 68 .. .. .. 98 .. .. .. 54 .. .. .. 96 .. 47 59 .. .. 72 .. .. 43 .. 47 50

.. .. 94 .. 95 .. .. .. .. 67 .. 90 .. 86 .. .. .. .. 73c 62 68 76 .. 58 .. 96 .. 100 82 78 75 95 66 .. 96 99 100 .. 80 .. 78 74 99 43 99 .. .. 71 96 .. 80 98 71 72 .. .. 75

Reaching last grade of primary education

Female 1991 2008a

89 .. 79 .. .. .. 99 .. .. .. .. 90 31 51 .. 81 .. .. 58 61 .. 66 .. 39 22 .. .. .. 59 55 68 73 61 .. .. .. 99 .. .. .. 57 .. .. .. 97 .. 46 53 .. .. 65 .. .. 35 .. 46 43

.. .. 95 .. 98 .. .. .. .. 66 .. 92 .. 85 .. .. .. .. 78 c 68 71 79 .. 48 .. 97 .. 100 89 77 79 97 66 .. 96 99 99 .. 83 .. 82 72 98 49 100 .. .. 72 95 .. 78 97 70 64 .. .. 80

Male 2008a

Female 2008a

.. .. 91 .. 93 98 .. 96 100 67 99 86 .. 85 .. .. .. 93 61c 56 60 68 .. 51 .. .. .. 100 82 78 71 93 62 97 96 99 99 .. 79 .. 74 74 99 35 99 .. .. 68 95 95 75 98 65 68 .. .. 74

.. .. 95 .. 97 97 .. 99 97 66 99 88 .. 82 .. .. .. 94 67c 64 63 69 .. 41 .. .. .. 100 89 73 71 96 59 99 95 99 99 .. 82 .. 78 72 98 41 100 .. .. 72 94 96 71 97 64 57 .. .. 79

% of enrollment Male Female 2009a 2009a

.. 2 13 .. 7 0b .. 0 0b 14 0b 4 14 1 0b 6 .. 2 11 32 10 15 .. 24 22 3 0b 1 2 15 21 6 19 0b 1 1 0 9 6 4 7 14 1 6 1 .. .. 6 0b 1 7 1 13 15 .. .. 6

.. 1 8 .. 5 0b .. 0 0b 13 0b 3 14 1 0b 4 .. 1 11 32 8 14 .. 24 24 2 0b 1 2 16 19 4 19 0b 0b 1 0 5 5 2 5 13 0b 5 0b .. .. 5 0b 1 6 1 11 16 .. .. 5

% Male 2008a

Female 2008a

.. .. 90 .. 93 100 .. 100 100 .. 100 100 .. 96 .. 98 .. 95 56c 48 80 42 .. 45 64 86 .. 100 100 83 65 97 47 100 99 99 95 88 81 .. 92 85 97 84 100 .. .. 83 99 99 91 .. 93 50 .. .. 82

.. .. 92 .. 96 98 .. 99 98 .. 100 99 .. 94 .. 97 .. 95 51c 23 81 45 .. 45 65 100 .. 100 100 76 62 91 45 99 98 99 98 92 77 .. 92 81 99 87 100 .. .. 83 99 99 92 .. 90 40 .. .. 86


Gross intake ratio in first grade of primary education

Cohort survival rate

Repeaters in primary education

2.13 Transition rate to secondary education

% of grade 1 students Reaching grade 5

% of relevant age group

Hungary India Indonesia Iran, Islamic Rep. Iraq Ireland Israel Italy Jamaica Japan Jordan Kazakhstan Kenya Korea, Dem. Rep. Korea, Rep. Kosovo Kuwait Kyrgyz Republic Lao PDR Latvia Lebanon Lesotho Liberia Libya Lithuania Macedonia, FYR Madagascar Malawi Malaysia Mali Mauritania Mauritius Mexico Moldova Mongolia Morocco Mozambique Myanmar Namibia Nepal Netherlands New Zealand Nicaragua Niger Nigeria Norway Oman Pakistan Panama Papua New Guinea Paraguay Peru Philippines Poland Portugal Puerto Rico Qatar

Male 2009a

Female 2009a

Male 1991

103 132 125 100 105 99 96 102 90 102 99 105 .. .. 106 .. 95 97 124 104 100 106 117 .. 97 92 198 136 89 102 112 99 122 94 147 107 163 140 98 .. 101 .. 158 97 102 97 88 111 105 .. 101 100 139 .. 107 97 103

103 124 122 100 103 101 98 101 86 102 99 106 .. .. 104 .. 93 97 115 105 105 98 107 .. 94 93 196 144 89 89 119 99 122 93 142 106 156 135 99 .. 101 .. 148 83 83 99 86 96 103 .. 97 100 130 .. 103 94 108

.. .. .. 75 75 .. .. .. 92 100 93 .. .. .. 92 .. .. .. 34 .. .. 53 .. .. .. .. 31 37 86 48 52 .. 81 .. .. 70 42 .. 52 44 .. 96 39 68 .. 99 77 .. .. 55 58 .. .. .. .. .. 98

Reaching last grade of primary education

2008a

Female 1991 2008a

Male 2008a

Female 2008a

.. 67 83 94 .. 98 100 99 .. 100 .. .. .. .. 98 .. 95 .. 66 98 94 56 64 .. .. .. 48 51 96 88 48 96 93 .. 94 84 56c 70 90 60 99 .. 48 66c .. 99 .. 61 88 .. 82 87 75 .. .. .. 92

.. .. .. 67 70 .. .. .. 94 100 89 .. .. .. 92 .. .. .. 32 .. .. 77 .. .. .. .. 31 33 87 42 47 .. 82 .. .. 64 34 .. 57 32 .. 95 48 65 .. 100 78 .. .. 52 60 .. .. .. .. .. 99

99 67 77 94 .. .. 99 99 .. 100 .. 98 c .. .. 98 .. 95 96 66 97 90 38 49 .. 98 98 48 42 96 81 40 94 90 95 94 78 37c 70 80 60 .. .. 45 63c .. 99 .. 61 86 .. 77 82 71 .. .. .. 91

99 70 83 95 .. .. 98 100 .. 100 .. 99c .. .. 99 .. 96 97 68 94 93 56 43 .. 98 97 50 42 96 77 42 98 93 96 95 78 34 c 69 85 64 .. .. 52 60 c .. 99 .. 60 88 .. 81 84 80 .. .. .. 97

.. 70 89 94 .. 100 98 100 .. 100 .. .. .. .. 99 .. 96 .. 68 94 96 69 56 .. .. .. 50 50 97 85 51 99 95 .. 95 85 51c 69 93 64 100 .. 55 62c .. 100 .. 60 91 .. 85 88 82 .. .. .. 99

% of enrollment Male Female 2009a 2009a

2 3 4 2 19 1 2 0b 3 0 1 0b .. .. 0b .. 1 0b 15d 5 11 23 6 .. 1 0b 21 19 .. 13 2 4 4 0b 0b 13 7 0b 18 17 .. .. 13 5 .. .. 1 3 6 .. 5 7 3 2 .. .. 0b

1 3 3 2 14 1 1 0b 3 0 1 0b .. .. 0b .. 1 0b 13d 2 7 16 7 .. 1 0b 20 18 .. 14 2 3 3 0b 0b 9 7 0b 14 17 .. .. 9 5 .. .. 2 3 4 .. 3 7 2 1 .. .. 0b

% Male 2008a

Female 2008a

99 81 91 96 .. .. 71 100 .. .. 99 100 c .. .. 100 .. 99 99 80 92 84 68 64 .. 99 99 57 75 100 72 38 64 94 99 96 80 52c 74 80 81 .. .. .. 56c 44 100 .. 73 96 .. 88 94 100 .. .. .. 100

99 81 93 97 .. .. 70 100 .. .. 98 100 c .. .. 100 .. 100 100 77 97 89 66 60 .. 99 100 55 74 99 68 31 75 93 98 99 78 55c 73 83 81 .. .. .. 62c 44 100 .. 72 97 .. 89 93 98 .. .. .. 100

2011 World Development Indicators

85

people

Education efficiency


2.13

Education efficiency Gross intake ratio in first grade of primary education

Cohort survival rate

Repeaters in primary education

Transition rate to secondary education

% of grade 1 students Reaching grade 5

% of relevant age group

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

Male 2009a

Female 2009a

101 .. 194 102 96 95 201 .. 100 97 .. 92 105 92 86 105 104 93 117 106 99 .. 142 105 102 106 101 .. 140 100 113 .. 103 101 94 101 .. 77 110 116 .. 114 w 133 114 115 .. 115 105 .. .. 104 126 121 102 102

99 .. 189 101 102 94 182 .. 99 97 .. 87 106 93 76 101 103 96 113 101 100 .. 134 102 100 107 98 .. 143 100 113 .. 109 111 91 98 .. 77 98 119 .. 110 w 126 110 110 .. 111 107 .. .. 101 117 113 104 101

Male 1991

.. .. 49 80 78 .. .. .. .. .. .. 61 .. 97 .. 58 99 72 87 .. 69 .. .. 55 98 76 93 .. .. .. 78 .. .. 98 .. 69 .. .. .. .. 70 .. w .. .. .. .. .. .. .. .. .. .. .. .. ..

2008a

Female 1991 2008a

Male 2008a

Female 2008a

.. .. 46 99 69 .. .. 99 .. .. .. .. 99 88 89 75 100 .. .. .. 79 .. 72 80 97 96 94 .. 57 .. 97 .. .. 93 .. 92 .. .. .. 71 .. .. w .. .. .. .. .. .. .. .. .. 68 .. .. ..

.. .. 51 76 68 .. .. .. .. .. .. 67 .. 98 .. 64 99 72 85 .. 71 .. .. 38 99 70 92 .. .. .. 80 .. .. 100 .. 80 .. .. .. .. 72 .. w .. .. .. .. .. .. .. .. .. .. .. .. ..

93 .. .. 98 56 99 .. 99 97 .. .. .. 99 88 86 70 100 .. 93 .. 71 .. 68 76 93 94 94 .. 54 96 97 .. .. 93 98 89 .. 99 .. 55 .. .. w .. .. .. .. .. .. .. .. .. 68 .. .. 98

94 .. .. 91 59 97 .. 99 98 .. .. .. 100 89 98 74 100 .. 94 .. 77 .. 78 62 93 95 94 .. 53 98 97 .. .. 96 99 95 .. 97 .. 52 .. .. w .. .. .. .. .. .. .. .. .. 70 .. .. 99

a. Provisional data. b. Less than 0.5. c. Data are for 2009. d. Data are for 2010.

86

2011 World Development Indicators

Reaching last grade of primary education

.. .. 51 93 71 .. .. 99 .. .. .. .. 100 89 100 86 100 .. .. .. 83 .. 80 71 95 96 94 .. 58 .. 97 .. .. 96 .. 96 .. .. .. 70 .. .. w .. .. .. .. .. .. .. .. .. 70 .. .. ..

% of enrollment Male Female 2009a 2009a

2 .. 15 4 8 1 10 0b 3 1 .. 8 3 1 4 21 0 2 9 0b 2 12 21 23 7 10 2 .. 14 0b 2 0 0 8 0b 4 .. 0 6 6 .. 5w 11 4 4 .. 5 1 .. .. 9 4 10 1 2

1 .. 14 4 7 1 10 0b 3 0b .. 8 2 1 4 15 0 1 7 0b 2 6 18 22 5 6 2 .. 14 0b 2 0 0 5 0b 3 .. 0 5 6 .. 4w 11 3 3 .. 4 1 .. .. 5 4 10 1 1

% Male 2008a

Female 2008a

97 .. .. 93 62 100 .. 86 97 .. .. 90 .. 95 90 .. 100 99 94 98 40 85 86 66 86 79 .. .. 58 100 98 .. .. 81 100 97 .. 97 .. 66 .. .. w .. .. .. .. .. .. .. .. .. 80 66 .. ..

97 .. .. 100 57 99 .. 92 97 .. .. 91 .. 97 98 .. 100 100 96 98 32 89 88 58 92 86 .. .. 55 100 99 .. .. 93 99 97 .. 97 .. 67 .. .. w ..  .. .. .. .. .. .. .. .. 80 65 .. ..


About the data

2.13

people

Education efficiency Definitions

The United Nations Educational, Scientific, and Cul-

data on repeaters by grade for the most recent of

• Gross intake ratio in first grade of primary edu-

tural Organization (UNESCO) Institute for Statistics

those two years to reflect current patterns of grade

cation is the number of new entrants in grade 1,

calculates indicators of students’ progress through

transition. Rates approaching 100 percent indicate

regardless of age, expressed as a percentage of the

school. These indicators measure an education sys-

high retention and low dropout levels.

population of the official school age. • Cohort sur-

tem’s success in reaching students, efficiently mov-

Data on repeaters are often used to indicate an

vival rate is the percentage of children enrolled in

ing students from one grade to the next, and trans-

education system’s internal efficiency. Repeaters not

the first grade of primary education who eventually

mitting knowledge at a particular level of education.

only increase the cost of education for the family

reach grade 5 or the last grade of primary education.

The gross intake ratio to the first grade of primary

and the school system, but also use limited school

The estimate is based on the reconstructed cohort

education indicates the level of access to primary

resources. Country policies on repetition and promo-

method (see About the data). • Repeaters in primary

education and the education system’s capacity to

tion differ. In some cases the number of repeaters

education are the number of students enrolled in the

provide access to primary education. A low gross

is controlled because of limited capacity. In other

same grade as in the previous year as a percentage

intake ratio in grade 1 reflects the fact that many chil-

cases the number of repeaters is almost 0 because

of all students enrolled in primary school. • Transi-

dren do not enter primary school even though school

of automatic promotion—suggesting a system that

tion rate to secondary education is the number of

attendance, at least through the primary level, is

is highly efficient but that may not be endowing stu-

new entrants to the first grade of secondary edu-

mandatory in most countries. Because the gross

dents with enough cognitive skills.

cation (general programs only) in a given year as a

intake ratio includes all new entrants regardless of

The transition rate from primary to secondary

percentage of the number of pupils enrolled in the

age, it can exceed 100 percent in some situations,

school conveys the degree of access or transition

final grade of primary education in the previous year.

such as immediately after fees have been abolished

between the two levels. As completing primary edu-

or when the number of reenrolled children is large.

cation is a prerequisite for participating in lower

The indicator is not calculated when new entrants

secondary school, growing numbers of primary

and repeaters are not correctly distinguished in

completers will inevitably create pressure for more

grade 1.

available places at the secondary level. A low transi-

The survival rate to grade 5 and to the last grade

tion rate can signal such problems as an inadequate

of primary education shows the percentage of stu-

examination and promotion system or insufficient

dents entering primary school who are expected to

secondary school capacity. The quality of data on

reach the specified grade. It measures an education

the transition rate is affected when new entrants and

system’s holding power and internal efficiency. Sur-

repeaters are not correctly distinguished in the first

vival rates are calculated based on the reconstructed

grade of secondary school. Students who interrupt

cohort method, which uses data on enrollment by

their studies after completing primary school could

grade for the two most recent consecutive years and

also affect data quality. In 2006 the UNESCO Institute for Statistics

There are more overage children among the poor in primary school in Zambia

Percent of total enrollment 100

Overage children Underage children

changed its convention for citing the reference year.

2.13a

For more information, see About the data for table 2.11.

On-time Children

75

50

25

0

Poorest wealth quintile

Middle

Richest wealth quintile

Data sources Data on education efficiency are from the UNESCO

Source: World Bank, EdStats.

Institute for Statistics, www.uis.unesco.org.

2011 World Development Indicators

87


2.14

Education completion and outcomes

1991

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

88

Total

28 .. 80 33 100 105 .. .. 95 41 94 79 22 71 .. 90 93 90 20 46 45 53 .. 28 18 .. 107 102 73 48 54 79 42 85 99 92 98 61 91 .. 65 18 .. 23 97 106 62 45 .. 100 64 99 .. 17 5 27 64

2011 World Development Indicators

Primary completion rate

Youth literacy rate

% of relevant age group

% ages 15–24

2009a

1991

.. 90 91 .. 102 98 .. 99 92 61 96 86 62 99 .. 95 .. 90 43 52 83 73 .. 38 33 95 .. 93 115 56 74 96 46 100 98 95 101 90 103 95 89 48 100 55 98 .. .. 79 107 104 83 101 80 62 .. .. 90

41 .. 86 .. .. .. .. .. 96 .. 95 76 30 78 .. 83 .. 88 25 49 .. 57 .. 37 29 .. .. .. 70 61 59 77 53 .. .. 91 98 .. 91 .. 64 21 .. 28 98 .. 59 56 .. 99 71 99 .. 24 7 29 67

Male 2009a

.. 90 90 .. 100 96 .. 99 92 58 93 84 71 99 .. 93 .. 91 46 54 83 80 .. 47 42 101 .. 92 113 66 77 95 54 99 98 95 100 90 101 97 88 52 100 57 99 .. .. 76 110 103 85 102 83 71 .. .. 87

Female 1991 2009a

14 .. 73 .. .. .. .. .. 94 .. 95 82 14 64 .. 98 .. 92 15 43 .. 49 .. 20 7 .. .. .. 76 36 49 81 32 .. .. 93 98 .. 92 .. 66 15 .. 18 97 .. 65 34 .. 100 56 98 .. 9 3 26 61

.. 89 91 .. 104 100 .. 98 91 63 92 88 53 98 .. 97 .. 89 40 51 84 67 .. 29 24 88 .. 93 117 46 72 97 39 100 98 95 101 89 104 93 91 43 101 53 97 .. .. 83 104 104 81 101 77 53 .. .. 93

1990

.. .. 86 .. .. 100 .. .. .. .. 100 .. .. .. .. .. .. .. .. 59 .. .. .. 63 26 .. 97 .. .. .. .. .. 60 .. .. .. .. .. 97 71 .. .. 100 .. .. .. .. .. .. .. .. .. .. .. .. .. ..

Male 2005–09b

.. 99 94 81 99 100 .. .. 100 74 100 .. 65 99 100 94 97 98 47 77 89 89 .. 72 54 99 99 .. 97 73 87 98 72 100 100 .. .. 95 97 88 95 92 100 56 .. .. 99 71 100 .. 81 99 89 68 78 .. 93

1990

.. .. 62 .. .. 100 .. .. .. .. 100 .. .. .. .. .. .. .. .. 48 .. .. .. 35 9 .. 91 .. .. .. .. .. 38 .. .. .. .. .. 96 54 .. .. 100 .. .. .. .. .. .. .. .. .. .. .. .. .. ..

Adult literacy PISA rate mathematics literacy

% ages 15 and older Female 2005–09b

.. 99 89 66 99 100 .. .. 100 77 100 .. 43 99 100 97 99 97 33 76 86 77 .. 57 39 99 99 .. 98 62 78 99 61 100 100 .. .. 97 97 82 95 86 100 33 .. .. 97 60 100 .. 79 99 84 54 64 .. 95

Total 2005–09b

Mean score 2009

.. 96 73 70 98 100 .. .. 100 56 100 .. 42 91 98 84 90 98 29 67 78 71 .. 55 34 99 94 .. 93 67 .. 96 55 99 100 .. .. 88 84 66 84 67 100 30 .. .. 88 46 100 .. 67 97 74 39 52 49 84

.. 377 .. .. 388 .. 514 496 431 .. .. 515 .. .. .. .. 386 428 .. .. .. .. 527 .. .. 421 .. 555 381 .. .. .. .. 460 .. 493 503 .. .. .. .. .. 512 .. 541 497 .. .. .. 513 .. 466 .. .. .. .. ..


1991

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

82 64 93 88 58 103 .. 98 94 102 101 103 .. .. 99 .. 57 .. 41 .. .. 59 .. .. .. 98 36 31 91 9 33 115 88 .. .. 48 26 .. 74 51 .. .. 42 17 .. 100 74 .. 86 46 68 .. 88 96 .. .. 71

Total

Primary completion rate

Youth literacy rate

% of relevant age group

% ages 15–24

2009a

1991

95 95 109 101 64 99 99 104 89 101 100 106 .. .. 99 .. 93 94 75 95 85 70 58 .. 92 92 79 59 97 59 64 89 104 93 93 80 57 99 87 .. .. .. 75 40 79 98 80 61 102 .. 94 101 94 96 .. .. 108

89 76 .. 93 63 103 .. 98 90 102 101 103 .. .. 99 .. 58 .. 46 .. .. 42 .. .. .. .. 35 35 91 12 39 115 91 .. .. 57 32 .. 67 70 .. .. 43 21 .. 100 78 .. 86 51 68 .. 85 .. .. .. 71

Male 2009a

97 95 109 101 73 99 99 104 88 100 99 106 .. .. 100 .. 94 94 78 97 83 60 63 .. 92 91 79 58 97 67 63 89 104 94 94 84 63 98 83 .. .. .. 71 47 84 98 80 68 102 .. 93 101 91 .. .. .. 109

Female 1991 2009a

1990

90 52 .. 82 52 103 .. 97 98 102 101 103 .. .. 100 .. 56 .. 36 .. .. 76 .. .. .. .. 37 27 91 7 26 115 92 .. .. 39 21 .. 81 41 .. .. 53 13 .. 100 70 .. 86 42 69 .. 86 .. .. .. 72

.. .. 97 85 .. .. .. .. .. .. .. 100 .. .. .. .. 91 .. .. 100 .. .. .. .. 100 .. .. 70 .. .. .. 91 96 100 .. .. .. .. .. .. .. .. .. .. .. .. .. .. 95 .. .. .. 96 .. .. 92 89

94 94 110 101 54 99 100 104 90 101 100 106 .. .. 97 .. 93 95 71 93 87 81 53 .. 92 93 79 60 97 52 66 90 105 91 92 77 51 100 91 .. .. .. 78 34 74 97 79 54 101 .. 95 101 97 .. .. .. 106

Male 2005–09b

99 88 100 99 85 .. .. 100 92 .. 99 100 92 100 .. .. 99 100 89 100 98 86 70 100 100 99 66 87 98 47 71 96 99 99 95 87 78 96 91 87 .. .. 85 52 78 .. 98 79 97 65 99 98 97 100 100 87 98

1990

.. .. 95 66 .. .. .. .. .. .. .. 100 .. .. .. .. 84 .. .. 100 .. .. .. .. 100 .. .. 49 .. .. .. 92 95 100 .. .. .. .. .. .. .. .. .. .. .. .. .. .. 95 .. .. .. 97 .. .. 94 91

2.14

people

Education completion and outcomes

Adult literacy PISA rate mathematics literacy

% ages 15 and older Female 2005–09b

99 74 99 99 80 .. .. 100 98 .. 99 100 94 100 .. .. 99 100 79 100 99 98 81 100 100 99 64 86 99 31 64 98 98 100 97 72 64 95 95 77 .. .. 89 23 65 .. 98 61 96 70 99 97 98 100 100 88 98

Total 2005–09b

Mean score 2009

99 63 92 85 78 .. .. 99 86 .. 92 100 87 100 .. .. 94 99 73 100 90 90 59 89 100 97 64 74 92 26 57 88 93 98 97 56 55 92 89 59 .. .. 78 29 61 .. 87 56 94 60 95 90 95 100 95 90 95

490 .. 371 .. .. 487 447 483 .. 529 387 405 .. .. 546 .. .. 331 .. 482 .. .. .. .. 477 .. .. .. .. .. .. .. 419 .. .. .. .. .. .. .. 526 519 .. .. .. 498 .. .. 360 .. .. 365 .. 495 487 .. 368

2011 World Development Indicators

89


2.14

Education completion and outcomes

1991

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

96 92 50 .. 39 .. .. .. 95 95 .. 76 104 101 .. 61 96 53 89 .. 55 .. .. 35 102 74 90 .. .. 92 103 .. .. 94 80 81 .. .. .. .. 97 79 w 44 83 82 88 78 101 92 84 .. 62 51 .. 101

Primary completion rate

Youth literacy rate

% of relevant age group

% ages 15–24

Male 2009a

2009a

1991

96 95 54 93 57 96 88 .. 96 96 .. 93 100 97 57 72 94 94 112 98 102 .. 80 61 93 93 93 .. 72 95 99 .. 95 106 92 95 .. 82 61 87 .. 88 w 63 92 90 100 87 99 96 101 95 79 64 98 ..

96 92 51 .. 48 .. .. .. 95 .. .. 72 104 101 .. 57 96 53 94 .. 56 .. .. 48 99 79 93 .. .. 99 104 .. .. 91 .. 76 .. .. .. .. 99 86 w .. 89 89 89 85 105 93 84 .. 75 57 .. 100

96 .. 52 95 56 97 101 .. 96 97 .. 93 100 97 53 75 95 93 113 97 102 .. 80 71 93 93 95 .. 72 98 100 .. 94 104 93 94 .. 82 72 92 .. 90 w 66 93 92 100 89 98 97 100 97 82 69 98 ..

Female 1991 2009a

1990

96 93 50 .. 31 .. .. .. 96 .. .. 80 103 101 .. 64 96 54 84 .. 55 .. .. 22 105 70 86 .. .. 99 103 .. .. 96 .. 86 .. .. .. .. 96 75 w .. 77 74 88 73 97 92 85 .. 52 47 .. 100

.. 100 .. .. 49 .. .. 99 .. .. .. .. .. .. .. 83 .. .. .. 100 86 .. .. .. 99 .. 97 .. .. .. 81 .. .. 98 .. 95 94 .. .. 67 .. 87 w 66 88 87 94 86 96 99 91 84 71 73 99 ..

a. Provisional data. b. Data are for the most recent year available. c. Data are for 2010.

90

2011 World Development Indicators

96 .. 56 90 57 96 75 .. 96 96 .. 94 100 98 47 69 94 95 111 93 102 .. 79 52 93 93 92 .. 73 99 98 .. 97 108 91 96 .. 81 49 82 .. 87 w 60 91 89 100 85 100 95 102 92 76 60 98 ..

Male 2005–09b

97 100 77 99 74 .. 68 100 .. 100 .. 97 100 97 89 92 .. .. 96 100 78 98 .. 85 100 98 99 100 90 c 100 94 .. .. 98 100 98 97 99 96 82 98 92 w 76 94 93 98 91 99 99 97 93 85 77 99 ..

1990

Adult literacy PISA rate mathematics literacy

% ages 15 and older Female 2005–09b

.. 100 .. .. 28 .. .. 99 .. .. .. .. .. .. .. 84 .. .. .. 100 78 .. .. .. 99 .. 88 .. .. .. 85 .. .. 99 .. 96 93 .. .. 66 .. 78 w 52 78 74 92 75 91 98 92 67 47 58 99 ..

98 100 77 97 56 .. 48 100 .. 100 .. 98 100 99 83 95 .. .. 93 100 76 98 .. 68 100 96 97 100 85c 100 97 .. .. 100 100 99 96 99 72 67 99 87 w 69 88 86 97 85 99 99 97 87 72 67 99 ..

Total 2005–09b

Mean score 2009

98 100 71 86 50 .. 41 95 .. 100 .. 89 98 91 70 87 .. .. 84 100 73 94 51 57 99 78 91 100 73c 100 90 .. .. 98 99 95 93 95 62 71 92 84 w 62 83 80 92 80 94 98 91 74 61 62 98 ..

427 468 .. .. .. 442 .. 562 497 501 .. .. 483 .. .. .. 494 534 .. .. .. 419 .. .. 414 371 445 .. .. .. .. 492 487 427 .. .. .. .. .. .. ..                            


About the data

2.14

people

Education completion and outcomes Definitions

Many governments publish statistics that indicate

Many countries estimate the number of literate

• Primary completion rate is approximated by the

how their education systems are working and devel-

people from self-reported data. Some use educa-

gross intake ratio to last grade of primary educa-

oping—statistics on enrollment and such efficiency

tional attainment data as a proxy but apply different

tion, which is the total number of new entrants in

indicators as repetition rates, pupil–teacher ratios,

lengths of school attendance or levels of completion.

the last grade of primary education, regardless of

and cohort progression. The World Bank and the

Because definitions and methodologies of data col-

age, expressed as a percentage of the population

United Nations Educational, Scientific, and Cultural

lection differ across countries, data should be used

at the entrance age to the last grade of primary.

Organization (UNESCO) Institute for Statistics jointly

cautiously.

• Youth literacy rate is the percentage of the popula-

developed the primary completion rate indicator.

The reported literacy data are compiled by the

tion ages 15–24 that can, with understanding, both

Increasingly used as a core indicator of an educa-

UNESCO Institute for Statistics based on national cen-

read and write a short simple statement on their

tion system’s performance, it reflects an education

suses and household surveys during 1985–2009. For

everyday life. • Adult literacy rate is the percentage

system’s coverage and the educational attainment

countries without recent literacy data, the UNESCO

of the population ages 15 and older that can, with

of students. The indicator is a key measure of edu-

Institute for Statistics estimates literacy rates with

understanding, both read and write a short simple

cation outcome at the primary level and of progress

the Global Age-specific Literacy Projections Model

statement on their everyday life. • PISA mathemat-

toward the Millennium Development Goals and the

(GALP). For detailed information on sources, defini-

ics literacy is the country’s mean mathematics

Education for All initiative. However, a high primary

tions, and methodology, consult www.uis.unesco.org.

score from the Programme for International Student

completion rate does not necessarily mean high lev-

Literacy statistics for most countries cover the

els of student learning. The primary completion rate reflects the primary

younger ages or are confined to age ranges that tend

cycle as defined by the International Standard Classi-

to inflate literacy rates. The youth literacy rate for

fication of Education (ISCED 97), ranging from three or

ages 15–24 reflects recent progress in education: it

four years of primary education (in a very small num-

measures the accumulated outcomes of primary edu-

ber of countries) to five or six years (in most coun-

cation over the previous 10 years or so by indicating

tries) and seven (in a small number of countries).

the proportion of people who have passed through

The table shows the primary completion rate, also

the primary education system and acquired basic

called the gross intake ratio to last grade of primary

literacy and numeracy skills. Generally, literacy also

education. It is the total number of new entrants in

encompasses numeracy, the ability to make simple

the last grade of primary education, regardless of age,

arithmetic calculations.

expressed as a percentage of the population at the

In many countries national assessments enable

entrance age to the last grade of primary education.

ministries of education to monitor progress in learn-

Data limitations preclude adjusting for students who

ing outcomes. Of the handful of internationally or

drop out during the final year of primary education.

regionally comparable assessments, one of the

Thus, this rate is a proxy that should be taken as an

largest is the Programme for International Student

upper estimate of the actual primary completion rate.

Assessment (PISA). Coordinated by the Organisation

There are many reasons why the primary comple-

for Economic Co-operation and Development (OECD),

tion rate can exceed 100 percent. The numerator

it measures the knowledge and skills of 15-year-olds,

may include late entrants and overage children

the age at which students in most countries are near-

who have repeated one or more grades of primary

ing the end of their compulsory time in school. The

education as well as children who entered school

assessment tests reading, mathematical, and sci-

early, while the denominator is the number of chil-

entific literacy in terms of general competencies—

dren at the entrance age to the last grade of primary

that is, how well students can apply the knowledge

education.

and skills they have learned at school to real-life

Basic student outcomes include achievements in reading and mathematics judged against established

Assessment (PISA).

population ages 15 and older, but some include

challenges. It does not test how well a student has mastered a school’s specific curriculum.

standards. The UNESCO Institute for Statistics has

The table presents the mean PISA mathematical

established literacy as an outcome indicator based

literacy score, as demonstrated through students’

on an internationally agreed definition. The literacy

ability to analyze, reason, and communicate effec-

rate is the percentage of the population who can,

tively while posing, solving, and interpreting math-

with understanding, both read and write a short,

ematical problems that involve quantitative, spatial,

simple statement about their everyday life. In prac-

probabilistic, or other mathematical concepts. The

tice, literacy is difficult to measure. To estimate

average score in 2009 was 496. Because the figures

literacy using such a definition requires census or

are derived from samples, the scores reflect a small

survey measurements under controlled conditions.

measure of statistical uncertainty.

Data sources Data on education completion and outcomes are from the UNESCO Institute for Statistics. Data on PISA mathematics literacy are from the OECD.

2011 World Development Indicators

91


2.15

Education gaps by income and gender Survey year

Armenia Azerbaijan Bangladesh Belize Benin Bolivia Burundi Cambodia Cameroon Colombia Côte d’Ivoire Dominican Republic Egypt, Arab Rep. Ethiopia Georgia Ghana Guatemala Guinea Guinea-Bissau Guyana Haiti Kazakhstan Kenya Kosovo Lesotho Macedonia, FYR Madagascar Malawi Malawi Mali Mauritania Moldova Mozambique Namibia Nepal Nicaragua Niger Nigeria Panama Peru Rwanda Serbia Somalia Swaziland Syrian Arab Republic Tanzania Togo Turkey Uganda Vietnam Yemen, Rep. Zambia Zimbabwe

92

2005 2006 2006 2006 2006 2003 2005 2005 2006 2005 2006 2007 2005 2005 2006 2006 2000 2005 2006 2006 2005 2006 2003 2000 2004 2005 2003/04 2004 2006 2006 2007 2005 2003 2006 2001 2001 2006 2003 2003 2004 2005 2005 2005 2006 2006 2004 2006 2003 2006 2006 2006 2007 1999

Gross intake rate in grade 1

Gross primary participation rate

Average years of schooling

Primary completion rate

Children out of school

% of relevant age group Poorest Richest quintile quintile

% of relevant age group Poorest Richest quintile quintile

Ages 15–19 Poorest Richest quintile quintile

% of relevant age group Richest quintile Male

% of relevant age group Poorest Richest quintile quintile

93 92 144 80 67 92 201 208 108 161 51 130 107 86 90 107 176 55 135 74 177 118 134 104 169 102 250 235 234 41 67 96 128 112 184 149 50 78 125 121 274 90 13 147 110 123 115 108 180 99 66 135 106

2011 World Development Indicators

80 118 147 89 107 95 191 151 75 84 77 112 97 124 104 121 124 119 184 76 188 101 125 119 111 190 153 145 207 98 96 84 143 104 141 106 90 101 116 90 195 98 44 117 149 123 148 111 144 100 109 123 111

106 100 96 106 61 108 91 113 93 127 57 113 95 47 101 81 81 52 94 105 87 106 92 95 116 89 118 98 133 46 62 99 75 118 109 85 35 70 108 118 131 98 8 117 102 82 99 97 107 108 50 105 144

102 108 105 113 114 129 144 134 116 99 110 107 99 112 103 117 114 121 166 101 159 103 106 104 124 97 145 122 169 110 116 95 143 109 139 105 89 108 102 96 151 100 93 114 107 119 128 97 124 100 101 112 144

9 9 8 8 6 6 4 5 6 6 5 7 9 3 15 5 4 5 4 10 4 9 6 9 5 8 3 5 5 5 5 9 3 7 5 4 4 7 7 7 3 9 8 6 7 5 6 6 5 .. 7 5 7

10 11 13 11 8 9 7 8 14 10 8 11 12 6 14 8 8 7 7 10 7 9 9 11 8 10 8 8 7 8 9 12 6 10 8 9 7 10 11 11 5 10 10 9 8 7 7 7 8 .. 10 9 10

Poorest quintile

119 94 65 59 31 76 20 42 43 94 47 69 84 14 102 62 15 32 34 109 31 102 40 82 36 120 42 24 30 36 17 97 13 81 49 34 31 48 100 106 31 86 2 69 92 32 40 95 27 99 25 50 36

116 109 97 130 95 98 70 121 111 109 127 109 92 90 102 88 80 93 125 118 136 115 76 94 122 119 141 81 80 79 89 100 100 109 96 124 71 71 94 99 88 96 58 110 93 108 82 85 68 104 103 101 80

113 103 83 107 67 90 44 88 90 100 88 88 92 46 106 93 34 76 80 91 73 102 71 98 69 133 77 47 49 55 48 96 57 94 69 78 60 70 105 100 48 94 26 85 93 58 67 100 50 96 84 88 51

Female

112 105 86 72 52 81 39 85 74 103 71 106 88 33 104 86 36 48 54 112 82 97 72 83 85 78 77 35 52 41 52 98 43 90 62 83 30 54 88 97 42 89 20 98 92 60 56 81 42 103 31 73 57

2 20 12 5 57 22 5 37 3 11 4 12 12 74 2 22 7 60 12 2 69 0 38 1 18 0 33 23 0 67 2 2 46 11 33 40 74 52 1 6 13 1 87 17 0 44 1 20 25 3 2 22 22

1 11 6 7 12 5 3 13 2 2 3 4 1 30 1 12 3 16 11 1 24 1 11 4 3 0 3 4 0 20 2 1 7 2 6 4 28 6 1 1 8 0 46 4 0 15 1 5 7 2 2 3 8


About the data

2.15

people

Education gaps by income and gender Definitions

The data in the table describe basic information on

exclusion. To that extent the index provides only a

• Survey year is the year in which the underlying

school participation and educational attainment

partial view of the multidimensional concepts of pov-

data were collected. • Gross intake rate in grade 1

by individuals in different socioeconomic groups

erty, inequality, and inequity.

is the number of students in the first grade of pri-

within countries. The data are from Demographic

Creating one index that includes all asset indica-

mary education regardless of age as a percentage

and Health Surveys (DHS) conducted by Macro

tors limits the types of analysis that can be per-

of the population of the official primary school

International with the support of the U.S. Agency for

formed. In particular, the use of a unified index does

entrance age. These data may differ from those in

International Development, Multiple Indicator Clus-

not permit a disaggregated analysis to examine

table 2.13. • Gross primary participation rate is

ter Surveys (MICS) conducted by the United Nations

which asset indicators have a more or less important

the ratio of total students attending primary school

Children’s Fund (UNICEF), and Living Standards

association with education status. In addition, some

regardless of age to the population of the age group

Measurement Study conducted by the World Bank

asset indicators may reflect household wealth better

that officially corresponds to primary education.

Development Economics Research Group. These

in some countries than in others—or reflect differ-

• Average years of schooling are the years of for-

large-scale household sample surveys, conducted

ent degrees of wealth in different countries. Taking

mal schooling received, on average, by youths and

periodically in developing countries, collect infor-

such information into account and creating country

adults ages 15–19. • Primary completion rate is

mation on a large number of health, nutrition, and

specific asset indexes with country-specific choices

the total number of students regardless of age in the

population measures as well as on respondents’

of asset indicators might produce a more effective

last grade of primary school, minus the number of

social, demographic, and economic characteristics

and accurate index for each country. The asset index

repeaters in that grade, divided by the total number

using detailed questionnaires. The data presented

used in the table does not have this flexibility.

of children of official graduation age. These data dif-

here draw on responses to individual and household

The analysis was carried out for around 80 coun-

fer from those in table 2.14 because the source is

tries. The table only shows the estimates for the

different. • Children out of school are the number

Typically, those surveys collect basic information

poorest and richest quintiles, gender, and latest

of children in the official primary school ages who

on educational attainment and enrollment levels

data; the full set of estimates for all indicators, other

are not attending primary or secondary education,

from every household member ages 5 or 6 and older

subgroups including urban and rural areas, and older

expressed as a percentage of children of the official

as part of the household’s socioeconomic charac-

data are available in the country reports (see Data

primary school ages. Children in the official primary

teristics. The surveys are not intended for the col-

sources). The data in the table differ from data for

school age, who are attending pre-primary education,

lection of detailed education data. As a result, the

similar indicators in preceding tables either because

are considered out-of-school. These data differ from

education section of the surveys does not replace

the indicator refers to a period a few years preceding

those in table 2.12 because the source is different.

education flows, nor are as detailed as, for instance,

the survey date or because the indicator definition

the health section for the case of the DHS and MICS.

or methodology is different. Findings should be used

Still, the education data are very useful for providing

with caution because of measurement error inherent

micro-level information on education that cannot be

in the use of survey data.

questionnaires.

obtained from administrative data, such as information on children not attending school. Socioeconomic status as displayed in the table is based on a household’s assets, including ownership of consumer items, features of the household’s dwelling, and other characteristics related to wealth. Each household asset on which information was collected was assigned a weight generated through principalcomponent analysis which was then used to create break-points defining wealth quintiles, expressed as quintiles of individuals in the population.

Data sources Data on education gaps by income and gender are

The selection of the asset index for defining socio-

from an analysis of Demographic and Health Sur-

economic status was based on pragmatic rather than

veys by Macro International, Multiple Indicators

conceptual considerations: Demographic and Health

Cluster surveys by UNICEF, and Living Standards

Surveys do not collect consumption data but do have

Measurement Study by World Bank, and these

detailed information on households’ ownership of

sources are analyzed by the EdStats team of the

consumer goods and access to a variety of goods

World Bank Human Development Network Edu-

and services. Like income or consumption, the asset

cation using ADePT Education. Country reports,

index defines disparities primarily in economic terms.

further updates, and ADePT Education software

It therefore excludes other possibilities of disparities

are available at www.worldbank.org/education/

among groups, such as those based on gender, edu-

edstats/.

cation, ethnic background, or other facets of social

2011 World Development Indicators

93


2.16

Health systems Health expenditure

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

94

Health workers

Outpatient visits

PPP $

per 1,000 people Nurses and Physicians midwives

per 1,000 people

per capita

2008

2008

2004–09a

2004–09a

2000–09a

47b 281 272 148 c 695 143 4,180d 5,201 240 17 351 5,243 32 75 506 530 721 482 37 19c 43 65c 4,445 20 49 762 146 .. 317 13 81 618 61 1,230 672 1,469 6,133 261 216 97 217 10 c 1,074 14 4,481 4,966 264 c 27 258 4,720 55 3,110 184 21 17c 40 121

57b 569 437 183c 1,062 224 3,365d 4,150 395 44 688 4,096 61 187 937 1,053 875 974 82 50 c 118 117c 3,867 32 86 1,088 265 .. 517 23 108 1,059 88 1,553 495 1,830 3,814 465 466 261 410 18 c 1,325 37 3,299 3,851 384 c 75 433 3,922 114 3,010 308 58 32c 69 248

0.2 1.1 1.2 0.1 3.2 3.7 3.0 4.7 3.8 0.3 5.1 3.0 0.1 .. 1.4 0.3 1.7 3.6 0.1 0.0 0.2 0.2 1.9 0.1 0.0 1.3 1.4 .. 1.4 0.1 0.1 .. 0.1 2.7 6.4 3.6 3.4 .. .. 2.8 1.6 0.1 3.4 0.0 2.7 3.5 0.3 0.0 4.5 3.5 0.1 6.0 .. 0.1 0.0 .. ..

Total % of GDP

Public % of total

Out of pocket % of total

External resources % of total

$

2008

2008

2008

2008

7.4b 6.8 5.4 3.3c 8.4 3.8 8.5d 10.5 4.3 3.3 5.6 11.1 4.1 4.4 10.3 7.6 8.4 7.1 5.9 13.0 c 5.7 5.3c 9.8 4.3 6.4 7.5 4.3 .. 5.9 7.3 2.7 9.4 5.4 7.8 12.0 7.1 9.9 5.7 5.3 4.8 6.0 3.1c 6.1 4.3 8.8 11.2 2.6c 5.5 8.7 10.5 7.8 10.1 6.5 5.5 6.0 c 6.1 6.3

21.5b 39.4 86.1 85.0 c 62.6 44.5 65.4 d 73.7 19.3 31.4 72.2 66.8 51.7 63.1 58.2 78.2 44.0 57.8 59.1 40.0 c 23.8 22.7c 69.5 39.3 50.6 44.0 47.3 .. 83.9 54.2 49.9 66.9 16.9 84.9 95.5 80.1 80.1 37.1 42.3 42.2 59.6 44.9c 77.8 51.9 70.7 75.9 43.7c 48.1 30.9 74.6 50.0 60.9 35.7 13.6 26.0 c 22.1 58.6

77.7b 58.6 13.2 15.0 c 22.2 51.8 17.9d 15.1 73.3 66.2 19.9 20.5 44.7 30.1 41.8 7.2 31.9 36.5 38.1 38.1c 64.4 73.5c 15.5 57.7 47.8 36.5 43.5 .. 7.9 39.2 50.1 29.3 75.6 14.5 4.1 15.7 13.6 41.8 50.4 56.5 35.8 55.1c 19.7 38.5 18.5 7.4 56.3c 25.1 66.5 11.8 39.4 37.0 57.4 85.9 40.7c 47.4 34.5

17.3b 2.1 0.0 3.0 c 0.0 10.4 0.0 d 0.0 0.6 5.8 0.2 0.0 17.7 9.1 1.3 4.2 0.0 0.0 29.2 34.5c 17.1 5.5c 0.0 31.5 5.3 0.0 0.2 .. 0.1 18.8 4.7 0.1 5.9 0.0 0.2 0.0 0.0 1.6 1.1 0.6 3.5 60.8 c 1.5 40.7 0.0 0.0 2.3c 38.0 10.5 0.0 14.0 0.0 1.8 10.1 77.3c 34.7 10.4

2011 World Development Indicators

Hospital beds

Per capita

2004–09a

0.5 4.0 2.0 1.4 0.5 4.9 9.6 7.8 8.4 0.3 12.6 0.3 0.8 .. 4.7 2.8 6.5 4.7 0.7 0.2 0.8 1.6 10.1 0.4 0.3 .. 1.4 .. .. 0.5 0.8 .. 0.5 5.6 8.6 8.6 14.5 .. .. 3.5 0.4 0.6 6.8 0.2 15.5 8.9 5.0 0.6 3.9 10.8 1.1 3.7 .. 0.0 0.6 .. ..

0.4 2.9 1.7 0.8 4.0 4.1 3.8 7.7 7.9 0.4 11.2 6.6 0.5 1.1 3.0 1.8 2.4 6.5 0.9 0.7 0.1 1.5 3.4 1.2 0.4 2.1 4.1 .. 1.0 0.8 1.6 1.2 0.4 5.5 5.9 7.2 3.6 1.0 1.5 1.7 1.1 1.2 5.7 0.2 6.5 7.1 1.3 1.1 3.3 8.2 0.9 4.8 0.6 0.3 1.0 1.3 0.8

.. 1.5 .. .. .. 2.8 6.2 6.7 4.6 .. 13.2 7.0 .. .. 3.3 .. .. .. .. .. .. .. 6.3 .. .. .. .. .. .. .. .. .. .. 6.4 .. 15.0 4.1 .. .. .. .. .. 6.9 .. 4.3 6.9 .. .. 2.2 7.0 .. .. .. .. .. .. ..


Health expenditure

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

Health workers

Total % of GDP

Public % of total

Out of pocket % of total

External resources % of total

$

2008

2008

2008

2008

2008

68.9 32.4 54.4 42.4 70.2c,e 76.9 58.4 76.3 50.4 80.5 62.7f 58.5 36.3 .. 53.9 .. 76.3 48.4 17.6 60.0 48.3 63.3 33.0 70.3c 68.3 68.2 70.2 59.4 44.1 47.1 61.4 c 34.8 46.9 50.6g 81.4 36.3 75.2 8.8 54.6 37.7 75.3 80.2 54.6 57.7 36.7c 78.6 76.4 32.3 69.3 80.1 40.1 59.4 34.7 67.4 67.4 .. 79.8

23.9 50.3 32.1 55.6 29.8 c,e 14.4 30.5 20.2 35.2 14.5 30.8f 41.0 49.2 .. 35.0 .. 21.7 45.0 62.6 38.7 40.7 25.3 35.0 29.7c 26.8 31.6 20.2 11.6 40.9 52.6 38.6c 57.8 49.3 48.3g 14.6 55.0 7.0 87.1 8.1 45.1 5.7 14.0 41.8 40.7 60.4 c 15.5 14.4 53.7 25.7 8.2 52.8 30.6 53.9 22.4 22.1 .. 14.8

0.0 1.6 1.7 0.0 8.2c,e 0.0 0.0 0.0 1.5 0.0 1.8f 0.2 26.8 .. 0.0 .. 0.0 12.6 16.1 0.0 4.8 19.3 47.0 0.1c 1.1 1.8 16.1 87.0 0.0 22.2 27.4 c 2.0 0.0 4.7g 7.5 0.2 80.8 10.7 21.4 11.0 0.0 0.0 10.3 26.3 4.6c 0.0 0.0 4.8 0.2 20.6 1.6 0.8 1.5 0.0 0.0 .. 0.0

7.2 4.2 2.3 5.5 3.2c,e 8.7 7.6 8.7 4.8 8.3 9.4f 3.9 4.2 .. 6.5 .. 2.0 5.7 4.0 6.6 8.5 7.6 11.9 3.0 c 6.6 6.8 4.4 6.5 4.3 5.6 2.6c 5.5 5.9 10.7g 3.8 5.3 4.7 2.0 6.9 6.0 9.9 9.7 9.4 5.9 5.2c 8.5 2.1 2.6 7.2 3.2 6.0 4.5 3.7 7.0 10.6 .. 2.1

Hospital beds

Outpatient visits

PPP $

per 1,000 people Nurses and Physicians midwives

per 1,000 people

per capita

2008

2004–09a

2004–09a

2000–09a

Per capita

1,119 45 51 254 109c,e 5,253 2,093 3,343 256 3,190 325f 333 33 .. 1,245 .. 990 54 34 979 604 60 26 458 c 931 328 22 18 353 39 27c 402 588 181g 73 149 21 10 284 24 5,243 2,917 105 21 73c 8,019 454 22 493 39 161 200 68 971 2,434 .. 1,775

2.16

1,506 122 91 613 107c,e 3,796 2,093 2,836 364 2,817 496f 444 66 .. 1,806 .. 932 123 84 1,206 1,009 119 46 502c 1,318 738 46 50 621 65 54 c 681 837 320 g 131 231 39 23 440 66 4,233 2,655 251 40 113c 5,207 593 62 924 70 281 381 129 1,271 2,578 .. 1,689

3.1 0.6 0.3 0.9 0.7 3.2 3.6 4.2 .. 2.1 2.5 3.8 0.1 .. 2.0 .. 1.8 2.3 0.3 3.0 3.5 .. 0.0 1.9 3.7 2.5 0.2 0.0 0.9 0.0 0.1 1.1 2.9 2.7 2.8 0.6 0.0 0.5 0.4 0.2 3.9 2.4 .. 0.0 0.4 4.1 1.9 0.8 .. 0.1 .. 0.9 1.2 2.1 3.8 .. 2.8

2004–09a

6.3 1.3 2.0 1.6 1.4 15.7 6.2 6.5 .. 4.1 4.0 7.8 .. .. 5.3 .. 4.6 5.7 1.0 4.8 2.2 .. 0.3 6.8 7.3 4.3 0.3 0.3 2.7 0.3 0.7 3.7 4.0 6.7 3.5 0.9 0.3 0.8 2.8 0.5 0.2 10.9 .. 0.1 1.6 14.8 4.1 0.6 .. 0.5 .. 1.3 6.0 5.7 5.3 .. 7.4

7.0 0.9 .. 1.4 1.3 5.2 5.8 3.7 1.7 13.8 1.8 7.6 1.4 .. 12.3 .. 1.8 5.1 1.2 6.4 3.5 1.3 0.7 3.7 6.8 4.6 0.3 1.1 1.8 0.6 0.4 3.3 1.6 6.1 5.9 1.1 0.8 0.6 2.7 5.0 4.3 .. 0.9 0.3 0.5 3.5 1.9 0.6 2.2 .. 1.3 1.5 0.5 6.6 3.4 .. 1.4

2011 World Development Indicators

12.9 .. .. .. .. .. 7.1 6.1 .. 14.4 .. 6.7 .. .. .. .. .. 3.6 .. 5.5 .. .. .. .. 6.6 6.0 0.5 .. .. .. .. .. 2.5 6.0 .. .. .. .. .. .. 5.4 4.4 .. .. .. .. .. .. .. .. .. .. .. 6.1 3.9 .. ..

95

people

Health systems


2.16

Health systems Health expenditure

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

Health workers

Total % of GDP

Public % of total

Out of pocket % of total

External resources % of total

$

2008

2008

2008

2008

2008

78.9 64.3 47.8 68.2 55.4 62.5 6.5 34.1 67.1 68.6 .. 39.7 69.7 43.7 33.1 60.8 78.1 59.1 38.8 27.7 71.9 74.3 73.4 24.5 48.9 54.1 73.1 49.1c 17.4 55.9 67.1 82.6 47.8 63.1 50.5 44.9 38.5 .. 30.1 62.0 .. 60.5 w 41.9 51.4 45.5 55.4 51.2 48.2 65.4 50.3 53.0 32.6 42.9 62.2 73.7

17.6 29.1 23.2 17.0 35.0 35.5 83.7 62.1 24.9 12.8 .. 17.9 20.7 48.8 64.1 16.6 15.6 30.8 61.2 68.8 18.3 17.5 6.8 63.5 41.8 40.0 17.4 50.9c 54.0 40.9 21.7 11.1 12.7 12.1 48.5 49.3 55.5 .. 68.9 28.3 .. 17.9 w 47.9 37.0 45.0 31.4 37.2 42.2 28.2 34.3 44.3 51.5 36.5 14.2 14.2

0.0 0.0 42.6 0.0 11.4 0.4 17.0 0.0 0.0 0.0 .. 1.2 0.0 1.8 4.3 11.1 0.0 0.0 0.5 10.5 59.2 0.3 21.8 14.1 0.3 0.5 0.0 0.3c 27.9 0.4 0.0 0.0 0.0 0.2 2.4 0.0 1.7 .. 4.6 38.4 .. 0.2 w 24.2 0.6 1.1 0.2 1.1 0.5 0.3 0.2 1.0 2.4 9.3 0.0 0.0

5.4 4.8 9.4 3.6 5.7 10.0 13.3 3.3 8.0 8.3 .. 8.2 9.0 4.1 6.9 5.8 9.4 10.7 3.1 5.0 4.5 4.1 13.8 5.9 4.7 6.2 6.1 2.2c 8.4 6.8 2.5 8.7 15.2 7.8 4.9 5.4 7.2 .. 5.3 5.9 .. 9.4 w 5.3 5.3 4.3 6.3 5.3 4.2 5.4 7.2 5.0 4.0 6.1 11.0 10.0

Outpatient visits

PPP $

per 1,000 people Nurses and Physicians midwives

per 1,000 people

per capita

2008

2004–09a

2004–09a

2000–09a

Per capita

517 568 45 676 62 499 47 1,404 1,395 2,238 .. 459 3,132 83 97 141 4,858 6,988 71 37 22 164 71 38 908 248 623 82c 44 268 1,427 3,771 7,164 725 51 597 76 .. 67 68 .. 857 w 25 186 95 531 163 125 448 542 176 40 74 4,455 4,132

Hospital beds

840 985 102 831 102 867 104 1,833 1,849 2,420 .. 843 2,941 187 147 287 3,622 4,815 123 95 57 328 126 70 1,237 501 845 146c 112 502 868 3,222 7,164 982 134 683 201 .. 137 80 .. 901 w 55 314 188 792 277 231 738 733 350 106 132 4,136 3,458

1.9 4.3 0.0 0.9 0.1 2.0 0.0 1.8 3.0 2.5 0.0 0.8 3.7 0.5 0.3 0.2 3.6 4.1 1.5 2.0 0.0 0.3 0.1 0.1 1.2 1.2 1.6 2.4 0.1 3.1 1.9 2.7 2.7 3.7 2.6 .. 1.2 .. 0.3 0.1 0.2 1.4 w 0.2 1.3 1.0 2.3 1.1 1.2 3.2 2.2 1.5 0.6 0.2 2.9 3.8

2004–09a

4.2 8.5 0.5 2.1 0.4 4.4 0.2 5.9 6.6 8.2 0.1 4.1 5.2 1.9 0.8 6.3 11.6 16.0 1.9 5.0 0.2 1.5 2.2 0.3 3.6 3.3 1.9 4.5 1.3 8.5 4.1 10.3 9.8 5.6 10.8 .. 1.0 .. 0.7 0.7 0.7 3.0 w 0.5 2.3 1.7 4.8 2.0 1.7 6.8 4.8 2.2 1.1 1.0 7.9 7.5

6.5 9.7 1.6 2.2 0.3 5.4 0.4 3.1 6.6 4.7 .. 2.8 3.2 3.1 0.7 2.1 .. 5.3 1.5 5.4 1.1 .. .. 0.9 2.5 2.1 2.4 4.1 0.4 8.7 1.9 3.4 3.1 2.9 4.8 1.3 2.9 .. 0.7 1.9 3.0 2.9 w .. 2.4 1.9 4.5 2.3 4.0 7.3 .. 1.6 0.9 .. 6.1 5.8

5.6 9.0 .. .. .. .. .. .. 12.5 6.6 .. .. 9.5 .. .. .. 2.8 .. .. 8.3 .. .. .. .. .. .. 3.1 3.7 .. 10.8 .. 4.9 9.0 .. 8.7 .. .. .. .. .. .. .. w .. .. .. .. .. .. 7.6 .. .. .. .. 8.5 6.8

a. Data are for the most recent year available. b. GDP includes measures of illicit activities such as opium production. Government expenditures include external assistance (external budget). c. Derived from incomplete data. d. Excludes expenditure in residential facilities for care of the aged. e. Excludes northern Iraq. f. Includes contributions from the United Nations Relief and Works Agency for Palestine. g. Excludes Transdniestria.

96

2011 World Development Indicators


About the data

2.16

Definitions

Health systems—the combined arrangements of

this reason, data for this indicator should not be

• Total health expenditure is the sum of public and

institutions and actions whose primary purpose

compared across editions.

private health expenditure. It covers the provision

is to promote, restore, or maintain health (World

External resources for health are disbursements

of health services (preventive and curative), family

Health Organization, World Health Report 2000)—

to recipient countries as reported by donors, lagged

planning and nutrition activities, and emergency aid

are increasingly being recognized as key to com-

one year to account for the delay between disburse-

for health but excludes provision of water and sani-

bating disease and improving the health status of

ment and expenditure. Disbursement data are not

tation. • Public health expenditure is recurrent and

populations. The World Bank’s Healthy Develop-

available before 2002, so commitments are used.

capital spending from central and local governments,

ment: Strategy for Health, Nutrition, and Population

Except where a reliable full national health account

external borrowing and grants (including donations

Results emphasizes the need to strengthen health

study has been done, most data are from the Organ-

from international agencies and nongovernmental

systems, which are weak in many countries, in order

isation for Economic Co-operation and Development

organizations), and social (or compulsory) health

to increase the effectiveness of programs aimed at

Development Assistance Committee’s Creditor

insurance funds. • Out-of-pocket health expendi-

reducing specific diseases and further reduce mor-

Reporting System database, which compiles data

ture is the percentage of total expenditure that is

bidity and mortality (World Bank 2007). To evaluate

from government expenditure accounts, government

direct household outlays, including gratuities and

health systems, the World Health Organization (WHO)

records on external assistance, routine surveys of

in-kind payments, for health practitioners and phar-

has recommended that key components—such as

external financing assistance, and special services.

maceutical suppliers, therapeutic appliances, and

financing, service delivery, workforce, governance,

Because of the variety of sources, care should be

other goods and services whose primary intent is

and information—be monitored using several key

taken in interpreting the data.

to restore or enhance health. • External resources

indicators (WHO 2008b). The data in the table are