Page 1

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.


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

2011 World Development Indicators

v


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.

2011 World Development Indicators

vii


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

viii

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

2011 World Development Indicators

ix


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

x

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

2011 World Development Indicators

xi


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/.

xii

2011 World Development Indicators


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

2011 World Development Indicators

xiii


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/.

xiv

2011 World Development Indicators


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/.

2011 World Development Indicators

xv


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

xvi

2011 World Development Indicators


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/.

2011 World Development Indicators

xvii


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/.

xviii

2011 World Development Indicators


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

Private and nongovernmental organizations 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/.

2011 World Development Indicators

xix


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/.

xx

2011 World Development Indicators


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/.

2011 World Development Indicators

xxi


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

xxii

2011 World Development Indicators


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

xxiii


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

2

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.

4

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.

8

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

In countries where the fiscal year spans two cal-

for health are funds or services in kind that are pro-

a subset of the first four indicators. Monitoring

endar years, expenditure data have been allocated

vided by entities not part of the country in ques-

health systems allows the effectiveness, efficiency,

to the later year (for example, 2008 data cover fis-

tion. The resources may come from international

and equity of different health system models to be

cal year 2007/08). Many low-income countries use

organizations, other countries through bilateral

compared. Health system data also help identify

Demographic and Health Surveys or Multiple Indica-

arrangements, or foreign nongovernmental orga-

weaknesses and strengths and areas that need

tor Cluster Surveys funded by donors to obtain health

nizations and are part of public and private health

investment, such as additional health facilities,

system data.

expenditure. • Health expenditure per capita is

better health information systems, or better trained human resources.

Data on health worker (physicians, nurses, and

total health expenditure divided by population in

midwives) density show the availability of medical

U.S. dollars and in international dollars converted

Health expenditure data are broken down into pub-

personnel. The WHO estimates that at least 2.5

using 2005 purchasing power parity (PPP) rates from

lic and private expenditures. In general, low-income

physicians, nurses, and midwives per 1,000 people

the World Bank’s International Comparison Project.

economies have a higher share of private health

are needed to provide adequate coverage with pri-

• Physicians include generalist and specialist medi-

expenditure than do middle- and high-income coun-

mary care interventions associated with achieving

cal practitioners. • Nurses and midwives include pro-

tries, and out-of-pocket expenditure (direct payments

the Millennium Development Goals (WHO, World

fessional nurses and midwives, auxiliary nurses and

by households to providers) makes up the largest

Health Report 2006). The WHO compiles data from

midwives, enrolled nurses and midwives, and other

proportion of private expenditure. High out-of-pocket

household and labor force surveys, censuses, and

personnel, such as dental nurses and primary care

expenditures may discourage people from access-

administrative records. Data comparability is limited

nurses. • Hospital beds are inpatient beds for both

ing preventive or curative care and can impoverish

by differences in definitions and training of medical

acute and chronic care available in public, private,

households that cannot afford needed care. Health

personnel varies. In addition, human resources tend

general, and specialized hospitals and rehabilita-

financing data are collected through national health

to be concentrated in urban areas, so that average

tion centers. • Outpatient visits per capita are the

accounts, which systematically, comprehensively,

densities do not provide a full picture of health per-

number of visits to health care facilities per capita,

and consistently monitoring health system resource

sonnel available to the entire population.

including repeat visits.

flows. To establish a national health account, coun-

Availability and use of health services, shown by

tries must define the boundaries of the health system

hospital beds per 1,000 people and outpatient visits

and classify health expenditure information along

per capita, reflect both demand- and supply-side fac-

several dimensions, including sources of financing,

tors. In the absence of a consistent definition these

Data sources

providers of health services, functional use of health

are crude indicators of the extent of physical, finan-

Data on health expenditures are from the WHO’s

expenditures, and beneficiaries of expenditures. The

cial, and other barriers to health care.

National Health Account database (latest updates

accounting system can then provide an accurate pic-

are available at www.who.int/nha/), supple-

ture of resource envelopes and financial flows and

mented by country data. Data on physicians, and

allow analysis of the equity and efficiency of financing

nurses and midwives, are from WHO’s Global Atlas

to inform policy.

of the Health Workforce. For the latest updates and

This year’s table presents out-of-pocket expendi-

metadata, see http://apps.who.int/globalatlas/.

ture as a percentage of total health expenditure; pre-

Data on hospital beds and outpatient visits are

vious editions presented out-of-pocket expenditure

from the WHO, supplemented by country data.

as a percentage of private health expenditure. For

2011 World Development Indicators

97

people

Health systems


2.17

Health information Year last national health account completed

Number of national health accounts completed

Year of last health survey

Year of last census

Completeness

%

1995–2009

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

98

2009 2003 1997 2009 2007 2008 2008 2008 2008 2007 2009 2002 2006 2007 2008 2007 1995 2009

2008 2007 2003 2009 2005 2003 2008

2008 2007 2008 2008 2008 2009 2008 2008 2008 2008 2004 2009 2008 2002 2008

2006 2005

2011 World Development Indicators

0 3 3 0 1 6 13 14 0 13 0 6 4 13 6 3 7 6 6 1 0 1 15 0 0 5 13 0 9 7 1 2 2 0 0 14 13 8 7 3 14 0 10 4 14 14 0 3 9 14 1 0 14 0 0 1 3

2001–11

2003 2008/09 2006 2006/07 2005

2006 2007 2005 2006 2008 2006 2000 1996 2006 2005 2005 2006 2006 2004

2005 2010 2009 1993 2006 2006 1993 2007 2004 2008 2008 2002 2005

2000 2005/06 2005 2008 2002 2005 2010 2005/06 2005/06

2001 2008 2010 2001 2006 2001 2009 2001 2009 2001 2002 2001 2001 2010 2001 2006 2008 2008 2005 2006 2003 2009 2002 2010 2006 2006 2007 2000 2001 2002 2001 2001 2010 2010 2006 2007 2000 2007 2010 2006 2003 2003 2002 2010 2001 2002 2009 2003 2001

Birth registration 2004–09a

.. 99 99 .. 91 96 .. .. 94 10 .. .. 60 .. 100 72 91 .. 64 60 66 70 .. 49 9 99 .. .. 90 31 81 .. 55 .. 100 .. .. 78 85 99 99 .. .. 7 .. .. .. 55 92 .. 71 .. .. 43 39 81 94

Infant death reporting 2004–09a

.. 28 .. .. 100 38 100 90 24 .. 55 100 .. .. 54 35 48 79 29 .. 0 .. 100 .. .. 100 .. 66 52 .. .. 90 .. 75 99 84 97 1 58 47 36 .. 68 .. 84 95 .. .. 54 96 95 78 62 .. .. .. 100

Total death reporting 2004–09a

.. 76 90 .. 100 100 96 100 100 .. 96 97 .. 30 92 47 87 100 88 .. 100 .. 98 .. .. 100 99 91 71 .. .. 98 .. 100 100 94 97 54 86 97 75 .. 94 88 98 100 .. .. 83 99 .. 95 93 .. .. .. 99


Year last national health account completed

Number of national health accounts completed

Year of last health survey

Year of last census

2.17

people

Health information

Completeness

%

1995–2009

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

2008 2004 2008 2007 2008 2006 2008 2000 2007 2008 2007 2006 2008

2009 2007 2005 2008 2008 2007 2006 2006 2004 2004 2009 2003 2006 2006 2007 2008 2005 2008 2008 2008 2006 2005 2008 1998 2006 2003 2000 2008 2005 2007 2008 2007

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

2001–11

2005/06 2007 2000 2006

2005 2009 2006 2008/09 2010

1996 2005/06 2006 2000 2009/10 2009 2000 2005 2008/09 2006 2006 2007 1995 2005 2005 2006 2009 2000 2006/07 2006

2006/07 2006 2008 1995 2006/07 2003 1996 2004 2008 2008

1996

2001 2001 2010 2006 2006 2009 2001 2001 2010 2004 2009 2009 2008 2005 2010 2009 2005 2000 2006 2008 2006 2001 2002 2008 2010 2009 2000 2000 2010 2004 2010 2004 2007 2001 2001 2001 2006 2005 2001 2006 2001 2010 2010 2000 2002 2007 2010 2002 2001 2010 2010

Birth registration 2004–09a

.. 41 53 .. 95 .. .. .. 89 .. .. 99 60 .. .. .. .. 94 72 .. .. 26 4 .. .. 94 75 .. .. 53 56 .. .. .. 98 .. 31 .. 67 35 .. .. .. 32 30 .. .. 27 .. .. .. 93 .. .. .. .. ..

Infant death reporting 2004–09a

Total death reporting 2004–09a

84 .. .. .. 100 75 90 99 76 88 .. 95 37 43 80 .. 100 78 .. 79 .. .. .. .. 68 87 .. .. 62 .. .. 80 89 62 60 .. .. 56 .. .. 84 100 66 .. .. 97 100 85 70 .. 34 41 39 95 85 100 95

2011 World Development Indicators

97 .. .. 99 100 99 99 98 68 98 76 82 39 91 92 .. 100 95 .. 96 72 .. .. .. 95 99 .. 75 100 .. .. 97 100 89 96 .. .. 55 100 .. 97 98 68 .. 1 100 97 84 88 .. 71 70 100 100 95 95 77

99


2.17

Health information Year last national health account completed

Number of national health accounts completed

Year of last health survey

Year of last census

Completeness

%

1995–2009

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

2006 2007 2006 2005 2009 2006 2008 2008 1998 2008 2006 2008 2008 2009 2008 2006 2007 2002 2000 2005 2005 2006 2008 2008 2009 2008

2007 2007 2006 2001

a. Data are for the most recent year available.

100

2011 World Development Indicators

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

2001–11

1999 1996 2007 2007 2008/09 2005/06 2008 2005

2006 2003 2006/07 2006 2006/07

2006 2005 2007/08 2005/06 2009 2006 2006 2006 2003 2006 2009/10 2007

2009 2006 2000 2006 2006 2006 2007 2005/06

2002 2010 2002 2010 2002 2002 2004 2010 2001 2002 2001 2001 2001 2008 2007 2010 2004 2010 2002 2010 2010 2010 2000 2004 2000 2002 2001 2010 2001 2010 2004 2001 2009 2007 2004 2000 2002

Birth registration 2004–09a

.. .. 82 .. 55 99 51 .. .. .. 3 92 .. 97 33 30 .. .. 95 88 22 99 .. 78 96 .. 94 96 21 100 .. .. .. .. 100 .. 88 96 22 14 74

Infant death reporting 2004–09a

76 80 .. 94 .. 38 .. 93 93 72 .. 81 99 63 .. .. 83 100 .. 19 .. 86 .. .. 50 .. 56 .. .. 90 75 100 100 78 .. 62 72 31 .. .. ..

Total death reporting 2004–09a

96 95 .. 100 .. 90 .. 72 98 96 .. 81 100 91 .. .. 99 99 100 69 .. 65 .. .. 94 98 100 .. .. 100 100 95 100 100 .. 84 83 66 15 .. ..


About the data

2.17

Definitions

According to the World Health Organization (WHO),

the institutional frameworks needed to ensure data

• Year last national health account completed is the

health information systems are crucial for moni-

quality, including independence, transparency, and

latest year for which the health expenditure data are

toring and evaluating health systems, which are

access. Benchmarks include the availability of inde-

available using the national health account approach.

increasingly recognized as important for combating

pendent coordination mechanisms and micro- and

• Number of national health accounts completed is

disease and improving health status. Health informa-

meta-data (WHO 2008a).

the number of national health accounts completed

tion systems underpin decisionmaking through four

The indicators in the table are all related to data

between 1995 and 2008. • Year of last health sur-

data functions: generation, compilation, analysis and

generation, including the years the last national

vey is the latest year the national survey that collects

synthesis, and communication and use. The health

health account, last health survey, and latest popu-

health information was conducted. • Year of last cen-

information system collects data from the health sec-

lation census were completed. Frequency of data col-

sus is the latest year a census was conducted in the

tor and other relevant sectors; analyzes the data and

lection, a benchmark of data generation, is shown

last 10 years. • Completeness of birth registration is

ensures their overall quality, relevance, and timeli-

as the number of years for which a national health

the percentage of children under age 5 whose births

ness; and converts data into information for health-

account was completed between 1995 and 2009.

were registered at the time of the survey. The numera-

related decisionmaking (WHO 2008b).

National health account data may be collected

tor of completeness of birth registration includes chil-

Numerous indicators have been proposed to

using different approaches such as Organisation for

dren whose birth certificate was seen by the interviewer

assess a country’s health information system.

Economic Co-operation and Development (OECD)

or whose mother or caretaker says the birth has been

They can be grouped into two broad types: indica-

System of Health Accounts, WHO National Health

registered. • Completeness of infant death reporting

tors related to data generation using core sources

Account producers guide approach, local national

is the number of infant deaths reported by national

and methods (health surveys, civil registration, cen-

health accounting methods, or Pan American

statistical authorities to the United Nations Statistics

suses, facility reporting, health system resource

Health O ­ rganization/WHO satellite health accounts

Division’s Demographic Yearbook divided by the number

tracking) and indicators related to capacity for

approach.

of infant deaths estimated by the United Nations Popu-

data synthesis, analysis, and validation. Indicators

Indicators related to data generation include com-

lation Division. • Completeness of total death report-

related to data generation reflect a country’s capac-

pleteness of birth registration, infant death report-

ing is the number of total deaths from civil registration

ity to collect relevant data at suitable intervals using

ing, and total death reporting.

system reported by national statistical authorities to

the most appropriate data sources. Benchmarks

the United Nations Statistics Division’s Demographic

include periodicity, timeliness, contents, and avail-

Yearbook divided by the number of total deaths esti-

ability. Indicators related to capacity for synthesis,

mated by the United Nations Population Division.

analysis, and validation measure the dimensions of Data sources Data on year last national health account completed South Asia has the highest number of unregistered births

2.17a

and number of national health accounts completed were compiled by staff in the World Health Organiza-

Number of unregistered births, 2007 (millions) Latin America and Caribbean 1.3

CEE/CIS 0.4

Middle East and North Africa 2.4

tion’s Health Financing Department and the World Bank’s Health, Nutrition, and Population Unit using data on the health expenditures reported by the WHO and OECD and consultation with colleagues from countries and other international organizations. Data on year of last health survey are from Macro

East Asia and Pacific, excluding China 3.5

International and the United Nations Children’s Eastern and Southern Africa 9.7

Fund (UNICEF). Data on year of last census are from South Asia 24.3

West and Central Africa 9.8

United Nations Statistics Division’s 2011 World Population and Housing Census Program (http:// unstats.un.org/unsd/demographic/sources/census/2010_PHC/default.htm.) Data on completeness of birth registration are compiled by UNICEF in State of the World’s Children 2010 based mostly on household surveys and ministry of health data. Data used

Too many people, especially poor, are never counted. They are born, live, and die uncounted and ignored. Around 50 million, or 40 percent of children born in 2007, have not been registered. Source: United Nations Children’s Fund Childinfo.

to calculate completeness of infant death reporting and total death reporting are from the United Nations Statistics Division’s Population and Vital Statistics Report and the United Nations Population Division’s World Population Prospects: The 2008 Revision.

2011 World Development Indicators

101

people

Health information


2.18

Disease prevention coverage and quality Access to an improved water source

Afghanistan Albania Algeria Angola Argentina Armenia Australia Austria Azerbaijan 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

102

Access to improved sanitation facilities

% of population 1990 2008

% of population 1990 2008

.. .. 94 36 94 .. 100 100 70 78 100 100 56 70 .. 93 88 100 41 70 35 50 100 58 38 90 67 .. 88 45 .. 93 76 .. 82 100 100 88 72 90 74 43 98 17 100 100 .. 74 81 100 54 96 82 52 .. 47 72

.. .. 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

48 97 83 50 97 96 100 100 80 80 100 100 75 86 99 95 97 100 76 72 61 74 100 67 50 96 89 .. 92 46 71 97 80 99 94 100 100 86 94 99 87 61 98 38 100 100 87 92 98 100 82 100 94 71 61 63 86

2011 World Development Indicators

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

Child immunization rate

% of children ages 12–23 monthsb Measles DTP3 2009 2009

76 97 88 77 99 96 94 83 67 89 99 94 72 86 93 94 99 96 75 91 92 74 93 62 23 96 94 .. 95 76 76 81 67 98 96 98 84 79 66 95 95 95 95 75 98 90 55 96 83 96 93 99 92 51 76 59 99

83 98 93 73 94 93 92 83 73 94 96 99 83 85 90 96 99 94 82 92 94 80 80 54 23 97 97 .. 92 77 91 86 81 96 96 99 89 82 75 97 91 99 95 79 99 99 45 98 88 93 94 99 92 57 68 59 98

Children with acute respiratory infection taken to health provider

Children with diarrhea who received oral rehydration and continuous feeding

Children sleeping under treated netsa

Children with fever receiving antimalarial drugs

% of children under age 5 with ARI 2004–09c

% of children under age 5 with diarrhea 2004–09c

% of children under age 5 2004–09c

% of children under age 5 with fever 2004–09c

.. 70 53 .. .. 36 .. .. 33 37 90 .. 36 51 91 .. 50 .. 39 38 48 35 .. 32 12 .. .. .. 62 42 48 .. 35 .. .. .. .. 70 .. 73 67 .. .. 19 .. .. .. 69 74 .. 51 .. .. 42 57 31 56

.. 63 24 .. .. 59 .. .. 31 68 54 .. 42 .. 53 .. .. .. 42 23 50 22 .. 47 27 .. .. .. 39 42 39 .. 45 .. .. .. .. 55 .. 19 .. .. .. 15 .. .. .. 38 37 .. 45 .. .. 38 25 43 49

.. .. .. 17.7 .. .. .. .. .. .. .. .. 20.1 .. .. .. .. .. 9.6 8.3 4.2 13.1 .. 15.1 .. .. .. .. .. 5.8 6.1 .. 3.0 .. .. .. .. .. .. .. .. .. .. 33.1 .. .. .. 49.0 .. .. 28.2 .. .. 4.5 39.0 .. ..

.. .. .. 29.3 .. .. .. .. .. .. .. .. 54.0 .. .. .. .. .. 48.0 30.0 0.2 57.8 .. 57.0 53.0 .. .. .. .. 29.8 48.0 .. 36.0 .. .. .. .. 0.6 .. .. .. .. .. 9.5 .. .. .. 62.6 .. .. 43.0 .. .. 43.5 45.7 5.1 0.5

Tuberculosis

Treatment success rate

Case detection rate

% of new registered cases

% of new estimated cases

2008

2009

88 91 90 70 44 73 80 47 56 91 71 76 89 84 92 65 71 85 76 90 95 76 78 71 54   72 94 68 76 87 76 89 76 58 88 68 41 75 78 89 91 76 60 84 72 .. 53 84 73 68 86 .. 83 78 70 82   85

48 94 100 75 67 70 89 48 75 44 140 88 47 64 91 62 86 86 14 25 60 70 93 60 26 130 75 89 70 46 69 93 27 76 120 70 79 60 51 63 92 58 89 50 110 77 42 47 100 91 31 92 33 26 59 60 68


Access to an improved water source

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

Access to improved sanitation facilities

% of population 1990 2008

% of population 1990 2008

96 72 71 91 81 100 100 100 93 100 97 96 43 100 .. .. 99 .. .. 99 100 61 58 54 .. .. 31 40 88 29 30 99 85 .. 58 74 36 57 64 76 100 100 74 35 47 100 80 86 84 41 52 75 84 100 96 .. 100

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

100 88 80 .. 79 100 100 100 94 100 96 95 59 100 98 .. 99 90 57 99 100 85 68 .. .. 100 41 80 100 56 49 99 94 90 76 81 47 71 92 88 100 100 85 48 58 100 88 90 93 40 86 82 91 100 99 .. 100

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

Child immunization rate

% of children ages 12–23 monthsb Measles DTP3 2009 2009

99 71 82 99 69 89 96 91 88 94 95 99 74 98 93 .. 97 99 59 96 53 85 64 98 96 96 64 92 95 71 59 99 95 90 94 98 77 87 76 79 96 89 99 73 41 92 97 80 85 58 91 91 88 98 95 .. 99

99 66 82 99 65 93 93 96 90 98 98 98 75 93 94 .. 98 95 57 95 74 83 64 98 98 96 78 93 95 74 64 99 89 85 95 99 76 90 83 82 97 92 98 70 42 92 98 85 84 64 92 93 87 99 96 .. 99

Children with acute respiratory infection taken to health provider

Children with diarrhea who received oral rehydration and continuous feeding

Children sleeping under treated netsa

Children with fever receiving antimalarial drugs

% of children under age 5 with ARI 2004–09c

% of children under age 5 with diarrhea 2004–09c

% of children under age 5 2004–09c

.. 69 66 .. 82 .. .. .. 75 .. 75 71 56 93 .. .. .. 62 32 .. .. 66 62 .. .. 93 42 52 .. 38 45 .. .. 60 63 38 65 .. 72 43 .. .. .. 47 45 .. .. 69 .. 63 .. 72 50 .. .. .. ..

.. 33 54 .. 64 .. .. .. 39 .. 32 48 .. .. .. .. .. 22 49 .. .. 53 47 .. .. 45 47 27 .. 38 32 .. .. 48 47 46 47 .. 48 37 .. .. .. 34 25 .. .. 37 .. .. .. 60 60 .. .. .. ..

.. .. 3.3 .. .. .. .. .. .. .. .. .. 46.1 .. .. .. .. .. 40.5 .. .. .. 26.4 .. .. .. 45.8 24.7 .. 27.1 2.1 .. .. .. .. .. 22.8 .. 10.5 .. .. .. .. 42.8 5.5 .. .. .. .. .. .. .. .. .. .. .. ..

2.18 Tuberculosis

Treatment success rate

Case detection rate

% of children under age 5 with fever 2004–09c

% of new registered cases

% of new estimated cases

2008

2009

.. 8.2 0.8 .. .. .. .. .. .. .. .. .. 23.2 .. .. .. .. .. 8.2 .. .. .. 67.2 .. .. .. 19.7 24.9 .. 31.7 20.7 .. .. .. .. .. 36.7 .. 9.8 0.1 .. .. .. 33.0 33.2 .. .. 3.3 .. .. .. .. 0.0 .. .. .. ..

53 87 91 83 88 76 81 .. 64 48 84 64 85 89 84 .. 80 84 93 33 77 73 79 69 82 89 81 87 78 82 68 87 85 62 87 85 84 85 82 89 85 73 89 81 78 84 98 90 79 64 81 82 88 74 87 63 73

82 67 67 74 48 89 89 66 78 89 100 80 85 93 89 .. 89 66 68 94 78 93 52 82 81 98 44 49 76 16 24 41 99 68 75 93 46 64 76 73 89 89 90 36 19 91 89 63 94 73 78 97 57 84 86 89 89

2011 World Development Indicators

103

people

Disease prevention coverage and quality


2.18

Disease prevention coverage and quality Access to an improved water source

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

Access to improved sanitation facilities

% of population 1990 2008

% of population 1990 2008

.. 93 68 89 61 .. .. 100 .. 100 .. 83 100 67 65 .. 100 100 85 .. 55 91 .. 49 88 81 85 .. 43 .. 100 100 99 96 90 90 58 .. .. 49 78 77 w 55 74 70 89 72 69 91 85 87 74 49 99 100

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

.. 96 65 .. 69 99 49 100 100 99 30 91 100 90 57 69 100 100 89 70 54 98 69 60 94 94 99 .. 67 98 100 100 99 100 87 .. 94 91 62 60 82 87 w 64 88 86 95 84 88 95 93 87 87 60 100 100

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

Child immunization rate

% of children ages 12–23 monthsb Measles DTP3 2009 2009

97 98 92 98 79 95 71 95 99 95 24 62 98 96 82 95 97 90 81 89 91 98 70 84 94 98 97 99 68 94 92 86 92 94 95 83 97 .. 58 85 76 82 w 78 82 79 93 81 91 96 93 87 75 68 93 94

97 98 97 98 86 95 75 97 99 96 31 69 96 97 84 95 98 95 80 93 85 99 72 89 90 99 96 96 64 90 92 93 95 95 98 83 96 .. 66 81 73 82 w 80 81 79 93 81 93 95 92 88 72 70 95 96

Children with acute respiratory infection taken to health provider

Children with diarrhea who received oral rehydration and continuous feeding

Children sleeping under treated netsa

Children with fever receiving antimalarial drugs

% of children under age 5 with ARI 2004–09c

% of children under age 5 with diarrhea 2004–09c

% of children under age 5 2004–09c

% of children under age 5 with fever 2004–09c

.. .. 55.7 .. 29.2 .. 25.8 .. .. .. 11.4 .. .. 2.9 27.6 0.6 .. .. .. 1.3 63.8 d .. .. 38.4 .. .. .. .. 9.7 .. .. .. .. .. .. .. 5.0 .. .. 41.1 17.3 .. w .. .. .. .. .. .. .. .. .. .. 20.2 .. ..

.. .. 5.6 .. 9.1 .. 30.1 .. .. .. 7.9 .. .. 0.3 54.2 0.6 .. .. .. 1.9 59.1d .. .. 47.7 .. .. .. .. 61.3 .. .. .. .. .. .. .. 2.6 .. .. 43.3 23.6 .. w 30.6 .. .. .. .. .. .. .. .. 7.2 34.4 .. ..

.. .. 28 .. 47 93 46 .. .. .. 13 .. .. 58 90 73 .. .. 77 64 59 84 71 23 74 59 .. 83 73 .. .. .. .. .. 68 .. 83 .. .. 68 25 .. w 45 .. .. .. .. .. .. .. .. 67 .. .. ..

.. .. 24 .. 43 71 57 .. .. .. 7 .. .. 67 56 22 .. .. 34 22 53 46 .. 22 32 62 22 25 39 .. .. .. .. .. 28 .. 65 .. 48 56 35 .. w 39 .. .. .. .. .. .. .. .. 37 33 .. ..

Tuberculosis

Treatment success rate

Case detection rate

% of new registered cases

% of new estimated cases

2008

2009

37 57 87 61 84 86 86 81 93 80 81 76 .. 85 81 68 87 .. 86 82 88 82 85 79 67 86 92 83 70 62 68 78 85 83 81 83 92 94 85 88 74 86 w 86 .. 89 72 .. 92 67 77 86 88 79 69 ..

79 84 19 89 31 89 31 89 89 80 42 74 89 70 52 67 89 89 88 44 77 69 84 10 89 86 77 92 44 78 61 94 89 96 50 68 54 4 67 80 46 62 w 50 .. 63 79 .. 70 78 73 78 64 48 87 ..

a. For malaria prevention only. b. Refers to children who were immunized before 12 months or in some cases at any time before the survey (12–23 months). c. Data are for the most recent year available. d. Data are for 2010.

104

2011 World Development Indicators


About the data

2.18

Definitions

People’s health is influenced by the environment

the use of oral rehydration therapy have changed over

• Access to an improved water source refers to

in which they live. Lack of clean water and basic

time based on scientific progress, so it is difficult

people with access to at least 20 liters of water

sanitation is the main reason diseases transmitted

to accurately compare use rates across countries.

a person a day from an improved source, such as

by feces are so common in developing countries.

Until the current recommended method for home

piped water into a dwelling, public tap, tubewell,

Access to drinking water from an improved source

management of diarrhea is adopted and applied in

protected dug well, and rainwater collection, within

and access to improved sanitation do not ensure

all countries, the data should be used with caution.

1 kilometer of the dwelling. • Access to improved

safety or adequacy, as these characteristics are

Also, the prevalence of diarrhea may vary by season.

sanitation facilities refers to people with at least

not tested at the time of the surveys. But improved

Since country surveys are administered at different

adequate access to excreta disposal facilities that

drinking water technologies and improved sanitation

times, data comparability is further affected.

can effectively prevent human, animal, and insect

facilities are more likely than those characterized

Malaria is endemic to the poorest countries in the

contact with excreta. Improved facilities range from

as unimproved to provide safe drinking water and to

world, mainly in tropical and subtropical regions of

protected pit latrines to flush toilets. • Child immu-

prevent contact with human excreta. The data are

Africa, Asia, and the Americas. Insecticide-treated

nization rate refers to children ages 12–23 months

derived by the Joint Monitoring Programme (JMP)

nets, properly used and maintained, are one of the

who, before 12 months or at any time before the

of the World Health Organization (WHO) and United

most important malaria-preventive strategies to limit

survey, had received one dose of measles vaccine

Nations Children’s Fund (UNICEF) based on national

human-mosquito contact.

and three doses of diphtheria, pertussis (whooping

censuses and nationally representative household

Prompt and effective treatment of malaria is a criti-

cough), and tetanus (DTP3) vaccine. • Children with

surveys. The coverage rates for water and sanita-

cal element of malaria control. It is vital that suffer-

acute respiratory infection (ARI) taken to health

tion are based on information from service users

ers, especially children under age 5, start treatment

provider are children under age 5 with ARI in the

on the facilities their households actually use rather

within 24 hours of the onset of symptoms, to pre-

two weeks before the survey who were taken to an

than on information from service providers, which

vent progression—often rapid—to severe malaria

appropriate health provider. • Children with diarrhea

may include nonfunctioning systems. While the esti-

and death.

who received oral rehydration and continuous feed-

mates are based on use, the JMP reports use as

Data on the success rate of tuberculosis treatment

ing are children under age 5 with diarrhea in the two

access, because access is the term used in the Mil-

are provided for countries that have submitted data

weeks before the survey who received either oral

lennium Development Goal target for drinking water

to the WHO. The treatment success rate for tuber-

rehydration therapy or increased fluids, with con-

and sanitation.

culosis provides a useful indicator of the quality of

tinuous feeding. • Children sleeping under treated

Governments in developing countries usually

health services. A low rate suggests that infectious

nets are children under age 5 who slept under an

finance immunization against measles and diphthe-

patients may not be receiving adequate treatment.

insecticide-treated net to prevent malaria the night

ria, pertussis (whooping cough), and tetanus (DTP)

An important complement to the tuberculosis treat-

before the survey. • Children with fever receiving

as part of the basic public health package. In many

ment success rate is the case detection rate, which

antimalarial drugs are children under age 5 who were

developing countries lack of precise information on

indicates whether there is adequate coverage by the

ill with fever in the two weeks before the survey and

the size of the cohort of one-year-old children makes

recommended case detection and treatment strat-

received any appropriate (locally defined) antimalarial

immunization coverage difficult to estimate from

egy. Uncertainty bounds for the case detection rate,

drugs. • Tuberculosis treatment success rate is new

program statistics. The data shown here are based

not shown in the table, are available at http://data.

registered infectious tuberculosis cases that were

on an assessment of national immunization cover-

worldbank.org or the original source.

cured or that completed a full course of treatment as

age rates by the WHO and UNICEF. The assessment

Editions before 2010 included the tuberculosis

a percentage of smear-positive cases registered for

considered both administrative data from service

detection rates by DOTS, the internationally rec-

treatment outcome evaluation. • Tuberculosis case

providers and household survey data on children’s

ommended strategy for tuberculosis control. This

detection rate is newly identified tuberculosis cases

immunization histories. Based on the data available,

year’s edition, like last year’s, shows the tuberculo-

(including relapses) as a percentage of estimated

consideration of potential biases, and contributions

sis detection rate for all detection methods, so data

incident cases (case detection, all forms).

of local experts, the most likely true level of immuni-

on the case detection rate cannot be compared with

zation coverage was determined for each year. Acute

data in previous editions.

respiratory infection continues to be a leading cause

For indicators that are from household surveys, the

of death among young children, killing about 2 million

year in the table refers to the survey year. For more

children under age 5 in developing countries each

information, consult the original sources.

year. Data are drawn mostly from household health surveys in which mothers report on number of episodes and treatment for acute respiratory infection. Since 1990 diarrhea-related deaths among children have declined tremendously. Most diarrhea-related deaths are due to dehydration, and many of these deaths can be prevented with the use of oral rehydration salts at home. However, recommendations for

Data sources Data on access to water and sanitation are from the WHO and UNICEF’s Progress on Sanitation and Drinking Water (2010). Data on immunization are from WHO and UNICEF estimates (www.who.int/ immunization_monitoring). Data on children with ARI, with diarrhea, sleeping under treated nets, and receiving antimalarial drugs are from UNICEF’s State of the World’s Children 2010, Childinfo, and Demographic and Health Surveys by Macro International. Data on tuberculosis are from the WHO’s Global Tuberculosis Control: A Short Update to the 2010 Report.

2011 World Development Indicators

105

people

Disease prevention coverage and quality


2.19

Reproductive health Total fertility rate

births per woman 1990 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

106

8.0 2.9 4.7 7.2 3.0 2.5 1.9 1.5 2.7 4.4 1.9 1.6 6.7 4.9 1.7 4.7 2.8 1.8 6.8 6.6 5.8 5.9 1.8 5.8 6.7 2.6 2.3 1.3 3.1 7.1 5.4 3.2 6.3 1.6 1.8 1.9 1.7 3.5 3.7 4.6 4.0 6.2 2.0 7.1 1.8 1.8 5.2 6.1 2.2 1.5 5.6 1.4 5.6 6.7 5.9 5.4 5.1

6.5 1.9 2.3 5.6 2.2 1.7 1.9 1.4 2.3 2.3 1.5 1.9 5.4 3.4 1.2 2.8 1.8 1.6 5.8 4.5 2.9 4.5 1.6 4.7 6.1 1.9 1.8 1.0 2.4 5.9 4.3 1.9 4.5 1.5 1.5 1.5 1.8 2.6 2.5 2.8 2.3 4.5 1.6 5.2 1.9 2.0 3.2 5.0 1.6 1.4 3.9 1.5 4.0 5.3 5.7 3.4 3.2

2011 World Development Indicators

Adolescent Unmet Contraceptive Pregnant fertility women need for prevalence rate receiving contraception rate prenatal care births per 1,000 women ages 15–19 2009

117 14 7 121 56 35 14 12 33 68 20 7 108 76 15 50 74 40 125 18 37 122 12 96 155 59 10 6 72 191 106 67 122 14 46 10 6 107 82 37 81 62 20 94 11 6 85 87 44 7 61 8 104 147 125 45 90

% of married women ages 15–49 2004–09a

.. .. 11 .. .. 13 .. .. 23 17 .. .. 30 .. 23 .. .. .. 31 .. 25 3 .. .. 21 .. .. .. 6 24 16 .. 29 .. 8 .. .. 11 .. 9 .. .. .. 34 .. .. .. .. .. .. 35 .. .. 21 25 38 17

any method % of married women ages 15–49 2004–09a

% 2004–09a

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

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

Births attended by skilled health staff

Maternal mortality ratio

% of total 1990 2004–09a

National estimates Modeled estimates 2004–09a 1990 2008

Lifetime risk of maternal death

per 100,000 live births

.. .. 77 .. 96 .. 100 .. .. .. .. .. .. 43 97 77 72 .. .. .. .. 58 .. .. .. .. 50 .. 82 .. .. 98 .. 100 .. .. .. 93 .. 37 52 .. .. .. .. .. .. 44 .. .. 40 .. .. 31 .. 23 45

24 99 95 47 95 100 .. .. 88 24 100 .. 74 71 100 95 97 100 54 34 71b 63 100 44 14 100 99 100 96 74 83 99 57 100 b 100 100 .. 98 98 79 96 .. 100 6 .. .. .. 57 98 100 57 .. 51 46 39 26 67

.. 21 .. .. 40 27 .. .. 26 348 3 .. 397 310 3 198 75 6 307 615 461 669 .. 543 1,099 18 34 .. 76 549 781 27 543 13b 47 6 .. 159 60 55 59 .. 7 673 .. .. .. .. 14 .. 451 .. 133 980 405 630 ..

1,700 48 250 1,000 72 51 10 10 64 870 37 7 790 510 18 83 120 24 770 1,200 690 680 6 880 1,300 56 110 .. 140 900 460 35 690 8 63 15 7 220 230 220 200 930 48 990 7 13 260 750 58 13 630 6 140 1,200 1,200 670 210

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

Probability 1 woman in: 2008

11 1,700 340 29 600 1,900 7,400 14,300 1,200 110 5,100 10,900 43 150 9,300 180 860 5,800 28 25 110 35 5,600 27 14 2,000 1,500 .. 460 24 39 1,100 44 5,200 1,400 8,500 10,900 320 270 380 350 72 5,300 40 7,600 6,600 110 49 1,300 11,100 66 31,800 210 26 18 93 240


Total fertility rate

births per woman 1990 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

1.8 4.0 3.1 4.8 6.0 2.1 2.8 1.3 2.9 1.5 5.5 2.7 6.0 2.4 1.6 3.9 3.5 3.7 6.0 2.0 3.1 4.9 6.5 4.8 2.0 2.1 6.3 7.0 3.7 6.7 5.9 2.3 3.4 2.4 4.2 4.0 6.2 3.4 5.2 5.2 1.6 2.2 4.8 7.9 6.6 1.9 6.6 6.1 3.0 4.8 4.5 3.8 4.3 2.0 1.4 2.2 4.4

1.3 2.7 2.1 1.8 3.9 2.1 3.0 1.4 2.4 1.4 3.4 2.6 4.9 1.9 1.3 2.3 2.2 2.8 3.4 1.3 1.8 3.3 5.8 2.6 1.5 1.4 4.6 5.5 2.5 6.5 4.4 1.5 2.1 1.5 2.0 2.3 5.0 2.3 3.3 2.8 1.8 2.1 2.7 7.1 5.6 2.0 3.0 3.9 2.5 4.0 3.0 2.5 3.0 1.4 1.3 1.7 2.4

Adolescent Unmet Contraceptive Pregnant women fertility need for prevalence receiving rate contraception rate prenatal care births per 1,000 women ages 15–19 2009

19 64 37 17 80 15 14 5 75 5 24 29 101 0 6 .. 13 32 34 14 16 69 136 3 20 21 127 127 12 155 82 41 63 33 15 19 139 18 67 91 4 21 111 152 118 8 10 42 80 50 69 52 43 13 15 50 15

% of married women ages 15–49 2004–09a

.. 13 9 .. .. .. .. .. .. .. 11 .. .. .. .. .. .. 1 .. .. .. 31 36 .. .. 34 24 28 .. 31 25 .. .. 7 14 10 .. .. 7 25 .. .. 8 16 .. .. .. 25 .. .. .. 8 22 .. .. .. ..

any method % of married women ages 15–49 2004–09a

% 2004–09a

.. 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 .. ..

.. 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 .. .. .. ..

2.19

Births attended by skilled health staff

Maternal mortality ratio

% of total 1990 2004–09a

National estimates Modeled estimates 2004–09a 1990 2008

Lifetime risk of maternal death

per 100,000 live births

.. .. 32 .. 54 .. .. .. 79 100 87 .. 50 .. 98 .. .. .. .. .. .. .. .. .. .. .. 57 55 .. .. 40 91 .. .. .. 31 .. .. 68 7 .. .. .. 15 33 100 .. 19 .. .. 66 80 .. .. 98 .. ..

100 53 75 97 80 .. .. .. 95 100 99 100 44 .. .. .. .. 98 20 100 98 62 46 .. 100 100 44 54 99 49 61 99 93 100 99 63 55 64 81 19 .. .. 74 33 39 .. 99 39 92 53 82 83 62 100 .. 100 ..

17 254 228 25 84 .. .. .. .. .. 19 37 488 77 .. .. .. 55 405 8 .. 762 994 .. 9 4 498 807 29 464 686 .. 63 38 81 132 .. 316 449 281 .. .. 77 648 545 .. 17 276 60 733 118 .. 162 5 .. .. ..

23 570 620 150 93 6 12 10 66 12 110 78 380 270 18 .. 10 77 1,200 57 52 370 1,100 100 34 16 710 910 56 1,200 780 72 93 62 130 270 1,000 420 180 870 10 18 190 1,400 1,100 9 49 490 86 340 130 250 180 17 15 29 15

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

2011 World Development Indicators

Probability 1 woman in: 2008

5,500 140 190 1,500 300 17,800 5,100 15,200 450 12,200 510 950 38 230 4,700 .. 4,500 450 49 3,600 2,000 62 20 540 5,800 7,300 45 36 1,200 22 41 1,600 500 2,000 730 360 37 180 160 80 7,100 3,800 300 16 23 7,600 1,600 93 520 94 310 370 320 13,300 9,800 3,000 4,400

107

people

Reproductive health


2.19

Reproductive health Total fertility rate

births per woman 1990 2009

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

1.8 1.9 6.8 5.8 6.7 1.8 5.5 1.9 2.1 1.5 6.6 3.7 1.3 2.5 6.0 5.7 2.1 1.6 5.5 5.2 6.2 2.1 5.3 6.3 2.4 3.5 3.1 4.3 7.1 1.8 4.4 1.8 2.1 2.5 4.1 3.4 3.7 6.4 8.1 6.5 5.2 3.3 w 5.6 3.3 3.4 3.0 3.6 2.6 2.3 3.2 4.9 4.3 6.3 1.8 1.5

1.4 1.6 5.3 3.0 4.7 1.4 5.2 1.2 1.4 1.5 6.4 2.5 1.4 2.3 4.1 3.5 1.9 1.5 3.1 3.4 5.5 1.8 6.4 4.2 1.6 2.1 2.1 2.4 6.3 1.5 1.9 2.0 2.1 2.0 2.7 2.5 2.0 4.9 5.1 5.7 3.4 2.5 w 4.2 2.4 2.5 2.0 2.7 1.9 1.8 2.2 2.7 2.8 5.1 1.7 1.6

Adolescent Unmet Contraceptive Pregnant fertility women need for prevalence rate receiving contraception rate prenatal care births per 1,000 women ages 15–19 2009

% of married women ages 15–49 2004–09a

29 24 35 25 97 21 124 4 20 5 69 56 12 29 53 78 7 5 55 27 128 36 52 62 34 7 36 18 142 27 15 22 33 60 13 89 16 73 64 133 61 50 w 97 46 45 49 54 17 27 71 34 63 112 18 8

.. .. 38 .. 32 29 .. .. .. .. 26 .. .. .. 6 24 .. .. 11 24 22 .. .. 41 27 .. 18 .. 41 10 .. .. .. .. 8 .. .. .. 24 27 13 .. w 25 .. .. .. .. .. .. .. .. 15 24 .. ..

a. Data are for the most recent year available. b. Data are for 2010.

108

2011 World Development Indicators

any method % of married women ages 15–49 2004–09a

70 80 36 24 12 41 8 .. .. .. 15 .. 66 68 8 51 .. .. 58 37 26 77 22b 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 .. ..

Births attended by skilled health staff

Maternal mortality ratio

% of total 1990 2004–09a

National estimates Modeled estimates 2004–09a 1990 2008

Probability 1 woman in: 2008

14 32b 750 14 401 6 857 .. 4 26 1,044 .. .. 39 1,107 589 .. .. .. 38 578 12 .. .. .. .. 29 15 435 16 .. .. 13 34 21 61 75 .. .. 591 555

2,700 1,900 35 1,300 46 7,500 21 10,000 13,300 4,100 14 100 11,400 1,100 32 75 11,400 7,600 610 430 23 1,200 44 67 1,100 860 1,900 500 35 3,000 4,200 4,700 2,100 1,700 1,400 540 850 .. 91 38 42 140 w 39 190 160 570 120 580 1,700 480 380 110 31 3,900 10,100

Lifetime risk of maternal death

per 100,000 live births % 2004–09a

94 .. .. .. 96 26 .. .. 94 .. 98 .. 87 .. .. .. .. .. .. 100 26 .. .. .. .. .. 99 .. 64 69 85 .. .. .. .. .. 84 .. 80 .. 76 53 98 .. .. .. 84 31 96 .. 96 69 95 .. 99 .. 94 38 99 .. .. .. .. .. .. 99 96 .. 99 .. .. .. 91 .. 99 .. 47 16 94 51 93 70 82 w 50 w 67 .. 85 46  83 41 95 .. 82 46 91 48 .. .. 95 72 83 47 70 32 71 .. .. .. .. ..

99 100 52 96 52 99 42 100 100 100 33 .. .. 99 49 69 .. 100 93 88 43 97 .. 62 98 95 95 100 42 99 .. .. .. 99 100 .. 88 99 36 47 60 65 w 41 71  66 96 64 89 97 89 80 47 44 .. ..

 

170 27 74 39 1,100 540 41 24 750 410 13 8 1,300 970 6 9 15 6 11 18 1,100 1,200 230 410 7 6 91 39 830 750 260 420 7 5 8 10 120 46 120 64 880 790 50 48 650 370 650 350 86 55 130 60 68 23 91 77 670 430 49 26 28 10 10 12 12 24 39 27 53 30 84 68 170 56 .. .. 540 210 390 470 390 790 400 w 260 w 850 580  350 200 400 230 120 82 440 290 200 89 69 32 140 86 210 88 610 290 870 650 15 15 11 7


2.19

About the data Reproductive health is a state of physical and mental

estimates of maternal mortality that it produces per-

using contraception. • Contraceptive prevalence rate

well-being in relation to the reproductive system and its

tain to 12 years or so before the survey, making them

is the percentage of women married or in union ages

functions and processes. Means of achieving reproduc-

unsuitable for monitoring recent changes or observ-

15–49 who are practicing, or whose sexual partners

tive health include education and services during preg-

ing the impact of interventions. In addition, measure-

are practicing, any form of contraception. • Pregnant

nancy and childbirth, safe and effective contraception,

ment of maternal mortality is subject to many types of

women receiving prenatal care are the percentage of

and prevention and treatment of sexually transmitted

errors. Even in high-income countries with vital regis-

women attended at least once during pregnancy by

diseases. Complications of pregnancy and childbirth

tration systems, misclassification of maternal deaths

skilled health personnel for reasons related to preg-

are the leading cause of death and disability among

has been found to lead to serious underestimation.

nancy. • Births attended by skilled health staff are the

women of reproductive age in developing countries.

The national estimates of maternal mortality ratios

percentage of deliveries attended by personnel trained

Total and adolescent fertility rates are based on data

in the table are based on national surveys, vital regis-

to give the necessary care to women during pregnancy,

on registered live births from vital registration systems

tration records, and surveillance data or are derived

labor, and postpartum; to conduct deliveries on their

or, in the absence of such systems, from censuses

from community and hospital records. The modeled

own; and to care for newborns. • Maternal mortality

or sample surveys. The estimated rates are generally

estimates are based on an exercise by the World

ratio is the number of women who die from pregnancy-

considered reliable measures of fertility in the recent

Health Organization (WHO), United Nations Children’s

related causes during pregnancy and childbirth per

past. Where no empirical information on age-specific

Fund (UNICEF), United Nations Population Fund

100,000 live births. • Lifetime risk of maternal death

fertility rates is available, a model is used to estimate

(UNFPA), and World Bank. This year’s estimates of

refers to the probability that a 15-year-old girl will

the share of births to adolescents. For countries with-

maternal mortality include country-level time-series

eventually die from a maternal cause if throughout her

out vital registration systems fertility rates are gener-

data for the first time. For countries with complete

lifetime she experiences the risks of maternal death

ally based on extrapolations from trends observed in

vital registration systems with good attribution of

and the overall level of fertility and mortality that are

censuses or surveys from earlier years.

cause of death, the data are used to directly estimate

observed for a given population. Data are presented

More couples in developing countries want to limit

maternal mortality. For countries without complete

as 1 in the number of women who are likely to die from

or postpone childbearing but are not using effective

registration data but with other types of data and for

a maternal cause.

contraception. These couples have an unmet need for

countries with no empirical national data, maternal

contraception. Common reasons are lack of knowledge

mortality is estimated with a multilevel regression

about contraceptive methods and concerns about pos-

model using available national-level maternal mortal-

sible side effects. This indicator excludes women not

ity data and socioeconomic information, including

Data on total fertility are compiled from the United

exposed to the risk of unintended pregnancy because

fertility, birth attendants, and GDP. The methodol-

Nations Population Division’s World Population

of menopause, infertility, or postpartum anovulation.

ogy of this year’s interagency estimates differs from

Prospects: The 2008 Revision, census reports

Data sources

Contraceptive prevalence reflects all methods—

previous years’, so the data should not be compared

and other statistical publications from national

ineffective traditional methods as well as highly effec-

with data in previous editions. For further information

statistical offices, household surveys conducted

tive modern methods. Contraceptive prevalence rates

on methodology, see the original source.

by national agencies, Macro International, and the

are obtained mainly from household surveys, includ-

Neither set of ratios can be assumed to provide an

U.S. Centers for Disease Control and Prevention,

ing Demographic and Health Surveys, Multiple Indicator

exact estimate of maternal mortality for any of the

Eurostat’s Demographic Statistics, and the U.S.

Cluster Surveys, and contraceptive prevalence surveys

countries in the table.

Bureau of the Census International Data Base.

(see Primary data documentation for the most recent

In countries with a high risk of maternal death,

Data on adolescent fertility are from World Popu-

survey and year). Unmarried women are often excluded

many girls die before reaching reproductive age. Life-

lation Prospects: The 2008 Revision, with annual

from such surveys, which may bias the estimates.

time risk of maternal mortality refers to the prob-

data linearly interpolated by the Development

ability that a 15-year-old girl will eventually die from

Data Group. Data on women with unmet need for

a maternal cause.

contraception and contraceptive prevalence are

Good prenatal and postnatal care improves maternal health and reduces maternal and infant mortality. Indicators on use of antenatal care services, however,

For the indicators that are from household surveys,

from household surveys, including Demographic

provide no information on the content or quality of the

the year in the table refers to the survey year. For

and Health Surveys by Macro International and

services. Data on antenatal care are obtained mostly

more information, consult the original sources.

Multiple Indicator Cluster Surveys by UNICEF.

from household surveys, which ask women who have had a live birth whether and from whom they received

Data on pregnant women receiving prenatal Definitions

antenatal care. The share of births attended by skilled

care, births attended by skilled health staff, and national estimates of maternal mortality ratios are

health staff is an indicator of a health system’s ability

• Total fertility rate is the number of children that would

from UNICEF’s State of the World’s Children 2011

to provide adequate care for pregnant women.

be born to a woman if she were to live to the end of

and Childinfo and Demographic and Health Sur-

Maternal mortality ratios are generally of unknown

her childbearing years and bear children in accordance

veys by Macro International. Modeled estimates

reliability, as are many other cause-specific mortality

with current age-specific fertility rates. • Adolescent

of maternal mortality ratios and lifetime risk of

indicators. Household surveys such as Demographic

fertility rate is the number of births per 1,000 women

maternal death are from WHO, UNICEF, UNFPA

and Health Surveys attempt to measure maternal mor-

ages 15–19. • Unmet need for contraception is the

and the World Bank’s Trends in Maternal Mortal-

tality by asking respondents about survivorship of sis-

percentage of fertile, married women of reproductive

ity: 1990–2008 (2010).

ters. The main disadvantage of this method is that the

age who do not want to become pregnant and are not

2011 World Development Indicators

109

people

Reproductive health


2.20

Nutrition Prevalence of undernourishment

% of population 1990–92 2005–09

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

110

.. 10 <5 67 <5 45 <5 <5 27 38 <5 <5 20 29 8 19 11 <5 14 44 38 33 <5 44 60 7 18c .. 15 26 42 <5 15 18 6 <5 <5 28 23 <5 13 67 10 69 <5 <5 6 14 58 <5 27 <5 15 20 22 63 19

.. <5 <5 41 <5 22 <5 <5 <5 27 <5 <5 12 27 <5 25 6 10 9 62 22 21 <5 40 37 <5 10 c .. 10 69 15 <5 14 <5 <5 <5 <5 24 15 <5 9 64 <5 41 <5 <5 <5 19 <5 <5 5 <5 21 17 22 57 12

2011 World Development Indicators

Prevalence of child Prevalence Lowmalnutrition of overweight birthweight children babies

% of children under age 5 Underweight Stunting 2004–09a 2004–09a

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

59.3 27.0 15.9 .. 8.2 18.2 .. .. 26.8 43.2 4.5 .. 44.7 27.2 11.8 .. 7.1 8.8 35.1 .. 39.5 36.4 .. .. 44.8 2.0 11.7 .. 16.2 45.8 31.2 .. 40.1 0.6 .. .. .. 10.1 29.0 30.7 .. .. .. 50.7 .. .. .. 27.6 14.7 1.3 28.6 .. .. 40.0 47.7 29.7 29.9

% of children under age 5 2004–09a

4.6 25.2 12.9 .. 9.9 11.7 .. .. 13.9 1.1 9.7 .. 11.4 8.7 25.6 .. 7.3 13.6 7.7 .. 2.0 9.6 .. .. 4.4 9.5 5.9 .. 4.2 6.8 8.5 .. 9.0 8.1 .. .. .. 8.3 5.1 20.5 .. .. .. 5.1 .. .. .. 2.7 21.0 3.5 5.9 .. .. 5.1 17.0 3.9 5.8

Exclusive breast­ feeding

% of births 2004–09a

% of children under 6 months 2004–09a

.. 7 6 .. 7 7 .. .. 10 22 4 .. 15 6 5 13 8 9 16 11 9 11 .. 13 22 6 3 .. 6 10 13 7 17 5 5 .. .. 11 10 13 .. .. .. 20 .. .. .. 20 5 .. 13 .. .. 12 24 25 10

83 39 7 .. .. 33 .. .. 12 43 9 .. 43 60 18 20 40 .. 16 45 66 21 .. 23 2 85 28 .. 47 36 19 15 4 98 26 .. .. 9 40 53 31 .. .. 49 .. .. .. 41 11 .. 63 .. 50 48 16 41 30

Consumption Vitamin A of iodized supplemensalt tation

% of households 2004–09a

28 76 61 45 .. 97 .. .. 54 84 55 .. 67 89 62 .. 96 100 34 98 73 49 .. 62 56 .. 96 .. .. 79 82 .. 84 88 88 .. .. 19 .. 79 .. .. .. 20 .. .. .. 7 100 .. 32 .. 76 41 1 3 ..

Prevalence of anemia

% % of children Children Pregnant 6–59 months under age 5 women a 2009 2004–09 2004–09a

95 .. .. 28 .. .. .. .. 79 b 91 .. .. 56 45 .. 89 .. .. 100 90 98 .. .. 87 71 .. .. .. .. 89 8 .. 88 .. .. .. .. .. .. 68b 20 44 .. 84 .. .. 0 28 .. .. 90 .. 43 94 80 .. ..

38 31 43 .. 17 37 8 11 .. 58 27 9 78 52 27 .. 55 27 .. 56 62 68 8 .. 71 24 .. .. 28 71 66 .. 69 23 27 18 9 35 38 49 .. 70 23 75 11 8 44 .. 41 8 .. 12 .. 76 75 .. ..

61 34 43 57 31 .. 12 15 .. 39 26 13 75 37 35 21 29 30 .. 47 57 51 12 .. 60 28 .. .. 31 67 55 .. 55 28 39 22 12 40 38 34 .. 55 23 63 15 11 46 .. 42 12 .. 19 .. .. 58 50 21


Prevalence of undernourishment

% of population 1990–92 2005–09

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

<5 20 16 <5 .. <5 <5 <5 11 <5 <5 <5 33 21 <5 .. 20 17 31 <5 <5 15 30 <5 <5 11 21 43 <5 27 12 7 <5 5 28 6 59 47 32 21 <5 <5 50 37 16 <5 .. 25 18 .. 16 27 24 <5 <5 .. ..

<5 21 13 <5 .. <5 <5 <5 5 <5 5 <5 31 33 <5 .. 5 10 23 <5 <5 14 33 <5 <5 <5 25 28 <5 12 7 5 <5 6 26 <5 38 16 19 16 <5 <5 19 20 6 <5 .. 26 15 .. 11 15 15 <5 <5 .. ..

Prevalence of child Prevalence Lowmalnutrition of overweight birthweight children babies

% of children under age 5 Underweight Stunting 2004–09a 2004–09a

.. 43.5 17.5d .. 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 .. .. .. .. ..

.. 47.9 35.6d .. 27.5 .. .. .. 3.7 .. 8.3 17.5 35.2 43.1 .. .. 3.8 18.1 47.6 .. 16.5 45.2 39.4 21.0 .. 11.5 49.2 53.2 .. 38.5 24.2 .. 15.5 11.3 27.5 23.1 .. .. 29.6 49.3 .. .. 18.8 54.8 41.0 .. .. .. .. 43.9 .. 29.8 .. .. .. .. ..

% of children under age 5 2004–09a

.. 1.9 11.2 .. 15.0 .. .. .. 7.5 .. 6.6 14.8 5.0 .. .. .. 9.0 10.7 1.3 .. 16.7 6.8 4.2 22.4 .. 16.2 6.2 11.3 .. 4.7 2.3 .. 7.6 9.1 14.2 13.3 .. .. 4.6 0.6 .. .. 5.2 3.5 10.5 .. .. .. .. 3.4 .. 9.1 .. .. .. .. ..

Exclusive breast­ feeding

2.20

Consumption Vitamin A of iodized supplemensalt tation

% of births 2004–09a

% of children under 6 months 2004–09a

% of households 2004–09a

.. 28 11d 7 15 .. .. .. 14 .. 13 6 8 .. .. .. .. 5 11 .. .. 13 14 .. .. 6 16 13 11 19 34 .. 8 6 5 .. 15 .. 16 21 .. .. 8 27 12 .. 9 32 .. 10 9 8 21 .. .. .. ..

.. 46 15d 23 25 .. .. .. 15 .. 22 17 32 65 .. .. .. 32 26 .. .. 54 29 .. .. 16 51 57 .. 38 35 .. .. 46 57 31 37 .. 24 53 .. .. 31 10 13 .. .. 37 .. 56 22 70 34 .. .. .. ..

.. 51 62d 99 28 .. .. .. .. .. .. 92 98 40 .. .. .. 76 84 .. 92 91 .. .. .. 94 53 50 .. 79 23 .. .. 60 83 21 25 93 .. .. .. .. .. 46 .. .. .. .. .. 92 94 91 45 .. .. .. ..

people

Nutrition

Prevalence of anemia

% % of children Children Pregnant 6–59 months under age 5 women a 2009 2004–09 2004–09a

.. 66 84 .. .. .. .. .. .. .. .. .. 51 99 .. .. .. 99 88 .. .. 85 92 .. .. .. 95 95 .. 100 89 .. .. .. 95 .. 97 95 .. 95 .. .. 6 95 78 .. .. 91 .. 12 .. .. 91 .. .. .. ..

19 74 44 35 56 10 12 11 .. 11 .. .. .. .. .. .. .. .. .. 27 .. 49 .. 34 24 .. 68 73 32 .. 68 .. 24 41 21 .. .. 63 41 48 9 11 17 81 .. 6 42 .. .. 60 30 50 21 23 13 .. ..

2011 World Development Indicators

21 50 44 .. 38 15 17 15 .. 15 .. 26 .. .. 23 .. 31 34 56 25 32 25 .. 34 24 32 50 47 38 .. 53 .. 21 36 37 .. 52 50 31 42 13 18 .. 61 .. 9 .. .. .. 55 39 43 43 25 17 .. 29

111


2.20

Nutrition Prevalence of undernourishment

% of population 1990–92 2005–09

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 <5 44 <5 22 <5e 45 .. <5 <5 .. <5 <5 28 39 12 <5 <5 <5 34 28 26 39 43 11 <5 <5 9 19 <5 <5 <5 <5 5 5 10 31 10 30 35 40 17 w 38 17 19 8 19 20 7 13 7 23 31 5 5

<5 <5 34 <5 17 8e 35 .. <5 <5 .. <5 <5 19 22 18 <5 <5 <5 30 34 16 31 30 11 <5 <5 6 21 <5 <5 <5 <5 <5 11 8 11 18 31 43 30 14 w 31 13 15 6 16 11 6 9 7 22 26 5 5

Prevalence of child Prevalence Lowmalnutrition of overweight birthweight children babies

% of children under age 5 Underweight Stunting 2004–09a 2004–09a

.. .. 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 .. ..

.. .. 51.7 9.3 20.1 8.1 37.4 .. .. .. 42.1 .. .. 19.2 37.9 29.5 .. .. 28.6 33.1 44.4 15.7 .. 27.8 .. 9.0 15.6 .. 38.7 .. .. .. 3.9 13.9 19.6 15.6 30.5 11.8 .. 45.8 35.8 31.7 w 44.0 30.0 33.1 .. 33.3 19.0 .. 14.1 25.0 47.5 42.0 .. ..

% of children under age 5 2004–09a

.. .. 6.7 6.1 2.4 19.3 10.1 .. .. .. 4.7 .. .. 0.8 5.3 11.4 .. .. 18.7 6.7 4.9 8.0 .. 4.7 .. 8.8 9.1 .. 4.9 .. .. .. 8.0 9.4 12.8 6.1 3.0 11.4 .. 8.4 9.1 6.1 w 4.9 6.3 5.9 .. 6.0 6.6 .. 7.2 16.6 1.9 7.0 .. ..

% of births 2004–09a

8 6 6 .. 19 6 14 .. .. .. 11 .. .. 17 .. 9 .. .. 9 10 10 9 .. 12 19 5 11 4 14 4 .. .. .. 8 5 8 5 7 .. 11 11 15 w 15 15 17 8 15 6 7 8 10 27 14 .. ..

Exclusive breast­ feeding

% of children under 6 months 2004–09a

16 .. 88 .. 34 15 11 .. .. .. 9 .. .. 76 34 33 .. .. 29 25 50d 5 52d 48 13 6 42 11 60 18 .. .. .. 57 26 .. 17 27 .. 61 26 37 w 44 35 34 .. 37 29 .. 44 31 46 33 .. ..

Consumption Vitamin A of iodized supplemensalt tation

% of households 2004–09a

74 .. 88 .. 41 32 58 .. .. .. 1 .. .. 92 11 80 .. .. .. 62 43 47 60 25 28 .. 69 87 96 18 .. .. .. .. 53 .. 93 86 .. .. 91 71 w 62 73 71 .. 71 87 .. 89 69 55 52 .. ..

Prevalence of anemia

% % of children Children Pregnant 6–59 months under age 5 women a 2009 2004–09 2004–09a

.. .. 94 .. 97 .. 99 .. .. .. 62 39 .. .. 84 27 .. .. .. 87 94 .. 45 100 .. .. .. .. 64 .. .. .. .. .. 65 .. 99b .. 47b 91 77 .. w 86 .. .. .. .. .. .. .. .. 73 81 .. ..

40 27 56 33 70 .. 83 19 23 14 .. .. 13 .. 85 47 9 6 41 .. 72 .. .. 52 30 .. 33 .. 73 .. 28 .. .. 19 .. 33 .. .. 68 .. 58 .. w 66 .. .. 36 .. .. 30 38 48 71 .. .. 10

30 21 .. 32 58 .. 60 24 25 19 .. 22 18 .. 58 24 13 .. 39 45 58 .. .. 50 30 .. 40 30 64 27 28 15 6 27 .. 40 .. .. 58 .. 47 .. w 56 .. .. 31 .. .. 31 33 .. 49 .. 13 14

a. Data are for the most recent year available. b. Country’s vitamin A supplementation programs do not target children all the way up to 59 months of age. c. Includes Hong Kong SAR, China; Macao SAR, China; and Taiwan, China. d. Data are for 2010. e. Includes Montenegro.

112

2011 World Development Indicators


About the data

2.20

Definitions

Data on undernourishment are from the Food and

emerging evidence that low-birthweight babies are

• Prevalence of undernourishment is the percent­

Agriculture Organization (FAO) of the United Nations

more prone to noncommunicable diseases such as

age of the population whose dietary energy consump-

and measure food deprivation based on average

diabetes and cardiovascular diseases. Estimates of

tion is continuously below a minimum requirement

food available for human consumption per person,

low-birthweight infants are drawn mostly from hos-

for maintaining a healthy life and carrying out light

the level of inequality in access to food, and the

pital records and household surveys. Many births

physical activity with an acceptable minimum weight

minimum calories required for an average person.

in developing countries take place at home and are

for height. • Prevalence of child malnutrition is the

From a policy and program standpoint, however,

seldom recorded. A hospital birth may indicate higher

percentage of children under age 5 whose weight for

this measure has its limits. First, food insecurity

income and therefore better nutrition, or it could indi-

age (underweight) or height for age (stunting) is more

exists even where food availability is not a problem

cate a higher risk birth. The data should therefore be

than two standard deviations below the median for

because of inad­equate access of poor households

used with caution.

the international reference population ages 0–59

to food. Second, food insecurity is an individual

Improved breastfeeding can save an estimated 1.3

months. Height is measured by recumbent length

or household phe­nomenon, and the average food

million children a year. Breast milk alone contains

for children up to two years old and by stature while

available to each person, even corrected for possible

all the nutrients, antibodies, hormones, and antioxi-

standing for older children. Data are based on the

effects of low income, is not a good predictor of food

dants an infant needs to thrive. It protects babies

WHO child growth standards released in 2006.

insecurity among the population. And third, nutrition

from diarrhea and acute respiratory infections, stimu-

• Prevalence of over­weight children is the percent-

security is determined not only by food security but

lates their immune systems and response to vacci-

also by the quality of care of mothers and children

nation, and may confer cognitive benefits. The data

and the quality of the household’s health environ-

on breastfeeding are derived from national surveys.

ment (Smith and Haddad 2000).

Iodine deficiency is the single most important

Estimates of child malnutrition, based on preva-

cause of preventable mental retardation, and it

lence of underweight and stunting, are from national

contributes significantly to the risk of stillbirth and

survey data. The proportion of underweight children

miscarriage. Widely used and inexpensive, iodized

is the most common malnutrition indicator. Being

salt is the best source of iodine, and a global cam-

even mildly underweight increases the risk of death

paign to iodize edible salt is significantly reducing

and inhibits cognitive development in children. And

the risks. The data on iodized salt are derived from

it perpetuates the problem across generations, as

household surveys.

malnourished women are more likely to have low-

Vitamin A is essential for immune system function-

birthweight babies. Stunting, or being below median

ing. Vitamin A deficiency, a leading cause of blind-

height for age, is often used as a proxy for multi-

ness, also causes a greater risk of dying from a range

faceted deprivation and as an indicator of long-term

of childhood ailments such as measles, malaria,

changes in malnutrition. Estimates of overweight

and diarrhea. Giving vitamin A to new breastfeed-

children are also from national survey data. Over-

ing mothers helps protect their children during the

weight children have become a growing concern in

first months of life. Food fortification with vitamin A

developing countries. Research shows an associa-

is being introduced in many developing countries.

tion between childhood obesity and a high preva-

Data on anemia are compiled by the WHO based

lence of diabetes, respiratory disease, high blood

mainly on nationally representative surveys, which

pressure, and psychosocial and orthopedic disorders

measured hemoglobin in the blood. WHO’s hemoglo-

(de Onis and Blössner 2000).

bin thresholds were then used to determine anemia

age of children under age 5 whose weight for height is more than two stan­dard deviations above the median for the interna­tional reference population of the corresponding age as established by the WHO child growth stan­ d ards released in 2006. • Low-birthweight babies are the percentage of newborns weighing less than 2.5 kilograms within the first hours of life, before significant postnatal weight loss has occurred. • Exclusive breastfeeding is the percentage of children less than six months old who were fed breast milk alone (no other liquids) in the past 24 hours. • Consumption of iodized salt is the percentage of households that use edible salt fortified with iodine. • Vitamin A supplementation is the percentage of children ages 6–59 months who received at least two doses of vitamin A in the previous year. • Prevalence of anemia, children under age 5, is the percentage of children under age 5 whose hemoglobin level is less than 110 grams per liter at sea level. • Prevalence of anemia, pregnant women, is the percentage of pregnant women whose hemoglobin level is less than 110 grams per liter at sea level.

Data sources

New international growth reference standards for

status based on age, sex, and physiological status.

infants and young children were released in 2006

Children under age 5 and pregnant women have the

Data on undernourishment are from www.fao.

by the World Health Organization (WHO) to monitor

highest risk for anemia. Data should be used with

org/faostat/foodsecurity/index_en.htm. Data

children’s nutritional status. Differences in growth

caution because surveys differ in quality, coverage,

on malnutrition and overweight children are from

to age 5 are influenced more by nutrition, feeding

age group interviewed, and treatment of missing val-

the WHO’s Global Database on Child Growth and

practices, environment, and healthcare than by

ues across countries and over time.

Malnutrition (www.who.int/nutgrowthdb). Data on

genetics or ethnicity. The previously reported data

For indicators from household surveys, the year in

low-birthweight babies, breastfeeding, iodized salt

were based on the U.S. National Center for Health

the table refers to the survey year. For more informa-

consumption, and vitamin A supplementation are

Statistics–WHO growth reference. Because of the

tion, consult the original sources.

from the United Nations Children’s Fund’s State of

change in standards, the data in this edition should

the World’s Children 2011 and Childinfo. Data on

not be compared with data in editions prior to 2008.

anemia are from the WHO’s Worldwide Prevalence

Low birthweight, which is associated with maternal

of Anemia 1993­–2005 (2008c) and Integrated

malnutrition, raises the risk of infant mortality and

WHO Nutrition Global Databases.

stunts growth in infancy and childhood. There is also

2011 World Development Indicators

113

people

Nutrition


2.21

Health risk factors and future challenges Prevalence of smoking

% of adults

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

114

Male 2006

Female 2006

.. 43 26 .. 34 61 22 47 .. 43 64 30 13 34 49 .. 19 49 13 .. 55c 9 21 .. 12 42 59 .. .. 10 9 26 11 34 e 36 35 35 15 23 24 .. 15 48 8 33 36 .. 17 57 37 7 63 24 .. .. .. ..

.. 4 0 .. 24 3 19 41 .. 1 22 24 1 26 35 .. 12 38 1 .. 20 c 1 18 .. 1 31 4 .. .. 1 0 7 1 27e 28 27 30 11 5 1 .. 1 25 1 23 27 .. 1 6 26 1 39 4 .. .. .. ..

2011 World Development Indicators

Prevalence of HIVa

Incidence of Prevalence tuberculosis of diabetes

Total % of population ages 15–49

per 100,000 people

% of population ages 20–79

2009

2010

1990

2009

8.6 4.5 8.5 3.5 5.7 7.8 5.7 8.9 7.5 6.6 7.6 5.3 4.6 6.0 7.1 5.4 6.4 6.5 3.8 1.8 5.2 3.9 9.2 4.5 3.7 5.7 4.2 8.5 5.2 3.2 5.1 9.3 4.7 6.9 9.5 6.4 5.6 11.2 5.9 11.4 9.0 2.5 7.6 2.5 5.7 6.7 5.0 4.3 7.5 8.9 4.3 6.0 8.6 4.3 3.9 7.2 9.1

.. .. <0.1 0.5 0.3 <0.1 0.1 <0.1 <0.1 <0.1 <0.1 <0.1 0.2 0.1 .. 3.5 .. <0.1 3.9 3.9 0.5 0.6 0.1 3.1 1.1 <0.1 .. .. 0.2 .. 5.2 <0.1 2.4 <0.1 <0.1 <0.1 <0.1 0.4 0.3 <0.1 0.1 0.3 <0.1 .. <0.1 0.3 0.9 0.1 <0.1 0.1 0.3 0.1 0.1 1.1 0.3 1.3 1.1

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

Female % of total population with HIV

Condom use

Youth % of population ages 15–24

% of population ages 15–24

2009

Male 2009

Female 2009

Male 2004–09b

.. .. 30 60 32 <43 31 29 60 30 50 31 58 32 .. 57 .. 29 60 60 63 58 21 61 59 31 .. .. 33 .. 59 29 58 <33 31 <42 27 59 31 23 34 60 31 .. <36 32 58 58 43 18 59 31 33 59 60 60 32

.. .. 0.1 0.6 0.3 <0.1 0.1 0.3 <0.1 <0.1 <0.1 <0.1 0.3 0.1 .. 5.2 .. <0.1 0.5 1.0 0.1 1.6 0.1 1.0 1.0 0.2 .. .. 0.2 .. 1.2 0.2 0.7 <0.1 0.1 <0.1 0.1 0.3 0.2 <0.1 0.4 0.2 0.3 .. 0.1 0.2 1.4 0.9 <0.1 0.1 0.5 0.1 0.5 0.4 0.8 0.6 0.3

.. .. <0.1 1.6 0.2 <0.1  0.1 0.2 0.1 <0.1  0.1 <0.1  0.7 0.1 .. 11.8 .. <0.1  0.8 2.1 0.1 3.9 0.1 2.2 2.5 0.1 .. .. 0.1 .. 2.6 0.1 1.5 <0.1  0.1 <0.1  0.1 0.7 0.2 <0.1  0.3 0.4 0.2 .. <0.1  0.1 3.5 2.4 <0.1 <0.1 1.3 0.1 0.3 0.9 2.0 1.3 0.2

.. .. .. .. .. 68 .. .. 25 .. .. .. 39 .. .. .. .. .. .. .. 31 52 .. .. 18 .. .. .. .. 16 36 .. .. .. .. .. .. 58 .. .. .. .. .. 18 .. .. .. .. .. .. .. .. .. 35 .. 42 ..

Female 2004–09b

.. .. .. .. .. 5 .. .. 1 .. .. .. 10 .. .. .. .. .. .. .. 3 24 .. .. 2 .. .. .. 24 26 16 .. .. .. .. .. .. 19 .. .. .. .. .. 2 .. .. .. .. .. .. .. .. .. 10 .. 37 7


Prevalence of smoking

% of adults

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 2006

Female 2006

45 28 66f 24 29 34 31 34 18 42 59 43 23 58 53 .. 36 46 60 53 31 .. 10 .. 50 .. .. 17 49 13 24 34 36 45 46 27 19 40 22 30 33 22 .. .. 8 30 20 30 .. .. 33 .. 50 30 34 .. ..

35 1 5f 2 3 28 18 19 8 13 10 9 1 .. 6 .. 4 2 13 24 7 .. .. .. 22 .. .. 2 2 1 1 1 12 5 6 0 1 13 8 28 28 20 .. .. 0 30 0 3 .. .. 14 .. 11 38 15 .. ..

Prevalence of HIVa

Incidence of Prevalence tuberculosis of diabetes

per 100,000 people

% of population ages 20–79

2009

2010

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

6.4 7.8 4.8 8.0 10.2 5.2 6.5 5.9 10.6 5.0 10.1 5.8 3.5 5.3 7.9 .. 14.6 5.2 5.6 7.6 7.8 3.9 4.7 9.0 7.6 6.9 3.2 2.3 11.6 4.2 4.8 16.2 10.8 7.6 1.6 8.3 4.0 3.2 4.4 3.9 5.3 5.2 10.0 3.9 4.7 3.6 13.4 9.1 9.6 3.0 4.9 6.2 7.7 7.6 9.7 10.6 15.4

Total % of population ages 15–49 1990

2009

0.1 0.1 <0.1 <0.1 .. <0.1 <0.1 0.3 2.1 <0.1 .. <0.1 3.9 .. <0.1 .. .. <0.1 <0.1 <0.1 <0.1 0.8 0.3 .. <0.1 .. 0.2 7.2 0.1 0.4 0.2 <0.1 0.4 <0.1 <0.1 <0.1 1.2 0.2 1.6 0.2 0.1 0.1 <0.1 0.1 1.3 <0.1 <0.1 <0.1 0.2 <0.1 <0.1 0.4 <0.1 <0.1 0.1 .. <0.1

<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

Female % of total population with HIV

2.21 Condom use

Youth % of population ages 15–24

% of population ages 15–24

2009

Male 2009

Female 2009

Male 2004–09b

<33 39 30 29 .. 29 29 33 33 34 .. 60 59 .. 31 .. .. 29 42 30 31 62 61 .. <33 .. 31 59 11 62 31 29 27 42 <29 32 61 35 59 33 30 <37 31 53 59 30 <33 29 31 58 31 25 30 31 31 .. <50

<0.1 0.1 0.1 <0.1 .. 0.1 0.1 <0.1 1.0 <0.1 .. 0.1 1.8 .. <0.1 .. .. 0.1 0.1 0.2 0.1 5.4 0.3 .. <0.1 .. 0.1 3.1 0.1 0.2 0.4 0.3 0.2 0.1 <0.1 0.1 3.1 0.3 2.3 0.2 0.1 <0.1 0.1 0.2 1.2 <0.1 <0.1 0.1 0.4 0.3 0.2 0.2 <0.1 <0.1 0.3 .. <0.1

<0.1 0.1 <0.1 <0.1 .. 0.1 <0.1 <0.1 0.7 <0.1 .. 0.2 4.1 .. <0.1 .. .. 0.1 0.2 0.1 <0.1 14.2 0.7 .. <0.1 .. 0.1 6.8 <0.1 0.5 0.3 0.2 0.1 0.1 <0.1 0.1 8.6 0.3 5.8 0.1 <0.1 <0.1  0.1 0.5 2.9 <0.1  <0.1  <0.1  0.3 0.8 0.1 0.1 <0.1  <0.1  0.2 .. <0.1 

.. 15 .. .. .. .. .. .. 74 .. .. .. 64 .. .. .. .. .. .. .. .. 44 19 .. .. .. 6 32 .. 29 .. .. .. 55 .. .. .. .. 78 24 .. .. .. 14 50 .. .. .. .. .. .. .. .. .. .. .. ..

2011 World Development Indicators

Female 2004–09b

.. 6 .. .. .. .. .. .. 66 .. .. .. 40 .. .. .. .. .. .. .. .. 26 9 .. .. .. 3 9 .. 4 .. .. .. 22 .. .. .. .. 55 8 .. .. .. 1 36 .. .. .. .. .. .. .. .. .. .. .. ..

115

people

Health risk factors and future challenges


2.21

Health risk factors and future challenges Prevalence of smoking

% of adults Male 2006

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

46 60 g .. 22 13 40 .. 34 41 32 .. 27 37 27 25 21 17 32 40 .. 20 40 .. .. .. 53 48e .. 17 65 24 26 25 39 23 32 41 .. 28 17 28 39 w 28 42 43 38 40 56 58 27 28 30 14 33 37

Female 2006

24 22g .. 3 1 27 .. 5 20 21 .. 8 27 0 2 2 23 23 .. .. 2 2 .. .. .. 6 15e .. 2 24 2 24 19 29 3 27 2 .. 6 2 2 8w 4 6 3 16 6 4 22 15 2 2 2 21 25

Prevalence of HIVa

Incidence of Prevalence tuberculosis of diabetes

Total % of population ages 15–49

Female % of total population with HIV

Condom use

Youth % of population ages 15–24

per 100,000 people

% of population ages 20–79

2009

2010

1990

2009

2009

Male 2009

Female 2009

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

6.9 7.6 1.6 16.8 4.7 6.9 4.4 10.2 6.4 7.7 3.0 4.5 6.6 10.9 4.2 4.2 5.2 8.9 10.8 5.0 3.2 7.1 3.5 4.3 11.7 9.3 8.0 5.3 2.2 7.6 18.7 3.6 10.3 5.7 5.2 6.5 3.5 8.6 3.0 4.0 4.1 6.4 w 4.4 6.3 6.0 7.5 6.1 4.6 7.3 7.4 9.1 7.8 3.8 7.9 7.1

<0.1 <0.1 5.2 .. 0.2 0.1 <0.1 <0.1 <0.1 <0.1 0.1 0.7 0.4 <0.1 0.1 2.3 0.1 0.2 .. <0.1 4.8 1.0 .. 0.6 0.2 <0.1 <0.1 .. 10.2 0.1 .. 0.1 0.5 0.1 <0.1 .. <0.1 .. .. 12.7 10.1 0.3 2.0 0.2 0.2 0.3 0.3 0.1 0.1 0.4 0.1 0.1 2.4 0.2 0.2

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

30 49 61 .. 59 24 60 30 <17 <29 47 62 24 <32 58 58 31 32 .. 30 59 40 .. 59 33 <37 30 .. 58 49 .. 31 25 32 29 .. 30 .. .. 57 60 37 w 46 .. .. 36 39 .. 42 .. 28 36 58 28 27

0.1 0.2 1.3 .. 0.3 0.1 0.6 <0.1 <0.1 <0.1 0.4 4.5 0.2 <0.1 0.5 6.5 <0.1 0.2 .. <0.1 1.7 .. .. 0.9 1.0 <0.1 <0.1 .. 2.3 0.2 .. 0.2 0.3 0.3 <0.1 .. 0.1 .. .. 4.2 3.3 0.4 w 0.9 .. .. 0.5 .. 0.1 0.1 0.2 0.1 0.1 1.5 0.2 0.1

<0.1 0.3 1.9 .. 0.7 0.1 1.5 <0.1  <0.1  <0.1  0.6 13.6 0.1 <0.1  1.3 15.6 <0.1  0.1 .. <0.1  3.9 .. .. 2.2 0.7 <0.1  <0.1  .. 4.8 0.3 .. 0.1 0.2 0.2 <0.1  .. 0.1 .. .. 8.9 6.9 0.7 w 2.0 .. .. 1.2 .. 0.1 0.2 0.2 0.1 0.1  3.8 0.1 0.1

% of population ages 15–24 Male 2004–09b

.. .. 19 .. 48 .. 20 .. .. .. .. .. .. .. .. 66 .. .. .. .. 36 .. .. .. .. .. .. .. 36 64 .. .. .. .. .. .. 16 .. .. 39 52 .. w .. .. .. .. .. .. .. .. .. 15 36 .. ..

Female 2004–09b

.. .. 5 .. 5 .. 9 .. .. .. .. .. .. .. .. 44 .. .. .. .. 13 .. .. .. .. .. .. .. 13 43 .. .. .. .. .. .. 8 .. .. 17 9 .. w .. .. .. .. .. .. .. .. .. 6 19 .. ..

a. See plausible bounds in the database and original source. b. Data are for the most recent year available. c. Data are for 2010. d. Includes Hong Kong SAR, China. e. Data are for 2008. f. Data are for 2007. g. Data are for 2009.

116

2011 World Development Indicators


About the data

2.21

Definitions

The limited availability of data on health status is a

many developing countries most new infections

• Prevalence of smoking is the adjusted and age-

major constraint in assessing the health situation in

occur in young adults, with young women especially

standardized prevalence estimate of smoking among

developing countries. Surveillance data are lacking

vulnerable.

adults. The age range varies but in most countries is

for many major public health concerns. Estimates

Data on HIV are from the Joint United Nations

18 and older or 15 and older. • Incidence of tuber-

of prevalence and incidence are available for some

Programme on HIV/AIDS (UNAIDS) Global Report:

culosis is the number of new and relapse cases of

diseases but are often unreliable and incomplete.

UNAIDS Report Global AIDS Epidemic 2010. Changes

tuberculosis (all types) per 100,000 people. • Preva-

National health authorities differ widely in capacity

in procedures and assumptions for estimating the

lence of diabetes refers to the percentage of people

and willingness to collect or report information. To

data and better coordination with countries have

ages 20–79 who have type 1 or type 2 diabetes.

compensate for this and improve reliability and inter-

resulted in improved estimates of HIV and AIDS. For

• Prevalence of HIV is the percentage of people who

national comparability, the World Health Organiza-

example, improved software was used to model the

are infected with HIV. Total and youth rates are per-

tion (WHO) prepares estimates in accordance with

course of HIV epidemics and their impacts, making

centages of the relevant age group. Female rate is as

epidemiological models and statistical standards.

full use of information on HIV prevalence trends from

a percentage of the total population living with HIV.

Smoking is the most common form of tobacco use

surveillance data as well as survey data. The soft-

• Condom use is the percentage of the population

and the prevalence of smoking is therefore a good

ware explicitly includes the effect of antiretroviral

ages 15–24 who used a condom at last intercourse

measure of the tobacco epidemic (Corrao and others

therapy (ART) when calculating HIV incidence and

in the last 12 months.

2000). Tobacco use causes heart and other vascular

models reducted infectivity among people receiv-

diseases and cancers of the lung and other organs.

ing ART, which is having an increasing impact on

Given the long delay between starting to smoke and

HIV prevalence, with HIV-positive people living lon-

the onset of disease, the health impact of smoking

ger lives. The software also allows for changes in

in developing countries will increase rapidly only in

urbanization over time—important because preva-

the next few decades. Because the data present a

lence is higher in urban areas and because many

one-time estimate, with no information on intensity

countries have seen rapid urbanization over the past

or duration of smoking, and because the definition of

two decades.

adult varies, the data should be used with caution.

The estimates include plausible bounds, not shown

Tuberculosis is one of the main causes of adult

in the table, which reflect the certainty associated

deaths from a single infectious agent in develop-

with each of the estimates. The bounds are avail-

ing countries. In developed countries tuberculosis

able at http://data.worldbank.org or from the original

has reemerged largely as a result of cases among

source.

immigrants. Since tuberculosis incidence cannot

Data on condom use are from household surveys

be directly measured, estimates are obtained by

and refer to condom use at last intercourse. How-

eliciting expert opinion or are derived from mea-

ever, condoms are not as effective at preventing the

surements of prevalence or mortality. These esti-

transmission of HIV unless used consistently. Some

mates include uncertainty intervals, which are not

surveys have asked directly about consistent use,

shown in the table, which are available at http://

but the question is subject to recall and other biases.

data.worldbank.org or from the original source.

Caution should be used in interpreting the data.

Diabetes, an important cause of ill health and a

For indicators from household surveys, the year in

risk factor for other diseases in developed countries,

the table refers to the survey year. For more informa-

is spreading rapidly in developing countries. Highest

tion, consult the original sources.

among the elderly, prevalence rates are rising among younger and productive populations in developing

Data sources

countries. Economic development has led to the

Data on smoking are from the WHO’s Report on

spread of Western lifestyles and diet to develop-

the Global Tobacco Epidemic 2009: Implementing

ing countries, resulting in a substantial increase in

Smoke-Free Environments. Data on tuberculosis

diabetes. Without effective prevention and control

are from the WHO’s Global Tuberculosis Control

programs, diabetes will likely continue to increase.

Report 2010. Data on diabetes are from the Inter-

Data are estimated based on sample surveys.

national Diabetes Federation’s Diabetes Atlas,

Adult HIV prevalence rates reflect the rate of HIV

3rd edition. Data on prevalence of HIV are from

infection in each country’s population. Low national

UNAIDS’s Global Report: UNAIDS Report on the

prevalence rates can be misleading, however. They

Global AIDS Epidemic 2010. Data on condom use

often disguise epidemics that are initially concen-

are from Demographic and Health Surveys by

trated in certain localities or population groups and

Macro International.

threaten to spill over into the wider population. In

2011 World Development Indicators

117

people

Health risk factors and future challenges


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

118

Mortality Life expectancy at birth

Infant mortality rate

Under-five mortality rate

Child mortality rate

Adult mortality rate

Survival to age 65

years

per 1,000 live births 1990 2009

per 1,000 1990 2009

per 1,000 Male Female 2004–09a,b 2004–09a,b

per 1,000 Male Female 2005–09a 2005–09a

% of cohort Male Female 2009 2009

1990

2009

41 72 67 42 72 68 77 76 65 54 71 76 54 59 67 64 66 72 47 46 55 55 77 49 51 74 68 c 77 68 48 59 76 58 72 75 71 75 68 69 63 66 48 69 47 75 77 61 51 70 75 57 77 62 48 44 55 66

44 77 73 48 76 74 82 80 70 67 70 81 62 66 75 55 73 73 53 51 62 51 81 47 49 79 73c 83 73 48 54 79 58 76 79 77 79 73 75 70 71 60 75 56 80 81 61 56 72 80 57 80 71 58 48 61 72

2011 World Development Indicators

167 41 51 153 25 48 8 8 78 102 20 9 111 84 21 46 46 14 110 114 85 91 7 115 120 18 37 .. 28 126 67 16 105 11 10 10 8 48 41 66 48 92 13 124 6 7 68 104 41 7 76 9 57 137 142 105 43

134 14 29 98 13 20 4 3 30 41 11 4 75 40 13 43 17 8 91 101 68 95 5 112 124 7 17 .. 16 126 81 10 83 5 4 3 3 27 20 18 15 39 4 67 3 3 52 78 26 4 47 3 33 88 115 64 25

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

.. 3 .. .. .. 8 .. .. 9 16 .. .. 64 18 .. .. .. .. .. 65 20 73 .. 74 96 .. .. .. 4 70 49 .. .. 1 .. .. .. 6 5 5 .. .. .. 56 .. .. .. 46 5 .. 38 .. .. 89 110 33 8

.. 1 .. .. .. 3 .. .. 5 20 .. .. 65 20 .. .. .. .. .. 65 20 72 .. 82 101 .. .. .. 3 64 43 .. .. 1 .. .. .. 4 5 5 .. .. .. 56 .. .. .. 39 4 .. 28 .. .. 86 88 36 9

435 98 118 406 163 162 82 99 178 206 330 108 207 232 132 487 226 213 331 382 288 401 92 452 358 129 147 198 397 373 111 305 144 108 143 107 206 164 161 285 374 283 334 129 121 317 324 195 102 323 92 232 252 398 284 170 75

409 51 98 350 75 79 47 50 108 172 115 62 170 172 61 505 118 91 277 346 218 398 55 426 317 64 88 92 348 350 59 271 57 68 65 67 134 86 105 121 281 92 293 57 55 276 264 77 54 286 37 127 195 347 223 119 33

34 82 78 37 75 73 88 85 69 66 54 85 62 64 79 42 67 72 46 42 56 43 87 36 42 81 76 72 38 46 82 53 77 83 79 83 70 76 72 63 46 64 49 84 85 56 48 70 85 51 86 68 55 39 57 73 88

36 90 82 44 87 85 93 93 79 71 83 92 67 72 89 43 81 87 52 47 64 45 92 41 47 90 83 84 44 50 90 59 90 89 90 89 79 86 80 81 58 87 54 93 93 61 55 84 92 56 94 80 63 45 64 80 94


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

2.22

Life expectancy at birth

Infant mortality rate

Under-five mortality rate

Child mortality rate

Adult mortality rate

Survival to age 65

years

per 1,000 live births 1990 2009

per 1,000 1990 2009

per 1,000 Male Female 2004–09a,b 2004–09a,b

per 1,000 Male Female 2005–09a 2005–09a

% of cohort Male Female 2009 2009

1990

2009

69 58 62 65 65 75 77 77 71 79 67 68 60 70 71 68 75 68 54 69 69 59 49 68 71 71 51 49 70 43 56 69 71 67 61 64 43 59 62 54 77 75 64 42 45 77 70 61 72 55 68 66 65 71 74 75 70

74 64 71 72 68 80 82 81 72 83 73 68 55 67 80 70 78 67 65 73 72 45 59 75 73 74 61 54 75 49 57 73 75 69 67 72 48 62 62 67 81 80 73 52 48 81 76 67 76 61 72 73 72 76 79 79 76

15 84 56 55 42 8 10 8 28 5 32 51 64 23 8 .. 14 63 108 12 33 74 165 32 12 32 102 129 16 139 81 21 36 30 73 69 155 84 49 99 7 9 52 144 126 7 37 101 25 67 34 62 41 15 12 .. 17

5 50 30 26 35 4 3 3 26 2 22 26 55 26 5 .. 8 32 46 7 11 61 80 17 5 10 41 69 6 101 74 15 15 15 24 33 96 54 34 39 4 5 22 76 86 3 9 71 16 52 19 19 26 6 3 .. 10

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

.. 9 13 .. 6 .. .. .. 5 .. 3 5 27 .. .. .. .. 8 .. .. .. 22 62 .. .. 2 30 52 .. 117 53 .. .. 7 11 9 .. .. 24 21 .. .. .. 138 91 .. .. 14 .. .. .. 13 10 .. .. .. ..

.. 12 12 .. 7 .. .. .. 6 .. 7 4 25 .. .. .. .. 4 .. .. .. 19 64 .. .. 1 31 54 .. 114 44 .. .. 4 10 11 .. .. 19 18 .. .. .. 135 93 .. .. 22 .. .. .. 4 9 .. .. .. ..

250 256 162 142 211 88 86 82 221 86 159 400 392 169 105 .. 84 257 222 311 150 666 251 144 346 132 266 434 147 386 304 230 137 279 284 144 489 250 346 196 81 87 201 344 404 82 96 162 136 344 170 162 153 209 124 130 109

104 170 113 96 105 56 48 43 116 43 109 151 403 117 41 .. 51 122 180 114 98 633 206 89 116 79 216 395 84 355 236 114 76 125 180 94 469 188 334 171 59 58 113 295 380 50 71 131 72 251 123 100 99 80 53 52 100

68 59 72 75 66 87 87 86 70 88 74 47 47 67 83 .. 85 61 63 64 74 25 56 75 60 77 57 44 76 39 50 67 79 60 58 74 36 58 55 67 87 87 71 44 40 88 83 68 79 50 73 74 74 73 83 80 82

2011 World Development Indicators

86 68 81 82 81 92 93 94 81 95 82 76 48 77 93 .. 90 78 70 86 83 29 63 84 86 85 63 49 85 42 59 81 87 78 71 83 40 66 59 71 92 91 81 49 42 92 87 72 87 61 80 83 83 89 92 91 83

119

people

Mortality


2.22

Mortality Life expectancy at birth

Infant mortality rate

Under-five mortality rate

Child mortality rate

Adult mortality rate

Survival to age 65

years

per 1,000 live births 1990 2009

per 1,000 1990 2009

per 1,000 Male Female 2004–09a,b 2004–09a,b

per 1,000 Male Female 2005–09a 2005–09a

% of cohort Male Female 2009 2009

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

70 69 33 68 52 71 40 74 71 73 45 61 77 70 53 60 78 77 68 63 51 69 46 58 69 70 65 63 48 70 73 76 75 73 67 71 65 68 54 51 61 65 w 52 64 63 68 63 67 68 68 64 58 50 75 76

2009

73 69 51 73 56 74 48 81 75 79 50 52 82 74 58 46 81 82 74 67 56 69 62 63 70 74 72 65 53 69 78 80 79 76 68 74 75 74 63 46 45 69 w 57 69 68 72 67 72 70 74 71 64 53 80 81

25 23 103 35 73 25 166 6 13 9 109 48 8 23 78 67 6 7 30 91 99 27 138 89 30 40 69 81 111 18 15 8 9 21 61 27 39 35 88 108 54 64 w 108 61 66 41 70 41 43 42 57 89 109 10 8

10 11 70 18 51 6 123 2 6 2 109 43 4 13 69 52 2 4 14 52 68 12 48 64 31 18 19 42 79 13 7 5 7 11 32 15 20 25 51 86 56 43 w 76 38 43 19 47 21 19 19 27 55 81 6 3

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

.. .. 69 3 43 4 67 .. .. .. 53 .. .. .. 38 32 .. .. 5 18 56 .. .. 55 5 .. 6 .. 75 4 .. .. .. .. 11 .. 5 3 10 66 21 .. w 52 .. .. .. .. .. .. .. .. 11 68 .. ..

.. .. 55 4 39 3 61 .. .. .. 54 .. .. .. 30 30 .. .. 3 13 52 .. .. 43 8 .. 6 .. 62 1 .. .. .. .. 7 .. 4 3 11 55 21 .. w 49 .. .. .. .. .. .. .. .. 15 65 .. ..

192 396 397 137 325 153e 498 80 195 149 368 575 106 192 302 605 78 78 120 208 369 291 259 238 236 122 149 298 401 385 76 100 141 139 237 175 134 125 247 528 687 213 w 312 201 201 201 216 158 286 190 155 242 390 120 107

82 147 351 88 266 82e 464 41 73 57 315 517 44 76 257 638 48 46 81 137 355 170 224 197 139 70 83 151 399 142 63 61 81 63 135 91 88 90 198 518 664 151 w 275 134 136 122 153 99 123 103 104 169 358 63 52

70 47 40 76 48 75 30 86 72 81 42 32 86 71 53 30 88 88 79 64 49 63 58 61 63 78 74 55 44 53 86 86 84 77 62 74 78 78 60 33 24 68 w 52 67 67 67 65 74 59 72 73 61 44 84 85

86 78 47 85 55 86 34 93 88 92 47 41 94 86 59 29 93 93 86 74 52 77 63 68 78 87 84 73 47 80 89 91 89 89 75 84 85 84 67 35 27 77 w 58 77 75 81 74 82 80 83 81 69 48 91 93

a. Data are for the most recent year available. b. Refers to a survey year. Values were estimated directly from surveys and cover the 5 or 10 years preceding the survey. c. Includes Taiwan, China. d. Excludes the French overseas departments of French Guiana, Guadeloupe, Martinique, and Réunion. e. Includes Kosovo.

120

2011 World Development Indicators


About the data

2.22

Definitions

Mortality rates for different age groups (infants, chil-

weighted least squares method to fit a regression

• Life expectancy at birth is the number of years

dren, and adults) and overall mortality indicators (life

line to the relationship between mortality rates and

a newborn infant would live if prevailing patterns of

expectancy at birth or survival to a given age) are

their reference dates and then extrapolate the trend

mortality at the time of its birth were to stay the

important indicators of health status in a country.

to the present. (For further discussion of childhood

same throughout its life. • Infant mortality rate is

Because data on the incidence and prevalence of

mortality estimates, see UNICEF, WHO, World Bank,

the number of infants dying before reaching one year

diseases are frequently unavailable, mortality rates

and United Nations Population Division 2010; for a

of age, per 1,000 live births in a given year. • Under-

are often used to identify vulnerable populations.

graphic presentation and detailed background data,

five mortality rate is the probability per 1,000 that a

And they are among the indicators most frequently

see www.childmortality.org.)

newborn baby will die before reaching age 5, if sub-

Infant and child mortality rates are higher for boys

ject to current age-specific mortality rates. • Child

than for girls in countries in which parental gender

mortality rate is the probability per 1,000 of dying

The main sources of mortality data are vital reg-

preferences are insignificant. Child mortality cap-

between ages 1 and 5—that is, the probability of a

istration systems and direct or indirect estimates

tures the effect of gender discrimination better than

1-year-old dying before reaching age 5—if subject to

based on sample surveys or censuses. A “complete”

infant mortality does, as malnutrition and medical

current age-specific mortality rates. • Adult mortal-

vital registration system—covering at least 90 per-

interventions are more important in this age group.

ity rate is the probability per 1,000 of dying between

cent of vital events in the population—is the best

Where female child mortality is higher, as in some

the ages of 15 and 60—that is, the probability of a

source of age-specific mortality data. Where reliable

countries in South Asia, girls probably have unequal

15-year-old dying before reaching age 60—if subject

age-specific mortality data are available, life expec-

access to resources. Child mortality rates in the

to current age-specific mortality rates between those

tancy at birth is directly estimated from the life table

table are not compatible with infant mortality and

ages. • Survival to age 65 refers to the percent-

constructed from age-specific mortality data.

under-five mortality rates because of differences in

age of a hypothetical cohort of newborn infants that

But complete vital registration systems are fairly

methodology and reference year. Child mortality data

would survive to age 65, if subject to current age-

uncommon in developing countries. Thus estimates

were estimated directly from surveys and cover the

specific mortality rates.

must be obtained from sample surveys or derived

10 years preceding the survey. In addition to esti-

by applying indirect estimation techniques to reg-

mates from Demographic Health Surveys, estimates

istration, census, or survey data (see table 2.17

derived from Multiple Indicator Cluster Surveys have

and Primary data documentation). Survey data are

been added to the table; they cover the 5 years pre-

Data on infant and under-five mortality are from

subject to recall error, and surveys estimating infant

ceding the survey.

Levels and Trends in Child Mortality, Report 2010

used to compare socioeconomic development across countries.

Data sources

deaths require large samples because households

Rates for adult mortality and survival to age 65

by the Inter-agency Group for Child Mortality Esti-

in which a birth has occurred during a given year

come from life tables. Adult mortality rates increased

mation, covered in About the data, based mainly

cannot ordinarily be preselected for sampling. Indi-

notably in a dozen countries in Sub-Saharan Africa

on household surveys, censuses, and vital regis-

rect estimates rely on model life tables that may be

between 1995–2000 and 2000–05 and in several

tration data, supplemented by the World Bank’s

inappropriate for the population concerned. Because

countries in Europe and Central Asia during the first

Human Development Network estimates based

life expectancy at birth is estimated using infant mor-

half of the 1990s. In Sub-Saharan Africa the increase

on vital registration and sample registration data.

tality data and model life tables for many develop-

stems from AIDS-related mortality and affects both

Data on child mortality are from Demographic and

ing countries, similar reliability issues arise for this

sexes, though women are more affected. In Europe

Health Surveys by Macro International and World

indicator. Extrapolations based on outdated surveys

and Central Asia the causes are more diverse (high

Bank calculations based on infant and under-five

may not be reliable for monitoring changes in health

prevalence of smoking, high-fat diet, excessive alco-

mortality from Multiple Indicator Cluster Surveys

status or for comparative analytical work.

hol use, stressful conditions related to the economic

by UNICEF. Data on survival to age 65 and most

transition) and affect men more.

data on adult mortality are linear interpolations of

Estimates of infant and under-five mortality tend to vary by source and method for a given time and place.

The percentage of a hypothetical cohort surviv-

five-year data from World Population Prospects: The

Years for available estimates also vary by country,

ing to age 65 reflects both child and adult mortality

2008 Revision. Remaining data on adult mortality

making comparison across countries and over time

rates. Like life expectancy, it is a synthetic mea-

are from the Human Mortality Database by the Uni-

difficult. To make infant and under-five mortality esti-

sure based on current age-specific mortality rates.

versity of California, Berkeley, and the Max Planck

mates comparable and to ensure consistency across

It shows that even in countries where mortality is

Institute for Demographic Research (www.mortal-

estimates by different agencies, the Inter-agency

high, a certain share of the current birth cohort will

ity.org). Data on life expectancy at birth are World

Group for Child Mortality Estimation, comprising the

live well beyond the life expectancy at birth, while in

Bank calculations based on male and female data

United Nations Children’s Fund (UNICEF), the United

low-mortality countries close to 90 percent will reach

from World Population Prospects: The 2008 Revi-

Nations Population Division, the World Health Organi-

at least age 65.

sion (for more than half of countries, most of them

zation (WHO), the World Bank, and other universities

Annual data series from the United Nations are

developing countries), census reports and other

and research institutes, developed and adopted a

interpolated based on five-year estimates and thus

statistical publications from national statistical

statistical method that uses all available informa-

may not reflect actual events.

offices, Eurostat’s Demographic Statistics, and the

tion to reconcile differences. The method uses the

people

Mortality

U.S. Bureau of the Census International Data Base.

2011 World Development Indicators

121 Text figures, tables, and boxes


Environment


Introduction

Environmental sustainability

T

he United Nations Conference on the Human Environment, held in Stockholm in 1972, drew worldwide attention to the growing impact of human activity on the environment and to the need for sustainable management of environmental resources. Twenty years later the United Nations Conference on Environment and Development in Rio de Janeiro adopted a comprehensive plan of action for a sustainable future. That plan later became part of the Millennium Declaration, with some of the more important targets included in Millennium Development Goal 7: ensuring environmental sustainability. Understanding climate change is a central issue for environmental sustainability and for development policy. Public policy should help people cope with new or worsened risks, facilitate investments in clean energy technologies, and adapt land and water management to better protect a threatened natural environment while feeding an expanding and more prosperous population. The World Bank Group plays a key role in financing climate change adaptation and mitigation. Since 1999 it has led in forming carbon markets, which are now directing funds toward clean low-carbon development. At the UN Climate Change Conference in Copenhagen in 2009, it launched the Carbon Partnership Facility, the latest addition in a family of carbon funds and facilities. The facility assists developing countries in pursuing low-carbon growth and in accelerating reductions of greenhouse gas emissions; it uses carbon finance innovatively to leverage capital for both public and private investment in clean technologies. At the UN Framework Convention on Climate Change conference in Cancun in 2010, the World Bank joined global leaders and policymakers in the Roadmap for Action: Agriculture, Food Security, and Climate Change, which outlines concrete actions linking agricultural investments and policies with the transition to climatesmart growth. It highlights a “triple-win” approach: increasing farm productivity and incomes, making agriculture more resilient to climate change, and making agriculture part of the solution to climate change by sequestering more carbon in the soil and biomass.

Environmental indicators Monitoring progress toward the environment targets of the Millennium Development Goals and measuring the complexity of environmental phenomena require new measurement frameworks and new data. This year’s Environment section of World Development Indicators includes a new table on natural resource rents that measures human dependence on environmental assets. And in recognition of the mainstreaming of green accounting, the data on adjusted net savings­— ­gross savings adjusted for capital depreciation, resource depletion, pollution damage, and human capital investment­— ­have been moved to the Economy section (table 4.11), joining a new table showing corresponding adjustments to national income (table 4.10). Together these tables provide a clearer picture of the impact of the environment on the long-term sustainability of economic growth. Other indicators in this section describe land use, agriculture and food production, forests and biodiversity, water resources, energy use and efficiency, urbanization, environmental impacts, government commitments, and threatened species. Where possible, the indicators come from international sources to facilitate cross-­ c ountry comparison. Important to keep in mind is that country coverage may be uneven, ecosystems span national boundaries, and natural resource use may differ locally, regionally, and globally. For example, greenhouse gas emissions and climate change may be measured globally, but their effects are also manifested locally, shaping people’s lives and opportunities.

3

2011 World Development Indicators

123


Measuring dependence on environmental assets Accounting for the contribution of natural resources to economic output is important in building an analytical framework for sustainable development. The extraction or harvesting of natural resources can produce substantial rents­— ­revenues above the cost of extracting them—which are calculated as the difference between the price of a commodity and the average cost of producing it. This is done by estimating the world price of units of specific commodities and subtracting estimates of the average unit costs of extraction or harvesting. These unit rents are then multiplied by the physical quantities countries extract or harvest to determine the rents for each commodity, as a share of gross national income (GNI). Table 3.16 presents data on rents from oil, gas, coal, and other mineral production and from forests as a share of GNI. In some countries those rents, especially from fossil fuels The 10 countries with the highest natural resource rents are primarily oil and gas producers  Natural resource rents, 2009 (percent of GNI)

Forest

3a Minerals

Coal

Natural gas

Oil

75

50

Mainstreaming environmental and wealth accounting in country statistical systems

25

0

Iraq

Congo, Rep.

Libya

Saudi Arabia

Gabon

Azerbaijan Turkmenistan

Oman

Angola

Chad

Source: Table 3.16.

Countries with negative adjusted net savings are depleting natural capital without replacing it and are becoming poorer

3b

Adjusted net savings in resource-rich countries, 2008 (percent of GNI) 50 Botswana

China

25 0 –25 –50

and minerals, account for 30–50 percent of GNI (figure 3a)­— ­almost 70 percent in Iraq. Rents from nonrenewable resources­—­fossil fuels and minerals­— ­as well as rents from overharvesting of forests indicate the liquidation of a country’s capital stock. When countries use such rents to support current consumption rather than to invest in new capital to replace what is being used up, they are, in effect, borrowing against their future. For resource-rich countries­—­where resource rents are at least 5 percent of GNI­—­transforming nonrenewable natural capital into other forms of wealth is a major development challenge. Figure 3b plots adjusted net savings­— ­net national savings plus education expenditure, minus energy depletion, mineral depletion, net forest depletion, and carbon dioxide and particulate emissions damage­ —­ against energy and mineral rents for resource-rich countries. Countries with negative adjusted net savings, such as Angola and Republic of Congo, are depleting natural capital without replacing it and becoming poorer over time. Countries with positive adjusted net savings, such as Botswana and China, are adding to wealth and well-being and reducing natural resource depletion by investing in other types of capital. (See About the data for tables 4.10 and 4.11.)

Angola Congo, Rep. 0

25

50

75

Energy and mineral rents (percent of GNI) Note: The underlying data were produced as part of a long-term World Bank project on measuring sustainable development. Estimates of natural resource rents are used in calculating comprehensive wealth and adjusted net savings, which are now in tables 4.10 and 4.11. For further discussion of wealth accounting, see The Changing Wealth of Nations (World Bank 2011). Source: World Development Indicators data files.

124

2011 World Development Indicators

There has been considerable effort over the past 20 years to develop statistical methods for environmental accounting (a broad framework that includes natural capital accounting) under the aegis of the United Nations Statistical Commission. The commission established the London Group on Environmental Accounting and later a high-level body, the UN Committee of Experts on Environmental and Economic Accounting, to develop methodological guidelines. In 2003 the United Nations and other international organizations produced the Handbook of National Accounting: Integrated Environmental and Economic Accounting (UN and others 2003). It is currently under revision and will become part of the statistical standard, like the System of National Accounts, which establishes methodology for national accounts. Other institutions and individual scholars have also done work on wealth accounting over


environment

the past 20 years. Official statistical offices in more than 30 countries have institutionalized wealth accounting, and 16 of them regularly compile at least one type of natural resource asset account. The majority of countries focus on mineral and energy assets, but some, notably Australia and Norway, construct more comprehensive accounts for natural capital. National statistical offices, the academic community, and nongovernmental organizations have produced empirical work on natural capital accounting nationally, regionally, and locally. Together, these studies have deepened our knowledge of wealth accounting, leading to better understanding of the prospects for growth

and poverty reduction, especially in resourcerich countries. Stiglitz, Sen, and Fitoussi (2009) offer further support for the comprehensive wealth approach to sustainable development. They propose ways to modify and extend conventional national accounts to provide a more accurate and useful guide for policy. An important part of the proposed changes, to better reflect the sustainability of economies, is comprehensive wealth. They recommend compiling accounts for all assets (natural, human-made, and human capital) and changes in those assets, which correspond to the components of adjusted net savings.

2011 World Development Indicators

125


Tables

3.1

Rural population and land use Rural population

% of total 1990 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

126

82 64 48 63 13 33 15 34 46 80 34 4 66 44 61 58 25 34 86 94 87 59 23 63 79 17 73 1 32 72 46 49 60 46 27 25 15 45 45 57 51 84 29 87 39 26 31 62 45 27 64 41 59 72 72 72 60

76 53 34 42 8 36 11 33 48 72 26 3 58 34 52 40 14 29 80 89 78 42 20 61 73 11 56 0 25 65 38 36 51 43 24 27 13 30 34 57 39 79 31 83 36 22 15 43 47 26 49 39 51 65 70 52 52

2011 World Development Indicators

average annual % growth 1990–2009

2.1 –1.2 –0.1 0.8 –1.6 –0.2 –0.1 0.2 1.3 1.2 –1.7 –1.3 2.7 0.6 –1.5 –0.1 –1.7 –1.6 2.7 1.7 1.6 0.7 0.1 2.0 2.8 –0.7 –0.5 .. 0.5 2.5 1.2 0.5 1.8 –0.8 –0.2 0.4 –0.4 –0.4 0.0 2.0 –0.6 2.1 –0.5 2.5 0.1 –0.2 –1.5 1.5 –1.0 0.0 1.1 0.2 1.6 2.1 2.3 0.1 1.5

Land area

Land use

% of land area

thousand sq. km 2009

Forest area 1990 2010

Permanent cropland 1990 2008

Arable land 1990 2008

652.2 27.4 2,381.7 1,246.7 2,736.7 28.5 7,682.3 82.5 82.6 130.2 202.9 30.3 110.6 1,083.3 51.2 566.7 8,459.4 108.6 273.6 25.7 176.5 472.7 9,093.5 623.0 1,259.2 743.5 9,327.5 1.0 1,109.5 2,267.1 341.5 51.1 318.0 56.0 106.4 77.3 42.4 48.3 248.4 995.5 20.7 101.0 42.4 1,000.0 303.9 547.7 257.7 10.0 69.5 348.6 227.5 128.9 107.2 245.7 28.1 27.6 111.9

2.1 28.8 0.7 48.9 12.7 12.2 20.1 45.8 11.2 11.5 38.4 22.4 52.1 58.0 43.2 24.2 68.0 30.1 25.0 11.3 73.3 51.4 34.1 37.2 10.4 20.5 16.8 .. 56.3 70.7 66.5 50.2 32.1 33.1 19.2 34.0 10.5 40.8 49.9 0.0 18.2 16.0 49.3 15.1 71.9 26.5 85.4 44.2 40.0 30.8 32.7 25.6 44.3 29.6 78.8 4.2 72.7

0.2 4.6 0.2 0.4 0.4 2.1 0.0 1.0 3.7 2.5 0.9 0.5a 0.9 0.1 2.9 0.0 0.8 2.7 0.2 14.0 0.6 2.6 0.7 0.1 0.0 0.3 0.8 .. 1.5 0.5 0.1 4.9 11.0 2.0 4.2 3.1 0.2 9.3 4.8 0.4 12.5 0.0 0.3 0.5 0.0 2.2 0.6 0.5 4.8 1.3 6.6 8.3 4.5 2.0 4.2 11.6 3.2

12.1 21.1 3.0 2.3 9.6 14.9 6.2 17.3 20.5 70.0 30.0 23.3a 14.6 1.9 16.6 0.7 6.0 34.9 12.9 36.2 20.9 12.6 5.0 3.1 2.6 3.8 13.3 .. 3.0 2.9 1.4 5.1 7.6 21.7 31.6 41.1 60.4 18.6 5.8 2.3 26.5 4.9 26.3 10.0 7.4 32.9 1.1 18.2 11.4 34.3 11.9 22.5 12.1 3.3 8.9 28.3 13.1

2.1 28.3 0.6 46.9 10.7 9.2 19.4 47.1 11.3 11.1 42.5 22.4 41.2 52.8 42.7 20.0 61.4 36.2 20.6 6.7 57.2 42.1 34.1 36.3 9.2 21.8 22.2 .. 54.5 68.0 65.6 51.0 32.7 34.3 27.0 34.4 12.8 40.8 39.7 0.1 13.9 15.2 52.3 12.3 72.9 29.1 85.4 48.0 39.5 31.8 21.7 30.3 34.1 26.6 71.9 3.7 46.4

0.2 3.2 0.4 0.2 0.4 1.9 0.0 0.8 2.8 6.1 0.6 0.8 2.7 0.2 1.8 0.0 0.9 1.7 0.2 15.2 0.9 2.5 0.8 0.1 0.0 0.6 1.5 .. 1.5 0.3 0.2 5.9 13.4 1.5 3.8 3.1 0.2 10.3 5.1 0.8 11.1 0.0 0.2 0.9 0.0 2.0 0.6 0.5 1.7 0.6 12.5 8.7 8.8 2.8 8.9 10.9 3.7

11.9 22.3 3.1 2.7 11.7 15.8 5.7 16.7 22.5 60.7 27.2 27.9 23.1 3.3 19.7 0.4 7.2 28.2 23.0 35.0 22.1 12.6 5.0 3.1 3.4 1.7 11.6 .. 1.6 3.0 1.4 3.9 8.8 15.4 33.5 39.2 56.6 16.6 5.0 2.8 33.1 6.6 14.1 13.6 7.4 33.3 1.3 39.0 6.7 34.2 19.3 16.3 12.4 9.8 10.7 36.3 9.1

Arable land hectares per 100 people 1990 2008

42.6 17.6 28.0 27.2 81.2 1.5 280.7 18.5 0.8 7.9 0.5 0.2 33.7 31.5 3.5 31.1 33.9 44.2 39.9 16.4 38.1 48.6 163.7 65.6 53.6 21.2 10.9 .. 10.0 18.0 19.6 8.4 19.3 2.4 32.0 32.1 49.8 12.2 15.6 4.0 10.3 0.1 3.5 1.4 45.5 31.7 31.8 20.3 1.0 15.1 18.0 28.5 14.6 13.1 24.5 11.0 29.8

26.9 19.4 21.8 18.9 80.2 14.6 205.4 16.5 21.4 4.9 57.0 7.9 29.4 37.1 26.7 13.0 31.8 40.2 41.4 11.1 26.8 31.2 135.4 44.5 39.4 7.5 8.2 .. 4.1 10.4 13.6 4.4 13.6 19.4 31.9 29.0 43.7 8.0 9.2 3.4 11.2 13.6 44.6 16.9 42.4 29.3 22.4 23.5 10.9 14.5 18.8 18.7 9.7 24.4 19.0 10.1 13.9


Rural population

% of total 1990 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

34 75 69 44 30 43 10 33 51 37 28 44 82 42 26 .. 2 62 85 31 17 86 55 24 32 42 76 88 50 77 60 56 29 53 43 52 79 75 72 91 31 15 48 85 65 28 34 69 46 85 51 31 51 39 52 28 8

32 70 47 31 34 38 8 32 47 33 22 42 78 37 18 .. 2 64 68 32 13 74 39 22 33 33 70 81 29 67 59 58 23 59 43 44 62 67 63 82 18 13 43 83 51 23 28 63 26 88 39 29 34 39 40 1 4

average annual % growth 1990–2009

–0.5 1.3 –0.6 –0.3 3.2 0.7 1.7 0.1 0.2 –0.4 2.1 –0.4 2.5 0.3 –1.2 .. 0.3 1.1 1.0 –0.7 0.4 0.5 1.4 1.6 –0.5 –1.0 2.5 2.0 –0.7 1.5 2.5 1.1 0.1 –0.5 0.9 0.5 1.5 0.4 1.5 1.7 –2.5 0.5 1.2 3.4 1.2 –0.5 1.3 1.9 –1.1 2.7 0.7 1.1 –0.1 0.0 –1.0 –15.0 2.7

Land area

3.1

environment

Rural population and land use Land use

% of land area

thousand sq. km 2009

Forest area 1990 2010

Permanent cropland 1990 2008

Arable land 1990 2008

89.6 2,973.2 1,811.6 1,628.6 437.4 68.9 21.6 294.1 10.8 364.5 88.2 2,699.7 569.1 120.4 96.9 10.9b 17.8 191.8 230.8 62.2 10.2 30.4 96.3 1,759.5 62.7 25.2 581.5 94.1 328.6 1,220.2 1,030.7 2.0 1,944.0 32.9 1,553.6 446.3 786.4 653.5 823.3 143.4 33.8 263.3 120.3 1,266.7 910.8 305.5 309.5 770.9 74.3 452.9 397.3 1,280.0 298.2 304.2 91.5 8.9 11.6

20.0 21.5 65.4 6.8 1.8 6.7 6.1 25.8 31.9 68.4 1.1 1.3 6.5 68.1 64.5 .. 0.2 4.4 75.0 51.1 12.8 1.3 51.2 0.1 31.0 35.9 23.5 41.4 68.1 11.5 0.4 19.2 36.2 9.7 8.1 11.3 55.2 60.0 10.6 33.7 10.2 29.3 37.5 1.5 18.9 30.0 0.0 3.3 51.0 69.6 53.3 54.8 22.0 29.2 36.4 32.4 0.0

2.6 2.2 6.5 0.8 0.7 0.0 4.1 10.1 9.2 1.3 0.8 0.1 0.8 1.5 1.6 .. 0.1 0.4 0.3 0.4 11.9 0.1 1.6 0.2 0.7 2.2 1.0 1.4 16.0 0.1 0.0 3.0 1.0 12.8 0.0 1.6 0.3 0.8 0.0 0.5 0.9 0.2 1.6 0.0 2.8 0.0 0.1 0.6 2.1 1.2 0.2 0.3 14.8 1.1 8.5 5.6 0.1

56.2 54.8 11.2 9.3 13.3 15.1 15.9 30.6 11.0 13.1 2.0 13.0 8.8 19.0 19.8 .. 0.2 6.9 3.5 27.2 17.9 10.4 3.6 1.0 46.0 23.8 4.7 23.9 5.2 1.7 0.4 49.3 12.5 52.8 0.9 19.5 4.4 14.6 0.8 16.0 26.0 10.0 10.8 8.7 32.4 2.8 0.1 26.6 6.7 0.4 5.3 2.7 18.4 47.3 25.6 7.3 0.9

22.6 23.0 52.1 6.8 1.9 10.7 7.1 31.1 31.1 68.5 1.1 1.2 6.1 47.1 64.2 .. 0.3 5.0 68.2 53.9 13.4 1.4 44.9 0.1 34.5 39.6 21.6 34.4 62.3 10.2 0.2 17.2 33.3 11.7 7.0 11.5 49.6 48.6 8.9 25.4 10.8 31.4 25.9 1.0 9.9 32.9 0.0 2.2 43.7 63.4 44.3 53.1 25.7 30.7 37.8 62.2 0.0

2.2 3.8 8.3 1.1 0.6 0.0 3.6 9.0 10.2 0.9 0.9 0.0 0.9 1.7 1.9 .. 0.2 0.4 0.4 0.1 13.9 0.1 2.3 0.2 0.4 1.4 1.0 1.3 17.6 0.1 0.0 2.0 1.4 9.2 0.0 2.1 0.3 1.7 0.0 0.8 1.0 0.3 1.9 0.0 3.3 0.0 0.1 1.1 2.0 1.4 0.3 0.6 16.8 1.3 6.4 4.2 0.3

51.0 53.2 12.1 10.5 11.9 16.0 13.9 24.2 11.5 11.8 1.7 8.4 9.3 22.4 16.0 27.6 0.6 6.7 5.4 18.8 14.1 11.7 4.2 1.0 29.7 17.1 5.1 37.2 5.5 4.0 0.4 42.9 12.8 55.4 0.5 18.0 5.7 16.2 1.0 16.4 31.6 1.7 15.8 11.4 41.2 2.8 0.2 26.4 7.4 0.6 10.6 2.9 17.8 41.3 11.5 6.8 1.1

Arable land hectares per 100 people 1990 2008

48.7 19.2 11.4 27.9 30.7 29.7 7.4 15.9 5.0 3.9 5.6 0.3 21.3 11.4 4.6 .. 0.2 1.2 19.0 2.0 6.2 19.8 16.2 41.4 1.5 3.0 24.1 23.8 9.4 23.7 20.1 9.5 29.2 39.7 61.8 35.1 25.5 23.4 46.6 12.0 5.9 76.7 31.4 139.6 30.3 20.3 1.9 19.0 20.7 4.6 49.7 16.1 8.8 37.7 23.7 1.8 2.1

2011 World Development Indicators

45.6 13.9 9.7 23.7 16.9 24.9 4.1 11.9 4.7 3.4 2.6 144.8 13.7 11.3 3.2 16.8 0.4 24.2 20.1 51.6 3.4 17.3 10.5 27.8 55.4 21.2 15.4 23.6 6.7 38.2 12.4 6.9 23.3 50.1 32.2 25.5 20.1 21.4 37.6 8.2 6.5 10.6 33.5 98.6 24.8 17.7 2.0 12.2 16.1 4.1 67.3 12.7 5.9 33.0 9.9 1.5 1.0

127


3.1

Rural population and land use Rural population

% of total 1990 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

47 27 95 23 61 50 67 0 44 50 70 48 25 83 73 77 17 27 51 68 81 71 79 70 92 42 41 55 89 33 21 11 25 11 60 16 80 32 79 61 71 57 w 78 62 70 33 64 71 37 29 48 75 72 27 29

46 27 81 18 57 48 62 0 43 52 63 39 23 85 56 75 15 27 45 74 74 66 72 57 86 33 31 51 87 32 22 10 18 8 63 6 72 28 69 64 62 50 w 71 52 59 25 55 55 36 21 42 70 63 23 27

average annual % growth 1990–2009

Land area

thousand sq. km 2009

–0.5 229.9 –0.1 16,376.9 1.0 24.7 0.8 2,000.0 c 2.4 192.5 –0.4 88.4 1.3 71.6 .. 0.7 0.1 48.1 0.3 20.1 1.1 627.3 0.7 1,214.5 0.5 499.1 1.0 62.7 0.9 2,376.0 1.5 17.2 –0.1 410.3 0.7 40.0 2.0 183.6 1.8 140.0 2.4 885.8 0.6 510.9 1.8 14.9 1.7 54.4 0.2 5.1 0.0 155.4 0.0 769.6 1.4 469.9 3.1 197.1 –0.8 579.3 5.0 83.6 –0.2 241.9 –0.6 9,147.4 –1.6 175.0 1.9 425.4 –2.9 882.1 0.9 310.1 3.0 6.0 2.7 528.0 2.9 743.4 0.2 386.9 0.6 w 129,561.8 s 1.8 17,303.9 0.4 78,352.9 0.5 30,841.8 –0.3 47,511.0 0.7 95,656.7 –0.3 15,853.7 0.0 22,686.7 –0.3 20,116.2 1.3 8,643.6 1.4 4,771.2 1.9 23,585.4 –0.3 33,905.1 –0.1 2,552.0

a. Includes Luxembourg. b. Data are from national sources. c. Provisional estimate.

128

2011 World Development Indicators

Land use

Forest area 1990 2010

Permanent cropland 1990 2008

Arable land 1990 2008

Arable land hectares per 100 people 1990 2008

27.8 49.4 12.9 0.5 48.6 26.2 43.5 3.0 40.0 59.0 13.2 6.8 27.7 37.5 32.1 27.4 66.5 28.8 2.0 2.9 46.8 38.3 65.0 12.6 47.0 4.1 12.6 8.8 24.1 16.0 2.9 10.8 32.4 5.3 7.2 59.0 28.8 1.5 1.0 71.0 57.3 32.1 w 31.9 33.9 26.2 38.8 33.5 29.0 38.4 51.6 2.4 16.6 31.3 28.1 33.6

2.6 0.1 12.4 0.0 0.2 .. 1.9 1.5 1.0 1.8 0.0 0.7 9.7 15.9 0.0 0.7 0.0 0.6 4.0 0.9 1.1 6.1 3.9 1.7 6.8 12.5 3.9 0.1 9.4 1.9 0.2 0.3 0.2 0.3 0.9 0.9 3.2 19.1 0.2 0.0 0.3 1.1 w 0.7 1.4 1.8 1.0 1.2 2.2 0.4 0.9 0.8 1.8 0.8 0.7 4.8

41.2 8.1 35.7 1.7 16.1 .. 6.8 1.5 32.5 9.9 1.6 11.1 30.7 14.4 5.4 10.5 6.9 10.3 26.6 6.1 10.2 34.2 7.4 38.6 7.0 18.7 32.0 2.9 25.4 57.6 0.4 27.4 20.3 7.2 10.5 3.2 16.4 18.1 2.9 3.1 7.5 9.0 w 6.4 8.4 14.9 4.2 8.1 12.1 2.1 6.6 5.9 42.6 6.2 11.8 26.7

40.7 0.0 12.3 20.9 41.0 4.5 11.9 0.0 31.0 1.8 15.5 38.2 39.5 5.3 47.2 20.8 33.2 6.1 38.4 1.0 35.4 30.9 14.9 53.5 3.0 35.7 43.9 36.7 28.2 0.1 1.9 11.6 74.4 40.6 21.8 14.3 8.1 .. 12.4 29.0 27.6 22.2 w 20.1 17.7 15.8 24.5 18.0 12.0 12.1 30.3 22.4 18.0 28.4 41.5 22.1

% of land area

28.6 49.4 17.6 0.5 44.0 30.7 38.1 2.9 40.2 62.2 10.8 4.7 36.4 29.7 29.4 32.7 68.7 31.0 2.7 2.9 37.7 37.1 49.9 5.3 44.1 6.5 14.7 8.8 15.2 16.8 3.8 11.9 33.2 10.0 7.7 52.5 44.5 1.5 1.0 66.5 40.4 31.1 w 28.2 32.8 25.9 37.2 31.9 29.6 38.6 47.0 2.4 17.1 28.0 28.9 37.3

1.6 0.1 11.3 0.1 0.3 3.4 1.9 0.3 0.5 1.3 0.0 0.8 9.6 15.1 0.1 0.8 0.0 0.6 5.3 1.0 1.5 7.1 4.4 3.1 4.3 14.2 3.8 0.1 11.4 1.6 2.4 0.2 0.3 0.2 0.8 0.7 10.0 19.5 0.6 0.0 0.3 1.1 w 0.9 1.4 2.4 0.7 1.3 3.1 0.4 1.0 1.0 2.9 1.0 0.7 4.2

37.9 7.4 52.3 1.7 18.2 37.4 25.1 0.7 28.7 9.0 1.6 11.9 25.0 19.9 8.7 10.3 6.4 10.2 25.6 5.3 10.8 29.8 10.8 45.2 4.9 18.2 28.0 3.9 28.7 56.1 0.8 24.8 18.6 9.4 10.1 3.1 20.3 16.8 2.4 3.2 9.6 10.7 w 8.6 11.0 16.0 7.8 10.6 11.3 10.4 7.4 6.0 41.5 8.5 10.9 24.4

40.5 85.7 13.3 13.9 28.7 44.9 32.3 0.0 25.6 9.0 11.2 29.7 27.4 6.2 50.1 15.2 28.5 5.3 22.8 10.8 22.6 22.6 14.6 38.1 1.9 27.5 29.2 36.7 17.8 70.2 1.4 9.8 56.0 49.2 15.7 9.7 7.3 2.8 5.6 18.7 29.9 20.7 w 17.9 18.2 13.1 37.4 18.2 9.3 58.8 26.4 16.0 12.8 24.4 34.0 18.9


About the data

3.1

environment

Rural population and land use Definitions

With more than 3 billion people, including 70 percent

Satellite images show land use that differs from

• Rural population is calculated as the difference

of the world’s poor people, living in rural areas, ade-

that of ground-based measures in area under cultiva-

between the total population and the urban popula-

quate indicators to monitor progress in rural areas

tion and type of land use. Moreover, land use data

tion (see Definitions for tables 2.1 and 3.11). • Land

are essential. However, few indicators are disaggre-

in some countries (India is an example) are based

area is a country’s total area, excluding area under

gated between rural and urban areas (for some that

on reporting systems designed for collecting tax rev-

inland water bodies and national claims to the con-

are, see tables 2.7, 3.5, and 3.11). The table shows

enue. With land taxes no longer a major source of

tinental shelf and to exclusive economic zones. In

indicators of rural population and land use. Rural

government revenue, the quality and coverage of land

most cases the definition of inland water bodies

population is approximated as the midyear nonurban

use data have declined. Data on forest area may be

includes major rivers and lakes. (See table 1.1 for

population. While a practical means of identifying the

particularly unreliable because of irregular surveys

the total surface area of countries.) Variations from

rural population, it is not precise (see box 3.1a for

and differences in definitions (see About the data

year to year may be due to updated or revised data

further discussion).

for table 3.4). The forest area statistics released by

rather than to change in area. • Land use is a coun-

The data in the table show that land use patterns

FAO between 1948 and 1963 were based mostly on

try’s total area, excluding area under inland water

are changing. They also indicate major differences

data from country questionnaires. Remote sensing,

bodies and national claims to the continental shelf

in resource endowments and uses among countries.

statistical modeling, and expert analysis of country

and to exclusive economic zones. In most cases defi-

True comparability of the data is limited, however,

surveys have been applied since 1980 to improve

nitions of inland water bodies includes major rivers

by variations in definitions, statistical methods, and

the forest coverage estimates. FAO’s Global Forest

and lakes. (See table 1.1 for the total surface area of

quality of data. Countries use different definitions of

Resources Assessment 2010 covers 233 countries

countries.) Variations from year to year may be due

rural and urban population and land use. The Food

and is the most comprehensive assessment of for-

to updated or revised data rather than to change in

and Agriculture Organization of the United Nations

ests, forestry, and the benefits of forest resources

area. • Forest area is land under natural or planted

(FAO), the primary compiler of the data, occasion-

in both scope and number of countries and people

stands of trees of at least 5 meters in situ, whether

ally adjusts its definitions of land use categories

involved. It examines status and trends for about 90

productive or not, and excludes tree stands in agricul-

and revises earlier data. Because the data reflect

variables on the extent, condition, uses, and values

tural production systems (for example, in fruit planta-

changes in reporting procedures as well as actual

of forests and other wooded land.

tions and agroforestry systems) and trees in urban

changes in land use, apparent trends should be inter-

parks and gardens. • Permanent cropland is land

preted cautiously.

cultivated with crops that occupy the land for long periods and need not be replanted after each har-

What is rural? Urban?

3.1a

vest, such as cocoa, coffee, and rubber. Land under

The rural population identified in table 3.1 is approximated as the difference between total population

flowering shrubs, fruit trees, nut trees, and vines

and urban population, calculated using the urban share reported by the United Nations Population

is included, but land under trees grown for wood or

Division. There is no universal standard for distinguishing rural from urban areas, and any urban-rural

timber is not. • Arable land is land defined by the FAO

dichotomy is an oversimplification (see About the data for table 3.11). The two distinct images—isolated

as under temporary crops (double-cropped areas are

farm, thriving metropolis—represent poles on a continuum. Life changes along a variety of dimensions,

counted once), temporary meadows for mowing or

moving from the most remote forest outpost through fields and pastures, past tiny hamlets, through

pasture, land under market or kitchen gardens, and

small towns with weekly farm markets, into intensively cultivated areas near large towns and small cities,

land temporarily fallow. Land abandoned as a result

eventually reaching the center of a megacity. Along the way access to infrastructure, social services, and

of shifting cultivation is excluded.

nonfarm employment increase, and with them population density and income. Because rurality has many dimensions, for policy purposes the rural-urban dichotomy presented in tables 3.1, 3.5, and 3.11 is inadequate. A 2005 World Bank Policy Research Paper proposes an operational definition of rurality based on population density and distance to large cities (Chomitz, Buys, and Thomas 2005). The report argues that these criteria are important gradients along which economic behavior and appropriate development

Data sources

interventions vary substantially. Where population densities are low, markets of all kinds are thin, and the

Data on urban population shares used to estimate

unit cost of delivering most social services and many types of infrastructure is high. Where large urban

rural population are from the United Nations Popu-

areas are distant, farm-gate or factory-gate prices of outputs will be low and input prices will be high, and

lation Division’s World Urbanization Prospects: The

it will be difficult to recruit skilled people to public service or private enterprises. Thus, low population

2009 Revision, and data on total population are

density and remoteness together define a set of rural areas that face special development challenges.

World Bank estimates. Data on land area, perma-

Using these criteria and the Gridded Population of the World (CIESIN 2005), the authors’ estimates of

nent cropland, and arable land are from the FAO’s

the rural population for Latin America and the Caribbean differ substantially from those in table 3.1. Their

electronic files. The FAO gathers these data from

estimates range from 13 percent of the population, based on a population density of less than 20 people

national agencies through annual questionnaires

per square kilometer, to 64 percent, based on a population density of more than 500 people per square

and by analyzing the results of national agricultural

kilometer. Taking remoteness into account, the estimated rural population would be 13–52 percent. The

censuses. Data on forest area are from the FAO’s

estimate for Latin America and the Caribbean in table 3.1 is 21 percent.

Global Forest Resources Assessment 2010.

2011 World Development Indicators

129


3.2

Agricultural inputs Agricultural landa

% of land area 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

130

58 41 16 46 47 41 60 42 53 77 46 44b 21 33 43 46 29 56 35 83 25 19 7 8 38 21 57 .. 41 10 31 45 60 43 63 55 66 53 28 3 68 73 32 31 8 56 20 64 46 52 55 72 40 49 51 58 30

Average annual precipitation

% irrigated 2008

58 43 17 46 49 61 54 38 58 71 44 45 31 34 42 46 31 48 45 85 31 19 7 8 39 21 56 .. 38 10 31 35 64 23 62 55 63 52 30 4 75 75 19 35 8 53 20 66 36 49 69 36 39 56 58 65 28

2011 World Development Indicators

5.8 10.0 2.1 .. .. 8.9 0.4 1.4 30.0 62.0 0.7 1.7 .. .. .. 0.0 .. 1.4 .. .. .. .. .. .. .. 5.6 10.2 .. .. .. .. .. .. 0.7 .. 0.2 9.5 .. 10.2 .. 2.1 .. .. 0.5 2.8 5.4 .. .. 4.0 .. .. 27.4 .. .. .. .. ..

Land under cereal production

thousand hectares

millimeters 2008

1990

327 1,485 89 1,010 591 562 534 1,110 447 2,666 618 847 1,039 1,146 1,028 416 1,782 608 748 1,274 1,904 1,604 537 1,343 322 1,522 .. .. 2,612 1,543 1,646 2,926 1,348 1,113 1,335 677 703 1,410 2,087 51 1,724 384 626 848 536 867 1,831 836 1,026 700 1,187 652 1,996 1,651 1,577 1,440 1,976

2,253.0 321.0 2,366.0 775.1 9,015.0 162.8 13,428.8 948.4 627.0 11,140.6 2,603.0 368.2b 643.9 582.5 304.1 205.1 18,512.4 2,055.3 2,528.9 217.5 1,900.0 657.6 21,547.9 110.5 1,075.4 823.5 93,555.2 .. 1,742.8 1,863.6 9.6 92.6 1,400.0 592.7 230.5 1,613.6 1,570.3 122.2 802.2 2,283.4 425.4 329.3 453.7 4,040.3 1,212.6 9,060.4 14.4 90.0 248.5 6,944.9 853.0 1,470.4 718.5 729.6 109.3 351.5 465.1

2009

3,188.0 146.2 3,176.3 1,752.1 8,031.6 169.3 19,805.6 838.0 1,113.8 12,032.5 2,418.0 345.0 976.1 897.0 295.8 85.7 20,220.4 1,829.2 4,178.6 222.0 2,888.0 1,223.3 14,863.2 264.3 2,486.7 567.5 88,592.8 .. 1,186.0 1,977.3 27.5 74.4 853.5 562.7 419.9 1,544.4 1,497.7 158.2 819.1 3,129.8 363.0 492.3 316.4 8,748.0 1,133.1 9,388.2 20.5 295.2 193.8 6,908.4 1,570.7 1,174.8 855.9 1,863.0 152.6 437.0 382.6

Fertilizer consumption

% of fertilizer production 2008

146.9 .. 226.4 .. 305.9 .. 161.3 .. .. 141.1 22.0 .. .. .. .. .. 313.5 68.4 .. .. .. .. 25.1 .. .. 102.8 99.2 .. 278.7 .. .. .. .. 70.7 554.4 117.1 .. .. .. 68.5 .. .. 65.4 .. 77.4 153.8 .. .. 13.0 55.7 .. 472.4 .. .. .. .. ..

kilograms per hectare of arable land 2008

3.2 38.4 6.8 8.3 38.8 18.1 33.9 109.6 20.9 164.5 237.4 .. 0.0 5.5 11.9 .. 165.7 81.8 3.9 2.2 22.7 8.6 56.9 .. .. 588.8 468.0 .. 492.4 1.0 1.1 707.5 18.9 387.6 39.7 135.1 128.3 .. 214.1 723.6 118.4 0.0 100.3 7.7 134.2 146.1 14.1 2.6 37.1 160.4 6.4 143.8 92.0 1.5 .. .. 107.7

Agricultural employment

Agricultural machinery

% of total employment 1990 2008

Tractors per 100 sq. km of arable land 1990 2008

.. .. .. 5.1 0.4 .. 5.6 7.9 30.9 64.9 .. 3.1 .. 1.2 .. .. 22.8 18.5 .. .. .. .. 4.1 .. 83.0 19.3 53.4 0.9 1.4 .. .. 25.9 .. .. 24.9 7.7 5.5 20.3 7.5 39.0 10.2 .. 21.0 .. 8.8 5.6 41.6 64.7 .. 4.1 62.0 23.9 12.9 .. .. 65.6 50.1

.. 58.0 .. .. 0.8 46.2 3.4 5.6 38.7 48.1 .. 1.8 .. .. .. 29.9 19.3 7.5 .. .. .. .. 2.5 .. .. 12.3 .. 0.2 18.4 .. .. 13.2 .. 12.8 18.7 3.3 2.7 14.5 8.3 31.2 18.9 .. 3.7 8.6 4.5 3.0 .. .. 53.4 2.2 .. 11.4 33.2 .. .. .. 39.2

0.2 212.4 129.1 .. 100.2 345.5 .. 2,373.7 194.8 2.4 206.9 1,523.3b 1.0 24.8 235.3 140.5 143.8 135.8 2.4 1.8 3.3 0.9 164.8 .. .. 127.6 66.6 .. 96.8 .. .. .. 19.9 35.2 226.2 264.6 634.7 25.9 54.2 249.6 .. 5.0 455.3 .. 916.5 800.0 .. .. 295.6 1,309.4 7.1 744.2 .. 45.0 0.8 2.6 30.9

1.2 121.9 139.6 .. .. 327.8 .. 2,390.3 116.1 3.9 89.8 1,127.1 .. .. .. 134.8 129.2 173.5 .. .. 11.8 .. 162.5 .. .. 425.9 277.1 .. .. .. .. .. .. .. 203.2 276.4 486.3 .. .. 372.1 .. .. 604.7 .. 784.7 635.3 .. .. 594.0 646.0 4.5 1,196.9 .. .. .. .. ..


Agricultural landa

% of land area 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 Romania

72 61 25 38 23 82 27 57 44 16 12 82 47 21 22 .. 8 53 7 41 59 76 26 9 54 51 62 45 22 26 38 56 53 78 81 68 61 16 47 29 59 61 33 26 79 3 3 34 29 2 43 17 37 62 43 49 64

64 60 27 30 22 61 23 46 43 13 11 77 48 25 19 52 8 56 10 29 67 78 27 9 43 42 70 58 24 32 38 48 53 76 75 67 62 18 47 29 57 43 43 34 86 3 6 34 30 2 51 17 40 53 38 21 59

Average annual precipitation

% irrigated 2008

1.4 .. 16.3 19.0 .. .. .. 19.2 .. 35.1 9.5 .. 0.1 .. 51.6 .. .. 9.3 .. 0.0 19.9 .. .. .. .. 2.7 2.2 .. .. .. .. 21.4 5.2 9.1 .. 4.4 .. 24.8 .. 27.7 10.6 .. .. .. .. 5.4 .. 73.0 .. .. .. .. .. 0.5 12.0 8.5 1.9

millimeters 2008

589 1,083 2,702 228 216 1,118 435 832 2,051 1,668 111 250 630 1,054 1,274 .. 121 533 1,834 641 661 788 2,391 56 656 619 1,513 1,181 2,875 282 92 2,041 752 450 241 346 1,032 2,091 285 1,500 778 1,732 2,391 151 1,150 1,414 125 494 2,692 3,142 1,130 1,738 2,348 600 854 2,054 637

Land under cereal production

thousand hectares 1990

2,778.6 102,536.5 13,660.5 9,468.1 3,256.3 298.9 113.8 4,413.4 2.1 2,471.5 105.8 22,152.4 1,785.5 1,605.0 1,441.0 .. 0.5 578.0 687.0 696.7 41.2 233.5 175.0 404.1 1,134.0 235.2 1,326.9 1,425.3 700.7 2,438.7 118.9 0.6 10,543.1 675.6 654.1 5,603.3 1,549.5 5,221.4 214.2 3,045.2 195.3 172.5 320.0 6,882.3 15,400.0 356.4 2.4 11,864.1 184.6 1.7 393.7 683.7 7,138.5 8,530.9 760.0 0.5 5,704.1

2009

2,883.6 99,880.0 17,044.2 9,095.5 2,141.4 293.5 79.1 3,453.8 1.5 1,936.2 48.3 16,575.0 2,329.0 1,265.5 1,018.5 .. 1.4 612.4 1,048.9 540.9 67.9 179.2 190.0 342.9 1,103.5 178.9 1,476.5 1,780.1 678.6 3,988.4 242.9 0.1 10,182.4 881.6 252.4 5,316.7 1,892.0 8,912.0 307.2 3,418.0 220.8 162.7 438.2 9,929.1 18,899.0 305.9 5.0 13,689.0 144.3 3.3 1,344.8 1,286.1 7,216.3 8,582.8 305.9 0.3 5,265.5

Fertilizer consumption

% of fertilizer production 2008

227.2 190.6 117.6 148.1 132.8 .. 2.6 264.9 .. 135.7 3.1 38.1 .. .. 134.0 .. 3.2 .. .. .. 8.6 .. .. 17.2 13.5 .. .. 3,197.3 242.6 .. .. .. 319.7 .. .. 40.0 .. 1,515.4 .. .. 17.9 320.3 .. .. 1,929.1 24.4 2.4 115.6 .. .. .. .. 973.8 100.0 190.1 .. 42.1

kilograms per hectare of arable land 2008

94.3 153.5 189.1 90.9 43.8 480.3 252.6 156.0 51.3 278.2 337.4 3.1 33.3 .. 479.5 .. 1,250.9 19.0 .. 124.3 56.2 .. .. 27.3 79.1 56.2 4.3 1.7 929.9 9.0 .. 210.1 44.7 12.5 8.2 53.8 0.0 3.3 0.3 7.7 269.1 1,721.0 32.3 0.4 13.3 219.0 395.0 163.3 35.3 78.6 66.8 81.6 131.2 190.4 236.5 .. 45.6

3.2

environment

Agricultural inputs Agricultural employment

Agricultural machinery

% of total employment 1990 2008

Tractors per 100 sq. km of arable land 1990 2008

18.2 .. 55.9 .. .. 15.1 4.1 8.8 27.3 7.2 6.6 .. .. .. 17.9 .. 1.3 32.7 .. .. .. .. .. .. .. .. .. .. 26.0 .. .. 16.7 22.6 33.8 39.5 3.9 .. 69.7 48.2 81.2 4.5 10.6 39.3 .. .. 6.4 9.3 51.1 29.1 .. 1.9 1.2 45.2 25.2 17.9 3.6 29.1

4.5 .. 41.2 22.8 .. 5.6 1.6 3.8 18.2 4.2 .. .. .. .. 7.4 .. .. 36.3 .. 7.7 .. .. .. .. 7.7 18.2 82.0 .. 14.8 .. .. 9.1 13.5 32.8 40.6 43.3 .. .. .. .. 2.7 7.2 29.1 .. .. 2.8 .. 43.6 14.7 .. 29.5 9.3 36.1 14.7 11.5 1.1 28.7

97.7 60.7 2.2 141.5 65.8 1,623.4 798.8 1,586.5 .. 4,492.9 340.4 62.0 20.0 .. 211.0 .. 220.0 189.4 .. 363.7 174.9 57.7 .. 184.3 256.0 730.3 4.9 .. 152.9 10.2 8.4 .. 123.5 310.1 80.3 45.0 .. 13.6 .. 21.9 2,073.1 .. 20.0 0.2 4.7 1,779.1 41.1 129.7 102.0 59.4 71.6 36.3 65.2 823.6 563.1 438.5 140.6

2011 World Development Indicators

261.9 .. .. 182.8 .. 1,476.4 705.3 .. .. 4,382.4 366.8 17.7 .. .. 1,632.5 .. 95.6 191.1 .. 501.4 .. .. .. .. 631.6 1,243.8 .. .. .. 2.7 9.8 .. 97.2 197.5 38.0 .. .. 10.9 .. 122.9 1,301.5 .. .. .. 6.6 1,539.1 .. 204.8 .. .. 61.5 .. .. 1,246.0 1,397.7 525.0 200.4

131


3.2

Agricultural inputs Agricultural landa

% of land area 1990 2008

Russian Federation Rwanda Qatar 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

14 76 5 .. 46 .. 39 3 51 28 70 80 61 37 52 72 8 40 73 32 38 42 21 59 15 56 52 69 61 72 3 75 47 85 65 25 21 62 45 27 34 37 w 35 37 49 30 37 49 28 34 24 55 42 39 51

% irrigated 2008

13 82 6 .. 48 57 58 1 40 25 70 82 56 42 58 71 8 39 76 34 39 38 25 67 11 64 51 69 66 71 7 73 45 85 63 24 32 61 45 30 41 38 w 37 38 50 30 38 48 28 35 23 55 45 37 44

2.0 .. .. .. 0.7 0.5 .. .. 1.3 0.8 .. .. 11.9 .. 1.3 .. .. .. 9.8 15.0 .. .. .. .. .. 4.0 13.3 .. .. 5.3 .. .. .. 1.2 .. .. .. 4.6 3.3 .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. ..

Average annual precipitation

millimeters 2008

460 1,212 74 59 686 .. 2,526 2,497 824 1,162 282 495 636 1,712 416 788 624 1,537 252 691 1,071 1,622 .. 1,168 2,200 207 593 161 1,180 565 78 1,220 715 1,265 206 1,875 1,821 402 167 1,020 657                          

Land under cereal production

thousand hectares 1990

59,541.3 254.1 1.1 974.6 1,229.0 .. 468.6 .. 836.6 112.5 732.5 6,162.9 7,551.4 869.8 3,734.6 85.7 1,285.2 211.9 4,134.4 266.5 2,629.3 10,536.9 82.6 648.0 5.9 1,445.7 13,640.1 331.3 1,055.0 12,542.3 1.3 3,657.3 65,700.0 514.6 1,225.3 753.9 6,474.6 0.0 844.9 895.2 1,576.1 708,090.3 s 63,834.3 482,334.4 307,764.8 174,569.6 546,168.7 142,232.3 127,839.3 47,401.7 29,953.1 131,803.8 66,938.5 161,921.6 34,697.5

2009

2011 World Development Indicators

% of fertilizer production 2008

kilograms per hectare of arable land 2008

41,715.7 11.9 15.9 368.2 .. 8.3 2.0 0.3 276.9 466.4 14.5 75.2 1,647.2 53.7 2.4 1,919.0 252.9 115.2 1,111.4 .. .. 1,918.9 .. .. 768.7 81.1 130.0 101.9 .. 283.6 470.2 .. .. 3,318.7 262.4 49.7 6,043.3 84.8 106.5 1,017.9 2,961.4 284.3 9,453.8 .. 3.6 48.7 .. .. 1,032.1 414.7 142.1 153.0 .. 226.3 2,774.1 184.5 88.0 409.5 452.5 0.0 5,087.0 .. 6.0 12,282.7 1,462.8 130.9 103.4 .. .. 826.7 .. 4.9 2.1 17.8 2,337.2 876.6 8.7 32.1 11,955.9 242.3 88.7 970.3 .. .. 1,826.0 .. 3.4 15,114.8 40.7 32.8 0.0 7.6 336.3 3,173.0 133.5 208.2 58,001.4 96.2 103.1 1,049.4 1,492.7 118.3 1,607.5 .. .. 1,237.1 74.5 232.9 8,528.5 218.6 286.6 35.0 .. .. 672.8 .. 14.3 1,062.5 .. 50.1 2,236.8 164.7 27.9 708,451.8 s 94.9 w 119.4 w 92,275.5 221.9 16.7 468,186.3 103.8 139.6 321,963.9 115.0 191.8 146,222.5 76.9 70.7 560,461.8 104.8 123.1 148,824.2 108.5 .. 104,480.3 26.9 33.3 50,290.2 279.7 111.8 27,642.2 57.6 95.3 133,310.2 176.9 148.0 95,914.7 578.1 11.6 147,990.0 73.6 109.3 31,367.7 95.1 150.5

a. Includes permanent pastures, arable land, and land under permanent crops. b. Includes Luxembourg.

132

Fertilizer consumption

Agricultural employment

Agricultural machinery

% of total employment 1990 2008

Tractors per 100 sq. km of arable land 1990 2008

13.9 90.1 .. .. .. .. .. 0.4 .. 10.7 .. .. 11.5 47.8 .. .. 3.4 4.2 26.5 44.7 .. 63.3 .. .. 12.3 25.8 46.9 .. .. 19.8 .. 2.1 2.9 0.0 .. 13.4 .. .. 52.6 49.8 .. .. w .. .. .. 20.8 .. 53.6 22.9 18.7 .. .. .. 6.5 7.2

9.0 97.8 .. 1.0 3.0 84.0 4.7 19.2 33.7 1.6 20.8 .. .. 4.1 1.1 .. 4.0 197.5 10.2 .. .. 16.0 8.8 107.9 4.3 483.1 31.3 .. .. 7.2 .. 228.9 2.2 601.9 3.9 2,783.1 .. 128.1 .. 415.4 74.6 8.2 41.7 33.0 .. 8.5 .. 0.5 4.3 .. .. 82.4 26.2 279.8 .. 464.7 .. .. 16.7 153.3 4.9 51.4 1.4 760.6 1.4 238.4 11.0 260.4 .. .. 8.7 .. .. 47.0 15.6 442.2 .. 39.0 .. 27.2 .. 60.1 .. w 186.4 w .. 15.7 .. 91.7 .. 66.0 14.9 125.1 .. 85.0 .. 53.2 16.3 132.3 15.8 120.9 .. 114.6 .. 62.2 .. 20.2 3.5 474.9 3.8 977.3

30.0 .. 56.2 .. .. 17.7 .. .. 154.7 .. 12.0 .. 824.4 .. 12.4 87.1 592.4 2,597.2 233.9 216.1 .. .. .. 0.5 .. 142.6 488.5 .. .. 103.3 .. .. 257.6 222.4 .. .. .. 767.9 .. .. .. .. w .. .. .. .. .. .. 111.0 .. 190.2 119.9 .. .. 811.1


3.2

environment

Agricultural inputs About the data Agriculture is still a major sector in many economies,

land as share of total agricultural land area and data

(July–June). Previous editions of World Development

and agricultural activities provide developing coun-

on average precipitation to illustrate how countries

Indicators reported data on a crop year basis, but

tries with food and revenue. But agricultural activi-

obtain water for agricultural use.

this edition uses the calendar year, as adopted by the FAO. Caution should thus be used when compar-

ties also can degrade natural resources. Poor farming

The data here and in table 3.3 are collected by

practices can cause soil erosion and loss of soil fertil-

the Food and Agriculture Organization of the United

ity. Efforts to increase productivity by using chemical

Nations (FAO) through annual questionnaires. The

fertilizers, pesticides, and intensive irrigation have

FAO tries to impose standard definitions and report-

environmental costs and health impacts. Excessive

ing methods, but complete consistency across

• Agricultural land is permanent pastures, arable,

use of chemical fertilizers can alter the chemistry of

countries and over time is not possible. Thus, data

and land under permanent crops. Permanent pasture

soil. Pesticide poisoning is common in developing

on agricultural land in different climates may not

is land used for five or more years for forage, includ-

countries. And salinization of irrigated land dimin-

be comparable. For example, permanent pastures

ing natural and cultivated crops. Arable land includes

ishes soil fertility. Thus, inappropriate use of inputs

are quite different in nature and intensity in African

land defined by the FAO as land under temporary

for agricultural production has far-reaching effects.

countries and dry Middle Eastern countries. Data

crops (double-cropped areas are counted once),

ing data over time. Definitions

The table provides indicators of major inputs to

on agricul-tural employment, in particular, should

temporary meadows for mowing or for pasture, land

agricultural production: land, fertilizer, labor, and

be used with caution. In many countries much agri-

under market or kitchen gardens, and land tempo-

machinery. There is no single correct mix of inputs:

cultural employment is informal and unrecorded,

rarily fallow. Land abandoned as a result of shift-

appropriate levels and application rates vary by coun-

including substantial work performed by women

ing cultivation is excluded. Land under permanent

try and over time and depend on the type of crops, the

and children. To address some of these concerns,

crops is land cultivated with crops that occupy the

climate and soils, and the production process used.

this indicator is heavily footnoted in the database in

land for long periods and need not be replanted after

sources, definition, and coverage.

each harvest, such as cocoa, coffee, and rubber.

The agriculture sector is the most water-intensive sector, and water delivery in agriculture is increas-

Fertilizer consumption measures the quantity of

Land under flowering shrubs, fruit trees, nut trees,

ingly important. The table shows irrigated agricultural

plant nutrients. Consumption is calculated as pro-

and vines is included, but land under trees grown

duction plus imports minus exports. Because some

for wood or timber is not. • Irrigated land refers to

chemical compounds used for fertilizers have other

areas purposely provided with water, including land

industrial applications, the consumption data may

irrigated by controlled flooding. • Average annual

overstate the quantity available for crops. Fertilizer

precipitation is the long-term average in depth

consumption as a share of production shows the

(over space and time) of annual precipitation in the

agriculture sector’s vulnerability to import and energy

country. Precipitation is defined as any kind of water

price fluctuation. The FAO recently revised the time

that falls from clouds as a liquid or a solid. • Land

series for fertilizer consumption and irrigation for

under cereal production refers to harvested areas,

2002 onward, but recent data are not available for

although some countries report only sown or culti-

all countries. FAO collects fertilizer statistics for pro-

vated area. • Fertilizer consumption is the quantity

duction, imports, exports, and consumption through

of plant nutrients applied to arable land. Fertilizer

the new FAO fertilizer resources questionnaire. In

products cover nitrogen, potash, and phosphate

the previous release, the data were based on total

fertilizers (including ground rock phosphate). Tradi-

consumption of fertilizers, but the data in the recent

tional nutrients—animal and plant manures—are

release are based on the nutrients in fertilizers.

not included. • Fertilizer production is fertilizer

Some countries compile fertilizer data on a calendar

consumption, exports, and nonfertilizer use of fertil-

year basis, while others do so on a crop year basis

izer products minus fertilizer imports. • Agricultural

Nearly 40 percent of land globally is devoted to agriculture

3.2a

Total land area in 2008: 130 million sq. km

Permanent pastures 26%

Other 31%

Arable land 11% Forests 31% Permanent crops 1% Note: Agricultural land includes permanent pastures, arable land, and land under permanent crops. Source: Tables 3.1 and 3.2.

employment is employment in agriculture, forestry,

Rainfed agriculture plays a significant role in Sub-Saharan agriculture where about 95 percent of cropland depends on precipitation, 2008

3.2b

hunting, and fishing (see table 2.3). • Agricultural machinery refers to wheel and crawler tractors (excluding garden tractors) in use in agriculture at

Sierra Leone Liberia Mauritius Gabon Guinea Congo, Rep. Cameroon Guinea-Bissau Congo, Dem. Rep. Madagascar

the end of the calendar year specified or during the first quarter of the following year.

Data sources 0

500

1,000

1,500

2,000

Average annual precipitation (millimeters per year) Source: Table 3.2.

2,500

3,000

Data on agricultural inputs are from electronic files that the FAO makes available to the World Bank and from the FAO web site (www.fao.org).

2011 World Development Indicators

133


3.3

Agricultural output and productivity Crop production index

1999–2001 = 100 1990 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

134

96.0 107.0 66.0 56.0 64.0 106.0 58.0 97.0 136.0 74.0 110.0 72.0a 53.0 62.0 107.0 102.0 74.0 160.0 62.0 109.0 66.0 69.0 90.0 75.0 59.0 74.0 68.0 .. 91.0 122.0 81.0 68.0 71.0 80.0 119.0 .. 113.0 118.0 75.0 66.0 95.0 .. 121.0 .. 116.0 96.0 81.0 55.0 122.0 86.0 43.0 75.0 73.0 71.0 72.0 111.0 100.0

2011 World Development Indicators

176.0 140.0 196.0 250.0 101.0 195.0 88.0 108.0 152.0 130.0 161.0 113.0 110.0 126.0 125.0 120.0 149.0 103.0 144.0 108.0 202.0 117.0 119.0 110.0 118.0 113.0 130.0 .. 120.0 97.0 116.0 118.0 109.0 96.0 82.0 98.0 113.0 111.0 114.0 136.0 102.0 154.0 127.0 153.0 115.0 101.0 104.0 114.0 61.0 102.0 156.0 76.0 138.0 133.0 120.0 110.0 153.0

Food production index

1999–2001 = 100 1990 2009

77.0 82.0 69.0 61.0 72.0 108.0 68.0 89.0 107.0 72.0 135.0 85.0a 58.0 72.0 119.0 109.0 65.0 151.0 62.0 109.0 64.0 72.0 84.0 68.0 64.0 71.0 59.0 .. 81.0 119.0 79.0 68.0 73.0 100.0 127.0 .. 100.0 101.0 67.0 64.0 85.0 .. 180.0 .. 113.0 97.0 91.0 60.0 99.0 102.0 46.0 84.0 72.0 72.0 73.0 101.0 90.0

127.0 115.0 163.0 198.0 106.0 191.0 95.0 97.0 151.0 132.0 157.0 96.0 116.0 133.0 138.0 113.0 148.0 76.0 136.0 110.0 184.0 120.0 119.0 123.0 125.0 120.0 133.0 .. 128.0 98.0 123.0 126.0 120.0 104.0 83.0 99.0 107.0 131.0 126.0 139.0 116.0 126.0 134.0 151.0 104.0 98.0 103.0 117.0 66.0 103.0 155.0 83.0 141.0 133.0 122.0 112.0 145.0

Livestock production index

1999–2001 = 100 1990 2009

63.0 65.0 80.0 72.0 90.0 108.0 82.0 90.0 102.0 70.0 146.0 88.0a 88.0 80.0 119.0 110.0 58.0 165.0 65.0 131.0 59.0 83.0 76.0 64.0 82.0 66.0 45.0 .. 79.0 102.0 73.0 77.0 90.0 126.0 152.0 .. 86.0 74.0 61.0 62.0 74.0 .. 192.0 .. 111.0 95.0 84.0 95.0 78.0 115.0 89.0 105.0 81.0 56.0 78.0 65.0 66.0

89.0 92.0 121.0 92.0 108.0 177.0 95.0 94.0 152.0 137.0 150.0 91.0 135.0 139.0 167.0 112.0 142.0 63.0 132.0 118.0 109.0 105.0 105.0 132.0 120.0 134.0 133.0 .. 140.0 96.0 157.0 126.0 132.0 129.0 109.0 90.0 104.0 151.0 140.0 134.0 131.0 101.0 118.0 140.0 100.0 94.0 100.0 132.0 67.0 105.0 127.0 99.0 118.0 167.0 128.0 111.0 133.0

Cereal yield

Agricultural productivity

kilograms per hectare 1990 2009

Agriculture value added per worker 2000 $ 1990 2009

1,201 2,794 688 321 2,232 1,843 1,716 5,577 2,113 2,491 2,741 5,755a 848 1,361 3,553 265 1,755 3,954 600 1,349 1,362 1,241 2,636 807 559 3,620 4,323 .. 2,475 800 624 3,097 887 3,975 2,342 .. 6,118 3,996 1,724 5,703 1,939 .. 1,304 .. 3,543 6,083 1,643 1,004 1,998 5,411 989 3,036 1,998 1,455 1,531 1,027 1,468

1,983 4,315 1,654 588 3,167 2,230 1,764 6,136 2,607 3,890 3,372 9,632 1,330 2,089 4,539 465 3,526 3,413 1,035 1,313 2,947 1,574 3,301 948 812 5,472 5,460 .. 4,017 772 776 3,770 1,724 6,117 2,069 5,074 6,810 4,246 2,974 7,635 2,727 938 2,761 1,652 3,760 7,460 1,663 1,053 1,917 7,201 1,660 4,103 1,624 1,711 1,422 961 1,752

.. 764 1,703 200 6,702 1,607 20,150 13,413 1,000 251 2,042 .. 422 681 .. 770 1,625 3,983 113 116 .. 419 28,898 321 168 3,453 263 .. 3,122 209 .. 2,984 653 5,546 4,117 .. 14,588 1,925 2,105 1,737 1,742 .. 3,288 .. 17,163 21,423 1,216 272 2,359 13,669 .. 6,707 2,243 156 .. .. 1,180

.. .. 2,184 313 9,987 5,049 29,257 24,715 1,342 435 5,184 42,035 .. 733 14,299 597 3,760 10,227 .. .. 411 730 44,752 .. .. 6,618 525 .. 2,861 168 .. 5,232 926 15,137 3,647 5,687 45,905 4,579 1,766 3,024 2,778 66 3,207 215 43,813 58,070 1,869 275 1,872 31,659 .. 10,779 2,783 225 .. .. 1,958


Crop production index

1999–2001 = 100 1990 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

119.0 78.0 80.0 71.0 105.0 91.0 112.0 88.0 83.0 115.0 102.0 163.0 79.0 111.0 88.0 .. 57.0 68.0 65.0 129.0 99.0 101.0 71.0 78.0 79.0 108.0 93.0 54.0 74.0 68.0 60.0 108.0 81.0 135.0 293.0 100.0 70.0 60.0 77.0 75.0 92.0 78.0 70.0 64.0 60.0 143.0 59.0 77.0 116.0 76.0 93.0 51.0 87.0 124.0 110.0 153.0 51.0

102.0 116.0 145.0 117.0 83.0 84.0 108.0 91.0 95.0 89.0 157.0 150.0 107.0 107.0 96.0 .. 122.0 108.0 157.0 147.0 96.0 72.0 115.0 102.0 138.0 112.0 115.0 141.0 141.0 162.0 116.0 95.0 111.0 100.0 270.0 142.0 130.0 151.0 140.0 135.0 100.0 109.0 117.0 210.0 134.0 90.0 95.0 125.0 114.0 112.0 122.0 145.0 131.0 99.0 86.0 105.0 121.0

Food production index

1999–2001 = 100 1990 2009

123.0 75.0 80.0 69.0 111.0 94.0 90.0 90.0 75.0 109.0 81.0 163.0 83.0 104.0 79.0 .. 53.0 78.0 60.0 222.0 92.0 91.0 88.0 77.0 157.0 108.0 91.0 47.0 67.0 79.0 86.0 95.0 74.0 159.0 101.0 93.0 68.0 61.0 98.0 76.0 102.0 74.0 63.0 61.0 60.0 112.0 57.0 67.0 91.0 77.0 76.0 55.0 78.0 119.0 101.0 125.0 64.0

100.0 119.0 146.0 124.0 92.0 90.0 122.0 95.0 100.0 95.0 156.0 145.0 126.0 112.0 101.0 .. 114.0 103.0 148.0 138.0 111.0 72.0 131.0 109.0 138.0 115.0 114.0 129.0 144.0 183.0 116.0 106.0 117.0 105.0 110.0 140.0 102.0 162.0 101.0 130.0 94.0 115.0 135.0 186.0 135.0 95.0 102.0 132.0 116.0 119.0 136.0 153.0 131.0 111.0 95.0 94.0 77.0

Livestock production index

1999–2001 = 100 1990 2009

140.0 70.0 81.0 64.0 140.0 93.0 69.0 94.0 64.0 106.0 52.0 178.0 89.0 124.0 62.0 .. 63.0 110.0 57.0 274.0 64.0 86.0 91.0 77.0 185.0 101.0 99.0 78.0 71.0 94.0 91.0 57.0 68.0 197.0 94.0 81.0 47.0 51.0 101.0 78.0 101.0 77.0 59.0 56.0 70.0 101.0 65.0 64.0 67.0 79.0 81.0 65.0 57.0 123.0 82.0 119.0 77.0

85.0 133.0 157.0 142.0 128.0 93.0 130.0 103.0 110.0 101.0 139.0 141.0 147.0 133.0 106.0 .. 102.0 105.0 126.0 126.0 149.0 78.0 127.0 116.0 116.0 115.0 111.0 153.0 133.0 153.0 115.0 138.0 123.0 98.0 101.0 128.0 89.0 248.0 90.0 120.0 101.0 114.0 149.0 153.0 121.0 95.0 122.0 135.0 116.0 127.0 134.0 158.0 134.0 110.0 103.0 91.0 46.0

3.3

environment

Agricultural output and productivity Cereal yield

Agricultural productivity

kilograms per hectare 1990 2009

Agriculture value added per worker 2000 $ 1990 2009

4,521 1,891 3,800 1,445 1,061 6,577 3,484 3,945 1,116 5,846 1,220 1,338 1,562 3,926 5,853 .. 3,653 2,772 2,268 1,641 1,878 1,036 1,029 674 1,938 2,652 1,945 992 2,740 726 870 4,193 2,424 2,928 1,098 1,120 474 2,762 457 1,920 6,959 5,034 1,524 310 1,148 4,399 2,160 1,766 1,867 2,395 1,979 2,601 2,065 3,284 1,878 1,080 2,897

4,713 2,471 4,813 2,291 1,222 6,798 3,250 5,035 1,253 5,920 1,044 1,254 1,204 3,698 7,073 .. 2,679 3,034 3,808 3,075 2,828 421 1,553 623 3,450 3,387 2,291 1,599 3,750 1,588 873 7,895 3,111 2,417 1,552 1,003 846 3,585 465 2,374 9,032 6,922 1,872 489 1,598 3,094 3,358 2,803 2,735 3,727 2,358 3,910 3,229 3,475 3,455 1,897 3,820

4,232 362 512 1,906 .. .. .. 10,410 2,224 20,934 2,077 1,781 400 .. 5,338 .. .. 684 387 1,896 .. 260 .. .. .. 2,413 214 89 3,850 406 653 3,446 2,275 1,349 1,241 1,806 132 .. 1,267 247 23,593 19,782 .. 235 .. 17,454 1,037 739 2,303 563 1,657 907 911 1,605 4,495 .. ..

2011 World Development Indicators

10,948 468 734 3,061 .. 13,573 .. 29,498 2,716 52,062 3,030 2,033 334 .. 19,105 .. .. 1,041 516 3,636 41,037 207 .. .. 5,369 5,811 192 162 6,529 523 408 5,556 3,364 1,531 1,888 3,306 220 .. 1,638 238 45,969 25,446 2,495 .. .. 40,666 .. 903 4,185 672 1,338 1,545 1,204 2,776 6,764 .. ..

135


3.3

Agricultural output and productivity Crop production index

Food production index

1999–2001 = 100 1990 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

98.0 126.0 97.0 118.0 72.0 98.0 b 127.0 223.0 .. 82.0 144.0 86.0 91.0 90.0 49.0 112.0 126.0 112.0 66.0 123.0 91.0 76.0 88.0 71.0 120.0 93.0 87.0 98.0 77.0 131.0 16.0 101.0 86.0 66.0 107.0 77.0 57.0 .. 75.0 81.0 77.0 81.0c w 76.2 73.6 71.9 79.2 73.8 69.9 111.3 75.8 74.7 78.0 71.1 90.7 90.9

100.0 136.0 132.0 124.0 130.0 .. 204.0 440.0 102.0 94.0 96.0 111.0 97.0 115.0 112.0 101.0 103.0 103.0 115.0 148.0 154.0 129.0 105.0 109.0 89.0 119.0 112.0 128.0 109.0 155.0 52.0 96.0 112.0 187.0 145.0 116.0 137.0 104.0 130.0 170.0 55.0 122.2 w 134.1 128.2 128.6 126.9 128.7 133.1 129.3 128.1 127.3 119.3 128.7 103.9 96.9

1999–2001 = 100 1990 2009

105.0 129.0 94.0 103.0 73.0 109.0 b 121.0 335.0 .. 76.0 101.0 87.0 88.0 91.0 51.0 106.0 110.0 104.0 71.0 134.0 86.0 77.0 94.0 74.0 90.0 81.0 88.0 60.0 78.0 147.0 19.0 105.0 82.0 74.0 93.0 73.0 59.0 .. 71.0 87.0 87.0 80.0c w 76.5 68.8 66.5 75.6 69.4 62.7 117.7 71.2 72.8 74.5 72.9 90.4 95.6

a. Includes Luxembourg. b. Includes Montenegro. c. FAO estimate.

136

2011 World Development Indicators

107.0 130.0 134.0 124.0 134.0 .. 201.0 132.0 97.0 97.0 104.0 122.0 97.0 120.0 119.0 115.0 100.0 104.0 131.0 162.0 134.0 126.0 111.0 132.0 125.0 115.0 119.0 137.0 112.0 123.0 45.0 98.0 115.0 144.0 155.0 122.0 138.0 102.0 144.0 135.0 82.0 123.0 w 134.4 130.2 130.5 129.3 130.5 135.1 126.2 131.2 131.6 122.7 130.0 106.3 97.7

Livestock production index

1999–2001 = 100 1990 2009

124.0 147.0 79.0 64.0 80.0 103.0 b 105.0 481.0 .. 76.0 95.0 93.0 77.0 88.0 58.0 108.0 100.0 103.0 74.0 196.0 75.0 74.0 89.0 85.0 72.0 57.0 91.0 64.0 79.0 170.0 54.0 105.0 81.0 83.0 99.0 73.0 50.0 .. 63.0 83.0 79.0 82.0c w 78.3 62.5 56.5 75.1 63.3 48.8 136.8 69.7 69.3 69.2 80.1 90.7 98.2

115.0 118.0 158.0 135.0 144.0 .. 144.0 105.0 80.0 97.0 105.0 130.0 103.0 123.0 123.0 140.0 93.0 106.0 143.0 168.0 104.0 111.0 114.0 137.0 149.0 110.0 125.0 129.0 120.0 101.0 125.0 99.0 108.0 127.0 137.0 128.0 157.0 93.0 165.0 106.0 107.0 120.3 w 131.0 131.5 132.6 129.4 131.4 135.2 119.1 132.6 134.7 132.9 125.1 104.1 100.0

Cereal yield

Agricultural productivity

kilograms per hectare 1990 2009

Agriculture value added per worker 2000 $ 1990 2009

3,011 1,743 1,043 4,245 795 2,926 b 1,202 .. .. 3,279 793 1,877 2,485 2,965 456 1,278 4,964 5,984 750 1,020 1,506 2,009 1,608 747 2,826 1,143 2,214 2,210 1,498 2,834 2,216 6,171 4,755 2,182 1,777 2,486 3,073 .. 908 1,352 1,625 2,755c w 1,561 2,563 2,696 2,103 2,429 3,795 2,596 2,089 1,471 1,926 1,033 4,138 4,490

2,825 2,279 1,097 5,212 1,135 4,626 989 .. 4,335 5,266 417 4,395 2,957 3,722 587 560 5,086 6,579 1,707 2,250 1,224 2,954 1,276 1,136 2,659 1,401 2,808 2,974 1,539 3,004 2,000 7,008 7,238 4,047 4,578 3,826 5,075 1,684 1,003 2,068 313 3,514 w 1,966 3,210 3,446 2,690 3,005 4,843 2,471 3,282 2,352 2,628 1,302 5,439 5,822

2,351 1,917 172 7,863 252 .. .. .. .. 13,217 .. 2,290 8,947 678 501 1,025 23,307 20,451 2,613 370 220 446 .. 351 1,825 2,736 2,175 1,272 177 1,232 9,042 21,400 18,523 6,166 1,427 4,443 225 .. 428 212 270 803 w 236 489 360 2,270 460 313 2,188 2,227 1,760 372 304 14,116 11,982

8,993 3,031 .. 20,431 245 .. .. 49,867 9,728 67,838 .. 3,641 21,831 926 922 1,176 51,057 26,726 4,717 542 283 708 .. .. 1,502 3,602 3,491 2,930 203 2,461 .. 26,370 49,512 9,064 2,584 7,941 356 .. .. 216 141 998 w 278 777 604 3,683 704 570 3,182 3,436 2,896 495 318 25,066 26,730


About the data

3.3

environment

Agricultural output and productivity Definitions

The agricultural production indexes in the table are

production. These prices, expressed in international

• Crop production index is agricultural production

prepared by the Food and Agriculture Organization of

dollars (equivalent in purchasing power to the U.S.

for each period relative to the average over the base

the United Nations (FAO). The FAO obtains data from

dollar), are derived using a Geary-Khamis formula

period 1999–2001. It includes all crops except fod-

official and semiofficial reports of crop yields, area

applied to agricultural outputs (see Inter-Secretariat

der crops. The regional and income group aggregates

under production, and livestock numbers. If data are

Working Group on National Accounts 1993, sections

for the FAO’s production indexes are calculated from

unavailable, the FAO makes estimates. The indexes

16.93–96). This method assigns a single price to

the underlying values in international dollars, normal-

are calculated using the Laspeyres formula: produc-

each commodity so that, for example, one metric ton

ized to the average over the base period 1999–2001.

tion quantities of each commodity are weighted by

of wheat has the same price regardless of where it

• Food production index covers food crops that are

average international commodity prices in the base

was produced. The use of international prices elimi-

considered edible and that contain nutrients. Cof-

period and summed for each year. Because the FAO’s

nates fluctuations in the value of output due to transi-

fee and tea are excluded because, although edible,

indexes are based on the concept of agriculture as a

tory movements of nominal exchange rates unrelated

they have no nutritive value. • Livestock production

single enterprise, estimates of the amounts retained

to the purchasing power of the domestic currency.

index includes meat and milk from all sources, dairy

for seed and feed are subtracted from the produc-

Data on cereal yield may be affected by a variety

products such as cheese, and eggs, honey, raw silk,

tion data to avoid double counting. The aggregates

of reporting and timing differences. Millet and sor-

wool, and hides and skins. • Cereal yield, measured

represent production available for any use except as

ghum, which are grown as feed for livestock and poul-

in kilograms per hectare of harvested land, includes

seed and feed and presented as “net”. The FAO’s

try in Europe and North America, are used as food

wheat, rice, maize, barley, oats, rye, millet, sorghum,

indexes may differ from those from other sources

in Africa, Asia, and countries of the former Soviet

buckwheat, and mixed grains. Production data on

because of differences in coverage, weights, con-

Union. So some cereal crops are excluded from the

cereals refer to crops harvested for dry grain only.

cepts, time periods, calculation methods, and use

data for some countries and included elsewhere,

Cereal crops harvested for hay or harvested green for

of international prices.

depending on their use.

food, feed, or silage, and those used for grazing, are

To facilitate cross-country comparisons, the

excluded. The FAO allocates production data to the

FAO uses international commodity prices to value

calendar year in which the bulk of the harvest took place. But most of a crop harvested near the end of

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

a year will be used in the following year. • Agricul-

3.3a

tural productivity is the ratio of agricultural value added, measured in 2000 U.S. dollars, to the num-

Index (1999–2001=100)

ber of workers in agriculture. Agricultural productivity is measured by value added per unit of input. (For

160 World

Middle income

120

further discussion of the calculation of value added in national accounts, see About the data for tables 4.1 and 4.2.) Agricultural value added includes that

90 High income

Low income

60

from forestry and fishing. Thus interpretations of land productivity should be made with caution.

30 0

1990

1995

2000

2005

2008

Source: Table 3.3.

Cereal yield in Sub-Saharan Africa increased between 1990 and 2009 but still is the lowest among the regions

3.3b

6 Kilograms per hectare (thousands)

1990

2009

5 4

Data sources

3

Data on agricultural production indexes, cereal yield, and agricultural employment are from elec-

2

tronic files that the FAO makes available to the 1 0

Source: Table 3.3.

World Bank. The files may contain more recent information than published versions. Data on agriEast Asia &

Europe & Central Asia

Latin America & Caribbean

Middle East & North Africa

South Asia

Sub-Saharan Africa

cultural value added are from the World Bank’s national accounts files.

2011 World Development Indicators

137


3.4

Deforestation and biodiversity Forest area

Average annual deforestationa

thousand sq. km

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

138

1990

2010

14 8 17 610 348 3 1,545 38 9 15 78 7 58 628 22 137 5,748 33 68 3 129 243 3,101 232 131 153 1,571 .. 625 1,604 227 26 102 19 21 26 4 20 138 0 4 16 21 151 219 145 220 4 28 107 74 33 47 73 22 1 81

14 8 15 585 294 3 1,493 39 9 14 86 7 46 572 22 114 5,195 39 56 2 101 199 3,101 226 115 162 2,069 .. 605 1,541 224 26 104 19 29 27 5 20 99 1 3 15 22 123 222 160 220 5 27 111 49 39 37 65 20 1 52

2011 World Development Indicators

% 1990–2000 2000–10

0.00 0.26 0.54 0.21 0.88 1.31 –0.03 –0.16 0.00 0.18 –0.62 0.15 1.29 0.44 0.11 0.90 0.51 –0.14 0.91 3.71 1.14 0.94 0.00 0.13 0.62 –0.37 –1.20 .. 0.16 0.20 0.08 0.76 –0.10 –0.19 –1.70 –0.03 –0.89 0.00 1.53 –2.98 1.26 0.28 –0.71 0.97 –0.26 –0.55 0.00 –0.42 0.04 –0.31 1.99 –0.88 1.20 0.51 0.44 0.62 2.38

0.00 –0.09 0.57 0.21 0.80 1.48 0.37 –0.13 0.00 0.18 –0.42 –0.16 1.03 0.49 0.00 0.99 0.49 –1.53 1.00 1.40 1.33 1.04 0.00 0.13 0.66 –0.25 –1.57 .. 0.17 0.20 0.06 –0.92 –0.07 –0.18 –1.66 –0.08 –1.13 0.00 1.81 –1.72 1.45 0.28 0.12 1.08 0.14 –0.38 0.00 –0.40 0.09 0.00 2.08 –0.81 1.39 0.53 0.47 0.76 2.06

Threatened species

GEF benefits index for biodiversity

Terrestrial protected areas

Marine protected areas

% of total land area

% of territorial waters

Mammals

Birds

Fish

Higher plantsb

0–100 (no biodiversity to maximum biodiversity)

2010

2010

2010

2010

2008

11 3 14 15 37 9 55 3 7 34 4 3 11 20 4 7 80 7 9 10 37 39 12 8 13 20 74 2 51 30 11 9 24 7 14 2 2 6 43 17 5 10 1 32 1 9 14 10 10 6 16 10 16 22 12 5 7

13 6 11 21 50 10 52 8 15 29 4 2 5 33 6 9 123 12 6 10 24 16 15 7 9 34 85 17 91 34 3 19 14 10 17 6 2 14 71 10 5 10 3 23 4 7 5 6 10 6 9 11 10 13 3 13 9

5 38 33 37 36 3 100 11 10 19 2 10 27 0 31 2 80 18 4 17 28 110 32 3 1 19 97 11 50 81 45 46 43 56 30 2 14 17 49 36 12 18 4 14 5 40 59 21 9 21 42 73 20 63 30 17 22

2 0 15 33 44 1 67 4 0 16 0 1 14 72 1 0 387 0 3 2 30 378 2 17 2 41 453 6 227 83 37 116 106 3 166 4 3 30 1,837 2 27 3 0 26 1 15 120 4 0 12 118 13 82 22 4 29 113

3.4 0.2 2.9 8.3 17.7 0.2 87.7 0.3 0.8 1.4 0.0 0.0 0.2 12.5 0.4 1.4 100.0 0.8 0.3 0.3 3.5 12.5 21.5 1.5 2.2 15.3 66.6 .. 51.5 19.9 3.6 9.7 3.4 0.6 12.5 0.1 0.2 6.0 29.3 2.9 0.9 0.8 0.1 8.4 0.2 5.3 3.0 0.1 0.6 0.6 1.9 2.8 8.0 2.3 0.6 5.2 7.2

1990

0.4 4.3 6.3 12.4 4.6 6.9 7.4 20.1 6.2 1.5 6.5 0.6 23.8 8.5 0.5 30.3 10.8 1.9 13.3 3.8 0.0 7.0 6.0 14.4 9.4 16.0 13.5 41.1 20.3 10.0 5.4 18.7 22.6 7.1 4.3 13.7 4.8 22.1 21.6 1.9 0.6 4.9 19.6 17.7 4.2 10.1 4.2 1.5 2.8 31.8 13.9 5.7 26.0 6.8 7.6 0.3 13.6

2009

0.4 9.8 6.3 12.4 5.4 8.0 10.5 22.9 7.1 1.6 7.3 0.9 23.8 18.2 0.6 30.9 28.0 9.1 13.9 4.8 24.0 9.2 8.0 14.7 9.4 16.5 16.6 41.8 20.4 10.0 9.4 20.9 22.6 7.3 6.2 15.1 5.0 22.1 25.1 5.9 0.8 5.0 20.0 18.4 9.1 15.1 14.9 1.5 3.7 40.5 14.0 13.8 30.6 6.8 16.1 0.3 18.2

1990

2009

.. 0.1 0.2 0.1 0.8 .. 10.9 .. .. 0.4 .. 0.0 0.0 .. 0.7 .. 11.4 0.1 .. .. 0.0 0.4 0.8 .. .. 3.4 0.4 0.0 3.7 3.7 0.0 12.1 0.1 1.2 1.3 .. 3.7 30.4 0.1 4.4 3.2 0.0 26.1 .. 3.5 1.1 0.2 0.1 0.2 35.7 0.0 0.6 0.3 0.0 2.7 0.0 0.0

.. 1.5 0.3 0.1 1.1 .. 28.3 .. .. 0.8 .. 0.0 0.0 .. 0.7 .. 20.1 3.0 .. .. 0.9 0.4 1.2 .. .. 3.7 1.4 0.0 5.9 4.3 2.1 12.3 0.1 1.2 2.7 .. 3.8 30.4 13.0 9.3 3.2 0.0 26.1 .. 5.0 3.4 7.1 0.1 0.4 36.3 0.0 2.5 12.5 0.0 45.8 0.0 1.9


Forest area

Average annual deforestationa

thousand sq. km

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

2010

18 639 1,185 111 8 5 1 76 3 250 1 34 37 82 64 .. 0 8 173 32 1 0 49 2 19 9 137 39 224 141 4 0 703 3 125 50 434 392 88 48 3 77 45 19 172 91 0 25 38 315 212 702 66 89 33 3 0

20 684 944 111 8 7 2 91 3 250 1 33 35 57 62 5c 0 10 158 34 1 0 43 2 22 10 126 32 205 125 2 0 648 4 109 51 390 318 73 36 4 83 31 12 90 101 0 17 33 287 176 680 77 93 35 6 0

% 1990–2000 2000–10

–0.57 –0.22 1.75 0.00 –0.17 –3.16 –1.49 –0.98 0.12 0.03 0.00 0.17 0.35 1.67 0.13 .. –5.24 –0.26 0.46 –0.21 0.00 –0.49 0.63 0.00 –0.38 –0.49 0.42 0.88 0.36 0.58 2.66 0.00 0.52 –0.16 0.67 0.06 0.52 1.17 0.87 2.09 –0.43 –0.69 1.67 3.74 2.68 –0.19 0.00 1.76 1.18 0.45 0.88 0.14 –0.80 –0.20 –0.28 –4.92 0.00

–0.62 –0.46 0.51 0.00 –0.09 –1.53 –0.07 –0.90 0.12 –0.04 0.00 0.17 0.33 2.00 0.11 .. –1.84 –1.07 0.48 –0.34 –0.45 –0.47 0.67 0.00 –0.67 –0.41 0.44 0.97 0.54 0.61 2.66 1.08 0.30 –1.77 0.72 –0.22 0.54 0.93 0.96 0.70 –0.14 0.00 2.01 0.98 3.67 –0.79 0.00 2.24 0.36 0.48 0.96 0.18 –0.74 –0.30 –0.10 –1.75 0.00

Threatened species

3.4

environment

Deforestation and biodiversity GEF benefits index for biodiversity

Terrestrial protected areas

Marine protected areas

% of total land area

% of territorial waters

Mammals

Birds

Fish

Higher plantsb

0–100 (no biodiversity to maximum biodiversity)

2010

2010

2010

2010

2008

2 94 183 16 13 5 15 7 5 28 13 16 28 9 9 .. 6 6 45 1 10 2 19 12 3 5 63 7 70 12 15 6 99 4 11 18 12 45 12 31 4 9 6 12 27 7 9 23 15 39 8 54 39 5 11 3 2

9 78 119 21 18 1 13 8 10 40 10 21 30 22 30 .. 9 12 22 3 7 7 11 4 4 10 35 14 45 7 9 11 55 9 21 10 23 41 24 33 2 70 11 6 13 2 10 26 17 37 27 96 72 6 9 8 5

8 122 138 29 11 18 35 42 17 59 13 14 66 12 17 .. 11 3 23 5 21 1 52 21 5 14 83 101 60 3 30 12 150 9 1 45 52 33 25 8 12 21 26 4 56 18 24 33 36 41 0 19 65 6 47 15 11

1 255 393 1 0 1 0 27 209 15 1 16 129 6 3 .. 0 14 22 0 1 4 47 2 0 0 280 14 692 6 0 88 255 0 0 31 52 42 26 7 0 21 43 2 172 2 6 2 202 143 10 274 222 4 21 53 0

0.2 39.9 81.0 7.3 1.6 0.6 0.8 3.8 4.4 36.0 0.4 5.1 8.8 0.7 1.7 .. 0.1 1.1 5.0 0.0 0.2 0.3 2.6 1.6 0.0 0.2 29.2 3.5 13.9 1.5 1.3 3.3 68.7 0.0 4.2 3.5 7.2 10.0 5.2 2.1 0.2 20.2 3.3 0.9 6.0 1.3 3.7 4.9 10.9 25.4 2.8 33.4 32.3 0.5 5.5 4.0 0.1

1990

4.6 5.0 10.0 5.2 0.1 0.6 17.2 5.0 10.2 13.2 8.4 2.4 11.5 3.9 2.2 .. 1.6 6.4 0.8 6.4 0.5 0.5 18.1 0.1 1.4 4.2 2.1 15.0 16.9 2.3 0.5 1.7 2.4 0.9 4.1 1.2 14.8 3.1 14.4 7.7 11.0 25.0 15.4 6.8 11.6 4.7 0.0 10.3 17.2 1.9 2.9 4.7 8.7 15.3 5.9 10.1 0.0

2009

5.1 5.3 14.1 7.1 0.1 1.0 18.7 9.9 18.9 16.3 9.4 2.5 11.6 4.0 2.4 .. 1.6 6.9 16.3 17.8 0.5 0.5 18.1 0.1 4.5 4.8 2.9 15.0 17.9 2.4 0.5 4.5 11.1 1.4 13.4 1.5 15.8 6.3 14.5 17.0 12.4 25.8 36.7 6.8 12.8 14.4 10.7 10.3 18.7 3.1 5.4 13.6 10.9 21.8 5.9 10.1 0.7

1990

2009

.. 1.5 0.5 1.3 0.0 0.1 1.0 0.5 0.2 2.0 0.0 .. 5.1 0.1 5.0 .. 0.0 .. .. 4.6 0.0 .. 0.0 0.0 0.8 .. 0.0 .. 1.1 .. 32.1 0.3 1.9 .. .. 0.7 1.8 0.3 0.5 .. 13.5 0.4 0.7 .. 0.2 1.0 0.0 1.8 3.1 0.3 .. 2.8 0.2 3.8 1.8 1.5 0.0

.. 1.7 1.9 1.9 0.0 0.1 1.0 16.7 4.2 5.6 20.8 .. 10.4 0.1 5.3 .. 0.0 .. .. 6.6 0.1 .. 0.0 0.0 2.7 .. 0.1 .. 1.6 .. 32.1 0.3 16.7 .. .. 1.2 3.3 0.3 0.5 .. 21.2 7.1 20.1 .. 0.2 2.3 1.3 1.8 4.0 0.3 .. 2.8 1.5 4.5 1.8 1.6 0.3

2011 World Development Indicators

139


3.4

Deforestation and biodiversity Forest area

Average annual deforestationa

thousand sq. km 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

2010

% 1990–2000 2000–10

64 66 0.01 8,090 8,091 0.00 3 4 –0.79 10 10 0.00 93 85 0.49 23 27 –0.62 31 27 0.65 0 0 0.00 19 19 0.01 12 13 –0.37 83 67 0.97 82 57 1.67 138 182 –2.09 24 19 1.20 764 699 0.80 5 6 –0.93 273 282 –0.04 12 12 –0.37 4 5 –1.51 4 4 –0.05 415 334 1.02 195 190 0.28 10 7 1.22 7 3 3.37 2 2 0.29 6 10 –2.67 97 113 –0.47 41 41 0.00 48 30 2.03 93 97 –0.25 2 3 –2.38 26 29 –0.68 2,963 3,040 –0.13 9 17 –4.38 30 33 –0.54 520 463 0.57 94 138 –2.28 0 0 0.00 5 5 0.00 528 495 0.32 222 156 1.58 41,582 s 40,204 s 0.20 w 5,524 4,881 0.63 26,552 25,660 0.24 8,103 7,996 0.26 18,449 17,664 0.23 32,076 30,541 0.31 4,602 4,698 0.17 8,703 8,750 –0.02 10,389 9,460 0.48 207 211 –0.08 795 817 0.01 7,379 6,605 0.58 9,506 9,663 –0.13 838 930 –0.73

–0.32 0.00 –2.37 0.00 0.49 –0.98 0.69 0.00 –0.06 –0.16 1.07 2.00 –0.68 1.12 0.08 –0.84 –0.29 –0.38 –1.29 0.00 1.13 0.02 1.40 5.13 0.35 –1.86 –1.11 0.00 2.55 –0.20 –0.22 –0.31 –0.13 –2.13 –0.20 0.60 –1.64 0.00 0.00 0.33 1.88 0.13 w 0.61 0.10 –0.13 0.20 0.18 –0.38 –0.03 0.45 –0.13 –0.27 0.52 –0.03 –0.31

Threatened species

2011 World Development Indicators

Terrestrial protected areas

Marine protected areas

% of total land area

% of territorial waters

Mammals

Birds

Fish

Higher plantsb

0–100 (no biodiversity to maximum biodiversity)

2010

2010

2010

2010

2008

7 12 18 1 32 18 35 8 20 12 9 4 9 14 22 3 16 9 41 9 6 11 11 1 17 10 45 48 11 17 25 57 3 7 5 2 4 4 26 0 15 11 26 21 24 39 81 97 16 15 62 55 30 14 41 283 15 14 17 18 5 9 4 11 1 3 11 3 2 2 9 3 16 13 33 3 8 9 5 14 35 42 172 298 57 45 72 91 4 7 5 0 11 3 24 10 2 2 19 1 13 7 31 7 17 15 67 5 9 15 11 3 41 22 19 61 11 12 21 1 7 10 13 0 5 2 41 14 37 74 177 245 11 23 35 1 10 15 7 15 32 27 34 70 54 40 46 146 3 8 0 0 9 14 21 159 9 14 20 9 9 13 3 16 1,131 s 1,240 s 1,851 s 8,724 s

a. Negative values indicate an increase in forest area. b. Flowering plants. c. National sources.

140

GEF benefits index for biodiversity

0.7 34.1 0.9 3.2 1.0 0.2 1.3 0.1 0.1 0.2 6.1 20.7 6.8 7.9 5.1 0.1 0.3 0.2 0.9 0.7 14.8 8.0 0.6 0.3 2.2 0.5 6.2 1.8 2.8 0.5 0.2 3.5 94.2 1.2 1.1 25.3 12.1 .. 3.2 3.8 1.9

1990

2.8 8.2 9.9 7.6 24.1 3.0 5.0 5.0 19.5 7.5 0.6 6.5 7.7 19.6 4.7 3.0 7.1 14.5 0.3 1.9 26.5 14.2 0.0 11.3 30.5 1.3 1.7 3.0 7.3 1.8 0.3 21.8 14.8 0.3 2.1 39.3 4.4 .. 0.0 36.0 18.0 9.1 w 10.0 8.6 8.8 8.4 8.9 10.8 6.6 10.5 3.1 5.5 11.0 9.9 11.1

2009

7.1 9.0 10.0 31.3 24.1 6.0 5.0 5.4 23.5 12.1 0.6 6.9 8.6 20.8 4.9 3.0 11.3 22.8 0.6 4.1 27.7 19.6 6.0 11.3 31.2 1.3 1.9 3.0 9.7 3.5 5.6 24.4 14.8 0.3 2.3 53.7 6.2 .. 0.5 36.0 28.0 12.5 w 11.2 12.4 11.5 13.0 12.2 14.9 7.4 20.8 4.0 6.1 11.7 13.4 15.4

1990

2009

1.5 3.1 .. 0.6 5.8 .. 0.0 0.0 .. 0.0 0.0 0.7 0.6 0.1 0.0 .. 3.7 .. 0.0 .. 3.7 4.0 0.0 0.0 0.2 1.1 2.4 .. .. 4.1 0.3 4.7 18.3 0.2 .. 7.0 0.3 .. 0.0 .. .. 4.8 w .. 2.9 0.8 4.1 3.2 0.5 3.1 6.7 0.9 1.5 3.2 8.7 6.5

33.2 9.1 .. 3.4 12.4 .. 0.0 1.6 .. 0.6 0.0 6.5 3.4 1.1 0.0 .. 5.3 .. 0.6 .. 10.0 4.3 6.7 0.0 2.8 1.2 2.4 .. .. 4.9 2.6 5.2 24.7 0.2 .. 15.3 2.1 .. 1.9 .. .. 9.2 w .. 6.6 2.0 9.4 6.6 1.5 8.8 13.1 2.0 1.7 4.7 15.1 10.1


About the data

3.4

environment

Deforestation and biodiversity Definitions

As threats to biodiversity mount, the international com-

ecoregions, and threatened ecoregions. To combine

• Forest area is land spanning more than 0.5 hectares

munity is increasingly focusing on conserving diversity.

these dimensions into one measure, the indicator

with trees higher than 5 meters and a canopy cover

Deforestation is a major cause of loss of biodiversity,

uses dimensional weights that reflect the consensus

of more than 10 percent or with trees able to reach

and habitat conservation is vital for stemming this

of conservation scientists at the GEF, IUCN, WWF Inter-

these thresholds in situ. It does not include land that

loss. Conservation efforts have focused on protecting

national, and other nongovernmental organizations.

is predominantly under agricultural or urban land use.

areas of high biodiversity. The Food and Agriculture

The World Conservation Monitoring Centre (WCMC)

• Average annual deforestation is the permanent con-

Organization of the United Nations (FAO) Global Forest

compiles data on protected areas, numbers of certain

version of natural forest area to other uses, including

Resources Assessment 2010 provides detailed informa-

species, and numbers of those species under threat

agriculture, ranching, settlements, and infrastruc-

tion on forest cover in 2010 and adjusted estimates

from various sources. Because of differences in defini-

ture. Deforested areas do not include areas logged

of forest cover in 1990 and 2000. The current survey

tions, reporting practices, and reporting periods, cross-

but intended for regeneration or areas degraded by

uses a uniform definition of forest. Because of space

country comparability is limited. Nationally protected

fuelwood gathering, acid precipitation, or forest fires.

limitations, the table does not break down forest cover

areas are defined using the six IUCN management cat-

• Threatened species are the number of species clas-

between natural forest and plantation, a breakdown

egories for areas of at least 1,000 hectares: scientific

sified by the IUCN as endangered, vulnerable, rare,

the FAO provides for developing countries. Thus the

reserves and strict nature reserves with limited public

indeterminate, out of danger, or insufficiently known.

deforestation data in the table may underestimate the

access; national parks of national or international sig-

Mammals exclude whales and porpoises. Birds are

rate at which natural forest is disappearing in some

nificance and not materially affected by human activ-

listed for the country where their breeding or wintering

countries.

ity; natural monuments and natural landscapes with

ranges are located. Plants are native vascular plant

The number of threatened species is an important

unique aspects; managed nature reserves and wildlife

species. • GEF benefits index for biodiversity is a

measure of the immediate need for conservation in

sanctuaries; protected landscapes (which may include

composite index of relative biodiversity potential based

an area. Global analyses of the status of threatened

cultural landscapes); and areas managed mainly for the

on the species represented in each country and their

species have been carried out for few groups of organ-

sustainable use of natural systems to ensure long-term

threat status and diversity of habitat types. The index

isms. Only for mammals, birds, and amphibians has the

protection and maintenance of biological diversity. The

has been normalized from 0 (no biodiversity potential)

status of virtually all known species been assessed.

data in the table cover these six categories as well as

to 100 (maximum biodiversity potential). • Nationally

Threatened species are defined using the International

terrestrial protected areas that are not assigned to a

protected areas are totally or partially protected areas

Union for Conservation of Nature’s (IUCN) classifica-

category by the IUCN. Designating an area as protected

of at least 1,000 hectares that are designated as sci-

tion: endangered (in danger of extinction and unlikely

does not mean that protection is in force. And for small

entific reserves with limited public access, national

to survive if causal factors continue operating) and vul-

countries that only have protected areas smaller than

parks, natural monuments, nature reserves or wildlife

nerable (likely to move into the endangered category in

1,000 hectares, the size limit in the definition leads to

sanctuaries, and protected landscapes. Terrestrial

the near future if causal factors continue operating).

an underestimate of protected areas.

protected areas exclude marine areas, unclassified

The Global Environment Facility’s (GEF) benefits

Due to variations in consistency and methods of

areas, littoral (intertidal) areas, and sites protected

index for biodiversity is a comprehensive indicator

collection, data quality is highly variable across coun-

under local or provincial law. Marine protected areas

of national biodiversity status and is used to guide

tries. Some countries update their information more

are areas of intertidal or subtidal terrain—and overly-

its biodiversity priorities. For each country the biodi-

frequently than others, some have more accurate data

ing water and associated flora and fauna and histori-

versity indicator incorporates the best available and

on extent of coverage, and many underreport the num-

cal and cultural features—that have been reserved to

comparable information in four relevant dimensions:

ber or extent of protected areas.

protect part or the entire enclosed environment.

represented species, threatened species, represented

3.4a

At least 33 percent of assessed species are estimated to be threatened

Data sources Data on forest area are from the FAO’s Global Forest Resources Assessment 2010 and the FAO’s data web

Species (thousands)

site. Data on species are from the electronic files

80

of the United Nations Environment Programme and WCMC, the 2010 IUCN Red List of Threatened Spe-

60

cies, and Froese and Pauly’s (2008) FishBase database. The GEF benefits index for biodiversity is from

Assessed

40

Kiran Dev Pandey, Piet Buys, Ken Chomitz, and David Wheeler’s, “Biodiversity Conservation Indicators: New Threatened

20

Tools for Priority Setting at the Global Environment Facility” (2006). Data on protected areas are from the United Nations Environment Programme and WCMC,

0 2000

2002

2004

Source: International Union for Conservation of Nature.

2006

2008

2010

based on data from national authorities and national legislation and international agreements.

2011 World Development Indicators

141


3.5

Freshwater Internal renewable freshwater resourcesa

Flows billion cu. m 2007

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

142

55 27 11 148 276 9 492 55 8 105 37 12 10 304 36 2 5,418 21 13 10 121 273 2,850 141 15 884 2,813 .. 2,112 900 222 112 77 38 38 13 6 21 432 2 18 3 13 122 107 200 164 3 58 107 30 58 109 226 16 13 96

Per capita cu. m 2007

1,946 8,588 332 8,431 6,989 2,952 23,348 6,626 946 666 3,834 1,129 1,227 31,868 9,395 1,268 28,498 2,742 849 1,283 8,417 14,630 86,426 33,119 1,412 53,137 2,134 .. 47,611 14,395 62,516 25,209 3,819 8,499 3,402 1,272 1,099 2,139 32,379 22 2,907 586 9,475 1,551 20,232 3,229 115,340 1,857 13,339 1,301 1,325 5,182 8,177 23,505 10,383 1,338 13,372

2011 World Development Indicators

Annual freshwater withdrawals

billion cu. m 2007b

23.3 1.7 6.1 0.4 29.2 3.0 23.9 2.1 12.2 79.4 2.8 0.0 0.1 1.4 0.0 0.2 59.3 10.5 0.8 0.3 4.1 1.0 46.0 0.0 0.2 12.6 554.1 .. 10.7 0.4 0.0 2.7 0.9 0.0 8.2 2.6 1.3 3.4 17.0 68.3 1.3 0.6 0.2 5.6 2.5 31.8 0.1 0.0 1.6 47.1 1.0 7.8 2.0 1.5 0.2 1.0 0.9

% of internal resources 2007b

42.3 6.4 54.0 0.2 10.6 32.5 4.9 3.8 150.5 75.6 7.5 .. 1.3 0.5 .. 8.1 1.1 50.0 6.4 2.9 3.4 0.4 1.6 0.0 1.5 1.4 22.4 .. 0.5 0.0 0.0 2.4 1.2 .. 21.5 19.6 21.2 16.1 3.9 3,794.4 7.2 20.8 1.2 4.6 2.3 22.4 0.1 1.0 2.8 44.0 3.2 13.4 1.8 0.7 1.1 7.6 0.9

% for agriculture 2007b

98 62 65 60 74 66 75 1 76 96 30 .. 45 81 .. 41 62 19 86 77 98 74 12 4 83 64 65 .. 46 31 9 53 65 .. 69 2 43 66 82 86 59 95 5 94 3 12 42 65 65 20 66 80 80 90 82 94 80

Water productivity

% for industry 2007b

0 11 13 17 9 4 10 64 19 1 47 .. 23 7 .. 18 18 78 1 6 0 8 69 16 0 25 23 .. 4 17 22 17 12 .. 12 57 25 2 5 6 16 0 38 0 84 69 8 12 13 68 10 3 13 2 5 1 12

% for domestic 2007b

2 27 22 23 17 30 15 35 4 3 23 .. 32 13 .. 41 20 3 13 17 1 18 20 80 17 11 12 .. 50 53 70 29 24 .. 19 41 32 32 12 8 25 5 57 6 14 18 50 23 22 12 24 16 6 8 13 5 8

GDP/water use 2000 $ per cu. m 2007b

.. 3 12 61 13 1 22 105 1 1 8 .. 23 7 .. 41 14 2 5 3 2 13 19 39 13 8 4 .. 13 16 89 9 11 .. 6 30 141 10 1 2 13 1 64 2 62 38 49 19 3 44 7 22 12 3 1 4 12

Access to an improved water source

% of rural population 2008

39 98 79 38 80 93 100 100 71 78 99 100 69 67 98 90 84 100 72 71 56 51 99 51 44 75 82 .. 73 28 34 91 68 97 89 100 100 84 88 98 76 57 97 26 100 100 41 86 96 100 74 99 90 61 51 55 77

% of urban population 2008

78 96 85 60 98 98 100 100 88 85 100 100 84 96 100 99 99 100 95 83 81 92 100 92 67 99 98 .. 99 80 95 100 93 100 96 100 100 87 97 100 94 74 99 98 100 100 95 96 100 100 90 100 98 89 83 71 95


Internal renewable freshwater resourcesa

Flows billion cu. m 2007

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

6 1,276 2,019 129 35 49 1 183 9 430 1 75 21 67 65 .. 0 46 190 17 5 5 200 1 16 5 337 16 580 60 0c 3 409 1 35 29 100 1003 6 198 11 327 190 4 221 382 1 55 147 801 94 1,616 479 54 38 7 0

Per capita cu. m 2007

597 1,134 8,987 1,809 1,175 11,246 104 3,074 3,514 3,365 120 4,871 548 2,824 1,338 .. 0 8,873 31,256 7,355 1,153 2,574 55,138 97 4,610 2,647 18,114 1,118 21,841 4,835 127 2,182 3,885 273 13,326 929 4,586 20,415 2,949 7,007 671 77,336 33,912 248 1,496 81,119 514 338 44,094 124,716 15,343 56,685 5,399 1,406 3,582 1,801 45

Annual freshwater withdrawals

billion cu. m 2007b

7.6 40.4 82.8 93.3 66.0 .. 2.0 44.4 0.4 88.4 0.9 35.0 2.7 9.0 18.6 .. 0.5 10.1 3.0 0.3 1.3 0.1 0.1 4.3 0.3 0.0 15.0 1.0 9.0 6.5 1.7 0.7 78.2 2.3 0.4 12.6 0.6 33.2 0.3 10.2 7.9 2.1 1.3 2.2 8.0 2.2 1.3 169.4 0.8 0.1 0.5 20.1 28.5 16.2 11.3 0.0 0.4

% of internal resources 2007b

127.3 51.2 2.9 72.6 187.5 .. 260.5 24.3 4.4 20.6 138.0 46.4 13.2 13.5 28.7 .. .. 21.7 1.6 1.8 27.3 1.0 0.1 721.0 1.7 .. 4.4 6.3 1.6 10.9 425.0 26.4 19.1 231.0 1.3 43.4 0.6 3.8 4.9 5.1 72.2 0.6 0.7 62.3 3.6 0.6 94.4 308.0 0.6 0.0 0.5 1.2 6.0 30.2 29.6 .. 870.6

% for agriculture 2007b

32 91 82 92 79 .. 58 45 49 62 65 82 79 55 48 .. 54 94 90 13 60 20 55 83 7 .. 96 80 62 90 88 68 77 33 52 87 87 89 71 96 34 42 83 95 69 11 88 96 28 1 71 82 74 8 78 .. 59

Water productivity

% for industry 2007b

59 2 7 1 15 .. 6 37 17 18 4 17 4 25 16 .. 2 3 6 33 11 40 18 3 15 .. 2 5 21 1 3 3 5 58 27 3 2 1 5 1 60 9 2 0 10 67 1 2 5 42 8 10 9 79 12 .. 2

% for domestic 2007b

9 7 12 7 7 .. 36 18 34 20 31 2 17 20 36 .. 44 3 4 53 29 40 27 14 78 .. 3 15 17 9 9 30 17 10 20 10 11 10 24 3 6 48 15 4 21 23 10 2 67 56 20 8 17 13 10 .. 39

GDP/water use 2000 $ per cu. m 2007b

8 1 1.2 2 0 125 79 27 25 59 14 1 6 .. 40 .. 67 0 1 48 17 18 5 11 73 .. 0 2 15 1 1 8 9 1 4 4 12 .. 19 1 55 31 4 1 9 90 20 1 21 59 18 4 4 14 11 .. 90

3.5

environment

Freshwater

Access to an improved water source

% of rural population 2008

100 84 71 .. 55 100 100 100 89 100 91 90 52 100 88 .. 99 85 51 96 100 81 51 .. .. 99 29 77 99 44 47 99 87 85 49 60 29 69 88 87 100 100 68 39 42 100 77 87 83 33 66 61 87 100 100 .. 100

2011 World Development Indicators

% of urban population 2008

100 96 89 98 91 100 100 100 98 100 98 99 83 100 100 .. 99 99 72 100 100 97 79 .. .. 100 71 95 100 81 52 100 96 96 97 98 77 75 99 93 100 100 98 96 75 100 92 95 97 87 99 90 93 100 99 .. 100

143


3.5

Freshwater Internal renewable freshwater resourcesa

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 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

Flows billion cu. m 2007

Per capita cu. m 2007

42 4,313 10 2 26 44 c 160 1 13 19 6 45 111 50 30 3 171 40 7 66 84 210 12 4 4 227 1 39 53 0 145 2,818 59 16 722 367 1 2 80 12 43,464 s 4,418 29,421 11,728 17,694 33,839 9,454 5,059 13,425 225 1,819 3,858 9,624 955

1,963 30,350 1,005 99 2,169 5,419c 29,518 131 2,334 9,251 687 928 2,478 2,499 742 2,293 18,692 5,350 349 9,855 2,035 3,135 1,825 2,891 410 3,109 273 1,273 1,142 34 2,378 9,344 17,750 608 26,287 4,304 212 94 6,513 985 6,616 w 5,452 6,271 3,155 18,142 6,150 4,940 12,911 24,001 714 1,194 4,826 9,017 2,932

Annual freshwater withdrawals

billion cu. m 2007b

23.2 76.7 0.2 23.7 2.2 0.0 c 0.4 0.0 0.0 0.0 3.3 12.5 35.6 12.6 37.3 1.0 3.0 2.6 16.7 12.0 5.2 87.1 0.2 0.3 2.6 40.1 24.7 0.0 37.5 4.0 9.5 477.8 3.2 58.3 8.4 71.4 0.4 3.4 1.7 4.2 3,850.0 s 240.9 2,672.1 2,103.9 568.2 2,913.0 959.0 351.9 264.9 275.6 941.1 120.5 937.0 200.2

% of internal resources 2007b

54.8 1.8 1.6 986.1 8.6 .. 0.2 .. .. .. 55.0 27.9 32.0 25.2 124.4 39.5 1.7 6.4 238.4 18.0 6.2 41.5 1.5 8.1 62.9 17.7 1,812.5 .. 70.7 2,665.3 6.6 17.1 5.3 357.0 1.2 19.5 .. 161.9 2.2 34.3 9.0 w 5.6 9.1 17.9 3.2 8.6 10.2 7.0 2.0 122.3 51.7 3.2 10.5 22.0

% for agriculture 2007b

57 18 68 88 93 .. 92 .. .. .. 99 63 68 95 97 97 9 2 88 92 89 95 45 6 82 74 98 40 52 83 3 40 96 93 47 68 45 90 76 79 70 w 93 78 81 65 79 74 63 71 86 90 87 42 38

Water productivity

Access to an improved water source

% for industry 2007b

% for domestic 2007b

GDP/water use 2000 $ per cu. m 2007b

% of rural population 2008

% of urban population 2008

34 63 8 3 3 .. 3 .. .. .. 0 6 19 2 1 1 54 74 4 5 0 2 2 26 4 11 1 16 35 2 75 46 1 2 7 24 7 2 7 7 20 w 2 14 12 19 13 20 27 10 6 4 3 43 48

9 19 24 9 4 .. 5 .. .. .. 0 31 13 2 3 2 37 24 9 4 10 2 53 68 14 15 2 43 12 15 22 14 3 5 46 8 48 8 17 14 10 w 5 9 7 15 8 7 10 19 8 6 10 15 15

2 5 19 10 3 .. 4 .. .. .. .. 14 21 2 1 2 103 111 2 0 3 2 9 46 10 9 0 .. 1 28 185 24 8 0 19 1 .. 4 3 1 10 w 1 3 2 7 3 3 3 10 2 1 4 32 34

.. 89 62 .. 52 98 26 .. 100 99 9 78 100 88 52 61 100 100 84 61 45 98 41 93 84 96 72 64 97 100 100 94 100 81 75 92 91 57 46 72 78 w 56 81 81 86 76 81 89 80 80 83 47 98 100

.. 98 77 97 92 99 86 100 100 100 67 99 100 98 64 92 100 100 94 94 80 99 87 98 99 100 97 91 98 100 100 100 100 98 94 99 91 72 87 99 96 w 85 95 94 98 94 96 98 97 95 95 82 100 100

a. Excludes river flows from other countries because of data unreliability. b. Data are for the most recent year available (see Primary data documentation). c. Includes Kosovo and Montenegro.

144

2011 World Development Indicators


About the data

3.5

environment

Freshwater Definitions

The data on freshwater resources are based on

to variations in collection and estimation methods.

• Internal renewable freshwater resources are

estimates of runoff into rivers and recharge of

In addition, inflows and outflows are estimated at

the average annual flows of rivers and groundwater

ground­water. These estimates are based on differ-

different times and at different levels of quality and

from rainfall in the country. Natural incoming flows

ent sources and refer to different years, so cross-

precision, requiring caution in interpreting the data,

originating outside a country’s borders are excluded.

country comparisons should be made with caution.

particularly for water-short countries, notably in the

Overlapping water resources between surface run-

Because the data are collected intermittently, they

Middle East and North Africa.

off and groundwater recharge are also deducted.

may hide significant variations in total renewable

Water productivity is an indication only of the

• Renewable internal freshwater resources per

water resources from year to year. The data also

efficiency by which each country uses its water

capita are calculated using the World Bank’s popu-

fail to distinguish between seasonal and geographic

resources. Given the different economic structure

lation estimates (see table 2.1). • Annual freshwater

variations in water availability within countries. Data

of each country, these indicators should be used

withdrawals are total water withdrawals, not count-

for small countries and countries in arid and semiarid

carefully, taking into account the countries’ sectoral

ing evaporation losses from storage basins. With-

zones are less reliable than those for larger countries

activities and natural resource endowments.

drawals also include water from desalination plants

The data on access to an improved water source

in countries where they are a significant source. With-

Caution should also be used in comparing data

measure the percentage of the population with ready

drawals can exceed 100 percent of total renewable

on annual freshwater withdrawals, which are subject

access to water for domestic purposes. The data

resources where extraction from nonrenewable aqui-

are based on surveys and estimates provided by

fers or desalination plants is considerable or where

governments to the Joint Monitoring Programme of

water reuse is significant. Withdrawals for agriculture

the World Health Organization (WHO) and the United

and industry are total withdrawals for irrigation and

Nations Children’s Fund (UNICEF). The coverage

livestock production and for direct industrial use

rates are based on information from service users

(including for cooling thermoelectric plants). With-

on actual household use rather than on information

drawals for domestic uses include drinking water,

from service providers, which may include nonfunc-

municipal use or supply, and use for public services,

tioning systems. Access to drinking water from an

commercial establishments, and homes. • Water

improved source does not ensure that the water

productivity is calculated as GDP in constant prices

is safe or adequate, as these characteristics are

divided by annual total water withdrawal. • Access

not tested at the time of survey. While information

to an improved water source is the percentage of the

on access to an improved water source is widely

population with reasonable access to an adequate

used, it is extremely subjective, and such terms as

amount of water from an improved source, such as

safe, improved, adequate, and reasonable may have

piped water into a dwelling, plot, or yard; public tap

different meaning in different countries despite offi-

or standpipe; tubewell or borehole; protected dug

cial WHO definitions (see Definitions). Even in high-

well or spring; and rainwater collection. Unimproved

income countries treated water may not always be

sources include unprotected dug wells or springs,

safe to drink. Access to an improved water source is

carts with small tank or drum, bottled water, and

equated with connection to a supply system; it does

tanker trucks. Reasonable access is defined as the

not take into account variations in the quality and

availability of at least 20 liters a person a day from

cost (broadly defined) of the service.

a source within 1 kilometer of the dwelling.

and countries with greater rainfall.

Agriculture is still the largest user of water, accounting for some 70 3.5a percent of global withdrawals . . . Percent 100

Domestic

Agriculture

Industry

80

60

40

20

0 Low income

Lower middle income

Upper middle income

High income

World

Source: Table 3.5.

. . . and approaching 90 percent in some developing regions Percent 100

Domestic

Industry

3.5b Agriculture

80

60

Data sources Data on freshwater resources and withdrawals

40

are from the Food and Agriculture Organization of the United Nations AQUASTAT data. The GDP

20

estimates used to calculate water productivity are from the World Bank national accounts data-

0 East Europe Latin Middle Asia & America East & & Central & North Pacific Asia Caribbean Africa

Source: Table 3.5.

South SubAsia Saharan Africa

base. Data on access to water are from WHO and UNICEF’s Progress on sanitation and drinking water (2010).

2011 World Development Indicators

145


3.6

Water pollution Emissions of organic water pollutants

thousand kilograms per day 1990 2007a

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

146

.. 2.4 .. .. 181.4 .. .. 90.5 41.3 250.8 .. 107.8 .. 11.3 .. 2.5 .. 124.3 .. .. 3.8 .. 300.9 .. .. .. .. .. .. .. .. .. .. 48.5 .. 177.1 84.5 88.6 28.6 206.5 .. 2.4 21.7 18.5 72.5 326.5 .. 0.8 .. 806.6 .. 50.9 .. .. .. 5.2 ..

0.2 3.6 .. .. 155.5 .. .. 84.4 20.0 303.0 .. 95.9 .. 11.5 .. 3.2 .. 102.1 .. .. .. .. 306.6 .. .. 92.5 9,428.9 .. 87.0 .. .. .. .. 42.9 .. 146.5 61.0 .. 44.7 .. .. 2.5 16.0 32.2 55.3 569.4 .. .. .. 936.2 16.0 60.8 .. .. .. .. ..

2011 World Development Indicators

Industry shares of emissions of organic water pollutants

kilograms per day per worker

% of total Stone, Food and ceramics, beverages and glass

Primary metals

Paper and pulp

Chemicals

Textiles

Wood

Other

2007a

2007a

2007a

2007a

2007a

2007a

2007a

27.9 .. .. .. 15.8 .. .. 9.3 18.5 3.0 .. 18.6 .. 13.1 .. .. .. 8.0 .. .. .. .. 10.9 .. .. 13.7 13.0 .. 17.3 .. .. .. .. 9.5 .. 10.9 13.1 .. 12.8 .. .. 9.5 7.1 10.9 8.7 15.0 .. .. .. 12.4 15.9 10.1 .. .. .. .. ..

14.1 39.8 .. .. 30.5 .. .. 12.2 19.6 7.6 .. 16.4 .. 35.4 .. 43.8 .. 17.7 .. .. .. .. 14.0 .. .. 35.1 7.4 .. 21.3 .. .. .. .. 17.6 .. 10.9 16.4 .. 46.4 .. .. 27.3 14.6 34.7 9.0 16.6 .. .. .. 11.4 18.6 23.9 .. .. .. .. ..

11.7 .. .. .. 3.5 .. .. 5.8 8.4 2.6 .. 3.1 .. 7.7 .. 0.6 .. 4.8 .. .. .. .. 2.8 .. .. 3.6 6.3 .. 5.3 .. .. .. .. 5.9 .. 6.4 4.8 .. 4.4 .. .. 9.6 5.5 8.3 4.4 3.8 .. .. .. 3.4 4.1 7.0 .. .. .. .. ..

23.3 60.2 .. .. 14.3 .. .. 4.3 11.7 79.3 .. 5.5 .. 18.4 .. 3.9 .. 26.8 .. .. .. .. 7.3 .. .. 9.1 20.6 .. 24.1 .. .. .. .. 14.5 .. 7.4 1.5 .. 12.3 .. .. 29.0 8.0 27.9 2.8 4.8 .. .. .. 2.4 10.2 14.4 .. .. .. .. ..

1990

2007a

2007a

.. 0.25 .. .. 0.21 .. .. 0.15 0.15 0.15 .. 0.17 .. 0.24 .. 0.30 .. 0.17 .. .. 0.17 .. 0.17 .. .. .. .. .. .. .. .. .. .. 0.17 .. 0.14 0.18 0.18 0.24 0.19 .. 0.19 0.15 0.23 0.19 0.11 .. 0.27 .. 0.13 .. 0.19 .. .. .. 0.20 ..

0.21 0.25 .. .. 0.23 .. .. 0.14 0.18 0.14 .. 0.17 .. 0.25 .. 0.23 .. 0.17 .. .. .. .. 0.16 .. .. 0.25 0.13 .. 0.20 .. .. .. .. 0.17 .. 0.13 0.16 .. 0.28 .. .. 0.20 0.14 0.24 0.14 0.16 .. .. .. 0.14 0.17 0.20 .. .. .. .. ..

.. .. .. .. 3.8 .. .. 5.7 8.8 0.7 .. 6.4 .. 0.9 .. .. .. 3.7 .. .. .. .. 4.3 .. .. 7.6 7.2 .. 2.3 .. .. .. .. 3.1 .. 5.4 1.4 .. 1.8 .. .. 0.2 0.4 1.4 1.0 3.2 .. .. .. 3.8 3.0 3.9 .. .. .. .. ..

19.7 .. .. .. 8.4 .. .. 7.1 3.0 2.3 .. 7.9 .. 9.8 .. 2.4 .. 4.3 .. .. .. .. 8.9 .. .. 6.3 3.9 .. 8.9 .. .. .. .. 7.2 .. 4.8 11.5 .. 7.8 .. .. 4.4 7.3 6.0 15.4 7.4 .. .. .. 7.1 3.8 9.0 .. .. .. .. ..

.. .. .. .. 2.1 .. .. 6.0 1.5 0.5 .. 2.2 .. 5.3 .. .. .. 3.0 .. .. .. .. 6.5 .. .. 6.9 1.7 .. 0.9 .. .. .. .. 4.9 .. 4.4 4.0 .. 2.2 .. .. 0.1 16.4 1.5 7.3 2.4 .. .. .. 1.9 33.3 2.8 .. .. .. .. ..

3.1 11.9 .. .. 21.6 .. .. 49.5 28.6 4.2 .. 40.0 .. 9.5 .. 50.0 .. 31.7 .. .. .. .. 45.3 .. .. 17.7 39.9 .. 19.9 .. .. .. .. 37.2 .. 49.8 47.3 .. 12.3 .. .. 20.3 40.8 9.3 51.4 46.9 .. .. .. 57.6 11.2 28.9 .. .. .. .. ..


Emissions of organic water pollutants

thousand kilograms per day 1990 2007a

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

122.1 .. 721.8 131.6 7.7 36.1 54.6 378.3 .. 1,455.0 15.0 123.5 .. .. 366.9 .. .. 28.9 4.3 39.8 14.7 .. .. .. 54.0 27.0 .. 37.2 .. .. .. 16.8 425.0 29.2 .. .. .. .. .. 26.4 142.3 46.7 .. .. .. 51.8 3.8 .. 10.3 .. 15.3 .. 169.0 446.7 140.6 .. ..

110.6 .. 883.0 160.8 7.7 28.4 52.7 479.2 .. 1,126.9 29.1 97.4 .. .. 319.6 .. .. 12.2 4.3 28.4 14.7 5.3 .. .. 42.2 20.3 92.8 32.7 208.3 .. .. 15.4 .. 18.8 8.8 74.0 .. .. .. 26.8 128.2 61.6 .. .. .. 46.9 7.6 153.7 13.7 .. 10.8 .. 144.6 359.7 87.7 .. 6.4

3.6

environment

Water pollution Industry shares of emissions of organic water pollutants

kilograms per day per worker 1990

2007a

0.18 .. 0.18 0.16 0.27 0.19 0.16 0.13 .. 0.14 0.18 0.23 .. .. 0.12 .. .. 0.14 0.14 0.12 0.19 .. .. .. 0.15 0.20 .. 0.40 .. .. .. 0.16 0.18 0.44 .. .. .. .. .. 0.14 0.20 0.24 .. .. .. 0.20 0.15 .. 0.30 .. 0.20 .. 0.17 0.16 0.14 .. ..

0.15 .. 0.19 0.15 0.27 0.16 0.16 0.13 .. 0.15 0.18 0.24 .. .. 0.11 .. .. 0.20 0.14 0.18 0.19 0.13 .. .. 0.17 0.18 0.14 0.39 0.12 .. .. 0.17 .. 0.45 0.22 0.16 .. .. .. 0.16 0.19 0.23 .. .. .. 0.18 0.16 0.17 0.32 .. 0.28 .. 0.15 0.16 0.17 .. 0.12

% of total Stone, Food and ceramics, beverages and glass

Primary metals

Paper and pulp

Chemicals

Textiles

Wood

Other

2007a

2007a

2007a

2007a

2007a

2007a

2007a

2007a

10.6 .. 12.0 12.8 29.9 17.6 13.4 10.3 .. 11.2 13.7 8.9 .. .. 12.1 .. .. 8.5 3.8 5.8 6.0 0.3 .. .. 8.3 6.3 12.4 3.7 16.5 .. .. 5.9 .. .. 3.3 7.9 .. .. .. 7.2 14.1 8.6 .. .. .. 7.5 17.8 9.1 6.9 .. 16.7 .. 9.5 11.3 3.4 .. 10.5

15.2 .. 23.1 16.1 16.9 14.8 16.4 9.3 .. 15.0 20.8 18.7 .. .. 6.3 .. .. 24.2 9.2 21.1 25.5 2.6 .. .. 20.5 15.1 7.6 82.1 9.1 .. .. 14.7 .. 95.2 27.2 16.3 .. .. .. 19.2 18.2 31.1 .. .. .. 19.1 20.4 15.1 55.2 .. 42.6 .. 14.4 18.1 19.8 .. 6.5

3.7 .. 4.0 13.8 5.4 5.9 2.9 5.4 .. 3.6 11.5 9.3 .. .. 3.0 .. .. 17.5 7.5 4.4 12.9 0.8 .. .. 4.7 3.2 2.8 0.6 3.8 .. .. .. .. .. 9.5 6.5 .. .. .. 29.9 4.0 3.1 .. .. .. 4.3 20.5 4.3 4.0 .. 5.9 .. 2.7 5.5 5.2 .. 18.1

9.1 .. 29.2 11.2 9.1 0.8 7.9 13.6 .. 5.3 18.6 3.9 .. .. 9.3 .. .. 9.8 49.2 11.8 16.7 93.5 .. .. 17.6 44.7 58.9 7.5 6.6 .. .. 63.9 .. .. 41.6 43.5 .. .. .. 29.4 2.1 5.8 .. .. .. 2.0 2.4 55.6 4.7 .. 11.0 .. 21.6 10.3 16.3 .. 20.7

2.7 .. 1.4 7.1 13.1 1.3 1.6 3.5 .. 3.3 2.3 33.3 .. .. 4.2 .. .. 9.8 1.8 2.7 0.5 0.9 .. .. 0.9 5.8 0.3 .. 2.8 .. .. 0.4 .. .. 3.7 1.0 .. .. .. 1.6 3.1 2.0 .. .. .. 4.9 4.0 2.2 0.9 .. 3.1 .. 2.6 3.3 0.2 .. 3.7

6.4 .. 4.1 2.8 25.6 10.2 8.9 5.2 .. 7.0 6.1 2.3 .. .. 5.4 .. .. 6.3 2.2 7.7 7.5 0.5 .. .. 5.7 4.7 1.6 1.4 4.9 .. .. 3.6 .. 3.8 5.1 2.9 .. .. .. 3.9 13.4 12.2 .. .. .. 12.1 4.6 1.9 11.6 .. 9.3 .. 4.2 5.1 8.1 .. 6.7

3.3 .. 6.3 0.7 .. 3.8 1.2 2.9 .. 2.0 2.3 0.6 .. .. 0.9 .. .. 1.6 21.4 19.1 4.5 .. .. .. 11.4 2.9 6.3 1.1 7.8 .. .. 0.7 .. .. 5.4 2.0 .. .. .. 2.0 2.6 8.0 .. .. .. 6.0 4.0 0.4 1.6 .. 4.5 .. 2.1 4.9 8.5 .. 12.5

2011 World Development Indicators

49.0 .. 19.9 35.5 .. 45.5 47.6 49.6 .. 52.5 24.5 23.0 .. .. 58.9 .. .. 22.4 4.9 27.3 26.3 1.4 .. .. 30.8 17.3 10.0 3.6 48.5 .. .. 10.9 .. 0.9 4.1 19.9 .. .. .. 6.8 42.5 29.3 .. .. .. 44.2 26.3 11.2 15.0 .. 6.9 .. 42.9 41.5 38.5 .. 21.3

147


3.6

Water pollution Emissions of organic water pollutants

thousand kilograms per day 1990 2007a

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

411.2 1,521.4 8.1 .. 6.1 .. .. 33.1 72.8 28.1 .. 260.5 348.0 .. .. .. 116.8 .. 59.7 29.1 .. 369.4 .. .. 7.0 .. 175.8 .. 3.3 .. .. 599.9 2,307.0 .. .. .. 141.0 .. 12.6 .. 29.3

222.1 1,381.7 8.1 106.6 6.6 .. .. 38.3 47.9 28.8 .. 229.6 378.8 266.1 38.6 .. 96.9 .. 80.4 12.8 30.3 581.4 .. .. 7.6 .. 346.4 .. 2.1 498.2 .. 521.7 1,850.8 .. .. .. 544.8 .. 46.5 .. ..

Industry shares of emissions of organic water pollutants

kilograms per day per worker 1990

2007a

0.12 0.16 0.37 .. 0.30 .. .. 0.09 0.13 0.13 .. 0.17 0.16 .. .. .. 0.15 .. 0.16 0.17 .. 0.15 .. .. 0.23 .. 0.18 .. 0.29 .. .. 0.16 0.14 .. .. .. 0.16 .. 0.24 .. 0.20

0.15 0.17 0.37 0.18 0.29 .. .. 0.09 0.14 0.13 .. 0.17 0.15 0.19 0.29 .. 0.14 .. 0.16 0.24 0.34 0.15 .. .. 0.29 .. 0.15 .. 0.23 0.19 .. 0.17 0.14 .. .. .. 0.14 .. 0.21 .. ..

% of total Stone, Food and ceramics, beverages and glass

Primary metals

Paper and pulp

Chemicals

Textiles

Wood

Other

2007a

2007a

2007a

2007a

2007a

2007a

2007a

2007a

7.1 11.6 9.0 11.6 23.8 .. .. 11.9 9.1 12.2 .. 10.6 10.8 9.0 7.0 .. 9.9 .. 7.3 2.0 8.6 12.4 .. .. 21.3 .. 8.6 .. 7.3 11.2 .. 13.5 13.1 .. .. .. 6.8 .. 7.4 .. ..

13.9 17.9 77.1 20.0 44.6 .. .. 5.3 10.7 7.7 .. 15.7 15.3 22.4 57.5 .. 8.6 .. 19.9 18.0 61.2 16.4 .. .. 39.3 .. 12.4 .. 34.8 19.7 .. 14.9 12.0 .. .. .. 12.7 .. 35.9 .. ..

4.0 8.3 4.3 10.7 3.9 .. .. 1.3 6.0 4.1 .. 5.2 7.9 6.3 14.2 .. 2.6 .. 11.3 8.9 1.9 4.7 .. .. 8.0 .. 6.6 .. 13.3 6.8 .. 3.6 3.9 .. .. .. 6.4 .. 14.6 .. ..

25.0 6.3 1.9 14.4 10.5 .. .. 2.3 5.0 10.8 .. 10.4 8.4 43.6 8.0 .. 1.2 .. 32.0 38.4 12.7 20.5 .. .. 7.7 .. 32.2 .. 17.2 5.6 .. 4.3 4.3 .. .. .. 40.2 .. 15.5 .. ..

4.5 8.4 .. 3.2 4.9 .. .. 0.5 7.9 4.6 .. 9.9 3.1 2.6 0.6 .. 5.3 .. 1.6 28.2 2.6 1.9 .. .. 4.8 .. 3.8 .. .. 13.9 .. 2.7 3.5 .. .. .. 1.4 .. .. .. ..

3.5 4.9 .. 6.9 6.3 .. .. 5.5 5.4 6.1 .. 6.6 8.0 4.3 1.9 .. 11.9 .. 1.9 2.7 4.8 4.2 .. .. 18.2 .. 3.8 .. 7.8 4.3 .. 12.5 8.1 .. .. .. 3.5 .. 2.1 .. ..

5.3 4.2 2.9 3.3 0.8 .. .. 0.5 4.2 4.9 .. 4.2 3.8 2.5 1.7 .. 5.6 .. 5.2 0.3 2.9 2.8 .. .. 8.5 .. 1.7 .. 2.3 2.1 .. 2.5 4.1 .. .. .. 3.3 .. 5.1 .. ..

36.8 38.4 4.8 30.0 5.3 .. .. 72.7 51.7 49.6 .. 37.4 42.7 9.3 9.1 .. 54.9 .. 20.9 1.8 5.3 37.2 .. .. 5.0 .. 30.9 .. 19.6 36.5 .. 46.1 51.1 .. .. .. 25.8 .. 19.4 .. ..

a. Data are derived using the United Nations Industrial Development Organizationâ&#x20AC;&#x2122;s (UNIDO) industry database four-digit International Standard Classification (ISIC). Data in italics are for the most recent year available and are derived using UNIDOâ&#x20AC;&#x2122;s industry database at the three-digit ISIC.

148

2011 World Development Indicators


About the data

3.6

environment

Water pollution Definitions

Emissions of organic pollutants from industrial

emissions of organic water pollutants. Such data are

• Emissions of organic water pollutants are mea-

activities are a major cause of degradation of water

fairly reliable because sampling techniques for mea-

sured as biochemical oxygen demand, or the amount

quality. Water quality and pollution levels are gener-

suring water pollution are more widely understood

of oxygen that bacteria in water will consume in

ally measured as concentration or load—the rate of

and much less expensive than those for air pollution.

breaking down waste, a standard water treatment

occurrence of a substance in an aqueous solution.

Hettige, Mani, and Wheeler (1998) used plant- and

test for the presence of organic pollutants. Emis-

Polluting substances include organic matter, metals,

sector-level information on emissions and employ-

sions per worker are total emissions divided by the

minerals, sediment, bacteria, and toxic chemicals.

ment from 13 national environmental protection

number of industrial workers. • Industry shares of

The table focuses on organic water pollution result-

agencies and sector-level information on output

emissions of organic water pollutants are emissions

ing from industrial activities. Because water pollu-

and employment from the United Nations Industrial

from manufacturing activities as defined by two-digit

tion tends to be sensitive to local conditions, the

Development Organization (UNIDO). Their economet-

divisions of the International Standard Industrial

national-level data in the table may not reflect the

ric analysis found that the ratio of BOD to employ-

Classification revision 3.

quality of water in specific locations.

ment in each industrial sector is about the same

The data in the table come from an international

across countries. This finding allowed the authors to

study of industrial emissions that may have been

estimate BOD loads across countries and over time.

the first to include data from developing countries

The estimated BOD intensities per unit of employ-

(Hettige, Mani, and Wheeler 1998). These data were

ment were multiplied by sectoral employment num-

updated through 2007 by the World Bank’s Develop-

bers from UNIDO’s industry database for 1980–98.

ment Research Group. Unlike estimates from earlier

These estimates of sectoral emissions were then

studies based on engineering or economic models,

used to calculate kilograms of emissions of organic

these estimates are based on actual measurements

water pollutants per day for each country and year.

of plant-level water pollution. The focus is on organic

The data in the table were derived by updating these

water pollution caused by organic waste, measured in

estimates through 2007.

terms of biochemical oxygen demand (BOD), because the data for this indicator are the most plentiful and reliable for cross-country comparisons of emissions. BOD measures the strength of an organic waste by the amount of oxygen consumed in breaking it down. A sewage overload in natural waters exhausts the water’s dissolved oxygen content. Wastewater treatment, by contrast, reduces BOD. Data on water pollution are more readily available than are other emissions data because most industrial pollution control programs start by regulating

Emissions of organic water pollutants vary among countries from 1990 to 2007 Kilograms per day (millions)

1990–98

3.6a 2000–07

3.0

2.0

1.5

Data sources 1.0

Data on water pollutants are from Hettige, Mani, and Wheeler, “Industrial Pollution in Economic

0.5

Development: Kuznets Revisited” (1998). The data were updated through 2007 by the World

0.0 United States

Russian Federation

Japan

Germany

Indonesia

Thailand

France

Vietnam

United Kingdom

Bank’s Development Research Group using the same methodology as the initial study. Data on

Note: Data are for the most recent year available during the period specified.

industrial sectoral employment are from UNIDO’s

Source: Table 3.6.

industry database.

2011 World Development Indicators

149


3.7

Energy production and use Energy production

Energy use

Alternative and nuclear energy production % of total

Total million metric tons of oil equivalent

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

150

1990

2008

.. 2.4 100.1 28.7 48.4 0.1 157.5 8.1 21.3 10.8 3.3 13.1 1.8 4.9 4.6 0.9 104.2 9.6 .. .. .. 11.0 273.8 .. .. 7.4 886.3 0.0 48.2 12.0 8.7 1.0 3.4 5.1 6.6 40.1 10.1 1.0 16.5 54.9 1.7 0.7 5.1 14.1 12.1 111.9 14.6 .. 1.8 186.2 4.4 9.2 3.4 .. .. 1.3 1.7

.. 1.2 162.0 105.8 82.9 0.8 302.1 11.0 58.6 23.4 4.0 14.5 1.8 16.8 4.3 1.0 228.1 10.2 .. .. 3.6 10.1 407.4 .. .. 9.0 1,993.3 0.1 93.6 22.7 13.2 2.7 11.4 3.9 5.1 32.8 26.6 1.7 28.5 87.5 3.0 0.5 4.2 29.6 16.6 136.6 13.5 .. 1.1 134.1 6.9 9.9 5.4 .. .. 2.0 2.1

2011 World Development Indicators

Total million metric tons of oil equivalent 1990

.. 2.7 22.2 5.9 46.1 7.7 86.2 24.8 25.8 12.7 45.5 48.3 1.7 2.8 7.0 1.3 140.2 28.6 .. .. .. 5.0 208.7 .. .. 13.8 863.0 8.7 24.2 11.8 0.8 2.0 4.3 9.0 16.5 48.8 17.3 4.1 6.0 31.8 2.5 0.9 9.6 14.9 28.4 223.9 1.2 .. 12.1 351.4 5.3 21.4 4.4 .. .. 1.6 2.4

2008

.. 2.1 37.1 11.0 76.4 3.0 130.1 33.2 13.4 27.9 28.1 58.6 3.0 5.7 6.0 2.1 248.5 19.8 .. .. 5.2 7.1 266.8 .. .. 31.4 2,116.4 14.1 30.8 22.3 1.4 4.9 10.3 9.1 12.1 44.6 19.0 8.2 10.3 70.7 4.9 0.7 5.4 31.7 35.3 266.5 2.1 .. 3.0 335.3 9.5 30.4 8.1 .. .. 2.8 4.6

average annual % growth 1990–2008

.. 2.0 2.8 3.6 2.5 –2.8 2.3 1.9 –2.7 4.6 -1.8 1.0 3.2 3.2 2.6 2.5 3.1 –1.2 .. .. 3.5 2.2 1.6 .. .. 4.8 4.9 2.5 0.7 3.9 3.0 5.0 5.1 1.4 –1.1 0.2 0.2 3.8 3.9 4.8 3.7 –2.1 –1.8 3.5 1.7 1.0 2.6 .. -6.8 -0.1 3.4 2.4 3.8 .. .. 3.6 3.7

Per capita kilograms of oil equivalent 1990

.. 809 878 552 1,418 2,171 5,053 3,214 3,609 110 4,470 4,844 346 416 1,627 933 938 3,277 .. .. .. 407 7,509 .. .. 1,049 760 1,534 730 319 326 658 343 1,884 1,558 4,705 3,374 556 583 551 463 276 6,101 308 5,692 3,946 1,275 .. 2,217 4,424 353 2,110 498 .. .. 219 486

2008

.. 664 1,078 609 1,915 974 6,071 3,988 1,540 175 2,907 5,471 347 587 1,588 1,102 1,295 2,595 .. .. 358 372 8,008 .. .. 1,871 1,598 2,026 684 346 378 1,084 499 2,047 1,076 4,282 3,460 820 767 867 796 138 4,026 393 6,635 4,279 1,431 .. 694 4,083 405 2,707 590 .. .. 281 632

Fossil fuel

Combustible renewables and waste

1990

2008

1990

2008

.. 76.5 99.9 25.5 88.7 97.2 93.9 79.2 100.0 45.5 95.6 76.0 4.8 69.1 93.9 66.1 51.2 84.3 .. .. .. 18.7 74.5 .. .. 75.1 75.5 100.0 67.4 11.2 35.0 48.3 23.3 86.5 64.3 93.2 89.6 74.8 79.1 94.0 31.4 19.3 100.0 5.5 55.5 58.1 32.0 .. 88.6 86.8 18.2 94.6 28.1 .. .. 19.7 30.0

.. 63.7 99.8 33.5 89.8 73.4 94.6 71.6 98.9 68.4 92.1 73.8 37.1 82.1 92.8 67.2 52.6 76.2 .. .. 29.7 23.9 74.9 .. .. 77.6 86.9 94.9 72.7 4.0 43.5 45.6 25.0 85.1 89.9 81.2 80.4 79.2 83.9 96.1 38.4 19.8 88.3 6.7 48.0 51.0 43.8 .. 66.6 80.1 27.8 92.8 42.9 .. .. 28.3 54.1

.. 13.6 0.1 73.5 3.7 0.1 4.6 10.0 0.0 53.9 0.4 1.6 94.2 27.2 2.3 33.4 34.1 0.6 .. .. .. 76.7 4.0 .. .. 19.3 23.2 0.6 22.8 84.7 59.5 36.6 73.5 3.5 35.6 0.0 6.6 24.4 13.8 3.3 48.2 80.7 2.0 93.9 16.1 4.9 62.9 .. 3.8 1.4 73.7 4.2 68.5 .. .. 77.8 62.9

.. 10.3 0.1 63.5 3.7 0.0 4.2 16.3 0.0 31.1 5.5 4.0 61.0 14.4 3.1 22.3 31.6 3.8 .. .. 69.6 71.0 4.5 .. .. 15.5 9.6 0.4 14.7 93.4 51.3 17.3 74.0 3.6 10.0 4.9 15.6 18.9 6.3 2.1 31.2 80.0 11.7 92.4 21.8 5.2 52.5 .. 12.7 7.0 66.8 3.4 53.3 .. .. 71.2 41.7

% of total energy use 1990

.. 9.2 0.1 1.1 7.5 1.7 1.5 11.0 0.2 0.6 0.0 23.1 0.0 3.6 3.8 0.1 13.1 13.9 .. .. .. 4.6 21.5 .. .. 5.5 1.3 0.0 9.8 4.1 5.3 14.4 2.6 3.6 0.1 6.9 0.3 0.7 7.2 2.7 20.3 0.0 0.0 0.6 20.9 38.7 5.2 .. 5.4 11.8 9.3 1.0 3.4 .. .. 2.5 8.2

2008

.. 15.9 0.1 3.0 5.9 26.6 1.2 10.8 1.4 0.5 0.0 20.4 0.0 3.4 6.5 0.0 14.3 22.3 .. .. 0.1 5.1 21.6 .. .. 6.6 3.5 0.0 13.0 2.9 2.3 37.3 1.6 5.1 0.1 16.0 3.3 1.8 9.4 1.9 30.3 0.0 0.2 0.9 21.2 45.3 3.7 .. 21.1 13.3 5.6 2.2 4.0 .. .. 0.6 4.3


3.7

Energy use

Energy production

environment

Energy production and use

Alternative and nuclear energy production % of total

Total million metric tons of oil equivalent

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

2008

14.6 291.8 172.2 179.8 104.9 3.5 0.4 25.3 0.5 75.2 0.2 90.5 9.0 28.9 22.6 .. 50.4 2.5 .. 1.1 0.1 .. .. 73.2 4.9 1.3 .. .. 48.8 .. .. .. 193.4 0.1 2.7 0.8 5.6 10.7 0.2 5.5 60.5 11.4 1.5 .. 150.5 119.1 38.3 34.3 0.6 .. 4.6 10.6 15.7 103.9 3.4 .. 26.6

10.5 468.3 347.0 326.9 117.7 1.5 3.3 26.9 0.5 88.7 0.3 148.2 15.1 20.8 44.7 .. 152.8 1.2 .. 1.8 0.2 .. .. 103.7 3.9 1.7 .. .. 93.1 .. .. .. 233.6 0.1 3.9 0.6 11.5 23.1 0.3 8.7 66.5 14.9 2.2 .. 226.8 219.7 63.5 63.3 0.7 .. 7.4 12.3 23.3 71.4 4.4 .. 124.8

Total million metric tons of oil equivalent 1990

28.7 318.9 103.9 68.3 18.1 10.0 11.5 146.6 2.8 439.3 3.3 72.7 10.9 33.2 93.1 .. 7.8 7.5 .. 7.9 2.2 .. .. 11.3 16.1 2.5 .. .. 22.0 .. .. .. 121.3 9.9 3.4 6.9 5.9 10.7 0.7 5.8 65.7 12.7 2.1 .. 70.6 21.0 3.9 43.0 1.5 .. 3.1 9.7 27.5 103.1 16.7 .. 6.9

2008

26.5 621.0 198.7 202.1 34.0 15.0 22.0 176.0 4.4 495.8 7.1 70.9 18.0 20.3 226.9 .. 26.3 2.9 .. 4.5 5.2 .. .. 18.2 9.2 3.1 .. .. 72.7 .. .. .. 180.6 3.2 3.2 15.0 9.3 15.7 1.8 9.8 79.7 16.9 3.5 .. 111.2 29.7 16.4 82.8 2.9 .. 4.4 14.7 41.1 97.9 24.2 .. 24.1

average annual % growth 1990–2008

0.0 3.6 3.5 6.1 3.8 2.8 3.5 1.4 2.7 0.8 4.3 –0.8 2.8 –2.1 4.8 .. 7.2 -3.8 .. -2.3 3.5 .. .. 2.2 –2.1 0.9 .. .. 6.1 .. .. .. 2.1 –5.2 –0.9 4.0 2.8 2.4 5.2 3.1 1.0 1.5 3.1 .. 2.5 1.6 6.6 3.7 3.4 .. 1.5 2.3 2.2 -0.5 2.6 .. 7.1

Per capita kilograms of oil equivalent 1990

2,762 375 586 1,256 957 2,849 2,462 2,584 1,167 3,556 1,028 4,450 467 1,649 2,171 .. 3,681 1,693 .. 2,941 755 .. .. 2,596 4,357 1,298 .. .. 1,215 .. .. .. 1,457 2,261 1,541 280 437 261 446 303 4,392 3,682 506 .. 725 4,952 2,105 398 618 .. 723 447 440 2,705 1,691 .. 14,732

2008

2,636 545 874 2,808 1,107 3,385 3,011 2,942 1,633 3,883 1,215 4,525 465 851 4,669 .. 9,637 542 .. 1,979 1,250 .. .. 2,895 2,733 1,520 .. .. 2,693 .. .. .. 1,698 867 1,193 474 416 316 823 340 4,845 3,967 621 .. 735 6,222 5,903 499 853 .. 699 510 455 2,567 2,274 .. 18,830

Fossil fuel

Combustible renewables and waste

1990

2008

1990

2008

81.5 55.7 54.3 98.2 98.6 84.6 97.2 93.4 82.6 84.5 98.2 96.9 17.5 93.1 83.8 .. 99.9 93.5 .. 81.8 93.5 .. .. 98.9 75.8 98.0 .. .. 88.8 .. .. .. 88.1 100.0 97.0 93.8 5.5 14.4 62.0 5.1 96.0 67.3 28.3 .. 19.3 51.9 100.0 52.8 58.4 .. 21.3 63.3 45.8 97.8 80.4 .. 99.9

77.8 71.1 65.6 99.4 99.4 90.2 96.6 89.9 88.5 83.0 98.0 98.8 16.2 88.9 81.2 .. 100.0 69.2 .. 64.3 95.3 .. .. 99.1 60.8 84.2 .. .. 95.1 .. .. .. 88.8 89.1 96.2 93.7 7.3 31.0 71.6 10.9 92.5 66.7 38.5 .. 18.3 58.6 100.0 61.8 75.7 .. 28.2 76.1 56.9 93.8 78.3 .. 100.0

2.3 41.9 43.3 1.0 0.1 1.1 0.0 0.6 17.1 1.1 0.1 0.2 77.9 2.9 0.8 .. 0.1 0.1 .. 8.4 4.6 .. .. 1.1 1.8 0.0 .. .. 9.7 .. .. .. 6.1 0.4 2.5 4.6 93.9 84.7 16.0 93.7 1.4 4.3 53.9 .. 80.2 4.9 0.0 43.7 28.3 .. 72.5 27.5 35.2 2.2 14.8 .. 0.1

5.8 26.3 26.7 0.5 0.1 1.8 0.0 3.0 11.1 1.4 0.1 0.2 76.9 5.1 1.3 .. 0.0 0.1 .. 24.8 2.7 .. .. 0.9 8.8 5.6 .. .. 4.1 .. .. .. 4.6 2.5 3.3 3.2 81.9 66.8 11.2 86.4 3.9 6.1 52.3 .. 81.2 4.6 0.0 34.8 12.3 .. 53.7 12.8 18.6 6.0 13.0 .. 0.0

% of total energy use 1990

12.8 2.4 2.4 0.8 1.2 0.6 3.1 3.9 0.3 14.4 1.8 0.9 4.5 4.0 15.4 .. 0.0 11.5 .. 4.9 1.9 .. .. 0.0 28.2 1.7 .. .. 1.6 .. .. .. 5.9 0.2 0.0 1.5 0.4 1.0 17.5 1.3 1.4 28.1 17.5 .. 0.5 49.6 0.0 3.6 12.7 .. 76.0 9.2 19.0 0.1 4.8 .. 0.0

2011 World Development Indicators

2008

15.2 2.4 7.7 0.2 0.1 2.0 4.8 5.1 0.4 15.6 1.6 0.9 7.0 6.0 17.5 .. 0.0 32.3 .. 6.1 1.0 .. .. 0.0 29.1 2.6 .. .. 0.9 .. .. .. 6.7 0.2 0.0 0.7 14.0 2.2 7.0 2.7 1.9 27.0 9.2 .. 0.4 40.7 0.0 3.4 11.8 .. 109.4 11.2 24.5 0.3 5.4 .. 0.0

151


3.7

Energy production and use Energy production

Energy use

Alternative and nuclear energy production % of total

Total million metric tons of oil equivalent 1990

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

40.8 1,293.1 .. 370.6 1.0 13.4 a .. 0.0 5.3 3.1 .. 114.5 34.6 4.2 8.8 .. 29.7 10.0 22.3 2.0 9.1 26.5 .. 1.1 12.6 5.7 25.8 74.9 .. 135.8 110.2 208.0 1,652.5 1.1 38.6 148.9 24.7 .. 9.4 4.9 8.6 8,840.1 t 172.8 4,796.0 2,168.7 2,627.2 4,966.5 1,226.1 1,769.6 609.0 558.6 349.5 475.6 3,892.9 476.5

2008

28.8 1,253.9 .. 579.0 1.2 9.9 .. 0.0 6.4 3.7 .. 163.0 30.4 5.1 34.9 .. 33.2 12.7 23.5 1.5 17.5 63.9 .. 2.1 40.0 7.5 29.0 68.6 .. 81.3 180.5 166.7 1,706.1 1.4 62.0 180.7 71.4 .. 15.3 6.8 8.5 12,357.7 t 264.0 7,284.5 4,001.4 3,284.5 7,544.8 2,658.9 1,772.9 922.0 856.3 573.6 810.3 4,843.0 463.1

a. Includes Kosovo and Montenegro.

152

2011 World Development Indicators

Total million metric tons of oil equivalent 1990

2008

62.3 39.4 879.2 686.8 .. .. 59.0 161.6 1.7 2.9 19.3a 16.0 .. .. 11.5 18.5 21.3 18.3 5.7 7.7 .. .. 90.9 134.5 90.1 138.8 5.5 8.9 10.6 15.4 .. .. 47.2 49.6 24.0 26.7 11.4 19.7 5.3 2.5 9.7 19.0 42.0 107.2 .. .. 1.3 2.6 6.0 19.4 4.9 9.2 52.8 98.5 19.6 18.8 .. .. 251.8 136.1 19.9 58.4 205.9 208.5 1,915.0 2,283.7 2.3 4.2 46.4 50.5 43.6 64.1 24.3 59.4 .. .. 2.5 7.5 5.4 7.4 9.3 9.5 8,569.9 t 11,899.4 t 200.3 279.9 3,864.0 6,002.2 1,993.4 3,842.7 1,871.1 2,161.8 4,049.8 6,266.2 1,139.4 2,655.4 1,577.0 1,215.0 454.0 729.2 185.5 431.3 389.2 756.8 310.5 497.4 4,544.3 5,672.5 1,059.7 1,226.5

average annual % growth 1990–2008

Per capita kilograms of oil equivalent 1990

2008

–1.9 2,683 1,830 –1.1 5,929 4,838 .. .. .. 4.8 3,631 6,514 3.5 224 234 0.2 2,550a 2,181 .. .. .. 1.8 3,760 3,828 –0.1 4,037 3,385 2.0 2,858 3,827 .. .. .. 2.2 2,581 2,756 3.0 2,320 3,047 3.3 322 443 2.6 392 372 .. .. .. 0.4 5,514 5,379 0.6 3,581 3,491 2.8 895 957 –3.1 1,001 365 4.1 382 446 5.1 742 1,591 .. .. .. 4.3 322 397 7.6 4,899 14,557 3.7 607 889 3.6 941 1,333 1.5 5,352 3,730 .. .. .. –3.0 4,852 2,943 5.4 10,645 13,030 0.1 3,597 3,395 1.1 7,672 7,503 1.8 725 1,254 0.6 2,261 1,849 1.6 2,206 2,295 5.2 367 689 .. .. .. 6.2 204 326 1.7 683 583 –0.1 889 763 1.9 w 1,669 w 1,835 w 2.1 380 357 2.5 1,029 1,261 3.6 679 1,019 1.0 2,283 2,177 2.5 966 1,157 4.6 716 1,380 –1.1 4,038 3,030 2.5 1,044 1,290 4.7 814 1,329 3.6 348 495 2.6 676 678 1.4 4,649 5,131 1.0 3,527 3,763

Fossil fuel 1990

96.1 93.4 .. 100.0 43.2 90.6a .. 100.0 81.6 71.3 .. 86.1 77.4 24.1 17.5 .. 37.3 59.3 97.9 71.3 6.9 63.9 .. 15.0 99.2 87.0 81.8 100.0 .. 91.8 100.0 90.7 86.4 58.7 99.2 91.5 20.4 .. 97.0 15.6 44.8 81.0 w 39.8 78.9 70.1 88.2 77.4 71.5 93.0 71.2 97.2 53.8 41.2 84.2 79.8

2008

Combustible renewables and waste 1990

2008

% of total energy use 1990

2008

79.4 1.0 10.3 1.6 11.2 90.9 1.4 0.9 5.2 8.4 .. .. .. .. .. 100.0 0.0 0.0 0.0 0.0 57.3 56.8 41.7 0.0 0.7 89.5 6.0a 5.0 4.2a 5.4 .. .. .. .. .. 100.0 0.0 0.0 0.0 0.0 70.0 0.8 3.7 15.5 26.0 69.4 4.7 6.7 25.6 25.6 .. .. .. .. .. 87.2 11.5 10.4 2.5 2.6 81.7 4.5 4.2 18.1 14.6 43.4 71.0 52.6 4.9 4.0 31.2 81.8 68.0 0.8 0.8 .. .. .. .. .. 33.1 11.7 20.0 50.9 45.9 52.7 4.8 8.1 36.7 39.6 98.7 0.0 0.0 2.1 1.3 42.3 0.0 0.0 26.7 54.7 10.6 91.7 88.2 1.4 1.2 80.6 34.9 18.7 1.0 0.6 .. .. .. .. .. 14.3 82.8 83.1 0.6 0.3 99.9 0.8 0.1 0.0 0.0 86.3 12.9 13.6 0.1 0.1 90.6 13.7 4.9 4.6 4.6 100.0 0.0 0.0 0.3 0.0 .. .. .. .. .. 81.8 0.1 0.7 8.2 17.9 100.0 0.0 0.0 0.0 0.0 90.2 0.3 2.2 8.5 7.1 85.0 3.3 3.7 10.3 11.2 64.9 24.3 23.9 26.8 9.3 98.1 0.0 0.0 1.2 1.9 87.6 1.2 0.8 7.3 11.7 54.0 77.7 41.8 1.9 3.8 .. .. .. .. .. 99.0 3.1 1.0 0.0 0.0 7.5 74.3 81.0 12.7 11.3 26.1 50.9 65.3 4.0 3.9 81.1 w 10.1 w 9.8 w 8.7 w 9.1 w 29.2 56.0 66.2 4.4 4.4 81.5 16.9 13.3 4.1 5.2 79.0 27.1 16.9 2.9 4.2 86.0 6.1 6.8 5.4 7.0 79.7 18.4 15.2 4.1 5.2 83.7 26.6 12.4 1.9 4.0 89.7 1.5 1.7 5.3 8.7 72.4 19.7 16.8 9.2 10.8 98.3 1.7 1.1 1.1 0.6 68.9 43.6 28.5 2.5 2.5 39.8 56.6 57.7 2.3 2.5 82.6 2.8 3.9 12.8 13.3 75.0 3.2 5.9 16.7 18.6


About the data

3.7

environment

Energy production and use Definitions

In developing economies growth in energy use is

Data sources). All forms of energy—primary energy

• Energy production refers to forms of primary

closely related to growth in the modern sectors—

and primary electricity—are converted into oil equiva-

energy—petroleum (crude oil, natural gas liquids,

industry, motorized transport, and urban areas—

lents. A notional thermal efficiency of 33 percent is

and oil from nonconventional sources), natural gas,

but energy use also reflects climatic, geographic,

assumed for converting nuclear electricity into oil

solid fuels (coal, lignite, and other derived fuels),

and economic factors (such as the relative price

equivalents and 100 percent efficiency for converting

and combustible renewables and waste—and pri-

of energy). Energy use has been growing rapidly in

hydroelectric power.

mary electricity, all converted into oil equivalents

low- and middle-income economies, but high-income

The IEA makes these estimates in consultation

(see About the data). • Energy use refers to the use

economies still use almost five times as much energy

with national statistical offices, oil companies, elec-

of primary energy before transformation to other

on a per capita basis.

tric utilities, and national energy experts. The IEA

end-use fuels, which is equal to indigenous produc-

Energy data are compiled by the International

occasionally revises its time series to reflect politi-

tion plus imports and stock changes, minus exports

Energy Agency (IEA). IEA data for economies that

cal changes, and energy statistics undergo contin-

and fuels supplied to ships and aircraft engaged in

are not members of the Organisation for Economic

ual changes in coverage or methodology as more

international transport (see About the data). • Fos-

Co-operation and Development (OECD) are based

detailed energy accounts become available. Breaks

sil fuel comprises coal, oil, petroleum, and natural

on national energy data adjusted to conform to

in series are therefore unavoidable.

gas products. • Combustible renewables and waste

annual questionnaires completed by OECD member

comprise solid biomass, liquid biomass, biogas,

governments.

industrial waste, and municipal waste. • Alternative

Total energy use refers to the use of primary energy

and nuclear energy production is noncarbohydrate

before transformation to other end-use fuels (such

energy that does not produce carbon dioxide when

as electricity and refined petroleum products). It

generated. It includes hydropower and nuclear, geo-

includes energy from combustible renewables and

thermal, and solar power, among others.

waste—solid biomass and animal products, gas and liquid from biomass, and industrial and municipal waste. Biomass is any plant matter used directly as fuel or converted into fuel, heat, or electricity. Data for combustible renewables and waste are often based on small surveys or other incomplete information and thus give only a broad impression of developments and are not strictly comparable across countries. The IEA reports include country notes that explain some of these differences (see

A person in a high-income economy uses more than 14 times as much energy on average as a person in a low3.7a income economy in 2008 Energy use per capita (thousands of kilograms of oil equivalent)

1990

2008

6

Fossil fuels are still the primary global energy source in 2008

Percent 100

Fossil fuel

Combustible renewables and waste

3.7b Alternative and nuclear energy

80

5 4

60

3

40

Data sources

2

Data on energy production and use are from IEA

20

1 0 High income

Upper middle income

Source: Table 3.7.

Middle income

Lower middle income

Low income

World

electronic files and are published in IEA’s annual publications, Energy Statistics and Balances of

0 High income

Upper middle income

Lower middle income

Low income

World

Non-OECD Countries, Energy Statistics of OECD Countries, and Energy Balances of OECD Countries.

Source: Table 3.7.

2011 World Development Indicators

153


3.8

Energy dependency and efficiency and carbon dioxide emissions Net energy importsa

GDP per unit of energy use

% of energy use

2005 PPP $ per kilogram of oil equivalent

1990

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

154

.. 8 –351 –387 –5 98 –83 67 17 16 93 73 –7 –77 34 28 26 66 .. .. .. –120 –31 .. .. 46 –3 100 –99 –2 –997 49 22 43 60 18 42 75 –175 –72 31 19 47 5 57 50 –1,139 .. 85 47 17 57 24 .. .. 20 29

2008

.. 45 –337 –865 –9 73 –132 67 –338 16 86 75 39 –195 28 53 8 48 .. .. 30 –42 -53 .. .. 71 6 100 –204 –2 -868 45 –11 57 58 26 –40 79 –176 –24 38 20 22 7 53 49 –552 .. 64 60 27 68 33 .. .. 28 55

2011 World Development Indicators

1990

.. 4.8 7.1 5.8 5.3 1.4 4.7 8.0 1.3 6.2 1.5 5.2 3.2 7.0 .. 7.6 7.7 2.3 .. .. .. 5.1 3.6 .. .. 6.3 1.4 15.5 8.4 1.9 10.7 9.5 5.5 7.1 .. 3.5 7.5 6.7 9.4 5.8 8.0 1.9 1.7 1.8 4.1 6.3 11.8 .. 2.4 5.8 2.5 8.3 6.7 .. .. 6.4 5.5

2008

.. 11.0 6.8 8.8 6.9 5.8 5.7 9.1 5.3 7.1 4.0 6.1 3.9 6.7 4.7 11.6 7.4 4.6 .. .. 5.0 5.4 4.5 .. .. 7.2 3.6 20.0 12.0 0.8 9.6 9.6 3.1 8.5 .. 5.4 9.9 9.2 9.9 5.8 7.9 3.8 4.7 2.0 5.1 7.4 9.4 .. 6.6 8.3 3.4 10.0 7.4 .. .. 3.7 5.7

Carbon dioxide emissions

Total million metric tons 1990

2.7 7.5 78.8 4.4 112.5 3.7 292.9 60.9 44.1 15.5 98.5 107.5 0.7 5.5 4.7 2.2 208.7 76.6 0.6 0.3 0.5 1.7 449.7 0.2 0.1 34.9 2,458.7 27.6 57.3 4.1 1.2 3.0 5.8 25.0 33.3 162.6 50.4 9.6 16.8 75.9 2.6 .. 28.2 3.0 50.9 398.7 6.1 0.2 15.3 960.2 3.9 72.7 5.1 1.1 0.3 1.0 2.6

2007

0.7 4.2 140.0 24.7 183.6 5.1 373.7 68.7 31.7 43.7 66.7 103.0 3.9 13.2 29.0 5.0 368.0 51.7 1.7 0.2 4.4 6.2 556.9 0.3 0.4 71.6 6,533.0 39.9 63.4 2.4 1.6 8.1 6.4 24.8 27.0 124.9 50.0 20.7 30.0 184.5 6.7 0.6 20.5 6.5 64.1 371.5 2.0 0.4 6.0 787.3 9.8 98.0 12.9 1.4 0.3 2.4 8.8

Carbon intensity kilograms per kilogram of oil equivalent energy use

Per capita metric tons

kilograms per 2005 PPP $ of GDP

1990

2007

1990

2007

1990

2007

.. 2.8 3.6 0.8 2.4 0.5 3.4 2.5 1.9 1.2 2.6 2.2 0.4 2.0 1.0 1.7 1.5 2.7 .. .. .. 0.3 2.2 .. .. 2.5 2.8 3.1 2.4 0.3 1.5 1.5 1.3 2.8 2.0 3.3 2.9 2.3 2.8 2.4 1.1 .. 2.9 0.2 1.8 1.8 5.2 .. 1.4 2.8 0.7 3.4 1.1 .. .. 0.6 1.1

.. 2.0 3.8 2.3 2.5 1.8 3.0 2.1 2.7 1.7 2.4 1.8 1.3 2.4 5.2 2.5 1.6 2.6 .. .. 0.9 0.8 2.1 .. .. 2.3 3.3 2.9 2.2 0.1 1.3 1.7 0.6 2.7 2.7 2.7 2.5 2.6 2.5 2.7 1.4 0.8 3.6 0.3 1.8 1.4 1.1 .. 1.8 2.4 1.0 3.0 1.6 .. .. 0.9 1.9

0.1 2.3 3.1 0.4 3.5 1.2 17.2 7.9 7.0 0.1 10.9 10.8 0.1 0.8 1.6 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 5.2 3.1 15.7 9.8 1.3 1.6 1.3 0.5 .. 18.0 0.1 10.2 7.0 6.6 0.2 3.2 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

.. 0.6 0.5 0.1 0.5 0.4 0.7 0.3 1.5 0.2 1.7 0.4 0.1 0.3 .. 0.2 0.2 1.2 0.1 0.1 .. 0.1 0.6 0.1 0.0 0.4 2.0 0.2 0.3 0.2 0.1 0.2 0.2 0.4 .. 1.0 0.4 0.3 0.3 0.4 0.1 .. 1.8 0.1 0.4 0.3 0.4 0.2 0.6 0.4 0.3 0.4 0.2 0.2 0.2 0.1 0.2

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


Net energy importsa

GDP per unit of energy use

% of energy use

2005 PPP $ per kilogram of oil equivalent

1990

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

49 8 –66 –163 –480 65 96 83 83 83 95 –24 18 13 76 .. –544 67 .. 86 94 .. .. –546 69 49 .. .. –122 .. .. .. –60 99 20 89 5 0 67 5 8 10 29 .. –113 –467 –888 20 59 .. –49 –9 43 –1 80 .. –286

2008

60 25 –75 –62 –246 90 85 85 88 82 96 –109 16 -3 80 .. –481 58 .. 60 96 .. .. –469 58 45 .. .. –28 .. .. .. -29 97 –23 96 –23 –47 82 11 16 12 39 .. –104 –640 –286 24 76 .. –69 16 43 27 82 .. –418

1990

4.4 3.3 3.6 5.0 .. 6.2 7.3 9.2 5.1 7.3 3.2 1.6 3.0 .. 5.2 .. 2.8 1.5 .. 3.4 7.5 .. .. .. 2.9 6.4 .. .. 5.5 .. .. .. 6.9 1.7 1.4 9.7 0.9 .. 9.4 2.3 6.0 5.1 3.7 .. 2.0 6.5 7.1 4.2 9.8 .. 5.5 10.0 5.4 3.0 9.6 .. ..

2008

6.8 5.1 4.2 3.7 2.9 11.6 8.5 9.6 4.4 8.1 4.2 2.3 3.1 .. 5.5 .. 4.8 3.8 .. 7.9 8.8 .. .. 5.2 6.4 5.8 .. .. 4.9 .. .. .. 7.9 3.1 2.8 8.4 1.9 .. 7.3 3.0 7.9 6.3 4.1 .. 2.6 7.9 4.0 4.7 13.8 .. 6.2 15.4 7.1 6.4 9.7 .. 4.5

3.8

environment

Energy dependency and efficiency and carbon dioxide emissions Carbon dioxide emissions

Total million metric tons 1990

63.4 690.0 149.4 227.0 52.5 30.3 33.5 424.7 8.0 1,152.3 10.4 261.1 5.8 244.6 241.5 .. 40.7 11.0 0.2 13.3 9.1 .. 0.5 40.3 22.1 10.8 1.0 0.6 56.5 0.4 2.7 1.5 357.2 21.0 10.0 23.5 1.0 4.3 0.0 0.6 164.0 23.9 2.6 1.0 45.3 31.3 10.3 68.5 3.1 2.1 2.3 21.1 44.5 347.6 44.3 .. 11.8

2007

56.4 1,611.0 396.8 495.6 100.0 44.3 66.7 456.1 14.0 1,253.5 21.4 227.2 11.2 70.7 502.9 .. 86.1 6.1 1.5 7.8 13.3 .. 0.7 57.3 15.3 11.3 2.2 1.1 194.3 0.6 1.9 3.9 471.1 4.7 10.6 46.4 2.6 13.2 3.0 3.4 173.1 32.6 4.6 0.9 95.2 42.7 37.3 156.3 7.2 3.4 4.1 43.0 70.9 317.1 58.1 .. 63.0

Carbon intensity kilograms per kilogram of oil equivalent energy use

Per capita metric tons

kilograms per 2005 PPP $ of GDP

1990

2007

1990

2007

1990

2007

2.2 2.2 1.5 3.3 2.9 3.0 2.9 2.9 2.9 2.6 3.2 4.0 0.5 7.4 2.6 .. 5.2 1.6 .. 1.9 4.0 .. .. 3.6 1.5 6.4 .. .. 2.5 .. .. .. 2.9 2.4 2.9 3.4 0.2 0.4 0.0 0.1 2.5 1.8 1.3 .. 0.6 1.5 2.4 1.6 2.1 .. 0.7 2.2 1.6 3.4 2.6 .. 1.7

2.1 2.7 2.1 2.7 3.0 2.9 3.0 2.6 2.8 2.4 3.0 3.4 0.6 3.8 2.3 .. 3.4 2.1 .. 1.7 3.3 .. .. 3.2 1.7 3.7 .. .. 2.7 .. .. .. 2.6 1.4 3.4 3.2 0.3 0.8 1.9 0.4 2.2 1.9 1.3 .. 0.9 1.6 2.4 1.9 2.6 .. 1.0 3.1 1.8 3.3 2.3 .. 2.8

6.1 0.8 0.8 4.2 2.8 8.6 7.2 7.5 3.3 9.3 3.3 18.0 0.2 12.1 5.6 .. 19.2 2.8 0.1 5.6 3.1 .. 0.2 9.2 6.8 8.3 0.1 0.1 3.1 0.0 1.3 1.4 4.3 5.4 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

0.5 0.7 0.4 0.7 .. 0.5 0.4 0.3 0.6 0.4 1.0 2.5 0.2 .. 0.5 .. 0.6 1.1 0.1 0.6 0.5 .. 0.5 .. 0.5 1.0 0.1 0.1 0.5 0.1 0.9 0.2 0.4 1.4 2.0 0.4 0.2 .. 0.0 0.0 0.4 0.4 0.3 0.2 0.3 0.2 0.4 0.4 0.2 0.3 0.1 0.2 0.3 1.1 0.3 .. ..

0.3 0.5 0.5 0.7 1.1 0.2 0.4 0.3 0.7 0.3 0.8 1.4 0.2 .. 0.4 .. 0.7 0.6 0.1 0.2 0.3 .. 0.5 0.6 0.3 0.7 0.1 0.1 0.6 0.0 0.3 0.3 0.3 0.5 1.3 0.4 0.2 .. 0.2 0.1 0.3 0.3 0.3 0.1 0.3 0.2 0.6 0.4 0.2 0.3 0.2 0.2 0.3 0.5 0.2 .. 0.7

2011 World Development Indicators

155


3.8

Energy dependency and efficiency and carbon dioxide emissions Net energy importsa

GDP per unit of energy use

% of energy use

2005 PPP $ per kilogram of oil equivalent

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

34 –47 .. –528 43 31 .. 100 75 46 .. –26 62 24 17 .. 37 59 –96 62 7 37 .. 17 –111 –16 51 –281 .. 46 –454 –1 14 49 17 –242 -2 .. –273 9 8 –3c w 14 –24 –9 –40 –23 –8 –12 –34 –201 10 –54 15 55

2008

27 –83 .. –258 57 38 .. 100 65 53 .. –21 78 43 –127 .. 33 52 –19 40 8 40 .. 17 –106 18 71 –265 .. 40 –209 20 25 67 –23 –182 –20 .. –104 8 10 –4c w 6 –21 –4 –52 –20 0 –46 –26 –99 24 –63 15 62

Carbon dioxide emissions

Total million metric tons

1990

2008

1990

2007

2.9 2.1 .. 5.3 6.3 4.6 .. 6.2 3.1 5.7 .. 3.1 8.5 6.3 2.5 .. 4.5 9.3 3.3 3.1 2.2 5.3 .. 2.7 2.2 6.6 8.3 0.7 .. 1.7 4.8 6.6 4.2 10.1 0.9 4.3 2.5 .. 8.7 1.8 .. 4.2 w 2.6 3.0 2.4 3.7 3.0 2.0 2.2 6.9 5.7 3.5 2.8 5.3 6.6

6.4 3.1 .. 3.3 7.1 4.7 .. 12.5 6.1 7.1 .. 3.5 9.3 9.5 5.3 .. 6.4 10.9 4.4 4.8 2.6 4.7 .. 1.9 1.7 8.3 8.9 1.7 .. 2.3 4.2 10.0 5.8 9.3 1.3 5.1 3.7 .. 6.8 2.1 .. 5.5 w 3.2 4.4 4.0 5.2 4.4 3.8 3.6 7.7 4.7 5.2 3.2 6.6 8.2

158.7 2,073.5 0.7 214.9 3.2 45.3b 0.4 46.9 44.3 12.3 0.0 333.2 227.4 3.8 5.6 0.4 51.7 42.9 37.4 21.3 2.4 95.8 .. 0.8 16.9 13.3 150.7 28.0 0.8 611.0 54.8 569.8 4,861.0 4.0 113.9 122.1 21.4 .. 10.1 2.4 15.5 22,529.9d t 357.6 9,758.0 4,772.5 4,984.4 10,115.2 3,091.2 4,214.9 1,017.3 578.7 781.4 465.1 11,669.7 2,595.7

94.1 1,536.1 0.7 402.1 5.5 53.5b 1.3 54.1 37.0 15.1 0.6 433.2 359.0 12.3 11.5 1.1 49.2 38.0 69.8 7.2 6.0 277.3 0.2 1.3 37.0 23.8 288.4 45.8 3.2 317.3 135.4 539.2 5,832.2 6.2 116.0 165.4 111.3 2.3 22.0 2.7 9.6 30,649.4d t 228.2 15,574.9 10,391.5 5,175.3 15,802.5 7,693.8 2,897.1 1,538.1 1,177.0 1,828.9 679.5 13,761.0 2,656.8

Carbon intensity kilograms per kilogram of oil equivalent energy use

Per capita metric tons

1990

2007

1990

2007

2.5 2.7 .. 3.6 1.9 1.5b .. 4.1 2.6 3.2 .. 3.7 2.5 0.7 0.5 .. 1.1 1.8 3.3 4.3 0.2 2.3 .. 0.6 2.8 2.7 2.9 1.6 .. 2.7 2.8 2.8 2.5 1.8 2.8 2.8 0.9 .. 3.3 0.5 1.7 2.6d w 2.2 2.6 2.4 2.7 2.5 2.7 2.7 2.2 3.1 2.0 1.7 2.6 2.4

2.4 2.3 .. 2.7 2.0 .. .. 2.0 2.1 2.1 .. 3.2 2.5 1.3 0.8 .. 1.0 1.5 3.6 1.9 0.3 2.7 .. 0.5 2.4 2.7 2.9 2.5 .. 2.3 2.6 2.6 2.5 2.0 2.4 2.6 2.0 .. 3.0 0.4 1.0 2.5d w 1.0 2.7 2.9 2.5 2.7 3.1 2.4 2.2 2.9 2.5 1.6 2.4 2.2

6.8 15.8 0.1 13.2 0.4 6.4b 0.1 15.4 10.4 9.1 0.0 9.5 5.9 0.2 0.2 0.5 6.0 6.4 2.9 4.5 0.1 1.7 .. 0.2 13.9 1.6 2.7 8.6 0.0 13.3 29.3 10.0 19.5 1.3 6.3 6.2 0.3 .. 0.8 0.3 1.5 4.3d 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 6.3b 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 4.6d 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

kilograms per 2005 PPP $ of GDP 1990

2007

0.9 1.2 0.1 0.7 0.3 .. 0.1 0.7 0.8 0.6 .. 1.2 0.3 0.1 0.2 0.1 0.2 0.2 1.0 1.5 0.1 0.4 .. 0.2 1.3 0.4 0.3 2.3 0.1 1.6 0.6 0.4 0.6 0.2 3.1 0.6 0.4 .. 0.5 0.2 .. 0.6d w 0.8 0.8 1.0 0.7 0.8 1.4 1.2 0.3 0.6 0.6 0.6 0.5 0.4

a. Negative values indicate that a country is a net exporter. b. Includes Kosovo and Montenegro. c. Deviation from zero is due to statistical errors and changes in stock. d. Includes emissions not allocated to specific countries.

156

2011 World Development Indicators

0.4 0.8 0.1 0.8 0.3 .. 0.3 0.2 0.4 0.3 .. 1.0 0.3 0.2 0.2 0.2 0.2 0.1 0.8 0.6 0.1 0.6 0.3 0.3 1.2 0.3 0.3 1.6 0.1 1.0 0.6 0.3 0.4 0.2 1.9 0.5 0.5 .. 0.4 0.2 .. 0.5d w 0.3 0.6 0.7 0.5 0.6 0.8 0.7 0.3 0.6 0.5 0.4 0.4 0.3


About the data

3.8

environment

Energy dependency and efficiency and carbon dioxide emissions Definitions

Because commercial energy is widely traded, its pro-

elemental carbon, were converted to actual carbon

• Net energy imports are estimated as energy use

duction and use need to be distinguished. Net energy

dioxide mass by multiplying them by 3.664 (the ratio

less production, both measured in oil equivalents.

imports show the extent to which an economy’s use

of the mass of carbon to that of carbon dioxide).

• GDP per unit of energy use is the ratio of gross

exceeds its production. High-income economies are

Although estimates of global carbon dioxide emis-

domestic product (GDP) per kilogram of oil equiva-

net energy importers; middle-income economies are

sions are probably accurate within 10 percent (as cal-

lent of energy use, with GDP converted to 2005

their main suppliers.

culated from global average fuel chemistry and use),

international dollars using purchasing power parity

The ratio of gross domestic product (GDP) to energy

country estimates may have larger error bounds.

(PPP) rates. An international dollar has the same

use indicates energy efficiency. To produce compa-

Trends estimated from a consistent time series tend

purchasing power over GDP that a U.S. dollar has

rable and consistent estimates of real GDP across

to be more accurate than individual values. Each year

in the United States. Energy use refers to the use

economies relative to physical inputs to GDP—that

the CDIAC recalculates the entire time series since

of primary energy before transformation to other

is, units of energy use—GDP is converted to 2005

1949, incorporating recent findings and corrections.

end-use fuel, which is equal to indigenous produc-

international dollars using purchasing power parity

Estimates exclude fuels supplied to ships and aircraft

tion plus imports and stock changes minus exports

(PPP) rates. Differences in this ratio over time and

in international transport because of the difficulty of

and fuel supplied to ships and aircraft engaged in

across economies reflect structural changes in an

apportioning the fuels among benefiting countries.

international transport (see About the data for table

economy, changes in sectoral energy efficiency, and

The ratio of carbon dioxide per unit of energy shows

3.7). • Carbon dioxide emissions are emissions from

differences in fuel mixes.

carbon intensity, which is the amount of carbon diox-

the burning of fossil fuels and the manufacture of

Carbon dioxide emissions, largely by-products of

ide emitted as a result of using one unit of energy in

cement and include carbon dioxide produced during

energy production and use (see table 3.7), account

the process of production. The proportion of carbon

consumption of solid, liquid, and gas fuels and gas

for the largest share of greenhouse gases, which are

dioxide per unit of GDP indicates how clean produc-

flaring.

associated with global warming. Anthropogenic car-

tion processes are.

bon dioxide emissions result primarily from fossil fuel combustion and cement manufacturing. In combustion different fossil fuels release different amounts of carbon dioxide for the same level of energy use: oil releases about 50 percent more carbon dioxide than natural gas, and coal releases about twice as much. Cement manufacturing releases about half a metric ton of carbon dioxide for each metric ton of cement produced. The U.S. Department of Energy’s Carbon Dioxide Information Analysis Center (CDIAC) calculates annual anthropogenic emissions from data on fossil fuel consumption (from the United Nations Statistics Division’s World Energy Data Set) and world cement manufacturing (from the U.S. Bureau of Mines’s Cement Manufacturing Data Set). Carbon dioxide emissions, often calculated and reported as

3.8a

High-income economies depend on imported energy Net energy imports (% of energy use)

1990

2008

Low income Lower middle income Upper middle income

Data sources High income

Data on energy use are from the electronic files of the International Energy Agency. Data on car-

Euro area –60

–40

–20

0

20

Note: Negative values indicate that the income group is a net energy exporter. Source: Table 3.8.

40

60

80

bon dioxide emissions are from the CDIAC, Environmental Sciences Division, Oak Ridge National Laboratory, Tennessee, United States.

2011 World Development Indicators

157


3.9

Trends in greenhouse gas emissions Carbon dioxide emissions

average annual % growtha % changeb 1990– 1990– 2007 2007

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

158

–7.8 2.5 3.7 10.1 2.4 1.2 1.4 1.1 –2.2 6.5 –2.6 –0.3 9.1 4.0 14.1 4.1 3.3 –2.1 5.9 –4.7 16.0 4.2 1.5 1.0 10.5 4.6 5.2 2.0 –0.3 –3.7 –0.8 5.1 1.5 1.8 –1.3 –1.1 –1.0 4.7 2.9 5.2 4.7 7.4 –2.1 4.6 1.3 –0.3 –6.2 4.2 –6.2 –1.1 4.9 2.2 6.0 1.5 –0.4 7.6 7.4

–73.3 –43.3 77.6 459.0 63.1 21.8 27.6 12.7 –36.2 181.7 –39.9 –4.2 442.1 139.6 315.2 130.2 76.3 –32.5 188.8 –41.0 884.6 254.9 23.8 27.8 162.5 105.4 165.7 44.5 10.6 –40.2 33.6 174.7 10.1 –0.8 –18.9 –23.2 –0.8 116.9 78.1 143.2 155.9 .. –27.5 115.7 26.0 –6.8 –66.6 107.7 –65.1 –18.0 149.5 34.9 154.2 31.6 13.0 141.3 240.7

2011 World Development Indicators

Methane emissions Total thousand metric tons of carbon dioxide equivalent 2005

.. 2,407 54,219 45,409 101,821 2,962 126,488 8,515 36,607 92,414 11,498 10,063 4,080 30,350 2,741 4,501 492,160 10,867 .. .. 20,215 18,518 89,338 .. .. 18,149 1,333,098 2,820 58,108 56,445 5,584 2,580 10,997 3,864 9,455 11,497 7,935 6,081 17,125 46,996 3,131 2,467 2,108 52,243 9,742 77,252 8,218 .. 4,410 67,582 8,990 7,289 8,306 .. .. 4,006 5,191

Nitrous oxide emissions

% of total % changeb From energy processes Agricultural 1990– 2005 2005 2005

.. –5.1 33.1 –8.3 –8.3 2.5 9.7 –15.0 110.7 6.5 –32.8 –21.8 –15.8 30.9 –53.5 –22.6 56.4 –24.8 .. .. 35.0 37.1 30.8 .. .. 49.8 28.5 84.0 13.5 –41.6 –10.4 –31.3 –2.2 –60.5 –21.0 –40.3 –0.5 3.8 31.2 68.8 18.0 30.9 –36.8 32.8 –2.8 –0.3 1.4 .. –12.4 –44.8 24.2 2.1 74.7 .. .. 34.9 31.5

.. 20.0 83.2 15.6 18.9 50.8 29.7 21.7 82.0 10.0 7.6 11.6 15.6 25.6 46.7 8.6 7.6 13.0 .. .. 4.9 39.1 32.2 .. .. 24.4 45.8 26.7 19.9 10.2 32.2 9.5 16.9 57.0 11.2 49.4 16.4 7.8 31.2 50.7 12.4 11.2 42.3 14.3 7.4 44.3 90.4 .. 36.1 32.1 23.3 26.3 12.4 .. .. 12.1 7.2

.. 70.8 8.2 27.9 70.6 36.7 55.1 48.6 13.6 70.5 70.9 56.7 47.8 34.1 42.4 84.1 61.1 18.9 .. .. 76.1 42.4 29.3 .. .. 39.4 38.8 0.0 68.0 23.1 31.9 67.2 17.4 33.3 62.4 33.6 65.2 63.7 57.8 31.7 53.1 73.2 30.5 72.5 20.7 47.7 1.1 .. 50.8 43.8 39.5 50.0 48.8 .. .. 56.2 78.4

Total thousand metric tons of carbon dioxide equivalent 2005

.. 1,036 4,898 38,881 49,821 580 62,966 4,448 2,633 21,386 11,680 6,571 2,902 15,092 1,196 3,081 235,987 4,227 .. .. 5,794 9,127 40,171 .. .. 8,135 467,213 422 21,288 54,643 3,566 1,334 7,364 2,851 6,356 8,878 6,290 2,255 4,571 18,996 1,377 1,189 932 30,510 7,124 49,058 482 .. 2,019 56,560 4,899 5,977 5,376 .. .. 1,438 2,865

Other greenhouse gas emissions

% of total % changeb Energy and industry Agricultural 1990– 2005 2005 2005

.. –18.7 27.5 –6.7 29.6 –27.6 –0.1 –13.5 0.4 42.1 –28.3 –27.6 –21.5 3.2 –40.8 –44.1 52.6 –55.2 .. .. 46.9 –13.3 –5.5 .. .. 57.5 48.5 –1.0 5.2 –37.3 –17.2 –26.2 –1.6 –24.5 –31.8 –10.2 –21.5 11.0 42.3 60.7 7.7 15.6 –50.7 19.4 –4.1 –30.6 57.9 .. –26.9 –23.9 –5.5 –17.1 121.2 .. .. 59.6 26.1

.. 7.1 22.6 0.4 3.9 1.2 10.3 31.0 8.3 7.5 23.1 38.1 4.0 0.7 24.7 1.4 3.4 36.0 .. .. 3.5 2.6 23.7 .. .. 16.6 12.9 38.5 4.4 2.2 1.0 4.5 2.7 36.6 15.1 53.0 18.0 7.8 3.8 8.3 8.2 3.8 21.5 5.2 42.8 24.2 10.0 .. 35.5 38.2 9.3 22.1 5.5 .. .. 6.2 3.8

.. 78.4 58.6 38.4 89.2 81.6 78.2 52.5 77.5 83.1 72.9 44.3 61.5 36.5 57.8 92.0 67.0 48.1 .. .. 66.1 75.9 58.9 .. .. 73.4 74.3 0.0 86.1 31.3 51.8 85.4 29.3 52.4 78.7 36.9 73.4 76.8 84.9 80.0 76.2 90.9 60.5 88.8 41.7 66.8 23.3 .. 56.9 52.2 70.5 58.2 56.8 .. .. 84.2 85.9

Total thousand metric tons of carbon dioxide equivalent 2005

.. 62 489 20 785 335 6,505 2,329 89 0 467 2,106 0 0 571 0 11,816 383 .. .. 0 419 21,943 .. .. 13 141,394 119 83 0 5 62 0 59 129 1,121 1,422 0 63 3,181 77 0 40 10 826 15,539 9 .. 12 31,543 15 1,842 481 .. .. 0 0

% changeb 1990– 2005

.. .. 50.0 .. –65.8 .. 33.5 46.2 –49.5 .. .. 583.8 .. .. –7.4 .. 40.5 .. .. .. .. –55.0 69.7 .. .. –29.5 1,073.0 –68.6 98.3 .. .. .. .. –93.4 .. .. 458.3 .. .. 54.5 .. .. 1,790.5 .. 724.4 57.1 .. .. .. 8.1 –97.5 –20.9 .. .. .. .. ..


Carbon dioxide emissions

average annual % growtha % changeb 1990– 1990– 2007 2007

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

–0.6 4.8 4.6 4.7 3.7 2.5 3.6 0.6 2.4 0.4 4.3 –2.1 4.6 –9.2 4.0 .. 9.0 –4.1 14.0 –4.4 3.0 .. 5.3 2.2 –3.1 –0.4 5.1 3.8 6.4 1.9 –4.6 6.2 1.6 –10.5 –0.7 3.8 5.4 6.9 42.7 8.1 0.0 2.2 4.4 –1.2 5.9 2.9 8.2 4.8 4.4 5.3 3.0 3.6 3.1 –1.0 2.0 .. 5.9

–11.0 133.5 165.5 118.3 90.5 46.1 99.0 7.4 75.3 8.8 106.2 –22.9 92.9 –71.1 108.2 .. 111.4 –51.2 554.7 –47.9 46.8 .. 39.4 42.2 –38.9 –29.3 128.3 72.5 243.6 37.4 –26.8 165.7 31.9 –80.1 5.4 97.1 159.7 208.5 .. 439.9 5.6 36.5 73.6 –4.6 110.0 36.5 260.5 128.1 131.2 57.2 82.7 103.1 59.2 –8.8 31.2 .. 435.5

Methane emissions Total thousand metric tons of carbon dioxide equivalent 2005

7,767 583,978 208,944 114,585 15,937 15,331 3,517 40,790 1,302 42,771 1,796 47,119 22,130 18,195 32,069 .. 14,380 3,591 .. 3,108 1,003 .. .. 14,682 5,516 1,403 .. .. 46,501 .. .. .. 128,209 3,372 6,067 10,573 12,843 77,211 5,057 22,142 21,259 27,635 6,018 .. 130,317 16,870 17,849 137,401 3,219 .. 15,388 17,187 51,889 70,023 12,173 .. 15,706

Nitrous oxide emissions

% of total % changeb From energy processes Agricultural 1990– 2005 2005 2005

–22.9 10.5 18.4 32.5 –45.8 14.3 83.8 –13.4 14.4 –36.5 111.5 –27.3 23.3 –15.0 2.4 .. 119.4 –38.1 .. –42.1 46.6 .. .. –34.7 –34.1 –36.5 .. .. 64.7 .. .. .. 26.3 –17.5 –25.9 15.8 18.2 –7.4 47.2 9.7 –30.4 3.6 26.3 .. 10.9 47.2 194.9 50.7 16.5 .. 2.0 22.7 28.6 –36.6 22.4 .. 387.2

29.1 15.9 25.5 70.6 58.4 11.9 18.4 14.7 11.4 8.1 25.0 66.2 16.9 58.6 19.9 .. 93.4 6.8 .. 53.6 9.7 .. .. 86.3 32.0 32.1 .. .. 69.3 .. .. .. 40.2 45.2 2.5 8.0 22.7 12.6 0.3 5.9 23.4 3.6 6.6 .. 68.9 74.6 94.1 23.7 4.0 .. 3.9 13.5 9.3 62.0 13.8 .. 96.5

33.6 64.4 46.4 18.2 18.6 76.7 31.2 39.8 50.3 71.2 21.8 25.3 65.5 23.5 38.6 .. 1.1 72.3 .. 27.7 25.5 .. .. 5.7 33.8 46.6 .. .. 12.4 .. .. .. 42.3 29.4 92.1 51.7 44.2 69.0 94.9 82.9 43.4 90.2 74.8 .. 19.8 12.6 3.0 63.5 79.2 .. 84.1 61.3 63.7 21.9 35.4 .. 0.4

Total thousand metric tons of carbon dioxide equivalent 2005

6,961 212,927 123,275 26,644 3,440 7,486 1,793 28,620 599 29,785 667 17,594 10,542 3,422 13,548 .. 650 1,510 .. 1,253 672 .. .. 1,285 2,451 599 .. .. 15,087 .. .. .. 42,514 849 3,489 5,814 9,501 30,932 3,797 4,516 14,596 12,930 3,340 .. 21,565 4,737 561 26,838 1,204 .. 9,067 7,560 12,950 30,198 5,958 .. 200

Other greenhouse gas emissions

% of total % changeb Energy and industry Agricultural 1990– 2005 2005 2005

–31.2 33.3 43.5 41.1 –9.9 –8.3 41.6 –5.4 29.5 –17.0 39.6 –46.2 14.3 –60.6 34.7 .. 157.1 –57.7 .. –58.7 79.1 .. .. 9.2 –45.7 –33.9 .. .. 13.5 .. .. .. 8.9 –51.0 –30.0 12.2 –12.7 –23.9 47.1 26.0 –10.7 23.5 10.1 .. 12.6 –3.1 82.6 46.0 18.1 .. 0.6 35.4 34.0 4.7 24.3 .. 105.1

3.9

environment

Trends in greenhouse gas emissions

30.9 12.8 3.7 11.4 9.7 4.5 15.3 39.1 12.1 41.6 8.2 12.8 5.0 13.2 41.3 .. 27.7 11.2 .. 11.6 12.6 .. .. 11.2 5.0 15.9 .. .. 6.7 .. .. .. 10.6 5.5 2.2 3.0 3.4 2.6 1.1 13.0 52.5 3.5 3.3 .. 9.1 46.5 16.0 14.5 4.9 .. 1.7 2.9 9.1 33.5 22.0 .. 33.9

60.1 73.4 71.5 75.3 63.3 90.5 53.0 43.7 59.0 27.9 55.4 62.5 88.8 62.3 35.9 .. 16.9 72.6 .. 77.4 58.8 .. .. 51.9 86.0 63.9 .. .. 64.9 .. .. .. 75.2 73.5 93.2 82.6 71.4 42.9 94.3 76.8 39.5 94.2 91.7 .. 77.3 39.0 68.0 74.2 83.7 .. 82.6 81.9 73.1 57.7 43.8 .. 25.0

Total thousand metric tons of carbon dioxide equivalent 2005

1,552 8,433 1,027 2,569 86 1,151 1,981 13,968 51 53,786 112 339 0 2,794 10,221 .. 931 24 .. 890 0 .. .. 280 656 120 .. .. 994 .. .. .. 4,555 8 0 0 282 0 0 0 3,750 973 0 .. 669 5,202 175 819 0 .. 0 330 365 2,451 783 .. 0

2011 World Development Indicators

% changeb 1990– 2005

121.1 –11.9 –40.6 –2.9 –66.0 3,062.9 88.7 211.1 .. 81.1 .. .. .. .. 66.0 .. 253.9 .. .. .. .. .. .. –0.7 .. .. .. .. 66.3 .. .. .. 53.1 .. .. .. .. .. .. .. –40.9 3.4 .. .. 176.6 –39.4 .. –18.8 .. .. .. .. 125.6 360.6 606.6 .. ..

159


3.9

Trends in greenhouse gas emissions Carbon dioxide emissions

average annual % growtha % changeb 1990– 1990– 2007 2007

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

–2.9 –2.2 0.6 2.6 3.0 0.6c 7.4 0.3 –1.6 0.6 38.1 1.3 2.9 7.5 6.4 10.0 –0.5 –0.3 3.6 –6.9 4.9 5.7 .. 3.6 4.1 3.3 3.4 2.8 8.3 –4.4 5.5 –0.5 1.2 2.0 0.1 2.9 11.7 19.7 4.5 –0.2 –3.3 1.8 w –4.1 2.6 4.3 0.3 2.4 4.7 –2.0 2.4 4.2 4.8 2.2 1.0 0.2

–40.7 –34.3 4.8 87.1 72.1 –20.1c 237.7 15.4 –32.8 –17.3 .. 30.0 57.9 226.3 107.3 150.0 –4.8 –11.6 86.6 –69.9 154.7 189.6 .. 70.1 118.4 79.9 91.4 44.7 291.9 –54.0 147.3 –5.4 20.0 55.7 –9.7 35.5 420.3 .. 117.4 10.0 –37.9 36.0 w –36.2 59.6 117.7 3.8 56.2 148.9 –31.3 51.2 103.4 134.1 46.1 17.9 2.4

Methane emissions Total thousand metric tons of carbon dioxide equivalent 2005

24,331 562,801 .. 48,152 7,129 7,782 .. 2,237 3,911 3,498 .. 63,785 36,338 10,210 67,441 .. 11,311 4,748 12,458 3,898 32,024 83,257 .. 2,889 10,070 8,160 64,251 27,984 .. 70,360 23,283 65,788 548,074 19,589 39,602 61,183 82,978 .. 6,677 19,294 9,539 7,135,973 s 464,616 5,128,922 3,120,011 2,008,911 5,593,538 1,928,355 933,500 1,008,557 287,084 846,145 589,897 1,542,435 315,597

Nitrous oxide emissions

% of total % changeb From energy processes Agricultural 1990– 2005 2005 2005

–35.1 –18.3 .. 67.4 35.1 –58.7 .. 136.6 –39.7 0.6 .. 24.6 11.9 –11.2 55.5 .. 1.3 –17.1 –10.8 –9.3 24.0 5.7 .. 5.0 32.0 106.2 46.4 –5.0 .. –42.2 58.0 –44.1 –14.4 24.1 24.0 5.9 40.1 .. 73.5 –28.4 –5.7 6.2 w –4.0 13.9 18.6 7.3 12.2 24.5 –17.2 30.3 20.0 14.6 5.4 –10.8 –18.0

42.7 79.3 .. 83.6 9.9 41.5 .. 60.1 18.2 30.7 .. 45.4 10.4 5.3 7.1 .. 9.9 19.8 53.8 12.8 12.6 16.9 .. 23.5 83.9 55.6 16.0 75.2 .. 62.1 93.1 24.8 41.0 1.5 57.3 47.4 22.7 .. 17.0 6.7 11.4 37.3 w 13.6 39.0 35.1 45.0 36.9 39.2 67.5 17.3 64.7 16.1 30.5 38.9 25.9

36.0 9.1 .. 4.0 68.3 43.7 .. 1.3 39.0 32.1 .. 31.4 56.8 65.2 85.2 .. 28.1 67.6 28.1 68.6 63.2 66.0 .. 39.8 0.7 25.5 33.6 21.6 .. 23.3 2.6 38.2 34.8 94.3 33.7 40.0 63.9 .. 54.9 59.3 73.3 42.6 w 60.7 42.6 46.9 35.8 44.1 43.6 17.4 58.6 20.7 65.4 44.0 37.0 46.9

Total thousand metric tons of carbon dioxide equivalent 2005

11,537 76,121 .. 6,501 4,083 4,581 .. 1,068 3,354 1,156 .. 24,048 26,529 2,056 49,472 .. 5,865 2,415 5,509 1,378 21,647 22,304 .. 1,738 230 2,366 32,781 4,276 .. 26,097 1,169 30,565 317,153 7,017 10,003 14,935 23,030 .. 3,250 25,068 6,114 2,852,592 s 239,126 1,799,128 1,153,692 645,436 2,038,253 707,496 213,150 442,132 73,539 267,722 334,216 814,339 218,258

Other greenhouse gas emissions

% of total % changeb Energy and industry Agricultural 1990– 2005 2005 2005

–44.0 –48.7 .. 17.5 37.2 –8.8 .. 162.8 –37.1 –12.2 .. 12.9 6.5 18.0 34.9 .. –13.1 –15.5 33.4 0.2 0.8 15.1 .. –21.3 12.4 18.0 12.8 93.8 .. –51.4 78.7 –44.7 1.8 16.1 9.4 23.4 98.3 .. 57.4 –29.7 –16.1 5.8 w –16.7 18.3 30.5 1.5 12.8 38.0 –38.8 32.9 36.8 34.9 –7.6 –8.4 –17.8

32.4 27.8 .. 14.0 2.7 24.2 .. 77.6 52.0 13.2 .. 12.6 18.7 12.1 1.3 .. 26.8 20.8 9.0 1.4 2.5 21.7 .. 5.6 11.5 21.4 22.7 16.4 .. 42.8 18.3 24.8 30.6 1.4 6.2 5.0 6.1 .. 11.2 2.6 3.7 15.4 w 4.1 10.7 10.8 10.6 10.0 10.5 25.4 4.6 10.7 12.6 3.7 29.1 31.7

Total thousand metric tons of carbon dioxide equivalent 2005

56.2 746 44.3 59,673 .. .. 46.1 2,193 88.5 0 63.6 4,493 .. .. 2.8 2,532 37.7 395 70.4 473 .. .. 59.8 2,552 62.6 9,080 65.1 0 92.6 0 .. .. 60.2 2,078 59.3 2,109 78.1 0 86.9 383 78.8 0 65.5 1,104 .. .. 67.5 0 60.3 0 66.4 0 66.4 5,066 78.1 73 .. .. 45.6 693 43.6 1,075 60.0 10,403 56.4 239,517 96.9 59 84.2 608 75.2 2,468 83.0 0 .. .. 72.5 0 71.7 0 85.2 0 66.2 w 724,183 s 63.6 .. 70.8 259,893 72.3 159,984 68.1 99,909 70.0 263,401 72.2 .. 56.6 74,802 72.4 20,972 74.5 6,717 74.3 9,253 66.1 .. 56.9 460,781 55.4 84,190

% changeb 1990– 2005

–62.8 130.6 .. –10.6 .. 353.7 .. 396.0 478.0 –38.5 .. 71.1 47.7 .. .. .. 133.8 97.4 .. –86.3 .. –22.8 .. .. .. .. 96.9 .. .. 209.3 27.4 96.7 158.7 .. .. –24.0 .. .. .. .. .. 122.4 w .. 208.5 439.4 83.1 200.5 .. 112.0 23.5 20.7 –12.5 .. 93.7 37.2

a. Calculated using the least squares method, which accounts for ups and downs of all data points in the period (see Statistical methods). b. Calculated as the change in emission since 1990, which is the baseline for Kyoto Protocal requirements. c. Includes Kosovo and Montenegro.

160

2011 World Development Indicators


About the data

3.9

environment

Trends in greenhouse gas emissions Definitions

Greenhouse gases—which include carbon dioxide,

compared. A kilogram of methane is 21 times as

• Carbon dioxide emissions are emissions from

methane, nitrous oxide, hydrofluorocarbons, per-

effective at trapping heat in the earth’s atmosphere

the burning of fossil fuels and the manufacture of

fluorocarbons, and sulfur hexafluoride—contribute

as a kilogram of carbon dioxide within 100 years.

cement and include carbon dioxide produced during

Nitrous oxide emissions are mainly from fossil fuel

consumption of solid, liquid, and gas fuels and gas

Carbon dioxide emissions, largely a byproduct of

combustion, fertilizers, rainforest fires, and animal

flaring. • Methane emissions are emissions from

energy production and use (see table 3.7), account

waste. Nitrous oxide is a powerful greenhouse gas,

human activities such as agriculture and from indus-

for the largest share of greenhouse gases. Anthro-

with an estimated atmospheric lifetime of 114 years,

trial methane production. • Methane emissions from

pogenic carbon dioxide emissions result primarily

compared with 12 years for methane. The per kilo-

energy processes are emissions from the produc-

from fossil fuel combustion and cement manufactur-

gram global warming potential of nitrous oxide is

tion, handling, transmission, and combustion of fos-

ing. Burning oil releases more carbon dioxide than

nearly 310 times that of carbon dioxide within 100

sil fuels and biofuels. • Agricultural methane emis-

burning natural gas, and burning coal releases even

years.

sions are emissions from animals, animal waste, rice

to climate change.

more for the same level of energy use. Cement manu-

Other greenhouse gases covered under the Kyoto

production, agricultural waste burning (nonenergy,

facturing releases about half a metric ton of carbon

Protocol are hydrofluorocarbons, perfluorocarbons,

on-site), and savannah burning. • Nitrous oxide

dioxide for each metric ton of cement produced.

and sulfur hexafluoride. Although emissions of these

emissions are emissions from agricultural biomass

Methane emissions result largely from agricultural

artificial gases are small, they are more powerful

burning, industrial activities, and livestock manage-

activities, industrial production landfills and waste-

greenhouse gases than carbon dioxide, with much

ment. • Nitrous oxide emissions from energy pro-

water treatment, and other sources such as tropi-

higher atmospheric lifetimes and high global warm-

cesses are emissions produced by the combustion

cal forest and other vegetation fires. The emissions

ing potential.

of fossil fuels and biofuels. • Agricultural nitrous

are usually expressed in carbon dioxide equivalents

For a discussion of carbon dioxide sources and

oxide emissions are emissions produced through

using the global warming potential, which allows

the methodology behind emissions calculation, see

fertilizer use (synthetic and animal manure), ani-

the effective contributions of different gases to be

About the data for table 3.8.

mal waste management, agricultural waste burning (nonenergy, on-site), and savannah burning. • Other greenhouse gas emissions include hydrofluorocar-

The six largest contributors to methane emissions account for about 50 percent of emissions

3.9a

bons, perfluorocarbons, and sulfur hexafluoride, which are to be curbed under the Kyoto Protocol.

Methane emissions, 2005 (million metric tons of carbon dioxide equivalent) 1,400

Hydrofluorocarbons, used as a replacement for chlorofluorocarbons, are used mainly in refrigeration and semiconductor manufacturing. Perfluorocarbons,

1,050

also used as a replacement for chlorofluorocarbons in manufacturing semiconductors, are a byproduct of

700

aluminum smelting and uranium enrichment. Sulfur 350

hexafluoride is used largely to insulate high-voltage electric power equipment.

0

China

India

Russian Federation

United States

Brazil

Indonesia

Source: Table 3.9.

The five largest contributors to nitrous oxide emissions account for about 50 percent of emissions

3.9b

Nitrous oxide emissions, 2005 (million metric tons of carbon dioxide equivalent) 500 375

Data sources Data on carbon dioxide emissions are from the

250

Carbon Dioxide Information Analysis Center, Environmental Sciences Division, Oak Ridge National

125

0

Laboratory, Tennessee, United States. Data on methane, nitrous oxide, and other greenhouse China

Source: Table 3.9.

United States

Brazil

India

Indonesia

gases emissions are compiled by the International Energy Agency.

2011 World Development Indicators

161


3.10

Sources of electricity Electricity production

Sources of electricitya

% of total billion kilowatt hours 1990

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

162

.. 3.2 16.1 0.8 50.7 10.4 154.3 49.3 23.2 7.7 39.5 70.3 0.0 2.1 14.6 0.9 222.8 42.1 .. .. .. 2.7 482.0 .. .. 18.4 621.2 28.9 36.4 5.7 0.5 3.5 2.0 9.2 15.0 62.3 26.0 3.7 6.3 42.3 2.2 0.1 17.4 1.2 54.4 417.2 1.0 .. 13.7 547.7 5.7 34.8 2.3 .. .. 0.6 2.3

2008

.. 3.8 40.2 4.0 121.4 5.8 257.1 64.4 23.9 35.0 35.0 83.6 0.1 6.2 13.3 0.6 463.4 44.6 .. .. 1.5 5.6 651.2 .. .. 59.7 3,456.9 38.0 56.0 7.5 0.5 9.5 5.8 12.2 17.7 83.2 36.4 15.4 18.6 131.0 6.0 0.3 10.6 3.8 77.4 570.3 2.0 .. 8.4 631.2 8.4 62.9 8.7 .. .. 0.5 6.5

2011 World Development Indicators

Coal

Natural Gas

Oil

Hydropower

Nuclear power

1990

2008

1990

2008

1990

2008

1990

2008

1990

2008

.. 0.0 0.0 0.0 1.3 0.0 78.7 14.2 0.0 0.0 0.0 28.2 0.0 0.0 71.8 88.1 2.1 50.3 .. .. .. 0.0 17.1 .. .. 38.3 71.3 98.3 10.1 0.0 0.0 0.0 0.0 6.8 0.0 76.4 90.7 1.2 0.0 0.0 0.0 0.0 85.8 0.0 18.5 8.5 0.0 .. 0.0 58.7 0.0 72.4 0.0 .. .. 0.0 0.0

.. 0.0 0.0 0.0 2.3 0.0 76.9 10.7 0.0 1.8 0.0 8.7 0.0 0.0 64.4 100.0 2.7 52.1 .. .. 0.0 0.0 17.2 .. .. 23.6 79.1 68.2 5.4 0.0 0.0 0.0 0.0 20.4 0.0 59.9 48.0 13.8 0.0 0.0 0.0 0.0 91.0 0.0 11.8 4.8 0.0 .. 0.0 46.0 0.0 53.0 13.0 .. .. 0.0 0.0

.. 0.0 93.7 0.0 39.2 16.4 9.3 15.7 0.0 84.3 58.1 7.7 0.0 37.6 0.0 0.0 0.3 7.6 .. .. .. 0.0 2.0 .. .. 2.1 0.4 0.0 12.4 0.0 0.0 0.0 0.0 20.2 0.2 0.6 2.7 0.0 0.0 39.6 0.0 0.0 5.5 0.0 8.6 0.7 16.4 .. 15.6 7.4 0.0 0.3 0.0 .. .. 0.0 0.0

.. 0.0 97.3 0.0 53.6 26.2 15.0 17.4 84.1 89.0 96.9 29.5 0.0 46.5 0.0 0.0 6.3 5.3 .. .. 0.0 7.7 6.2 .. .. 3.7 0.9 31.5 10.3 0.4 18.7 0.0 65.1 20.1 0.0 1.2 19.0 12.9 7.3 68.4 0.0 0.0 4.0 0.0 14.5 3.8 24.7 .. 15.2 13.9 0.0 21.9 0.0 .. .. 0.0 0.0

.. 10.9 5.4 13.8 9.8 68.6 2.3 3.8 97.0 4.3 41.8 1.9 100.0 5.3 7.3 11.9 2.2 2.9 .. .. .. 1.5 3.4 .. .. 9.2 7.9 1.7 1.0 0.4 0.6 2.5 33.3 31.6 91.4 0.9 3.4 88.6 21.5 36.9 6.9 100.0 8.3 11.6 3.1 2.1 11.2 .. 29.2 1.9 0.0 22.3 9.0 .. .. 20.6 1.7

.. 0.0 2.0 3.7 11.7 0.0 1.1 1.9 6.6 5.0 2.7 0.5 99.3 14.0 1.3 0.0 3.8 0.6 .. .. 96.5 15.9 1.5 .. .. 26.9 0.7 0.3 0.3 0.2 0.0 7.1 0.2 16.2 97.0 0.2 3.1 61.8 29.8 19.7 38.6 99.3 0.3 12.4 0.5 1.0 31.2 .. 0.0 1.5 25.9 15.9 26.6 .. .. 62.8 61.9

.. 89.1 0.8 86.2 35.2 15.0 9.2 63.9 3.0 11.4 0.1 0.4 0.0 55.3 20.9 0.0 92.8 4.5 .. .. .. 98.5 61.6 .. .. 48.5 20.4 0.0 75.6 99.6 99.4 97.5 66.7 41.3 0.8 1.9 0.1 9.4 78.5 23.5 73.5 0.0 0.0 88.4 20.0 12.9 72.1 .. 55.2 3.2 100.0 5.1 76.0 .. .. 76.5 98.3

.. 100.0 0.7 96.3 24.9 31.1 4.6 59.0 9.3 4.2 0.1 0.5 0.7 36.6 34.3 0.0 79.8 6.3 .. .. 3.1 76.2 58.7 .. .. 40.5 16.9 0.0 82.8 99.4 81.3 78.0 32.7 42.7 0.8 2.4 0.1 11.2 60.7 11.2 34.2 0.0 0.3 87.3 22.1 11.2 43.8 .. 84.8 3.3 74.1 5.3 42.6 .. .. 37.2 35.0

.. 0.0 0.0 0.0 14.3 0.0 0.0 0.0 0.0 0.0 0.0 60.8 0.0 0.0 0.0 0.0 1.0 34.8 .. .. .. 0.0 15.1 .. .. 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 20.2 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 35.3 75.3 0.0 .. 0.0 27.8 0.0 0.0 0.0 .. .. 0.0 0.0

.. 0.0 0.0 0.0 6.0 42.6 0.0 0.0 0.0 0.0 0.0 54.5 0.0 0.0 0.0 0.0 3.0 35.4 .. .. 0.0 0.0 14.4 .. .. 0.0 2.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 31.9 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 29.6 77.1 0.0 .. 0.0 23.5 0.0 0.0 0.0 .. .. 0.0 0.0


Electricity production

3.10

Sources of electricitya

% of total billion kilowatt hours 1990

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

28.4 289.4 33.3 59.1 24.0 14.2 20.9 213.1 2.5 835.5 3.6 87.4 3.2 27.7 105.4 .. 18.5 15.7 .. 6.6 1.5 .. .. 10.2 28.4 5.8 .. .. 23.0 .. .. .. 115.8 16.2 3.5 9.6 0.5 2.5 1.4 0.9 71.9 32.3 1.4 .. 13.5 121.6 4.5 37.7 2.7 .. 27.2 13.8 27.4 134.4 28.4 .. 4.8

2008

40.0 830.1 149.4 214.5 36.8 29.4 56.4 313.5 7.8 1,075.0 13.8 80.3 7.1 23.2 443.9 .. 51.7 11.9 .. 5.3 10.6 .. .. 28.7 13.3 6.3 .. .. 97.4 .. .. .. 258.9 3.6 4.1 20.8 15.1 6.6 2.1 3.1 107.6 43.8 3.4 .. 21.1 141.7 15.7 91.6 6.4 .. 55.5 32.4 60.8 155.6 45.5 .. 21.6

Coal

Natural Gas

Oil

Hydropower

Nuclear power

1990

2008

1990

2008

1990

2008

1990

2008

1990

2008

30.5 66.2 31.5 0.0 0.0 41.6 50.1 16.8 0.0 14.0 0.0 71.1 0.0 40.1 16.8 .. 0.0 13.1 .. 0.0 0.0 .. .. 0.0 0.0 89.7 .. .. 12.3 .. .. .. 6.7 30.8 92.4 23.0 13.9 1.6 1.5 0.0 38.3 2.1 0.0 .. 0.1 0.1 0.0 0.1 0.0 .. 0.0 0.0 7.0 97.5 32.1 .. 0.0

18.0 68.6 41.1 0.2 0.0 17.8 62.7 15.5 0.0 26.8 0.0 70.3 0.0 36.0 43.2 .. 0.0 3.5 .. 0.0 0.0 .. .. 0.0 0.0 83.8 .. .. 26.9 .. .. .. 8.3 0.0 96.1 56.2 0.0 0.0 31.1 0.0 24.9 11.0 0.0 .. 0.0 0.1 0.0 0.1 0.0 .. 0.0 2.7 25.9 92.2 24.6 .. 0.0

15.7 3.4 2.3 52.5 0.0 27.7 0.0 18.6 0.0 20.0 11.9 10.5 0.0 0.0 9.1 .. 45.7 23.5 .. 26.1 0.0 .. .. 0.0 23.8 0.0 .. .. 20.4 .. .. .. 12.5 42.3 0.0 0.0 0.0 39.3 0.0 0.0 50.9 17.7 0.0 .. 53.7 0.0 81.6 33.6 0.0 .. 0.0 1.7 0.0 0.1 0.0 .. 100.0

37.9 9.9 16.9 80.8 0.0 54.7 26.2 55.1 0.0 26.3 80.6 10.7 0.0 0.0 18.3 .. 30.4 6.1 .. 39.0 0.0 .. .. 41.0 15.2 0.0 .. .. 63.6 .. .. .. 50.6 95.6 0.0 13.8 0.1 35.7 0.0 0.0 58.9 24.3 0.0 .. 58.2 0.3 82.0 32.4 0.0 .. 0.0 28.0 32.2 2.0 33.4 .. 100.0

4.8 3.5 42.7 37.3 89.2 10.0 49.9 48.2 92.4 18.5 87.8 10.0 7.1 3.6 17.9 .. 54.3 0.0 .. 5.4 66.7 .. .. 100.0 14.6 1.8 .. .. 50.0 .. .. .. 53.6 25.4 7.6 64.4 23.6 10.9 3.3 0.1 4.3 0.0 39.8 .. 13.7 0.0 18.4 20.6 14.7 .. 0.0 21.5 45.3 1.2 33.1 .. 0.0

0.9 4.1 28.8 16.6 98.5 5.9 10.6 10.0 96.0 9.7 18.9 9.7 38.4 3.4 3.5 .. 69.6 0.0 .. 0.0 96.5 .. .. 59.0 4.2 2.9 .. .. 1.9 .. .. .. 19.0 0.4 3.9 24.2 0.0 3.5 1.4 0.4 1.9 0.3 64.5 .. 14.7 0.0 18.0 35.4 37.9 .. 0.0 9.0 8.0 1.5 9.1 .. 0.0

0.6 24.8 20.2 10.3 10.8 4.9 0.0 14.8 3.6 10.7 0.3 8.4 76.6 56.3 6.0 .. 0.0 63.5 .. 67.6 33.3 .. .. 0.0 1.5 8.5 .. .. 17.3 .. .. .. 20.3 1.6 0.0 12.7 62.6 48.1 95.2 99.9 0.1 71.9 28.8 .. 32.6 99.6 0.0 44.9 83.2 .. 99.9 75.8 22.1 1.1 32.3 .. 0.0

0.5 13.8 7.7 2.3 1.5 3.3 0.0 13.3 2.0 7.1 0.4 9.3 40.4 60.6 0.7 .. 0.0 90.4 .. 58.9 3.5 .. .. 0.0 3.0 13.3 .. .. 7.7 .. .. .. 15.1 2.3 0.0 4.5 99.9 60.8 67.5 99.6 0.1 51.0 15.9 .. 27.1 98.5 0.0 30.3 61.8 .. 100.0 58.7 16.2 1.4 15.0 .. 0.0

48.3 2.1 0.0 0.0 0.0 0.0 0.0 0.0 0.0 24.2 0.0 0.0 0.0 0.0 50.2 .. 0.0 0.0 .. 0.0 0.0 .. .. 0.0 60.0 0.0 .. .. 0.0 .. .. .. 2.5 0.0 0.0 0.0 0.0 0.0 0.0 0.0 4.9 0.0 0.0 .. 0.0 0.0 0.0 0.8 0.0 .. 0.0 0.0 0.0 0.0 0.0 .. 0.0

37.0 1.8 0.0 0.0 0.0 0.0 0.0 0.0 0.0 24.0 0.0 0.0 0.0 0.0 34.0 .. 0.0 0.0 .. 0.0 0.0 .. .. 0.0 74.2 0.0 .. .. 0.0 .. .. .. 3.8 0.0 0.0 0.0 0.0 0.0 0.0 0.0 3.9 0.0 0.0 .. 0.0 0.0 0.0 1.8 0.0 .. 0.0 0.0 0.0 0.0 0.0 .. 0.0

2011 World Development Indicators

163

environment

Sources of electricity


3.10

Sources of electricity Electricity production

Sources of electricitya

% of total billion kilowatt hours 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

64.3 1,082.2 .. 69.2 0.9 40.9 .. 15.7 25.5 12.4 .. 165.4 151.2 3.2 1.5 .. 146.0 55.0 11.6 18.1 1.6 44.2 .. 0.2 3.6 5.8 57.5 14.6 .. 298.6 17.1 317.8 3,202.8 7.4 56.3 59.3 8.7 .. 1.7 8.0 9.4 11,839.5 t 138.9 3,984.6 1,654.0 2,330.2 4,122.5 796.3 1,935.8 598.1 187.9 341.7 260.2 7,736.5 1,694.1

2008

65.0 1,038.4 .. 204.2 2.4 36.8 .. 41.7 28.8 16.4 .. 255.5 311.1 9.2 4.5 .. 149.9 67.1 41.0 16.1 4.4 147.4 .. 0.1 7.9 15.3 198.4 15.0 .. 192.5 86.3 385.3 4,343.8 8.8 49.4 119.3 73.0 .. 6.5 9.7 8.0 20,201.4 t 206.7 8,948.5 5,548.8 3,402.8 9,174.7 4,044.1 1,864.6 1,285.3 566.8 977.2 424.1 11,079.9 2,352.2

Coal

Natural Gas

1990

2008

1990

28.8 14.3 .. 0.0 0.0 69.1 .. 0.0 31.9 31.3 .. 94.3 40.1 0.0 0.0 .. 1.1 0.1 0.0 0.0 0.0 25.0 .. 0.0 0.0 0.0 35.1 0.0 .. 38.2 0.0 65.0 53.1 0.0 7.4 0.0 23.1 .. 0.0 0.5 53.3 37.3 w 13.2 32.4 47.7 21.6 31.8 61.0 23.1 4.0 1.2 56.1 62.2 40.2 33.7

39.8 18.9 .. 0.0 0.0 72.4 .. 0.0 17.9 32.5 .. 94.2 16.1 0.0 0.0 .. 1.1 0.0 0.0 0.0 2.7 21.4 .. 0.0 0.0 0.0 29.1 0.0 .. 35.6 0.0 32.9 49.1 0.0 4.1 0.0 20.8 .. 0.0 0.0 46.3 40.8 w 6.4 47.4 63.3 21.3 46.4 71.6 25.3 4.5 2.1 58.3 58.0 36.1 22.4

35.1 47.3 .. 48.1 2.3 3.2 .. 0.0 7.1 0.0 .. 0.0 1.0 0.0 0.0 .. 0.3 0.6 20.5 9.1 0.0 40.2 .. 0.0 99.0 63.7 17.7 95.2 .. 16.7 96.3 1.6 11.9 0.0 76.4 26.2 0.1 .. 0.0 0.0 0.0 14.6 w 9.2 22.3 11.7 29.8 21.8 3.4 36.7 9.4 36.9 8.5 2.8 10.7 8.6

Oil

Hydropower

2008

1990

2008

15.3 47.6 .. 43.1 1.7 1.1 .. 80.3 5.6 2.9 .. 0.0 39.1 0.0 0.0 .. 0.4 1.1 31.3 1.9 36.2 69.4 .. 0.0 99.6 88.7 49.7 100.0 .. 11.4 98.3 45.9 21.0 0.0 70.0 14.7 41.5 .. 0.0 0.0 0.0 21.3 w 17.5 19.8 9.9 35.8 19.7 6.7 40.2 20.7 62.5 14.6 4.4 22.5 24.0

18.4 11.5 .. 51.9 93.0 4.6 .. 100.0 6.4 7.9 .. 0.0 5.7 0.2 36.8 .. 0.9 0.7 56.0 0.0 4.9 23.5 .. 39.9 0.1 35.5 6.9 0.0 .. 16.1 3.7 10.9 4.1 5.1 4.4 11.5 15.0 .. 100.0 0.3 0.0 10.2 w 1.7 14.6 14.2 14.9 14.1 12.6 13.6 17.8 48.3 5.3 1.9 8.1 9.6

1.1 1.5 .. 56.9 85.8 0.5 .. 19.7 2.4 0.1 .. 0.1 5.8 55.1 67.6 .. 0.6 0.2 61.7 0.0 0.9 1.1 .. 24.4 0.2 10.8 3.8 0.0 .. 0.4 1.7 1.6 1.3 39.1 2.9 12.5 2.1 .. 100.0 0.3 0.3 5.1 w 5.0 6.0 5.0 7.6 6.0 2.0 2.0 13.5 29.4 7.5 3.8 4.4 3.9

1990

17.7 15.3 .. 0.0 0.0 23.1 .. 0.0 7.4 23.7 .. 0.6 16.8 99.8 63.2 .. 49.7 54.2 23.5 90.9 95.1 11.3 .. 60.1 0.0 0.8 40.2 4.8 .. 3.5 0.0 1.6 8.5 94.2 11.8 62.3 61.8 .. 0.0 99.2 46.7 18.0 w 53.7 23.1 20.3 25.1 24.1 21.4 14.5 64.4 12.4 27.4 15.9 14.7 11.1

a. Shares may not sum to 100 percent because some sources of generated electricity (such as wind, solar, and geothermal) are not shown.

164

2011 World Development Indicators

2008

26.5 15.9 .. 0.0 9.5 26.0 .. 0.0 14.0 24.5 .. 0.5 7.6 44.7 32.4 .. 46.1 53.7 7.0 98.1 60.1 4.8 .. 74.0 0.0 0.2 16.8 0.0 .. 5.9 0.0 1.3 5.9 51.4 23.0 72.8 35.6 .. 0.0 99.7 53.4 15.8 w 48.9 20.4 16.8 26.4 21.0 16.4 16.4 55.3 4.4 15.4 17.2 11.3 9.5

Nuclear power 1990

2008

0.0 10.9 .. 0.0 0.0 0.0 .. 0.0 47.2 37.1 .. 5.1 35.9 0.0 0.0 .. 46.7 43.0 0.0 0.0 0.0 0.0 .. 0.0 0.0 0.0 0.0 0.0 .. 25.5 0.0 20.7 19.1 0.0 0.0 0.0 0.0 .. 0.0 0.0 0.0 17.0 w 0.0 6.4 5.0 7.3 6.1 0.0 11.7 2.1 0.0 1.9 3.2 22.7 35.6

17.3 15.7 .. 0.0 0.0 0.0 .. 0.0 58.1 38.3 .. 5.1 19.0 0.0 0.0 .. 42.6 41.3 0.0 0.0 0.0 0.0 .. 0.0 0.0 0.0 0.0 0.0 .. 46.7 0.0 13.6 19.3 0.0 0.0 0.0 0.0 .. 0.0 0.0 0.0 13.5 w 0.0 4.7 3.2 7.2 4.6 1.7 15.7 2.4 0.0 1.7 3.1 20.8 31.6


About the data

3.10

Definitions

Use of energy is important in improving people’s

as more detailed energy accounts have become

• Electricity production is measured at the termi-

standard of living. But electricity generation also

available. Breaks in series are therefore unavoidable.

nals of all alternator sets in a station. In addition to

can damage the environment. Whether such damage

hydropower, coal, oil, gas, and nuclear power gen-

occurs depends largely on how electricity is gener-

eration, it covers generation by geothermal, solar,

ated. For example, burning coal releases twice as

wind, and tide and wave energy as well as that from

much carbon dioxide—a major contributor to global

combustible renewables and waste. Production

warming—as does burning an equivalent amount

includes the output of electric plants designed to

of natural gas (see About the data for table 3.8).

produce electricity only, as well as that of combined

Nuclear energy does not generate carbon dioxide

heat and power plants. • Sources of electricity are

emissions, but it produces other dangerous waste

the inputs used to generate electricity: coal, gas, oil,

products. The table provides information on electric-

hydropower, and nuclear power. • Coal is all coal and

ity production by source.

brown coal, both primary (including hard coal and

The International Energy Agency (IEA) compiles

lignite-brown coal) and derived fuels (including pat-

data on energy inputs used to generate electricity.

ent fuel, coke oven coke, gas coke, coke oven gas,

IEA data for countries that are not members of the

and blast furnace gas). Peat is also included in this

Organisation for Economic Co-operation and Devel-

category. • Gas is natural gas but not natural gas

opment (OECD) are based on national energy data

liquids. • Oil is crude oil and petroleum products.

adjusted to conform to annual questionnaires com-

• Hydropower is electricity produced by hydroelectric

pleted by OECD member governments. In addition,

power plants. • Nuclear power is electricity produced

estimates are sometimes made to complete major

by nuclear power plants.

aggregates from which key data are missing, and adjustments are made to compensate for differences in definitions. The IEA makes these estimates in consultation with national statistical offices, oil companies, electric utilities, and national energy experts. It occasionally revises its time series to reflect political changes. For example, the IEA has constructed historical energy statistics for countries of the former Soviet Union. In addition, energy statistics for other countries have undergone continuous changes in coverage or methodology in recent years More than 50 percent of electricity in Latin America is produced 3.10a by hydropower Percent

Other Hydropower

Nuclear power Natural gas

Oil Coal

Lower middle-income countries produce the majority of their power 3.10b from coal Percent

100

100

80

80

60

60

40

40

20

20

0

Other Hydropower

Nuclear power Natural gas

Oil Coal

Data sources Data on electricity production are from the IEA’s

0 East Asia Europe Latin America Middle & Pacific & Central & East Asia Caribbean & North Africa

Source: Table 3.10.

South Asia

Low income

Lower middle income

Upper middle income

Low & middle income

High income

electronic files and its annual publications Energy Statistics and Balances of Non-OECD Countries, Energy Statistics of OECD Countries, and Energy

Source: Table 3.10.

Balances of OECD Countries.

2011 World Development Indicators

165

environment

Sources of electricity


3.11

Urbanization Urban population

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

166

millions 1990 2009

% of total population 1990 2009

3 1 13 4 28 2 15 5 4 23 7 10 2 4 2 1 112 6 1 0 1 5 21 1 1 11 311 6 23 10 1 2 5 3 8 8 4 4 6 25 3 0 1 6 3 42 1 0 3 58 5 6 4 2 0 2 2

18 36 52 37 87 68 85 66 54 20 66 96 35 56 39 42 75 66 14 6 13 41 77 37 21 83 27 100 68 28 54 51 40 54 73 75 85 55 55 44 49 16 71 13 61 74 69 38 55 73 36 59 41 28 28 29 40

7 1 23 11 37 2 19 6 5 45 7 11 4 7 2 1 167 5 3 1 3 11 27 2 3 15 586 7 34 23 2 3 10 3 8 8 5 7 9 35 4 1 1 14 3 49 1 1 2 60 12 7 7 4 0 5 4

2011 World Development Indicators

24 47 66 58 92 64 89 67 52 28 74 97 42 66 48 60 86 71 20 11 22 58 81 39 27 89 44 100 75 35 62 64 49 58 76 74 87 70 66 43 61 21 69 17 64 78 86 57 53 74 51 61 49 35 30 48 48

average annual % growth 1990–2009

4.0 1.2 2.9 5.2 1.4 -1.0 1.5 0.6 0.9 3.5 0.3 0.5 4.3 3.0 0.4 3.8 2.1 -0.3 5.0 4.8 5.2 4.3 1.3 2.4 4.6 1.7 3.3 1.1 2.2 4.2 2.8 3.3 3.9 -0.1 0.5 0.0 0.5 2.9 2.5 1.8 1.9 4.0 -1.0 4.5 0.5 0.8 3.6 5.5 -1.5 0.2 4.2 0.8 3.3 3.8 2.7 4.6 3.2

Population in urban agglomerations of more than 1 million

Population in largest city

% of total population 1990 2009

% of urban population 1990 2009

% of urban population 1990 2008

7 .. 7 15 39 33 60 20 24 8 16 17 .. 25 .. .. 35 14 6 .. 6 14 40 .. .. 35 9 100 31 13 29 24 17 .. 20 12 20 21 26 21 18 .. .. 4 17 23 .. .. 22 8 13 30 9 15 .. 16 12

38 21 14 40 37 49 25 30 45 29 24 17 30 29 24 22 13 21 44 66 50 19 18 43 38 42 3 100 21 35 53 47 42 27 27 16 24 37 28 36 37 72 43 29 28 22 62 66 41 6 22 51 22 52 53 56 29

.. .. 99 58 93 95 100 100 .. 59 .. 100 14 29 .. 58 81 100 28 41 38 65 100 21 20 91 48 .. 80 23 .. 94 38 .. 86 100 100 83 86 91 88 58 .. 21 100 100 .. .. 97 100 11 100 84 18 .. 44 68

12 .. 8 24 39 36 59 20 22 13 19 18 .. 33 .. .. 40 16 11 .. 10 19 44 .. .. 35 17 100 37 17 35 31 19 .. 19 11 21 21 33 18 25 .. .. 3 21 23 .. .. 26 8 17 29 8 16 .. 26 13

49 29 12 42 35 56 23 30 43 32 26 18 22 25 22 17 12 22 56 51 46 18 20 41 27 39 3 99 24 37 57 48 38 27 25 15 24 30 29 31 41 60 43 20 33 21 49 45 50 6 19 47 16 45 63 55 28

Access to improved sanitation facilities

60 98 98 86 91 95 100 100 51 56 91 100 24 34 99 74 87 100 33 49 67 56 100 43 23 98 58 .. 81 23 31 95 36 99 94 99 100 87 96 97 89 52 96 29 100 100 33 68 96 100 18 99 89 34 49 24 80

% of rural population 1990 2008

.. .. 77 6 73 .. 100 100 .. 34 .. 100 1 6 .. 20 35 98 2 44 5 35 99 5 2 48 38 .. 43 4 .. 91 8 .. 64 98 100 61 48 57 62 0 .. 1 100 100 .. .. 95 100 4 92 51 6 .. 19 28

30 98 88 18 77 80 100 100 39 52 97 100 4 9 92 39 37 100 6 46 18 35 99 28 4 83 52 .. 55 23 29 96 11 98 81 97 100 74 84 92 83 4 94 8 100 100 30 65 93 100 7 97 73 11 9 10 62


Urban population

millions 1990 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

7 217 54 31 13 2 4 38 1 78 2 9 4 12 32 .. 2 2 1 2 2 0 1 3 2 1 3 1 9 2 1 0 59 2 1 12 3 10 0 2 10 3 2 1 34 3 1 33 1 1 2 15 30 23 5 3 0

7 345 121 50 21 3 7 41 1 85 5 9 9 15 40 .. 3 2 2 2 4 1 2 5 2 1 6 3 20 4 1 1 83 1 2 18 9 17 1 5 14 4 3 3 76 4 2 62 3 1 4 21 60 23 6 4 1

% of total population 1990 2009

66 26 31 56 70 57 90 67 49 63 72 56 18 58 74 .. 98 38 15 69 83 14 45 76 68 58 24 12 50 23 40 44 71 47 57 48 21 25 28 9 69 85 52 15 35 72 66 31 54 15 49 69 49 61 48 72 92

68 30 53 69 67 62 92 68 54 67 78 58 22 63 82 .. 98 36 32 68 87 26 61 78 67 67 30 19 71 33 41 43 78 41 57 56 38 33 37 18 82 87 57 17 49 78 72 37 74 13 61 72 66 61 60 99 96

average annual % growth 1990â&#x20AC;&#x201C;2009

0.0 2.4 4.2 2.6 2.4 1.7 2.5 0.4 1.1 0.5 3.9 0.0 3.8 1.3 1.2 .. 1.5 0.8 6.0 -1.0 2.1 4.6 4.7 2.2 -0.6 1.2 4.2 5.2 4.1 3.9 2.8 0.8 1.8 -1.6 1.0 2.1 5.8 2.6 3.8 5.9 1.5 1.3 2.2 3.9 4.2 1.1 2.7 3.3 3.6 1.6 3.3 1.7 3.6 0.0 1.6 2.3 6.0

Population in urban agglomerations of more than 1â&#x20AC;Żmillion

Population in largest city

% of total population 1990 2009

% of urban population 1990 2009

% of urban population 1990 2008

29 4 15 21 31 46 48 9 49 42 37 12 32 21 33 .. 67 38 70 49 52 50 106 26 23 40 36 24 12 37 53 30 26 32 45 22 27 29 35 23 9 30 34 35 14 22 27 22 65 32 53 39 26 7 54 60 54

100 49 58 86 .. 100 100 .. 82 100 98 96 24 .. 100 .. 100 94 .. .. 100 29 21 97 .. .. 14 50 88 36 29 93 80 .. .. 81 36 .. 66 41 100 .. 59 19 39 100 97 73 73 78 61 71 70 96 97 .. 100

19 10 10 24 26 26 56 19 .. 46 27 7 6 13 51 .. 65 .. .. .. 43 .. .. 20 .. .. 8 .. 8 9 .. .. 34 .. .. 18 6 9 .. .. 13 25 18 5 12 .. .. 16 35 .. 26 27 14 4 37 44 ..

17 13 9 24 23 24 57 17 .. 49 18 9 8 12 48 .. 80 .. .. .. 45 .. 29 17 .. .. 9 .. 9 13 .. .. 36 .. .. 19 7 11 .. .. 12 32 23 7 15 .. .. 18 39 .. 31 30 14 4 39 69 ..

25 6 8 14 27 39 47 8 40 43 23 15 39 19 25 .. 81 44 39 46 52 41 37 22 24 35 31 28 8 38 52 28 23 43 62 18 18 26 42 19 8 36 29 40 13 23 31 21 53 37 51 42 19 7 44 70 32

3.11

environment

Urbanization

Access to improved sanitation facilities

100 54 67 .. 76 100 100 .. 82 100 98 97 27 .. 100 .. 100 94 86 82 100 40 25 97 .. 92 15 51 96 45 50 93 90 85 64 83 38 86 60 51 100 .. 63 34 36 100 97 72 75 71 90 81 80 96 100 .. 100

% of rural population 1990 2008

100 7 22 78 .. 98 100 .. 83 100 .. 97 27 .. 100 .. 100 .. .. .. .. 32 3 96 .. .. 6 41 81 23 8 90 30 .. .. 27 4 .. 9 8 100 88 26 2 36 100 61 8 40 42 15 16 46 .. 87 .. 100

2011 World Development Indicators

100 21 36 .. 66 98 100 .. 84 100 97 98 32 .. 100 .. 100 93 38 71 .. 25 4 96 .. 82 10 57 95 32 9 90 68 74 32 52 4 79 17 27 100 .. 37 4 28 100 .. 29 51 41 40 36 69 80 100 .. 100

167


3.11

Urbanization Urban population

millions 1990 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

168

12 109 0 12 3 4 1 3 3 1 2 18 29 3 7 0 7 5 6 2 5 17 0 1 0 5 33 2 2 35 1 51 188 3 8 17 13 1 3 3 3 2,257 s 121 1,437 883 554 1,558 461 246 308 117 281 145 699 213

12 103 2 21 5 4 2 5 3 1 3 30 36 3 19 0 8 6 12 2 11 23 0 3 0 7 52 3 4 31 4 56 252 3 10 27 25 3 7 5 5 3,398 s 243 2,309 1,559 750 2,552 875 258 452 191 467 310 845 241

2011 World Development Indicators

% of total population 1990 2009

53 73 5 77 39 50 33 100 57 50 30 52 75 17 27 23 83 73 49 32 19 29 21 30 9 58 59 45 11 67 79 89 75 89 40 84 20 68 21 39 29 43 w 22 38 30 68 36 29 63 71 52 25 28 73 71

54 73 19 82 43 52 38 100 57 48 37 61 77 15 44 25 85 74 55 26 26 34 28 43 14 67 69 49 13 68 78 90 82 92 37 94 28 72 31 36 38 50 w 29 48 41 75 45 45 64 79 58 30 37 77 73

average annual % growth 1990â&#x20AC;&#x201C;2009

-0.3 -0.3 8.3 2.7 3.1 0.0 2.5 2.6 0.1 -0.1 2.9 2.6 1.0 0.2 5.0 2.2 0.5 0.8 3.2 0.5 4.5 1.7 3.8 4.6 3.0 2.1 2.3 2.2 4.1 -0.5 4.7 0.5 1.5 0.6 1.1 2.5 3.2 4.1 5.5 2.0 2.3 2.2 w 3.7 2.5 3.0 1.6 2.6 3.4 0.2 2.0 2.6 2.7 4.0 1.0 0.6

Population in urban agglomerations of more than 1â&#x20AC;Żmillion

Population in largest city

% of total population

% of urban population

1990

9 17 .. 34 19 15 .. 99 .. .. 16 28 22 .. 9 .. 12 15 30 .. 5 10 .. 16 .. .. 23 .. 4 12 25 26 42 50 10 34 9 .. 5 10 10 17 w 8 14 11 25 13 .. 16 32 20 10 11 .. 18

2009

9 18 .. 41 22 15 .. 95 .. .. 15 34 23 .. 12 .. 14 15 32 .. 7 10 .. 24 .. .. 28 .. 5 14 33 26 45 49 8 32 12 .. 9 11 13 20 w 11 18 15 29 17 .. 18 35 20 13 14 .. 18

1990

17 8 57 19 48 30 39 99 .. 27 53 10 15 21 33 22 15 20 25 35 27 35 79 52 44 14 20 25 38 7 32 15 9 56 26 17 25 .. 25 24 35 17 w 33 14 11 19 16 9 14 24 26 9 27 20 16

2009

17 10 49 23 52 29 40 95 .. 26 40 12 16 22 27 25 16 20 26 38 28 30 53 56 32 11 20 25 36 9 42 15 8 53 22 11 24 .. 30 31 34 16 w 32 13 11 19 15 7 16 22 22 12 26 19 16

Access to improved sanitation facilities

% of urban population 1990 2008

88 93 35 100 62 .. .. 99 100 100 .. 80 100 85 63 .. 100 100 94 93 27 93 .. 25 93 95 96 99 35 97 98 100 100 95 95 89 61 .. 64 62 58 77 w 39 69 58 87 67 54 94 81 90 53 43 100 100

88 93 50 100 69 96 24 100 100 100 52 84 100 88 55 61 100 100 96 95 32 95 76 24 92 96 97 99 38 97 98 100 100 100 100 .. 94 91 94 59 56 76 w 44 71 63 90 69 64 94 86 92 57 43 100 100

% of rural population 1990 2008

52 70 22 .. 22 .. .. .. 100 100 .. 58 100 67 23 .. 100 100 72 .. 23 74 .. 8 93 44 66 97 40 91 95 100 99 83 76 45 29 .. 6 36 37 35 w 19 31 28 58 29 37 75 38 57 11 21 99 99

54 70 55 .. 38 88 6 .. 99 100 6 65 100 92 18 53 100 100 95 94 21 96 40 3 92 64 75 97 49 90 95 100 99 99 100 .. 67 84 33 43 37 45 w 32 43 41 67 41 54 80 54 76 27 24 98 100


About the data

3.11

environment

Urbanization Definitions

There is no consistent and universally accepted

populous nations were to change their definition of

• Urban population is the midyear population of

standard for distinguishing urban from rural areas, in

urban centers. According to China’s State Statis-

areas defined as urban in each country and reported

part because of the wide variety of situations across

tical Bureau, by the end of 1996 urban residents

to the United Nations (see About the data). • Popula-

countries (see About the data for table 3.1). Most

accounted for about 43 percent of China’s popula-

tion in urban agglomerations of more than 1 million

countries use an urban classification related to the

tion, more than double the 20 percent considered

is the percentage of a country’s population living in

size or characteristics of settlements. Some define

urban in 1994. In addition to the continuous migra-

metropolitan areas that in 2005 had a population of

urban areas based on the presence of certain infra-

tion of people from rural to urban areas, one of the

more than 1 million. • Population in largest city is

structure and services. And other countries designate

main reasons for this shift was the rapid growth in

the percentage of a country’s urban population living

urban areas based on administrative arrangements.

the hundreds of towns reclassified as cities in recent

in that country’s largest metropolitan area. • Access

years.

to improved sanitation facilities is the percentage

The population of a city or metropolitan area depends on the boundaries chosen. For example, in

Because the estimates in the table are based on

of the urban or rural population with access to at

1990 Beijing, China, contained 2.3 million people in

national definitions of what constitutes a city or met-

least adequate excreta disposal facilities (private or

87 square kilometers of “inner city” and 5.4 million

ropolitan area, cross-country comparisons should be

shared but not public) that can effectively prevent

in 158 square kilometers of “core city.” The popula-

made with caution. To estimate urban populations,

human, animal, and insect contact with excreta.

tion of “inner city and inner suburban districts” was

UN ratios of urban to total population were applied

Improved facilities range from simple but protected

6.3 million and that of “inner city, inner and outer

to the World Bank’s estimates of total population

pit latrines to flush toilets with a sewerage connec-

suburban districts, and inner and outer counties”

(see table 2.1).

tion. To be effective, facilities must be correctly con-

was 10.8 million. (Most countries use the last defini-

The table shows access to improved sanitation

tion.) For further discussion of urban-rural issues see

facilities for both urban and rural populations to

box 3.1a in About the data for table 3.1.

allow comparison of access. Definitions of access

Estimates of the world’s urban population would change significantly if China, India, and a few other

structed and properly maintained.

and urban areas vary, however, so comparisons between countries can be misleading.

Urban population is increasing in developing economies, especially in low and lower middle-income economies

3.11a

Urban population (millions) 1,600

1990

2009

1,200

800

400

0 Low income

Lower middle income

Upper middle income

High income

Source: Table 3.11.

Latin America and Caribbean has the greatest share of urban population, even greater than the high-income economies in 2009

3.11b Urban

Percent

Rural

100

Data sources Data on urban population and the population in

75

urban agglomerations and in the largest city are from the United Nations Population Division’s

50

World Urbanization Prospects: The 2009 Revision. Data on total population are World Bank

25

estimates. Data on access to sanitation are from 0

the World Health Organization and United Nations East Asia & Pacific

Europe & Central Asia

Source: Tables 3.1 and 3.11.

Latin America Middle East & & Caribbean North Africa

South Asia

Sub-Saharan Africa

High income

Children’s Fund’s Progress on sanitation and drinking water (2010).

2011 World Development Indicators

169


3.12

Urban housing conditions Census year

Household size

number of people National Urban

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 Georgia Germany Ghana Greece Guatemala Guinea Guinea-Bissau Haiti Honduras

170

2001 1998 2001 2001 2001 2001 1999 2001 1999 2001 1992 2001 2001 2000 2001 1996 1990 2005 1987 2001 2003 1993 2002 2000 1993 1984 1984 2000 1998 2001 2002 2001 2001 2002 2001 1996 1992 2000 1994 2000 1999 2003 1993 2002 2001 2000 2001 2002 1996 1982 2001

.. 4.2 4.9 .. 3.6 4.1 3.8 2.4 4.7 4.8 .. 2.6 5.9 4.2 .. 4.2 3.8 2.7 6.2 4.7 5.0 5.2 2.6 5.2 5.1 3.4 3.4 .. 4.8 5.4 10.5 4.0 5.4 3.0 3.1 2.4 2.2 3.9 3.5 4.7 .. .. 2.4 4.8 2.2 2.5 5.2 8.9 3.5 2.3 5.1 3.0 4.4 6.7 .. 4.2 4.4

2011 World Development Indicators

.. 3.9 .. .. .. 4.0 .. .. 4.4 4.8 .. .. .. 4.3 .. 3.9 3.7 2.7 5.8 .. 4.9 5.1 .. 5.8 5.1 3.5 3.2 .. .. .. .. .. .. .. .. .. .. .. 3.7 .. .. .. 2.3 4.7 .. .. .. .. 3.5 .. 5.1 .. 4.7 .. .. .. ..

Overcrowding

Households living in overcrowded dwellingsa % of total National Urban

.. .. .. .. 19 4 1 2 .. .. .. 0b .. 40 .. 27 .. .. 30 .. 35 67 .. 32 .. .. .. .. 27b 55 .. 22 .. .. 5 .. .. .. 30 .. 63 .. 3 .. .. .. .. .. .. .. .. 1 .. 63 .. 26 ..

.. .. .. .. .. 6 .. .. .. .. .. .. .. .. .. 47 .. .. 53 .. 32 77 .. 36b .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. ..

Durable dwelling units

Home ownership

Buildings with durable structure % of total National Urban

Privately owned dwellings % of total National Urban

.. .. .. .. 97 93 .. .. .. 21b .. .. 26 43 .. 88 .. 79 .. .. 79 77 .. 78 .. 91 82 .. 83b .. .. 88 .. .. .. .. .. 97 81 .. 67 .. .. .. .. .. .. 18 .. .. 45 .. 67 .. .. .. 69

.. .. .. .. .. 93 .. .. .. 42b .. .. .. 58 .. 90 b .. 89 .. .. 88 .. .. 92 .. 92 .. .. .. .. .. .. .. .. .. .. .. .. 88 .. 83 .. .. 23 .. .. .. .. .. .. .. .. 80 .. .. .. 85

.. 65b 67 .. .. 95 .. 48 74 88b .. 67 59 70 .. 61 74 98 .. .. 58 73 64 85 .. 66 88 .. 68b .. 76 72 .. .. .. 52 .. .. 68 b .. 70 .. .. .. 64 55 .. 68 .. 43 57 .. 81 76 .. 92 ..

.. 30 b .. .. .. 90 .. .. 62 61b .. .. .. 59 .. 47 75 98 .. .. 57 48 .. 74 .. 65 74 .. .. .. .. .. .. .. .. .. .. .. 58b .. 68 .. .. 54 .. .. .. .. .. .. .. .. 74 .. .. 68 ..

Multiunit dwellings

Vacancy rate

% of total National Urban

Unoccupied dwellings % of total National Urban

.. .. .. .. 4 1 .. .. 4 .. .. 32b .. 3b .. 1 .. .. .. .. 27 27 32 .. .. 13 .. .. 13 .. .. 2 .. .. .. 49 .. 8 9 75 3 .. 72 .. 44 .. .. .. .. .. 53 .. 2 .. .. .. ..

.. .. .. .. .. 1 .. .. 5 .. .. .. .. 5b .. .. .. .. .. .. 32 42 .. .. .. 15 .. .. .. .. .. 3 .. .. .. .. .. .. 14 .. 6 .. .. .. .. .. .. .. .. .. .. .. 4 .. .. .. ..

.. 12 19 .. 16b .. .. .. .. .. .. .. .. 6 .. .. .. 23 .. .. .. .. 8 .. .. 11 1 .. 10 b .. .. 9 .. 12 .. 12 .. 11 12 .. 11 .. 13 .. .. 7 .. .. .. 7 5 .. 13 .. .. 9 14

.. 13 .. .. .. .. .. .. .. .. .. .. .. 4 .. .. .. 17 .. .. .. .. .. .. .. 10 .. .. .. .. .. 6 .. .. .. .. .. .. 7 .. 11 .. .. .. .. .. .. .. .. .. .. .. 11 .. .. 19 ..


Census year

Household size

number of people National Urban

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

2001 2001 2000 1996 1997 2002 1995 2001 2001 2000 2004 1999 2000 1993 1995 1999 1995 2000 2001 1974 2001 2002 1993 1998 2000 1998 1988 2000 2005 2003 2000 1982 1997 2001 2001 2001 1995 2001 1991 1980 2003 1998 2000 1990 2002 2007 2000 1988 2001 2005

2.6 5.3 4.0 4.8 7.7 3.0 3.5 2.8 3.5 2.7 5.3 .. 4.6 3.8 4.4 .. 6.4 4.4 6.1 3.0 .. 5.0 4.8 6.4 2.6 3.6 4.9 4.4 4.5 5.6 .. 3.9 4.0 .. 4.4 5.9 4.4 .. 5.3 5.4 .. 2.8 5.3 6.4 5.0 2.7 7.1 6.8 4.1 4.5b 4.6 3.9 4.9 3.2 2.8 2.8 ..

.. 5.3 .. 4.6 7.2 .. .. .. .. .. 5.1 .. 3.4 .. .. .. .. 3.6 6.1 2.6 .. .. .. .. .. 3.6 4.8 4.4 4.4 .. .. 3.8 3.9 .. 4.5 5.3 4.9 .. .. 4.9 .. .. .. 6.0 4.7 .. .. 6.8 .. 6.5 4.5 3.9 .. .. .. . ..

Overcrowding

Households living in overcrowded dwellingsa % of total National Urban

2 77 .. 33b .. .. .. .. .. .. 35 .. .. 23 .. .. .. .. .. 4 .. 10 b 31 .. 7 8b 64 30 .. .. .. 6 24 .. .. .. 37 .. .. .. .. 1b .. .. .. 1 .. .. 28b .. 38 b 35 .. .. .. 1 ..

.. 71 .. 26b .. .. .. .. .. .. 34 .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. 57 .. .. .. .. 7 20 .. .. .. 28 .. .. .. .. .. .. .. .. .. .. .. .. .. ..b 31 .. .. .. .. ..

Durable dwelling units

Home ownership

Buildings with durable structure % of total National Urban

Privately owned dwellings % of total National Urban

.. 83 .. 72 88 .. .. .. 98 b .. .. .. 35 .. .. .. .. .. 49 88 .. .. 20 .. .. 95b .. 48 .. .. .. 91 .. .. .. .. 7 .. .. .. .. .. 79 .. .. .. .. 58 88 .. 95b .. .. .. .. .. ..

.. 81 .. 76 96 .. .. .. .. .. .. .. 72 .. .. .. .. .. 77 .. .. .. .. .. .. 95b .. 84 .. .. .. 94 .. .. .. .. 20 .. .. .. .. .. 87 .. .. .. .. 86 98b .. 98b .. .. .. .. .. ..

.. 87 .. 73 70 .. .. .. 58 b 61 64 .. 72 50 .. .. .. .. 96 58 .. 84 1 .. .. 48b 81 86 .. .. .. 87 .. .. .. .. 92 .. .. 88 .. 65 84 77 .. 67 .. 81 80 .. 79 .. 71 .. 76 75 ..

.. 67 .. 67 66 .. .. .. .. .. 60 .. 25 .. .. .. .. .. 86 .. .. .. .. .. .. .. 59 47 .. .. .. 81 .. .. .. .. 83 .. .. .. .. .. 86 40 .. .. .. .. 66b 44 75 .. .. .. .. .. ..

Multiunit dwellings

Vacancy rate

% of total National Urban

Unoccupied dwellings % of total National Urban

.. .. .. .. 4 8b .. .. 2b 37 72 .. .. 15 .. .. 9b .. .. 74 .. 0 .. .. .. .. .. .. 10 b .. .. .. .. .. 48 .. 1 .. .. .. .. 17 0 .. .. 38 .. .. 10 b .. 1b .. 12 .. 86 .. ..

.. .. .. .. 5 .. .. .. .. .. 80 .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. 16b .. .. .. .. .. 56 .. 1 .. .. .. .. .. 0 .. .. .. .. .. 10 b 8 2b .. .. .. .. ..

4 6 .. .. 13 .. .. 21 .. .. .. .. 39 .. .. .. 11 .. .. 0 .. .. .. 7 .. 7b .. .. .. .. .. 7 3 .. .. .. 0 .. .. 0 .. 10 8 .. .. .. .. .. 14 .. 6b .. 1 .. .. ..

2011 World Development Indicators

.. 9 .. .. 15 .. .. .. .. .. .. .. 17 .. .. .. .. .. .. .. .. .. .. .. .. 3b .. .. .. .. .. 6 2 .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. 6b .. .. .. .. ..

171

environment

3.12

Urban housing conditions


3.12

Urban housing conditions Census year

Household size

number of people National Urban

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

2002 2002 2002 2004 2002 2001 1985 2000 2002 1975 2007 2001 2001 1993 1997 1990 2000 1981 2000 2002 2000

2000 1994 1990 2002 2003 2001 2005 1996 2001 1999 1997 1994 2000 1992

2.9 2.8 4.4 5.5 9.2 2.9 6.8 4.4 .. 2.8 .. 3.0 2.9 3.8 5.8 5.4 2.0 2.2 6.3 .. 4.9 3.8 .. .. 3.7 8.0 5.0 .. 4.7 .. .. .. 2.5 3.3 ..  4.4 4.6 7.1  6.7 5.3  4.8

Overcrowding

Households living in overcrowded dwellingsa % of total National Urban

2.8 2.7 3.7 .. 8.0 2.2 .. .. .. 2.7 .. 2.8 .. .. 6.0 3.7 .. .. 6.0 .. 4.5 .. .. .. .. .. .. .. 3.9 .. .. 2.4 .. 3.4 ..  .. 4.5 ..  6.8 5.9  4.2

a. More than two people per room. b. Data are from a previous census.

172

2011 World Development Indicators

20 7 43 .. 72 .. .. .. .. 14 .. 16 1 .. .. .. .. 1 .. .. 33b .. .. .. 9b .. .. .. .. .. .. .. 0 22b ..  .. .. ..  54b ..  ..

20 5 36 .. 68 .. .. .. .. 17 .. 15 .. .. .. .. .. .. .. .. 7b .. .. .. .. .. .. .. .. .. .. .. .. .. ..  .. .. ..  6b ..  ..

Durable dwelling units

Home ownership

Buildings with durable structure % of total National Urban

Privately owned dwellings % of total National Urban

.. .. 13 92b .. .. 34 .. .. .. .. .. .. 93b .. .. .. .. .. .. .. 93 .. .. 98b 99 .. .. 19 .. .. .. .. .. ..  .. 77 ..  .. ..  ..

.. .. 31 .. .. .. .. .. .. .. .. .. .. 92b .. .. .. .. .. .. .. 93 .. .. .. .. .. .. 61 .. .. .. .. .. ..  .. 89 ..  .. ..  ..

84 .. 79 43 74 .. 68 .. .. 91 .. 43 82 70 b 86b .. .. 34 .. .. 82b 81 .. .. 74b 71 70 .. 76 .. .. .. 74 57b ..  78 95 78  88b 94  94

72 .. 41 .. 54 .. .. .. .. 87 .. 40 .. 58b 58b .. .. .. .. .. 43b 62 .. .. .. 89 b .. .. 28 .. .. 69 .. 57b ..  .. 86 ..  68b 30  30

Multiunit dwellings

Vacancy rate

% of total National Urban

Unoccupied dwellings % of total National Urban

.. 73 36 .. .. .. .. .. .. 33 .. .. .. 1 0b .. 54 77 .. .. .. 3 .. .. 17b 6 .. .. 37 .. .. .. 26 .. ..  14 .. 45  3b ..  6

.. 86 60 .. .. .. .. .. .. 56 .. .. .. 14b 1b .. .. .. .. .. .. .. .. .. .. 10 b .. .. 71 .. .. 19 .. .. ..  .. .. ..  11b ..  ..

.. .. .. .. .. .. .. .. .. .. .. .. .. 13 .. .. 1 .. .. .. .. 3 .. .. .. 15 .. .. .. .. .. .. .. 13b ..  16 .. ..  .. ..  ..

.. .. .. .. .. .. .. .. .. .. .. .. .. 1b .. .. .. .. .. .. .. .. .. .. .. 12b .. .. .. .. .. .. .. 13b ..  .. .. ..  .. ..  ..


About the data

3.12

Definitions

Urbanization can yield important social benefits,

There is a strong demand for quantitative indi-

• Census year is the year in which the underlying

improving access to public services and the job mar-

cators that can measure housing conditions on a

data were collected. • Household size is the aver-

ket. It also leads to significant demands for services.

regular basis to monitor progress. However, data

age number of people within a household, calcu-

Inadequate living quarters and demand for housing

deficiencies and lack of rigorous quantitative analy-

lated by dividing total population by the number

and shelter are major concerns for policymakers.

sis hamper informed decisionmaking on desirable

of households in the country and in urban areas.

The unmet demand for affordable housing, along

policies to improve housing conditions. The data

• Overcrowding refers to the number of households

with urban poverty, has led to the emergence of

in the table are from housing and population cen-

living in dwellings with two or more people per room

slums in many poor countries. Improving the shel-

suses, collected using similar definitions. The table

as a percentage of total households in the country

ter situation requires a better understanding of the

will incorporate household survey data in future edi-

and in urban areas. • Durable dwelling units are

mechanisms governing housing markets and the pro-

tions. The table focuses attention on urban areas,

the number of housing units in structures made of

cesses governing housing availability. That requires

where housing conditions are typically most severe.

durable building materials (concrete, stone, cement,

good data and adequate policy-oriented analysis so

Not all the compiled indicators are presented in the

brick, asbestos, zinc, and stucco) expected to main-

that housing policy can be formulated in a global

table because of space limitations.

tain their stability for 20 years or longer under local

comparative perspective and drawn from lessons

conditions with normal maintenance and repair, tak-

learned in other countries. Housing policies and

ing into account location and environmental hazards

outcomes affect such broad socioeconomic condi-

such as floods, mudslides, and earthquakes, as a

tions as the infant mortality rate, performance in

percentage of total dwellings. • Home ownership

school, household saving, productivity levels, capital

refers to the number of privately owned dwellings as

formation, and government budget deficits. A good

a percentage of total dwellings. When the number

understanding of housing conditions thus requires

of private dwellings is not available from the census

an extensive set of indicators within a reasonable

data, the share of households that own their housing

framework.

unit is used. Privately owned and owner-occupied units are included, depending on the definition used

3.12a

Selected housing indicators for smaller economies

Census Household Overcrowding Durable Home Multiunit Vacancy year size dwelling ownership dwellings rate units Households

Antigua and Barbuda Bahamas Bahrain Barbados Belize Cape Verde Cayman Islands Equatorial Guinea Fiji Guam Isle of Man Maldives Marshall Islands Netherlands Antilles New Caledonia Northern Mariana Islands Palau Seychelles Solomon Islands St. Vincent & Grenadines Turks and Caicos Virgin Islands (UK) Western Samoa

2001 1990 2001 1990 2000 1990 1999 1993 1996 2000 2001 2000 1999 2001 1989 1995 2000 1997 1999 1991 1990 1991 1991

number of people

living in overcrowded dwellingsa % of total

3.0 3.8 5.9 3.5 4.6 5.1 3.1 7.5 5.4 4.0 2.4 6.6 7.8 2.9 4.1 4.9 5.7 4.2 6.3 3.9 3.3 3.0 7.3

.. 12 .. 3 .. 28 .. 14 .. 2b 0 .. .. 24b .. 9b 8 15b 51 .. 4 2 ..

Privately Buildings owned with durable dwellings structure % of total % of total

a. More than two people per room. b. Data are from a previous census. Source: National population and housing censuses.

99 b 99 94b 100 93 78 100 56b 60 93 .. 93 95 99 77 99 76 97 23 98 96 99 42

65b 55 51 76 63 72 53 75 65 48 68 .. 72 60 53 33 79 78 85 71 66 40 90

in the census data. State- and community-owned units and rented, squatted, and rent-free units are excluded. • Multiunit dwellings are the number of multiunit dwellings, such as apartments, flats, condominiums, barracks, boardinghouses, orphan-

Unoccupied dwellings % of total % of total

3b 13 28 9 4 2 38 14 7 29 16 1 12 16 9 27 11 .. 1 7 11 46 47

22 14 6 9 .. .. 19 .. .. 19 .. 15 8 12 13 17 3 0 .. .. .. .. 30

ages, retirement houses, hostels, hotels, and collective dwellings, as a percentage of total dwellings. • Vacancy rate is the percentage of completed dwelling units that are currently unoccupied. It includes all vacant units, whether on the market or not (such as second homes).

Data sources Data on urban housing conditions are from national population and housing censuses.

2011 World Development Indicators

173

environment

Urban housing conditions


3.13

Traffic and congestion Motor vehicles

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

174

Passenger Road cars density

Road sector energy consumption

Fuel price

Particulate matter concentration

per 1,000 people

per kilometer of road

per 1,000 people

km. of road per 100 sq. km. of land area

Diesel

Urban-populationweighted PM10 micrograms per cubic meter

2008

2008

2008

2008

2008

2008

2008

2008

2010

2010

1990

2008

27 114 112 40 314 105 687 562 89 2 282 543 21 68 135 113 198 353 11 6 20 .. 605 0 6 172 37 73 58 5 26 163 20 388 38 513 477 123 63 43 84 11 477 3 534 598 .. 7 116 554 33 560 117 .. 33 .. 97

19 20 35 .. .. 42 18 42 13 2 .. 38 .. 7 23 7 18 67 2 .. 6 .. 14 0 2 36 13 248 16 .. .. 19 5 59 7 41 36 .. 19 33 .. .. 11 4 36 39 .. 3 16 71 13 54 .. .. 15 .. ..

19 84 72 8 .. 96 551 514 72 1 240 479 17 18 119 56 158 310 7 2 18 11 399 0 .. 109 27 55 41 .. 15 126 16 346 21 424 377 62 38 31 41 6 412 1 461 495 .. 5 95 502 21 443 .. .. 27 .. 69

6 63 5 .. 8 26 11 132 61 166 46 503 17 6 43 4 21 36 34 44 21 11 14 .. 3 .. 39 187 14 7 5 74 25 52 .. 166 170 .. 15 10 .. .. 128 4 23 173 3 33 29 180 24 88 .. 18 12 .. ..

.. 32 16 11 18 10 18 22 12 6 6 15 23 25 15 31 23 13 .. .. 7 10 17 .. .. 18 5 10 25 1 26 30 4 21 3 13 23 18 38 17 16 5 13 4 11 16 10 .. 19 15 12 21 23 .. .. 9 21

.. 213 173 65 346 100 1,091 877 188 11 161 827 79 149 242 340 298 335 .. .. 26 36 1,324 .. .. 345 85 204 171 3 98 320 21 432 29 553 779 144 289 145 131 7 542 16 740 666 143 .. 135 609 49 581 134 .. .. 25 135

.. 184 92 31 179 0 327 605 72 5 89 659 27 72 151 136 148 196 .. .. 14 17 336 .. .. 191 36 149 81 0 67 159 14 249 21 330 442 48 123 79 58 6 286 13 419 483 106 .. 45 309 23 192 64 .. .. 0 73

.. 24 63 30 102 64 645 199 108 2 51 134 47 48 84 186 73 78 .. .. 11 18 889 .. .. 137 45 47 70 3 27 144 6 153 5 189 312 89 151 54 67 1 239 2 284 129 31 .. 81 243 23 359 63 .. .. 23 55

1.15 1.46 0.32 0.65 0.96 1.08 1.27 1.63 0.75 1.09 1.08 1.87 1.04 0.70 1.42 0.93 1.58 1.51 1.44 1.43 1.15 1.20 1.21 1.71 1.32 1.38 1.11 1.92 1.41 1.28 1.27 1.14 1.68 1.59 1.72 1.75 2.00 1.23 0.53 0.48 0.92 2.54 1.54 0.91 1.94 1.98 1.14 0.79 1.13 1.90 0.82 2.05 0.95 0.95 .. 1.16 1.04

1.00 1.40 0.19 0.43 1.05 0.99 1.23 1.55 0.56 0.63 0.86 1.62 1.21 0.54 1.42 0.97 1.14 1.58 1.28 1.42 0.98 1.10 1.08 1.69 1.31 1.02 1.04 1.32 0.95 1.27 0.84 0.97 1.30 1.49 1.24 1.69 1.79 1.03 0.28 0.32 0.89 1.07 1.57 0.78 1.60 1.72 0.90 0.75 1.13 1.68 0.83 1.78 0.85 0.95 .. 0.89 0.92

68 92 113 111 104 481 22 39 132 237 23 30 78 113 36 93 39 108 144 68 88 122 25 60 209 92 115 .. 38 71 129 43 87 45 42 67 29 43 36 212 44 141 44 108 22 18 9 136 204 27 38 64 69 103 114 68 44

37 46 69 55 68 69 14 29 33 134 7 21 45 74 19 69 21 51 64 31 41 47 15 34 81 62 66 .. 20 40 68 32 32 27 23 18 16 16 20 97 28 71 13 59 15 13 7 62 49 16 24 32 60 53 47 35 42

2011 World Development Indicators

kilograms of oil equivalent per capita

$ per liter

% of total consumption

Total

Diesel fuel

Gasoline fuel

Super grade gasoline


Motor vehicles

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

Passenger Road cars density

Road sector energy consumption

per 1,000 people

per kilometer of road

per 1,000 people

km. of road per 100 sq. km. of land area

2008

2008

2008

2008

2008

2008

384 15 77 128 .. 534 313 673 188 593 146 197 21 .. 346 .. 507 59 21 474 .. .. 3 291 546 144 27 9 334 9 .. 159 264 139 72 71 13 7 109 5 515 733 57 5 31 575 225 11 120 9 82 55 33 495 509 642 724

20 4 40 53 .. 24 126 83 24 63 110 33 10 .. 161 .. 233 9 10 15 .. .. .. .. 23 21 10 .. 83 .. .. 99 77 39 4 38 10 13 4 .. 62 33 16 4 .. 29 12 8 30 .. .. 15 14 49 70 .. ..

304 10 43 113 .. 451 260 596 138 319 102 164 15 .. 257 .. 282 44 2 412 .. .. 2 225 498 129 8 4 298 7 .. 123 181 101 48 53 9 5 52 3 449 616 17 4 31 461 174 9 131 6 39 35 11 422 495 614 335

212 129 23 10 .. 137 82 162 202 318 9 3 11 21 105 .. 32 17 15 108 67 .. .. .. 124 54 .. 13 30 2 1 99 19 38 3 13 4 4 .. 12 328 35 16 1 21 29 17 33 18 .. .. 8 67 123 90 287 67

16 7 12 19 30 29 16 21 12 14 22 6 6 2 12 .. 14 17 .. 24 29 .. .. 19 18 13 .. .. 19 .. .. .. 28 10 13 24 4 7 33 3 15 25 13 .. 8 12 11 13 17 .. 26 29 17 15 25 .. 12

435 36 103 522 330 996 481 626 204 541 264 277 26 17 559 .. 1,343 94 .. 481 360 .. .. 542 486 194 .. .. 523 .. .. .. 472 85 157 112 18 22 274 11 708 1,004 79 .. 58 733 665 63 148 .. 181 148 76 391 579 .. 2,245

Fuel price

Particulate matter concentration

% of total consumption

Total

Diesel fuel

Gasoline fuel

Super grade gasoline

Diesel

Urban-populationweighted PM10 micrograms per cubicâ&#x20AC;Żmeter

2008

2008

2010

2010

1990

2008

149 10 67 249 129 385 300 181 190 331 150 238 9 7 152 .. 868 89 .. 163 334 .. .. 192 122 58 .. .. 310 .. .. .. 312 26 139 15 4 8 170 2 252 529 32 .. 46 271 567 9 138 .. 31 29 28 105 140 .. 756

1.67 1.15 0.79 0.10 0.78 1.78 1.85 1.87 0.98 1.60 1.04 0.71 1.33 1.51 1.52 1.63 0.23 0.85 1.26 1.48 1.13 0.97 0.98 0.17 1.59 1.52 1.52 1.71 0.59 1.42 1.16 1.55 0.81 1.21 1.11 1.23 1.11 0.80 1.06 1.18 2.13 1.47 1.09 1.07 0.44 2.12 0.31 0.86 0.85 0.94 1.28 1.41 1.05 1.57 1.85 0.65 0.19

1.61 0.82 0.51 0.02 0.56 1.69 1.87 1.69 0.98 1.37 0.73 0.51 1.27 1.40 1.35 1.60 0.21 0.79 0.97 1.49 0.77 1.07 0.96 0.13 1.42 1.27 1.26 1.54 0.56 1.25 0.99 1.23 0.72 1.08 1.04 0.88 0.86 0.80 1.09 0.91 1.71 0.97 0.99 1.16 0.77 2.01 0.38 0.92 0.77 0.90 1.01 1.10 0.84 1.50 1.58 0.78 0.19

33 111 133 86 164 23 66 41 55 42 107 42 64 180 51 .. 77 76 87 38 64 123 68 101 52 45 91 93 35 259 145 21 67 109 190 38 112 113 73 67 45 14 44 199 195 21 136 220 58 35 106 96 56 60 49 23 71

16 59 72 55 138 13 28 23 37 27 33 15 30 59 31 .. 95 26 39 13 36 46 31 76 17 20 33 35 20 112 69 18 33 36 111 27 26 46 48 32 31 12 23 96 46 16 94 109 34 18 67 51 19 35 21 21 35

kilograms of oil equivalent per capita

254 22 31 223 185 570 155 389 0 175 105 25 16 9 278 .. 401 0 .. 293 3 .. .. 325 275 106 .. .. 193 .. .. .. 128 53 8 93 13 11 77 7 396 423 44 .. 8 437 56 40 0 .. 140 106 43 216 409 .. 1,388

$ per liter

2011 World Development Indicators

175

environment

3.13

Traffic and congestion


3.13

Traffic and congestion Motor vehicles

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

per 1,000 people

per kilometer of road

per 1,000 people

km. of road per 100 sq. km. of land area

2008

2008

2008

2008

219 245 4 .. 23 227 5 150 319 565 .. 159 606 61 28 89 521 567 62 38 73 .. .. 2 351 114 138 106 7 152 313 526 809 176 .. 147 13 39 35 18 106 .. w .. 42 23 .. .. 47 185 169 88 16 34 622 592

24 35 3 20 19 42 2 218 35 29 .. .. 41 13 .. 25 8 61 20 .. 3 .. .. .. .. 61 24 22 3 41 .. 77 38 .. .. .. 7 29 .. .. .. .. w .. .. 8 .. .. 16 30 .. .. 4 .. 38 ..

187 206 2 415 17 202 3 114 272 520 .. 108 486 19 20 46 464 522 27 29 4 54 .. 2 .. 76 92 80 3 138 293 462 451 151 .. 107 13 30 .. 11 91 118 w .. 36 15 129 35 33 152 118 66 10 25 432 418

83 6 53 11 8 45 .. 475 89 192 .. .. 132 148 .. 21 128 173 35 .. 9 35 .. 21 .. 12 54 .. 29 28 5 172 68 44 .. .. 48 85 14 .. 25 28 w .. 25 50 .. 22 36 8 18 12 129 .. 43 140

a. Includes Montenegro.

176

Passenger Road cars density

2011 World Development Indicators

Road sector energy consumption

Fuel price

Particulate matter concentration

% of total consumption

Total

Diesel fuel

Gasoline fuel

Super grade gasoline

Diesel

Urban-populationweighted PM10 micrograms per cubicâ&#x20AC;Żmeter

2008

2008

2008

2008

2010

2010

1990

2008

1.46 0.84 1.63 0.16 1.57 1.50 0.94 1.42 1.70 1.67 1.12 1.19 1.56 1.19 0.62 1.07 1.87 1.66 0.96 1.02 1.22 1.41 1.40 1.18 0.36 0.94 2.52 0.22 1.42 1.01 0.47 1.92 0.76 1.49 0.92 0.02 0.88 1.71 0.35 1.66 1.29 1.21 m 1.18 1.08 1.05 1.14 1.11 1.08 1.17 1.04 0.94 1.12 1.22 1.63 1.78

1.46 0.72 1.62 0.07 1.34 1.48 0.94 1.04 1.53 1.62 1.15 1.14 1.47 0.66 0.43 1.10 1.82 1.77 0.45 0.91 1.19 0.95 0.90 1.17 0.24 0.82 2.03 0.20 1.11 0.92 0.71 1.98 0.84 1.44 0.83 0.01 0.77 1.54 0.23 1.52 1.15 1.07 m 1.11 0.96 0.89 1.03 0.98 0.93 1.11 0.98 0.56 0.83 1.15 1.54 1.62

36 41 60 157 92 33a 87 107 46 38 94 33 41 94 282 55 15 35 145 112 56 77 .. 57 135 71 76 259 33 71 281 24 30 236 145 21 123 .. 137 124 55 80 w 128 96 121 57 98 112 58 58 124 133 119 38 33

12 16 27 104 81 14 a 38 31 13 29 31 22 28 74 159 35 11 22 69 43 22 55 .. 29 105 26 37 65 12 18 87 13 19 160 40 9 53 .. 67 39 40 46 w 60 53 63 31 54 61 24 32 71 72 49 24 20

kilograms of oil equivalent per capita

12 7 .. 20 24 12 .. 13 11 26 .. 11 23 19 14 .. 16 22 20 4 6 16 .. 11 4 17 14 5 .. 6 14 19 23 21 3 24 13 .. 27 2 4 14 w 5 10 8 15 10 7 8 23 19 7 8 19 18

216 318 .. 1,279 57 251 .. 494 379 985 .. 293 703 86 54 .. 844 754 189 15 26 262 .. 45 587 151 181 191 .. 177 1,884 641 1,703 259 63 553 90 .. 90 10 27 261 w 19 129 78 320 116 97 228 302 259 36 57 964 665

136 80 .. 568 45 182 .. 305 230 628 .. 121 539 56 36 .. 410 283 118 0 19 153 .. 15 229 101 114 0 .. 55 964 335 399 163 8 81 50 .. 18 0 15 103 w 10 56 37 127 51 42 82 121 128 23 24 356 422

67 222 .. 646 10 63 .. 167 115 316 .. 161 135 25 15 .. 365 441 62 12 6 73 .. 27 327 41 31 182 .. 114 829 271 1,148 81 49 416 38 .. 62 10 11 135 w 7 61 36 156 55 50 128 136 111 9 31 526 194

$ per liter


About the data

3.13

Definitions

Traffic congestion in urban areas constrains eco-

associations. If they lack data or do not respond,

• Motor vehicles include cars, buses, and freight

nomic productivity, damages people’s health, and

other agencies are contacted, including road direc-

vehicles but not two-wheelers. Population figures

degrades the quality of life. In recent years own-

torates, ministries of transport or public works, and

refer to the midyear population in the year for

ership of passenger cars has increased, and the

central statistical offices. As a result, data quality

which data are available. Roads refer to motor-

expansion of economic activity has led to more

is uneven. Coverage of each indicator may differ

ways, highways, main or national roads, and sec-

goods and services being transported by road over

across countries because of different definitions.

ondary or regional roads. A motorway is a road

greater distances (see table 5.10). These devel-

Comparability is also limited when time series data

designed and built for motor traffic that sepa-

opments have increased demand for roads and

are reported. The IRF is taking steps to improve the

rates the traffic flowing in opposite directions.

vehicles, adding to urban congestion, air pollution,

quality of the data in its World Road Statistics 2010.

• Passenger cars are road motor vehicles, other than

health hazards, and traffic accidents and injuries.

Because this effort covers 2003–08 only, time

two-wheelers, intended for the carriage of passen-

The data on motor vehicles, passenger cars, and

series data may not be comparable. Another rea-

gers and designed to seat no more than nine people

road density in the table are compiled by the Interna-

son is coverage. Road density is a rough indicator of

(including the driver). • Road density is the ratio of

tional Road Federation (IRF) through questionnaires

accessibility and does not capture road width, type,

the length of the country’s total road network to the

sent to national organizations. The IRF uses a hier-

or condition. Thus comparisons over time and across

country’s land area. The road network includes all

archy of sources to gather as much information as

countries should be made with caution.

roads in the country—motorways, highways, main

possible. Primary sources are national road Biogasoline consumption as a share of total consumption is 3.13a highest in Brazil . . .  Biogasoline consumption (percent of total consumption)

2000

2008

40

30

20

10

0

Brazil

France Canada Germany China

United States

Source: International Energy Agency.

. . . but the United States consumes the most biogasoline  3.13b Biogasoline consumption (thousand of tons of oil equivalent)

or national roads, secondary or regional roads, and

from petroleum products, natural gas, renewable and

other urban and rural roads. • Road sector energy

combustible waste, and electricity. Biodiesel and bio-

consumption is the total energy used in the road

gasoline, forms of renewable energy, are biodegrad-

sector, including energy from petroleum products,

able and emit less sulfur and carbon monoxide than

natural gas, combustible and renewable waste,

petroleum-derived ones. They can be produced from

and electricity. • Total energy consumption is the

vegetable oils, such as soybean, corn, palm, peanut,

country’s total energy consumption from all sources

or sunflower oil, and can be used directly only in a

(see table 3.7). • Gasoline is light hydrocarbon oil

modified internal combustion engine. Data are pro-

use in internal combustion engines such as motor

vided by the International Energy Agency.

vehicles, excluding aircraft. • Diesel is heavy oils

Data on fuel prices are compiled by the German

used as a fuel for internal combustion in diesel

Agency for International Cooperation (GIZ), from its

engines. • Fuel price is the pump price of super

global network, and other sources, including the

grade gasoline and of diesel fuel, converted from the

Allgemeiner Deutscher Automobile Club (for Europe)

local currency to U.S. dollars (see About the data).

and the Latin American Energy Organization for Latin

• Particulate matter concentration is fine sus-

America. Local prices are converted to U.S. dollars

pended particulates of less than 10 microns in diam-

using the exchange rate in the Financial Times inter-

eter (PM10) that are capable of penetrating deep

national monetary table on the survey date. When

into the respiratory tract and causing severe health

multiple exchange rates exist, the market, parallel,

damage. Data are urban-population-weighted PM10

or black market rate is used. Prices were compiled

levels in residential areas of cities with more than

in mid-November 2010, based on the crude oil price

100,000 residents. The estimates represent the

of $81 per barrel Brent.

average annual exposure level of the average urban

Considerable uncertainty surrounds estimates of 2000

2008

30

country comparisons of the relative risk of particulate

15

10

5

United States

Brazil

China

Canada France Germany

Source: International Energy Agency.

resident to outdoor particulate matter.

particulate matter concentrations, and caution should be used in interpreting them. They allow for cross-

25

0

Road sector energy consumption includes energy

Data sources

matter pollution facing urban residents. Major sources

Data on vehicles and road density are from the

of urban outdoor particulate matter pollution are

IRF’s electronic files and its annual World Road

traffic and industrial emissions, but nonanthropogenic

Statistics, except where noted. Data on road sector

sources such as dust storms may also be a substan-

energy consumption are from the IRF and the Inter-

tial contributor for some cities. Country technology

national Energy Agency. Data on fuel prices are

and pollution controls are important determinants of

from the GIZ’s electronic files. Data on particulate

particulate matter. Data on particulate matter for

matter concentrations are from Pandey and oth-

selected cities are in table 3.14.

ers’ “Ambient Particulate Matter Concentrations in Residential and Pollution Hotspot Areas of World Cities: New Estimates Based on the Global Model of Ambient Particulates (GMAPS)” (2006b).

2011 World Development Indicators

177

environment

Traffic and congestion


3.14

Air pollution City

City population

thousands 2009

Argentina Australia

Austria Belgium Brazil Bulgaria Canada

Chile China

Colombia Croatia Cuba Czech Republic Denmark Ecuador Egypt, Arab Rep. Finland France Germany

Ghana Greece Hungary Iceland India

178

Buenos Aires Córdoba Melbourne Perth Sydney Vienna Brussels Rio de Janeiro São Paulo Sofia Montréal Toronto Vancouver Santiago Anshan Beijing Changchun Chengdu Chongqing Dalian Guangzhou Guiyang Harbin Jinan Kunming Lanzhou Liupanshui Nanchang Shanghai Shenyang Shenzhen Tianjin Wuhan Zhengzhou Zibo Foshan Chengdu Xi’an Bogotá Zagreb Havana Prague Copenhagen Guayaquil Quito Cairo Helsinki Paris Berlin Frankfurt Munich Accra Athens Budapest Reykjavik Ahmadabad

2011 World Development Indicators

12,988 1,479 3,813 1,578 4,395 1,693 1,892 11,836 19,960 1,192 3,750 5,377 2,197 5,883 1,632 12,214 3,504 4,869 9,348 3,252 8,735 2,125 4,224 3,186 3,062 2,243 1,221 2,648 16,344 5,074 8,847 7,759 7,582 2,914 2,396 4,876 4,869 4,704 8,262 779 b 2,140 1,162 1,174 2,634 1,801 10,902 1,107 10,410 3,438 680 b 1,334 2,269 3,252 1,705 319b 5,606

Particulate matter concentration

Sulfur dioxide

Nitrogen dioxide

Urbanpopulationweighted PM10 micrograms per micrograms per micrograms per cubic meter cubic meter cubic meter 1990 2008 2001 a 2001 a

159 78 17 16 27 45 33 49 57 118 24 29 17 103 132 141 117 136 194 79 99 111 121 148 111 145 94 124 115 160 89 198 125 154 117 107 136 221 51 48 47 68 30 33 44 272 24 14 30 27 27 37 69 35 23 127

104 51 11 11 18 34 23 26 30 55 15 17 10 69 75 80 66 77 110 45 56 63 69 84 63 82 53 70 65 90 50 112 71 87 66 61 77 125 27 28 26 19 17 18 24 124 17 10 18 16 16 24 34 16 14 68

.. .. .. 5 28 14 20 129 43 39 10 17 14 29 115 90 21 77 340 61 57 424 23 132 19 102 102 69 53 99 .. 82 40 63 198 .. .. .. .. 31 1 14 7 15 22 69 4 14 18 11 8 .. 34 39 5 30

.. 97 30 19 81 42 48 .. 83 122 42 43 37 81 88 122 64 74 70 100 136 53 30 45 33 104 .. 29 73 73 .. 50 43 95 43 .. .. .. .. .. 5 33 54 .. .. .. 35 57 26 45 53 .. 64 51 42 21

About the data

Indoor and outdoor air pollution places a major burden on world health. More than half the world’s people rely on dung, wood, crop waste, or coal to meet basic energy needs. Cooking and heating with these fuels on open fires or stoves without chimneys lead to indoor air pollution, which is responsible for 1.6 million deaths a year—one every 20 seconds. In many urban areas air pollution exposure is the main environmental threat to health. Long-term exposure to high levels of soot and small particles contributes to a range of health effects, including respiratory diseases, lung cancer, and heart disease. Particulate pollution, alone or with sulfur dioxide, creates an enormous burden of ill health. Sulfur dioxide and nitrogen dioxide emissions lead to deposition of acid rain and other acidic compounds over long distances, which can lead to the leaching of trace minerals and nutrients critical to trees and plants. Sulfur dioxide emissions can damage human health, particularly that of the young and old. Nitrogen dioxide is emitted by bacteria, motor vehicles, industrial activities, nitrogen fertilizers, fuel and biomass combustion, and aerobic decomposition of organic matter in soils and oceans. Where coal is the primary fuel for power plants without effective dust controls, steel mills, industrial boilers, and domestic heating, high levels of urban air pollution are common—especially particulates and sulfur dioxide. Elsewhere the worst emissions are from petroleum product combustion. Sulfur dioxide and nitrogen dioxide concentration data are based on average observed concentrations at urban monitoring sites, which not all cities have. The data on particulate matter are estimated average annual concentrations in residential areas away from air pollution “hotspots,” such as industrial districts and transport corridors. The data are from the World Bank’s Development Research Group and Environment Department estimates of annual ambient concentrations of particulate matter in cities with populations exceeding 100,000 (Pandey and others 2006b). A country’s technology and pollution controls are important determinants of particulate matter concentrations. Pollutant concentrations are sensitive to local conditions, and even monitoring sites in the same city may register different levels. Thus these data should be considered only a general indication of air quality, and comparisons should be made with caution. Current World Health Organization (WHO) air quality guidelines are annual mean concentrations of 20 micrograms per cubic meter for particulate matter less than 10 microns in diameter and 40 micrograms for nitrogen dioxide and daily mean concentrations of 20 micrograms per cubic meter for sulfur dioxide.


City

City population

thousands 2009

Indonesia Iran, Islamic Rep. Ireland Italy

Japan

Kenya Korea, Rep

Malaysia Mexico Netherlands New Zealand Norway Philippines Poland

Portugal Romania Russian Federation Singapore Slovak Republic South Africa

Spain Sweden Switzerland Thailand Turkey Ukraine United Kingdom

United States

Venezuela, RB

Bangalore Chennai Delhi Hyderabad Kanpur Kolkata Lucknow Mumbai Nagpur Pune Jakarta Tehran Dublin Milan Rome Turin Osaka-Kobe Tokyo Yokohama Nairobi Pusan Seoul Taegu Kuala Lumpur Mexico City Amsterdam Auckland Oslo Manila Katowice Lódz Warsaw Lisbon Bucharest Moscow Omsk Singapore Bratislava Cape Town Durban Johannesburg Barcelona Madrid Stockholm Zurich Bangkok Ankara Istanbul Kiev Birmingham London Manchester Chicago Los Angeles New York-Newark Caracas

7,079 7,416 21,720 6,627 3,298 15,294 2,815 19,695 2,556 4,898 9,121 7,190 1,084 2,962 3,357 1,662 11,325 36,507 3,654b 3,375 3,439 9,778 2,458 1,493 19,319 1,044 1,360 875 11,449 309 b 742b 1,710 2,808 1,933 10,523 1,128 4,737 500 b 3,353 2,837 3,607 5,029 5,762 1,279 1,143 6,902 3,846 10,378 2,779 2,296 8,615 2,247 9,134 12,675 19,300 3,051

Particulate matter concentration

Sulfur dioxide

Nitrogen dioxide

Urbanpopulationweighted PM10 micrograms per micrograms per micrograms per cubic meter cubic meter cubic meter 1990 2008 2001 a 2001 a

69 57 229 62 166 195 167 96 85 71 138 86 24 46 44 66 48 54 42 67 52 55 59 36 89 45 13 27 78 62 61 67 44 40 42 44 107 44 20 40 42 43 37 14 33 88 74 87 91 22 27 24 33 45 28 32

37 30 122 33 89 104 89 51 45 38 74 55 13 26 25 38 31 35 27 32 31 33 36 20 43 31 11 20 26 36 36 39 19 14 16 17 31 13 13 27 28 29 25 10 21 63 36 42 22 11 17 12 21 29 18 14

.. 15 24 12 15 49 26 33 6 .. .. 209 20 31 .. .. 19 18 100 .. 60 44 81 24 74 10 3 8 33 83 21 16 8 10 109 20 20 21 21 31 19 11 24 3 11 11 55 120 14 9 25 26 14 9 26 33

.. 17 41 17 14 34 25 39 13 .. .. .. .. 248 .. .. 63 68 13 .. 51 60 62 .. 130 58 20 43 .. 79 43 32 52 71 .. 34 30 27 72 .. 31 43 66 20 39 23 46 .. 51 45 77 49 57 74 79 57

3.14

Definitions

• City population is the number of residents of the city or metropolitan area as defined by national authorities and reported to the United Nations. • Particulate matter concentration is fine suspended particulates of less than 10 microns in diameter (PM10) that are capable of penetrating deep into the respiratory tract and causing severe health damage. Data are urban-population-weighted PM10 levels in residential areas of cities with more than 100,000 residents. The estimates represent the average annual exposure level of the average urban resident to outdoor particulate matter. • Sulfur dioxide is an air pollutant produced when fossil fuels containing sulfur are burned. • Nitrogen dioxide is a poisonous, pungent gas formed when nitric oxide combines with hydrocarbons and sunlight, producing a photochemical reaction. These conditions occur in both natural and anthropogenic activities.

Data sources Data on city population are from the United Nations Population Division’s World Urbanization Prospects: The 2009 Revision. Data on particulate matter concentrations are from Kiran D. Pandey, David Wheeler, Bart Ostro, Uwe Deichman, Kirk Hamilton, and Kathrine Bolt’s “Ambient Particulate Matter Concentration in Residential and Pollution Hotspot Areas of World Cities: New Estimates Based on the Global Model of Ambient Particulates (GMAPS)” (2006). Data on sulfur dioxide and nitrogen dioxide concentrations are from the WHO’s Healthy Cities Air Management Information System and the World Resources Institute.

a. Data are for the most recent year available. b. Data are from national sources.

2011 World Development Indicators

179

environment

Air pollution


3.15

Government commitment Environ­ Biodiversity mental assessments, strategies strategies, or or action action plans plans

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

180

1993 2001 1992 1992

1994

1998 1991

1990

1993 1994

1988

1990

1993 1994 1999 1990

1991 1988 1994 1989 1989 1994

1990 1994 1998

1990 1994 2001

1993 1994 1988 1990 1990 1992 1991 2000

1994 1994 1993 1992 1994 1995 1998 1994 1995 1990 1992 1998

1995 1995 1988 1988

1991

1990 1989

1992

1988

1994 1994 1993 1999 1993

1988 1988 1991

2011 World Development Indicators

Participation in treatiesa

Climate changeb

Ozone layer

CFC control

2002 1995 1994 2000 1994 1994 1994 1994 1995 1994 2000 1996 1994 1995 2000 1994 1994 1995 1994 1997 1996 1995 1994 1995 1994 1995 1994

2004 d 1999d 1992d 2000 d 1990 1999d 1987d 1987 1996d 1990 d 1986e 1988 1993d 1994 d 1992f 1991d 1990 d 1990 d 1989 1997d 2001d 1989 d 1986 1993d 1989d 1990 1989d

2004 d 1999d 1992d 2000 d 1990 1999d 1989 1989 1996d 1990 d 1988 e 1988 1993d 1994 d 1992 f 1991d 1990 d 1990 d 1989 1997d 2001d 1989d 1988 1993d 1994 1990 1991d

1995 1995 1997 1994 1995 1996 1994 1994 1994 1999 1994 1995 1996 1995 1994 1994 1994 1994 1998 1994 1994 1994 1995 1994 1996 1994 1996 1996 1996

1990 d 1994 d 1994 d 1991d 1993d 1991e 1992d 1993e 1988 1993d 1990 d 1988 1992 2005d 1996d 1994 d 1986 1987g 1994 d 1990 d 1996d 1988 1989d 1988 1987d 1992d 2002d 2000 d 1993d

1993d 1994 d 1994 d 1991d 1993d 1991e 1992d 1993e 1988 1993d 1990 d 1988 1992 2005d 1996d 1994 d 1988 1988g 1994 d 1990 d 1996d 1988 1989 1988 1989d 1992d 2002d 2000 d 1993d

Law of the Seac

2003d 1996 1994 1995 2002d 1994 1995 2001 2006d 1998 1997 1995 1994f 1994 1994 1996 2005

1994 2003

1997 1996

1995 2008 1994 1994 1994f 1994 1996 2004 1994

2005d 1996 1996 1998 1994 1996d 1994 d 1994 1995 1997 1994 1994 1996 1994

Biological diversity b

Kyoto Protocolb

2002 1994 d 1995 1998 1994 1993e 1993 1994 2000 f 1994 1993 1996 1994 1994 2002d 1995 1994 1996 1993 1997 1995d 1994 1992 1995 1994 1994 1993

2005 2005 2007 2005 2005 2008 2005 2005 2005 2005 2005 2005 2005 2007 2005 2005 2005 2005 2005 2005 2005 2005 2008 2009 2005 2005

1994 1996 1994 1994 1994 1996 1994 1993g 1993 1996 1993 1994 1994 1996d 1994 1994 1994 e 1994 1997 1994 1994 d 1993 1994 1994 1995 1993 1995 1996 1995

2005 2005 2007 2005 2007 2007 2005 2005 2005 2005 2005 2005 2005 2005 2005 2005 2005 2005 2007 2005 2005 2005 2005 2005 2005 2005 2005 2005 2005

CITES

1985d 2003d 1983d 1981 1976 1982d 1998 d 1981 1995d 1983 1984 d 1979 2002 1977d 1975 1991d 1989d 1988d 1997 1981d 1975 1980 d 1989d 1975 1981d 1981 1976d 1983d 1975 1994 d 2000 d 1990 d 1993f 1977 1986d 1975 1978 1987d 1994 d 1992d 1989d 1976d 1978 1989d 1977d 1996d 1976 1975 1992d 1979 1981d 1990 d 1985d

CCD

Stockholm Convention

1996d 2000 d 1996 1997 1997 1997 2000 1997d 1998 d 1996 2001d 1997d 1996 1996 2002d 1996 1997 2001d 1996 1997 1997 1997 1996 1996 1996 1998 1997

2004 2006 2006d 2005 2003 2004 2002 2004 d 2007 2004 d 2006 2004 2003 2010 2002d 2004 2004 2004 2005 2006 2009 2001 2008 2004 2005 2004

1999 2004 1997 1998 1997 2001e 1997 2000 d 1996d 1997d 1996 1996 1997d 1996 1997 1996e 1997 1996d 1996 1999 1996 1997 1997 1998d 1997 1996 1996 1997

2008 2005d 2007 2007 2004 2007 2007 2002 2003 2007 2004 2003 2008 2005d 2008d 2003 2002e 2004g 2007 2006 2006 2002 2003 2006 2008 2007 2008 2005


Environ­ Biodiversity mental assessments, strategies strategies, or or action action plans plans

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

1995 1993 1993

1994 1993

1994 1991 1994

1992

1995 1995

1989

1988 1994 1991

1991 1988 1989

1988 1990 1988 2002 1995 1988 1994 1989 1992 1993 1994 1994 1994 1990

1994 1990 1992

1989 1993 1995

1991 1992 1994 1991 1993 1988 1989 1991

3.15

Participation in treatiesa

Climate changeb

Ozone layer

CFC control

Law of the Seac

Biological diversity b

Kyoto Protocolb

1994 1994 1994 1996

1988 d 1991d 1992d 1990 d

1989d 1992d 1992 1990 d

2002 1995 1994

1994 1994 1994 1996 1996 1995 1994 1995 1993e 1993 1994 1994 1994g 1994

2005 2005 2005 2005 2009 2005 2005 2005 2005 2005 2005 2009 2005 2005 2005

2002 1996g 1996g 1995 1994 1995 2000 2001 1996 1997d 1996 1994 1994 1995 1996 1992 1993 1995 1993 1995 1995 1995 1997 1993 1994 e 1993 1995 1995 1994 1993 1995 1994 1995 1993 1994 1993 1993 1996 1993

2005 2005 2005 2005 2007 2005 2005 2006 2005 2005 2005 2005 2005 2005 2005 2005 2005 2005 2005 2005 2005 2005 2005 2005 2005 2005 2005 2005 2005 2005 2005 2005 2005 2005 2005 2005 2005 2005 2005

1994 1996 1994 1995 1994 1994 1995 1994 1995 1994

1988d 1992d 1988 1993d 1988d 1989d 1998 d 1988d 1995d 1992

1988 1992 1988 1993d 1988 1989d 1998 d 1988 1995d 1992

1995 2000 1995 1995 1995 1995 2003 1999 1995 1998 1999 1994 1994 1995 1994 1994 1994 1995 1994 1996 1995 1995 1995 1994 1994 1994 1996 1995 1994 1994 1995 1994 1995 1994 1994 1994 1994 1994 1994

1992d 2000 d 1998 d 1995d 1993d 1994 d 1996d 1990 d 1995d 1994f 1996d 1991d 1989d 1994 d 1994 d 1992d 1987 1996d 1996d 1995 1994 d 1993d 1993d 1994 d 1988d 1987 1993d 1992d 1988 d 1986 1999 d 1992d 1989d 1992d 1992d 1989 1991d 1990 d 1988 d

1992d 2000 d 1998 d 1995d 1993d 1994 d 1996d 1990 d 1995d 1994f 1996d 1991d 1989d 1994 d 1994 d 1992d 1988 1996d 1996d 1995 1994 d 1993d 1993d 1994 d 1988 e 1988 1993d 1992d 1988 d 1988 1999d 1992d 1989 1992d 1992d 1993d 1991 1990 d 1988

1994 1996 1995 1994 1996 1995d 1994 1996 1994 1998 2004 d 1995 2007 2008 2003d 1994f 2001 1996 1994 1996 1994 1994 2007 1996 2007 1997 1996 1994 1998 1996 1996 2000 1994 1996 1994 1997 1996 1997 1994 1994 1998 1997

2005

CITES

1985d 1976 1978d 1976 2002 1979 1979 1997d 1980 1978d 2000 d 1978 1993d 2002 2004 d 1997d 2003 2005d 2003d 2001d 2000 d 1975 1982d 1977d 1994 d 1998 d 1975 1991d 2001d 1996d 1975 1981d 1997d 1990 d 1975d 1984 1989d 1977d 1975 1974 1976 1976d 1978 1975d 1976 1975 1981 1989 1980

CCD

1999d 1997 1998 1997 2010 1997 1996 1997 1998 d 1998 e 1997 1997 1997 2004 d 1999

Stockholm Convention

2008 2006 2006 2010

2007 2002d 2004 2007 2004 2002d 2007

1997 1997d 1996e 2003d 1996 1996 1998 d 1996 2003d 2002d 1997 1996 1997 1996 1996 1996 1996 1999d 1996 1997 1997 1997d 1997 1997 1996e 2000 d 1998 1996 1997 1996 1996 1997 1996 2001d 1997 1996 2000 2002 1996

2003 2005 2004 2003 2004 2004 2004 2005 2004 d 2005d 2007 2002e 2004 2005 2006 2004 2002 2005 2008 2003 2003 2004 2005 2004 2008 2004 d

1999

2004 d

2011 World Development Indicators

2006 2006 2006 2004 2003 2002 2002d 2005d 2006 2004 2005 2009

181

environment

Government commitment


3.15

Government commitment Environ­ Biodiversity mental assessments, strategies strategies, or or action action plans plans

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 Togo Trinidad and Tobago Tunisia Turkey Turkmenistan Uganda Ukraine United Arab Emirates United Kingdom United States Uruguay Uzbekistan Venezuela Vietnam West Bank and Gaza Yemen, Rep. Zambia Zimbabwe

1995 1999 1991

1994

1984

1991

1994 1993

1995

1994 1993 1994

1991

1999 1994

1988

1991 1994 1998

1988

1994 1999

1988

1995 1995

1994 1995

1993 1996 1994 1987

1992

Participation in treatiesa

Climate changeb

Ozone layer

CFC control

Law of the Seac

Biological diversity b

Kyoto Protocolb

1994 1995 1998 1995 1995

1993d 1986e 2001d 1993d 1993d

1993d 1988 e 2001d 1993d 1993

1996 1997

1994 1995 1996 2001g 1994

1995 1997 1994 1996 1997 1994 1994 1994 1997 1994 1994 1996 1998 1996 1995 1995 1994 1994 2004 1995 1994 1997 1996 1994 1994 1994 1994 1995 1995

2001d 1989d 1993f 1992 f 2001d 1990 d 1988d 1989d 1993d 1992d 1986 1987 1989d 1996d 1993d 1989d 1991d 1989d 1989d 1991d 1993d 1988 d 1986e 1989d 1987 1986 1989d 1993d 1988 d 1994 d

2001d 1989d 1993f 1992 f 2001d 1990 d 1988 1989d 1993d 1992d 1988 1988 1989d 1998 d 1993d 1989 1991 1989d 1989d 1991d 1993d 1988 1988 e 1989d 1988 1988 1991d 1993d 1989 1994 d

1994 1994 1996 1995f 1994 1997 1997 1994 1994

1995 1995 1994 1995 1994 1993 1994 1996 1997g 1996 2004 1995e 1996 1993 1997 1996g 1993 1995 2000 1994

2005 2005 2005 2005 2005 2008 2007 2006 2005 2005 2011 2005 2005 2005 2005 2006 2005 2005 2006 2009 2005 2005 2005 2005 2005 2009 2005 2005 2005 2005 2005

2006d

1993 1995g 1994 1994

1996 1994 1994

1996d 1990 d 1992d

1996d 1990 d 1992d

1994 1994 1994

1996 1993 1994

1996 1994

1996

1994 1994 1994 1994

1994 1999 1997d 1994

1994g 1995 1994g 1996

CITES

1994 d 1992 1980 d 1996d 1977d

CCD

2005 2005 2005 2005

1991d 1999d 1990 d 1976 1974 1975 1997d 1977 1994 d

1998 2003 1999 1997 1996 2008 1997 1999 2002 2001 2002 1997 1996 1999 1996 1997 1996 1996 1997 1997 1997 2001 1996 2000 1996 1998 1996 1997 2002 1999 1997 2001 1999 1996 1998 1998

2005 2006 2009

1997d 1980 d 1981d

1997 1996 1997

1994 d 1986d 1993 2000 d 1985d 1975 1986d 1979d 1982 1997d 1974 1974 2003d 1979 1983 1978 1984 d 1974 1996d

Stockholm Convention

a. Ratification of the treaty. b. Year the treaty entered into force in the country. c. Convention became effective November 16, 1994. d. Accession. e. Acceptance. f. Succession. g. Approval.

182

2011 World Development Indicators

2004 2002d 2003 2009 2003d 2005 2002 2004 2010d 2002 2004 2005 2006 2006 2002 2003 2005 2007 2004 2005 2004 2002d 2004 2009 2004 d 2002 2005 2004 2005 2002 2004 2006


About the data

3.15

Definitions

National environmental strategies and participation

­Environment and Development (the Earth Summit) in

• Environmental strategies or action plans pro-

in international treaties on environmental issues pro-

Rio de Janeiro, which produced Agenda 21—an array

vide a comprehensive analysis of conservation and

vide some evidence of government commitment to

of actions to address environmental challenges:

resource management issues that integrate envi-

sound environmental management. But the signing

• The Framework Convention on Climate Change

ronmental concerns with development. They include

of these treaties does not always imply ratification,

aims to stabilize atmospheric concentrations of

national conservation strategies, environmental

nor does it guarantee that governments will comply

greenhouse gases at levels that will prevent human

action plans, environmental management strategies,

with treaty obligations.

activities from interfering dangerously with the

and sustainable development strategies. The date

global climate.

is the year a country adopted a strategy or action

In many countries efforts to halt environmental degradation have failed, primarily because govern-

• The Vienna Convention for the Protection of the

plan. • Biodiversity assessments, strategies, or

ments have neglected to make this issue a priority, a

Ozone Layer aims to protect human health and the

action plans include biodiversity profiles (see About

reflection of competing claims on scarce resources.

environment by promoting research on the effects

the data). • Participation in treaties covers nine

To address this problem, many countries are prepar-

of changes in the ozone layer and on alternative

international treaties (see About the data). • Cli-

ing national environmental strategies—some focus-

substances (such as substitutes for chlorofluoro-

mate change refers to the Framework Convention

ing narrowly on environmental issues, and others

carbon) and technologies, monitoring the ozone

on Climate Change (signed in 1992). • Ozone layer

integrating environmental, economic, and social

layer, and taking measures to control the activities

refers to the Vienna Convention for the Protection

concerns. Among such initiatives are conservation

that produce adverse effects.

of the Ozone Layer (signed in 1985). • CFC control

strategies and environmental action plans. Some

• The Montreal Protocol for Chlorofluorocarbon

refers to the Protocol on Substances That Deplete

countries have also prepared country environmen-

Control requires that countries help protect the

the Ozone Layer (the Montreal Protocol for Chloro-

tal profiles and biodiversity strategies and profiles.

earth from excessive ultraviolet radiation by cut-

fluorocarbon Control) (signed in 1987). • Law of

National conservation strategies—promoted by

ting chlorofluorocarbon consumption by 20 percent

the Sea refers to the United Nations Convention on

the World Conservation Union (IUCN)—provide a

over their 1986 level by 1994 and by 50 percent

the Law of the Sea (signed in 1982). • Biological

comprehensive, cross-sectoral analysis of conser-

over their 1986 level by 1999, with allowances for

diversity refers to the Convention on Biological Diver-

vation and resource management issues to help inte-

increases in consumption by developing countries.

sity (signed at the Earth Summit in 1992). • Kyoto

grate environmental concerns with the development

• The United Nations Convention on the Law of the

Protocol refers to the protocol on climate change

process. Such strategies discuss current and future

Sea, which became effective in November 1994,

adopted at the third conference of the parties to the

needs, institutional capabilities, prevailing technical

establishes a comprehensive legal regime for seas

United Nations Framework Convention on Climate

conditions, and the status of natural resources in

and oceans, establishes rules for environmental

Change in December 1997. • CITES is the Conven-

a country.

standards and enforcement provisions, and devel-

tion on International Trade in Endangered Species of

ops international rules and national legislation to

Wild Fauna and Flora, an agreement among govern-

prevent and control marine pollution.

ments to ensure that the survival of wild animals

National environmental action plans, supported by the World Bank and other development agencies, describe a country’s main environmental concerns,

• The Convention on Biological Diversity promotes

and plants is not threatened by uncontrolled exploita-

identify the principal causes of environmental prob-

conservation of biodiversity through scientific

tion. Adopted in 1973, it entered into force in 1975.

lems, and formulate policies and actions to deal with

and technological cooperation among countries,

• CCD is the United Nations Convention to Combat

them. These plans are a continuing process in which

access to financial and genetic resources, and

Desertification, an international convention address-

governments develop comprehensive environmental

transfer of ecologically sound technologies.

ing the problems of land degradation in the world’s

policies, recommend specific actions, and outline

But 10 years after the Earth Summit in Rio de

drylands. Adopted in 1994, it entered into force in

the investment strategies, legislation, and institu-

Janeiro the World Summit on Sustainable Develop-

1996. • Stockholm Convention is an international

tional arrangements required to implement them.

ment in Johannesburg recognized that many of the

legally binding instrument to protect human health

Biodiversity profiles—prepared by the World Con-

proposed actions had yet to materialize. To help

and the environment from persistent organic pollut-

servation Monitoring Centre and the IUCN—provide

developing countries comply with their obligations

ants. Adopted in 2001, it entered into force in 2004.

basic background on species diversity, protected

under these agreements, the Global Environment

areas, major ecosystems and habitat types, and

Facility (GEF) was created to focus on global improve-

legislative and administrative support. In an effort

ment in biodiversity, climate change, international

to establish a scientific baseline for measuring prog-

waters, and ozone layer depletion. The UNEP, United

Data on environmental strategies and participa-

ress in biodiversity conservation, the United Nations

Nations Development Programme, and World Bank

tion in international environmental treaties are

Environment Programme (UNEP) coordinates global

manage the GEF according to the policies of its gov-

from the Secretariat of the United Nations Frame-

biodiversity assessments.

erning body of country representatives. The World

work Convention on Climate Change, the Ozone

Bank is responsible for the GEF Trust Fund and chairs

Secretariat of the UNEP, the World Resources

the GEF.

Institute, the UNEP, the Center for International

To address global issues, many governments have also signed international treaties and agreements

Data sources

launched in the wake of the 1972 United Nations

Earth Science Information Network, and the

Conference on the Human Environment in Stock-

United Nations Treaty Series.

holm and the 1992 United Nations Conference on

2011 World Development Indicators

183

environment

Government commitment


3.16 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

184

Contribution of natural resources to gross domestic product Total natural resources rents

Oil rents

Natural gas rents

Coal rents, hard and soft

Mineral rents

Forest rents

% of GDP

% of GDP

% of GDP

% of GDP

% of GDP

% of GDP

2009

2009

2009

2009

2009

2009

4.0 1.8 25.2 39.0 6.0 0.8 6.7 0.3 44.5 3.9 1.7 0.0 1.9 17.5 2.0 3.5 5.0 1.2 3.7 11.3 1.5 9.4 3.7 7.3 36.4 15.6 2.0 0.0 6.3 28.0 56.8 0.4 5.9 1.1 .. 0.3 1.8 0.8 15.7 10.7 0.5 1.4 0.7 5.0 0.6 0.1 45.0 3.2 0.3 0.1 8.6 0.1 1.6 10.5 3.5 0.7 1.8

2011 World Development Indicators

0.0 1.7 15.1 38.6 3.5 0.0 0.9 0.1 39.6 0.0 1.2 0.0 0.0 4.5 0.0 0.0 2.1 0.0 0.0 0.0 0.0 6.8 2.1 0.0 33.7 0.0 1.4 0.0 5.2 3.9 52.8 0.0 3.6 0.4 .. 0.0 1.4 0.0 15.3 5.3 0.0 0.0 0.0 0.0 0.0 0.0 39.9 0.0 0.2 0.0 0.0 0.0 0.7 0.0 0.0 0.0 0.0

0.0 0.0 9.7 0.1 1.9 0.0 0.8 0.1 4.9 3.2 0.1 0.0 0.0 10.3 0.0 0.0 0.1 0.0 0.0 0.0 0.0 0.3 0.6 0.0 0.0 0.1 0.2 0.0 0.5 0.0 0.0 0.0 1.0 0.5 .. 0.0 0.4 0.0 0.1 5.1 0.0 0.0 0.0 0.0 0.0 0.0 0.3 0.0 0.0 0.1 0.0 0.0 0.0 0.0 0.0 0.0 0.0

0.0 0.0 0.0 0.0 0.0 0.0 1.2 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.2 0.4 0.0 0.6 0.0 0.0 0.0 0.0 0.1 0.0 0.0 0.0 2.7 0.0 1.0 0.0 0.0 0.0 0.0 0.0 .. 0.3 0.0 0.0 0.0 0.0 0.0 0.0 1.1 0.0 0.0 0.0 0.0 0.0 0.0 0.1 0.0 0.2 0.0 0.0 0.0 0.0 0.0

.. 0.0 0.2 0.0 0.5 0.8 4.9 0.0 0.0 0.0 0.0 0.0 0.0 2.2 1.5 3.4 2.4 1.0 0.0 1.2 0.0 0.1 0.6 0.0 0.0 14.8 0.3 0.0 0.5 11.6 0.0 0.1 0.0 0.0 .. 0.0 0.0 0.8 0.0 0.2 0.0 0.0 0.0 0.2 0.1 0.0 0.1 0.0 0.0 .. 6.5 0.1 0.0 5.3 0.0 0.0 0.6

4.0 0.1 0.1 0.2 0.1 0.0 0.1 0.1 0.0 0.6 0.5 0.0 1.9 0.4 0.5 0.2 0.4 0.2 3.7 10.1 1.5 2.2 0.4 7.3 2.7 0.6 0.2 0.0 0.1 12.5 3.9 0.4 1.3 0.2 .. 0.2 0.0 0.0 0.2 0.1 0.5 1.4 0.7 4.8 0.5 0.0 4.7 3.2 0.1 0.0 2.1 0.0 0.9 5.3 3.5 0.7 1.2


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

3.16

Total natural resources rents

Oil rents

Natural gas rents

Coal rents, hard and soft

Mineral rents

Forest rents

% of GDP

% of GDP

% of GDP

% of GDP

% of GDP

% of GDP

2009

2009

2009

2009

2009

2009

0.5 4.0 5.9 28.4 68.6 0.1 0.3 0.1 1.2 0.0 1.7 27.3 1.4 .. 0.0 0.0 .. 0.5 1.9 1.1 0.0 1.8 15.6 48.4 1.4 0.1 2.0 2.5 12.3 1.3 30.1 0.0 6.8 0.2 12.7 2.3 8.5 .. 0.5 5.6 1.1 2.3 2.9 1.7 23.3 13.2 40.1 4.4 0.1 32.7 1.7 8.2 1.7 0.8 0.1 .. 28.6

0.2 0.8 2.4 21.4 68.1 0.0 0.0 0.1 0.0 0.0 0.0 20.9 0.0 .. 0.0 0.0 .. 0.5 0.0 0.0 0.0 0.0 0.0 44.7 0.1 0.0 0.0 0.0 6.1 0.0 0.0 0.0 5.5 0.1 1.4 0.0 0.0 .. 0.0 0.0 0.1 0.7 0.0 0.0 20.3 9.5 32.3 0.7 0.0 0.0 0.0 0.9 0.0 0.1 0.0 .. 14.0

0.2 0.5 1.3 6.6 0.5 0.0 0.2 0.0 0.0 0.0 0.1 4.7 0.0 .. 0.0 0.0 .. 0.0 0.0 0.0 0.0 0.0 0.0 3.7 0.0 0.0 0.0 0.0 5.7 0.0 0.0 0.0 0.7 0.0 0.0 0.0 5.1 .. 0.0 0.0 1.1 0.5 0.0 0.0 1.8 3.6 7.7 2.7 0.0 0.0 0.0 0.4 0.4 0.1 0.0 .. 14.6

0.1 2.2 2.5 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 4.3 0.0 .. 0.0 .. .. 0.3 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.1 0.0 3.9 0.0 0.0 .. 0.0 0.0 0.0 0.1 0.0 0.0 0.0 0.0 0.0 0.1 0.0 0.0 0.0 0.0 0.1 0.9 0.0 .. 0.0

0.0 1.7 1.6 0.3 0.0 0.0 0.1 0.0 1.1 .. 1.6 1.7 0.0 .. .. .. .. 0.0 0.0 0.0 0.0 0.0 0.7 0.0 0.0 0.0 0.1 0.0 0.0 0.0 29.4 0.0 0.4 0.0 11.0 2.2 0.0 .. 0.5 0.0 0.0 0.4 1.0 .. 0.0 0.0 0.0 0.0 0.0 29.7 0.0 6.8 1.1 0.4 .. .. 0.0

0.1 1.0 0.5 0.0 0.0 0.0 0.0 0.0 0.2 0.0 0.0 0.0 1.4 .. 0.0 .. .. 0.0 1.9 1.1 0.0 1.8 14.9 0.0 1.3 0.1 1.9 2.5 0.5 1.3 0.6 0.0 0.1 0.1 0.3 0.1 3.4 .. 0.0 5.6 0.0 0.6 1.8 1.7 1.2 0.1 0.0 1.0 0.1 3.0 1.7 0.1 0.2 0.2 0.1 .. ..

2011 World Development Indicators

185

environment

Contribution of natural resources to gross domestic product


3.16 Romania Russian Federation Rwanda Saudi Arabia Senegal Serbia and Montenegro 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

Contribution of natural resources to gross domestic product Total natural resources rents

Oil rents

Natural gas rents

Coal rents, hard and soft

Mineral rents

Forest rents

% of GDP

% of GDP

% of GDP

% of GDP

% of GDP

% of GDP

2009

2009

2009

2009

2009

2009

2.0 20.7 3.3 47.2 1.8 .. 4.5 0.0 0.3 0.1 .. 4.7 0.0 0.8 16.9 2.3 0.8 0.0 14.4 0.2 6.3 3.6 0.4 4.5 35.2 4.9 0.3 41.0 5.2 3.6 20.9 1.4 0.9 0.7 28.2 15.6 8.1 .. 19.7 18.4 5.2 3.7 w 6.3 8.7 6.8 11.2 8.7 5.3 13.7 7.0 22.7 5.8 14.2 1.6 0.2

0.9 13.4 0.0 43.8 0.0 .. 0.0 0.0 0.0 0.0 .. 0.1 0.0 0.0 16.0 0.0 0.0 0.0 12.4 0.1 0.0 1.6 0.0 0.0 10.3 3.8 0.1 17.1 0.0 0.9 17.6 1.0 0.5 0.0 2.8 13.8 6.0 .. 19.4 0.0 0.0 1.9 w 0.7 4.7 2.9 6.8 4.6 1.6 8.2 4.1 17.6 0.8 8.8 0.9 0.0

0.8 5.8 0.0 3.4 0.0 .. 0.0 0.0 0.0 0.0 .. 0.1 0.0 0.0 0.0 0.0 0.0 0.0 1.8 0.1 0.4 1.7 0.0 0.0 24.9 0.9 0.0 24.0 0.0 2.4 3.3 0.4 0.2 0.0 25.4 1.2 1.3 .. 0.3 0.0 0.0 0.6 w 0.9 1.4 0.8 2.1 1.3 0.6 3.7 0.5 4.6 0.8 0.5 0.3 0.1

0.2 1.0 0.0 0.0 0.0 .. 0.0 0.0 0.0 0.1 .. 4.2 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.1 0.0 0.1 0.0 0.0 0.0 0.0 0.1 0.0 0.0 2.4 0.0 0.0 0.2 0.0 0.1 0.0 2.2 .. 0.0 0.1 3.2 0.5 w 0.1 1.4 2.1 0.5 1.3 2.4 0.9 0.1 0.0 1.8 1.3 0.1 0.0

.. 1.1 0.0 0.0 0.4 .. 0.6 0.0 0.0 0.0 .. 3.3 .. 0.0 0.1 .. 0.4 0.0 0.2 0.0 3.5 0.0 0.0 2.1 0.0 .. 0.1 0.0 0.0 0.0 0.0 0.0 0.1 0.0 0.0 0.5 0.1 .. 0.0 16.4 1.9 0.5 w 2.0 1.0 0.6 1.5 1.0 0.4 0.7 2.0 0.4 1.4 1.8 0.2 ..

Note: Components may not sum to 100 percent because of rounding.

186

2011 World Development Indicators

0.2 0.3 3.3 0.0 1.3 .. 3.8 0.0 0.3 0.1 .. 1.2 0.0 0.8 0.8 2.3 0.5 0.0 0.0 0.0 2.4 0.3 0.4 2.4 0.0 0.2 0.1 .. 5.2 0.3 .. 0.0 0.1 0.7 0.0 0.0 0.7 .. 0.0 2.0 3.3 0.2 w 2.6 0.3 0.4 0.3 0.4 0.2 0.2 0.3 0.1 1.1 1.7 0.1 0.0


3.16

environment

Contribution of natural resources to gross domestic product About the data Accounting for the contribution of natural resources

the price of a commodity and the average cost of

savings measures the net additions or subtractions

to economic output is important in building an analyt-

producing it. This is done by estimating the world

from a country’s stock of tangible and intangible

ical framework for sustainable development. In some

price of units of specific commodities and subtract-

capital. This table is now included in the Economy

countries earnings from natural resources, especially

ing estimates of average unit costs of extraction

section as table 4.11 along with the closely related

from fossil fuels and minerals, account for a sizable

or harvesting costs (including a normal return on

table 4.10 “Toward a broader measure of income.”

share of GDP, and much of these come in the form of

capital). These unit rents are then multiplied by the

economic rents—revenues above the cost of extract-

physical quantities countries extract or harvest to

ing them. Natural resources give rise to economic

determine the rents for each commodity as a share

rents because they are not produced. For produced

of gross national income.

Definitions • Oil rents are the difference between the value of

goods and services competitive forces expand sup-

This definition of economic rent differs from that

crude oil production at world prices and total costs

ply until economic profits are driven to zero, but natu-

used in the System of National Accounts, where

of production. • Natural gas rents are the differ-

ral resources in fixed supply often command returns

rents are a form of property income, consisting of

ence between the value of natural gas production

well in excess of their cost of production. Rents from

payments to landowners by a tenant for the use of

at world prices and total costs of production. • Coal

nonrenewable resources—fossil fuels and miner-

the land or payments to the owners of subsoil assets

rents are the difference between the value of both

als—as well as rents from overharvesting of forests

by institutional units permitting them to extract sub-

hard and soft coal production at world prices and

indicate the liquidation of a country’s capital stock.

soil deposits.

their total costs of production. • Mineral rents are

When countries use such rents to support current

The Environment section of previous editions of the

the difference between the value of production for

consumption rather than to invest in new capital to

World Development Indicators included a table “Toward

a stock of minerals at world prices and their total

replace what is being used up, they are, in effect,

a broader measure of savings,” which showed the

costs of production. Minerals included in the calcu-

borrowing against their future.

derivation of adjusted net savings taking into account

lation are tin, gold, lead, zinc, iron, copper, nickel,

The estimates of natural resources rents shown in

consumption of fixed and natural capital and pollution

silver, bauxite, and phosphate. • Forest rents are

the table are calculated as the difference between

damage and additions to human capital. Adjusted net

roundwood harvest times the product of average prices and a region-specific rental rate (based on a number of reviews, World Bank 2011). • Total natu-

Oil dominates the contribution of natural resources in the Middle East and North Africa

3.16a

ral resources rents are the sum of oil rents, natural gas rents, coal rents (hard and soft), mineral rents,

Natural resources rents (percent of GDP)

Oil

Natural gas

Coal

Mineral

Forest

and forest rents.

25 20 15 10 5 0

East Asia & Pacific

Europe & Central Asia

Latin America & Carib.

Middle East & N. Africa

South Asia

Sub-Saharan Africa

Source: Table 3.16.

Upper middle-income countries have the highest contribution of natural resources to GDP

Natural resources rents (percent of GDP)

Oil

3.16b

Natural gas

Coal

Mineral

Forest

9.6 7.2

Data sources

4.8

Data on contributions of natural resources to

2.4 0.0

GDP are estimates based on sources and methLow income

Source: Table 3.16.

Lower middle income

Upper middle income

High income

World

ods described in The Changing Wealth of Nations: Measuring Sustainable Development in the New Millennium (World Bank 2011a).

2011 World Development Indicators

187

Text figures, tables, and boxes

World Development Indicators 2011 Part 1 of 2  

Looking for accurate, up-to-date data on development issues? 'World Development Indicators' is the World Bank's premier annual compilation o...