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World Development Indicators 2013

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This compilation of cross-country comparable statistics about development is organized around six themes—world view, people, environment, economy, states and markets, and global links. Each section includes an introduction, a set of six stories highlighting regional trends, a table of the most relevant and popular indicators, and an index to the full set of tables and indicators available online.It also reviews progress toward the Millennium Development Goals.





Burkina
Faso


Dominican
Republic Puerto


Rico (US)


U.S. Virgin
Islands (US)


St. Kitts
and Nevis


Antigua and Barbuda


Dominica


St. Lucia


Barbados


Grenada


Trinidad
and Tobago


St. Vincent and
the Grenadines


R.B. de Venezuela


Martinique (Fr)


Guadeloupe (Fr)


St. Martin (Fr)


St. Maarten (Neth)


Curaçao (Neth)


Aruba (Neth)


Poland


Czech Republic


Slovak Republic
Ukraine


Austria


Germany


San
Marino


Italy


Slovenia
Croatia


Bosnia and
Herzegovina Serbia


Hungary


Romania


Bulgaria


Albania
Greece


FYR
Macedonia


Samoa


American
Samoa (US)


Tonga
Fiji


Kiribati


French Polynesia (Fr)


N. Mariana Islands (US)


Guam (US)


Palau


Federated States of Micronesia Marshall Islands


Nauru Kiribati


Solomon
Islands


Tuvalu


Vanuatu Fiji


New
Caledonia


(Fr)


Haiti
Jamaica


Cuba
Cayman Is.(UK)


The Bahamas


Turks and Caicos Is. (UK)


Bermuda
(UK)


United States


Canada


Mexico


PanamaCosta Rica


Nicaragua
Honduras


El Salvador
Guatemala


Belize


Colombia
French Guiana (Fr)


Guyana
Suriname


R.B. de
Venezuela


Ecuador


Peru Brazil


Bolivia


Paraguay


Chile
Argentina


Uruguay


Greenland
(Den)


NorwayIceland


Isle of Man (UK)


Ireland
United


Kingdom


Faeroe
Islands
(Den) Sweden Finland


Denmark


Estonia
Latvia


Lithuania


Poland


Russian
Fed.


Belarus


Ukraine
Moldova


Romania


Bulgaria


Greece


Italy


Germany
Belgium


The Netherlands


Luxembourg


Channel Islands (UK)


Switzerland
Liechtenstein France


Andorra


Portugal
Spain


Monaco


Gibraltar (UK)
Malta


Morocco


Tunisia


Algeria


Western
Sahara


Mauritania


Mali


Senegal
The Gambia


Guinea-Bissau Guinea


Cape Verde


Sierra Leone


Liberia


Côte
d’Ivoire


Ghana


Togo


Benin


Niger


Nigeria


Libya Arab Rep.
of Egypt


Sudan


South
Sudan


Chad


Cameroon


Central
African


Republic


Equatorial Guinea
São Tomé and Príncipe


Gabon Congo


Angola


Dem.Rep.of
Congo


Eritrea


Djibouti


Ethiopia


Somalia


Kenya
Uganda


Rwanda


Burundi
Tanzania


Zambia Malawi


MozambiqueZimbabwe


BotswanaNamibia


Swaziland


LesothoSouth
Africa


Madagascar Mauritius


Seychelles


Comoros


Mayotte
(Fr)


Réunion (Fr)


Rep. of Yemen


Oman


United Arab
Emirates


Qatar
Bahrain


Saudi
Arabia


KuwaitIsrael
West Bank and Gaza Jordan


Lebanon
Syrian
Arab
Rep.


Cyprus


Iraq
Islamic Rep.


of Iran


Turkey
Azer-
baijanArmenia


Georgia


Turkmenistan


Uzbekistan


Kazakhstan


Afghanistan


Tajikistan


Kyrgyz
Rep.


Pakistan


India


Bhutan
Nepal


Bangladesh


Myanmar


Sri
Lanka


Maldives


Thailand


Lao
P.D.R.


Vietnam
Cambodia


Singapore


Malaysia
Brunei Darussalam


Philippines


Papua New GuineaIndonesia


Australia


New
Zealand


Japan
Rep.of
Korea


Dem.People’s
Rep.of Korea


Mongolia


China


Russian Federation


Antarctica


Timor-Leste


Vatican
City


IBRD 39817 MARCH 2013


Kosovo
Montenegro


Designed, edited, and produced by


Communications Development Incorporated,


Washington, D.C.,


with Peter Grundy Art & Design, London


Classified according to
World Bank estimates of


2011 GNI per capita


The world by income


Low ($1,025 or less)


Lower middle ($1,026–$4,035)


Upper middle ($4,036–$12,475)


High ($12,476 or more)


No data




2013 World DevelopmentIndicators




© 2013 International Bank for Reconstruction and Development / The World Bank
1818 H Street NW, Washington DC 20433
Telephone: 202-473-1000; Internet: www.worldbank.org


Some rights reserved
1 2 3 4 16 15 14 13


This work is a product of the staff of The World Bank with external contributions. Note that The World
Bank does not necessarily own each component of the content included in the work. The World Bank
therefore does not warrant that the use of the content contained in the work will not infringe on
the rights of third parties. The risk of claims resulting from such infringement rests solely with you.


The findings, interpretations, and conclusions expressed in this work do not necessarily reflect the
views of The World Bank, its Board of Executive Directors, or the governments they represent. The
World Bank does not guarantee the accuracy of the data included in this work. The boundaries,
colors, denominations, and other information shown on any map in this work do not imply any judg-
ment on the part of The World Bank concerning the legal status of any territory or the endorsement
or acceptance of such boundaries.


Nothing herein shall constitute or be considered to be a limitation upon or waiver of the privileges
and immunities of The World Bank, all of which are specifically reserved.


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Attribution—Please cite the work as follows: World Bank. 2013. World Development Indicators
2013. Washington, DC: World Bank. doi: 10.1596/978-0-8213-9824-1. License: Creative
Commons Attribution CC BY 3.0


Translations—If you create a translation of this work, please add the following disclaimer along
with the attribution: This translation was not created by The World Bank and should not be
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content or error in this translation.


All queries on rights and licenses should be addressed to the Office of the Publisher, The World
Bank, 1818 H Street NW, Washington, DC 20433, USA; fax: 202-522-2625; e-mail: pubrights@
worldbank.org.
ISBN (paper): 978-0-8213-9824-1
ISBN (electronic): 978-0-8213-9825-8
DOI: 10.1596/978-0-8213-9824-1


Cover photo: Arne Hoel/World Bank; Cover design: Communications Development Incorporated.


Other photos: page xviii, Arne Hoel/World Bank; page 34, Kim Eun Yeul/World Bank; page 50, Curt
Carnemark/World Bank; page 64, Gerardo Pesantez/ World Bank; page 78, Maria Fleischmann/
World Bank; page 92, Curt Carnemark/World Bank.




World Development Indicators 2013 iiiEconomy States and markets Global links Back


Preface
Welcome to World Development Indicators 2013, the
World Bank’s premier compilation of relevant, high-
quality, and internationally comparable statistics
about global development.


The first edition of World Development Indicators
in 1997 included this forecast: “The global economy
is undergoing an information revolution that will be
as significant in effect as the industrial revolution of
the nineteenth century.” At that time the number of
mobile phones worldwide was estimated to be less
than 2 per 100 people, with eight times as many
telephone mainlines. World Development Indicators
has tracked the revolution: this edition reports that
mobile phone subscriptions in 2011 grew to 85 per
100 people—a more than fortyfold increase.


This is just one example of how people were com-
municating and acquiring knowledge and how infor-
mation was changing. But in addition to measuring
the change, World Development Indicators has felt it
directly. Use of the online database and the tools
that access it—particularly the Open Data website
(http://data.worldbank.org), the web-based DataBank
query application (http://databank.worldbank.org),
and applications for mobile devices—has increased
dramatically.


And so we have refined and improved the pre-
sentation of this 17th edition. Our aim is to find the
best way to put data in the hands of policymakers,
development specialists, students, and the public,
so that they may use the data to reduce poverty and
solve the world’s most pressing development chal-
lenges. The biggest change is that the data tables
previously published in the book are now available
online (http://wdi.worldbank.org/tables). This has
many advantages: The tables will reflect the latest


additions and revisions to the data. They will be avail-
able to a far greater audience. And they will be free
for everyone.


World Development Indicators 2013 is organized
around six themes—world view, people, environment,
economy, states and markets, and global links. Each
section includes an introduction, a set of six stories
highlighting regional trends, a table of the most rel-
evant and popular indicators, and an index to the full
set of tables and indicators available online. World
view also reviews progress toward the Millennium
Development Goals.


Other companion products include The Little Data
Book 2013, which provides an at-a-glance view of indi-
cators for each economy, and a new version of the
DataFinder mobile application, available in Chinese,
English, French, and Spanish and designed to reflect
the structure and tables of World Development Indica-
tors 2013, for both tablet and handheld devices and
for all major mobile platforms (http://data.worldbank
.org/apps).


World Development Indicators is the result of a
collaborative effort of many partners: the United
Nations family, the International Monetary Fund, the
International Telecommunication Union, the Organ-
isation for Economic Co-operation and Development,
the statistical offices of more than 200 economies,
and countless others. I extend my gratitude to them
all—and especially to government statisticians around
the world. Without their hard work, professionalism,
and dedication, measuring and monitoring trends in
global development would not be possible.


We hope you will find the new World Development
Indicators a useful resource, and we welcome any sug-
gestions to improve it at data@worldbank.org.


Shaida Badiee
Director


Development Economics Data Group




iv World Development Indicators 2013 Front User guide World view People Environment?


Acknowledgments
This book was prepared by a team led by Soong Sup
Lee under the management of Neil Fantom and com-
prising Azita Amjadi, Liu Cui, Federico Escaler, Mahyar
Eshragh-Tabary, Juan Feng, Masako Hiraga, Wendy
Ven-dee Huang, Bala Bhaskar Naidu Kalimili, Buyant
Khaltarkhuu, Elysee Kiti, Alison Kwong, Ibrahim Lev-
ent, Hiroko Maeda, Johan Mistiaen, Vanessa Moreira
da Silva, Maurice Nsabimana, Beatriz Prieto-Oramas,
William Prince, Evis Rucaj, Rubena Sukaj, Emi Suzuki,
Eric Swanson, Jomo Tariku, Rasiel Victor Vellos, and
Olga Victorovna Vybornaia, working closely with other
teams in the Development Economics Vice Presiden-
cy’s Development Data Group.


World Development Indicators electronic products
were prepared by a team led by Reza Farivari and com-
prising Ying Chi, Jean-Pierre Djomalieu, Ramgopal Era-
belly, Shelley Fu, Gytis Kanchas, Siddhesh Kaushik,
Ugendran Machakkalai, Nacer Megherbi, Shanmugam
Natarajan, Parastoo Oloumi, Manish Rathore, Ash-
ish Shah, Atsushi Shimo, Malarvizhi Veerappan, and
Vera Wen.


All work was carried out under the direction of
Shaida Badiee. Valuable advice was provided by


Tito Cordella, Doerte Doemeland, Zia M. Qureshi, and
David Rosenblatt.


The choice of indicators and text content was shaped
through close consultation with and substantial contri-
butions from staff in the World Bank’s four thematic
networks—Sustainable Development, Human Develop-
ment, 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 impor-
tant, the team received substantial help, guidance, and
data from external partners. For individual acknowl-
edgments of contributions to the book’s content, see
Credits. For a listing of our key partners, see Partners.


Communications Development Incorporated pro-
vided overall design direction, editing, and layout,
led by Meta de Coquereaumont, Jack Harlow, Bruce
Ross-Larson, and Christopher Trott. Elaine Wilson cre-
ated the cover and graphics and typeset the book.
Peter Grundy, of Peter Grundy Art & Design, and Diane
Broadley, of Broadley Design, designed the report.
Staff from The World Bank’s Office of the Publisher
oversaw printing and dissemination of the book.




World Development Indicators 2013 vEconomy States and markets Global links Back


Table of contents
Preface iii


Acknowledgments iv


Partners vi


User guide xii


1. World view 1


2. People 35


3. Environment 51


4. Economy 65


5. States and markets 79


6. Global links 93


Primary data documentation 107


Statistical methods 118


Credits 121


Introduction
Goal 1 Eradicate extreme poverty
Goal 2 Achieve universal primary education
Goal 3 Promote gender equality and


empower women
Goal 4 Reduce child mortality
Goal 5 Improve maternal health
Goal 6 Combat HIV/AIDS, malaria, and


other diseases
Goal 7 Ensure environmental sustainability
Goal 8 Develop a global partnership for


development
Targets and indicators for each goal
World view indicators
About the data
Online tables and indicators
Poverty indicators NEW!
About the data


Introduction
Highlights
Table of indicators
About the data
Online tables and indicators




vi World Development Indicators 2013 Front User guide World view People Environment?


Partners
Defining, gathering, and disseminating international
statistics is a collective effort of many people and
organizations. The indicators presented in World Devel-
opment 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 interpreta-


tion of statistical indicators. All these contributors
have a strong belief that available, accurate data will
improve the quality of public and private decisionmak-
ing.


The organizations listed here have made World
Development Indicators possible by sharing their data
and their expertise with us. More important, their col-
laboration 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, 2013.




World Development Indicators 2013 viiEconomy States and markets Global links Back


International and government agencies


Carbon Dioxide Information
Analysis Center


http://cdiac.ornl.gov


Centre for Research on the
Epidemiology of Disasters


www.emdat.be


Deutsche Gesellschaft für
Internationale Zusammenarbeit


www.giz.de


Food and Agriculture
Organization


www.fao.org


Internal Displacement
Monitoring Centre


www.internal-displacement.org/


International Civil
Aviation Organization


www.icao.int


International
Diabetes Federation


www.idf.org


International
Energy Agency


www.iea.org


International
Labour Organization


www.ilo.org


International
Monetary Fund


www.imf.org


International Telecommunication
Union


www.itu.int


Joint United Programme
on HIV/AIDS


www.unaids.org




viii World Development Indicators 2013 Front User guide World view People Environment?


Partners
National Science
Foundation


www.nsf.gov


The Office of U.S. Foreign
Disaster Assistance


www.globalcorps.com/ofda.html


Organisation for Economic
Co-operation and Development


www.oecd.org


Stockholm International
Peace Research Institute


www.sipri.org


Understanding
Children’s Work


www.ucw-project.org


United Nations


www.un.org


United Nations Centre for Human
Settlements, Global Urban Observatory


www.unhabitat.org


United Nations
Children’s Fund


www.unicef.org


United Nations Conference on
Trade and Development


www.unctad.org


United Nations Department of
Economic and Social Affairs,
Population Division


www.un.org/esa/population


United Nations Department of
Peacekeeping Operations


www.un.org/en/peacekeeping


United Nations Educational,
Scientific, and Cultural Organization,
Institute for Statistics


www.uis.unesco.org




World Development Indicators 2013 ixEconomy States and markets Global links Back


United Nations
Environment Programme


www.unep.org


United Nations Industrial
Development Organization


www.unido.org


United Nations
International Strategy
for Disaster Reduction


www.unisdr.org


United Nations Office on
Drugs and Crime


www.unodc.org


United Nations Office
of the High Commissioner
for Refugees


www.unhcr.org


United Nations
Population Fund


www.unfpa.org


Upsalla Conflict
Data Program


www.pcr.uu.se/research/UCDP


World Bank


http://data.worldbank.org


World Health Organization


www.who.int


World Intellectual
Property Organization


www.wipo.int


World Tourism
Organization


www.unwto.org


World Trade
Organization


www.wto.org




x World Development Indicators 2013 Front User guide World view People Environment?


Partners
Private and nongovernmental organizations


Center for International Earth
Science Information Network


www.ciesin.org


Containerisation
International


www.ci-online.co.uk


DHL


www.dhl.com


International Institute for
Strategic Studies


www.iiss.org


International
Road Federation


www.irfnet.org


Netcraft


http://news.netcraft.com




World Development Indicators 2013 xiEconomy States and markets Global links Back


PwC


www.pwc.com


Standard &
Poor’s


www.standardandpoors.com


World Conservation
Monitoring Centre


www.unep-wcmc.org


World Economic
Forum


www.weforum.org


World Resources
Institute


www.wri.org




xii World Development Indicators 2013 Front User guide World view People Environment?


User guide to tables


World Development Indicators 2013 4746 World Development Indicators 2013 Economy States and markets Global links Back Front Users guide World view People Environment


Environment 3
Deforestation Nationally


protected
areas


Internal
renewable
freshwater
resourcesb


Access to
improved


water
source


Access to
improved
sanitation
facilities


Urban
population


Particulate
matter


concentration


Carbon
dioxide


emissions


Energy use Electricity
production


Terrestrial and
marine areas


% of total
territorial area


urban-population-
weighted PM10
micrograms per


cubic meter
average
annual %


Per capita
cubic meters


% of total
population


% of total
population


average
annual


% growth
million


metric tons


Per capita
kilograms of
oil equivalent


billion
kilowatt
hours


2000–10 2011 2011 2010 2010 1990–2011 2010 2009 2010 2010


Afghanistan 0.00 0.4 1,335 50 37 4.0 30 6.3 .. ..


Albania –0.10 8.4 8,364 95 94 2.4 38 3.0 648 7.6


Algeria 0.57 6.2 313 83 95 2.6 69 121.3 1,138 45.6


American Samoa 0.19 16.7 .. .. .. 1.9 .. .. .. ..


Andorra 0.00 6.1 3,663 100 100 0.9 18 0.5 .. ..


Angola 0.21 12.1 7,544 51 58 4.1 58 26.7 716 5.3


Antigua and Barbuda 0.20 1.0 580 .. .. 1.0 13 0.5 .. ..


Argentina 0.81 5.3 6,771 .. .. 1.0 57 174.7 1,847 125.3


Armenia 1.48 8.0 2,212 98 90 0.3 45 4.5 791 6.5


Aruba 0.00 0.0 .. 100 .. 0.8 .. 2.3 .. ..


Australia 0.37 12.5 22,039 100 100 1.3 13 400.2 5,653 241.5


Austria –0.13 22.9 6,529 100 100 0.7 27 62.3 4,034 67.9


Azerbaijan 0.00 7.1 885 80 82 1.8 27 49.1 1,307 18.7


Bahamas, The 0.00 1.0 58 .. 100 1.5 .. 2.6 .. ..


Bahrain –3.55 0.7 3 .. .. 4.9 44 24.2 7,754 13.2


Bangladesh 0.18 1.6 698 81 56 3.0 115 51.0 209 42.3


Barbados 0.00 0.1 292 100 100 1.4 35 1.6 .. ..


Belarus –0.43 7.2 3,927 100 93 0.4 6 60.3 2,922 34.9


Belgium –0.16 13.2 1,089 100 100 1.2 21 103.6 5,586 93.8


Belize 0.67 20.6 44,868 98 90 3.0 12 0.4 .. ..


Benin 1.04 23.3 1,132 75 13 4.2 48 4.9 413 0.2


Bermuda 0.00 5.1 .. .. .. 0.7 .. 0.5 .. ..


Bhutan –0.34 28.3 105,653 96 44 3.9 20 0.4 .. ..


Bolivia 0.50 18.5 30,085 88 27 2.2 57 14.5 737 6.9


Bosnia and Herzegovina 0.00 0.6 9,461 99 95 0.9 21 30.1 1,703 17.1


Botswana 0.99 30.9 1,182 96 62 2.2 64 4.4 1,128 0.5


Brazil 0.50 26.0 27,551 98 79 1.2 18 367.1 1,363 515.7


Brunei Darussalam 0.44 29.6 20,939 .. .. 2.2 44 9.3 8,308 3.9


Bulgaria –1.53 8.9 2,858 100 100 –1.7 40 42.8 2,370 46.0


Burkina Faso 1.01 14.2 737 79 17 6.2 65 1.7 .. ..


Burundi 1.40 4.8 1,173 72 46 4.9 24 0.2 .. ..


Cambodia 1.34 23.4 8,431 64 31 2.1 42 4.6 355 1.0


Cameroon 1.05 9.0 13,629 77 49 3.3 59 6.7 363 5.9


Canada 0.00 6.2 82,647 100 100 1.2 15 513.9 7,380 607.8


Cape Verde –0.36 0.2 599 88 61 2.1 .. 0.3 .. ..


Cayman Islands 0.00 1.5 .. 96 96 0.9 .. 0.5 .. ..


Central African Republic 0.13 17.7 31,425 67 34 2.6 35 0.2 .. ..


Chad 0.66 9.4 1,301 51 13 3.0 83 0.4 .. ..


Channel Islands .. 0.5 .. .. .. 0.8 .. .. .. ..


Chile –0.25 13.3 51,188 96 96 1.1 46 66.7 1,807 60.4


China –1.57 16.0 2,093 91 64 3.0 59 7,687.1 1,807 4,208.3


Hong Kong SAR, China .. 41.8 .. .. .. 0.1 .. 37.0 1,951 38.3


Macao SAR, China .. .. .. .. .. 2.2 .. 1.5 .. ..


Colombia 0.17 20.5 45,006 92 77 1.7 19 71.2 696 56.8


Comoros 9.34 .. 1,592 95 36 2.9 30 0.1 .. ..


Congo, Dem. Rep. 0.20 10.0 13,283 45 24 4.3 35 2.7 360 7.9


Congo, Rep. 0.07 9.7 53,626 71 18 3.0 57 1.9 363 0.6


Deforestation Nationally
protected


areas


Internal
renewable
freshwater
resourcesb


Access to
improved


water
source


Access to
improved
sanitation
facilities


Urban
population


Particulate
matter


concentration


Carbon
dioxide


emissions


Energy use Electricity
production


Terrestrial and
marine areas


% of total
territorial area


urban-population-
weighted PM10
micrograms per


cubic meter
average
annual %


Per capita
cubic meters


% of total
population


% of total
population


average
annual


% growth
million


metric tons


Per capita
kilograms of
oil equivalent


billion
kilowatt
hours


2000–10 2011 2011 2010 2010 1990–2011 2010 2009 2010 2010


Costa Rica –0.93 17.6 23,780 97 95 2.2 27 8.3 998 9.6


Côte d’Ivoire –0.15 21.8 3,813 80 24 3.5 30 6.6 485 6.0


Croatia –0.19 9.5 8,562 99 99 0.2 22 21.5 1,932 14.0


Cuba –1.66 5.3 3,387 94 91 –0.1 15 31.6 975 17.4


Curacao .. .. .. .. .. .. .. .. .. ..


Cyprus –0.09 4.5 699 100 100 1.4 27 8.2 2,215 5.4


Czech Republic –0.08 15.1 1,253 100 98 –0.3 16 108.1 4,193 85.3


Denmark –1.14 4.1 1,077 100 100 0.6 15 45.7 3,470 38.8


Djibouti 0.00 0.0 331 88 50 2.0 28 0.5 .. ..


Dominica 0.58 3.7 .. .. .. 0.1 20 0.1 .. ..


Dominican Republic 0.00 24.1 2,088 86 83 2.1 14 20.3 840 15.9


Ecuador 1.81 38.0 29,456 94 92 2.2 19 30.1 836 17.7


Egypt, Arab Rep. –1.73 6.1 22 99 95 2.1 78 216.1 903 146.8


El Salvador 1.45 1.4 2,850 88 87 1.3 28 6.3 677 6.0


Equatorial Guinea 0.69 14.0 36,100 .. .. 3.2 6 4.8 .. ..


Eritrea 0.28 3.8 517 61 14 5.2 61 0.5 142 0.3


Estonia 0.12 22.6 9,486 98 95 0.1 9 16.0 4,155 13.0


Ethiopia 1.08 18.4 1,440 44 21 3.7 47 7.9 400 5.0


Faeroe Islands 0.00 .. .. .. .. 0.8 11 0.7 .. ..


Fiji –0.34 0.2 32,876 98 83 1.7 20 0.8 .. ..


Finland 0.14 8.5 19,858 100 100 0.6 15 53.6 6,787 80.7


France –0.39 17.1 3,057 100 100 1.2 12 363.4 4,031 564.3


French Polynesia –3.97 0.1 .. 100 98 1.1 .. 0.9 .. ..


Gabon 0.00 14.6 106,892 87 33 2.3 7 1.6 1,418 1.8


Gambia, The –0.41 1.3 1,689 89 68 3.7 60 0.4 .. ..


Georgia 0.09 3.4 12,958 98 95 1.0 49 5.8 700 10.1


Germany 0.00 42.3 1,308 100 100 0.2 16 734.6 4,003 622.1


Ghana 2.08 14.0 1,214 86 14 3.6 22 7.4 382 8.4


Greece –0.81 9.9 5,133 100 98 0.3 27 94.9 2,440 57.4


Greenland 0.00 40.1 .. 100 100 0.2 .. 0.6 .. ..


Grenada 0.00 0.1 .. .. 97 1.3 19 0.2 .. ..


Guam 0.00 3.6 .. 100 99 1.3 .. .. .. ..


Guatemala 1.40 29.5 7,400 92 78 3.4 51 15.2 713 8.8


Guinea 0.54 6.4 22,110 74 18 3.8 55 1.2 .. ..


Guinea-Bissau 0.48 26.9 10,342 64 20 3.6 48 0.3 .. ..


Guyana 0.00 4.8 318,766 94 84 0.5 20 1.6 .. ..


Haiti 0.76 0.1 1,285 69 17 3.8 35 2.3 229 0.6


Honduras 2.06 13.9 12,371 87 77 3.1 34 7.7 601 6.7


Hungary –0.62 5.1 602 100 100 0.4 15 48.7 2,567 37.4


Iceland –4.99 13.2 532,892 100 100 0.4 18 2.0 16,882 17.1


India –0.46 4.8 1,165 92 34 2.5 52 1,979.4 566 959.9


Indonesia 0.51 6.4 8,332 82 54 2.5 60 451.8 867 169.8


Iran, Islamic Rep. 0.00 6.9 1,718 96 100 1.3 56 602.1 2,817 233.0


Iraq –0.09 0.1 1,068 79 73 2.8 88 109.0 1,180 50.2


Ireland –1.53 1.2 10,707 100 99 2.7 13 41.6 3,218 28.4


Isle of Man 0.00 .. .. .. .. 0.5 .. .. .. ..


Israel –0.07 15.1 97 100 100 1.9 21 67.2 3,005 58.6


3 Environment


World Development Indicators is the World Bank’s premier


compilation of cross-country comparable data on develop-


ment. The database contains more than 1,200 time series


indicators for 214 economies and more than 30 country


groups, with data for many indicators going back more


than 50 years.


The 2013 edition of World Development Indicators has


been reconfigured to offer a more condensed presentation


of the principal indicators, arranged in their traditional sec-


tions, along with regional and topical highlights.


World view People Environment


Economy States and markets Global links


Tables
The tables include all World Bank member countries (188),


and all other economies with populations of more than


30,000 (214 total). Countries and economies are listed


alphabetically (except for Hong Kong SAR, China, and


Macao SAR, China, which appear after China).


The term country, used interchangeably with economy,


does not imply political independence but refers to any terri-


tory for which authorities report separate social or economic


statistics. When available, aggregate measures for income


and regional groups appear at the end of each table.


Aggregate measures for income groups
Aggregate measures for income groups include the 214


economies listed in the tables, plus Taiwan, China, when-


ever data are available. To maintain consistency in the


aggregate measures over time and between tables, miss-


ing data are imputed where possible.


Aggregate measures for regions
The aggregate measures for regions cover only low- and


middle-income economies.


The country composition of regions is based on the


World Bank’s analytical regions and may differ from com-


mon geographic usage. For regional classifications, see


the map on the inside back cover and the list on the back


cover flap. For further discussion of aggregation methods,


see Statistical methods.


Data presentation conventions
• A blank means not applicable or, for an aggregate, not


analytically meaningful.


• A billion is 1,000 million.


• A trillion is 1,000 billion.


• Figures in orange italics refer to years or periods other


than those specified or to growth rates calculated for


less than the full period specified.


• Data for years that are more than three years from the


range shown are footnoted.


• The cutoff date for data is February 1, 2013.




World Development Indicators 2013 xiiiEconomy States and markets Global links Back


World Development Indicators 2013 4746 World Development Indicators 2013 Economy States and markets Global links Back Front Users guide World view People Environment


Environment 3
Deforestation Nationally


protected
areas


Internal
renewable
freshwater
resourcesb


Access to
improved


water
source


Access to
improved
sanitation
facilities


Urban
population


Particulate
matter


concentration


Carbon
dioxide


emissions


Energy use Electricity
production


Terrestrial and
marine areas


% of total
territorial area


urban-population-
weighted PM10
micrograms per


cubic meter
average
annual %


Per capita
cubic meters


% of total
population


% of total
population


average
annual


% growth
million


metric tons


Per capita
kilograms of
oil equivalent


billion
kilowatt
hours


2000–10 2011 2011 2010 2010 1990–2011 2010 2009 2010 2010


Afghanistan 0.00 0.4 1,335 50 37 4.0 30 6.3 .. ..


Albania –0.10 8.4 8,364 95 94 2.4 38 3.0 648 7.6


Algeria 0.57 6.2 313 83 95 2.6 69 121.3 1,138 45.6


American Samoa 0.19 16.7 .. .. .. 1.9 .. .. .. ..


Andorra 0.00 6.1 3,663 100 100 0.9 18 0.5 .. ..


Angola 0.21 12.1 7,544 51 58 4.1 58 26.7 716 5.3


Antigua and Barbuda 0.20 1.0 580 .. .. 1.0 13 0.5 .. ..


Argentina 0.81 5.3 6,771 .. .. 1.0 57 174.7 1,847 125.3


Armenia 1.48 8.0 2,212 98 90 0.3 45 4.5 791 6.5


Aruba 0.00 0.0 .. 100 .. 0.8 .. 2.3 .. ..


Australia 0.37 12.5 22,039 100 100 1.3 13 400.2 5,653 241.5


Austria –0.13 22.9 6,529 100 100 0.7 27 62.3 4,034 67.9


Azerbaijan 0.00 7.1 885 80 82 1.8 27 49.1 1,307 18.7


Bahamas, The 0.00 1.0 58 .. 100 1.5 .. 2.6 .. ..


Bahrain –3.55 0.7 3 .. .. 4.9 44 24.2 7,754 13.2


Bangladesh 0.18 1.6 698 81 56 3.0 115 51.0 209 42.3


Barbados 0.00 0.1 292 100 100 1.4 35 1.6 .. ..


Belarus –0.43 7.2 3,927 100 93 0.4 6 60.3 2,922 34.9


Belgium –0.16 13.2 1,089 100 100 1.2 21 103.6 5,586 93.8


Belize 0.67 20.6 44,868 98 90 3.0 12 0.4 .. ..


Benin 1.04 23.3 1,132 75 13 4.2 48 4.9 413 0.2


Bermuda 0.00 5.1 .. .. .. 0.7 .. 0.5 .. ..


Bhutan –0.34 28.3 105,653 96 44 3.9 20 0.4 .. ..


Bolivia 0.50 18.5 30,085 88 27 2.2 57 14.5 737 6.9


Bosnia and Herzegovina 0.00 0.6 9,461 99 95 0.9 21 30.1 1,703 17.1


Botswana 0.99 30.9 1,182 96 62 2.2 64 4.4 1,128 0.5


Brazil 0.50 26.0 27,551 98 79 1.2 18 367.1 1,363 515.7


Brunei Darussalam 0.44 29.6 20,939 .. .. 2.2 44 9.3 8,308 3.9


Bulgaria –1.53 8.9 2,858 100 100 –1.7 40 42.8 2,370 46.0


Burkina Faso 1.01 14.2 737 79 17 6.2 65 1.7 .. ..


Burundi 1.40 4.8 1,173 72 46 4.9 24 0.2 .. ..


Cambodia 1.34 23.4 8,431 64 31 2.1 42 4.6 355 1.0


Cameroon 1.05 9.0 13,629 77 49 3.3 59 6.7 363 5.9


Canada 0.00 6.2 82,647 100 100 1.2 15 513.9 7,380 607.8


Cape Verde –0.36 0.2 599 88 61 2.1 .. 0.3 .. ..


Cayman Islands 0.00 1.5 .. 96 96 0.9 .. 0.5 .. ..


Central African Republic 0.13 17.7 31,425 67 34 2.6 35 0.2 .. ..


Chad 0.66 9.4 1,301 51 13 3.0 83 0.4 .. ..


Channel Islands .. 0.5 .. .. .. 0.8 .. .. .. ..


Chile –0.25 13.3 51,188 96 96 1.1 46 66.7 1,807 60.4


China –1.57 16.0 2,093 91 64 3.0 59 7,687.1 1,807 4,208.3


Hong Kong SAR, China .. 41.8 .. .. .. 0.1 .. 37.0 1,951 38.3


Macao SAR, China .. .. .. .. .. 2.2 .. 1.5 .. ..


Colombia 0.17 20.5 45,006 92 77 1.7 19 71.2 696 56.8


Comoros 9.34 .. 1,592 95 36 2.9 30 0.1 .. ..


Congo, Dem. Rep. 0.20 10.0 13,283 45 24 4.3 35 2.7 360 7.9


Congo, Rep. 0.07 9.7 53,626 71 18 3.0 57 1.9 363 0.6


Deforestation Nationally
protected


areas


Internal
renewable
freshwater
resourcesb


Access to
improved


water
source


Access to
improved
sanitation
facilities


Urban
population


Particulate
matter


concentration


Carbon
dioxide


emissions


Energy use Electricity
production


Terrestrial and
marine areas


% of total
territorial area


urban-population-
weighted PM10
micrograms per


cubic meter
average
annual %


Per capita
cubic meters


% of total
population


% of total
population


average
annual


% growth
million


metric tons


Per capita
kilograms of
oil equivalent


billion
kilowatt
hours


2000–10 2011 2011 2010 2010 1990–2011 2010 2009 2010 2010


Costa Rica –0.93 17.6 23,780 97 95 2.2 27 8.3 998 9.6


Côte d’Ivoire –0.15 21.8 3,813 80 24 3.5 30 6.6 485 6.0


Croatia –0.19 9.5 8,562 99 99 0.2 22 21.5 1,932 14.0


Cuba –1.66 5.3 3,387 94 91 –0.1 15 31.6 975 17.4


Curacao .. .. .. .. .. .. .. .. .. ..


Cyprus –0.09 4.5 699 100 100 1.4 27 8.2 2,215 5.4


Czech Republic –0.08 15.1 1,253 100 98 –0.3 16 108.1 4,193 85.3


Denmark –1.14 4.1 1,077 100 100 0.6 15 45.7 3,470 38.8


Djibouti 0.00 0.0 331 88 50 2.0 28 0.5 .. ..


Dominica 0.58 3.7 .. .. .. 0.1 20 0.1 .. ..


Dominican Republic 0.00 24.1 2,088 86 83 2.1 14 20.3 840 15.9


Ecuador 1.81 38.0 29,456 94 92 2.2 19 30.1 836 17.7


Egypt, Arab Rep. –1.73 6.1 22 99 95 2.1 78 216.1 903 146.8


El Salvador 1.45 1.4 2,850 88 87 1.3 28 6.3 677 6.0


Equatorial Guinea 0.69 14.0 36,100 .. .. 3.2 6 4.8 .. ..


Eritrea 0.28 3.8 517 61 14 5.2 61 0.5 142 0.3


Estonia 0.12 22.6 9,486 98 95 0.1 9 16.0 4,155 13.0


Ethiopia 1.08 18.4 1,440 44 21 3.7 47 7.9 400 5.0


Faeroe Islands 0.00 .. .. .. .. 0.8 11 0.7 .. ..


Fiji –0.34 0.2 32,876 98 83 1.7 20 0.8 .. ..


Finland 0.14 8.5 19,858 100 100 0.6 15 53.6 6,787 80.7


France –0.39 17.1 3,057 100 100 1.2 12 363.4 4,031 564.3


French Polynesia –3.97 0.1 .. 100 98 1.1 .. 0.9 .. ..


Gabon 0.00 14.6 106,892 87 33 2.3 7 1.6 1,418 1.8


Gambia, The –0.41 1.3 1,689 89 68 3.7 60 0.4 .. ..


Georgia 0.09 3.4 12,958 98 95 1.0 49 5.8 700 10.1


Germany 0.00 42.3 1,308 100 100 0.2 16 734.6 4,003 622.1


Ghana 2.08 14.0 1,214 86 14 3.6 22 7.4 382 8.4


Greece –0.81 9.9 5,133 100 98 0.3 27 94.9 2,440 57.4


Greenland 0.00 40.1 .. 100 100 0.2 .. 0.6 .. ..


Grenada 0.00 0.1 .. .. 97 1.3 19 0.2 .. ..


Guam 0.00 3.6 .. 100 99 1.3 .. .. .. ..


Guatemala 1.40 29.5 7,400 92 78 3.4 51 15.2 713 8.8


Guinea 0.54 6.4 22,110 74 18 3.8 55 1.2 .. ..


Guinea-Bissau 0.48 26.9 10,342 64 20 3.6 48 0.3 .. ..


Guyana 0.00 4.8 318,766 94 84 0.5 20 1.6 .. ..


Haiti 0.76 0.1 1,285 69 17 3.8 35 2.3 229 0.6


Honduras 2.06 13.9 12,371 87 77 3.1 34 7.7 601 6.7


Hungary –0.62 5.1 602 100 100 0.4 15 48.7 2,567 37.4


Iceland –4.99 13.2 532,892 100 100 0.4 18 2.0 16,882 17.1


India –0.46 4.8 1,165 92 34 2.5 52 1,979.4 566 959.9


Indonesia 0.51 6.4 8,332 82 54 2.5 60 451.8 867 169.8


Iran, Islamic Rep. 0.00 6.9 1,718 96 100 1.3 56 602.1 2,817 233.0


Iraq –0.09 0.1 1,068 79 73 2.8 88 109.0 1,180 50.2


Ireland –1.53 1.2 10,707 100 99 2.7 13 41.6 3,218 28.4


Isle of Man 0.00 .. .. .. .. 0.5 .. .. .. ..


Israel –0.07 15.1 97 100 100 1.9 21 67.2 3,005 58.6


3 Environment


Classification of economies
For operational and analytical purposes the World Bank’s


main criterion for classifying economies is gross national


income (GNI) per capita (calculated using the World Bank


Atlas method). Because GNI per capita changes over time,


the country composition of income groups may change


from one edition of World Development Indicators to the


next. Once the classification is fixed for an edition, based


on GNI per capita in the most recent year for which data


are available (2011 in this edition), all historical data pre-


sented are based on the same country grouping.


Low-income economies are those with a GNI per capita


of $1,025 or less in 2011. Middle-income economies are


those with a GNI per capita of more than $1,025 but less


than $12,475. Lower middle-income and upper middle-


income economies are separated at a GNI per capita of


$4,036. High-income economies are those with a GNI per


capita of $12,476 or more. The 17 participating member


countries of the euro area are presented as a subgroup


under high income economies.


Statistics
Additional information about the data is provided in Pri-


mary data documentation, which summarizes national and


international efforts to improve basic data collection and


gives country-level information on primary sources, census


years, fiscal years, statistical methods and concepts used,


and other background information. Statistical methods pro-


vides technical information on some of the general calcula-


tions and formulas used throughout the book.


Country notes
• Data for China do not include data for Hong Kong SAR,


China; Macao SAR, China; or Taiwan, China.


• Data for Indonesia include Timor-Leste through 1999.


• Data for Mayotte, to which a reference appeared in pre-


vious editions, are included in data for France.


• Data for Serbia do not include data for Kosovo or


Monte negro.


• Data for Sudan include South Sudan unless otherwise


noted.


Symbols


.. means that data are not available or that aggregates


cannot be calculated because of missing data in the


years shown.


0 or
0.0


means zero or small enough that the number would


round to zero at the displayed number of decimal places.


/ in dates, as in 2010/11, means that the period of


time, usually 12 months, straddles two calendar years


and refers to a crop year, a survey year, or a fiscal year.


$ means current U.S. dollars unless otherwise noted.


< means less than.




xiv World Development Indicators 2013 Front User guide World view People Environment?


User guide to WDI online tables
Statistical tables that were previously available in the


World Development Indicators print edition are now avail-


able online. Using an automated query process, these ref-


erence tables will be consistently updated based on the


revisions to the World Development Indicators database.


How to access WDI online tables
To access the WDI online tables, visit http://wdi.worldbank


.org/tables. To access a specific WDI online table directly,


use the URL http://wdi.worldbank.org/table/ and the


table number (for example, http://wdi.worldbank.org/


table/1.1 to view the first table in the World view sec-


tion). Each section of this book also lists the indicators


included by table and by code. To view a specific indi-


cator online, use the URL http://data.worldbank.org/


indicator/ and the indicator code (for example, http://data


.worldbank.org/indicator/SP.POP.TOTL to view a page for


total population).




World Development Indicators 2013 xvEconomy States and markets Global links Back


How to use DataBank
DataBank (http://databank.worldbank.org) is an online


web resource that provides simple and quick access to


collections of time series data. It has advanced functions


for selecting and displaying data, performing customized


queries, downloading data, and creating charts and maps.


Users can create dynamic custom reports based on their


selection of countries, indicators, and years. All these


reports can be easily edited, shared, and embedded as


widgets on websites or blogs. For more information, see


http://databank.worldbank.org/help.


Actions


Click to edit and revise the table in


DataBank


Click to print the table and corresponding


indicator metadata


Click to export the table to Excel


Click to export the table and corresponding


indicator metadata to PDF


Click to access the WDI Online Tables Help


file


Click the checkbox to highlight cell level


metadata and values from years other


than those specified; click the checkbox


again to reset to the default display


Click on a country


to view metadata


Click on an indicator


to view metadata


Breadcrumbs to show


where you’ve been




xvi World Development Indicators 2013 Front User guide World view People Environment?


User guide to DataFinder
DataFinder is a free mobile app that accesses the full


set of data from the World Development Indicators data-


base. Data can be displayed and saved in a table, chart,


or map and shared via email, Facebook, and Twitter.


DataFinder works on mobile devices (smartphone or


tablet computer) in both offline (no Internet connection)


and online (Wi-Fi or 3G/4G connection to the Internet)


modes.


• Select a topic to display all related indicators.


• Compare data for multiple countries.


• Select predefined queries.


• Create a new query that can be saved and edited later.


• View reports in table, chart, and map formats.


• Send the data as a CSV file attachment to an email.


• Share comments and screenshots via Facebook,


Twitter, or email.




World Development Indicators 2013 xviiEconomy States and markets Global links Back


Table view provides time series data tables of key devel-


opment indicators by country or topic. A compare option


shows the most recent year’s data for the selected country


and another country.


Chart view illustrates data trends and cross-country com-


parisons as line or bar charts.


Map view colors selected indicators on world and regional


maps. A motion option animates the data changes from


year to year.




xviii World Development Indicators 2013 Front User guide World view People Environment?


WORLD
VIEW




World Development Indicators 2013 1Economy States and markets Global links Back


1
The Millennium Declaration adopted by all the
members of the United Nations General Assem-
bly in 2000 represents a commitment to a more
effective, results-oriented development partner-
ship in the 21st century. Progress documented
here and in the annual reports of the United
Nations Secretary- General has been encourag-
ing: poverty rates have fallen, more children—
especially girls—are enrolled in and completing
school, and they are—on average—living longer
and healthier lives. Fewer mothers die in child
birth, and more women have access to reproduc-
tive health services.


The indicators used to monitor the Millen-
nium Development Goals have traced the path of
the HIV epidemic, the resurgence and retreat of
tuberculosis, and the step-by-step efforts to “roll
back malaria.” More people now have access
to reliable water supplies and basic sanitation
facilities. But forests continue to disappear and
with them the habitat for many species of plants
and animals, and greenhouse gases continue to
accumulate in the atmosphere.


From the start monitoring the Millennium
Development Goals posed three challenges:
selecting appropriate targets and indicators,
constructing an international database for global
monitoring, and significantly improving the qual-
ity, frequency, and availability of the relevant sta-
tistics. When they were adopted, the target year
of 2015 seemed comfortably far away, and the
baseline year of 1990 for measuring progress
seemed a reasonable starting point with well-
established data. As we near the end of that
25-year span, we have a better appreciation of
how great those challenges were.


Already there is discussion of the post-
2015 development agenda and the monitoring
framework needed to record commitments and


measure progress. The Millennium Development
Goals have contributed to the development of
a statistical infrastructure that is increasingly
capable of producing reliable statistics on vari-
ous topics. The post-2015 agenda and a well-
designed monitoring framework will build on that
infrastructure.


The international database for monitoring
the Millennium Development Goals is a valu-
able resource for analyzing many development
issues. The effort of building and maintaining
such a database should not be underestimated,
and it will take several years to implement a
new framework of goals and targets. To serve
as an analytical resource, the database will need
to include additional indicators, beyond those
directly associated with the targets and the core
data for conducting these indicators. New tech-
nologies and methods for reporting data should
improve the quality and timeliness of the result-
ing database. The quality of data will ultimately
depend on the capacity of national statistical
systems, where most data originate.


When the Millennium Development Goals
were adopted, few developing countries had
the capacity or resources to produce statistics
of the requisite quality or frequency. Despite
much progress, the statistical capacity-building
programs initiated over the last decade should
continue, and other statistical domains need
attention. Planning for post-2015 goals must
include concomitant plans for investments in
statistics—by governments and development
partners alike.


The effort to achieve the Millennium Develop-
ment Goals has been enormous. The next set of
goals will require an even larger effort. Without
good statistics, we will never know if we have
succeeded.




2 World Development Indicators 2013 Front User guide World view People Environment?


Goal 1 Eradicate extreme poverty
The world will not have eradicated extreme pov-
erty in 2015, but the Millennium Development
Goal target of halving world poverty will have
been met. The proportion of people living on less
than $1.25 a day fell from 43.1 percent in 1990
to 22.7 percent in 2008, reaching new lows in
all six developing country regions. While the
food, fuel, and financial crises over the past five
years worsened the situation of vulnerable popu-
lations and slowed poverty reduction in some
countries, global poverty rates continued to fall
in most regions. Preliminary estimates for 2010
confirm that the extreme poverty rate fell fur-
ther, to 20.6 percent, reaching the global target
five years early. Except in South Asia and Sub-
Saharan Africa the target has also been met at
the regional level (figure 1a).


Further progress is possible and likely
before the 2015 target date of the Millen-
nium Development Goals. Developing econo-
mies are expected to maintain GDP growth of
6.6–6.8 percent over the next three years, with
growth of GDP per capita around 5.5 percent.
Growth will be fastest in East Asia and Pacific
and South Asia, which still contain more than
half the world’s poorest people. Growth will
be slower in Sub- Saharan Africa, the poorest
region in the world, but faster than in the pre-
ceding years, quickening the pace of poverty
reduction. According to these forecasts, the
proportion of people living in extreme poverty
will fall to 16 percent by 2015. Based on cur-
rent trends, 59 of 112 economies with ade-
quate data are likely to achieve the first Millen-
nium Development Goal (figure 1b). The number
of people living in extreme poverty will continue
to fall to less than a billion in 2015 (figure 1c).
Of these, 40 percent will live in South Asia and
40 percent in Sub- Saharan Africa.


How fast poverty reduction will proceed
depends not just on the growth of GDP but
also on its distribution. Income distribution has
improved in some countries, such as Brazil, while


2005200019951990


People living on less than 2005 PPP $1.25 a day (billions)


Sub-Saharan Africa


South Asia


Europe & Central Asia


Middle East & North Africa


Latin America
& Caribbean


2015
forecast


2010
estimate


0.0


0.5


1.0


1.5


2.0
Forecast
2010–15


Fewer people are living
in extreme poverty


1c


Source: World Bank PovcalNet.


Sub-Saharan
Africa


South
Asia


Middle East
& North
Africa


Latin
America &
Caribbean


Europe
& Central


Asia


East Asia


Share of countries making progress toward reducing poverty (%)


100


50


0


50


100


Reached target On track Off track Seriously off track


Progress in reaching the
poverty target, 1990–2010


1b


Source: World Bank staff calculations.


0


25


50


75


2015
forecast


2010
estimate


2005200019951990


People living on less than 2005 PPP $1.25 a day (%)


Sub-Saharan Africa


South Asia


Europe &
Central Asia


Middle East & North Africa


Latin America
& Caribbean


Forecast
2010–15


Poverty rates
continue to fall


1a


Source: World Bank PovcalNet.




World Development Indicators 2013 3Economy States and markets Global links Back


worsening in others, such as China. To speed
progress toward eliminating extreme poverty,
development strategies should attempt to
increase not just the mean rate of growth but
also the share of income going to the poor-
est part of the population. Sub- Saharan Africa,
where average income is low and average income
of those below the poverty line is even lower, will
face great difficulties in bringing the poorest peo-
ple to an adequate standard of living (figure 1d).
Latin America and the Caribbean, where average
income is higher, must overcome extremely ineq-
uitable income distributions.


Two Millennium Development Goal indicators
address hunger and malnutrition. Child malnu-
trition, measured by comparing a child’s weight
with that of other children of similar age, reflects
a shortfall in food energy, poor feeding prac-
tices by mothers, and lack of essential nutri-
ents in the diet. Malnutrition in children often
begins at birth, when poorly nourished mothers
give birth to underweight babies. Malnourished
children develop more slowly, enter school later,
and perform less well. Malnutrition rates have
dropped substantially since 1990, from 28 per-
cent of children under age 5 in developing coun-
tries to 17 percent in 2011. Every developing
region except Sub- Saharan Africa is on track
to cut child malnutrition rates in half by 2015
(figure 1e). However, collecting data on malnutri-
tion through surveys with direct measurement of
children’s weight and height is costly, and many
countries lack the information to calculate time
trends.


Undernourishment, a shortage of food energy
to sustain normal daily activities, is affected by
changes in the average amount of food available
and its distribution. After steady declines in most
regions from 1991 to 2005, further improve-
ments in undernourishment have stalled, leav-
ing 13 percent of the world’s population, almost
900 million people, without adequate daily food
intake (figure 1f).


0


10


20


30


40


20112006200119961991


Undernourishment prevalence (% of population)


Sub-Saharan Africa


Europe & Central Asia


Middle East & North Africa


Latin America
& Caribbean East Asia & Pacic


South Asia


And fewer people lacking
sufficient food energy


1f


Source: Food and Agriculture Organization and World Development
Indicators database.


0


20


40


60


20112005200019951990


Malnutrition prevalence, weight for age (% of children under age 5)


Sub-Saharan Africa


South Asia


Middle East & North Africa


Latin America & Caribbean


East Asia & Pacic


Europe & Central Asia


Fewer malnourished
children


1e


Source: World Development Indicators database.


0.00


0.25


0.50


0.75


1.00


1.25


Sub-Saharan
Africa


South
Asia


Middle East
& North
Africa


Latin
America &
Caribbean


Europe
& Central


Asia


East Asia
& Pacic


Average daily income of people living on less than 2005 PPP
$1.25 a day, 2008 (2005 PPP $)


Poorer
than poor


1d


Source: World Bank PovcalNet.




4 World Development Indicators 2013 Front User guide World view People Environment?


0


5


10


15


20


25


30


Sub-Saharan
Africa


South
Asia


Middle East
& North
Africa


Latin
America &
Caribbean


Europe
& Central


Asia


East Asia
& Pacic


Children not attending primary school, 2010
(% of relevant age group)


G
ir


ls


B
oy


s


More girls than boys remain
out of school in most regions


2c


Source: United Nations Educational, Scientific and Cultural Organization
Institute of Statistics and World Development Indicators database.


Sub-Saharan
Africa


South
Asia


Middle East
& North
Africa


Latin
America &
Caribbean


Europe
& Central


Asia


East Asia
& Pacic


Share of countries making progress toward universal primary
education (%)


Reached target On track Off track Seriously off track
Insufcient data


100


50


0


50


100


Progress toward universal 
primary education, 1990–2010


2b


Source: World Bank staff calculations.


0


25


50


75


100


125


201520102005200019951990


Primary school completion rate (% of relevant age group)


Sub-Saharan Africa


Middle East &
North Africa


Latin America
& Caribbean


East Asia & Pacic


Europe & Central Asia


South Asia


Growth in complete
primary education has slowed


2a


Note: Dotted lines indicate progress needed to reach target.
Source: United Nations Educational, Scientific and Cultural Organization
Institute of Statistics and World Development Indicators database.


The commitment to provide primary education to
every child is the oldest of the Millennium Devel-
opment Goals, having been set at the first Educa-
tion for All conference in Jomtien, Thailand, more
than 20 years ago.


Progress among the poorest countries has
accelerated since 2000, particularly in South
Asia and Sub- Saharan Africa, but full enrollment
remains elusive. Many children start school but
drop out before completion, discouraged by cost,
distance, physical danger, and failure to progress.
Even as countries approach the target, the educa-
tion demands of modern economies expand, and
primary education will increasingly be of value
only as a stepping stone toward secondary and
higher education.


In most developing country regions school
enrollment picked up after the Millennium
Development Goals were promulgated in 2000,
when the completion rate was 80 percent. Sub-
Saharan Africa and South Asia, which started
out farthest behind, made substantial prog-
ress. By 2009 nearly 90 percent of children in
developing countries completed primary school,
but completion rates have stalled since, with
no appreciable gains in any region (figure 2a).
Three regions have attained or are close to
attaining complete primary education: East Asia
and Pacific, Europe and Central Asia, and Latin
America and the Caribbean (figure 2b). Comple-
tion rates in the Middle East and North Africa
have stayed at 90 percent since 2008. South
Asia has reached 88 percent, but progress
has been slow. And Sub- Saharan Africa lags
behind at 70 percent. Even if the schools in
these regions were to now enroll every eligible
child in the first grade, they would not be able
to achieve a full course of primary education by
2015. But it would help.


Many children enroll in primary school but
attend intermittently or drop out entirely. This
is particularly true for girls—almost all school


Goal 2 Achieve universal primary education




World Development Indicators 2013 5Economy States and markets Global links Back


0


25


50


75


100


Grade 9Grade 8Grade 7Grade 6Grade 5Grade 4Grade 3Grade 2Grade 1


Wealthiest quintile


Third quintile


Second quintile


Poorest quintile


Share of people ages 10–19 completing each grade of schooling,
by wealth quintile, 2010 (%)


Fourth quintile


Poverty is a barrier to
education in Senegal


2f


Source: Demographic and Health Surveys and World Bank EdStats
database.


0


25


50


75


100


Grade 9Grade 8Grade 7Grade 6Grade 5Grade 4Grade 3Grade 2Grade 1


Share of people ages 10–19 completing each grade of schooling,
by parents’ education level, 2011 (%)


No education


Incomplete primary


Incomplete secondary


Some higher


Primary


Parents’ education makes
a difference in Nepal


2e


Source: Demographic and Health Surveys and World Bank EdStats
database.


0


25


50


75


100


Grade 9Grade 8Grade 7Grade 6Grade 5Grade 4Grade 3Grade 2Grade 1


Share of people ages 10–19 completing each grade of schooling,
by location, 2010–11 (%)


Urban Cambodia


Rural Cambodia


Urban Ethiopia


Rural Ethiopia


Urban Senegal


Rural Senegal


Rural students
at a disadvantage


2d


Source: Demographic and Health Surveys and World Bank EdStats
database.


systems with low enrollment rates show under-
enrollment of girls in primary school, since
their work is needed at home (figure 2c). Other
obstacles discourage parents from sending their
children to school, including the need for boys
and girls during planting and harvest, lack of
suitable school facilities, absence of teachers,
and school fees. The problem is worst in South
Asia and Sub- Saharan Africa, where more than
46 million children of primary school age are not
in school.


Not all children have the same opportunities
to enroll in school or remain in school. Across
the world, children in rural areas are less likely
to enter school, and when they do, they are likely
to drop out sooner. In Ethiopia nearly all urban
children complete first grade, but fewer than
80  percent of rural children do (figure  2d). In
Senegal, where slightly more than 80 percent of
urban children complete first grade, barely half
of rural children begin the first grade and only
40 percent remain after nine years. Cambodia
follows a similar pattern.


Parents’ education makes a big difference
in how far children go in school. In Nepal, for
example, less than 90 percent of children whose
parents lack any education complete first grade
and barely 70 percent remain through the ninth
(figure  2e). But 95  percent of children from
households with some higher education stay
through nine grades, and many of those go onto
complete secondary school and enter tertiary
education.


Income inequality and educational quality are
closely linked. Take Senegal (figure 2f). Children
from wealthier households (as measured by a
household’s ownership of certain assets) are
more likely to enroll and stay in school than chil-
dren from poorer households. Thus children from
poor households are least likely to acquire the
one asset—human capital—that could most help
them to escape poverty.




6 World Development Indicators 2013 Front User guide World view People Environment?


Sub-Saharan
Africa


South
Asia


Middle East
& North
Africa


Latin
America &
Caribbean


Europe
& Central


Asia


East Asia
& Pacic


Share of countries making progress toward gender equality in
primary and secondary education (%)


Reached target On track Off track Seriously off track
Insufcient data


100


50


0


50


100


Progress toward gender equality
in education, 1990–2010


3b


Source: World Bank staff calculations.


Women make important contributions to eco-
nomic and social development. Expanding oppor-
tunities for them in the public and private sectors
is a core development strategy, and education
is the starting point. By enrolling and staying in
school, girls gains skills needed to enter the labor
market, care for families, and make decisions for
themselves. Achieving gender equality in educa-
tion is an important demonstration that young
women are full, contributing members of society.


Girls have made substantial gains in school
enrollment. In 1990 girls’ primary school enroll-
ment rate in developing countries was only
86 percent of boys’. By 2011 it was 97 percent
(figure 3a). Similar improvements have been
made in secondary schooling, where girls’ enroll-
ments have risen from 78 percent of boys’ to
96 percent over the same period. But the aver-
ages mask large differences across countries. At
the end of 2011, 31 upper middle-income coun-
tries had reached or exceeded equal enrollment
of girls in primary and secondary education, as
had 23  lower middle-income countries but only
9 low-income countries. South Asia and Sub-
Saharan Africa are lagging behind (figure 3b).


Patterns of school attendance at the national
level mirror those at the regional level: poor house-
holds are less likely than wealthy households to
keep their children in school, and girls from wealth-
ier households are more likely to enroll in school
and stay longer. Ethiopia is just one example of the
prevailing pattern documented by household sur-
veys from many developing countries (figure 3c).


More women are participating in public life at
the highest levels. The proportion of parliamen-
tary seats held by women continues to increase.
In Latin America and the Caribbean women now
hold 25 percent of all parliamentary seats (figure
3d). The most impressive gains have been made
in the Middle East and North Africa, where the pro-
portion of seats held by women more than tripled
between 1990 and 2012. Algeria leads the way
with 32 percent. In Nepal a third of parliamentary


Goal 3 Promote gender equality and empower women


0 25 50 75 100 125


Sub-Saharan
Africa


South
Asia


Middle East &
North Africa


Latin America
& Caribbean


Europe &
Central Asia


East Asia
& Pacic


Ratio of girls’ to boys’ gross enrollment, 2011 (%)


Primary


Secondary


Tertiary


A ragged kind of parity
in school enrollments


3a


Source: United Nations Educational, Scientific and Cultural Organization
Institute of Statistics and World Development Indicators database.




World Development Indicators 2013 7Economy States and markets Global links Back


0


10


20


30


40


50


Sub-Saharan
Africaa


South
Asia


Middle East
& North
Africa


Latin
America &
Caribbean


Europe
& Central


Asia


East Asia
& Pacica


Share of women employed in the nonagricultural sector,
median value, most recent year available, 2004–10 (% of total
nonagricultural employment)


Women still lack opportunities
in the labor market


3e


a. Data cover less than two-thirds of regional population.
Source: International Labour Organization and World Development
Indicators database.


0


5


10


15


20


25


20122005200019951990


Share of seats held by women in national parliaments (%)


Sub-Saharan Africa


Europe &
Central Asia


Middle East &
North Africa


Latin America
& Caribbean


East Asia & Pacic


South Asia


More women
in parliaments


3d


Source: Inter-Parliamentary Union and World Development Indicators
database.


0


25


50


75


100


Grade 9Grade 8Grade 7Grade 6Grade 5Grade 4Grade 3Grade 2Grade 1


Share of people ages 10–19 completing each grade of schooling,
by sex and wealth quintile, 2011 (%)


Poorest quintile, boys


Poorest quintile, girls


Third quintile, boys


Wealthiest quintile, boys
Wealthiest quintile,


girls


Third quintile, girls


Girls are disadvantaged at
every income level in Ethiopia


3c


Source: Demographic and Health Surveys and World Bank EdStats
database.


seats were held by women in 2012. Rwanda con-
tinues to lead the world. Since 2008, 56 percent
of parliamentary seats have been held by women.


Women work long hours and make important
contributions to their families’ welfare, but many
in the informal sector are unpaid for their labor.
The largest proportion of women working in the
formal sector is in Europe and Central Asia,
where the median proportion of women in wage
employment outside the agricultural sector was
46  percent (figure  3e). Latin America and the
Caribbean is not far behind, with 42 percent of
women in nonagricultural employment. Women’s
share in paid employment in the nonagricultural
sector has risen marginally but remains less
than 20 percent in most countries in the Middle
East and North Africa and South Asia and less
than 35 percent in Sub- Saharan Africa. In these
regions full economic empowerment of women
remains a distant goal.


Lack of data hampers the ability to understand
women’s roles in the economy. The Evidence and
Data for Gender Equality (EDGE) Initiative is a
new partnership, jointly managed by UN Women
and the United Nations Statistics Division, in col-
laboration with member states, the World Bank,
the Organisation for Economic Co-operation and
Development, and others, that seeks to acceler-
ate the work of gathering indicators on women’s
education, employment, entrepreneurship, and
asset ownership. During its initial phase EDGE
will lay the ground work for a database of basic
education and employment indicators, develop-
ing standards and guidelines for entrepreneur-
ship and assets indicators, and pilot data in sev-
eral countries. Relevant indicators could include
the percentage distribution of the employed
population, by sector and sex; the proportion of
employed who are employer, by sex; the length of
maternity leave; the percentage of firms owned
by women; the proportion of the population with
access to credit, by sex; and the proportion of the
population who own land, by sex.




8 World Development Indicators 2013 Front User guide World view People Environment?


Sub-Saharan
Africa


South
Asia


Middle East
& North
Africa


Latin
America &
Caribbean


Europe
& Central


Asia


East Asia
& Pacic


Share of countries making progress toward reducing child mortality
(%)


100


50


0


50


100


Reached target On track Off track Seriously off track
Insufcient data


Progress toward reducing
child mortality, 1990–2010


4b


Source: World Bank staff calculations.


0


1


2


3


4


Sub-Saharan
Africa


South
Asia


Middle East
& North
Africa


Latin
America &
Caribbean


Europe
& Central


Asia


East Asia
& Pacic


Deaths of children under age 5, 2011 (millions)


Children (ages 1–4)
Infants (ages 1–11 months)
Neonatals (ages 0–1 month)


As mortality rates fall, a larger proportion
of deaths occur in the first month


4c


Source: Inter-agency Group for Child Mortality Estimation and World
Development Indicators database.


0


50


100


150


200


20112005200019951990


Under-ve mortality rate (per 1,000 live births)


Sub-Saharan Africa


Europe &
Central Asia Latin America& Caribbean


East Asia
& Pacic South Asia


Middle East &
North Africa


Under-five mortality rates
continue to fall


4a


Source: Inter-agency Group for Child Mortality Estimation and World
Development Indicators database.


In 1990, 12 million children died before their fifth
birthday. By 1999 fewer than 10 million did. And
in 2012 7 million did. In developing countries the
under-five mortality rate fell from an average of 95
per 1,000 live births in 1990 to 56 in 2011, but
rates in Sub- Saharan Africa and South Asia remain
much higher (figure 4a). Currently, 41 countries
are poised to reach the Millennium Development
Goal target of a two-thirds reduction in under-five
mortality rates by 2015 (figure 4b). Faster improve-
ments over the last decade suggest that many
countries are accelerating progress and another
25 could reach the target as soon as 2020. Look-
ing past 2015, still faster progress is possible if
high mortality countries give priority to addressing
the causes of child mortality. Concomitant reduc-
tions in fertility rates, particularly among adoles-
cents, will also help.


Most children die from causes that are read-
ily preventable or curable with existing interven-
tions, such as pneumonia (18 percent), diarrhea
(11 percent), and malaria (7 percent). Almost 70
percent of deaths of children under age 5 occur
in the first year of life, and 60 percent of those
in the first month (figure 4c). Preterm birth com-
plications account for 14 percent of deaths, and
complications during birth another 9 percent (UN
Inter-Agency Group for Child Mortality Estimation
2012). Therefore reducing child mortality requires
addressing the causes of neonatal and infant
deaths: inadequate care at birth and afterward,
malnutrition, poor sanitation, and exposure to
acute and chronic disease. Lower infant and child
mortality rates are, in turn, the largest contribu-
tors to higher life expectancy in most countries.


Childhood vaccinations are a proven, cost-
effective way of reducing childhood illness and
death. But despite years of vaccination campaigns,
many children in low- and lower middle-income
economies remain unprotected. To be success-
ful, vaccination campaigns must reach all children
and be sustained over time. Thus it is worrisome
that measles vaccination rates in the two highest


Goal 4 Reduce child mortality




World Development Indicators 2013 9Economy States and markets Global links Back


0 1 2 3 4


Turkey


Mexico


Madagascar


Philippines


Malawi


Egypt, Arab Rep.


Brazil


Angola


Mozambique


Niger


Uganda


Afghanistan


Tanzania


Indonesia


Bangladesh


Ethiopia


Pakistan


China


Nigeria


India


Deaths of children under age 5, 2011 (millions)
At 2011 mortality rate Averted based on 1990 mortality rate


Five million deaths
averted in 20 countries


4e


Source: World Bank staff calculations.


0


25


50


75


100


Children ages 12–23 months immunized against measles (%)


Sub-Saharan Africa


Europe & Central Asia


Latin America
& Caribbean


East Asia
& Pacic


South Asia


Middle East & North Africa


20112005200019951990


Measles immunization
rates are stagnating


4d


Source: World Health Organization, United Nations Children’s Fund, and
World Development Indicators database.


mortality regions, South Asia and Sub- Saharan
Africa, have stagnated in the last three years, at
less than 80 percent coverage (figure 4d).


Twenty countries in the developing world
accounted for 4.5 million deaths among children
under age 5 in 2011, or 65 percent of all such
deaths worldwide (figure 4e). These countries are
mostly large, often with high birth rates, but many
have substantially reduced mortality rates over
the past two decades. Of the 20, 11 have reached
or are likely to achieve a two-thirds reduction in
their under-five mortality rate by 2015: Bangla-
desh, Brazil, China, the Arab Republic of Egypt,
Ethiopia, Indonesia, Madagascar, Malawi, Mexico,
Niger, and Turkey. Had the mortality rates of 1990
prevailed in 2011, these 11 countries would have
experienced 2 million more deaths. The remaining
nine, where progress has been slower, have nev-
ertheless averted 3 million deaths. If India were
on track to reach the target, another 440,000
deaths would have been averted.


The data used to monitor child mortality are pro-
duced by the Inter-agency Group for Child Mortal-
ity Estimation (IGME), which evaluates data from
existing sources and then fits a statistical model
to data points that are judged to be reliable. The
model produces a trend line for under-five mortality
rates in each country. Infant mortality and neona-
tal mortality rates are derived from under-five mor-
tality estimates. The data come from household
surveys and, where available, vital registration sys-
tems. But surveys are slow and costly. While they
remain important tools for investigating certain
complex, micro-level problems, vital registration
systems are usually better sources of timely statis-
tics. Recent IGME estimates of under-five mortality
include new data from vital registration systems
for about 70 countries. But many countries lack
complete reporting of vital events, and even those
that do often misreport cause of death. Vital reg-
istration supplemented by surveys and censuses
offers the best approach for improving knowledge
of morbidity and mortality in all age groups.




10 World Development Indicators 2013 Front User guide World view People Environment?


0


2


4


6


Sub-Saharan
Africa


South
Asia


Middle East
& North
Africa


Latin
America &
Caribbean


Europe
& Central


Asia


East Asia
& Pacic


Lifetime risk of maternal death (%)


1
9


9
0


2
0


1
0


Reducing the
risk to mothers


5c


Source: Maternal Mortality Estimation Inter-Agency Group and World
Development Indicators database.


Sub-Saharan
Africa


South
Asia


Middle East
& North
Africa


Latin
America &
Caribbean


Europe
& Central


Asia


East Asia
& Pacic


Share of countries making progress toward reducing maternal
mortality (%)


100


50


0


50


100


Reached target On track Off track Seriously off track
Insufcient data


Progress toward reducing
maternal mortality, 1990–2010


5b


Source: World Bank staff calculations.


20102005200019951990


Maternal mortality ratio, modeled estimate (per 100,000 live births)


0


250


500


750


1,000


Sub-Saharan Africa


South Asia


Latin America
& Caribbean


Europe & Central Asia


East Asia & Pacic


Middle East & North Africa


Maternal deaths are more likely in
South Asia and Sub- Saharan Africa


5a


Source: Maternal Mortality Estimation Inter-Agency Group and World
Development Indicators database.


An estimated 287,000 maternal deaths occurred
worldwide in 2010, all but 1,700 of them in
developing countries. More than half of mater-
nal deaths occur in Sub- Saharan Africa and a
quarter in South Asia. And while the number of
maternal deaths remains high, South Asia has
made great progress in reducing them, reaching
a maternal mortality ratio of 220 per 100,000
live births in 2010, down from 620 in 1990, a
drop of 65 percent. The Middle East and North
Africa and East Asia and Pacific have also
reduced their maternal morality ratios more than
60 percent (figure 5a).


These are impressive achievements, but prog-
ress in reducing maternal mortality has been
slow, far slower than targeted by the Millennium
Development Goals, which call for reducing the
maternal mortality ratio by 75 percent between
1990 and 2015. But few countries and no devel-
oping region on average will achieve this target.
Based on progress through 2010, 8 countries
have achieved a 75 percent reduction, and 10
more are on track to reach the 2015 target (fig-
ure 5b). This is an improvement over the 2008
assessment, suggesting that progress is accel-
erating. Because of the reductions in Cambodia,
China, Lao People’s Democratic Republic, and
Vietnam, 74 percent of people in East Asia and
Pacific live in a country that has reached the tar-
get (Vietnam) or is on track to do so by 2015.
On average a third of people in low- and middle-
income countries live in countries that have
reached the target or are on track to do so.


The maternal mortality ratio gives the risk of a
maternal death at each birth, a risk compounded
with each pregnancy. And because women in
poor countries have more children under riskier
conditions, their lifetime risk of maternal death
may be 100 times greater than for women in high-
income countries. Improved health care and lower
fertility rates have reduced the lifetime risk in all
regions, but women ages 15–49 in Sub- Saharan
Africa still face a 2.5 percent chance of dying


Goal 5 Improve maternal health




World Development Indicators 2013 11Economy States and markets Global links Back


0


25


50


75


100


Sub-Saharan
Africa


South
Asia


Middle East
& North
Africaa


Latin
America &
Caribbeana


Europe
& Central


Asia


East Asia
& Pacic


Births attended by skilled health staff, average of most recent year
available for 2007–11 (% of total)


Every mother
needs care


5f


a. Data are for 1998–2002.
Source: United Nations Children’s Fund and World Development
Indicators database.


0


50


100


150


20112009200720052003200119991997


Adolescent fertility rate (births per 1,000 women ages 15–19)


Sub-Saharan Africa


South Asia


Latin America & Caribbean


Middle East & North Africa


East Asia & Pacic


Europe &
Central Asia


Fewer young women
giving birth


5e


Source: United Nations Population Division and World Development
Indicators database.


0


10


20


30


40


50


Sub-Saharan
Africa


(31 countries)


South
Asia


(5 countries)


Middle East
& North
Africa


(6 countries)


Latin
America &
Caribbean
(9 countries)


Europe
& Central


Asia
(10 countries)


East Asia
& Pacic


(7 countries)


Unmet need for contraception, most recent year available,
2006–10 (% of women married or in union ages 15–49)


Regional median


A wide range
of needs


5d


Source: Demographic and Household Surveys, Multiple Indicator Cluster
Surveys, and World Development Indicators database.


in childbirth, down from more than 5 percent in
1990 (figure 5c). In Chad and Somalia, both frag-
ile states, lifetime risk is still more than 6 per-
cent, meaning more than 1 woman in 16 will die
in childbirth.


Reducing maternal mortality requires a com-
prehensive approach to women’s reproductive
health, starting with family planning and access
to contraception. In countries with data half of
women who are married or in union use some
method of birth control. Household surveys of
women show that some 200 million women
want to delay or cease childbearing, and a sub-
stantial proportion say that their last birth was
unwanted or mistimed (United Nations 2012).
Figure 5d shows the share of women of child-
bearing age who say they need but are not using
contraception. There are large differences within
each region. More surveys have been carried out
in Sub- Saharan Africa than in any other region,
and many show a large unmet need for family
planning.


Women who give birth at an early age are likely
to bear more children and are at greater risk of
death or serious complications from pregnancy.
The adolescent birth rate is highest in Sub-
Saharan Africa and is declining slowly. A rapid
decrease in South Asia has been led by Maldives,
Afghanistan, and Pakistan (figure 5e).


Many health problems among pregnant women
are preventable or treatable through visits with
trained health workers before childbirth. Skilled
attendants at delivery and access to hospital
treatments are essential for dealing with life-
threatening emergencies such as severe bleed-
ing and hypertensive disorders. In South Asia and
Sub- Saharan Africa fewer than half of births are
attended by doctors, nurses, or trained midwives
(figure 5f). Having skilled health workers present
for deliveries is key to reducing maternal mortal-
ity. In many places women have only untrained
caregivers or family members to attend them dur-
ing childbirth.




12 World Development Indicators 2013 Front User guide World view People Environment?


0


25


50


75


People with comprehensive and correct knowledge about HIV,
most recent year available (% of adults ages 15–49)


W
om


en
M


en


Tan
zan


ia
Ke


nya


Ug
an


da


Ma
law


i


Mo
zam


biq
ue


Za
mb


ia


Na
mi
bia


Zim
ba


bw
e


Le
so
tho


Sw
azi


lan
d


Knowledge to
control HIV/AIDS


6c


Note: Comprehensive and correct knowledge about HIV entails knowledge
of two ways to prevent HIV and rejecting three misconceptions.
Source: Joint United Nations Programme on HIV/AIDS and World
Development Indicators database.


Sub-Saharan
Africa


South
Asia


Middle East
& North
Africa


Latin
America &
Caribbean


Europe
& Central


Asia


East Asia
& Pacic


Share of countries making progress against HIV/AIDS (%)


100


50


0


50


100


Halted and reversed Halted or reversed Stable low prevalence
Not improving Insufcient data


Progress toward halting and reversing
the HIV epidemic, 1990–2010


6b


Source: World Bank staff calculations.


0


1


2


3


4


5


6


20112005200019951990


HIV prevalence (% of population ages 15–49)


Sub-Saharan Africa


High income
Latin America & Caribbean


Other developing regions


HIV prevalence in Sub- Saharan
Africa continues to fall


6a


Source: Joint United Nations Programme on HIV/AIDS and World
Development Indicators database.


Epidemic diseases exact a huge toll in human
suffering and lost opportunities for development.
Poverty, armed conflict, and natural disasters
contribute to the spread of disease and are made
worse by it. In Africa the spread of HIV/AIDS has
reversed decades of improvement in life expec-
tancy and left millions of children orphaned.
Malaria takes a large toll on young children and
weakens adults at great cost to their productiv-
ity. Tuberculosis killed 1.4 million people in 2010,
most of them ages 15–45, and sickened millions
more.


There were 34 million people living with HIV/
AIDS in 2011, and 2.5 million more people
acquired the disease. Sub- Saharan Africa remains
the center of the HIV/AIDS epidemic, but the pro-
portion of adults living with AIDS has begun to
fall even as the survival rate of those with access
to antiretroviral drugs has increased (figures 6a
and 6b). By the end of 2010, 6.5 million people
worldwide were receiving antiretroviral drugs. This
represented the largest one-year increase in cov-
erage but still fell far short of universal access
(United Nations 2012).


Altering the course of the HIV epidemic requires
changes in behaviors by those already infected by
the virus and those at risk of becoming infected.
Knowledge of the cause of the disease, its trans-
mission, and what can be done to avoid it is the
starting point. The ability to reject false informa-
tion is another important kind of knowledge. But
significant gaps in knowledge remain. In 26 of
31 countries with a generalized epidemic and in
which nationally representative surveys were car-
ried out recently, less than half of young women
have comprehensive and correct knowledge about
HIV (UNAIDS 2012). And less than half of men in
21 of 25 countries had correct knowledge. In only


Goal 6 Combat HIV/AIDS, malaria, and other diseases




World Development Indicators 2013 13Economy States and markets Global links Back


0 20 40 60 80


Mauritania (2004)
Congo, Rep. (2005)


Mali
Rwanda


Niger
Tanzania


Togo
São Tomé and Príncipe


Gabon
Zambia
Eritrea


Burkina Faso
Kenya


Madagascar
Burundi
Uganda
Malawi
Ghana


Congo, Dem. Rep.
Liberia


Central African Rep.
Guinea-Bissau


Senegal
Namibia


Gambia, The
Ethiopia


Sierra Leone
Nigeria
Angola
Sudan


Cameroon
Benin


Mozambique
Somalia


Chad
Zimbabwe
Comoros


Guinea
Côte d’Ivoire


Swaziland


Use of insecticide-treated nets (% of population under age 5)
First observation (2000 or earlier) Most recent observation (2006 or later)


Use of insecticide-treated nets
increasing in Sub- Saharan Africa


6e


Source: United Nations Children’s Fund and World Development
Indicators database.


0


100


200


300


400


20112005200019951990


Tuberculosis prevalence, incidence, and deaths in low- and
middle-income countries (per 100,000 people)


Prevalence


Incidence


Deaths


Fewer people contracting, living with,
and dying from tuberculosis


6d


Source: World Health Organization and World Development Indicators
database.


3 of the 10 countries with the highest HIV preva-
lence rates in 2011 did more than half the men
and women tested demonstrate knowledge of two
ways to prevent HIV and reject three misconcep-
tions (figure 6c). In Kenya men scored better than
50 percent, but women fell short. Clearly more
work is to be done.


In 2011 there were 8.8 million people newly
diagnosed with tuberculosis, but incidence, prev-
alence, and death rates from tuberculosis are
falling (figure 6d). If these trends are sustained,
the world could achieve the target of halting and
reversing the spread of this disease by 2015.
People living with HIV/AIDS, which reduces resis-
tance to tuberculosis, are particularly vulnerable,
as are refugees, displaced persons, and prison-
ers living in close quarters and unsanitary con-
ditions. Well-managed medical intervention using
appropriate drug therapy is crucial to stopping the
spread of tuberculosis.


There are 300–500 million cases of malaria
each year, causing more than 1 million deaths.
Malaria is a disease of poverty. But there has
been progress against it. In 2011 Armenia was
added to the list of countries certified free of the
disease. Although malaria occurs in all regions,
Sub- Saharan Africa is where the most lethal form
of the malaria parasite is most abundant. Insec-
ticide-treated nets have proved to be an effec-
tive preventative, and their use in the region is
growing: from 2 percent of the population under
age 5 in 2000 to 39 percent in 2010 (figure 6e).
Better testing and the use of combination thera-
pies with artemisinin-based drugs are improving
the treatment of at-risk populations. But malaria
is difficult to control. There is evidence of emerg-
ing resistance to artemisinins and to pyrethroid
insecticides used to treat mosquito nets.




14 World Development Indicators 2013 Front User guide World view People Environment?


0


25


50


75


100


Sub-Saharan
Africa


South
Asia


Middle East
& North
Africa


Latin
America &
Caribbean


Europe
& Central


Asia


East Asia
& Pacic


Share of population with access to improved water sources (%)


1
9


9
0


2
0


1
0


Better access to
improved water sources


7c


Source: Joint Monitoring Programme of the World Health Organization
and United Nations Children’s Fund and World Development Indicators
database.


High
income


Sub-Saharan
Africa


South
Asia


Middle East
& North
Africa


Latin
America &
Caribbean


Europe
& Central


Asia


East Asia
& Pacic


Average annual change in forest area (thousands of square
kilometers)


1
9


9
0


–2
0


0
0


2
0


0
0


–1
0


–500


–250


0


250


Forest losses
and gains


7b


Source: Food and Agriculture Organization and World Development
Indicators database.


0


10


20


30


40


Carbon dioxide emissions (millions of metric tons)


Lower middle income


Upper middle income


High income


Low income


20092005200019951990


Carbon dioxide emissions
dropped slightly in 2009


7a


Source: Carbon Dioxide Information Analysis Center and World
Development Indicators database.


The seventh goal of the Millennium Development
Goals is the most far-reaching, affecting each
person now and in the future. It addresses the
condition of the natural and built environments:
reversing the loss of natural resources, pre-
serving biodiversity, increasing access to safe
water and sanitation, and improving living con-
ditions of people in slums. The overall theme is
sustainability, an equilibrium in which people’s
lives can improve without depleting natural and
manmade capital stocks.


The failure to reach a comprehensive agree-
ment on limiting greenhouse gas emissions leaves
billions of people vulnerable to climate change.
Although the global financial crisis caused a slight
decrease in carbon dioxide emissions, such emis-
sions are expected to rise as economic activity
resumes in large industrial economies (figure 7a).


The loss of forests threatens the livelihood of
poor people, destroys the habitat that harbors bio-
diversity, and eliminates an important carbon sink
that helps moderate the climate. Net losses since
1990 have been substantial, especially in Latin
American and the Caribbean and Sub- Saharan
Africa, and only partly compensated by net gains
elsewhere. The rate of deforestation slowed in the
past decade, but on current trends zero net losses
will not be reached for another 20 years (figure 7b).


Protecting forests and other terrestrial and
marine areas helps protect plant and animal
habitats and preserve the diversity of species. By
2010, 13 percent of the world’s land area had
been protected, but only 1.6 percent of oceans
had similar protection. Such measures have
slowed the rate of species extinction, but sub-
stantial losses continue (United Nations 2012).


The Millennium Development Goals call for
halving the proportion of the population without
access to improved sanitation facilities and water
sources by 2015. In 1990 more than 1 billion
people lacked access to drinking water from a
convenient, protected source. In developing coun-
tries the proportion of people with access to an


Goal 7 Ensure environmental sustainability




World Development Indicators 2013 15Economy States and markets Global links Back


–10


0


10


20


Adjusted
net savings


Depletion
adjusted
savings


Net savings
plus education


expeditures


Net
savings


Gross
savings


Share of gross national income, 2008 (%)


Depreciation
of xed
capital Educationexpenditures


Depletion
of natural
resources


Pollution
damages


A nonsustainable path
in Sub-Saharan Africa


7f


Source: World Bank 2011.


0


10


20


30


40


50


20102005200019951990


Resource rents (% of GDP)


Sub-Saharan
Africa


Latin America & Caribbean


Middle East & North Africa


Europe &
Central Asia


South AsiaEast Asia & Pacic


Resource rents are a large share of
GDP in Africa and the Middle East


7e


Source: World Bank staff calculations and World Development Indicators
database.


0


25


50


75


100


Sub-Saharan
Africa


South
Asia


Middle East
& North
Africa


Latin
America &
Caribbean


Europe
& Central


Asia


East Asia
& Pacic


Share of population with access to improved sanitation facilities,
2010 (%)


R
ur


al


U
rb


an


Rural areas lack
sanitation facilities


7d


Source: Joint Monitoring Programme of the World Health Organization
and United Nations Children’s Fund and World Development Indicators
database.


improved water source rose from 71 percent in
1990 to 86 in 2010 (figure 7c).


In 1990 only 37 percent of the people living in
low- and middle-income economies had access to
a flush toilet or other form of improved sanitation.
By 2010 the access rate had risen to 56  per-
cent. But 2.7 billion people still lack access to
improved sanitation, and more than 1 billion prac-
tice open defecation, posing enormous health
risks. The situation is worse in rural areas, where
43 percent of the population have access to
improved sanitation; in urban areas the access
rate is 30 percentage points higher (figure 7d).
This large disparity, especially in South Asia and
Sub- Saharan Africa, is the principal reason the
sanitation target of the Millennium Development
Goals will not be met.


Achieving sustainable development also
requires managing natural resources carefully,
since high economic growth can deplete natural
capital, such as forests and minerals. Countries
that rely heavily on extractive industries have
seen large increases in natural resource rents,
but their growth will not be sustainable unless
they invest in productive assets, including human
capital (figure 7e).


The World Bank has constructed a global data-
base to monitor the sustainability of economic
progress using wealth accounts, including indi-
cators of adjusted net savings and adjusted net
national income. Wealth is defined comprehen-
sively to include stocks of manufactured capital,
natural capital, human capital, and social capital.
Development is conceived as building wealth and
managing a portfolio of assets. The challenge of
development is to manage not just the total vol-
ume of assets but also the composition of the
asset portfolio—that is, how much to invest in
different types of capital. Adjusted net savings is
a sustainability indicator that measures whether a
country is building its wealth sustainably (positive
values) or running it down on an unsustainable
development path (negative values; figure 7f).




16 World Development Indicators 2013 Front User guide World view People Environment?


0


25


50


75


100


20102008200620042002200019981996


Goods (excluding arms) admitted free of tariffs from developing
countries (% total merchandise imports, excluding arms)


Norway


Australia


Japan


United States


European
Union


More opportunities for exporters
in developing countries


8c


Source: World Trade Organization, International Trade Center, United
Nations Conference on Trade and Development, and World Development
Indicators database.


20112005200019951990


Agricultural support ($ billions)


United States


Korea, Rep.


Turkey


European Union


Japan


0


50


100


150


Domestic subsidies to
agriculture exceed aid flows


8b


Source: Organisation for Economic Co-operation and Development
StatExtracts.


Ofcial development assistance from Development Assistance
Committee members (2010 $ billions)


Multilateral net ofcial development assistance,
excluding debt relief and humanitarian assistance


Bilateral net ofcial development assistance,
excluding debt relief and humanitarian assistance


Humanitarian assistance


Net debt relief


0


50


100


150


200


20112005200019951990


Aid flows
decline


8a


Source: Organisation for Economic Co-operation and Development
StatExtracts.


The eighth and final goal distinguishes the Mil-
lennium Development Goals from previous reso-
lutions and targeted programs. It recognizes the
multidimensional nature of development and the
need for wealthy countries and developing coun-
tries to work together to create an environment in
which rapid, sustainable development is possible.
Along with increased aid flows and debt relief for
the poorest, highly indebted countries, goal 8 rec-
ognizes the need to reduce barriers to trade and
to share the benefits of new medical and commu-
nication technologies. It is also a reminder that
development challenges differ for large and small
countries and for those that are landlocked or iso-
lated by large expanses of ocean. Building and
sustaining a partnership is an ongoing process
that does not stop at a specific date or when a
target is reached.


After falling through much of the 1990s, offi-
cial development assistance (ODA) from mem-
bers of the Organisation for Economic Co-oper-
ation and Development’s (OECD) Development
Assistance Committee (DAC) rose sharply after
2002, but a large part of the increase was in the
form of debt relief and humanitarian assistance
(figure 8a). The financial crisis that began in 2008
and fiscal austerity in many high-income econo-
mies have begun to undermine commitments
to increase ODA. Net disbursements of ODA by
members of the DAC rose to $134 billion in 2011,
but, after accounting for price and exchange
rate adjustments, fell 2.3 percent in real terms
from 2010. Aid from multilateral organizations
remained essentially unchanged at $34.7 billion,
a decrease of 6.6 percent in real terms. ODA from
DAC members has fallen back to 0.31 percent of
their combined gross national income, less than
half the UN target of 0.7 percent.


OECD members, mostly high-income econo-
mies but also some upper middle-income econo-
mies such as Chile, Mexico, and Turkey, continue
to spend more on support to domestic agricul-
tural producers than on ODA. In 2011 the OECD


Goal 8 Develop a global partnership for development




World Development Indicators 2013 17Economy States and markets Global links Back


0


20


40


60


80


201120082006200420022000


Internet users (per 100 people)


Sub-Saharan
Africa


Latin America & Caribbean


East Asia & Pacic


High income


Europe & Central Asia


South Asia


Middle East & North Africa


More people connecting
to the Internet


8f


Source: International Telecommunications Union and World Development
Indicators database.


20112005200019951990


Mobile phone subscriptions (per 100 people)


High income


Upper middle
income


Lower middle
income


Low income


0


25


50


75


100


125


Telecommunications
on the move


8e


Source: International Telecommunications Union and World Development
Indicators database.


0


10


20


30


40


50


20112005200019951990


Total debt service (% of exports of goods, services, and income)


Sub-Saharan Africa


South Asia


Latin America & Caribbean


Europe &
Central Asia


East Asia
& Pacic


Middle East & North Africa


Debt service burdens
continue to fall


8d


Source: World Development Indicators database.


estimate of agricultural subsidies was $252 bil-
lion, 41 percent of which was to EU producers
(figure 8b).


Many rich countries have pledged to open their
markets to exports from developing countries, and
the share of goods (excluding arms) admitted duty
free by OECD economies has been rising. However,
arcane rules of origin and phytosanitary standards
keep many countries from qualifying for duty-free
access. And uncertainty over market access may
inhibit development of export industries (figure 8c).


Growing economies, better debt management,
and debt relief for the poorest countries have
allowed developing countries to substantially
reduce their debt burdens. Despite the financial
crisis and a 2.3 percent contraction in the global
economy in 2009, the debt service to exports ratio
in low- and middle-income economies reached a
new low of 8.8 percent in 2011. In Europe and
Central Asia, where the debt service to exports
ratio rose to 26 percent in 2009, higher export
earnings have helped return the average to its
2007 level of 17.8 percent (figure 8d).


Telecommunications is an essential tool for
development, and new technologies are creating
new opportunities everywhere. The growth of fixed-
line phone systems has peaked in high-income
economies and will never reach the same level
of use in developing countries. In high-income
economies mobile phone subscriptions have now
passed 1 per person, and upper middle-income
economies are not far behind (figure 8e).


Mobile phones are one of several ways of
accessing the Internet. In 2000 Internet use was
spreading rapidly in high-income economies but
was barely under way in developing country regions.
Now developing countries are beginning to catch
up. Since 2000 Internet use per person in develop-
ing economies has grown 28 percent a year. Like
telephones, Internet use is strongly correlated with
income. The low-income economies of South Asia
and Sub- Saharan Africa lag behind, but even there
Internet access is spreading rapidly (figure 8f).




18 World Development Indicators 2013


Millennium Development Goals
Goals and targets from the Millennium Declaration Indicators for monitoring progress
Goal 1 Eradicate extreme poverty and hunger
Target 1.A Halve, between 1990 and 2015, the proportion of


people whose income is less than $1 a day
1.1 Proportion of population below $1 purchasing power


parity (PPP) a daya


1.2 Poverty gap ratio [incidence × depth of poverty]
1.3 Share of poorest quintile in national consumption


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


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


Target 1.C Halve, between 1990 and 2015, the proportion of
people who suffer from hunger


1.8 Prevalence of underweight children under five years of age
1.9 Proportion of population below minimum level of dietary


energy consumption


Goal 2 Achieve universal primary education
Target 2.A Ensure that by 2015 children everywhere, boys and


girls alike, will be able to complete a full course of
primary schooling


2.1 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


Goal 3 Promote gender equality and empower women
Target 3.A Eliminate gender disparity in primary and secondary


education, preferably by 2005, and in all levels of
education no later than 2015


3.1 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


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


under-five mortality rate
4.1 Under-five mortality rate
4.2 Infant mortality rate
4.3 Proportion of one-year-old children immunized against


measles


Goal 5 Improve maternal health
Target 5.A Reduce by three-quarters, between 1990 and 2015,


the maternal mortality ratio
5.1 Maternal mortality ratio
5.2 Proportion of births attended by skilled health personnel


Target 5.B Achieve by 2015 universal access to reproductive
health


5.3 Contraceptive prevalence rate
5.4 Adolescent birth rate
5.5 Antenatal care coverage (at least one visit and at least


four visits)
5.6 Unmet need for family planning


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
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


Target 6.B Achieve by 2010 universal access to treatment for
HIV/AIDS for all those who need it


6.5 Proportion of population with advanced HIV infection with
access to antiretroviral drugs


Target 6.C Have halted by 2015 and begun to reverse the
incidence of malaria and other major diseases


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


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


Front User guide World view People Environment?




World Development Indicators 2013 19Economy States and markets Global links Back


Goals and targets from the Millennium Declaration Indicators for monitoring progress
Goal 7 Ensure environmental sustainability
Target 7.A Integrate the principles of sustainable development


into country policies and programs and reverse the
loss of environmental resources


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


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


7.8 Proportion of population using an improved drinking water
source


7.9 Proportion of population using an improved sanitation
facility


Target 7.D Achieve by 2020 a significant improvement in the
lives of at least 100 million slum dwellers


7.10 Proportion of urban population living in slumsb


Goal 8 Develop a global partnership for development
Target 8.A Develop further an open, rule-based, predictable,


nondiscriminatory trading and financial system

(Includes a commitment to good governance,
development, and poverty reduction—both
nationally and internationally.)


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


Official development assistance (ODA)
8.1 Net ODA, total and to the least developed countries, as


percentage of OECD/DAC donors’ 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
sanitation)


8.3 Proportion of bilateral official development assistance of
OECD/DAC donors that is untied


8.4 ODA received in landlocked developing countries as a
proportion of their gross national incomes


8.5 ODA received in small island developing states as a
proportion of their gross national incomes


Market access
8.6 Proportion of total developed country imports (by value


and excluding arms) from developing countries and least
developed countries, admitted free of duty


8.7 Average tariffs imposed by developed countries on
agricultural products and textiles and clothing from
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


Debt sustainability
8.10 Total number of countries that have reached their HIPC


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.B Address the special needs of the least developed
countries

(Includes tariff and quota-free access for the least
developed countries’ exports; enhanced program of
debt relief for heavily indebted poor countries (HIPC)
and cancellation of official bilateral debt; and more
generous ODA for countries committed to poverty
reduction.)


Target 8.C Address the special needs of landlocked
developing countries and small island developing
states (through the Programme of Action for
the Sustainable Development of Small Island
Developing States and the outcome of the 22nd
special session of the General Assembly)


Target 8.D Deal comprehensively with the debt problems
of developing countries through national and
international measures in order to make debt
sustainable in the long term


Target 8.E In cooperation with pharmaceutical companies,
provide access to affordable essential drugs in
developing countries


8.13 Proportion of population with access to affordable
essential drugs on a sustainable basis


Target 8.F In cooperation with the private sector, make
available the benefits of new technologies,
especially information and communications


8.14 Fixed-line telephones per 100 population
8.15 Mobile cellular subscribers per 100 population
8.16 Internet users per 100 population


a. Where available, indicators based on national poverty lines should be used for monitoring country poverty trends.
b. The proportion of people living in slums is measured by a proxy, represented by the urban population living in households with at least one of these characteristics: lack of access to


improved water supply, lack of access to improved sanitation, overcrowding (three or more people per room), and dwellings made of nondurable material.




20 World Development Indicators 2013 Front User guide World view People Environment?


1 World view
Population Surface


area
Population


density
Urban


population
Gross national income Gross domestic


productAtlas method Purchasing power parity


millions
thousand
sq. km


people
per sq. km


% of total
population $ billions


Per capita
$ $ billions


Per capita
$ % growth


Per capita
% growth


2011 2011 2011 2011 2011 2011 2011 2011 2010–11 2010–11


Afghanistan 35.3 652.2 54 24 16.6 470 40.3a 1,140a 5.7 2.9


Albania 3.2 28.8 117 53 12.8 3,980 28.4 8,820 3.0 2.6


Algeria 36.0 2,381.7 15 73 160.8 4,470 299.0a 8,310a 2.5 1.0


American Samoa 0.1 0.2 348 93 .. ..b .. .. .. ..


Andorra 0.1 0.5 183 87 .. ..c .. .. .. ..


Angola 19.6 1,246.7 16 59 75.2 3,830d 102.7 5,230 3.9 1.1


Antigua and Barbuda 0.1 0.4 204 30 1.1 11,940 1.6a 17,900a –5.0 –6.0


Argentina 40.8 2,780.4 15 93 .. .. .. .. .. ..


Armenia 3.1 29.7 109 64 10.4 3,360 18.9 6,100 4.6 4.3


Aruba 0.1 0.2 601 47 .. ..c .. .. .. ..


Australia 22.3 7,741.2 3 89 1,111.4 49,790 862.0 38,610 1.9 0.7


Austria 8.4 83.9 102 68 405.7 48,170 354.0 42,030 2.7 2.3


Azerbaijan 9.2 86.6 111 54 48.5 5,290 82.1 8,950 1.0 –0.3


Bahamas, The 0.3 13.9 35 84 7.5 21,970 10.2a 29,790a 1.6 0.4


Bahrain 1.3 0.8 1,742 89 20.1 15,920 26.8 21,200 3.0 –3.1


Bangladesh 150.5 144.0 1,156 28 117.8 780 291.7 1,940 6.7 5.4


Barbados 0.3 0.4 637 44 3.5 12,660 5.2a 18,900a 1.2 –5.5


Belarus 9.5 207.6 47 75 55.2 5,830 137.0 14,460 5.3 5.5


Belgium 11.0 30.5 364 98 506.2 45,930 431.4 39,150 1.8 0.6


Belize 0.4 23.0 16 45 1.3 3,710 2.2a 6,090a 1.9 –1.5


Benin 9.1 112.6 82 45 7.1 780 14.6 1,610 3.5 0.7


Bermuda 0.1 0.1 1,294 100 .. ..c .. .. –1.9 –1.7


Bhutan 0.7 38.4 19 36 1.6 2,130 4.1 5,570 5.6 3.8


Bolivia 10.1 1,098.6 9 67 20.4 2,020 49.3 4,890 5.2 3.5


Bosnia and Herzegovina 3.8 51.2 74 48 18.0 4,780 34.5 9,190 1.7 1.9


Botswana 2.0 581.7 4 62 15.2 7,470 29.5 14,550 5.7 4.5


Brazil 196.7 8,514.9 23 85 2,107.7 10,720 2,245.8 11,420 2.7 1.8


Brunei Darussalam 0.4 5.8 77 76 12.5 31,800 19.6 49,910 2.2 0.4


Bulgaria 7.3 111.0 68 73 48.8 6,640 105.8 14,400 1.7 4.3


Burkina Faso 17.0 274.2 62 27 9.9 580 22.5 1,330 4.2 1.1


Burundi 8.6 27.8 334 11 2.2 250 5.2 610 4.2 1.9


Cambodia 14.3 181.0 81 20 11.7 820 31.8 2,230 7.1 5.8


Cameroon 20.0 475.4 42 52 24.1 1,210 46.7 2,330 4.2 2.0


Canada 34.5 9,984.7 4 81 1,570.9 45,550 1,367.6 39,660 2.5 1.4


Cape Verde 0.5 4.0 124 63 1.8 3,540 2.0 3,980 5.0 4.1


Cayman Islands 0.1 0.3 236 100 .. ..c .. .. .. ..


Central African Republic 4.5 623.0 7 39 2.1 480 3.6 810 3.3 1.3


Chad 11.5 1,284.0 9 22 8.3 720 17.7 1,540 1.6 –1.0


Channel Islands 0.2 0.2 810 31 .. ..c .. .. .. ..


Chile 17.3 756.1 23 89 212.0 12,280 282.1 16,330 6.0 5.0


China 1,344.1 9,600.0 144 51 6,643.2 4,940 11,270.8 8,390 9.3 8.8


Hong Kong SAR, China 7.1 1.1 6,787 100 254.6 36,010 370.2 52,350 4.9 4.8


Macao SAR, China 0.6 0.0e 19,848 100 24.7 45,460 31.0 56,950 20.7 18.1


Colombia 46.9 1,141.8 42 75 284.9 6,070 448.6 9,560 5.9 4.5


Comoros 0.8 1.9 405 28 0.6 770 0.8 1,110 2.2 –0.4


Congo, Dem. Rep. 67.8 2,344.9 30 34 13.1 190 23.2 340 6.9 4.1


Congo, Rep. 4.1 342.0 12 64 9.3 2,250 13.4 3,240 3.4 1.0




World Development Indicators 2013 21Economy States and markets Global links Back


World view 1
Population Surface


area
Population


density
Urban


population
Gross national income Gross domestic


productAtlas method Purchasing power parity


millions
thousand
sq. km


people
per sq. km


% of total
population $ billions


Per capita
$ $ billions


Per capita
$ % growth


Per capita
% growth


2011 2011 2011 2011 2011 2011 2011 2011 2010–11 2010–11


Costa Rica 4.7 51.1 93 65 36.1 7,640 56.1a 11,860a 4.2 2.7


Côte d’Ivoire 20.2 322.5 63 51 22.1 1,090 34.5 1,710 –4.7 –6.7


Croatia 4.4 56.6 79 58 59.6 13,540 82.7 18,780 0.0 0.3


Cuba 11.3 109.9 106 75 .. ..b .. .. 2.1 2.1


Curaçao 0.1 0.4 328 .. .. ..c .. .. .. ..


Cyprus 1.1 9.3 121 71 23.7f 29,450f 24.9f 30,970f 0.5f 0.3f


Czech Republic 10.5 78.9 136 73 196.3 18,700 257.0 24,490 1.9 2.1


Denmark 5.6 43.1 131 87 335.1 60,160 233.5 41,920 1.1 0.7


Djibouti 0.9 23.2 39 77 1.1 1,270 2.1 2,450 5.0 3.0


Dominica 0.1 0.8 90 67 0.5 7,030 0.9a 13,000a –0.3 –0.2


Dominican Republic 10.1 48.7 208 70 52.6 5,240 94.7a 9,420a 4.5 3.1


Ecuador 14.7 256.4 59 67 61.7 4,200 124.7 8,510 7.8 6.3


Egypt, Arab Rep. 82.5 1,001.5 83 44 214.7 2,600 504.8 6,120 1.8 0.1


El Salvador 6.2 21.0 301 65 21.7 3,480 41.4a 6,640a 1.5 0.9


Equatorial Guinea 0.7 28.1 26 40 11.3 15,670 18.4 25,620 7.8 4.8


Eritrea 5.4 117.6 54 21 2.3 430 3.1a 580a 8.7 5.5


Estonia 1.3 45.2 32 70 20.4 15,260 27.9 20,850 8.3 8.3


Ethiopia 84.7 1,104.3 85 17 31.0 370 93.8 1,110 7.3 5.0


Faeroe Islands 0.0g 1.4 35 41 .. ..c .. .. .. ..


Fiji 0.9 18.3 48 52 3.2 3,720 4.0 4,610 2.0 1.1


Finland 5.4 338.4 18 84 257.3 47,760 202.9 37,660 2.7 2.3


France 65.4 549.2 120 86 2,775.7 42,420 2,349.8 35,910 1.7 1.1


French Polynesia 0.3 4.0 75 51 .. ..c .. .. .. ..


Gabon 1.5 267.7 6 86 12.4 8,080 21.1 13,740 4.8 2.8


Gambia, The 1.8 11.3 178 57 0.9 500 3.1 1,750 –4.3 –6.9


Georgia 4.5h 69.7 79h 53h 12.8h 2,860h 24.0h 5,350h 7.0h 6.2h


Germany 81.8 357.1 235 74 3,617.7 44,230 3,287.6 40,190 3.0 3.0


Ghana 25.0 238.5 110 52 35.1 1,410 45.2 1,810 14.4 11.8


Greece 11.3 132.0 88 62 276.7 24,490 283.7 25,110 –7.1 –7.0


Greenland 0.1 410.5i 0j 85 1.5 26,020 .. .. –5.4 –5.4


Grenada 0.1 0.3 309 39 0.8 7,350 1.1a 10,350a 1.0 0.6


Guam 0.2 0.5 337 93 .. ..c .. .. .. ..


Guatemala 14.8 108.9 138 50 42.4 2,870 70.3a 4,760a 3.9 1.3


Guinea 10.2 245.9 42 36 4.4 430 10.5 1,020 3.9 1.5


Guinea-Bissau 1.5 36.1 55 44 0.9 600 1.9 1,230 5.7 3.5


Guyana 0.8 215.0 4 28 2.2 2,900 2.6a 3,460a 4.2 4.2


Haiti 10.1 27.8 367 53 7.1 700 11.9a 1,180a 5.6 4.2


Honduras 7.8 112.5 69 52 15.4 1,980 29.7a 3,820a 3.6 1.6


Hungary 10.0 93.0 110 69 126.9 12,730 202.5 20,310 1.7 2.0


Iceland 0.3 103.0 3 94 11.1 34,820 9.9 31,020 2.6 2.2


India 1,241.5 3,287.3 418 31 1,766.2 1,420 4,524.6 3,640 6.3 4.9


Indonesia 242.3 1,904.6 134 51 712.7 2,940 1,091.4 4,500 6.5 5.4


Iran, Islamic Rep. 74.8 1,745.2 46 69 330.4 4,520 835.5 11,420 .. ..


Iraq 33.0 435.2 76 67 87.0 2,640 123.5 3,750 9.9 6.8


Ireland 4.6 70.3 66 62 179.2 39,150 153.4 33,520 0.7 –1.5


Isle of Man 0.1 0.6 146 51 .. ..c .. .. .. ..


Israel 7.8 22.1 359 92 224.7 28,930 210.5 27,110 4.7 2.8




Front User guide World view People Environment?22 World Development Indicators 2013


1 World view
Population Surface


area
Population


density
Urban


population
Gross national income Gross domestic


productAtlas method Purchasing power parity


millions
thousand
sq. km


people
per sq. km


% of total
population $ billions


Per capita
$ $ billions


Per capita
$ % growth


Per capita
% growth


2011 2011 2011 2011 2011 2011 2011 2011 2010–11 2010–11


Italy 60.7 301.3 206 68 2,144.7 35,320 1,968.9 32,420 0.4 0.0


Jamaica 2.7 11.0 250 52 .. ..b .. .. –0.3 ..


Japan 127.8 377.9 351 91 5,739.5 44,900 4,516.3 35,330 –0.7 –1.0


Jordan 6.2 89.3 70 83 27.1 4,380 36.6 5,930 2.6 0.4


Kazakhstan 16.6 2,724.9 6 54 136.7 8,260 186.4 11,250 7.5 6.0


Kenya 41.6 580.4 73 24 34.1 820 71.1 1,710 4.4 1.6


Kiribati 0.1 0.8 125 44 0.2 2,030 0.3a 3,300a 1.8 0.2


Korea, Dem. Rep. 24.5 120.5 203 60 .. ..k .. .. .. ..


Korea, Rep. 49.8 99.9 513 83 1,039.0 20,870 1,511.7 30,370 3.6 2.9


Kosovo 1.8 10.9 166 .. 6.3 3,510 .. .. 5.0 3.4


Kuwait 2.8 17.8 158 98 133.8 48,900 147.0 53,720 8.2 5.1


Kyrgyz Republic 5.5 199.9 29 35 5.0 900 12.7 2,290 6.0 4.7


Lao PDR 6.3 236.8 27 34 7.1 1,130 16.2 2,580 8.0 6.5


Latvia 2.1 64.6 33 68 27.4 13,320d 39.3 19,090 5.5 14.7


Lebanon 4.3 10.5 416 87 38.9 9,140 61.6 14,470 3.0 2.2


Lesotho 2.2 30.4 72 28 2.7 1,210 4.5 2,050 4.2 3.1


Liberia 4.1 111.4 43 48 1.4 330 2.2 540 9.4 5.9


Libya 6.4 1,759.5 4 78 77.1 12,320 105.2a 16,800a 2.1 0.3


Liechtenstein 0.0g 0.2 227 14 4.9 137,070 .. .. –1.2 –1.9


Lithuania 3.0 65.3 48 67 39.3 12,980d 62.9 20,760 5.9 14.8


Luxembourg 0.5 2.6 200 85 40.1 77,390 33.2 64,110 1.7 –0.6


Macedonia, FYR 2.1 25.7 82 59 9.9 4,810 23.5 11,370 2.8 2.7


Madagascar 21.3 587.0 37 33 9.1 430 20.2 950 1.0 –1.9


Malawi 15.4 118.5 163 16 5.6 360 13.4 870 4.3 1.1


Malaysia 28.9 330.8 88 73 253.0 8,770 451.7 15,650 5.1 3.4


Maldives 0.3 0.3 1,067 41 1.8 5,720 2.4 7,430 7.5 6.1


Mali 15.8 1,240.2 13 35 9.7 610 16.8 1,060 2.7 –0.3


Malta 0.4 0.3 1,299 95 7.7 18,620 10.2 24,480 2.1 2.2


Marshall Islands 0.1 0.2 305 72 0.2 3,910 .. .. 5.0 3.5


Mauritania 3.5 1,030.7 3 42 3.6 1,030l 8.9 2,530 4.0 1.6


Mauritius 1.3 2.0 634 42 10.3 8,040 18.4 14,330 4.1 3.7


Mexico 114.8 1,964.4 59 78 1,081.8 9,420 1,766.4 15,390 3.9 2.7


Micronesia, Fed. Sts. 0.1 0.7 159 23 0.3 2,860 0.4a 3,580a 2.1 1.6


Moldova 3.6m 33.9 124m 48m 7.1m 1,980m 13.0m 3,640m 6.4m 6.5m


Monaco 0.0g 0.0e 17,714 100 6.5 183,150 .. .. –2.6 –2.7


Mongolia 2.8 1,564.1 2 69 6.5 2,310 12.0 4,290 17.5 15.7


Montenegro 0.6 13.8 47 63 4.5 7,140 8.7 13,700 3.2 3.1


Morocco 32.3 446.6 72 57 97.6n 2,970n 160.1n 4,880n 4.5n 3.5n


Mozambique 23.9 799.4 30 31 11.1 460 22.9 960 7.1 4.7


Myanmar 48.3 676.6 74 33 .. ..k .. .. .. ..


Namibia 2.3 824.3 3 38 10.9 4,700 15.4 6,610 4.8 3.0


Nepal 30.5 147.2 213 17 16.6 540 38.4 1,260 3.9 2.1


Netherlands 16.7 41.5 495 83 829.0 49,660 720.3 43,150 1.0 0.5


New Caledonia 0.3 18.6 14 62 .. ..c .. .. .. ..


New Zealand 4.4 267.7 17 86 127.3 29,140 126.3 28,930 1.0 0.1


Nicaragua 5.9 130.4 49 58 8.9 1,510 21.9a 3,730a 5.1 3.6


Niger 16.1 1,267.0 13 18 5.8 360 11.6 720 2.3 –1.2




World Development Indicators 2013 23Economy States and markets Global links Back


World view 1
Population Surface


area
Population


density
Urban


population
Gross national income Gross domestic


productAtlas method Purchasing power parity


millions
thousand
sq. km


people
per sq. km


% of total
population $ billions


Per capita
$ $ billions


Per capita
$ % growth


Per capita
% growth


2011 2011 2011 2011 2011 2011 2011 2011 2010–11 2010–11


Nigeria 162.5 923.8 178 50 207.3 1,280 372.8 2,290 7.4 4.7


Northern Mariana Islands 0.1 0.5 133 92 .. ..c .. .. .. ..


Norway 5.0 323.8 16 79 440.2 88,870 304.4 61,450 1.4 0.1


Oman 2.8 309.5 9 73 53.6 19,260 71.6 25,720 5.5 3.1


Pakistan 176.7 796.1 229 36 198.0 1,120 507.2 2,870 3.0 1.1


Palau 0.0g 0.5 45 84 0.1 6,510 0.2a 11,080a 5.8 5.1


Panama 3.6 75.4 48 75 26.7 7,470 51.8a 14,510a 10.6 8.9


Papua New Guinea 7.0 462.8 16 13 10.4 1,480 18.0a 2,570a 9.0 6.6


Paraguay 6.6 406.8 17 62 19.8 3,020 35.4 5,390 6.9 5.0


Peru 29.4 1,285.2 23 77 151.4 5,150 277.6 9,440 6.8 5.6


Philippines 94.9 300.0 318 49 209.7 2,210 393.0 4,140 3.9 2.2


Poland 38.5 312.7 127 61 477.0 12,380o 780.8 20,260 4.3 3.4


Portugal 10.6 92.1 115 61 225.6 21,370 259.9 24,620 –1.7 –0.9


Puerto Rico 3.7 8.9 418 99 61.6 16,560 .. .. –2.1 –1.6


Qatar 1.9 11.6 161 99 150.4 80,440 161.6 86,440 18.8 11.7


Romania 21.4 238.4 93 53 174.0 8,140 337.4 15,780 2.5 2.7


Russian Federation 143.0 17,098.2 9 74 1,522.3 10,650 2,917.7 20,410 4.3 3.9


Rwanda 10.9 26.3 444 19 6.2 570 13.9 1,270 8.3 5.1


Samoa 0.2 2.8 65 20 0.6 3,160 0.8a 4,270a 2.0 1.6


San Marino 0.0g 0.1 529 94 .. ..c .. .. .. ..


São Tomé and Príncipe 0.2 1.0 176 63 0.2 1,350 0.4 2,080 4.9 3.0


Saudi Arabia 28.1 2,149.7p 13 82 500.5 17,820 693.7 24,700 6.8 4.4


Senegal 12.8 196.7 66 43 13.7 1,070 24.8 1,940 2.6 –0.1


Serbia 7.3 88.4 83 56 41.3 5,690 83.8 11,550 2.0 2.5


Seychelles 0.1 0.5 187 54 1.0 11,270 2.3a 26,280a 5.0 5.6


Sierra Leone 6.0 71.7 84 39 2.8 460 6.8 1,140 6.0 3.7


Singapore 5.2 0.7 7,405 100 222.6 42,930 307.8 59,380 4.9 2.7


Sint Maarten 0.0g 0.0e 1,077 .. .. ..c .. .. .. ..


Slovak Republic 5.4 49.0 112 55 87.4 16,190 120.4 22,300 3.3 4.0


Slovenia 2.1 20.3 102 50 48.5 23,600 54.4 26,500 –0.2 –0.4


Solomon Islands 0.6 28.9 20 21 0.6 1,110 1.3a 2,350a 9.0 6.2


Somalia 9.6 637.7 15 38 .. ..k .. .. .. ..


South Africa 50.6 1,219.1 42 62 352.0 6,960 542.0 10,710 3.1 1.9


South Sudan 10.3p 644.3 .. 18 .. ..q .. .. 1.9 –1.7


Spain 46.2 505.4 93 77 1,428.3 30,930 1,451.7 31,440 0.4 0.2


Sri Lanka 20.9 65.6 333 15 53.8 2,580 115.2 5,520 8.3 7.1


St. Kitts and Nevis 0.1 0.3 204 32 0.7 12,610 0.9a 16,470a 2.1 0.9


St. Lucia 0.2 0.6 289 18 1.2 6,820 2.0a 11,220a 1.3 0.1


St. Martin 0.0g 0.1 563 .. .. ..c .. .. .. ..


St. Vincent and Grenadines 0.1 0.4 280 49 0.7 6,070 1.1a 10,440a 0.1 0.1


Sudan 34.3p,r 1,861.5r 18 33 58.3u 1,310u 94.7u 2,120u 4.7u 2.2u


Suriname 0.5 163.8 3 70 4.1 7,840 4.1a 7,730a 4.7 3.7


Swaziland 1.1 17.4 62 21 3.7 3,470 6.5 6,110 1.3 0.1


Sweden 9.4 450.3 23 85 502.5 53,170 398.9 42,210 3.9 3.1


Switzerland 7.9 41.3 198 74 604.1 76,350 415.6 52,530 1.9 0.8


Syrian Arab Republic 20.8 185.2 113 56 67.9 2,750 104.0 5,080 .. ..


Tajikistan 7.0 142.6 50 27 6.1 870 16.0 2,300 7.4 5.9




24 World Development Indicators 2013 Front User guide World view People Environment?


1 World view
Population Surface


area
Population


density
Urban


population
Gross national income Gross domestic


productAtlas method Purchasing power parity


millions
thousand
sq. km


people
per sq. km


% of total
population $ billions


Per capita
$ $ billions


Per capita
$ % growth


Per capita
% growth


2011 2011 2011 2011 2011 2011 2011 2011 2010–11 2010–11


Tanzania 46.2 947.3 52 27 24.2v 540v 67.1v 1,500v 6.4v 3.3v


Thailand 69.5 513.1 136 34 308.3 4,440 581.4 8,360 0.1 –0.5


Timor-Leste 1.2 14.9 79 28 3.1 2,730 5.9a 5,200a 10.6 7.5


Togo 6.2 56.8 113 38 3.5 570 6.4 1,040 4.9 2.7


Tonga 0.1 0.8 145 24 0.4 3,820 0.5a 5,000a 4.9 4.5


Trinidad and Tobago 1.3 5.1 262 14 21.3 15,840 32.7a 24,350a –4.1 –4.4


Tunisia 10.7 163.6 69 66 42.9 4,020d 96.0 8,990 –2.0 –3.1


Turkey 73.6 783.6 96 71 766.6 10,410 1,247.3 16,940 8.5 7.2


Turkmenistan 5.1 488.1 11 49 24.5 4,800 44.3a 8,690a 14.7 13.3


Turks and Caicos Islands 0.0g 1.0 41 94 .. ..c .. .. .. ..


Tuvalu 0.0g 0.0e 328 51 0.0 4,950 .. .. 1.2 1.0


Uganda 34.5 241.6 173 16 17.5 510 45.3 1,310 6.7 3.3


Ukraine 45.7 603.6 79 69 142.9 3,130 321.9 7,040 5.2 5.6


United Arab Emirates 7.9 83.6 94 84 321.7 40,760 377.9 47,890 4.9 –0.1


United Kingdom 62.7 243.6 259 80 2,370.4 37,780 2,255.9 35,950 0.8 0.0


United States 311.6 9,831.5 34 82 15,148.2 48,620 15,211.3 48,820 1.7 1.0


Uruguay 3.4 176.2 19 93 40.0 11,860 49.3 14,640 5.7 5.3


Uzbekistan 29.3 447.4 69 36 44.2 1,510 100.3a 3,420a 8.3 5.4


Vanuatu 0.2 12.2 20 25 0.7 2,730 1.1a 4,330a 1.4 –1.0


Venezuela, RB 29.3 912.1 33 94 346.1 11,820 363.9 12,430 4.2 2.6


Vietnam 87.8 331.1 283 31 111.1 1,270 285.5 3,250 5.9 4.8


Virgin Islands (U.S.) 0.1 0.4 313 95 .. ..c .. .. .. ..


West Bank and Gaza 3.9 6.0 652 74 .. ..q .. .. .. ..


Yemen, Rep. 24.8 528.0 47 32 26.4 1,070 53.7 2,170 –10.5 –13.2


Zambia 13.5 752.6 18 39 15.7 1,160 20.1 1,490 6.5 2.1


Zimbabwe 12.8 390.8 33 39 8.4 660 .. .. 9.4 7.8


World 6,974.2 s 134,269.2 s 54 w 52 w 66,354.3 t 9,514 w 80,624.2 t 11,560 w 2.7 w 1.6 w


Low income 816.8 16,582.1 51 28 466.0 571 1,125.4 1,378 6.0 3.7


Middle income 5,022.4 81,875.7 63 50 20,835.4 4,149 36,311.9 7,230 6.3 5.2


Lower middle income 2,532.7 20,841.9 126 39 4,488.5 1,772 9,719.0 3,837 5.5 3.9


Upper middle income 2,489.7 61,033.8 42 61 16,340.5 6,563 26,646.2 10,703 6.6 5.9


Low & middle income 5,839.2 98,457.7 61 47 21,324.4 3,652 37,436.4 6,411 6.3 5.0


East Asia & Pacific 1,974.2 16,301.7 125 49 8,387.3 4,248 14,344.6 7,266 8.3 7.6


Europe & Central Asia 408.1 23,613.7 18 65 3,156.6 7,734 5,845.7 14,323 5.9 5.4


Latin America & Carib. 589.0 20,393.6 29 79 5,050.3 8,574 6,822.0 11,582 4.7 3.6


Middle East & N. Africa 336.5 8,775.4 39 59 1,279.5 3,866 2,619.2 8,052 0.2 2.4


South Asia 1,656.5 5,131.1 347 31 2,174.5 1,313 5,523.5 3,335 6.1 4.6


Sub- Saharan Africa 874.8 24,242.3 37 37 1,100.8 1,258 1,946.2 2,225 4.7 2.1


High income 1,135.0 35,811.5 33 81 45,242.5 39,861 43,724.5 38,523 1.5 0.9


Euro area 332.9 2,628.4 131 76 12,871.5 38,661 11,735.7 35,250 1.5 1.2


a. Based on regression; others are extrapolated from the 2005 International Comparison Program benchmark estimates. b. Estimated to be upper middle income ($4,036–$12,475).
c. Estimated to be high income ($12,476 or more). d. Included in the aggregates for upper middle-income economies based on earlier data. e. Greater than 0 but less than 50. f. Data
are for the area controlled by the government of Cyprus. g. Greater than 0 but less than 50,000. h. Excludes Abkhazia and South Ossetia. i. Refers to area free from ice. j. Greater than
0 but less than 0.5. k. Estimated to be low income ($1,025 or less). l. Included in the aggregates for low-income economies based on earlier data. m. Excludes Transnistria. n. Includes
Former Spanish Sahara. o. Included in the aggregates for high-income economies based on earlier data. p. Provisional estimate. q. Estimated to be lower middle income ($1,026–$4,035).
r. Excludes South Sudan. s. Sum of available data (see Statistical methods). t. Missing data are imputed (see Statistical methods). u. Excludes South Sudan after July 9, 2011. v. Covers
mainland Tanzania only. w. Weighted average (see Statistical methods).




World Development Indicators 2013 25Economy States and markets Global links Back


World view 1
Population, land area, income (as measured by gross national


income, GNI), and output (as measured by gross domestic product,


GDP) are basic measures of the size of an economy. They also pro-


vide a broad indication of actual and potential resources and are


therefore used throughout World Development Indicators to normal-


ize other indicators.


Population


Population estimates are usually based on national population cen-


suses. Estimates for the years before and after the census are


interpolations or extrapolations based on demographic models.


Errors and undercounting occur even in high-income countries; in


developing countries errors may be substantial because of limits


in the transport, communications, and other resources required to


conduct and analyze a full census.


The quality and reliability of official demographic data are also


affected by public trust in the government, government commit-


ment to full and accurate enumeration, confidentiality and pro-


tection against misuse of census data, and census agencies’


independence from political influence. Moreover, comparability of


population indicators is limited by differences in the concepts,


definitions, collection procedures, and estimation methods used


by national statistical agencies and other organizations that col-


lect the data.


Of the 214 economies in the table, 180 (about 86 percent) con-


ducted a census during the 2000 census round (1995–2004). As


of January 2012, 141 countries have completed a census for the


2010 census round (2005–14). The currentness of a census and


the availability of complementary data from surveys or registration


systems are important indicators of demographic data quality. See


Primary data documentation for the most recent census or survey


year and for the completeness of registration. Some European coun-


tries’ registration systems offer complete information on population


in the absence of a census.


Current population estimates for developing countries that


lack recent census data and pre- and post-census estimates for


countries with census data are provided by the United Nations


Population Division and other agencies. The cohort compo-


nent method—a standard method for estimating and projecting


population—requires fertility, mortality, and net migration data,


often collected from sample surveys, which can be small or limited


in coverage. Population estimates are from demographic modeling


and so are susceptible to biases and errors from shortcomings in


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


cohort unit and five-year period data are used, interpolations to


obtain annual data or single age structure may not reflect actual


events or age composition.


Surface area


The surface area of an economy includes inland bodies of water


and some coastal waterways. Surface area thus differs from land


area, which excludes bodies of water, and from gross area, which


may include offshore territorial waters. Land area is particularly


important for understanding an economy’s agricultural capacity and


the environmental effects of human activity. Innovations in satellite


mapping and computer databases have resulted in more precise


measurements of land and water areas.


Urban population


There is no consistent and universally accepted standard for


distinguishing urban from rural areas, in part because of the


wide variety of situations across countries. Most countries use


an urban classification related to the size or characteristics


of settlements. Some define urban areas based on the pres-


ence of certain infrastructure and services. And other countries


designate urban areas based on administrative arrangements.


Because the estimates in the table are based on national defi-


nitions of what constitutes a city or metropolitan area, cross-


country comparisons should be made with caution. To estimate


urban populations, ratios of urban to total population obtained


from the United Nations were applied to the World Bank’s esti-


mates of total population.


Size of the economy


GNI measures total domestic and foreign value added claimed by


residents. GNI comprises GDP plus net receipts of primary income


(compensation of employees and property income) from nonresi-


dent sources. GDP is the sum of gross value added by all resident


producers in the economy plus any product taxes (less subsidies)


not included in the valuation of output. GNI is calculated without


deducting for depreciation of fabricated assets or for depletion and


degradation of natural resources. Value added is the net output of


an industry after adding up all outputs and subtracting intermedi-


ate inputs. The industrial origin of value added is determined by


the International Standard Industrial Classification revision 3. The


World Bank uses GNI per capita in U.S. dollars to classify countries


for analytical purposes and to determine borrowing eligibility. For


definitions of the income groups in World Development Indicators,


see User guide.


When calculating GNI in U.S. dollars from GNI reported in national


currencies, the World Bank follows the World Bank Atlas conversion


method, using a three-year average of exchange rates to smooth


the effects of transitory fluctuations in exchange rates. (For fur-


ther discussion of the World Bank Atlas method, see Statistical


methods.)


Because exchange rates do not always reflect differences in price


levels between countries, the table also converts GNI and GNI per


capita estimates into international dollars using purchasing power


parity (PPP) rates. PPP rates provide a standard measure allowing


comparison of real levels of expenditure between countries, just


as conventional price indexes allow comparison of real values over


time.


About the data




Front User guide World view People Environment?26 World Development Indicators 2013


1 World view
PPP rates are calculated by simultaneously comparing the prices


of similar goods and services among a large number of countries.


In the most recent round of price surveys conducted by the Interna-


tional Comparison Program (ICP) in 2005, 146 countries and ter-


ritories participated, including China for the first time, India for the


first time since 1985, and almost all African countries. The PPP


conversion factors presented in the table come from three sources.


For 47 high- and upper middle-income countries conversion fac-


tors are provided by Eurostat and the Organisation for Economic


Co-operation and Development (OECD); PPP estimates for these


countries incorporate new price data collected since 2005. For the


remaining 2005 ICP countries the PPP estimates are extrapolated


from the 2005 ICP benchmark results, which account for relative


price changes between each economy and the United States. For


countries that did not participate in the 2005 ICP round, the PPP


estimates are imputed using a statistical model. More information


on the results of the 2005 ICP is available at www.worldbank.org/


data/icp.


Growth rates of GDP and GDP per capita are calculated using the


least squares method and constant price data in local currency.


Constant price U.S. dollar series are used to calculate regional and


income group growth rates. The growth rates in the table are annual


averages. Methods of computing growth rates are described in Sta-


tistical methods.


Definitions


• Population is based on the de facto definition of population, which


counts all residents regardless of legal status or citizenship—


except for refugees not permanently settled in the country of asy-


lum, who are generally considered part of the population of their


country of origin. The values shown are midyear estimates. • Sur-


face area is a country’s total area, including areas under inland


bodies of water and some coastal waterways. • Population density


is midyear population divided by land area. • Urban population is


the midyear population of areas defined as urban in each country


and reported to the United Nations. • Gross national income, Atlas


method, is the sum of value added by all resident producers plus


any product taxes (less subsidies) not included in the valuation


of output plus net receipts of primary income (compensation of


employees and property income) from abroad. Data are in current


U.S. dollars converted using the World Bank Atlas method (see


Statistical methods). • Gross national income, purchasing power


parity, is GNI converted to international dollars using PPP rates. An


international dollar has the same purchasing power over GNI that


a U.S. dollar has in the United States. • Gross national income


per capita is GNI divided by midyear population. • Gross domestic


product is the sum of value added by all resident producers plus


any product taxes (less subsidies) not included in the valuation of


output. Growth is calculated from constant price GDP data in local


currency. • Gross domestic product per capita is GDP divided by


midyear population.


Data sources


The World Bank’s population estimates are compiled and produced


by its Development Data Group in consultation with its Human Devel-


opment Network, operational staff, and country offices. The United


Nations Population Division (2011) is a source of the demographic


data for more than half the countries, most of them developing


countries. Other important sources are census reports and other


statistical publications from national statistical offices; household


surveys by national agencies, ICF International (for MEASURE DHS),


and the U.S. Centers for Disease Control and Prevention; Eurostat’s


Demographic Statistics; the Secretariat of the Pacific Community’s


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


Census’s International Data Base.


Data on surface and land area are from the Food and Agricul-


ture Organization, which gathers these data from national agen-


cies through annual questionnaires and by analyzing the results of


national agricultural censuses.


Data on urban population shares are from United Nations Popula-


tion Division (2010).


GNI, GNI per capita, GDP growth, and GDP per capita growth are


estimated by World Bank staff based on national accounts data col-


lected by World Bank staff during economic missions or reported by


national statistical offices to other international organizations such


as the OECD. PPP conversion factors are estimates by Eurostat/


OECD and by World Bank staff based on data collected by the ICP.


References


Eurostat (Statistical Office of the European Communities). n.d. Demo-


graphic Statistics. http://epp.eurostat.ec.europa.eu/portal/page/


portal/eurostat/home/. Luxembourg.


ICF International. Various years. Demographic and Health Surveys.


http://www.measuredhs.com. Calverton, MD.


OECD (Organisation for Economic Co-operation and Development). n.d.


OECD.StatExtracts database. http://stats.oecd.org/. Paris.


UNAIDS (Joint United Nations Programme on HIV/AIDS). 2012. Global


Report: UNAIDS Report on the Global AIDS Epidemic 2012. www


.unaids.org/en/resources/publications/2012/. Geneva.


United Nations Inter-Agency Group for Child Mortality Estimation.


2012. Levels and Trends in Child Mortality: Report 2012. www


.childinfo.org/files/Child_Mortality_Report_2012.pdf. New York.


United Nations. 2012. The Millennium Development Goals Report


2012. New York.


United Nations Population Division. 2010. World Urbanization Pros-


pects: The 2009 Revision. New York: United Nations, Department of


Economic and Social Affairs.


———. 2011. World Population Prospects: The 2010 Revision. New


York: United Nations, Department of Economic and Social Affairs.


World Bank. 2011. The Changing Wealth of Nations: Measuring Sustain-


able Development for the New Millennium. Washington, DC.


———. n.d. PovcalNet online database. http://iresearch.worldbank


.org/PovcalNet/index.htm




World Development Indicators 2013 27Economy States and markets Global links Back


World view 1


1.1 Size of the economy


Population SP.POP.TOTL


Surface area AG.SRF.TOTL.K2


Population density EN.POP.DNST


Gross national income, Atlas method NY.GNP.ATLS.CD


Gross national income per capita, Atlas
method NY.GNP.PCAP.CD


Purchasing power parity gross national
income NY.GNP.MKTP.PP.CD


Purchasing power parity gross national
income, Per capita NY.GNP.PCAP.PP.CD


Gross domestic product NY.GDP.MKTP.KD.ZG


Gross domestic product, Per capita NY.GDP.PCAP.KD.ZG


1.2 Millennium Development Goals: eradicating poverty


and saving lives


Share of poorest quintile in national
consumption or income SI.DST.FRST.20


Vulnerable employment SL.EMP.VULN.ZS


Prevalence of malnutrition, Underweight SH.STA.MALN.ZS


Primary completion rate SE.PRM.CMPT.ZS


Ratio of girls to boys enrollments in primary
and secondary education SE.ENR.PRSC.FM.ZS


Under-five mortality rate SH.DYN.MORT


1.3 Millennium Development Goals: protecting our


common environment


Maternal mortality ratio, Modeled estimate SH.STA.MMRT


Contraceptive prevalence rate SP.DYN.CONU.ZS


HIV prevalence SH.DYN.AIDS.ZS


Incidence of tuberculosis SH.TBS.INCD


Carbon dioxide emissions per capita EN.ATM.CO2E.PC


Nationally protected terrestrial and marine
areas ER.PTD.TOTL.ZS


Access to improved sanitation facilities SH.STA.ACSN


Individuals using the Internet ..a


1.4 Millennium Development Goals: overcoming obstacles


This table provides data on net official
development assistance by donor, least
developed countries’ access to high-income
markets, and the Debt Initiative for Heavily
Indebted Poor Countries. ..a


1.5 Women in development


Female population SP.POP.TOTL.FE.ZS


Life expectancy at birth, Male SP.DYN.LE00.MA.IN


Life expectancy at birth, Female SP.DYN.LE00.FE.IN


Pregnant women receiving prenatal care SH.STA.ANVC.ZS


Teenage mothers SP.MTR.1519.ZS


Women in wage employment in
nonagricultural sector SL.EMP.INSV.FE.ZS


Unpaid family workers, Male SL.FAM.WORK.MA.ZS


Unpaid family workers, Female SL.FAM.WORK.FE.ZS


Female part-time employment SL.TLF.PART.TL.FE.ZS


Female legislators, senior officials, and
managers SG.GEN.LSOM.ZS


Women in parliaments SG.GEN.PARL.ZS


Female-headed households SP.HOU.FEMA.ZS


Data disaggregated by sex are available in
the World Development Indicators database.


a. Available online only as part of the table, not as an individual indicator.


To access the World Development Indicators online tables, use


the URL http://wdi.worldbank.org/table/ and the table number (for


example, http://wdi.worldbank.org/table/1.1). To view a specific


indicator online, use the URL http://data.worldbank.org/indicator/


and the indicator code (for example, http://data.worldbank.org/


indicator/SP.POP.TOTL).


About the dataOnline tables nd indicators




28 World Development Indicators 2013 Front User guide World view People Environment?


International poverty
line in local currency


Population below international poverty linesa


$1.25 a day $2 a day
Survey
yearb


Population
below


$1.25 a day
%


Poverty gap
at $1.25


a day
%


Population
below


$2 a day
%


Poverty
gap at


$2 a day
%


Survey
yearb


Population
below


$1.25 a day
%


Poverty gap
at $1.25


a day
%


Population
below


$2 a day
%


Poverty
gap at


$2 a day
%2005 2005


Albania 75.5 120.8 2005 <2 <0.5 7.9 1.5 2008 <2 <0.5 4.3 0.9


Algeria 48.4c 77.5c 1988 7.6 1.2 24.6 6.7 1995 6.8 1.4 23.6 6.5


Angola 88.1 141.0 .. .. .. .. 2000 54.3 29.9 70.2 42.4


Argentina 1.7 2.7 2009d,e 2.0 1.2 3.4 1.7 2010d,e <2 0.7 <2 0.9


Armenia 245.2 392.4 2008 <2 <0.5 12.4 2.3 2010 2.5 <0.5 19.9 4.0


Azerbaijan 2,170.9 3,473.5 2001 6.3 1.1 27.1 6.8 2008 <2 <0.5 2.8 0.6


Bangladesh 31.9 51.0 2005 50.5 14.2 80.3 34.3 2010 43.3 11.2 76.5 30.4


Belarus 949.5 1,519.2 2008 <2 <0.5 <2 <0.5 2010 <2 <0.5 <2 <0.5


Belize 1.8c 2.9c 1998f 11.3 4.7 26.3 10.0 1999f 12.2 5.5 22.0 9.9


Benin 344.0 550.4 .. .. .. .. 2003 47.3 15.7 75.3 33.5


Bhutan 23.1 36.9 2003 26.2 7.0 49.5 18.8 2007 10.2 1.8 29.8 8.5


Bolivia 3.2 5.1 2007e 13.1 6.6 24.7 10.9 2008e 15.6 8.6 24.9 13.1


Bosnia and Herzegovina 1.1 1.7 2004 <2 <0.5 <2 <0.5 2007 <2 <0.5 <2 <0.5


Botswana 4.2 6.8 1986 35.6 13.8 54.7 25.8 1994 31.2 11.0 49.4 22.3


Brazil 2.0 3.1 2008f 6.0 3.4 11.3 5.3 2009f 6.1 3.6 10.8 5.4


Bulgaria 0.9 1.5 2003 <2 <0.5 <2 <0.5 2007 <2 <0.5 <2 <0.5


Burkina Faso 303.0 484.8 2003 56.5 20.3 81.2 39.3 2009 44.6 14.7 72.6 31.7


Burundi 558.8 894.1 1998 86.4 47.3 95.4 64.1 2006 81.3 36.4 93.5 56.1


Cambodia 2,019.1 3,230.6 2008 22.8 4.9 53.3 17.4 2009 18.6 3.6 49.5 15.1


Cameroon 368.1 589.0 2001 10.8 2.3 32.5 9.5 2007 9.6 1.2 30.4 8.2


Cape Verde 97.7 156.3 .. .. .. .. 2002 21.0 6.1 40.9 15.2


Central African Republic 384.3 614.9 2003 62.4 28.3 81.9 45.3 2008 62.8 31.3 80.1 46.8


Chad 409.5 655.1 .. .. .. .. 2003 61.9 25.6 83.3 43.9


Chile 484.2 774.7 2006f <2 0.5 3.2 1.1 2009f <2 0.7 2.7 1.2


China 5.1g 8.2g 2008h 13.1 3.2 29.8 10.1 2009h 11.8 2.8 27.2 9.1


Colombia 1,489.7 2,383.5 2009f 9.7 4.7 18.5 8.2 2010f 8.2 3.8 15.8 6.8


Comoros 368.0 588.8 .. .. .. .. 2004 46.1 20.8 65.0 34.2


Congo, Dem. Rep. 395.3 632.5 .. .. .. .. 2006 87.7 52.8 95.2 67.6


Congo, Rep. 469.5 751.1 .. .. .. .. 2005 54.1 22.8 74.4 38.8


Costa Rica 348.7c 557.9c 2008f 2.4 1.5 5.0 2.3 2009f 3.1 1.8 6.0 2.7


Côte d’Ivoire 407.3 651.6 2002 23.3 6.8 46.8 17.6 2008 23.8 7.5 46.3 17.8


Croatia 5.6 8.9 2004 <2 <0.5 <2 <0.5 2008 <2 <0.5 <2 <0.5


Czech Republic 19.0 30.4 1993e <2 <0.5 <2 <0.5 1996e <2 <0.5 <2 <0.5


Djibouti 134.8 215.6 .. .. .. .. 2002 18.8 5.3 41.2 14.6


Dominican Republic 25.5c 40.8c 2009f 3.0 0.7 10.0 2.7 2010f 2.2 <0.5 9.9 2.4


Ecuador 0.6 1.0 2009f 6.4 2.9 13.5 5.5 2010f 4.6 2.1 10.6 4.1


Egypt, Arab Rep. 2.5 4.0 2005 2.0 <0.5 18.5 3.5 2008 <2 <0.5 15.4 2.8


El Salvador 6.0c 9.6c 2008f 5.4 1.9 14.0 4.8 2009f 9.0 4.4 16.9 7.6


Estonia 11.0 17.7 2003 <2 <0.5 2.6 <0.5 2004 <2 <0.5 <2 0.5


Ethiopia 3.4 5.5 2005 39.0 9.6 77.6 28.9 2011 30.7 8.2 66.0 23.6


Fiji 1.9 3.1 2003 29.2 11.3 48.7 21.8 2009 5.9 1.1 22.9 6.0


Gabon 554.7 887.5 .. .. .. .. 2005 4.8 0.9 19.6 5.0


Gambia, The 12.9 20.7 1998 65.6 33.8 81.2 49.1 2003 29.8 9.8 55.9 24.4


Georgia 1.0 1.6 2008 15.3 4.6 32.2 11.7 2010 18.0 5.8 35.6 13.7


Ghana 5,594.8 8,951.6 1998 39.1 14.4 63.3 28.5 2006 28.6 9.9 51.8 21.3


Guatemala 5.7c 9.1c 2004f 24.4 13.2 39.2 20.2 2006f 13.5 4.7 26.3 10.5


Guinea 1,849.5 2,959.1 2003 56.3 21.3 80.8 39.7 2007 43.3 15.0 69.6 31.0


Poverty rates




World Development Indicators 2013 29Economy States and markets Global links Back


International poverty
line in local currency


Population below international poverty linesa


$1.25 a day $2 a day
Survey
yearb


Population
below


$1.25 a day
%


Poverty gap
at $1.25


a day
%


Population
below


$2 a day
%


Poverty
gap at


$2 a day
%


Survey
yearb


Population
below


$1.25 a day
%


Poverty gap
at $1.25


a day
%


Population
below


$2 a day
%


Poverty
gap at


$2 a day
%2005 2005


Guinea-Bissau 355.3 568.6 1993 52.1 20.6 75.7 37.4 2002 48.9 16.6 78.0 34.9


Guyana 131.5c 210.3c 1993e 6.9 1.5 17.1 5.4 1998e 8.7 2.8 18.0 6.7


Haiti 24.2c 38.7c .. .. .. .. 2001 61.7 32.3 77.5 46.7


Honduras 12.1c 19.3c 2008f 21.4 11.8 32.6 17.5 2009f 17.9 9.4 29.8 14.9


Hungary 171.9 275.0 2004 <2 <0.5 <2 <0.5 2007 <2 <0.5 <2 <0.5


India 19.5i 31.2i 2005h 41.6 10.5 75.6 29.5 2010h 32.7 7.5 68.7 24.5


Indonesia 5,241.0i 8,385.7i 2009h 20.4 4.1 52.7 16.5 2010h 18.1 3.3 46.1 14.3


Iran, Islamic Rep. 3,393.5 5,429.6 1998 <2 <0.5 8.3 1.8 2005 <2 <0.5 8.0 1.8


Iraq 799.8 1,279.7 .. .. .. .. 2007 2.8 <0.5 21.4 4.4


Jamaica 54.2c 86.7c 2002 <2 <0.5 8.5 1.5 2004 <2 <0.5 5.4 0.8


Jordan 0.6 1.0 2008 <2 <0.5 2.1 <0.5 2010 <2 <0.5 <2 <0.5


Kazakhstan 81.2 129.9 2008 <2 <0.5 <2 <0.5 2009 <2 <0.5 <2 <0.5


Kenya 40.9 65.4 1997 19.6 4.6 42.7 14.7 2005 43.4 16.9 67.2 31.8


Kyrgyz Republic 16.2 26.0 2009 6.2 1.4 21.7 6.0 2010 6.7 1.5 22.9 6.4


Lao PDR 4,677.0 7,483.2 2002 44.0 12.1 76.9 31.1 2008 33.9 9.0 66.0 24.8


Latvia 0.4 0.7 2008 <2 <0.5 <2 <0.5 2009 <2 <0.5 <2 <0.5


Lesotho 4.3 6.9 1994 46.2 25.6 59.7 36.1 2003 43.4 20.8 62.3 33.1


Liberia 0.6 1.0 .. .. .. .. 2007 83.8 40.9 94.9 59.6


Lithuania 2.1 3.3 2004 <2 <0.5 <2 0.5 2008 <2 <0.5 <2 <0.5


Macedonia, FYR 29.5 47.2 2009 <2 <0.5 5.9 0.9 2010 <2 <0.5 6.9 1.2


Madagascar 945.5 1,512.8 2005 67.8 26.5 89.6 46.9 2010 81.3 43.3 92.6 60.1


Malawi 71.2 113.8 1998 83.1 46.0 93.5 62.3 2004 73.9 32.3 90.5 51.8


Malaysia 2.6 4.2 2007e <2 <0.5 2.9 <0.5 2009e <2 <0.5 2.3 <0.5


Maldives 358.3 573.5 .. .. .. .. 2004 <2 <0.5 12.2 2.5


Mali 362.1 579.4 2006 51.4 18.8 77.1 36.5 2010 50.4 16.4 78.7 35.2


Mauritania 157.1 251.3 2004 25.4 7.0 52.6 19.2 2008 23.4 6.8 47.7 17.7


Mexico 9.6 15.3 2008 <2 <0.5 5.2 1.3 2010 <2 <0.5 4.5 1.0


Micronesia, Fed. Sts. 0.8c 1.3c .. .. .. .. 2000d 31.2 16.3 44.7 24.5


Moldova 6.0 9.7 2009 <2 <0.5 7.1 1.2 2010 <2 <0.5 4.4 0.7


Montenegro 0.6 1.0 2008 <2 <0.5 <2 <0.5 2010 <2 <0.5 <2 <0.5


Morocco 6.9 11.0 2001 6.3 0.9 24.3 6.3 2007 2.5 0.5 14.0 3.2


Mozambique 14,532.1 23,251.4 2003 74.7 35.4 90.0 53.6 2008 59.6 25.1 81.8 42.9


Namibia 6.3 10.1 1993e 49.1 24.6 62.2 36.5 2004e 31.9 9.5 51.1 21.8


Nepal 33.1 52.9 2003 53.1 18.4 77.3 36.6 2010 24.8 5.6 57.3 19.0


Nicaragua 9.1c 14.6c 2001e 14.4 3.7 34.4 11.5 2005e 11.9 2.4 31.7 9.6


Niger 334.2 534.7 2005 50.2 18.3 75.3 35.6 2008 43.6 12.4 75.2 30.8


Nigeria 98.2 157.2 2004 63.1 28.7 83.1 45.9 2010 68.0 33.7 84.5 50.2


Pakistan 25.9 41.4 2006 22.6 4.1 61.0 18.8 2008 21.0 3.5 60.2 17.9


Panama 0.8c 1.2c 2009f 5.9 1.8 14.6 4.9 2010f 6.6 2.1 13.8 5.1


Papua New Guinea 2.1c 3.4c .. .. .. .. 1996 35.8 12.3 57.4 25.5


Paraguay 2,659.7 4,255.6 2009f 7.6 3.2 14.2 6.0 2010f 7.2 3.0 13.2 5.7


Peru 2.1 3.3 2009f 5.5 1.6 14.0 4.6 2010f 4.9 1.3 12.7 4.1


Philippines 30.2 48.4 2006 22.6 5.5 45.0 16.4 2009 18.4 3.7 41.5 13.8


Poland 2.7 4.3 2009 <2 <0.5 <2 <0.5 2010 <2 <0.5 <2 <0.5


Romania 2.1 3.4 2009 <2 <0.5 <2 0.5 2010 <2 <0.5 <2 <0.5


Russian Federation 16.7 26.8 2008 <2 <0.5 <2 <0.5 2009 <2 <0.5 <2 <0.5


Rwanda 295.9 473.5 2006 72.1 34.8 87.4 52.2 2011 63.2 26.6 82.4 44.6


Poverty rates




30 World Development Indicators 2013 Front User guide World view People Environment?


International poverty
line in local currency


Population below international poverty linesa


$1.25 a day $2 a day
Survey
yearb


Population
below


$1.25 a day
%


Poverty gap
at $1.25


a day
%


Population
below


$2 a day
%


Poverty
gap at


$2 a day
%


Survey
yearb


Population
below


$1.25 a day
%


Poverty gap
at $1.25


a day
%


Population
below


$2 a day
%


Poverty
gap at


$2 a day
%2005 2005


São Tomé and Príncipe 7,953.9 12,726.3 .. .. .. .. 2001 28.2 7.9 54.2 20.6


Senegal 372.8 596.5 2005 33.5 10.8 60.4 24.7 2011 29.6 9.1 55.2 21.9


Serbia 42.9 68.6 2009 <2 <0.5 <2 <0.5 2010 <2 <0.5 <2 <0.5


Seychelles 5.6c 9.0c 2000 <2 <0.5 <2 <0.5 2007 <2 <0.5 <2 <0.5


Sierra Leone 1,745.3 2,792.4 1990 62.8 44.8 75.0 54.0 2003 53.4 20.3 76.1 37.5


Slovak Republic 23.5 37.7 2008e <2 <0.5 <2 <0.5 2009e <2 <0.5 <2 <0.5


Slovenia 198.2 317.2 2003 <2 <0.5 <2 <0.5 2004 <2 <0.5 <2 <0.5


South Africa 5.7 9.1 2006 17.4 3.3 35.7 12.3 2009 13.8 2.3 31.3 10.2


Sri Lanka 50.0 80.1 2007 7.0 1.0 29.1 7.4 2010 4.1 0.7 23.9 5.4


St. Lucia 2.4c 3.8c .. .. .. .. 1995 20.9 7.2 40.6 15.5


Sudan 154.4 247.0 .. .. .. .. 2009 19.8 5.5 44.1 15.4


Suriname 2.3c 3.7c .. .. .. .. 1999 15.5 5.9 27.2 11.7


Swaziland 4.7 7.5 2001 62.9 29.4 81.0 45.8 2010 40.6 16.0 60.4 29.3


Syrian Arab Republic 30.8 49.3 .. .. .. .. 2004 <2 <0.5 16.9 3.3


Tajikistan 1.2 1.9 2007 14.7 4.4 37.0 12.2 2009 6.6 1.2 27.7 7.0


Tanzania 603.1 964.9 2000 84.6 41.6 95.3 60.3 2007 67.9 28.1 87.9 47.5


Thailand 21.8 34.9 2009j <2 <0.5 4.6 0.8 2010j <2 <0.5 4.1 0.7


Togo 352.8 564.5 2006 38.7 11.4 69.3 27.9 2011 28.2 8.8 52.7 20.9


Trinidad and Tobago 5.8c 9.2c 1988e <2 <0.5 8.6 1.9 1992e 4.2 1.1 13.5 3.9


Tunisia 0.9 1.4 2005 <2 <0.5 8.1 1.8 2010 <2 <0.5 4.3 1.1


Turkey 1.3 2.0 2008 <2 <0.5 4.2 0.7 2010 <2 <0.5 4.7 1.4


Turkmenistan 5,961.1c 9,537.7c 1993e 63.5 25.8 85.7 44.9 1998e 24.8 7.0 49.7 18.4


Uganda 930.8 1,489.2 2006 51.5 19.1 75.6 36.4 2009 38.0 12.2 64.7 27.4


Ukraine 2.1 3.4 2009 <2 <0.5 <2 <0.5 2010 <2 <0.5 <2 <0.5


Uruguay 19.1 30.6 2009f <2 <0.5 <2 <0.5 2010f <2 <0.5 <2 <0.5


Venezuela, RB 1,563.9 2,502.2 2005f 13.4 8.2 21.9 11.6 2006f 6.6 3.7 12.9 5.9


Vietnam 7,399.9 11,839.8 2006 21.4 5.3 48.1 16.3 2008 16.9 3.8 43.4 13.5


West Bank and Gaza 2.7c 4.3c 2007 <2 <0.5 2.5 0.5 2009 <2 <0.5 <2 <0.5


Yemen, Rep. 113.8 182.1 1998 12.9 3.0 36.4 11.1 2005 17.5 4.2 46.6 14.8


Zambia 3,537.9 5,660.7 2004 64.3 32.8 81.5 48.3 2006 68.5 37.0 82.6 51.8


a. Based on nominal per capita consumption averages and distributions estimated parametrically from grouped household survey data, unless otherwise noted. b. Refers to the year in
which the underlying household survey data were collected or, when the data collection period bridged two calendar years, the year in which most of the data were collected. c. Based on
purchasing power parity (PPP) dollars imputed using regression. d. Covers urban areas only. e. Based on per capita income averages and distribution data estimated parametrically from
grouped household survey data. f. Estimated nonparametrically from nominal income per capita distributions based on unit-record household survey data. g. PPP conversion factor based
on urban prices. h. Population-weighted average of urban and rural estimates. i. Based on benchmark national PPP estimate rescaled to account for cost-of-living differences in urban and
rural areas. j. Estimated nonparametrically from nominal consumption per capita distributions based on unit-record household survey data.


Poverty rates




World Development Indicators 2013 31Economy States and markets Global links Back


Poverty rates
Trends in poverty indicators by region, 1990–2015


Region 1990 1993 1996 1999 2002 2005 2008 2010 provisional 2015 projection Trend, 1990–2010


Share of population living on less than 2005 PPP $1.25 a day (%)


East Asia & Pacific 56.2 50.7 35.9 35.6 27.6 17.1 14.3 12.5 5.5


Europe & Central Asia 1.9 2.9 3.9 3.8 2.3 1.3 0.5 0.7 0.4


Latin America & Caribbean 12.2 11.4 11.1 11.9 11.9 8.7 6.5 5.5 4.9


Middle East & North Africa 5.8 4.8 4.8 5.0 4.2 3.5 2.7 2.4 2.6


South Asia 53.8 51.7 48.6 45.1 44.3 39.4 36.0 31.0 23.2


Sub- Saharan Africa 56.5 59.4 58.1 57.9 55.7 52.3 49.2 48.5 42.3


Total 43.1 41.0 34.8 34.1 30.8 25.1 22.7 20.6 15.5


People living on less than 2005 PPP $1.25 a day (millions)


East Asia & Pacific 926 871 640 656 523 332 284 251 115


Europe & Central Asia 9 14 18 18 11 6 2 3 2


Latin America & Caribbean 53 53 54 60 63 48 37 32 30


Middle East & North Africa 13 12 12 14 12 10 9 8 9


South Asia 617 632 631 619 640 598 571 507 406


Sub- Saharan Africa 290 330 349 376 390 395 399 414 408


Total 1,908 1,910 1,704 1,743 1,639 1,389 1,302 1,215 970


Regional distribution of people living on less than $1.25 a day (% of total population living on less than $1.25 a day)


East Asia & Pacific 48.5 45.6 37.6 37.6 31.9 23.9 21.8 20.7 11.8


Europe & Central Asia 0.5 0.7 1.1 1.0 0.6 0.5 0.2 0.3 0.2


Latin America & Caribbean 2.8 2.7 3.1 3.4 3.8 3.4 2.9 2.7 3.1


Middle East & North Africa 0.7 0.6 0.7 0.8 0.7 0.8 0.7 0.7 1.0


South Asia 32.3 33.1 37.0 35.5 39.1 43.1 43.8 41.7 41.9


Sub- Saharan Africa 15.2 17.3 20.5 21.6 23.8 28.4 30.7 34.1 42.1


Average daily consumption or income of people living on less than 2005 PPP $1.25 a day (2005 PPP $)


East Asia & Pacific 0.83 0.85 0.89 0.87 0.89 0.95 0.95 0.97 ..


Europe & Central Asia 0.90 0.90 0.91 0.92 0.93 0.88 0.88 0.85 ..


Latin America & Caribbean 0.71 0.69 0.68 0.67 0.65 0.63 0.62 0.60 ..


Middle East & North Africa 1.02 1.02 1.01 1.01 1.01 0.99 0.97 0.96 ..


South Asia 0.88 0.89 0.91 0.91 0.92 0.94 0.95 0.96 ..


Sub- Saharan Africa 0.69 0.68 0.69 0.69 0.70 0.71 0.71 0.71 ..


Total 0.82 0.83 0.85 0.84 0.85 0.87 0.87 0.87 ..


Survey coverage (% of total population represented by underlying survey data)


East Asia & Pacific 92.4 93.3 93.7 93.4 93.5 93.2 93.6 93.5 ..


Europe & Central Asia 81.5 87.3 97.1 93.9 96.3 94.7 89.9 85.3 ..


Latin America & Caribbean 94.9 91.8 95.9 97.7 97.5 95.9 94.5 86.9 ..


Middle East & North Africa 76.8 65.3 81.7 70.0 21.5 85.7 46.7 30.8 ..


South Asia 96.5 98.2 98.1 20.1 98.0 98.0 97.9 94.5 ..


Sub- Saharan Africa 46.0 68.8 68.0 53.1 65.7 82.7 81.7 64.1 ..


Total 86.4 89.4 91.6 68.2 87.8 93.0 90.2 84.7 ..


Source: World Bank PovcalNet.




Front User guide World view People Environment?32 World Development Indicators 2013


The World Bank produced its first global poverty estimates for


developing countries for World Development Report 1990: Poverty


(World Bank 1990) using household survey data for 22 coun-


tries (Ravallion, Datt, and van de Walle 1991). Since then there


has been considerable expansion in the number of countries


that field household income and expenditure surveys. The World


Bank’s Development Research Group maintains a database that


is updated regularly as new survey data become available (and


thus may contain more recent data or revisions that are not incor-


porated into the table) and conducts a major reassessment of


progress against poverty about every three years.


This year the database has been updated with global and regional


extreme poverty estimates for 2010, which are provisional due to


low coverage in household survey data availability for recent years


(2008–12). The projections to 2015 of poverty rates use the 2010


provisional estimates as a baseline and assume that average house-


hold income or consumption will grow in line with the aggregate eco-


nomic projections reported in this year’s Global Monitoring Report


(World Bank 2013) but that inequality within countries will remain


unchanged. This methodology was first used and described in the


Global Economic Prospects report (World Bank 2000). Estimates of


the number of people living in extreme poverty are based on popula-


tion projections in the World Bank’s HealthStats database (http://


datatopics.worldbank.org/hnp).


PovcalNet (http://iresearch.worldbank.org/PovcalNet) is an


interactive computational tool that allows users to replicate these


internationally comparable $1.25 and $2 a day global, regional and


country-level poverty estimates and to compute poverty measures


for custom country groupings and for different poverty lines. The


Poverty and Equity Data portal (http://povertydata.worldbank.org/


poverty/home) provides access to the database and user-friendly


dashboards with graphs and interactive maps that visualize trends in


key poverty and inequality indicators for different regions and coun-


tries. The country dashboards display trends in poverty measures


based on the national poverty lines (see online table 2.7) alongside


the internationally comparable estimates in the table, produced


from and consistent with PovcalNet.


Data availability


The World Bank’s internationally comparable poverty monitoring


database now draws on income or detailed consumption data


collected from interviews with 1.23 million randomly sampled


households through more than 850 household surveys collected


by national statistical offices in nearly 130 countries. Despite prog-


ress in the last decade, the challenges of measuring poverty remain.


The timeliness, frequency, quality, and comparability of household


surveys need to increase substantially, particularly in the poorest


countries. The availability and quality of poverty monitoring data


remain low in small states, fragile situations, and low-income coun-


tries and even some middle-income countries.


The low frequency and lack of comparability of the data available


in some countries create uncertainty over the magnitude of poverty


reduction. The table on trends in poverty indicators reports the


percentage of the regional and global population represented by


household survey samples collected during the reference year or


during the two preceding or two subsequent years (in other words,


within a five-year window centered on the reference year). Data cov-


erage in Sub- Saharan Africa and the Middle East and North Africa


remains low and variable. The need to improve household survey


programs for monitoring poverty is clearly urgent. But institutional,


political, and financial obstacles continue to limit data collection,


analysis, and public access.


Data quality


Besides the frequency and timeliness of survey data, other data


quality issues arise in measuring household living standards. The


surveys ask detailed questions on sources of income and how it


was spent, which must be carefully recorded by trained personnel.


Income is generally more difficult to measure accurately, and con-


sumption comes closer to the notion of living standards. And income


can vary over time even if living standards do not. But consumption


data are not always available: the latest estimates reported here


use consumption for about two-thirds of countries.


However, even similar surveys may not be strictly comparable


because of differences in timing, sampling frames, or the quality


and training of enumerators. Comparisons of countries at different


levels of development also pose a potential problem because of dif-


ferences in the relative importance of the consumption of nonmarket


goods. The local market value of all consumption in kind (includ-


ing own production, particularly important in underdeveloped rural


economies) should be included in total consumption expenditure, but


may not be. Most survey data now include valuations for consump-


tion or income from own production, but valuation methods vary.


The statistics reported here are based on consumption data or,


when unavailable, on income data. Analysis of some 20 countries


for which both were available from the same surveys found income


to yield a higher mean than consumption but also higher inequality.


When poverty measures based on consumption and income were


compared, the two effects roughly cancelled each other out: there


was no significant statistical difference.


Invariably some sampled households do not participate in


surveys because they refuse to do so or because nobody is


at home during the interview visit. This is referred to as “unit


nonresponse” and is distinct from “item nonresponse,” which


occurs when some of the sampled respondents participate but


refuse to answer certain questions, such as those pertaining to


income or consumption. To the extent that survey nonresponse


is random, there is no concern regarding biases in survey-based


inferences; the sample will still be representative of the popula-


tion. However, households with different income might not be


equally likely to respond. Richer households may be less likely


Poverty rates
About the data




World Development Indicators 2013 33Economy States and markets Global links Back


to participate because of the high opportunity cost of their time


or concerns about intrusion in their affairs. It is conceivable that


the poorest can likewise be underrepresented; some are home-


less or nomadic and hard to reach in standard household survey


designs, and some may be physically or socially isolated and


thus less likely to be interviewed. This can bias both poverty and


inequality measurement if not corrected for (Korinek, Mistiaen,


and Ravallion 2007).


International poverty lines


International comparisons of poverty estimates entail both concep-


tual and practical problems. Countries have different definitions of


poverty, and consistent comparisons across countries can be diffi-


cult. Local poverty lines tend to have higher purchasing power in rich


countries, where more generous standards are used, than in poor


countries. Poverty measures based on an international poverty line


attempt to hold the real value of the poverty line constant across


countries, as is done when making comparisons over time. Since


World Development Report 1990 the World Bank has aimed to apply


a common standard in measuring extreme poverty, anchored to what


poverty means in the world’s poorest countries. The welfare of people


living in different countries can be measured on a common scale by


adjusting for differences in the purchasing power of currencies. The


commonly used $1 a day standard, measured in 1985 international


prices and adjusted to local currency using purchasing power parities


(PPPs), was chosen for World Development Report 1990 because it


was typical of the poverty lines in low-income countries at the time.


Early editions of World Development Indicators used PPPs from the


Penn World Tables to convert values in local currency to equivalent


purchasing power measured in U.S dollars. Later editions used 1993


consumption PPP estimates produced by the World Bank.


International poverty lines were recently revised using the new


data on PPPs compiled in the 2005 round of the International Com-


parison Program, along with data from an expanded set of house-


hold income and expenditure surveys. The new extreme poverty


line is set at $1.25 a day in 2005 PPP terms, which represents the


mean of the poverty lines found in the poorest 15 countries ranked


by per capita consumption. The new poverty line maintains the same


standard for extreme poverty—the poverty line typical of the poorest


countries in the world—but updates it using the latest information


on the cost of living in developing countries. PPP exchange rates are


used to estimate global poverty because they take into account the


local prices of goods and services not traded internationally. But


PPP rates were designed for comparing aggregates from national


accounts, not for making international poverty comparisons. As a


result, there is no certainty that an international poverty line mea-


sures the same degree of need or deprivation across countries.


So-called poverty PPPs, designed to compare the consumption of


the poorest people in the world, might provide a better basis for


comparison of poverty across countries. Work on these measures


is ongoing.


Definitions


• International poverty line in local currency is the international


poverty lines of $1.25 and $2.00 a day in 2005 prices, converted


to local currency using the PPP conversion factors estimated by


the International Comparison Program. • Survey year is the year in


which the underlying data were collected or, when the data collection


period bridged two calendar years, the year in which most of the data


were collected. • Population below $1.25 a day and population


below $2 a day are the percentages of the population living on less


than $1.25 a day and $2 a day at 2005 international prices. As a


result of revisions in PPP exchange rates, consumer price indexes,


or welfare aggregates, poverty rates for individual countries cannot


be compared with poverty rates reported in earlier editions. The


PovcalNet online database and tool always contain the most recent


full time series of comparable country data. • Poverty gap is the


mean shortfall from the poverty line (counting the nonpoor as hav-


ing zero shortfall), expressed as a percentage of the poverty line.


This measure reflects the depth of poverty as well as its incidence.


Data sources


The poverty measures are prepared by the World Bank’s Develop-


ment Research Group. The international poverty lines are based on


nationally representative primary household surveys conducted by


national statistical offices or by private agencies under the supervi-


sion of government or international agencies and obtained from


government statistical offices and World Bank Group country depart-


ments. For details on data sources and methods used in deriving


the World Bank’s latest estimates, see http://iresearch.worldbank


.org/PovcalNet/index.htm.


References


Chen, Shaohua, and Martin Ravallion. 2011. “The Developing World Is


Poorer Than We Thought, But No Less Successful in the Fight Against


Poverty.” Quarterly Journal of Economics 125(4): 1577–1625.


Korinek, Anton, Johan A. Mistiaen, and Martin Ravallion. 2007. “An


Econometric Method of Correcting for Unit Nonresponse Bias in


Surveys.” Journal of Econometrics 136: 213–35.


Ravallion, Martin, Guarav Datt, and Dominique van de Walle. 1991.


“Quantifying Absolute Poverty in the Developing World.” Review of


Income and Wealth 37(4): 345–61.


Ravallion, Martin, Shaohua Chen, and Prem Sangraula. 2009. “Dol-


lar a Day Revisited.” World Bank Economic Review 23(2): 163–84.


World Bank. 1990. World Development Report 1990: Poverty. Wash-


ington, DC.


———. 2000. Global Economic Prospects and the Developing Coun-


tries. Washington, DC.


———. 2008. Poverty Data: A Supplement to World Development Indi-


cators 2008. Washington, DC.


———. 2013. Global Monitoring Report: Rural-Urban Dynamics and


the Millennium Development Goals. Washington, DC.


Poverty rates




34 World Development Indicators 2013 Front User guide World view People Environment?


PEOPLE




World Development Indicators 2013 35Economy States and markets Global links Back


The indicators in the People section present
demographic trends and forecasts alongside
indicators of education, health, jobs, social pro-
tection, poverty, and the distribution of income.
Together they provide a multidimensional portrait
of human development.


Data updates in this edition include provi-
sional estimates of global and regional extreme
poverty rates for 2010—measured as the propor-
tion of the population living on less than $1.25
a day. The availability, frequency, and quality of
poverty monitoring data remain low, especially in
small states and in countries and territories with
fragile situations. While estimates may change
marginally as additional country data become
available, it is now clear that the first Millennium
Development Goal target—cutting the global
extreme poverty rate to half its 1990 level—was
achieved before the 2015 target date. In 1990,
the benchmark year for the Millennium Devel-
opment Goals, the extreme poverty rate was
43.1 percent. Estimates for 2010 show that the
extreme poverty rate had fallen to 20.6 percent.


In addition to extreme poverty rates, the Peo-
ple section includes many other indicators used
to monitor the Millennium Development Goals.
Following the adoption of the Millennium Declara-
tion by the United Nations General Assembly in
2000, various international agencies including
the World Bank resolved to invest in using high-
quality harmonized data to monitor the Millen-
nium Development Goals. These efforts range
from providing technical and financial assis-
tance to strengthen country statistical systems
to fostering international collaboration through
participation in the United Nations Inter-Agency
and Expert Group on the Millennium Develop-
ment Goal Indicators and several thematic


inter-agency groups. These forums bring together
agency subject matter specialists with leading
academic and local experts. Successful the-
matic inter-agency efforts have improved esti-
mates on child mortality, child malnutrition, as
well as maternal mortality, one of the most chal-
lenging indicators to measure.


For 2013 the People section includes im-
proved child malnutrition indicators produced by
the United Nations Children’s Fund, the World
Health Organization, and the World Bank using
a harmonized dataset and the same statistical
methodology to estimate global, regional, and
income group aggregates.


Another result is the improvement in monitor-
ing HIV prevalence. Initial efforts relied solely on
country surveillance systems that collected data
from pregnant women who attended sentinel
antenatal clinics. Now the methodology measur-
ing this indicator uses all available information—
including blood test data collected through
nationally representative sample surveys—and
the models account for the effects of anti retro-
viral therapy and urbanization (see About the
data for online table 2.20).


In addition to providing insights into differ-
ences between countries and between groups of
countries, the People section includes indicators
disaggregated by location and by socioeconomic
and demographic strata within countries, such
as gender, age, and wealth quintile. These data
provide a deeper perspective on the disparities
within countries. New indicators for 2013 include
sex-disaggregated data on the under-five mortal-
ity rate, sex-disaggregated youth labor force par-
ticipation rates, and numerous indicators from
the World Bank’s Atlas of Social Protection Indica-
tors of Resilience and Equity database.


2




36 World Development Indicators 2013


Highlights


Front User guide World view People Environment?


East Asia & Pacific: Narrowing gaps in access to improved sanitation facilities and water sources


0


25


50


75


100


37


56


30


66


22


38


26
31


71


86


68


90


71


90


48


61


20101990201019902010199020101990


Share of population with access (%)
To improved sanitation facilities To improved water sources


Sub-Saharan
Africa


South
Asia


East Asia
& Pacic


Developing
countries


During the past 20 years the share of the developing world’s population


with access to improved sanitation facilities and water sources has


risen substantially. East Asia and Pacific has made above-average


progress on improving both and narrowing the gap between them,


driven by a rapid expansion in access to improved sanitation from


30 percent in 1990 to 66 percent in 2010. In South Asia progress on


access to an improved water source was almost on par with East Asia


and Pacific, but access to improved sanitation facilities remains very


low. Indeed, the region fares only marginally better than Sub- Saharan


Africa, which progressed substantially slower over the past two


decades. But both regions made progress on expanding the share of


the population with access to an improved water source.


Source: Online table 2.16.


Europe & Central Asia: Variation in prevalence of underweight children


0


5


10


15


20


25


Prevalence of child malnutrition, underweight,
most recent year, 2005–11 (% of children under age 5)


Su
b-


Sa
ha


ra
n


Af
ric


a


M
id


dl
e


Ea
st


&
No


rth
A


fri
ca


Ea
st


A
si


a
&


P
ac


i
c


La
tin


A
m


er
ic


a
&


C
ar


ib
be


an


Eu
ro


pe
&


Ce
nt


ra
l A


si
a


Ta
jik


is
ta


n


Al
ba


ni
a


Ar
m


en
ia


M
ol


do
va


Bu
lg


ar
ia


Ge
or


gi
a


Malnourishment in children has been linked to poverty, low levels of


education, and poor access to health services. It increases the risk of


death, inhibits cognitive development, and can adversely affect health


status during adulthood. Adequate nutrition is a cornerstone for devel-


opment, health, and the survival of current and future generations.


Europe and Central Asia has the lowest prevalence (1.5 percent) of


underweight children among developing regions, but there is substan-


tial variation across countries. At 3.2 percent, prevalence in Moldova


is more like that in Latin America and the Caribbean countries; at


5.3 percent, prevalence in Armenia is more like that in East Asia and


Pacific countries; and at 6.3 percent, prevalence in Albania is more


like that in Middle East and North African countries. Tajikistan, a low-


income country, at 15 percent has the highest proportion of under-


weight children in the region.
Source: Online table 2.18.


Latin America & Caribbean: Income inequality falling but remains among the world’s highest


25 35 45 55 65
25


35


45


55


65


Gini coefcient, most recent value, 2000–11


Gini coefcient (most recent value, 1990–99)


Inequality
increased


Inequality
decreased


Inequality
unchanged


Latin America & Caribbean countries
Other upper middle income countries
Other countries


The Gini coefficient, a common indicator of income inequality, mea-


sures how much the per capita income distribution within a country


deviates from perfect equality. (A Gini coefficient of 0 represents


perfect equality, and a value of 100 perfect inequality.) Trends in


developing countries over the past two decades suggest that many


countries have become less unequal, but trends differ by region and


by income. Inequality in Latin America and the Caribbean fell notably


in almost all upper middle-income countries but remains among the


highest worldwide. The Gini coefficient is higher than the Latin


America and the Caribbean average in only two other upper middle-


income countries and in only a few low- and lower middle- income


countries.


Source: Online table 2.9 and World Bank PovcalNet.




World Development Indicators 2013 37Economy States and markets Global links Back


Middle East & North Africa: Many educated women are not participating in the labor force


More than 70 percent of girls in the Middle East and North Africa


attend secondary school (higher than most developing countries), but


labor force participation rates have stagnated at around 20 percent


since 1990 (lower than most developing countries). Eight of the ten


countries with the largest gap between their labor force participation


rate and secondary enrollment ratio are in the Middle East and North


Africa. (Costa Rica and Sri Lanka are the only countries in the top 10


not from the region.) Morocco and the Republic of Yemen have the


smallest gap, but they also have the lowest secondary enrollment ratio.


In addition to whether women participate in economic activities, where


and in what condition women work are also important. The region also


has the largest gender gap in the share of vulnerable employment (see


online table 2.4).


0


25


50


75


100


Gross secondary enrollment ratio for girls (% of relevant age group)
Labor force participation rate for women (% of women ages 15
and older)


De
ve


lo
pi


ng
co


un
tri


es
,


20
11


M
id


dl
e


Ea
st


&
No


rth
A


fri
ca


,
20


11


Ye
m


en
, R


ep
.,


20
11


M
or


oc
co


,
20


07


Eg
yp


t,
Ar


ab
Re


p.
, 2


01
0


Ira
n,


Is
la


m
ic


Re
p.


, 2
01


1


Le
ba


no
n,


20
11


Jo
rd


an
,


20
10


Tu
ni


si
a,


20
09


Al
ge


ria
,


20
09


Source: Online tables 2.2 and 2.11.


South Asia: In India more girls than boys die before their fifth birthday


The ratio of girls’ to boys’ under-five mortality rate varies across


developing countries, but on average it is 0.96. East Asia and Pacific


has the largest variation among developing regions. At one extreme


is Palau, where mortality rates for boys are two-thirds higher than


that for girls. Biologically, boys are more vulnerable than girls, so


under-five mortality rates are usually higher for boys than girls. This


biological advantage can be reversed, however, by socioeconomic


factors such as gender inequalities in nutrition and medical care or


discrimination against girls. India and the Solomon Islands are the


only developing countries where more girls than boys die before their


fifth birthday. In India under-five mortality rates for girls and boys have


improved, but the ratio of girls’ to boys’ under-five deaths has stayed


at around 1.1 since 1990.
0.50 0.75 1.00


(parity)
1.25


Sub-Saharan
Africa


South
Asia


Middle East &
North Africa


Latin America
& Caribbean


Europe &
Central Asia


East Asia
& Pacic


Ratio of girls’ to boys’ under-ve mortality rate, 2011
Lowest in region Highest in region


1.06


0.92


0.93


0.96


1.09


0.98


0.59


0.73


0.75


0.85


0.81


0.83Eritrea


Sri Lanka


Tunisia


Jamaica


Belarus


Palau


South Sudan


India


Iran, Islamic Rep.


Grenada


Azerbaijan


Solomon Islands


Source: Online table 2.21.


Sub- Saharan Africa: Different demographic transition paths at varying speeds


Today, one of every nine children in Sub- Saharan Africa dies before


their fifth birthday, and fertility rates there remain higher than any-


where else in the developing world. Sub- Saharan countries pro-


gressed over the past four decades, but they are moving along dif-


ferent paths at varying speeds. Gabon, ahead of the curve, had the


under-five mortality and total fertility rates in 1980 that the region


has today. Niger made little progress between 1970 and 1990 but


has since seen rapid improvements in its under-five mortality rate


and a marginal decline in its fertility rate. In 1970 Côte d’Ivoire had


an average mortality rate but a high fertility rate. Its mortality rate


declined quickly until 1980, thereafter its fertility rate declined rap-


idly and overtook the region by 2010. Mali, which had the highest


under-five mortality rate in 1970, took until 2010 to reach the rate


the region passed in 1990.


3 4 5 6 7 8 9
0


100


200


300


400


Under-ve mortality rate (per 1,000 live births)


Total fertility rate (births per woman)


Côte d’Ivoire Gabon
Mali Niger
Sub-Saharan Africa


1970


1970
1990


2010


1970


2010


1970


1980


1980


2010


2010


2010


Source: Online table 2.21.




38 World Development Indicators 2013 Front User guide World view People Environment?


Prevalence
of child


malnutrition,
underweight


Under-five
mortality


rate


Maternal
mortality


ratio


Adolescent
fertility


rate


Prevalence
of HIV


Primary
completion


rate


Youth
literacy


rate


Labor force
participation


rate


Vulnerable
employment


Unemployment Female
legislators,


senior
officials, and


managers


Unpaid family
workers and
own-account


workers
% of total


employment
% of children
under age 5


per 1,000
live births


Modeled
estimate


per 100,000
live births


births per
1,000


women ages
15–19


% of
population


ages 15–49
% of relevant


age group
% ages
15–24


% ages 15
and older


% of total
labor force % of total


2005–11a 2011 2010 2011 2011 2011 2005–11a 2011 2007–11a 2007–11a 2007–11a


Afghanistan .. 101 460 103 <0.1 .. .. 49 .. .. ..


Albania 6.3 14 27 16 .. .. 99 60 .. 13.8 ..


Algeria 3.7 30 97 6 .. 94 92 44 .. 10.0 ..


American Samoa .. .. .. .. .. .. .. .. .. .. ..


Andorra .. 3 .. .. .. 63 .. .. .. .. ..


Angola 15.6 158 450 153 2.1 .. 73 70 .. .. ..


Antigua and Barbuda .. 8 .. .. .. 98 .. .. .. .. ..


Argentina 2.3 14 77 55 0.4 .. 99 61 19 7.2 ..


Armenia 5.3 18 30 34 0.2 83 100 59 38 28.6 22


Aruba .. .. .. 28 .. .. 99 .. 4 5.7 40


Australia .. 5 7 13 0.2 .. .. 66 9 5.1 37


Austria .. 4 4 10 0.4 .. .. 61 9 4.1 27


Azerbaijan 8.4 45 43 32 <0.1 93 100 65 55 5.4 7


Bahamas, The .. 16 47 29 2.8 .. .. 74 .. 13.7 52


Bahrain .. 10 20 15 .. .. 100 71 2 .. ..


Bangladesh 41.3 46 240 70 <0.1 .. 77 71 .. 5.0 ..


Barbados .. 20 51 41 0.9 111 .. 70 .. 11.2 47


Belarus 1.3 6 4 21 0.4 104 100 56 .. .. ..


Belgium .. 4 8 12 0.3 .. .. 54 10 7.1 30


Belize 4.9 17 53 72 2.3 110 .. 65 .. 8.2 ..


Benin 20.2 106 350 100 1.2 75 55 73 .. .. ..


Bermuda .. .. .. .. .. .. .. .. .. .. ..


Bhutan 12.7 54 180 46 0.3 103b 74 72 71 3.1 27


Bolivia 4.5 51 190 75 0.3 .. 99 72 55 3.4 29


Bosnia and Herzegovina 1.6 8 8 14 .. 76 100 46 25 27.6 ..


Botswana 11.2 26 160 45 23.4 .. 95 77 .. .. ..


Brazil 2.2 16 56 76 0.3 .. 98 70 25 8.3 36


Brunei Darussalam .. 7 24 23 .. 120 100 66 .. .. ..


Bulgaria .. 12 11 38 <0.1 .. 98 54 9 11.2 37


Burkina Faso 26.0 146 300 119 1.1 .. 39 84 .. 3.3 ..


Burundi 35.2 139 800 20 1.3 62 78 83 .. .. ..


Cambodia 29.0 43 250 35 0.6 90 87 83 69 0.2 21


Cameroon 16.6 127 690 118 4.6 78 83 71 76 3.8 ..


Canada .. 6 12 12 0.3 .. .. 67 .. 7.4 36


Cape Verde .. 21 79 72 1.0 95 98 67 .. .. ..


Cayman Islands .. .. .. .. .. .. 99 .. .. 4.0 44


Central African Republic 28.0 164 890 100 4.6 43 65 79 .. .. ..


Chad .. 169 1,100 143 3.1 38 47 72 .. .. ..


Channel Islands .. .. .. 9 .. .. .. .. .. .. ..


Chile 0.5 9 25 56 0.5 .. 99 60 24 7.1 ..


China 3.4 15 37 9 <0.1 .. 99 74 .. 4.1 ..


Hong Kong SAR, China .. .. .. 4 .. 91 .. 59 7 3.4 32


Macao SAR, China .. .. .. 4 .. .. 100 72 4 2.6 31


Colombia 3.4 18 92 69 0.5 112 98 67 49 11.6 ..


Comoros .. 79 280 52 <0.1 .. 86 58 .. .. ..


Congo, Dem. Rep. 28.2 168 540 177 .. .. 65 71 .. .. ..


Congo, Rep. 11.8 99 560 114 3.3 .. 80 71 .. .. ..


2 People




World Development Indicators 2013 39Economy States and markets Global links Back


People 2
Prevalence


of child
malnutrition,
underweight


Under-five
mortality


rate


Maternal
mortality


ratio


Adolescent
fertility


rate


Prevalence
of HIV


Primary
completion


rate


Youth
literacy


rate


Labor force
participation


rate


Vulnerable
employment


Unemployment Female
legislators,


senior
officials, and


managers


Unpaid family
workers and
own-account


workers
% of total


employment
% of children
under age 5


per 1,000
live births


Modeled
estimate


per 100,000
live births


births per
1,000


women ages
15–19


% of
population


ages 15–49
% of relevant


age group
% ages
15–24


% ages 15
and older


% of total
labor force % of total


2005–11a 2011 2010 2011 2011 2011 2005–11a 2011 2007–11a 2007–11a 2007–11a


Costa Rica 1.1 10 40 63 0.3 99 98 63 20 7.7 35


Côte d’Ivoire 29.4 115 400 110 3.0 59 67 67 .. .. ..


Croatia .. 5 17 13 <0.1 .. 100 53 18 13.4 25


Cuba 1.3 6 73 44 0.2 99 100 57 .. 2.5 ..


Curaçao .. .. .. .. .. .. .. .. .. .. ..


Cyprus .. 3 10 6 .. .. 100 65 14 7.7 14


Czech Republic .. 4 5 10 <0.1 .. .. 59 14 6.7 26


Denmark .. 4 12 5 0.2 .. .. 64 6 7.6 28


Djibouti 29.6 90 200 20 1.4 57b .. 52 .. .. ..


Dominica .. 12 .. .. .. 94 .. .. .. .. ..


Dominican Republic 3.4 25 150 105 0.7 92 97 65 37 12.4 31


Ecuador .. 23 110 81 0.4 112 99 68 42 4.2 ..


Egypt, Arab Rep. 6.8 21 66 42 <0.1 .. 88 49 27 9.0 11


El Salvador 6.6 15 81 77 0.6 101 96 62 38 7.0 29


Equatorial Guinea .. 118 240 116 4.7 52 98 87 .. .. ..


Eritrea .. 68 240 56 0.6 .. 89 85 .. .. ..


Estonia .. 4 2 18 1.3 .. 100 62 5 12.5 36


Ethiopia 29.2 77 350 53 1.4 58 55 84 .. .. ..


Faeroe Islands .. .. .. .. .. .. .. .. .. .. ..


Fiji .. 16 26 43 <0.1 103 .. 60 .. 8.6 ..


Finland .. 3 5 9 <0.1 .. .. 60 9 7.7 32


France .. 4 8 6 0.4 .. .. 56 7 9.3 39


French Polynesia .. .. .. 49 .. .. .. 58 .. 11.7 ..


Gabon .. 66 230 83 5.0 .. 98 61 .. .. ..


Gambia, The 15.8 101 360 69 1.5 66 67 78 .. .. ..


Georgia 1.1 21 67 41 0.2 .. 100 64 63 15.1 34


Germany 1.1 4 7 7 0.2 .. .. 60 7 5.9 30


Ghana 14.3 78 350 64 1.5 99b 81 69 .. .. ..


Greece 1.1 4 3 10 0.2 .. 99 55 29 17.7 23


Greenland .. .. .. .. .. .. .. .. .. .. ..


Grenada .. 13 24 37 .. .. .. .. .. .. ..


Guam .. .. .. 50 .. .. .. 61 .. .. ..


Guatemala 13.0 30 120 103 0.8 .. 87 68 .. 4.1 ..


Guinea 20.8 126 610 138 1.4 .. 63 72 .. .. ..


Guinea-Bissau 17.2 161 790 99 2.5 .. 72 73 .. .. ..


Guyana 11.1 36 280 57 1.1 85 .. 60 .. 21.0 ..


Haiti 18.9 70 350 42 1.8 .. 72 65 .. .. ..


Honduras 8.6 21 100 87 .. 101 95 62 53 4.8 ..


Hungary .. 6 21 14 <0.1 .. 99 51 7 10.9 40


Iceland .. 3 5 12 0.3 .. .. 75 8 7.1 40


India 43.5 61 200 77 .. .. 81 56 81 3.5 14


Indonesia 18.6 32 220 43 0.3 .. 99 68 57 6.6 22


Iran, Islamic Rep. .. 25 21 26 0.2 106 99 45 42 10.5 13


Iraq 7.1 38 63 88 .. .. 83 42 .. .. ..


Ireland .. 4 6 11 0.3 .. .. 61 12 14.4 33


Isle of Man .. .. .. .. .. .. .. .. .. .. ..


Israel .. 4 7 14 0.2 .. .. 57 7 5.6 32




40 World Development Indicators 2013 Front User guide World view People Environment?


2 People
Prevalence


of child
malnutrition,
underweight


Under-five
mortality


rate


Maternal
mortality


ratio


Adolescent
fertility


rate


Prevalence
of HIV


Primary
completion


rate


Youth
literacy


rate


Labor force
participation


rate


Vulnerable
employment


Unemployment Female
legislators,


senior
officials, and


managers


Unpaid family
workers and
own-account


workers
% of total


employment
% of children
under age 5


per 1,000
live births


Modeled
estimate


per 100,000
live births


births per
1,000


women ages
15–19


% of
population


ages 15–49
% of relevant


age group
% ages
15–24


% ages 15
and older


% of total
labor force % of total


2005–11a 2011 2010 2011 2011 2011 2005–11a 2011 2007–11a 2007–11a 2007–11a


Italy .. 4 4 5 0.4 .. 100 48 18 8.4 25


Jamaica 1.9 18 110 71 1.8 .. 95 64 37 12.7 ..


Japan .. 3 5 6 <0.1 .. .. 60 11 4.5 ..


Jordan 1.9 21 63 24 .. .. 99 42 9 12.9 ..


Kazakhstan 4.9 28 51 26 0.2 108b 100 72 30 5.4 38


Kenya 16.4 73 360 99 6.2 .. 93 67 .. .. ..


Kiribati .. 47 .. .. .. .. .. .. .. .. ..


Korea, Dem. Rep. 18.8 33 81 1 .. .. 100 78 .. .. ..


Korea, Rep. .. 5 16 5 <0.1 .. .. 60 25 3.4 10


Kosovo .. .. .. .. .. .. .. .. .. 45.4 ..


Kuwait 1.7 11 14 14 .. .. 99 68 .. .. ..


Kyrgyz Republic 2.7 31 71 33 0.4 96 100 67 .. 8.2 ..


Lao PDR 31.6 42 470 32 0.3 93 84 78 .. .. ..


Latvia .. 8 34 14 0.7 93 100 61 8 15.4 45


Lebanon 5.2 9 25 16 <0.1 87 99 46 28 9.0 8


Lesotho 13.5 86 620 63 23.3 68 92 66 .. 25.3 ..


Liberia 20.4 78 770 127 1.0 66 77 61 79 3.7 ..


Libya 5.6 16 58 3 .. .. 100 53 .. .. ..


Liechtenstein .. 2 .. .. .. 101 .. .. .. .. ..


Lithuania .. 6 8 17 <0.1 95 100 59 8 15.4 38


Luxembourg .. 3 20 9 0.3 .. .. 57 6 4.9 24


Macedonia, FYR 1.8 10 10 19 .. .. 99 56 23 31.4 28


Madagascar .. 62 240 125 0.3 73 65 86 .. .. ..


Malawi 13.8 83 460 108 10.0 71 87 83 .. .. ..


Malaysia 12.9 7 29 11 0.4 .. 98 60 22 3.4 25


Maldives 17.8 11 60 11 <0.1 107 99 66 .. .. ..


Mali 27.9 176 540 172 1.1 55 44 53 83 .. ..


Malta .. 6 8 13 <0.1 .. 98 51 9 6.4 23


Marshall Islands .. 26 .. .. .. 97 .. .. .. .. ..


Mauritania 15.9 112 510 73 1.1 .. 68 54 .. 31.2 ..


Mauritius .. 15 60 33 1.0 .. 97 60 15 7.9 23


Mexico 3.4 16 50 67 0.3 104 98 62 29 5.3 31


Micronesia, Fed. Sts. .. 42 100 20 .. .. .. .. .. .. ..


Moldova 3.2 16 41 30 0.5 91 99 42 29 6.7 38


Monaco .. 4 .. .. .. .. .. .. .. .. ..


Mongolia 5.3 31 63 19 <0.1 115 96 60 58 .. 47


Montenegro 2.2 7 8 15 .. 99b 99 .. .. 19.7 31


Morocco 3.1 33 100 12 0.2 99b 79 50 52 8.9 13


Mozambique 18.3 103 490 129 11.3 56 72 85 .. .. ..


Myanmar 22.6 62 200 13 0.6 .. 96 79 .. .. ..


Namibia 17.5 42 200 58 13.4 .. 93 64 14 37.6 ..


Nepal 29.1 48 170 90 0.3 .. 83 84 .. 2.7 ..


Netherlands .. 4 6 4 0.2 .. .. 65 11 4.4 30


New Caledonia .. .. .. 20 .. .. 100 58 .. .. ..


New Zealand .. 6 15 21 <0.1 .. .. 68 12 6.5 40


Nicaragua 5.7 26 95 106 0.2 .. 87 63 47 8.0 ..


Niger 39.9 125 590 196 0.8 46 37 65 .. .. ..




World Development Indicators 2013 41Economy States and markets Global links Back


People 2
Prevalence


of child
malnutrition,
underweight


Under-five
mortality


rate


Maternal
mortality


ratio


Adolescent
fertility


rate


Prevalence
of HIV


Primary
completion


rate


Youth
literacy


rate


Labor force
participation


rate


Vulnerable
employment


Unemployment Female
legislators,


senior
officials, and


managers


Unpaid family
workers and
own-account


workers
% of total


employment
% of children
under age 5


per 1,000
live births


Modeled
estimate


per 100,000
live births


births per
1,000


women ages
15–19


% of
population


ages 15–49
% of relevant


age group
% ages
15–24


% ages 15
and older


% of total
labor force % of total


2005–11a 2011 2010 2011 2011 2011 2005–11a 2011 2007–11a 2007–11a 2007–11a


Nigeria 26.7 124 630 113 3.7 .. 72 56 .. .. ..


Northern Mariana Islands .. .. .. .. .. .. .. .. .. .. ..


Norway .. 3 7 8 0.2 .. .. 66 5 3.3 31


Oman 8.6 9 32 9 .. 107 98 61 .. .. ..


Pakistan 30.9 72 260 29 <0.1 67 71 53 63 5.0 3


Palau .. 19 .. .. .. .. .. .. .. .. ..


Panama 3.9 20 92 77 0.8 101 98 66 29 4.5 46


Papua New Guinea 18.1 58 230 63 0.7 .. 68 72 .. 4.0 ..


Paraguay 3.4 22 99 68 0.3 .. 99 72 42 5.6 34


Peru 4.5 18 67 50 0.4 97 97 76 40 7.8 19


Philippines 20.7 25 99 48 <0.1 .. 98 64 41 7.0 55


Poland .. 6 5 13 <0.1 .. 100 56 18 9.6 38


Portugal .. 3 8 13 0.7 .. 100 62 16 12.7 33


Puerto Rico .. .. 20 51 .. .. 87 45 .. 15.7 43


Qatar .. 8 7 16 .. 96 97 86 0 0.6 10


Romania .. 13 27 29 <0.1 .. 97 56 32 7.4 31


Russian Federation .. 12 34 25 .. .. 100 63 6 6.6 37


Rwanda 11.7 54 340 36 2.9 .. 77 86 .. 2.4 ..


Samoa .. 19 100 26 .. 98 99 61 .. .. ..


San Marino .. 2 .. .. .. 93 .. .. .. 2.6 18


São Tomé and Príncipe 14.4 89 70 58 1.0 115 95 60 .. .. ..


Saudi Arabia 5.3 9 24 20 .. 106 98 50 .. 5.4 8


Senegal 19.2 65 370 93 0.7 63 65 77 .. .. ..


Serbia 1.8 7 12 20 <0.1 99 99 .. 27 19.2 33


Seychelles .. 14 .. .. .. 125 99 .. .. .. ..


Sierra Leone 21.3 185 890 112 1.6 74 59 68 .. .. ..


Singapore .. 3 3 6 <0.1 .. 100 67 10 2.9 34


Sint Maarten .. .. .. .. .. .. .. .. .. .. ..


Slovak Republic .. 8 6 17 <0.1 .. .. 59 12 13.5 31


Slovenia .. 3 12 5 <0.1 .. 100 59 13 8.2 38


Solomon Islands 11.5 22 93 66 .. .. .. 67 .. .. ..


Somalia 32.8 180 1,000 68 0.7 .. .. 57 .. .. ..


South Africa 8.7 47 300 52 17.3 .. 98 52 10 24.7 31


South Sudan .. 121 .. .. 3.1 .. .. .. .. .. ..


Spain .. 4 6 11 0.4 .. 100 59 11 21.6 30


Sri Lanka 21.6 12 35 22 <0.1 .. 98 55 42 4.9 24


St. Kitts and Nevis .. 7 .. .. .. 93 .. .. .. .. ..


St. Lucia .. 16 35 57 .. 93 .. 71 .. 14.0 ..


St. Martin .. .. .. .. .. .. .. .. .. .. ..


St. Vincent and Grenadines .. 21 48 55 .. .. .. 67 .. .. ..


Sudan 31.7 86d 730 55 0.4d .. 87 54 .. .. ..


Suriname 7.5 30 130 36 1.0 88 98 55 .. .. ..


Swaziland 7.3 104 320 71 26.0 77 94 57 .. .. ..


Sweden .. 3 4 6 0.2 .. .. 64 7 7.5 35


Switzerland .. 4 8 4 0.4 .. .. 68 9 4.1 33


Syrian Arab Republic 10.1 15 70 38 .. 106 95 43 33 8.4 9


Tajikistan 15.0 63 65 26 0.3 104 100 66 .. .. ..




42 World Development Indicators 2013 Front User guide World view People Environment?


2 People
Prevalence


of child
malnutrition,
underweight


Under-five
mortality


rate


Maternal
mortality


ratio


Adolescent
fertility


rate


Prevalence
of HIV


Primary
completion


rate


Youth
literacy


rate


Labor force
participation


rate


Vulnerable
employment


Unemployment Female
legislators,


senior
officials, and


managers


Unpaid family
workers and
own-account


workers
% of total


employment
% of children
under age 5


per 1,000
live births


Modeled
estimate


per 100,000
live births


births per
1,000


women ages
15–19


% of
population


ages 15–49
% of relevant


age group
% ages
15–24


% ages 15
and older


% of total
labor force % of total


2005–11a 2011 2010 2011 2011 2011 2005–11a 2011 2007–11a 2007–11a 2007–11a


Tanzania 16.2 68 460 129 5.8 81b 77 89 .. .. ..


Thailand 7.0 12 48 38 1.2 .. 98 72 54 0.7 25


Timor-Leste 45.3 54 300 55 .. 72 80 57 70 3.6 ..


Togo 20.5 110 300 57 3.4 77 82 81 .. .. ..


Tonga .. 15 110 19 .. .. 99 64 .. .. ..


Trinidad and Tobago .. 28 46 32 1.5 .. 100 66 .. 4.6 ..


Tunisia 3.3 16 56 5 <0.1 .. 97 48 .. 13.0 ..


Turkey .. 15 20 32 <0.1 .. 98 50 33 9.8 10


Turkmenistan .. 53 67 17 .. .. 100 61 .. .. ..


Turks and Caicos Islands .. .. .. .. .. .. .. .. .. 5.4 ..


Tuvalu 1.6 30 .. .. .. .. .. .. .. .. ..


Uganda 16.4 90 310 131 7.2 55 87 78 .. 4.2 ..


Ukraine .. 10 32 27 0.8 97 100 59 .. 7.9 39


United Arab Emirates .. 7 12 24 .. .. 95 79 1 4.0 10


United Kingdom .. 5 12 30 0.3 .. .. 62 12 7.8 34


United States .. 8 21 30 0.7 .. .. 64 .. 8.9 43


Uruguay .. 10 29 59 0.6 .. 99 66 22 6.0 40


Uzbekistan 4.4 49 28 13 .. 93 100 61 .. .. ..


Vanuatu 11.7 13 110 51 .. .. 94 71 70 4.6 29


Venezuela, RB 3.7 15 92 88 0.6 95 99 66 33 8.3 ..


Vietnam 20.2 22 59 24 0.5 104 97 77 63 2.0 ..


Virgin Islands (U.S.) .. .. .. 23 .. .. .. 62 .. .. ..


West Bank and Gaza 2.2 22 .. 49 .. 91 99 41 26 23.7 10


Yemen, Rep. .. 77 200 69 0.2 63 85 49 .. 14.6 ..


Zambia 14.9 83 440 140 12.5 .. 74 79 .. .. ..


Zimbabwe 10.1 67 570 56 14.9 .. 99 86 .. .. ..


World 15.7 w 51 w 210 w 53 w 0.8 w 90 w 90 w 64 w .. w 5.9 w


Low income 22.6c 95 410 92 2.4 68 74 75 .. ..


Middle income 16.0c 46 190 50 .. .. 91 64 .. 5.2


Lower middle income 24.3c 62 260 66 .. .. 84 58 71 4.9


Upper middle income 2.9c 20 62 30 0.6 .. 99 69 .. 5.0


Low & middle income 17.4c 56 230 57 1.0 89 88 65 .. 5.2


East Asia & Pacific 5.5c 21 83 19 0.2 .. 99 73 .. 4.2


Europe & Central Asia 1.5c 21 32 26 .. 98 99 59 18 8.0


Latin America & Carib. 3.1c 19 81 71 0.4 102 97 66 31 7.7


Middle East & N. Africa 6.3c 32 81 37 .. 90 91 46 .. 10.6


South Asia 33.2c 62 220 71 .. .. 79 57 78 3.5


Sub- Saharan Africa 21.4c 109 500 106 4.9 70 72 70 .. ..


High income 1.7c 6 14 17 0.4 100 100 60 .. 8.1


Euro area .. 4 6 8 0.3 99 100 57 11 10.1


a. Data are for the most recent year available. b. Data are for 2012. c. Calculated by World Bank staff using the United Nations Children’s Fund, World Health Organization, and World Bank
harmonized database and aggregation method. d. Excludes South Sudan.




World Development Indicators 2013 43Economy States and markets Global links Back


People 2
Though not included in the table due to space limitations, many


indicators in this section are available disaggregated by sex, place


of residence, wealth, and age in the World Development Indicators


database.


Child malnutrition


Good nutrition is the cornerstone for survival, health, and devel-


opment. Well-nourished children perform better in school, grow


into healthy adults, and in turn give their children a better start


in life. Well-nourished women face fewer risks during pregnancy


and childbirth, and their children set off on firmer developmental


paths, both physically and mentally. Undernourished children have


lower resistance to infection and are more likely to die from com-


mon childhood ailments such as diarrheal diseases and respiratory


infections. Frequent illness saps the nutritional status of those who


survive, locking them into a vicious cycle of recurring sickness and


faltering growth.


The proportion of underweight children is the most common child


malnutrition indicator. Being even mildly underweight increases the


risk of death and inhibits cognitive development in children. And


it perpetuates the problem across generations, as malnourished


women are more likely to have low-birthweight babies. Estimates


of prevalence of underweight children are from the World Health


Organization’s (WHO) Global Database on Child Growth and Malnu-


trition, a standardized compilation of child growth and malnutrition


data from national nutritional surveys. To better monitor global child


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


and the World Bank have jointly produced estimates for 2011 and


trends since 1990 for regions, income groups, and the world, using


a harmonized database and aggregation method.


Under-five mortality


Mortality rates for children and others are important indicators


of health status. When data on the incidence and prevalence of


diseases are unavailable, mortality rates may be used to identify


vulnerable populations. And they are among the indicators most


frequently used to compare socioeconomic development across


countries.


The main sources of mortality data are vital registration sys-


tems and direct or indirect estimates based on sample surveys


or censuses. A complete vital registration system—covering at


least 90 percent of vital events in the population—is the best


source of age-specific mortality data. But complete vital registra-


tion systems are fairly uncommon in developing countries. Thus


estimates must be obtained from sample surveys or derived by


applying indirect estimation techniques to registration, census,


or survey data (see Primary data documentation). Survey data are


subject to recall error.


To make estimates comparable and to ensure consistency across


estimates by different agencies, the United Nations Inter-agency


Group for Child Mortality Estimation, which comprises UNICEF,


WHO, the United Nations Population Division, the World Bank,


and other universities and research institutes, has developed and


adopted a statistical method that uses all available information to


reconcile differences. Trend lines are obtained by fitting a country-


specific regression model of mortality rates against their reference


dates. (For further discussion of childhood mortality estimates,


see UN Inter-agency Group for Child Mortality Estimation 2012; for


detailed background data and for a graphic presentation, see www


.childmortality.org).


Maternal mortality


Measurements of maternal mortality are subject to many types


of errors. In countries with incomplete vital registration systems,


deaths of women of reproductive age or their pregnancy status may


not be reported, or the cause of death may not be known. Even in


high-income countries with reliable vital registration systems, mis-


classification of maternal deaths has been found to lead to serious


underestimation. Surveys and censuses can be used to measure


maternal mortality by asking respondents about survivorship of sis-


ters. But these estimates are retrospective, referring to a period


approximately five years before the survey, and may be affected by


recall error. Further, they reflect pregnancy-related deaths (deaths


while pregnant or within 42 days of pregnancy termination, irrespec-


tive of the cause of death) and need to be adjusted to conform to


the strict definition of maternal death.


Maternal mortality ratios in the table are modeled estimates


based on work by the WHO, UNICEF, United Nations Population Fund


(UNFPA), and World Bank and include country-level time series data.


For countries without complete registration data but with other types


of data and for countries with no data, maternal mortality is esti-


mated with a multilevel regression model using available national


maternal mortality data and socioeconomic information, including


fertility, birth attendants, and gross domestic product. The meth-


odology differs from that used for previous estimates, so data pre-


sented here should not be compared across editions.


Adolescent fertility


Reproductive health is a state of physical and mental well-being


in relation to the reproductive system and its functions and pro-


cesses. Means of achieving reproductive health include education


and services during pregnancy and childbirth, safe and effective


contraception, and prevention and treatment of sexually transmitted


diseases. Complications of pregnancy and childbirth are the leading


cause of death and disability among women of reproductive age in


developing countries.


Adolescent pregnancies are high risk for both mother and child.


They are more likely to result in premature delivery, low birthweight,


delivery complications, and death. Many adolescent pregnancies are


unintended, but young girls may continue their pregnancies, giving


up opportunities for education and employment, or seek unsafe


abortions. Estimates of adolescent fertility rates are based on vital


About the data




44 World Development Indicators 2013 Front User guide World view People Environment?


2 People
registration systems or, in their absence, censuses or sample sur-


veys and are generally considered reliable measures of fertility in the


recent past. Where no empirical information on age-specific fertility


rates is available, a model is used to estimate the share of births to


adolescents. For countries without vital registration systems fertility


rates are generally based on extrapolations from trends observed


in censuses or surveys from earlier years.


Prevalence of HIV


HIV prevalence rates reflect the rate of HIV infection in each coun-


try’s population. Low national prevalence rates can be misleading,


however. They often disguise epidemics that are initially concen-


trated in certain localities or population groups and threaten to spill


over into the wider population. In many developing countries most


new infections occur in young adults, with young women especially


vulnerable.


Data on HIV prevalence are from the Joint United Nations Pro-


gramme on HIV/AIDS. Changes in procedures and assumptions for


estimating the data and better coordination with countries have


resulted in improved estimates. New models track the course of


HIV epidemics and their impacts, making full use of information


on HIV prevalence trends from surveillance data as well as survey


data. The models include the effect of antiretroviral therapy, take


into account reduced infectivity among people receiving antiretrovi-


ral therapy (which is having a larger impact on HIV prevalence and


allowing HIV-positive people to live longer), and allow for changes in


urbanization over time (important because prevalence is higher in


urban areas and because many countries have seen rapid urbaniza-


tion over the past two decades). The estimates include plausible


bounds, available at http://data.worldbank.org, which reflect the


certainty associated with each of the estimates.


Primary completion


Many governments publish statistics that indicate how their educa-


tion systems are working and developing—statistics on enrollment


and efficiency indicators such as repetition rates, pupil–teacher


ratios, and cohort progression. The primary completion rate, also


called the gross intake ratio to last grade of primary education, is


a core indicator of an education system’s performance. It reflects


an education system’s coverage and the educational attainment


of students. It is a key measure of progress toward the Millennium


Development Goals and the Education for All initiative. However, a


high primary completion rate does not necessarily mean high levels


of student learning.


The indicator reflects the primary cycle as defined by the Interna-


tional Standard Classification of Education (ISCED97), ranging from


three or four years of primary education (in a very small number of


countries) to five or six years (in most countries) and seven (in a


small number of countries). It is a proxy that should be taken as an


upper estimate of the actual primary completion rate, since data


limitations preclude adjusting for students who drop out during the


final year of primary education. There are many reasons why the pri-


mary completion rate may exceed 100 percent. The numerator may


include late entrants and overage children who have repeated one


or more grades of primary education as well as children who entered


school early, while the denominator is the number of children at the


entrance age for the last grade of primary education.


Youth literacy


The youth literacy rate for ages 15–24 is a standard measure of


recent progress in student achievement. It reflects the accumulated


outcomes of primary education by indicating the proportion of the


population that has acquired basic literacy and numeracy skills over


the previous 10 years or so. In practice, however, literacy is difficult


to measure. Estimating literacy rates requires census or survey mea-


surements under controlled conditions. Many countries estimate


the number of literate people from self-reported data. Some use


educational attainment data as a proxy but apply different lengths


of school attendance or levels of completion. Because definitions


and methods of data collection differ across countries, data should


be used cautiously. Generally, literacy encompasses numeracy, the


ability to make simple arithmetic calculations.


Data on youth literacy are compiled by the United Nations Educa-


tional, Scientific and Cultural Organization (UNESCO) Institute for


Statistics based on national censuses and household surveys dur-


ing 1985–2011 and, for countries without recent literacy data, using


the Global Age-Specific Literacy Projection Model.


Labor force participation


The labor force is the supply of labor available for producing goods


and services in an economy. It includes people who are currently


employed, people who are unemployed but seeking work, and first-


time job-seekers. Not everyone who works is included, however.


Unpaid workers, family workers, and students are often omitted,


and some countries do not count members of the armed forces.


Labor force size tends to vary during the year as seasonal workers


enter and leave.


Data on the labor force are compiled by the International Labour


Organization (ILO) from labor force surveys, censuses, and estab-


lishment censuses and surveys and from administrative records


such as employment exchange registers and unemployment insur-


ance schemes. Labor force surveys are the most comprehensive


source for internationally comparable labor force data. Labor force


data from population censuses are often based on a limited number


of questions on the economic characteristics of individuals, with


little scope to probe. Establishment censuses and surveys provide


data on the employed population only, not unemployed workers,


workers in small establishments, or workers in the informal sector


(ILO, Key Indicators of the Labour Market 2001–2002).


Besides the data sources, there are other important factors


that affect data comparability, such as census or survey reference


period, definition of working age, and geographic coverage. For




World Development Indicators 2013 45Economy States and markets Global links Back


People 2
country-level information on source, reference period, or definition,


consult the footnotes in the World Development Indicators data-


base or the ILO’s Key Indicators of the Labour Market, 7th edition,


database.


The labor force participation rates in the table are estimates


from the ILO’s Key Indicators of the Labour Market, 7th edition,


database. These harmonized estimates use strict data selection


criteria and enhanced methods to ensure comparability across


countries and over time to avoid the inconsistencies mentioned


above. Estimates are based mainly on labor force surveys, with


other sources (population censuses and nationally reported esti-


mates) used only when no survey data are available. Because other


employment data are mostly national estimates, caution should


be used when comparing labor force participation rate and other


employment data.


Vulnerable employment


The proportion of unpaid family workers and own-account workers in


total employment is derived from information on status in employ-


ment. Each group faces different economic risks, and unpaid family


workers and own-account workers are the most vulnerable—and


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


to have formal work arrangements, are the least likely to have social


protection and safety nets to guard against economic shocks, and


are often incapable of generating enough savings to offset these


shocks. A high proportion of unpaid family workers in a country


indicates weak development, little job growth, and often a large


rural economy.


Data on vulnerable employment are drawn from labor force and


general household sample surveys, censuses, and official esti-


mates. Besides the limitation mentioned for calculating labor force


participation rates, there are other reasons to limit comparability.


For example, information provided by the Organisation for Economic


Co-operation and Development relates only to civilian employment,


which can result in an underestimation of “employees” and “work-


ers not classified by status,” especially in countries with large


armed forces. While the categories of unpaid family workers and


own-account workers would not be affected, their relative shares


would be.


Unemployment


The ILO defines the unemployed as members of the economically


active population who are without work but available for and seek-


ing work, including people who have lost their jobs or who have


voluntarily left work. Some unemployment is unavoidable. At any


time some workers are temporarily unemployed—between jobs as


employers look for the right workers and workers search for better


jobs. Such unemployment, often called frictional unemployment,


results from the normal operation of labor markets.


Changes in unemployment over time may reflect changes in the


demand for and supply of labor, but they may also reflect changes


in reporting practices. In countries without unemployment or welfare


benefits people eke out a living in vulnerable employment. In coun-


tries with well-developed safety nets workers can afford to wait for


suitable or desirable jobs. But high and sustained unemployment


indicates serious inefficiencies in resource allocation.


The criteria for people considered to be seeking work, and the


treatment of people temporarily laid off or seeking work for the


first time, vary across countries. In many developing countries it


is especially difficult to measure employment and unemployment


in agriculture. The timing of a survey can maximize the effects of


seasonal unemployment in agriculture. And informal sector employ-


ment is difficult to quantify where informal activities are not tracked.


Data on unemployment are drawn from labor force sample surveys


and general household sample surveys, censuses, and official esti-


mates. Administrative records, such as social insurance statistics


and employment office statistics, are not included because of their


limitations in coverage.


Women tend to be excluded from the unemployment count for


various reasons. Women suffer more from discrimination and from


structural, social, and cultural barriers that impede them from seek-


ing work. Also, women are often responsible for the care of children


and the elderly and for household affairs. They may not be available


for work during the short reference period, as they need to make


arrangements before starting work. Further, women are considered


to be employed when they are working part-time or in temporary


jobs, despite the instability of these jobs or their active search for


more secure employment.


Female legislators, senior officials, and managers


Despite much progress in recent decades, gender inequalities


remain pervasive in many dimensions of life. But while gender


inequalities exist throughout the world, they are most prevalent in


developing countries. Inequalities in the allocation of education,


health care, nutrition, and political voice matter because of their


strong association with well-being, productivity, and economic


growth. These patterns of inequality begin at an early age, with boys


routinely receiving a larger share of education and health spending


than girls, for example. The share of women in high-skilled occu-


pations such as legislators, senior officials, and managers indi-


cates women’s status and role in the labor force and society at


large. Women are vastly underrepresented in decisionmaking posi-


tions in government, although there is some evidence of recent


improvement.




46 World Development Indicators 2013 Front User guide World view People Environment?


2 People
Definitions


• Prevalence of child malnutrition, underweight, is the percent-


age of children under age 5 whose weight for age is more than two


standard deviations below the median for the international refer-


ence population ages 0–59 months. Data are based on the WHO


child growth standards released in 2006. • Under-five mortality


rate is the probability of a child born in a specific year dying before


reaching age 5, if subject to the age-specific mortality rates of that


year. The probability is derived from life tables and is expressed as


a rate per 1,000 live births. • Maternal mortality ratio, modeled


estimate, is the number of women who die from pregnancy-related


causes while pregnant or within 42 days of pregnancy termination,


per 100,000 live births. • Adolescent fertility rate is the number


of births per 1,000 women ages 15–19. • Prevalence of HIV is the


percentage of people who are infected with HIV in the relevant age


group. • Primary completion rate is the number of new entrants


(enrollments minus repeaters) in the last grade of primary educa-


tion, regardless of age, divided by the population at the entrance age


for the last grade of primary education. Data limitations preclude


adjusting for students who drop out during the final year of primary


education. • Youth literacy rate is the percentage of the popula-


tion ages 15–24 that can, with understanding, both read and write


a short simple statement about their everyday life. • Labor force


participation rate is the proportion of the population ages 15 and


older that engages actively in the labor market, by either working or


looking for work during a reference period. • Vulnerable employment


is unpaid family workers and own-account workers as a percentage


of total employment. • Unemployment is the share of the labor force


without work but available for and seeking employment. Definitions


of labor force and unemployment may differ by country. • Female


legislators, senior officials, and managers are the percentage of


legislators, senior officials, and managers (International Standard


Classification of Occupations–88 category 1) who are female.


Data sources


Data on child malnutrition prevalence are from the WHO’s Global


Database on Child Growth and Malnutrition (www.who.int/


nutgrowthdb/en/). Data on under-five mortality rates are from UN


Inter-agency Group for Child Mortality Estimation (2012) and are


based mainly on household surveys, censuses, and vital registra-


tion data. Modeled estimates of maternal mortality ratios are from


WHO and others (2012). Data on adolescent fertility rates are from


United Nations Population Division (2011), with annual data linearly


interpolated by the World Bank’s Development Data Group. Data on


HIV prevalence are from UNAIDS (2012). Data on primary completion


rates and literacy rates are from the UNESCO Institute for Statistics


(www.uis.unesco.org). Data on labor force participation rates, vul-


nerable employment, unemployment, and female legislators, senior


officials, and managers are from the ILO’s Key Indicators of the


Labour Market, 7th edition, database.


References


De Onis, Mercedes, Monika Blössner, Elaine Borghi, Richard Mor-


ris, and Edward A. Frongillo. 2004. “Methodology for Estimating


Regional and Global Trends of Child Malnutrition.” International Jour-


nal of Epidemiology 33: 1260–70.


ILO (International Labour Organization).Various years. Key Indicators of


the Labour Market. Geneva: International Labour Office.


UNAIDS (Joint United Nations Programme on HIV/AIDS). 2012. Global


Report: UNAIDS Report on the Global AIDS Epidemic 2012. Geneva.


UNICEF (United Nations Children’s Fund), WHO (World Health Organi-


zation), and World Bank. 2012. Joint Child Malnutrition Estimates—


Levels and Trends. www.who.int/nutgrowthdb/jme_unicef_who_


wb.pdf. New York: UNICEF.


UN Inter-agency Group for Child Mortality Estimation. 2012. Levels and


Trends in Child Mortality: Report 2012. New York.


United Nations Population Division. 2011. World Population Prospects:


The 2010 Revision. New York: United Nations, Department of Eco-


nomic and Social Affairs.


WHO (World Health Organization), UNICEF (United Nations Children’s


Fund), UNFPA (United Nations Population Fund), and World Bank.


2012. Trends in Maternal Mortality: 1990 to 2010. Geneva: WHO.


World Bank. n.d. PovcalNet online database. http://iresearch


.worldbank.org/PovcalNet. Washington, DC.




World Development Indicators 2013 47Economy States and markets Global links Back


People 2


2.1 Population dynamics


Population SP.POP.TOTL


Population growth SP.POP.GROW


Population ages 0–14 SP.POP.0014.TO.ZS


Population ages 15–64 SP.POP.1564.TO.ZS


Population ages 65+ SP.POP.65UP.TO.ZS


Dependency ratio, Young SP.POP.DPND.YG


Dependency ratio, Old SP.POP.DPND.OL


Crude death rate SP.DYN.CDRT.IN


Crude birth rate SP.DYN.CBRT.IN


2.2 Labor force structure


Labor force participation rate, Male SL.TLF.CACT.MA.ZS


Labor force participation rate, Female SL.TLF.CACT.FE.ZS


Labor force, Total SL.TLF.TOTL.IN


Labor force, Average annual growth ..a,b


Labor force, Female SL.TLF.TOTL.FE.ZS


2.3 Employment by sector


Agriculture, Male SL.AGR.EMPL.MA.ZS


Agriculture, Female SL.AGR.EMPL.FE.ZS


Industry, Male SL.IND.EMPL.MA.ZS


Industry, Female SL.IND.EMPL.FE.ZS


Services, Male SL.SRV.EMPL.MA.ZS


Services, Female SL.SRV.EMPL.FE.ZS


2.4 Decent work and productive employment


Employment to population ratio, Total SL.EMP.TOTL.SP.ZS


Employment to population ratio, Youth SL.EMP.1524.SP.ZS


Vulnerable employment, Male SL.EMP.VULN.MA.ZS


Vulnerable employment, Female SL.EMP.VULN.FE.ZS


GDP per person employed SL.GDP.PCAP.EM.KD


2.5 Unemployment


Unemployment, Male SL.UEM.TOTL.MA.ZS


Unemployment, Female SL.UEM.TOTL.FE.ZS


Youth unemployment, Male SL.UEM.1524.MA.ZS


Youth unemployment, Female SL.UEM.1524.FE.ZS


Long-term unemployment, Total SL.UEM.LTRM.ZS


Long-term unemployment, Male SL.UEM.LTRM.MA.ZS


Long-term unemployment, Female SL.UEM.LTRM.FE.ZS


Unemployment by educational attainment,
Primary SL.UEM.PRIM.ZS


Unemployment by educational attainment,
Secondary SL.UEM.SECO.ZS


Unemployment by educational attainment,
Tertiary SL.UEM.TERT.ZS


2.6 Children at work


Children in employment, Total SL.TLF.0714.ZS


Children in employment, Male SL.TLF.0714.MA.ZS


Children in employment, Female SL.TLF.0714.FE.ZS


Work only SL.TLF.0714.WK.ZS


Study and work SL.TLF.0714.SW.ZS


Employment in agriculture SL.AGR.0714.ZS


Employment in manufacturing SL.MNF.0714.ZS


Employment in services SL.SRV.0714.ZS


Self-employed SL.SLF.0714.ZS


Wage workers SL.WAG0714.ZS


Unpaid family workers SL.FAM.0714.ZS


2.7 Poverty rates at national poverty lines


Poverty headcount ratio, Rural SI.POV.RUHC


Poverty headcount ratio, Urban SI.POV.URHC


Poverty headcount ratio, National SI.POV.NAHC


Poverty gap, Rural SI.POV.RUGP


Poverty gap, Urban SI.POV.URGP


Poverty gap, National SI.POV.NAGP


2.8 Poverty rates at international poverty lines


Population living below 2005 PPP $1.25
a day SI.POV.DDAY


Poverty gap at 2005 PPP $1.25 a day SI.POV.2DAY


Population living below 2005 PPP $2 a day SI.POV.GAPS


Poverty gap at 2005 PPP $2 a day SI.POV.GAP2


2.9 Distribution of income or consumption


Gini index SI.POV.GINI


Share of consumption or income, Lowest
10% of population SI.DST.FRST.10


Share of consumption or income, Lowest
20% of population SI.DST.FRST.20


Share of consumption or income, Second
20% of population SI.DST.02ND.20


Share of consumption or income, Third 20%
of population SI.DST.03RD.20


Share of consumption or income, Fourth
20% of population SI.DST.04TH.20


To access the World Development Indicators online tables, use


the URL http://wdi.worldbank.org/table/ and the table number (for


example, http://wdi.worldbank.org/table/2.1). To view a specific


indicator online, use the URL http://data.worldbank.org/indicator/


and the indicator code (for example, http://data.worldbank.org/


indicator/SP.POP.TOTL).


Online tables and indicators




48 World Development Indicators 2013 Front User guide World view People Environment?


2 People
Share of consumption or income, Highest
20% of population SI.DST.05TH.20


Share of consumption or income, Highest
10% of population SI.DST.10TH.10


2.10 Education inputs


Public expenditure per student, Primary SE.XPD.PRIM.PC.ZS


Public expenditure per student, Secondary SE.XPD.SECO.PC.ZS


Public expenditure per student, Tertiary SE.XPD.TERT.PC.ZS


Public expenditure on education, % of GDP SE.XPD.TOTL.GD.ZS


Public expenditure on education, % of total
government expenditure SE.XPD.TOTL.GB.ZS


Trained teachers in primary education SE.PRM.TCAQ.ZS


Primary school pupil-teacher ratio SE.PRM.ENRL.TC.ZS


2.11 Participation in education


Preprimary gross enrollment ratio SE.PRE.ENRR


Primary gross enrollment ratio SE.PRM.ENRR


Secondary gross enrollment ratio SE.SEC.ENRR


Tertiary gross enrollment ratio SE.TER.ENRR


Primary net enrollment rate SE.PRM.NENR


Secondary net enrollment rate SE.SEC.NENR


Primary adjusted net enrollment rate, Male SE.PRM.TENR.MA


Primary adjusted net enrollment rate, Female SE.PRM.TENR.FE


Primary school-age children out of school,
Male SE.PRM.UNER.MA


Primary school-age children out of school,
Female SE.PRM.UNER.FE


2.12 Education efficiency


Gross intake ratio in first grade of primary
education, Male SE.PRM.GINT.MA.ZS


Gross intake ratio in first grade of primary
education, Female SE.PRM.GINT.FE.ZS


Reaching grade 5, Male SE.PRM.PRS5.MA.ZS


Reaching grade 5, Female SE.PRM.PRS5.FE.ZS


Reaching last grade of primary education,
Male SE.PRM.PRSL.MA.ZS


Reaching last grade of primary education,
Female SE.PRM.PRSL.FE.ZS


Repeaters in primary education, Male SE.PRM.REPT.MA.ZS


Repeaters in primary education, Female SE.PRM.REPT.FE.ZS


Transition rate to secondary education, Male SE.SEC.PROG.MA.ZS


Transition rate to secondary education,
Female SE.SEC.PROG.FE.ZS


2.13 Education completion and outcomes


Primary completion rate, Total SE.PRM.CMPT.ZS


Primary completion rate, Male SE.PRM.CMPT.MA.ZS


Primary completion rate, Female SE.PRM.CMPT.FE.ZS


Youth literacy rate, Male SE.ADT.1524.LT.MA.ZS


Youth literacy rate, Female SE.ADT.1524.LT.FE.ZS


Adult literacy rate, Male SE.ADT.LITR.MA.ZS


Adult literacy rate, Female SE.ADT.LITR.FE.ZS


2.14 Education gaps by income and gender


This table provides education survey data
for the poorest and richest quintiles. ..b


2.15 Health systems


Total health expenditure SH.XPD.TOTL.ZS


Public health expenditure SH.XPD.PUBL


Out-of-pocket health expenditure SH.XPD.OOPC.ZS


External resources for health SH.XPD.EXTR.ZS


Health expenditure per capita, $ SH.XPD.PCAP


Health expenditure per capita, PPP $ SH.XPD.PCAP.PP.KD


Physicians SH.MED.PHYS.ZS


Nurses and midwives SH.MED.NUMW.P3


Community health workers SH.MED.CMHW.P3


Hospital beds SH.MED.BEDS.ZS


Completeness of birth registration SP.REG.BRTH.ZS


2.16 Disease prevention coverage and quality


Access to an improved water source SH.H2O.SAFE.ZS


Access to improved sanitation facilities SH.STA.ACSN


Child immunization rate, Measles SH.IMM.MEAS


Child immunization rate, DTP3 SH.IMM.IDPT


Children with acute respiratory infection
taken to health provider SH.STA.ARIC.ZS


Children with diarrhea who received oral
rehydration and continuous feeding SH.STA.ORCF.ZS


Children sleeping under treated bed nets SH.MLR.NETS.ZS


Children with fever receiving antimalarial
drugs SH.MLR.TRET.ZS


Tuberculosis treatment success rate SH.TBS.CURE.ZS


Tuberculosis case detection rate SH.TBS.DTEC.ZS


2.17 Reproductive health


Total fertility rate SP.DYN.TFRT.IN


Adolescent fertility rate SP.ADO.TFRT


Unmet need for contraception SP.UWT.TFRT


Contraceptive prevalence rate SP.DYN.CONU.ZS


Pregnant women receiving prenatal care SH.STA.ANVC.ZS


Births attended by skilled health staff SH.STA.BRTC.ZS


Maternal mortality ratio, National estimate SH.STA.MMRT.NE


Maternal mortality ratio, Modeled estimate SH.STA.MMRT


Lifetime risk of maternal mortality SH.MMR.RISK




World Development Indicators 2013 49Economy States and markets Global links Back


People 2
2.18 Nutrition and growth


Prevalence of undernourishment SN.ITK.DEFC.ZS


Prevalence of underweight, Male SH.STA.MALN.MA.ZS


Prevalence of underweight, Female SH.STA.MALN.FE.ZS


Prevalence of stunting, Male SH.STA.STNT.MA.ZS


Prevalence of stunting, Female SH.STA.STNT.FE.ZS


Prevalence of wasting, Male SH.STA.WAST.MA.ZS


Prevalence of wasting, Female SH.STA.WAST.FE.ZS


Prevalence of overweight children, Male SH.STA.OWGH.MA.ZS


Prevalence of overweight children, Female SH.STA.OWGH.FE.ZS


2.19 Nutrition intake and supplements


Low-birthweight babies SH.STA.BRTW.ZS


Exclusive breastfeeding SH.STA.BFED.ZS


Consumption of iodized salt SN.ITK.SALT.ZS


Vitamin A supplementation SN.ITK.VITA.ZS


Prevalence of anemia among children
under age 5 SH.ANM.CHLD.ZS


Prevalence of anemia among pregnant
women SH.PRG.ANEM


2.20 Health risk factors and future challenges


Prevalence of smoking, Male SH.PRV.SMOK.MA


Prevalence of smoking, Female SH.PRV.SMOK.FE


Incidence of tuberculosis SH.TBS.INCD


Prevalence of diabetes SH.STA.DIAB.ZS


Prevalence of HIV, Total SH.DYN.AIDS.ZS


Women’s share of population ages 15+
living with HIV SH.DYN.AIDS.FE.ZS


Prevalence of HIV, Youth male SH.HIV.1524.MA.ZS


Prevalence of HIV, Youth female SH.HIV.1524.FE.ZS


Antiretroviral therapy coverage SH.HIV.ARTC.ZS


Death from communicable diseases and
maternal, prenatal, and nutrition conditions SH.DTH.COMM.ZS


Death from non-communicable diseases SH.DTH.NCOM.ZS


Death from injuries SH.DTH.INJR.ZS


2.21 Mortality


Life expectancy at birth SP.DYN.LE00.IN


Neonatal mortality rate SH.DYN.NMRT


Infant mortality rate SP.DYN.IMRT.IN


Under-five mortality rate, Total SH.DYN.MORT


Under-five mortality rate, Male SH.DYN.MORT.MA


Under-five mortality rate, Female SH.DYN.MORT.FE


Adult mortality rate, Male SP.DYN.AMRT.MA


Adult mortality rate, Female SP.DYN.AMRT.FE


2.22 Health gaps by income


This table provides health survey data for
the poorest and richest quintiles. ..b


Data disaggregated by sex are available in
the World Development Indicators database.


a. Derived from data elsewhere in the World Development Indicators database.
b. Available online only as part of the table, not as an individual indicator.




50 World Development Indicators 2013 Front User guide World view People Environment?


ENVIRONMENT




World Development Indicators 2013 51Economy States and markets Global links Back


The Millennium Development Goals call for inte-
grating principles of environmental sustainability
into country policies and programs and reversing
environmental losses. Whether the world contin-
ues to sustain itself depends largely on properly
managing its natural resources. The indicators in
the Environment section measure resource use
and the way human activities affect the natural
and built environment. They include measures of
environmental goods (forest, water, cultivatable
land) and of degradation (pollution, deforesta-
tion, loss of habitat, and loss of biodiversity).
These indicators show that growing populations
and expanding economies have placed greater
demands on land, water, forests, minerals, and
energy sources. But new technologies, increas-
ing productivity, and better policies can ensure
that future development is environmentally and
socially sustainable.


Nowhere are these risks and opportunities
more intertwined than in the global effort to miti-
gate the effects of climate change. A continuing
rise in temperature, accompanied by changes
in precipitation patterns, is projected for this
century, as are more frequent, severe, and pro-
longed climate-related events such as floods
and droughts—posing risks for agriculture, food
production, and water supplies. Poor countries
and the poorest people in all countries are most
vulnerable to the changing climate.


The 2012 edition of World Development
Indicators included two new tables on climate


change. The first presents data on carbon diox-
ide emissions by economic sector from the
International Energy Agency’s annual time series
statistics. And the second contains country indi-
cators on climate variability, exposure to impact,
and resilience.


Other indicators in this section describe
land use, agriculture and food production, for-
ests and biodiversity, threatened species, water
resources, energy use and efficiency, electricity
production and use, greenhouse gas emissions,
urbanization, traffic and congestion, air pollution,
government commitments, and natural resource
rents.


Where possible, the indicators come from
international sources and are standardized to
facilitate comparison across countries. But eco-
systems span national boundaries, and access
to natural resources may vary within countries.
For example, water may be abundant in some
parts of a country but scarce in others, and
countries often share water resources. Land pro-
ductivity and optimal land use may be location
specific, but widely separated regions can have
common characteristics. Greenhouse gas emis-
sions and climate change are measured globally,
but their effects are experienced locally, shaping
people’s lives and opportunities. Measuring envi-
ronmental phenomena and their effects at the
subnational, national, and supranational levels
remains a major challenge for achieving long-
term, sustainable development.


3




52 World Development Indicators 2013


Highlights


Front User guide World view People Environment?


East Asia & Pacific: More access to improved sanitation facilities


0


20


40


60


80


100


20102005200019951990


Share of population with access to improved sanitation facilities (%)


East Asia & Pacic


Samoa


Tonga


Palau


Papua New Guinea


Cambodia


China


Timor-Leste


East Asia and Pacific has more than doubled the proportion of people


with access to improved sanitation facilities. This is an impressive


achievement, bringing access to basic sanitation facilities to more


than 700 million additional people, mostly in China. Because of its


size, China dominates the regional average of East Asia and Pacific.


But some countries progressed even faster than China, such as


Palau, with 100 percent access in 2010. At the other end of the


spectrum is Cambodia, where only 31 percent of the population has


access.


Source: Table 3.


Europe & Central Asia: Emissions fall but per capita carbon dioxide emissions remain high


0


10


20


30


Carbon dioxide emissions (metric tons per capita)


1
9


9
0


2
0


0
9


Eu
rop


e &
Ce


ntr
al


As
ia


a


No
rwa


y


Fin
lan


d


Gre
en


lan
d


Ne
the


rla
nd


s


Cz
ec


h R
ep


ub
lic


Ru
ssi


an
Fe


de
rat


ion


Es
ton


ia


Ka
zak


hs
tan


Fa
ero


e I
sla


nd
s


Lu
xem


bo
urg


Carbon dioxide emissions, largely a byproduct of energy production


and use, account for the largest portion of greenhouse gases released


each year. Greenhouse gases—including carbon dioxide, methane,


nitrous oxide, and other industrial gases (hydrofluorocarbons, perfluo-


rocarbons, and sulfur hexafluoride)—are associated with global warm-


ing and environmental damage. In 2009 the world released an esti-


mated 32 billion metric tons of carbon dioxide, up 44 percent from


1990. In Europe and Central Asia carbon dioxide emissions fell 25 per-


cent over the same period, but emissions per capita remain the highest


among developing regions. Emissions of other greenhouse gases have


also risen over the last two decades. In 2010 global emissions were


estimated at 7.5 billion metric tons of carbon dioxide equivalent for


methane, 2.9 billion for nitrous dioxide, and 1.0 billion for other indus-


trial gases.a. Includes countries at all income levels.
Source: Online table 3.8.


Latin America & Caribbean: The leader in clean and efficient energy


0


25


50


75


100


Middle East
& North
Africa


Sub-Saharan
Africa


South
Asia


East Asia
& Pacic


Europe
& Central


Asia


Latin
America &
Caribbean


Energy use, 2010 (%)


Alternative and nuclear energy
Combustible renewables and waste
Fossil fuel


Latin America and the Caribbean remains one of the world’s most efficient


energy-using regions, measured by the ratio of gross domestic product


to energy use. Latin American countries averaged $7.70 of output per


kilogram of oil-equivalent energy used in 2010 (in 2005 purchasing


power parity dollars), up 12 percent from 1990. Peru, Colombia, Pan-


ama, Costa Rica, and Uruguay were the region’s most efficient energy


users. Clean energy from noncarbon energy sources, which consists of


alternative energy (geothermal, solar, and hydropower) and nuclear


energy, is also on the rise, accounting for 9.2 percent of world energy


use in 2010, but 10.4 percent in Latin America and the Caribbean, high-


est among developing regions. The increase in carbon dioxide emissions


slowed, but the region’s emissions still rose more than 57 percent


between 1990 and 2009. Mexico, Brazil, República Bolivariana de Vene-


zuela, Argentina, and Colombia were the largest emitters.
Source: Online tables 3.6 and 3.8.




World Development Indicators 2013 53Economy States and markets Global links Back


Middle East & North Africa: The most water-stressed region


The world has about 42 trillion cubic meters of available freshwater,


but distribution is drastically uneven. The Middle East and North


Africa is the most water-stressed region, with less than 1 percent of


global renewable freshwater resources. At 226 billion cubic meters,


the region has only 673 cubic meters of water per person, the lowest


among developing regions. By contrast, Latin America and the Carib-


bean, with 32 percent of world resources, has 22,810 cubic meters per


person; Europe and Central Asia, with 12 percent, has 12,516 cubic


meters per person; and East Asia and Pacific, with 21 percent, has


4,446 cubic meters per person.


Renewable internal freshwater resources, 2011 (%)


Latin America
& Caribbean


32%


High income
21%


East Asia
& Pacic
21%


Europe &
Central Asia


12%


Sub-
Saharan


Africa
9%


South Asia 5% Middle East & North Africa 0.5%


Source: Online table 3.5.


South Asia: Some of the world’s most polluted cities


South Asia has some of the most polluted air in the world, as


measured by the concentration of fine suspended particulates of


fewer than 10 microns in diameter (PM10), capable of penetrating


deep into the respiratory tract and causing severe health damage.


The PM10 estimates measure the annual exposure of the average


urban resident to outdoor particulate matter. Globally, air pollution


has fallen from 78 micrograms per cubic meter to 41 over the last


two decades. Among developing regions the highest concentrations


are in South Asia (62) and the Middle East and North Africa (59).


City-level PM10 concentration data, however, indicate that Sub-


Saharan Africa has the most cities with high levels of pollution


among developing regions. Even so, the PM10 concentration has


dropped significantly in Sub- Saharan Africa, falling 65 percent over


1990–2010.
0 50 100 150 200


N’djamena, Chad


Shubra-El-Khema, Egypt


Osh, Kyrgyz Republic


Niamey, Niger


Bamako, Mali


Montevideo, Uruguay


Xi’an, China


Ségou, Mali


Saint-Louis, Senegal


Hyderabad, Pakistan


Muzaffarpur, India


Dhaka, Bangladesh


Maroua, Cameroon


Nyala, Sudan


Particulates of less than 10 microns in diameter, 2010
(micrograms per cubic meter)


Source: Online tables 3.13 and 3.14.


Sub- Saharan Africa: Use of biomass energy for cooking and heating increases health risks


Many poor people depend on biomass energy from plant materials or


animal waste for cooking and heating. Millions of deaths are caused


by indoor air pollution each year, due largely to indoor particulate pol-


lution. Many are children, who die of acute respiratory infections from


burning fuel wood, crop residues, or animal dung (WHO 2004). These


sources of energy account for 66 percent of total energy used by more


than 800 million low-income inhabitants of the world. In Sub- Saharan


Africa use of combustible renewables and waste, which account for


more than half of total energy use, has risen almost 3 percent over


the last two decades. For this region, where an estimated 67 percent


of the population lacks access to any form of electrical services, use


of biomass and coal—the primary cooking and heating fuel—is still


on the rise.


0


25


50


75


100


Combustible renewables and waste, 2010 (% of energy use)


Wo
rld


Su
b-S


ah
ara


n A
fric


a
Ke


nya



te


d’I
voi


re
Eri


tre
a


Za
mb


ia


Mo
zam


biq
ueTog


o
Nig


eri
a


Tan
zan


ia


Eth
iop


ia


Co
ng


o,
De


m.
Re


p.


Source: Online tables 1.1 and 3.6.




54 World Development Indicators 2013 Front User guide World view People Environment?


Deforestationa Nationally
protected


areas


Internal
renewable
freshwater
resourcesb


Access to
improved


water
source


Access to
improved
sanitation
facilities


Urban
population


Particulate
matter


concentration


Carbon
dioxide


emissions


Energy use Electricity
production


Terrestrial and
marine areas


% of total
territorial area


urban-population-
weighted PM10
micrograms per


cubic meter
average
annual %


Per capita
cubic meters


% of total
population


% of total
population


average
annual


% growth
million


metric tons


Per capita
kilograms of
oil equivalent


billion
kilowatt
hours


2000–10 2010 2011 2010 2010 1990–2011 2010 2009 2010 2010


Afghanistan 0.00 0.4 1,335 50 37 4.0 30 6.3 .. ..


Albania –0.10 8.4 8,364 95 94 2.4 38 3.0 648 7.6


Algeria 0.57 6.2 313 83 95 2.6 69 121.3 1,138 45.6


American Samoa 0.19 16.7 .. .. .. 1.9 .. .. .. ..


Andorra 0.00 6.1 3,663 100 100 0.9 18 0.5 .. ..


Angola 0.21 12.1 7,544 51 58 4.1 58 26.7 716 5.3


Antigua and Barbuda 0.20 1.0 580 .. .. 1.0 13 0.5 .. ..


Argentina 0.81 5.3 6,771 .. .. 1.0 57 174.7 1,847 125.3


Armenia 1.48 8.0 2,212 98 90 0.3 45 4.5 791 6.5


Aruba 0.00 0.0 .. 100 .. 0.8 .. 2.3 .. ..


Australia 0.37 12.5 22,039 100 100 1.3 13 400.2 5,653 241.5


Austria –0.13 22.9 6,529 100 100 0.7 27 62.3 4,034 67.9


Azerbaijan 0.00 7.1 885 80 82 1.8 27 49.1 1,307 18.7


Bahamas, The 0.00 1.0 58 .. 100 1.5 .. 2.6 .. ..


Bahrain –3.55 0.7 3 .. .. 4.9 44 24.2 7,754 13.2


Bangladesh 0.18 1.6 698 81 56 3.0 115 51.0 209 42.3


Barbados 0.00 0.1 292 100 100 1.4 35 1.6 .. ..


Belarus –0.43 7.2 3,927 100 93 0.4 6 60.3 2,922 34.9


Belgium –0.16 13.2 1,089 100 100 1.2 21 103.6 5,586 93.8


Belize 0.67 20.6 44,868 98 90 3.0 12 0.4 .. ..


Benin 1.04 23.3 1,132 75 13 4.2 48 4.9 413 0.2


Bermuda 0.00 5.1 .. .. .. 0.7 .. 0.5 .. ..


Bhutan –0.34 28.3 105,653 96 44 3.9 20 0.4 .. ..


Bolivia 0.50 18.5 30,085 88 27 2.2 57 14.5 737 6.9


Bosnia and Herzegovina 0.00 0.6 9,461 99 95 0.9 21 30.1 1,703 17.1


Botswana 0.99 30.9 1,182 96 62 2.2 64 4.4 1,128 0.5


Brazil 0.50 26.0 27,551 98 79 1.2 18 367.1 1,363 515.7


Brunei Darussalam 0.44 29.6 20,939 .. .. 2.2 44 9.3 8,308 3.9


Bulgaria –1.53 8.9 2,858 100 100 –1.7 40 42.8 2,370 46.0


Burkina Faso 1.01 14.2 737 79 17 6.2 65 1.7 .. ..


Burundi 1.40 4.8 1,173 72 46 4.9 24 0.2 .. ..


Cambodia 1.34 23.4 8,431 64 31 2.1 42 4.6 355 1.0


Cameroon 1.05 9.0 13,629 77 49 3.3 59 6.7 363 5.9


Canada 0.00 6.2 82,647 100 100 1.2 15 513.9 7,380 607.8


Cape Verde –0.36 0.2 599 88 61 2.1 .. 0.3 .. ..


Cayman Islands 0.00 1.5 .. 96 96 0.9 .. 0.5 .. ..


Central African Republic 0.13 17.7 31,425 67 34 2.6 35 0.2 .. ..


Chad 0.66 9.4 1,301 51 13 3.0 83 0.4 .. ..


Channel Islands .. 0.5 .. .. .. 0.8 .. .. .. ..


Chile –0.25 13.3 51,188 96 96 1.1 46 66.7 1,807 60.4


China –1.57 16.0 2,093 91 64 3.0 59 7,687.1 1,807 4,208.3


Hong Kong SAR, China .. 41.8 .. .. .. 0.1 .. 37.0 1,951 38.3


Macao SAR, China .. .. .. .. .. 2.2 .. 1.5 .. ..


Colombia 0.17 20.5 45,006 92 77 1.7 19 71.2 696 56.8


Comoros 9.34 .. 1,592 95 36 2.9 30 0.1 .. ..


Congo, Dem. Rep. 0.20 10.0 13,283 45 24 4.3 35 2.7 360 7.9


Congo, Rep. 0.07 9.7 53,626 71 18 3.0 57 1.9 363 0.6


3 Environment




World Development Indicators 2013 55Economy States and markets Global links Back


Environment 3
Deforestationa Nationally


protected
areas


Internal
renewable
freshwater
resourcesb


Access to
improved


water
source


Access to
improved
sanitation
facilities


Urban
population


Particulate
matter


concentration


Carbon
dioxide


emissions


Energy use Electricity
production


Terrestrial and
marine areas


% of total
territorial area


urban-population-
weighted PM10
micrograms per


cubic meter
average
annual %


Per capita
cubic meters


% of total
population


% of total
population


average
annual


% growth
million


metric tons


Per capita
kilograms of
oil equivalent


billion
kilowatt
hours


2000–10 2010 2011 2010 2010 1990–2011 2010 2009 2010 2010


Costa Rica –0.93 17.6 23,780 97 95 2.2 27 8.3 998 9.6


Côte d’Ivoire –0.15 21.8 3,813 80 24 3.5 30 6.6 485 6.0


Croatia –0.19 9.5 8,562 99 99 0.2 22 21.5 1,932 14.0


Cuba –1.66 5.3 3,387 94 91 –0.1 15 31.6 975 17.4


Curaçao .. .. .. .. .. .. .. .. .. ..


Cyprus –0.09 4.5 699 100 100 1.4 27 8.2 2,215 5.4


Czech Republic –0.08 15.1 1,253 100 98 –0.3 16 108.1 4,193 85.3


Denmark –1.14 4.1 1,077 100 100 0.6 15 45.7 3,470 38.8


Djibouti 0.00 0.0 331 88 50 2.0 28 0.5 .. ..


Dominica 0.58 3.7 .. .. .. 0.1 20 0.1 .. ..


Dominican Republic 0.00 24.1 2,088 86 83 2.1 14 20.3 840 15.9


Ecuador 1.81 38.0 29,456 94 92 2.2 19 30.1 836 17.7


Egypt, Arab Rep. –1.73 6.1 22 99 95 2.1 78 216.1 903 146.8


El Salvador 1.45 1.4 2,850 88 87 1.3 28 6.3 677 6.0


Equatorial Guinea 0.69 14.0 36,100 .. .. 3.2 6 4.8 .. ..


Eritrea 0.28 3.8 517 61 14 5.2 61 0.5 142 0.3


Estonia 0.12 22.6 9,486 98 95 0.1 9 16.0 4,155 13.0


Ethiopia 1.08 18.4 1,440 44 21 3.7 47 7.9 400 5.0


Faeroe Islands 0.00 .. .. .. .. 0.8 11 0.7 .. ..


Fiji –0.34 0.2 32,876 98 83 1.7 20 0.8 .. ..


Finland 0.14 8.5 19,858 100 100 0.6 15 53.6 6,787 80.7


France –0.39 17.1 3,057 100 100 1.2 12 363.4 4,031 564.3


French Polynesia –3.97 0.1 .. 100 98 1.1 .. 0.9 .. ..


Gabon 0.00 14.6 106,892 87 33 2.3 7 1.6 1,418 1.8


Gambia, The –0.41 1.3 1,689 89 68 3.7 60 0.4 .. ..


Georgia 0.09 3.4 12,958 98 95 1.0 49 5.8 700 10.1


Germany 0.00 42.3 1,308 100 100 0.2 16 734.6 4,003 622.1


Ghana 2.08 14.0 1,214 86 14 3.6 22 7.4 382 8.4


Greece –0.81 9.9 5,133 100 98 0.3 27 94.9 2,440 57.4


Greenland 0.00 40.1 .. 100 100 0.2 .. 0.6 .. ..


Grenada 0.00 0.1 .. .. 97 1.3 19 0.2 .. ..


Guam 0.00 3.6 .. 100 99 1.3 .. .. .. ..


Guatemala 1.40 29.5 7,400 92 78 3.4 51 15.2 713 8.8


Guinea 0.54 6.4 22,110 74 18 3.8 55 1.2 .. ..


Guinea-Bissau 0.48 26.9 10,342 64 20 3.6 48 0.3 .. ..


Guyana 0.00 4.8 318,766 94 84 0.5 20 1.6 .. ..


Haiti 0.76 0.1 1,285 69 17 3.8 35 2.3 229 0.6


Honduras 2.06 13.9 12,371 87 77 3.1 34 7.7 601 6.7


Hungary –0.62 5.1 602 100 100 0.4 15 48.7 2,567 37.4


Iceland –4.99 13.2 532,892 100 100 0.4 18 2.0 16,882 17.1


India –0.46 4.8 1,165 92 34 2.5 52 1,979.4 566 959.9


Indonesia 0.51 6.4 8,332 82 54 2.5 60 451.8 867 169.8


Iran, Islamic Rep. 0.00 6.9 1,718 96 100 1.3 56 602.1 2,817 233.0


Iraq –0.09 0.1 1,068 79 73 2.8 88 109.0 1,180 50.2


Ireland –1.53 1.2 10,707 100 99 2.7 13 41.6 3,218 28.4


Isle of Man 0.00 .. .. .. .. 0.5 .. .. .. ..


Israel –0.07 15.1 97 100 100 1.9 21 67.2 3,005 58.6




56 World Development Indicators 2013 Front User guide World view People Environment?


3 Environment
Deforestationa Nationally


protected
areas


Internal
renewable
freshwater
resourcesb


Access to
improved


water
source


Access to
improved
sanitation
facilities


Urban
population


Particulate
matter


concentration


Carbon
dioxide


emissions


Energy use Electricity
production


Terrestrial and
marine areas


% of total
territorial area


urban-population-
weighted PM10
micrograms per


cubic meter
average
annual %


Per capita
cubic meters


% of total
population


% of total
population


average
annual


% growth
million


metric tons


Per capita
kilograms of
oil equivalent


billion
kilowatt
hours


2000–10 2010 2011 2010 2010 1990–2011 2010 2009 2010 2010


Italy –0.90 15.9 3,005 100 .. 0.7 21 400.8 2,815 298.8


Jamaica 0.11 7.3 3,475 93 80 0.4 27 8.6 1,131 4.2


Japan –0.05 10.9 3,364 100 100 0.9 24 1,101.1 3,898 1,110.8


Jordan 0.00 1.9 110 97 98 2.5 30 22.5 1,191 14.8


Kazakhstan 0.17 2.5 3,886 95 97 1.3 18 225.8 4,595 82.6


Kenya 0.33 11.7 497 59 32 4.4 30 12.4 483 7.5


Kiribati 0.00 22.6 .. .. .. 1.8 .. 0.1 .. ..


Korea, Dem. Rep. 2.00 3.9 2,740 98 80 0.6 52 75.1 761 21.7


Korea, Rep. 0.11 3.0 1,303 98 100 1.1 30 509.4 5,060 496.7


Kosovo .. .. .. .. .. .. .. .. 1,372 5.2


Kuwait –2.57 1.1 0 99 100 2.9 91 80.2 12,204 57.0


Kyrgyz Republic –1.07 6.9 8,873 90 93 1.5 35 6.7 536 11.4


Lao PDR 0.49 16.6 30,280 67 63 4.7 45 1.8 .. ..


Latvia –0.34 16.4 8,133 99 78 –8.4 12 6.7 1,971 6.6


Lebanon –0.45 0.4 1,127 100 .. 0.9 25 21.0 1,526 15.7


Lesotho –0.47 0.5 2,384 78 26 3.7 38 .. .. ..


Liberia 0.67 1.6 48,443 73 18 4.1 31 0.5 .. ..


Libya 0.00 0.1 109 .. 97 1.3 65 62.9 3,013 31.6


Liechtenstein 0.00 42.4 .. .. .. 0.5 24 .. .. ..


Lithuania –0.68 14.4 5,135 92 86 –8.0 16 12.8 2,107 5.0


Luxembourg 0.00 20.0 1,930 100 100 2.5 12 10.1 8,343 3.2


Macedonia, FYR –0.41 4.9 2,616 100 88 0.4 17 11.3 1,402 7.3


Madagascar 0.45 2.5 15,810 46 15 4.8 28 1.8 .. ..


Malawi 0.97 15.0 1,049 83 51 4.1 29 1.1 .. ..


Malaysia 0.54 13.7 20,098 100 96 2.5 18 198.3 2,558 125.3


Maldives 0.00 .. 94 98 97 4.1 28 1.0 .. ..


Mali 0.61 2.4 3,788 64 22 4.9 111 0.6 .. ..


Malta 0.00 1.7 121 100 100 0.1 .. 2.5 2,013 2.1


Marshall Islands 0.00 0.6 .. 94 75 1.9 .. 0.1 .. ..


Mauritania 2.66 1.1 113 50 26 3.0 68 2.1 .. ..


Mauritius 1.00 0.7 2,139 99 89 0.4 16 3.8 .. ..


Mexico 0.30 11.9 3,563 96 85 1.6 30 446.2 1,570 271.0


Micronesia, Fed. Sts. –0.04 0.1 .. .. .. 0.9 .. 0.1 .. ..


Moldova –1.77 1.4 281 96 85 1.4 36 4.5 731 3.6


Monaco 0.00 98.1 .. 100 100 0.1 .. .. .. ..


Mongolia 0.73 13.4 12,428 82 51 2.9 96 14.5 1,189 4.5


Montenegro 0.00 11.5 .. 98 90 0.4 .. 3.1 1,303 4.2


Morocco –0.23 1.5 899 83 70 1.6 23 48.8 517 22.3


Mozambique 0.54 14.8 4,191 47 18 3.1 22 2.6 436 16.7


Myanmar 0.93 5.2 20,750 83 76 2.5 40 11.1 292 7.5


Namibia 0.97 14.7 2,651 93 32 3.3 42 3.6 702 1.5


Nepal 0.70 17.0 6,501 89 31 3.8 27 3.5 341 3.2


Netherlands –0.14 15.2 659 100 100 0.9 30 169.7 5,021 118.1


New Caledonia 0.00 23.9 .. .. .. 1.4 48 3.0 .. ..


New Zealand –0.01 20.0 74,230 100 .. 0.9 11 32.1 4,166 44.8


Nicaragua 2.01 36.8 32,318 85 52 1.9 21 4.5 542 3.7


Niger 0.98 7.1 218 49 9 5.0 96 1.2 .. ..




World Development Indicators 2013 57Economy States and markets Global links Back


Environment 3
Deforestationa Nationally


protected
areas


Internal
renewable
freshwater
resourcesb


Access to
improved


water
source


Access to
improved
sanitation
facilities


Urban
population


Particulate
matter


concentration


Carbon
dioxide


emissions


Energy use Electricity
production


Terrestrial and
marine areas


% of total
territorial area


urban-population-
weighted PM10
micrograms per


cubic meter
average
annual %


Per capita
cubic meters


% of total
population


% of total
population


average
annual


% growth
million


metric tons


Per capita
kilograms of
oil equivalent


billion
kilowatt
hours


2000–10 2010 2011 2010 2010 1990–2011 2010 2009 2010 2010


Nigeria 3.67 12.6 1,360 58 31 3.8 38 70.2 714 26.1


Northern Mariana Islands 0.53 28.4 .. 98 .. 0.5 .. .. .. ..


Norway –0.80 10.9 77,124 100 100 1.6 16 47.1 6,637 124.1


Oman 0.00 9.3 492 89 99 2.6 95 41.1 7,188 19.8


Pakistan 2.24 9.8 311 92 48 2.7 91 161.2 487 94.5


Palau –0.18 4.8 .. 85 100 1.6 .. 0.2 .. ..


Panama 0.36 11.5 41,275 93 69 2.3 45 7.8 1,073 7.5


Papua New Guinea 0.48 1.4 114,203 40 45 2.8 16 3.5 .. ..


Paraguay 0.97 5.4 14,311 86 71 2.6 64 4.5 742 54.1


Peru 0.18 13.1 54,966 85 71 1.5 42 47.4 667 35.9


Philippines –0.75 5.0 5,050 92 74 2.2 17 68.6 434 67.7


Poland –0.31 21.8 1,391 .. 90 0.8 33 298.9 2,657 157.1


Portugal –0.11 6.1 3,600 99 100 0.1 18 57.4 2,213 53.7


Puerto Rico –1.76 4.4 1,915 .. .. –0.3 15 .. .. ..


Qatar 0.00 1.4 30 100 100 6.3 20 70.3 12,799 28.1


Romania –0.32 7.8 1,978 89 73 –0.2 11 79.5 1,632 60.3


Russian Federation 0.00 9.2 30,169 97 70 0.6 15 1,574.4 4,927 1,036.1


Rwanda –2.38 10.0 868 65 55 4.6 21 0.7 .. ..


Samoa 0.00 1.2 .. 96 98 –0.5 .. 0.2 .. ..


San Marino 0.00 .. .. .. .. 0.7 8 .. .. ..


São Tomé and Príncipe 0.00 .. 12,936 89 26 2.9 28 0.1 .. ..


Saudi Arabia 0.00 29.9 85 .. .. 2.5 96 432.8 6,168 240.1


Senegal 0.49 23.5 2,021 72 52 3.4 77 4.6 272 3.0


Serbia –0.99 6.0 1,158 99 92 0.2 .. 46.3 2,141 37.4


Seychelles 0.00 0.9 .. .. .. 0.1 .. 0.7 .. ..


Sierra Leone 0.69 4.3 26,678 55 13 3.2 39 1.4 .. ..


Singapore 0.00 3.4 116 100 100 2.1 23 31.9 6,456 45.4


Sint Maarten .. .. .. .. .. .. .. .. .. ..


Slovak Republic –0.06 23.2 2,334 100 100 –0.7 13 33.9 3,280 27.5


Slovenia –0.16 13.1 9,095 99 100 0.1 26 15.3 3,520 16.2


Solomon Islands 0.25 0.1 80,939 .. .. 4.8 31 0.2 .. ..


Somalia 1.07 0.5 628 29 23 3.6 26 0.6 .. ..


South Africa 0.00 6.9 886 91 79 1.9 18 499.0 2,738 256.6


South Sudan .. .. .. .. .. 4.7 .. .. .. ..


Spain –0.68 7.6 2,408 100 100 0.4 24 288.2 2,773 299.9


Sri Lanka 1.12 15.0 2,530 91 92 1.6 65 12.7 478 10.8


St. Kitts and Nevis 0.00 0.8 452 99 96 1.5 15 0.3 .. ..


St. Lucia –0.07 2.0 .. 96 65 –2.6 31 0.4 .. ..


St. Martin 0.00 .. .. .. .. .. .. .. .. ..


St. Vincent and Grenadines –0.27 1.2 .. .. .. 0.8 22 0.2 .. ..


Sudan 0.08 4.2 672 58 26 2.6c 137 14.3 371 7.8


Suriname 0.01 12.2 166,220 92 83 1.5 20 2.5 .. ..


Swaziland –0.84 3.0 2,472 71 57 1.0 30 1.0 .. ..


Sweden –0.30 10.0 18,097 100 100 0.9 10 43.7 5,468 148.5


Switzerland –0.38 24.9 5,106 100 100 1.2 20 41.6 3,349 66.1


Syrian Arab Republic –1.29 0.6 343 90 95 2.5 54 65.3 1,063 46.4


Tajikistan 0.00 4.1 9,096 64 94 1.6 29 2.8 336 16.4




58 World Development Indicators 2013 Front User guide World view People Environment?


3 Environment
Deforestationa Nationally


protected
areas


Internal
renewable
freshwater
resourcesb


Access to
improved


water
source


Access to
improved
sanitation
facilities


Urban
population


Particulate
matter


concentration


Carbon
dioxide


emissions


Energy use Electricity
production


Terrestrial and
marine areas


% of total
territorial area


urban-population-
weighted PM10
micrograms per


cubic meter
average
annual %


Per capita
cubic meters


% of total
population


% of total
population


average
annual


% growth
million


metric tons


Per capita
kilograms of
oil equivalent


billion
kilowatt
hours


2000–10 2010 2011 2010 2010 1990–2011 2010 2009 2010 2010


Tanzania 1.13 26.9 1,817 53 10 4.8 19 7.0 448 4.4


Thailand 0.02 17.3 3,229 96 96 1.7 53 271.7 1,699 159.5


Timor-Leste 1.40 6.4 6,986 69 47 4.2 .. 0.2 .. ..


Togo 5.13 11.0 1,868 61 13 3.4 27 1.5 446 0.1


Tonga 0.00 9.4 .. 100 96 0.9 .. 0.2 .. ..


Trinidad and Tobago 0.32 9.6 2,852 94 92 2.3 97 47.8 15,913 8.5


Tunisia –1.86 1.3 393 94 85 1.5 23 25.2 913 16.1


Turkey –1.11 1.9 3,083 100 90 2.5 35 277.8 1,445 211.2


Turkmenistan 0.00 3.0 275 .. 98 1.9 36 48.2 4,226 16.7


Turks and Caicos Islands 0.00 3.5 .. 100 .. 2.6 .. 0.2 .. ..


Tuvalu 0.00 0.2 .. 98 85 1.0 .. .. .. ..


Uganda 2.56 10.3 1,130 72 34 5.9 10 3.5 .. ..


Ukraine –0.21 3.6 1,162 98 94 –0.1 15 272.2 2,845 188.6


United Arab Emirates –0.24 4.7 19 100 98 5.3 89 156.8 8,271 97.7


United Kingdom –0.31 18.1 2,311 100 100 0.9 13 474.6 3,252 378.0


United States –0.13 13.7 9,044 99 100 1.0 18 5,299.6 7,164 4,354.4


Uruguay –2.14 0.3 17,515 100 100 0.5 112 7.9 1,241 10.8


Uzbekistan –0.20 2.3 557 87 100 2.8 31 116.5 1,533 51.7


Vanuatu 0.00 0.5 .. 90 57 3.7 14 0.1 .. ..


Venezuela, RB 0.60 50.2 24,674 .. .. 1.7 10 184.8 2,669 118.3


Vietnam –1.65 4.6 4,092 95 76 3.1 54 142.3 681 94.9


Virgin Islands (U.S.) 0.80 1.5 .. .. .. 0.1 .. .. .. ..


West Bank and Gaza –0.10 0.6 207 85 92 3.3 .. 2.2 .. ..


Yemen, Rep. 0.00 0.7 85 55 53 4.9 34 24.0 298 7.8


Zambia 0.33 36.0 5,952 61 48 5.3 27 2.0 628 11.3


Zimbabwe 1.88 28.0 961 80 40 2.7 34 8.9 764 8.1


World 0.11 w 11.9 w 6,115 s 88 w 63 w 2.1 w 41 w 32,042.2d w 1,851 w 21,448.9 w


Low income 0.61 10.0 5,125 65 37 3.6 54 229.8 363 197.4


Middle income 0.08 12.0 5,819 90 59 2.3 46 17,344.8 1,310 10,122.1


Lower middle income 0.31 8.8 3,121 87 47 2.6 53 3,884.9 667 2,138.2


Upper middle income 0.02 13.1 8,550 93 73 2.1 42 13,460.8 1,948 7,981.7


Low & middle income 0.15 11.6 5,722 86 56 2.4 47 17,574.2 1,210 10,344.7


East Asia & Pacific –0.44 13.3 4,446 90 66 2.9 55 8,936.9 1,520 4,888.8


Europe & Central Asia –0.04 7.7 12,498 96 84 0.9 21 2,863.0 3,015 1,884.3


Latin America & Carib. 0.45 19.8 22,810 94 79 1.5 28 1,533.7 1,312 1,356.4


Middle East & N. Africa –0.15 4.0 673 89 88 2.1 59 1,320.9 1,372 638.4


South Asia –0.29 5.6 1,197 90 38 2.7 62 2,215.6 519 1,120.1


Sub- Saharan Africa 0.48 11.6 4,455 61 31 3.8 41 724.0 683 441.4


High income –0.04 12.7 8,195 100 100 1.0 22 12,727.0 5,000 11,163.7


Euro area –0.31 16.5 2,933 100 100 0.6 18 2,456.1 3,633 2,352.4


a. Negative values indicate an increase in forest area. b. River flows from other countries are not included because of data unreliability. c. Excludes South Sudan. d. Includes emissions
not allocated to specific countries.




World Development Indicators 2013 59Economy States and markets Global links Back


Environment 3
Environmental resources are needed to promote growth and poverty


reduction, but growth can create new stresses on the environment.


Deforestation, loss of biologically diverse habitat, depletion of water


resources, pollution, urbanization, and ever increasing demand for


energy production are some of the factors that must be considered


in shaping development strategies.


Loss of forests


Forests provide habitat for many species and act as carbon sinks.


If properly managed they also provide a livelihood for people who


manage and use forest resources. FAO (2010) uses a uniform defi-


nition of forest to provide information on forest cover in 2010 and


adjusted estimates of forest cover in 1990 and 2000. Data pre-


sented here do not distinguish natural forests from plantations, a


breakdown the FAO provides only for developing countries. Thus,


data may underestimate the rate at which natural forest is disap-


pearing in some countries.


Habitat protection and biodiversity


Deforestation is a major cause of loss of biodiversity, and habitat


conservation is vital for stemming this loss. Conservation efforts


have focused on protecting areas of high biodiversity. The World


Conservation Monitoring Centre (WCMC) and the United Nations


Environment Programme (UNEP) compile data on protected areas.


Differences in definitions, reporting practices, and reporting peri-


ods limit cross-country comparability. Nationally protected areas


are defined using the six International Union for Conservation of


Nature (IUCN) categories for areas of at least 1,000 hectares—


scientific reserves and strict nature reserves with limited public


access, national parks of national or international significance and


not materially affected by human activity, natural monuments and


natural landscapes with unique aspects, managed nature reserves


and wildlife sanctuaries, protected landscapes (which may include


cultural landscapes), and areas managed mainly for the sustainable


use of natural systems to ensure long-term protection and mainte-


nance of biological diversity—as well as terrestrial protected areas


not assigned to an IUCN category. Designating an area as protected


does not mean that protection is in force. For small countries with


protected areas smaller than 1,000 hectares, the size limit in the


definition leads to underestimation of protected areas. Due to varia-


tions in consistency and methods of collection, data quality is highly


variable across countries. Some countries update their information


more frequently than others, some have more accurate data on


extent of coverage, and many underreport the number or extent of


protected areas.


Freshwater resources


The data on freshwater resources are derived from estimates of


runoff into rivers and recharge of groundwater. These estimates


are derived from different sources and refer to different years, so


cross-country comparisons should be made with caution. Data are


collected intermittently and may hide substantial year-to-year varia-


tions in total renewable water resources. Data do not distinguish


between seasonal and geographic variations in water availability


within countries. Data for small countries and countries in arid and


semiarid zones are less reliable than data for larger countries and


countries with greater rainfall.


Water and sanitation


A reliable supply 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.


Data on access to an improved water source measure the percent-


age of the population with ready access to water for domestic pur-


poses, based on surveys and estimates of service users provided


by governments to the Joint Monitoring Programme of the World


Health Organization (WHO) and the United Nations Children’s Fund


(UNICEF). The coverage rates are based on information from service


users on household use rather than on information from service


providers, which may include nonfunctioning systems. Access to


drinking water from an improved source does not ensure that the


water is safe or adequate, as these characteristics are not tested


at the time of survey. While information on access to an improved


water source is widely used, it is extremely subjective; terms such as


“safe,” “improved,” “adequate,” and “reasonable” may have differ-


ent meanings in different countries despite official WHO definitions


(see Definitions). Even in high-income countries treated water may


not always be safe to drink. Access to an improved water source is


equated with connection to a supply system; it does not account for


variations in the quality and cost of the service.


Urbanization


There is no consistent and universally accepted standard for distin-


guishing urban from rural areas and, by extension, calculating their


populations. Most countries use a classification related to the size


or characteristics of settlements. Some define areas based on the


presence of certain infrastructure and services. Others designate


areas based on administrative arrangements. Because data are


based on national definitions, cross-country comparisons should


be made with caution.


Air pollution


Indoor and outdoor air pollution place 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 such health effects


as respiratory diseases, lung cancer, and heart disease. Particulate


About the data




60 World Development Indicators 2013 Front User guide World view People Environment?


3 Environment
pollution, alone or with sulfur dioxide, creates an enormous burden


of ill health.


Data on particulate matter are estimated average annual concen-


trations in residential areas away from air pollution “hotspots,” such


as industrial districts and transport corridors. Data are estimates


of annual ambient concentrations of particulate matter in cities of


more than 100,000 people by the World Bank’s Agriculture and


Environmental Services Department.


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. They


allow for cross-country comparisons of the relative risk of particulate


matter pollution facing urban residents. Major sources of urban


outdoor particulate matter pollution are traffic and industrial emis-


sions, but nonanthropogenic sources such as dust storms may also


be a substantial contributor for some cities. Country technology and


pollution controls are important determinants of particulate matter.


Current WHO air quality guidelines are annual mean concentrations


of 20 micrograms per cubic meter for particulate matter less than


10 microns in diameter.


Carbon dioxide emissions


Carbon dioxide emissions are the primary source of greenhouse


gases, which contribute to global warming, threatening human and


natural habitats. Fossil fuel combustion and cement manufacturing


are the primary sources of anthropogenic carbon dioxide emissions,


which the U.S. Department of Energy’s Carbon Dioxide Information


Analysis Center (CDIAC) calculates using data from the United


Nations Statistics Division’s World Energy Data Set and the U.S.


Bureau of Mines’s Cement Manufacturing Data Set. Carbon dioxide


emissions, often calculated and reported as elemental carbon, were


converted to actual carbon dioxide mass by multiplying them by


3.667 (the ratio of the mass of carbon to that of carbon dioxide).


Although estimates of global carbon dioxide emissions are probably


accurate within 10 percent (as calculated from global average fuel


chemistry and use), country estimates may have larger error bounds.


Trends estimated from a consistent time series tend to be more


accurate than individual values. Each year the CDIAC recalculates


the entire time series since 1949, incorporating recent findings and


corrections. Estimates exclude fuels supplied to ships and aircraft


in international transport because of the difficulty of apportioning


the fuels among benefiting countries.


Energy use


In developing economies growth in energy use is closely related to


growth in the modern sectors—industry, motorized transport, and


urban areas—but also reflects climatic, geographic, and economic


factors. Energy use has been growing rapidly in low- and middle-


income economies, but high-income economies still use almost five


times as much energy per capita.


Total energy use refers to the use of primary energy before trans-


formation to other end-use fuels (such as electricity and refined


petroleum products). It includes energy from combustible renew-


ables and 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 informa-


tion and thus give only a broad impression of developments and


are not strictly comparable across countries. The IEA reports


include country notes that explain some of these differences (see


Data sources). All forms of energy—primary energy and primary


electricity—are converted into oil equivalents. A notional thermal


efficiency of 33 percent is assumed for converting nuclear electric-


ity into oil equivalents and 100 percent efficiency for converting


hydroelectric power.


Electricity production


Use of energy is important in improving people’s standard of liv-


ing. But electricity generation also can damage the environment.


Whether such damage occurs depends largely on how electricity


is generated. For example, burning coal releases twice as much


carbon dioxide—a major contributor to global warming—as does


burning an equivalent amount of natural gas. Nuclear energy does


not generate carbon dioxide emissions, but it produces other dan-


gerous waste products.


The International Energy Agency (IEA) compiles data and data


on energy inputs used to generate electricity. Data for countries


that are not members of the Organisation for Economic Co-opera-


tion and Development (OECD) are based on national energy data


adjusted to conform to annual questionnaires completed by OECD


member governments. In addition, estimates are sometimes made


to complete major aggregates from which key data are missing,


and adjustments are made to compensate for differences in defini-


tions. 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 addi-


tion, energy statistics for other countries have undergone continu-


ous changes in coverage or methodology in recent years as more


detailed energy accounts have become available. Breaks in series


are therefore unavoidable.




World Development Indicators 2013 61Economy States and markets Global links Back


Environment 3
Definitions


• Deforestation is the permanent conversion of natural forest area


to other uses, including agriculture, ranching, settlements, and


infrastructure. Deforested areas do not include areas logged but


intended for regeneration or areas degraded by fuelwood gathering,


acid precipitation, or forest fires. • Nationally protected areas are


terrestrial and marine protected areas as a percentage of total ter-


ritorial area and include all nationally designated protected areas


with known location and extent. All overlaps between different desig-


nations and categories, buffered points, and polygons are removed,


and all undated protected areas are dated. • Internal renewable


freshwater resources are the average annual flows of rivers and


groundwater from rainfall in the country. Natural incoming flows origi-


nating outside a country’s borders and overlapping water resources


between surface runoff and groundwater recharge are excluded.


• Access to an improved water source is the percentage of the


population with reasonable access to an adequate amount of water


from an improved source, such as piped water into a dwelling, plot,


or yard; public tap or standpipe; tubewell or borehole; protected dug


well or spring; and rainwater collection. Unimproved sources include


unprotected dug wells or springs, carts with small tank or drum,


bottled water, and tanker trucks. Reasonable access is defined as


the availability of at least 20 liters a person a day from a source


within 1 kilometer of the dwelling • 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 pub-


lic) 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, facilities must be correctly constructed and properly


maintained. • Urban population is the midyear population of areas


defined as urban in each country and reported to the United Nations


divided by the World Bank estimate of total population. • 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. • Carbon dioxide emis-


sions are emissions from the burning of fossil fuels and the manu-


facture of cement and include carbon dioxide produced during con-


sumption of solid, liquid, and gas fuels and gas flaring. • Energy use


refers to the use of primary energy before transformation to other


end use fuels, which equals indigenous production plus imports


and stock changes, minus exports and fuels supplied to ships and


aircraft engaged in international transport. • Electricity production


is measured at the terminals of all alternator sets in a station. In


addition to hydropower, coal, oil, gas, and nuclear power generation,


it covers generation by geothermal, solar, wind, and tide and wave


energy as well as that from combustible renewables and waste. Pro-


duction includes the output of electric plants designed to produce


electricity only, as well as that of combined heat and power plants.


Data sources


Data on deforestation are from FAO (2010) and the FAO’s data


website. Data on protected areas, derived from the UNEP and


WCMC online databases, are based on data from national authori-


ties, national legislation, and international agreements. Data on


freshwater resources are from the FAO’s AQUASTAT database. Data


on access to water and sanitation are from WHO and UNICEF (2012).


Data on urban population are from the United Nations Population


Division (2011). Data on particulate matter concentrations are World


Bank estimates. Data on carbon dioxide emissions are from the


CDIAC. Data on energy use and electricity production are from IEA


electronic files and published in IEA’s annual publications, Energy


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


Countries, Energy Statistics of OECD Countries, and Energy Balances


of OECD Countries.


References


CDIAC (Carbon Dioxide Information Analysis Center). n.d. Online data-


base. http://cdiac.ornl.gov/home.html. Oak Ridge National Labora-


tory, Environmental Science Division, Oak Ridge, TN.


FAO (Food and Agriculture Organization of the United Nations). 2010.


Global Forest Resources Assessment 2010. Rome.


———. n.d. AQUASTAT. Online database. www.fao.org/nr/water/


aquastat/data/query/index.html. Rome.


IEA (International Energy Agency). Various years. Energy Balances of


Non-OECD Countries. Paris.


———.Various years. Energy Balances of OECD Countries. Paris.


———. Various years. Energy Statistics of Non-OECD Countries. Paris.


———.Various years. Energy Statistics of OECD Countries. Paris.


UNEP (United Nations Environment Programme) and WCMC (World


Conservation Monitoring Centre). 2012. Online databases www


.unep-wcmc-apps.org/species/dbases/about.cfm. Nairobi.


United Nations Population Division. 2011. World Urbanization Pros-


pects: The 2011 Revision. New York: United Nations, Department of


Economic and Social Affairs.


WHO (World Health Organization). 2004. Inheriting the World: The


Atlas of Children’s Health and the Environment. www.who.int/ceh/


publications/atlas/en/index.html. Geneva.


WHO (World Health Organization) and UNICEF (United Nations Chil-


dren’s Fund). 2012. Progress on Sanitation and Drinking Water.


Geneva: WHO.




62 World Development Indicators 2013 Front User guide World view People Environment?


3 Environment


3.1 Rural environment and land use


Rural population SP.RUR.TOTL.ZS


Rural population growth SP.RUR.TOTL.ZG


Land area AG.LND.TOTL.K2


Forest area AG.LND.FRST.ZS


Permanent cropland AG.LND.CROP.ZS


Arable land, % of land area AG.LND.ARBL.ZS


Arable land, hectares per person AG.LND.ARBL.HA.PC


3.2 Agricultural inputs


Agricultural land, % of land area AG.LND.AGRI.ZS


Agricultural land, % irrigated AG.LND.IRIG.AG.ZS


Average annual precipitation AG.LND.PRCP.MM


Land under cereal production AG.LND.CREL.HA


Fertilizer consumption, % of fertilizer
production AG.CON.FERT.PT.ZS


Fertilizer consumption, kilograms per
hectare of arable land AG.CON.FERT.ZS


Agricultural employment SL.AGR.EMPL.ZS


Tractors AG.LND.TRAC.ZS


3.3 Agricultural output and productivity


Crop production index AG.PRD.CROP.XD


Food production index AG.PRD.FOOD.XD


Livestock production index AG.PRD.LVSK.XD


Cereal yield AG.YLD.CREL.KG


Agriculture value added per worker EA.PRD.AGRI.KD


3.4 Deforestation and biodiversity


Forest area AG.LND.FRST.K2


Average annual deforestation ..a,b


Threatened species, Mammals EN.MAM.THRD.NO


Threatened species, Birds EN.BIR.THRD.NO


Threatened species, Fishes EN.FSH.THRD.NO


Threatened species, Higher plants EN.HPT.THRD.NO


Terrestrial protected areas ER.LND.PTLD.ZS


Marine protected areas ER.MRN.PTMR.ZS


3.5 Freshwater


Internal renewable freshwater resources ER.H2O.INTR.K3


Internal renewable freshwater resources,
Per capita ER.H2O.INTR.PC


Annual freshwater withdrawals, cu. m ER.H2O.FWTL.K3


Annual freshwater withdrawals, % of
internal resources ER.H2O.FWTL.ZS


Annual freshwater withdrawals, % for
agriculture ER.H2O.FWAG.ZS


Annual freshwater withdrawals, % for
industry ER.H2O.FWIN.ZS


Annual freshwater withdrawals, % of
domestic ER.H2O.FWDM.ZS


Water productivity, GDP/water use ER.GDP.FWTL.M3.KD


Access to an improved water source, % of
rural population SH.H2O.SAFE.RU.ZS


Access to an improved water source, % of
urban population SH.H2O.SAFE.UR.ZS


3.6 Energy production and use


Energy production EG.EGY.PROD.KT.OE


Energy use EG.USE.COMM.KT.OE


Energy use, Average annual growth ..a,b


Energy use, Per capita EG.USE.PCAP.KG.OE


Fossil fuel EG.USE.COMM.FO.ZS


Combustible renewable and waste EG.USE.CRNW.ZS


Alternative and nuclear energy production EG.USE.COMM.CL.ZS


3.7 Electricity production, sources, and access


Electricity production EG.ELC.PROD.KH


Coal sources EG.ELC.COAL.ZS


Natural gas sources EG.ELC.NGAS.ZS


Oil sources EG.ELC.PETR.ZS


Hydropower sources EG.ELC.HYRO.ZS


Renewable sources EG.ELC.RNWX.ZS


Nuclear power sources EG.ELC.NUCL.ZS


Access to electricity EG.ELC.ACCS.ZS


3.8 Energy dependency, efficiency and carbon dioxide


emissions


Net energy imports EG.IMP.CONS.ZS


GDP per unit of energy use EG.GDP.PUSE.KO.PP.KD


Carbon dioxide emissions, Total EN.ATM.CO2E.KT


Carbon dioxide emissions, Carbon intensity EN.ATM.CO2E.EG.ZS


Carbon dioxide emissions, Per capita EN.ATM.CO2E.PC


Carbon dioxide emissions, kilograms per
2005 PPP $ of GDP EN.ATM.CO2E.PP.GD.KD


3.9 Trends in greenhouse gas emissions


Carbon dioxide emissions, Average annual
growth ..a,b


Carbon dioxide emissions, % change ..a,b


Methane emissions, Total EN.ATM.METH.KT.CE


To access the World Development Indicators online tables, use


the URL http://wdi.worldbank.org/table/ and the table number (for


example, http://wdi.worldbank.org/table/3.1). To view a specific


indicator online, use the URL http://data.worldbank.org/indicator/


and the indicator code (for example, http://data.worldbank.org/


indicator/SP.RUR.TOTL.ZS).


Online tables and indicators




World Development Indicators 2013 63Economy States and markets Global links Back


Environment 3
Methane emissions, % change ..a,b


Methane emissions, From energy processes EN.ATM.METH.EG.ZS


Methane emissions, Agricultural EN.ATM.METH.AG.ZS


Nitrous oxide emissions, Total EN.ATM.NOXE.KT.CE


Nitrous oxide emissions, % change ..a,b


Nitrous oxide emissions, Energy and industry EN.ATM.NOXE.EI.ZS


Nitrous oxide emissions, Agriculture EN.ATM.NOXE.AG.ZS


Other greenhouse gas emissions, Total EN.ATM.GHGO.KT.CE


Other greenhouse gas emissions, % change ..a,b


3.10 Carbon dioxide emissions by sector


Electricity and heat production EN.CO2.ETOT.ZS


Manufacturing industries and construction EN.CO2.MANF.ZS


Residential buildings and commercial and
public services EN.CO2.BLDG.ZS


Transport EN.CO2.TRAN.ZS


Other sectors EN.CO2.OTHX.ZS


3.11 Climate variability, exposure to impact, and


resilience


Average daily minimum/maximum temperature ..b


Projected annual temperature ..b


Projected annual cool days/cold nights ..b


Projected annual hot days/warm nights ..b


Projected annual precipitation ..b


Land area with an elevation of 5 meters or less AG.LND.EL5M.ZS


Population living in areas with elevation of
5 meters or less EN.POP.EL5M.ZS


Population affected by droughts, floods,
and extreme temperatures EN.CLC.MDAT.ZS


Disaster risk reduction progress score EN.CLC.DRSK.XQ


3.12 Urbanization


Urban population SP.URB.TOTL


Urban population, % of total population SP.URB.TOTL.IN.ZS


Urban population, Average annual growth SP.URB.GROW


Population in urban agglomerations of
more than 1 million EN.URB.MCTY.TL.ZS


Population in the largest city EN.URB.LCTY.UR.ZS


Access to improved sanitation facilities,
% of urban population SH.STA.ACSN.UR


Access to improved sanitation facilities,
% of rural population SH.STA.ACSN.RU


3.13 Traffic and congestion


Motor vehicles, Per 1,000 people IS.VEH.NVEH.P3


Motor vehicles, Per kilometer of road IS.VEH.ROAD.K1


Passenger cars IS.VEH.PCAR.P3


Road density IS.ROD.DNST.K2


Road sector energy consumption, % of total
consumption IS.ROD.ENGY.ZS


Road sector energy consumption, Per capita IS.ROD.ENGY.PC


Diesel fuel consumption IS.ROD.DESL.PC


Gasoline fuel consumption IS.ROD.SGAS.PC


Pump price for super grade gasoline EP.PMP.SGAS.CD


Pump price for diesel EP.PMP.DESL.CD


Urban-population-weighted particulate
matter concentrations (PM10) EN.ATM.PM10.MC.M3


3.14 Air pollution


This table provides air pollution data for
major cities. ..b


3.15 Contribution of natural resources to gross domestic


product


Total natural resources rents NY.GDP.TOTL.RT.ZS


Oil rents NY.GDP.PETR.RT.ZS


Natural gas rents NY.GDP.NGAS.RT.ZS


Coal rents NY.GDP.COAL.RT.ZS


Mineral rents NY.GDP.MINR.RT.ZS


Forest rents NY.GDP.FRST.RT.ZS


a. Derived from data elsewhere in the World Development Indicators database.
b. Available online only as part of the table, not as an individual indicator.




64 World Development Indicators 2013 Front User guide World view People Environment?


ECONOMY




World Development Indicators 2013 65Economy States and markets Global links Back


The data in the Economy section provide a pic-
ture of the global economy and the economic
activity of more than 200 countries and ter-
ritories that produce, trade, and consume
the world’s output. They include measures of
macroeconomic performance and stability
and broader measures of income and savings
adjusted for pollution, depreciation, and deple-
tion of resources.


The world economy grew 2.3 percent in 2012,
to reach $71 trillion, and the share from develop-
ing economies grew to 34.3 percent. Growth is
expected to remain around 2.4 percent in 2013.
Low- and middle-income economies, estimated
to have grown 5.1 percent in 2012, are projected
to expand 5.5 percent in 2013. Growth in high-
income economies has been downgraded from
earlier forecasts to 1.3 percent in 2012 and
2013.


Beginning in August 2012, the International
Monetary Fund implemented the Balance of
Payments Manual 6 (BPM6) framework in its
major statistical publications. The World Bank
will implement BPM6 in its online databases
and publications in April 2013. Balance of pay-
ments data for 2005 onward will be presented
in accord with the BPM6. The historical BPM5
data series will end with data for 2008, which
can be accessed through the World Development
Indicators archives.


The change to the BPM6 framework will affect
some components of the balance of payments.
In the current account, “merchanting”—the pur-
chase of goods from a nonresident and the sub-
sequent resale to another nonresident without


the goods being present in the economy—has
been reclassified from services to goods, while
manufacturing services performed on physical
inputs owned by others along with maintenance
and repair services were reclassified from goods
to services. In the capital account, reverse
investment in direct investment has been
reclassified to present assets and liabilities on
a gross basis in the balance of payments and
international investment position. No changes
were made to the balances on current account,
capital account, or financial account. Levels of
reserves were not adjusted, nor were net errors
and omissions.


For many economies changes to major aggre-
gates and balancing items will be limited. The
change in the methodology for “goods for pro-
cessing” results in increases in imports and
exports of services (equivalent to the amounts
received or paid for manufacturing services) and
larger reductions in gross imports and exports of
goods (due to the elimination of imputed trans-
actions in goods that do not change ownership),
though net goods and services trade may not be
affected. The change in the recording of reverse
investment in foreign direct investment will result
in substantial increases in both international
investment position assets and liabilities for
many economies under BPM6, though net inter-
national investment position is not affected by
this change.


The complete balance of payments methodol-
ogy can be accessed through the International
Monetary Fund website (www.imf.org/external/
np/sta/bop/bop.htm).


4




66 World Development Indicators 2013


Highlights


Front User guide World view People Environment?


East Asia & Pacific: Service sector has potential for more growth


0


25


50


75


100


Value added in services as a share of GDP, 2010 (%)


GDP per capita, 2010 (2005 PPP $, log scale)
500 5,000 50,000


Malaysia


Cambodia


China


Fiji


Indonesia


Kiribati


Lao PDR


Papua New Guinea


Philippines


Samoa


Thailand


Tonga


Vietnam


Mongolia


Larger than
expected


Smaller
than
expected


The service sector contributed substantially to gross domestic product


(GDP) growth in many East Asia and Pacific economies in recent years,


constituting nearly half of GDP and contributing 3.7 percentage points


to an overall growth rate of 8.5 percent. Reflecting strong domestic


demand, continuing growth of services is consistent with long-term


trends of rising incomes in other regions. But despite recent growth,


the service sector in many East Asia and Pacific economies is smaller


than expected based on average income. This reflects the relative


success of manufacturing among the countries in the region. It may


also be the result of limited adoption of high-value, modern services


(information and communications technology, finance and professional


business services). Few East Asia and Pacific countries besides the


Philippines have developed robust industries focused on exporting


modern services (World Bank 2012a; Asian Development Bank 2012).
Source: World Development Indicators database.


Europe & Central Asia: Volatile food and energy prices pose a challenge


–500 –250 0 250


Moldova
Belarus


Turkey
Armenia


Kyrgyz Rep.
Georgia
Croatia


Macedonia, FYR
Ukraine
Bulgaria


Tajikistan
Poland
Serbia


Bosnia & Herzegovina
Kosovo
Albania


Romania
Montenegro
Uzbekistan


Russian Federation
Kazakhstan


Turkmenistan
Azerbaijan


Net energy exports or imports, 2010 (% of energy use)


Net energy
exporters


Net energy
importers


Europe and Central Asia is in general a net exporter of energy, but many


economies in the region depend heavily on energy imports and are


thus vulnerable to sudden price changes. Azerbaijan, Kazakhstan, the


Russian Federation, Turkmenistan, and Uzbekistan, the main export-


ers, stand to benefit from rising world energy prices. But net energy


imports account for 96 percent of energy use in Moldova, 84 percent


in Belarus, 69 percent in Turkey, 64 percent in Armenia, and 59 per-


cent in the Kyrgyz Republic. Most energy imports are oil, but Belarus,


Croatia, Poland, and Serbia are also large importers of electricity.


Some of these economies may face an acute deterioration of their


balance of payments positions if oil prices rise. And some are also


vulnerable to rising food prices, triggered by the substitution of crop-


based fuels for petroleum-based fuels.


Source: Online table 3.9.


Latin America & Caribbean: Tracking global uncertainties


–10


–5


0


5


10


201220112010200920082007


Mexico


Chile


Brazil
Latin America
& Caribbean


GDP growth (%) GDP growth in Latin America and the Caribbean fell 1.7 percentage


points from 2011 to 3.0 percent in 2012, the second largest drop


among developing country regions after Europe and Central Asia, where


growth fell 2.8 percentage points. The region’s GDP growth decelerated


due to slowing domestic demand and a weak external environment. The


slowdown was particularly severe in Brazil, the region’s largest econ-


omy, where global uncertainties and earlier fiscal, monetary, and credit


policy tightening to contain inflation risks had a large impact, especially


on private investment. In Chile growth remained buoyant and continued


to expand briskly, if slightly slower than in 2011. Growth in Central


America and the Caribbean slowed modestly, while growth in Mexico


(the second largest economy in the region) rose slightly in 2012, to 4


percent, benefiting from the fairly strong recovery in U.S. manufacturing


(De la Torre, Didier, and Pienknagura 2012; World Bank 2013).
Source: Online table 4.1.




World Development Indicators 2013 67Economy States and markets Global links Back


Middle East & North Africa: Recovery has been slow


Macroeconomic fundamentals weakened in most Middle East and


North Africa countries in 2011 and 2012, as growth slowed and


governments responded to social pressures with expansionary fis-


cal policies. High oil prices heightened current account and fiscal


deficits in oil importers, especially in places where governments


subsidize energy use. Domestic pressures coupled with a challeng-


ing global environment and spillovers from regional events also


weighed heavily on the economies of some oil importers such as


Jordan, Lebanon, and Morocco. Postrevolutionary economies such


as the Arab Republic of Egypt, Tunisia, and the Republic of Yemen


are recovering after the Arab Spring turmoil. However, recovery has


taken place in a weak global environment. The transition in these


countries is far from complete, and uncertainty around the reform pro-


cess continues to constrain private investment (World Bank 2012b).
–15


–10


–5


0


5


10


201220112010200920082007


GDP growth (%)


Lebanon


Tunisia


Yemen, Rep.


Morocco


Egypt, Arab Rep.


Algeria Jordan


Source: Online table 4.1.


South Asia: Revenues are low and stagnating


South Asian countries collect exceptionally low levels of tax revenue.


Revenue collection by central governments in the region averages


10–15 percent of GDP, compared with 20 percent in similar develop-


ing economies and higher rates in more developed economies. In most


South Asian countries the major source of revenue is the value added


tax. Some are changing their tax structures—India is adopting a goods


and services tax, for example—but the main problem remains low


collection rates for the value added tax and income tax. Most South


Asian countries have outdated tax laws and inadequate institutional


arrangements unsuitable to the growing complexity of their economies.


South Asian countries all need to upgrade their infrastructure and


improve social services such as education and health care, which will


require a substantial increase in fiscal spending and better sources


of revenue (World Bank 2012c).


0


5


10


15


20


2011201020082006200420022000


Afghanistan


Bangladesh Bhutan


Pakistan


India


Sri Lanka


Maldives


Tax revenue (% of GDP)


Nepal


Source: Online table 5.6.


Sub- Saharan Africa: Resilient growth in an uncertain global economic environment


Despite a sluggish global economy, economic conditions in Sub-


Saharan Africa held up well in 2011–12. Robust domestic demand,


high commodity prices, rising export volumes (due to new capacity in


the natural resources sector), and steady remittance flows supported


growth in 2012. GDP in Sub- Saharan Africa expanded at an average of


4.9 percent a year over 2000–11 and rose an estimated 4.6 percent


in 2012, the third most among developing regions. Excluding South


Africa, the region’s largest economy, GDP output rose 5.8 percent


in 2012, with a third of countries growing at least 6 percent. Growth


varied across the region, with steady expansion in most low-income


countries but slow growth in middle-income countries, such as South


Africa, that are more tightly integrated with the global economy and


in some countries affected by political instability, such as Mali (IMF


2012; World Bank 2013).
–5


0


5


10


15


20


25


201220112010200920082007


GDP growth (%)


Angola


Nigeria


South Africa


Sub-Saharan Africa


Mali


Source: Online table 4.1.




68 World Development Indicators 2013 Front User guide World view People Environment?


Gross domestic product Gross
savings


Adjusted
net savings


Current
account
balance


Central
government
cash surplus


or deficit


Central
government


debt


Consumer
price index


Broad
money


average annual % growth


Estimate Forecast % of GDP % of GNI % of GDP % of GDP % of GDP % growth % of GDP


2000–11 2011–12 2012–13 2011 2011 2011 2011 2011 2011 2011


Afghanistan .. .. .. .. .. .. –0.6 .. 5.7 35.8


Albania 5.2 0.8 1.6 12.7 0.9 –12.1 .. .. 3.5 81.8


Algeria 3.7 3.0 3.4 48.4 25.3 10.4 –0.3 .. 4.5 64.6


American Samoa .. .. .. .. .. .. .. .. .. ..


Andorra 5.9 .. .. .. .. .. .. .. .. ..


Angola 12.2 8.1 7.2 20.2 –21.4 12.5 .. .. 13.5 35.9


Antigua and Barbuda 2.7 1.7 2.4 26.8 .. –10.7 .. .. 3.5 101.4


Argentina .. .. .. 22.3 11.3 0.0 .. .. .. 28.8


Armenia 8.2 6.8 4.3 19.3 8.7 –10.8 –2.7 .. 7.7 29.5


Aruba .. .. .. .. .. .. .. .. 4.4 ..


Australia 3.1 .. .. 22.9 6.8 –2.8 –3.7 30.7 3.4 105.8


Austriaa 1.8 .. .. 25.5 16.4 0.5 –2.2 75.0 3.3 ..


Azerbaijan 16.0 2.0 4.2 45.9 7.5 27.0 1.4 6.4 7.9 27.8


Bahamas, The 0.5 .. .. 11.5 .. –14.0 –3.5 .. 3.2 80.5


Bahrain 6.3 .. .. 30.1 9.6 3.4 .. .. –0.4 91.2


Bangladesh 6.0 6.1 6.0 36.5 25.0 0.2 –0.9 .. 10.7 68.7


Barbados 0.9 .. .. 16.9 .. –5.3 –8.6 104.4 9.4 152.3


Belarus 7.8 2.8 4.0 26.3 17.6 –10.5 1.9 44.2 53.2 40.5


Belgiuma 1.5 .. .. 22.3 14.1 –1.4 –3.6 91.0 3.5 ..


Belize 3.7 4.0 2.8 .. .. –2.2 –1.6 .. –2.5 76.0


Benin 3.8 3.5 3.8 13.1 7.7 –8.1 –1.4 .. 2.7 40.0


Bermuda 1.9 .. .. .. .. .. .. .. .. ..


Bhutan 8.5 .. .. .. .. .. 0.5 56.8 8.8 67.1


Bolivia 4.2 4.7 4.4 26.1 6.9 2.2 .. .. 9.8 68.7


Bosnia and Herzegovina 4.2 .. .. 13.1 .. –8.8 –1.2 .. 3.7 56.7


Botswana 4.0 5.8 5.1 27.7 20.4 0.3 –1.7 .. 8.9 37.8


Brazil 3.8 0.9 3.4 17.2 6.8 –2.1 –2.6 52.8 6.6 74.4


Brunei Darussalam 1.2 .. .. 50.9 10.1 37.1 .. .. 2.0 67.2


Bulgaria 4.3 0.8 1.8 23.6 13.7 0.4 –2.0 15.5 4.2 75.6


Burkina Faso 5.8 6.4 6.7 15.6 5.5 –4.6 –2.4 .. 2.8 29.0


Burundi 3.5 4.1 4.3 1.7 –6.1 –12.2 .. .. 9.7 24.2


Cambodia 8.4 6.6 6.7 10.6 3.4 –5.5 –4.2 .. 5.5 39.1


Cameroon 3.2 4.6 4.8 12.2 0.8 –3.8 .. .. 2.9 23.1


Canada 1.9 .. .. 19.6 6.7 –2.8 –1.3 53.8 2.9 ..


Cape Verde 6.2 4.8 4.9 22.2 16.7 –16.0 –3.7 .. 4.5 77.0


Cayman Islands .. .. .. .. .. .. .. .. .. ..


Central African Republic 1.3 3.8 4.0 .. .. .. .. .. 1.3 19.2


Chad 8.7 .. .. .. .. .. .. .. –4.9 13.8


Channel Islands 0.5 .. .. .. .. .. .. .. .. ..


Chile 4.1 5.8 5.1 24.9 5.4 1.8 1.3 .. 3.3 76.1


China 10.8 7.9 8.4 52.7 36.4 2.8 .. .. 5.4 180.1


Hong Kong SAR, China 4.6 .. .. 29.4 18.1 5.2 4.1 35.5 5.3 328.2


Macao SAR, China 12.5 .. .. 55.9 .. 42.7 25.0 .. 5.8 101.9


Colombia 4.5 3.5 3.8 19.1 1.3 –3.0 0.3 53.2 3.4 39.9


Comoros 1.9 2.5 3.5 .. .. .. .. .. 0.9 34.9


Congo, Dem. Rep. 5.6 6.6 8.2 .. .. .. 3.8 .. .. 16.8


Congo, Rep. 4.5 4.7 5.6 .. .. .. .. .. 1.3 27.0


4 Economy




World Development Indicators 2013 69Economy States and markets Global links Back


Economy 4
Gross domestic product Gross


savings
Adjusted


net savings
Current
account
balance


Central
government
cash surplus


or deficit


Central
government


debt


Consumer
price index


Broad
money


average annual % growth


Estimate Forecast % of GDP % of GNI % of GDP % of GDP % of GDP % growth % of GDP


2000–11 2011–12 2012–13 2011 2011 2011 2011 2011 2011 2011


Costa Rica 4.8 4.6 4.0 14.7 9.0 –5.3 –3.5 .. 4.9 49.8


Côte d’Ivoire 1.0 8.2 7.0 12.7 4.1 2.0 .. .. 4.9 40.5


Croatia 2.6 .. .. 19.9 10.7 –0.7 –4.6 .. 2.3 72.9


Cuba 6.1 .. .. .. .. .. .. .. .. ..


Curaçao .. .. .. .. .. .. .. .. .. ..


Cyprusa 2.9b .. .. 8.8b 4.6b –4.7 –6.3 114.0 3.3 ..


Czech Republic 3.7 .. .. 21.7 12.6 –2.9 –4.4 38.1 1.9 73.9


Denmark 0.8 .. .. 23.5 15.0 5.7 –2.0 50.6 2.8 74.0


Djibouti 4.0 .. .. .. .. –6.8 .. .. 4.4 89.7


Dominica 3.5 0.4 1.2 5.0 –3.7 –17.5 .. .. 2.4 86.7


Dominican Republic 5.7 3.0 4.3 8.4 –1.7 –8.1 –2.9 .. 8.5 33.4


Ecuador 4.8 4.5 3.9 22.8 –3.2 –0.4 .. .. 4.5 35.6


Egypt, Arab Rep. 5.0 2.4 3.2 16.9 1.4 –2.4 –10.1 .. 10.1 76.1


El Salvador 2.0 1.8 2.3 8.9 0.7 –4.6 –2.2 48.0 5.1 45.7


Equatorial Guinea 14.4 .. .. .. .. .. .. .. 6.9 11.5


Eritrea 0.9 7.5 6.0 .. .. .. .. .. .. 114.7


Estoniaa 3.9 .. .. 25.1 16.3 2.2 1.0 7.1 5.0 59.8


Ethiopia 8.9 7.8 7.5 27.3 17.6 –2.6 –1.4 .. 33.2 ..


Faeroe Islands .. .. .. .. .. –0.7 .. .. .. ..


Fiji 1.2 .. .. .. .. –13.0 .. .. 8.7 66.3


Finlanda 1.9 .. .. 19.7 11.5 –1.2 –0.5 47.8 3.4 ..


Francea 1.2 .. .. 17.9 9.0 –2.0 –5.2 93.9 2.1 ..


French Polynesia .. .. .. .. .. .. .. .. .. ..


Gabon 2.4 4.7 3.5 .. .. .. .. .. 1.3 22.1


Gambia, The 3.4 3.9 10.7 12.1 6.6 7.5 .. .. 4.8 54.9


Georgia 6.6c 4.9c 5.1c 13.9c 4.9c –11.8 –1.2 32.6 49.4 29.3


Germanya 1.1 .. .. 23.9 13.8 5.6 –0.4 55.6 2.1 ..


Ghana 6.3 7.5 7.8 8.8 –6.6 –8.9 –4.0 .. 8.7 30.8


Greecea 1.8 .. .. 5.4 –5.5 –9.9 –9.8 106.5 3.3 ..


Greenland 1.7 .. .. .. .. .. .. .. .. ..


Grenada 2.2 .. .. –5.7 .. –26.6 –3.0 .. 3.0 94.3


Guam .. .. .. .. .. .. .. .. .. ..


Guatemala 3.6 3.1 3.2 10.7 0.9 –3.1 –2.8 24.6 6.2 45.5


Guinea 2.6 4.8 5.0 –6.5 –27.7 –22.8 .. .. 21.4 36.4


Guinea-Bissau 2.5 –2.8 3.0 .. .. –8.5 .. .. 5.0 40.5


Guyana 2.5 .. .. 14.5 –1.3 –7.1 .. .. 5.0 65.8


Haiti 0.7 2.2 6.0 24.6 17.0 –4.6 .. .. 8.4 47.2


Honduras 4.4 3.3 3.7 17.9 11.0 –8.6 –2.6 .. 6.8 52.2


Hungary 1.9 .. .. 20.6 13.0 0.9 3.6 80.9 4.0 63.7


Iceland 2.7 .. .. 7.3 0.9 –6.9 –5.3 119.3 4.0 96.3


India 7.8 5.5 5.9 34.6 22.5 –3.0 –3.7 48.5 8.9 76.7


Indonesia 5.4 6.1 6.3 31.8 17.1 0.2 –1.1 26.2 5.4 38.8


Iran, Islamic Rep. 5.4 .. .. .. .. .. 0.5 .. 20.6 45.0


Iraq 1.1 .. .. .. .. 22.7 .. .. 2.9 54.9


Irelanda 2.4 .. .. 14.0 5.5 1.2 –13.5 106.0 2.6 ..


Isle of Man 6.2 .. .. .. .. .. .. .. .. ..


Israel 3.6 .. .. 14.7 6.3 0.1 –4.4 .. 3.5 104.5




70 World Development Indicators 2013 Front User guide World view People Environment?


4 Economy
Gross domestic product Gross


savings
Adjusted


net savings
Current
account
balance


Central
government
cash surplus


or deficit


Central
government


debt


Consumer
price index


Broad
money


average annual % growth


Estimate Forecast % of GDP % of GNI % of GDP % of GDP % of GDP % growth % of GDP


2000–11 2011–12 2012–13 2011 2011 2011 2011 2011 2011 2011


Italya 0.4 .. .. 16.4 6.8 –3.1 –3.5 110.8 2.7 ..


Jamaica .. .. .. 8.4 1.3 –14.3 –5.1 .. 7.5 50.8


Japan 0.7 .. .. 21.7 10.4 2.0 –8.3 175.0 –0.3 239.2


Jordan 6.5 3.0 3.3 13.4 5.8 –12.0 –6.8 61.9 4.4 129.6


Kazakhstan 8.0 5.0 5.5 29.7 –4.3 7.5 7.7 9.9 8.3 35.4


Kenya 4.4 4.3 4.9 13.5 11.3 –9.9 –4.6 .. 14.0 51.0


Kiribati 0.4 .. .. .. .. .. .. .. .. ..


Korea, Dem. Rep. .. .. .. .. .. .. .. .. .. ..


Korea, Rep. 4.0 .. .. 31.5 22.2 2.4 1.8 .. 4.0 78.1


Kosovo 5.2 .. .. .. .. .. .. .. 7.3 41.0


Kuwait 6.0 .. .. 48.7 12.2 29.6 25.0 .. 4.7 56.9


Kyrgyz Republic 4.4 1.0 8.5 18.2 5.6 –6.1 –4.8 .. 16.5 ..


Lao PDR 7.3 8.2 7.5 16.3 –1.7 –2.5 –0.9 .. 7.6 35.9


Latvia 4.1 5.3 3.0 26.1 18.5 –2.2 –2.9 42.5 4.4 47.0


Lebanon 5.0 1.7 2.8 11.5 0.8 –12.1 –5.9 .. 4.0 242.0


Lesotho 3.8 4.3 5.2 15.1 .. –21.4 .. .. 5.0 37.9


Liberia 6.0 .. .. 30.4 23.0 –48.9 0.0 .. 8.5 38.2


Libya 5.4 .. .. .. .. 15.0 .. .. 2.5 58.0


Liechtenstein 2.5 .. .. .. .. .. .. .. .. ..


Lithuania 4.7 3.3 2.5 17.5 9.6 –1.4 –5.2 43.7 4.1 47.6


Luxembourga 2.9 .. .. 22.0 12.4 7.1 –0.4 16.8 3.4 ..


Macedonia, FYR 3.3 0.0 1.0 25.6 14.9 –3.0 .. .. 3.9 55.7


Madagascar 3.2 2.2 4.5 .. .. .. .. .. 9.5 24.9


Malawi 5.1 4.1 5.4 13.5 9.8 –13.6 .. .. 7.6 35.7


Malaysia 4.9 5.1 5.0 34.6 20.6 11.0 –4.8 51.8 3.2 138.5


Maldives 7.2 .. .. .. .. –23.9 –16.7 68.4 12.8 63.7


Mali 5.1 –1.5 3.5 8.5 –5.2 –12.6 –2.5 .. 2.9 29.1


Maltaa 1.8 .. .. 9.5 .. –0.2 –2.8 86.5 2.7 ..


Marshall Islands 1.5 .. .. .. .. .. .. .. .. ..


Mauritania 5.6 4.8 5.2 .. .. .. .. .. 5.7 32.8


Mauritius 3.9 3.3 3.6 13.8 4.8 –12.6 –1.1 36.3 6.5 103.3


Mexico 2.1 4.0 3.3 26.6 12.4 –1.0 .. .. 3.4 31.4


Micronesia, Fed. Sts. 0.0 .. .. .. .. .. .. .. .. 38.2


Moldova 5.1d 1.0d 3.0d 13.0d 10.2d –11.3 –1.8 23.8 7.7 49.9


Monaco 4.3 .. .. .. .. .. .. .. .. ..


Mongolia 7.4 11.8 16.2 31.1 –5.5 –31.5 –3.1 46.9 9.5 57.8


Montenegro 4.2 .. .. .. .. .. .. .. 3.2 47.4


Morocco 4.8e 3.0e 4.4e 28.1e 20.2e –8.0 –4.1 56.3 0.9 112.4


Mozambique 7.5 7.5 8.0 12.4 5.6 –19.1 .. .. 10.4 38.8


Myanmar .. .. .. .. .. .. .. .. 5.0 ..


Namibia 4.9 4.2 4.3 18.6 14.6 –1.2 .. .. 5.0 66.6


Nepal 3.9 4.2 4.0 34.2 27.9 1.5 –1.0 33.8 9.5 75.7


Netherlandsa 1.5 .. .. 26.1 15.1 9.7 –3.9 66.0 2.4 ..


New Caledonia .. .. .. .. .. .. .. .. .. ..


New Zealand 2.0 .. .. 18.8 11.2 –4.2 –7.3 63.6 4.4 95.8


Nicaragua 3.2 4.0 4.2 19.0 11.2 –14.0 0.5 .. 8.1 34.5


Niger 4.2 12.0 6.8 .. .. –25.1 .. .. 2.9 21.4




World Development Indicators 2013 71Economy States and markets Global links Back


Economy 4
Gross domestic product Gross


savings
Adjusted


net savings
Current
account
balance


Central
government
cash surplus


or deficit


Central
government


debt


Consumer
price index


Broad
money


average annual % growth


Estimate Forecast % of GDP % of GNI % of GDP % of GDP % of GDP % growth % of GDP


2000–11 2011–12 2012–13 2011 2011 2011 2011 2011 2011 2011


Nigeria 6.8 6.5 6.6 .. .. 3.6 .. .. 10.8 33.6


Northern Mariana Islands .. .. .. .. .. .. .. .. .. ..


Norway 1.6 .. .. 37.7 19.2 14.5 14.7 20.4 1.3 ..


Oman 4.9 .. .. .. .. 14.3 –0.8 5.1 4.1 35.7


Pakistan 4.9 3.8 3.9 20.4 9.3 –1.1 –6.5 .. 11.9 38.0


Palau 0.3 .. .. .. .. .. .. .. .. ..


Panama 7.2 10.0 7.5 17.7 9.4 –14.5 .. .. 5.9 98.1


Papua New Guinea 4.3 8.0 4.0 20.3 .. –6.7 .. .. 8.4 49.9


Paraguay 4.1 –1.0 8.5 10.6 3.6 –1.1 1.1 .. 8.3 45.0


Peru 6.2 6.3 5.8 23.4 5.4 –1.9 1.3 19.5 3.4 35.8


Philippines 4.9 6.0 6.2 25.1 14.6 3.1 –1.8 .. 4.6 59.8


Poland 4.3 .. .. 16.9 9.2 –4.9 –4.3 .. 4.2 58.0


Portugala 0.5 .. .. 11.7 –1.6 –6.5 –4.0 92.6 3.7 ..


Puerto Rico 0.0 .. .. .. .. .. .. .. .. ..


Qatar 13.6 .. .. .. .. .. 2.9 .. 1.9 49.2


Romania 4.4 0.6 1.6 24.8 14.4 –4.4 –4.9 .. 5.8 37.3


Russian Federation 5.1 3.5 3.6 30.3 7.2 5.3 3.4 9.5 8.4 52.7


Rwanda 7.9 7.7 7.5 11.3 4.5 –7.5 .. .. 5.7 ..


Samoa 2.6 .. .. .. .. –11.9 .. .. 5.2 47.2


San Marino 3.2 .. .. .. .. .. .. .. 2.6 ..


São Tomé and Príncipe .. .. .. .. .. –43.5 .. .. 11.9 35.8


Saudi Arabia 3.7 .. .. 46.8 3.8 27.5 .. .. 5.0 57.2


Senegal 4.1 3.7 4.8 21.8 17.3 –4.7 .. .. 3.4 40.2


Serbia 3.7 .. .. 16.4 .. –8.4 –4.2 .. 11.1 44.7


Seychelles 2.9 3.3 4.2 .. .. –21.4 5.6 75.3 2.6 57.9


Sierra Leone 6.6 25.0 11.1 9.8 –6.0 –37.9 –4.6 .. 16.2 21.6


Singapore 6.0 .. .. 46.6 35.7 23.3 9.8 112.7 5.3 135.7


Sint Maarten .. .. .. .. .. .. .. .. .. ..


Slovak Republica 5.1 .. .. 16.5 7.2 0.0 –4.9 45.6 3.9 ..


Sloveniaa 2.9 .. .. 21.4 13.2 0.0 –6.0 .. 1.8 ..


Solomon Islands 4.9 5.3 4.0 .. .. –30.1 .. .. 7.3 40.8


Somalia .. .. .. .. .. .. .. .. .. ..


South Africa 3.7 2.4 2.7 16.4 1.5 –3.4 –4.4 .. 5.0 76.1


South Sudan .. –2.0 11.0 .. .. .. .. .. .. ..


Spaina 2.1 .. .. 18.2 9.1 –3.5 –3.5 55.2 3.2 ..


Sri Lanka 5.8 6.1 6.8 22.1 13.4 –7.8 –6.4 .. 6.7 38.1


St. Kitts and Nevis 2.5 .. .. 23.4 .. –8.6 2.6 .. 5.9 138.3


St. Lucia 2.9 0.7 1.2 11.8 .. –22.5 .. .. 2.8 91.3


St. Martin .. .. .. .. .. .. .. .. .. ..


St. Vincent and Grenadines 3.1 1.2 1.5 –4.8 –12.2 –30.2 –3.7 .. 4.0 67.8


Sudan 7.1f 3.0f 3.2f 20.9f 0.1f 0.2 .. .. 13.0 24.5


Suriname 4.9 4.0 4.5 .. .. 5.8 .. .. 17.7 47.5


Swaziland 2.4 –2.0 1.0 –0.5 –5.1 –10.0 .. .. 6.1 29.6


Sweden 2.3 .. .. 26.2 18.7 6.5 0.5 38.2 3.0 86.6


Switzerland 1.9 .. .. 32.1 22.0 7.5 .. .. 0.2 167.8


Syrian Arab Republic 5.0 .. .. 16.8 –5.7 –0.6 .. .. 4.8 73.9


Tajikistan 8.0 .. .. 23.2 16.4 –12.1 .. .. 12.4 ..




72 World Development Indicators 2013 Front User guide World view People Environment?


4 Economy
Gross domestic product Gross


savings
Adjusted


net savings
Current
account
balance


Central
government
cash surplus


or deficit


Central
government


debt


Consumer
price index


Broad
money


average annual % growth


Estimate Forecast % of GDP % of GNI % of GDP % of GDP % of GDP % growth % of GDP


2000–11 2011–12 2012–13 2011 2011 2011 2011 2011 2011 2011


Tanzaniag 7.0 6.5 6.8 20.3 10.5 –16.6 .. .. 12.7 34.7


Thailand 4.2 4.7 5.0 31.0 20.8 4.1 –1.2 30.2 3.8 128.2


Timor-Leste 5.6 .. .. .. .. .. .. .. 13.5 30.6


Togo 2.6 4.0 4.4 12.2 5.3 –6.3 –1.1 .. 3.6 48.5


Tonga 1.0 .. .. 3.9 .. –17.0 .. .. 6.3 39.0


Trinidad and Tobago 5.6 .. .. .. .. 19.9 –4.8 21.3 5.1 65.1


Tunisia 4.4 2.4 3.2 15.9 5.8 –7.3 –3.7 44.0 3.6 67.6


Turkey 4.7 2.9 4.0 14.1 4.0 –10.0 –1.3 45.9 6.5 54.7


Turkmenistan 8.7 .. .. .. .. .. .. .. .. ..


Turks and Caicos Islands .. .. .. .. .. .. .. .. .. ..


Tuvalu 1.1 .. .. .. .. .. .. .. .. ..


Uganda 7.7 3.4 6.2 20.8 11.2 –13.5 –3.9 42.7 18.7 26.5


Ukraine 4.3 0.5 2.2 16.0 6.2 –5.5 –2.3 27.1 8.0 52.1


United Arab Emirates 4.8 .. .. .. .. .. .. .. 0.9 62.4


United Kingdom 1.7 .. .. 12.9 2.7 –1.9 –7.7 101.2 4.5 165.7


United States 1.6 .. .. 11.7 0.9 –3.2 –9.3 81.8 3.2 89.8


Uruguay 4.0 4.0 4.0 16.5 5.9 –3.1 –0.6 46.6 8.1 44.6


Uzbekistan 7.3 8.0 6.5 .. .. .. .. .. .. ..


Vanuatu 3.9 2.0 2.5 .. .. –15.2 .. .. 0.9 84.2


Venezuela, RB 4.4 5.2 1.8 30.7 1.4 8.6 .. .. 26.1 36.6


Vietnam 7.3 5.2 5.5 33.1 17.4 0.2 .. .. 18.7 109.3


Virgin Islands (U.S.) .. .. .. .. .. .. .. .. .. ..


West Bank and Gaza .. .. .. .. .. .. .. .. 2.8 ..


Yemen, Rep. 3.6 0.1 4.0 8.3 –10.0 –3.0 .. .. 16.4 30.1


Zambia 5.7 6.7 7.1 27.8 4.0 1.1 –1.5 .. 6.4 23.4


Zimbabwe –5.1 5.0 6.0 .. .. .. .. .. .. ..


World 2.7 w 2.3 w 2.4 w 19.4 w 6.7 w


Low income 5.5 5.6 6.1 26.5 15.6


Middle income 6.4 4.9 5.5 30.0 15.1


Lower middle income 6.2 4.8 5.5 28.1 13.4


Upper middle income 6.5 5.0 5.5 31.0 15.6


Low & middle income 6.4 4.9 5.5 29.9 15.1


East Asia & Pacific 9.4 7.5 7.9 47.7 30.3


Europe & Central Asia 5.2 3.0 3.6 22.9 6.2


Latin America & Carib. 3.9 3.0 3.6 21.5 8.6


Middle East & N. Africa 4.7 0.2 2.4 .. ..


South Asia 7.3 5.4 5.7 33.3 21.2


Sub- Saharan Africa 4.9 4.6 4.9 16.8 –2.4


High income 1.6 1.3 1.3 17.6 5.7


Euro area 1.2 –0.4 –0.1 20.1 8.6


a. As members of the European Monetary Union, these countries share a single currency, the euro. b. Refers to the area controlled by the government of the Republic of Cyprus. c. Excludes
Abkhazia and South Ossetia. d. Excludes Transnistria. e. Includes Former Spanish Sahara. f. Excludes South Sudan after July 9, 2011. g. Covers mainland Tanzania only.




World Development Indicators 2013 73Economy States and markets Global links Back


Economy 4
Economic data are organized by several different accounting con-


ventions: the system of national accounts, the balance of pay-


ments, government finance statistics, and international finance


statistics. There has been progress in unifying the concepts in the


system of national accounts, balance of payments, and government


finance statistics, but there are many national variations in the


implementation of these standards. For example, even though the


United Nations recommends using the 2008 System of National


Accounts (2008 SNA) methodology in compiling national accounts,


many are still using earlier versions, some as old as 1968. The


International Monetary Fund (IMF) has recently published a new


balance of payments methodology (BPM6), but many countries are


still using the previous version. Similarly, the standards and defini-


tions for government finance statistics were updated in 2001, but


several countries still report using the 1986 version. For individual


country information about methodology used, refer to Primary data


documentation.


Economic growth


An economy’s growth is measured by the change in the volume of its


output or in the real incomes of its residents. The 2008 SNA offers


three plausible indicators for calculating growth: the volume of gross


domestic product (GDP), real gross domestic income, and real gross


national income. Only growth in GDP is reported here.


Growth rates of GDP and its components are calculated using the


least squares method and constant price data in the local currency


for countries and using constant price U.S. dollar series for regional


and income groups. Local currency series are converted to constant


U.S. dollars using an exchange rate in the common reference year.


The growth rates are average annual and compound growth rates.


Methods of computing growth are described in Statistical methods.


Forecasts of growth rates come from World Bank (2013).


Rebasing national accounts


Rebasing of national accounts can alter the measured growth rate of


an economy and lead to breaks in series that affect the consistency


of data over time. When countries rebase their national accounts,


they update the weights assigned to various components to better


reflect current patterns of production or uses of output. The new


base year should represent normal operation of the economy—it


should be a year without major shocks or distortions. Some devel-


oping countries have not rebased their national accounts for many


years. Using an old base year can be misleading because implicit


price and volume weights become progressively less relevant and


useful.


To obtain comparable series of constant price data for comput-


ing aggregates, the World Bank rescales GDP and value added by


industrial origin to a common reference year. This year’s World Devel-


opment Indicators continues to use 2000 as the reference year.


Because rescaling changes the implicit weights used in forming


regional and income group aggregates, aggregate growth rates in


this year’s edition are not comparable with those from earlier edi-


tions with different base years.


Rescaling may result in a discrepancy between the rescaled GDP


and the sum of the rescaled components. To avoid distortions in the


growth rates, the discrepancy is left unallocated. As a result, the


weighted average of the growth rates of the components generally


does not equal the GDP growth rate.


Adjusted net savings


Adjusted net savings measure the change in a country’s real wealth


after accounting for the depreciation and depletion of a full range


of assets in the economy. If a country’s adjusted net savings are


positive and the accounting includes a sufficiently broad range of


assets, economic theory suggests that the present value of social


welfare is increasing. Conversely, persistently negative adjusted


net savings indicate that the present value of social welfare is


decreasing, suggesting that an economy is on an unsustainable


path.


Adjusted net savings are derived from standard national account-


ing measures of gross savings by making four adjustments. First,


estimates of fixed capital consumption of produced assets are


deducted to obtain net savings. Second, current public expendi-


tures on education are added to net savings (in standard national


accounting these expenditures are treated as consumption). Third,


estimates of the depletion of a variety of natural resources are


deducted to reflect the decline in asset values associated with their


extraction and harvest. And fourth, deductions are made for dam-


ages from carbon dioxide emissions and local pollution. By account-


ing for the depletion of natural resources and the degradation of


the environment, adjusted net savings goes beyond the definition


of savings or net savings in the SNA.


Balance of payments


The balance of payments records an economy’s transactions with


the rest of the world. Balance of payments accounts are divided


into two groups: the current account, which records transactions


in goods, services, primary income, and secondary income, and


the capital and financial account, which records capital transfers,


acquisition or disposal of nonproduced, nonfinancial assets, and


transactions in financial assets and liabilities. The current account


balance is one of the most analytically useful indicators of an exter-


nal imbalance.


A primary purpose of the balance of payments accounts is to


indicate the need to adjust an external imbalance. Where to draw


the line for analytical purposes requires a judgment concerning the


imbalance that best indicates the need for adjustment. There are a


number of definitions in common use for this and related analytical


purposes. The trade balance is the difference between exports and


imports of goods. From an analytical view it is arbitrary to distinguish


goods from services. For example, a unit of foreign exchange earned


by a freight company strengthens the balance of payments to the


About the data




74 World Development Indicators 2013 Front User guide World view People Environment?


4 Economy
same extent as the foreign exchange earned by a goods exporter.


Even so, the trade balance is useful because it is often the most


timely indicator of trends in the current account balance. Customs


authorities are typically able to provide data on trade in goods long


before data on trade in services are available.


Beginning in August 2012, the International Monetary Fund imple-


mented the Balance of Payments Manual 6 (BPM6) framework in


its major statistical publications. The World Bank will implement


BPM6 in its online databases and publications in April 2013. Bal-


ance of payments data for 2005 onward will be presented in accord


with the BPM6. The historical BPM5 data series will end with data


for 2008, which can be accessed through the World Development


Indicators archives.


The complete balance of payments methodology can be accessed


through the International Monetary Fund website (www.imf.org/


external/np/sta/bop/bop.htm).


Government finance


Central government cash surplus or deficit, a summary measure of


the ongoing sustainability of government operations, is comparable


to the national accounting concept of savings plus net capital trans-


fers receivable, or net operating balance in the 2001 update of the


IMF’s Government Finance Statistics Manual.


The 2001 manual, harmonized with the 1993 SNA, recommends


an accrual accounting method, focusing on all economic events


affecting assets, liabilities, revenues, and expenses, not just those


represented by cash transactions. It accounts for all changes in


stocks, so stock data at the end of an accounting period equal stock


data at the beginning of the period plus flows over the period. The


1986 manual considered only debt stocks.


For most countries central government finance data have been


consolidated into one account, but for others only budgetary central


government accounts are available. Countries reporting budgetary


data are noted in Primary data documentation. Because budgetary


accounts may not include all central government units (such as


social security funds), they usually provide an incomplete picture.


In federal states the central government accounts provide an incom-


plete view of total public finance.


Data on government revenue and expense are collected by the IMF


through questionnaires to member countries and by the Organisa-


tion for Economic Co-operation and Development (OECD). Despite


IMF efforts to standardize data collection, statistics are often incom-


plete, untimely, and not comparable across countries.


Government finance statistics are reported in local currency. The


indicators here are shown as percentages of GDP. Many countries


report government finance data by fiscal year; see Primary data


documentation for information on fiscal year end by country.


Financial accounts


Money and the financial accounts that record the supply of money


lie at the heart of a country’s financial system. There are several


commonly used definitions of the money supply. The narrowest, M1,


encompasses currency held by the public and demand deposits with


banks. M2 includes M1 plus time and savings deposits with banks


that require prior notice for withdrawal. M3 includes M2 as well as


various money market instruments, such as certificates of deposit


issued by banks, bank deposits denominated in foreign currency,


and deposits with financial institutions other than banks. However


defined, money is a liability of the banking system, distinguished


from other bank liabilities by the special role it plays as a medium


of exchange, a unit of account, and a store of value.


A general and continuing increase in an economy’s price level is


called inflation. The increase in the average prices of goods and


services in the economy should be distinguished from a change


in the relative prices of individual goods and services. Generally


accompanying an overall increase in the price level is a change in


the structure of relative prices, but it is only the average increase,


not the relative price changes, that constitutes inflation. A commonly


used measure of inflation is the consumer price index, which mea-


sures the prices of a representative basket of goods and services


purchased by a typical household. The consumer price index is usu-


ally calculated on the basis of periodic surveys of consumer prices.


Other price indices are derived implicitly from indexes of current and


constant price series.


Consumer price indexes are produced more frequently and so


are more current. They are constructed explicitly, using surveys


of the cost of a defined basket of consumer goods and services.


Nevertheless, consumer price indexes should be interpreted with


caution. The definition of a household, the basket of goods, and the


geographic (urban or rural) and income group coverage of consumer


price surveys can vary widely by country. In addition, weights are


derived from household expenditure surveys, which, for budgetary


reasons, tend to be conducted infrequently in developing countries,


impairing comparability over time. Although useful for measuring


consumer price inflation within a country, consumer price indexes


are of less value in comparing countries.




World Development Indicators 2013 75Economy States and markets Global links Back


Economy 4
Definitions


• Gross domestic product (GDP) at purchaser prices is the sum of


gross value added by all resident producers in the economy plus any


product taxes (less subsidies) not included in the valuation of output.


It is calculated without deducting for depreciation of fabricated capital


assets or for depletion and degradation of natural resources. Value


added is the net output of an industry after adding up all outputs and


subtracting intermediate inputs. • Gross savings are the difference


between gross national income and public and private consump-


tion, plus net current transfers. • Adjusted net savings measure


the change in value of a specified set of assets, excluding capital


gains. Adjusted net savings are net savings plus education expendi-


ture minus energy depletion, mineral depletion, net forest depletion,


and carbon dioxide and particulate emissions damage. • Current


account balance is the sum of net exports of goods and services, net


primary income, and net secondary income. • Central government


cash surplus or deficit is revenue (including grants) minus expense,


minus net acquisition of nonfinancial assets. In editions before 2005


nonfinancial assets were included under revenue and expenditure in


gross terms. This cash surplus or deficit is close to the earlier overall


budget balance (still missing is lending minus repayments, which are


included as a financing item under net acquisition of financial assets).


• Central government debt is the entire stock of direct government


fixed-term contractual obligations to others outstanding on a particu-


lar date. It includes domestic and foreign liabilities such as currency


and money deposits, securities other than shares, and loans. It is


the gross amount of government liabilities reduced by the amount


of equity and financial derivatives held by the government. Because


debt is a stock rather than a flow, it is measured as of a given date,


usually the last day of the fiscal year. • Consumer price index reflects


changes in the cost to the average consumer of acquiring a basket


of goods and services that may be fixed or may change at specified


intervals, such as yearly. The Laspeyres formula is generally used.


• Broad money (IFS line 35L..ZK) is the sum of currency outside


banks; demand deposits other than those of the central government;


the time, savings, and foreign currency deposits of resident sectors


other than the central government; bank and traveler’s checks; and


other securities such as certificates of deposit and commercial paper.


Data sources


Data on GDP for most countries are collected from national statisti-


cal organizations and central banks by visiting and resident World


Bank missions; data for selected high-income economies are from


the OECD. Data on gross savings are from World Bank national


accounts data files. Data on adjusted net savings are based on


a conceptual underpinning by Hamilton and Clemens (1999) and


calculated using data on consumption of fixed capital from the


United Nations Statistics Division’s National Accounts Statistics:


Main Aggregates and Detailed Tables, extrapolated to 2010; data


on education expenditure from the United Nations Educational,


Scientific, and Cultural Organization Institute for Statistics online


database, with missing data estimated by World Bank staff; data on


forest, energy, and mineral depletion based on sources and meth-


ods in World Bank (2011); data on carbon dioxide damage from


Fankhauser (1994); data on local pollution damage from Pandey and


others (2006). Data on current account balance are from the IMF’s


Balance of Payments Statistics Yearbook and International Financial


Statistics. Data on central government finances are from the IMF’s


Government Finance Statistics database. Data on the consumer


price index are from the IMF’s International Financial Statistics. Data


on broad money are from the IMF’s monthly International Financial


Statistics and annual International Financial Statistics Yearbook.


References


Asian Development Bank. 2012. Asian Development Outlook 2012


Update: Services and Asia’s Future Growth. Manila.


De la Torre, Augusto, Tatiana Didier, and Samuel Pienknagura. 2012.


Latin America Copes with Volatility, the Dark Side of Globalization.


Washington, DC: World Bank.


Fankhauser, Samuel. 1994. “The Social Costs of Greenhouse Gas Emis-


sions: An Expected Value Approach.” Energy Journal 15 (2): 157–84.


Hamilton, Kirk, and Michael Clemens. 1999. “Genuine Savings Rates in


Developing Countries.” World Bank Economic Review 13 (2): 333–56.


IMF (International Monetary Fund). 2001. Government Finance Statis-


tics Manual. Washington, DC.


———. 2012. Regional Economic Outlook: Sub- Saharan Africa—Main-


taining Growth in an Uncertain World, October 2012. Washington, DC.


www.imf.org/external/pubs/ft/reo/2012/afr/eng/sreo1012.htm.


Pandey, Kiran D., Katharine Bolt, Uwe Deichmann, Kirk Hamilton, Bart


Ostro, and David Wheeler. 2006. “The Human Cost of Air Pollution:


New Estimates for Developing Countries.” World Bank, Development


Research Group and Environment Department, Washington, DC.


United Nations Statistics Division. Various years. National Accounts


Statistics: Main Aggregates and Detailed Tables. Parts 1 and 2. New


York: United Nations.


World Bank. 2011. The Changing Wealth of Nations: Measuring Sustain-


able Development for the New Millennium. Washington, DC.


———. 2012a. East Asia and Pacific Economic Update 2012, Volume 2:


Remaining Resilient. Washington, DC.


———. 2012b. Middle East and North Africa Region Economic Devel-


opments and Prospects, October 2012: Looking Ahead After a Year


in Transition. Washington, DC.


———. 2012c. South Asia Economic Focus—A Review of Economic


Developments in South Asian Countries: Creating Fiscal Space


through Revenue Mobilization. Washington, DC.


———. 2013. Global Economic Prospects, Volume 6, January 13:


Assuring Growth over the Medium Term. Washington, DC.


———. Various years. World Development Indicators. Washington, DC.




76 World Development Indicators 2013 Front User guide World view People Environment?


4 Economy


4.1 Growth of output


Gross domestic product NY.GDP.MKTP.KD.ZG


Agriculture NV.AGR.TOTL.KD.ZG


Industry NV.IND.TOTL.KD.ZG


Manufacturing NV.IND.MANF.KD.ZG


Services NV.SRV.TETC.KD.ZG


4.2 Structure of output


Gross domestic product NY.GDP.MKTP.CD


Agriculture NV.AGR.TOTL.ZS


Industry NV.IND.TOTL.ZS


Manufacturing NV.IND.MANF.ZS


Services NV.SRV.TETC.ZS


4.3 Structure of manufacturing


Manufacturing value added NV.IND.MANF.CD


Food, beverages and tobacco NV.MNF.FBTO.ZS.UN


Textiles and clothing NV.MNF.TXTL.ZS.UN


Machinery and transport equipment NV.MNF.MTRN.ZS.UN


Chemicals NV.MNF.CHEM.ZS.UN


Other manufacturing NV.MNF.OTHR.ZS.UN


4.4 Structure of merchandise exports


Merchandise exports TX.VAL.MRCH.CD.WT


Food TX.VAL.FOOD.ZS.UN


Agricultural raw materials TX.VAL.AGRI.ZS.UN


Fuels TX.VAL.FUEL.ZS.UN


Ores and metals TX.VAL.MMTL.ZS.UN


Manufactures TX.VAL.MANF.ZS.UN


4.5 Structure of merchandise imports


Merchandise imports TM.VAL.MRCH.CD.WT


Food TM.VAL.FOOD.ZS.UN


Agricultural raw materials TM.VAL.AGRI.ZS.UN


Fuels TM.VAL.FUEL.ZS.UN


Ores and metals TM.VAL.MMTL.ZS.UN


Manufactures TM.VAL.MANF.ZS.UN


4.6 Structure of service exports


Commercial service exports TX.VAL.SERV.CD.WT


Transport TX.VAL.TRAN.ZS.WT


Travel TX.VAL.TRVL.ZS.WT


Insurance and financial services TX.VAL.INSF.ZS.WT


Computer, information, communications,
and other commercial services TX.VAL.OTHR.ZS.WT


4.7 Structure of service imports


Commercial service imports TM.VAL.SERV.CD.WT


Transport TM.VAL.TRAN.ZS.WT


Travel TM.VAL.TRVL.ZS.WT


Insurance and financial services TM.VAL.INSF.ZS.WT


Computer, information, communications,
and other commercial services TM.VAL.OTHR.ZS.WT


4.8 Structure of demand


Household final consumption expenditure NE.CON.PETC.ZS


General government final consumption
expenditure NE.CON.GOVT.ZS


Gross capital formation NE.GDI.TOTL.ZS


Exports of goods and services NE.EXP.GNFS.ZS


Imports of goods and services NE.IMP.GNFS.ZS


Gross savings NY.GNS.ICTR.ZS


4.9 Growth of consumption and investment


Household final consumption expenditure NE.CON.PRVT.KD.ZG


Household final consumption expenditure,
Per capita NE.CON.PRVT.PC.KD.ZG


General government final consumption
expenditure NE.CON.GOVT.KD.ZG


Gross capital formation NE.GDI.TOTL.KD.ZG


Exports of goods and services NE.EXP.GNFS.KD.ZG


Imports of goods and services NE.IMP.GNFS.KD.ZG


4.10 Toward a broader measure of national income


Gross domestic product, $ NY.GDP.MKTP.CD


Gross national income, $ NY.GNP.MKTP.CD


Consumption of fixed capital NY.ADJ.DKAP.GN.ZS


Natural resource depletion NY.ADJ.DRES.GN.ZS


Adjusted net national income NY.ADJ.NNTY.CD


Gross domestic product, % growth NY.GDP.MKTP.KD.ZG


Gross national income, % growth NY.GNP.MKTP.KD.ZG


Adjusted net national income NY.ADJ.NNTY.KD.ZG


4.11 Toward a broader measure of savings


Gross savings NY.ADJ.ICTR.GN.ZS


Consumption of fixed capital NY.ADJ.DKAP.GN.ZS


Education expenditure NY.ADJ.AEDU.GN.ZS


Net forest depletion NY.ADJ.DFOR.GN.ZS


Energy depletion NY.ADJ.DNGY.GN.ZS


Mineral depletion NY.ADJ.DMIN.GN.ZS


Carbon dioxide damage NY.ADJ.DCO2.GN.ZS


Local pollution damage NY.ADJ.DPEM.GN.ZS


Adjusted net savings NY.ADJ.SVNG.GN.ZS


To access the World Development Indicators online tables, use


the URL http://wdi.worldbank.org/table/ and the table number (for


example, http://wdi.worldbank.org/table/4.1). To view a specific


indicator online, use the URL http://data.worldbank.org/indicator/


and the indicator code (for example, http://data.worldbank.org/


indicator/NY.GDP.MKTP.KD.ZG).


Online tables and indicators




World Development Indicators 2013 77Economy States and markets Global links Back


Economy 4
4.12 Central government finances


Revenue GC.REV.XGRT.GD.ZS


Expense GC.XPN.TOTL.GD.ZS


Cash surplus or deficit GC.BAL.CASH.GD.ZS


Net incurrence of liabilities, Domestic GC.FIN.DOMS.GD.ZS


Net incurrence of liabilities, Foreign GC.FIN.FRGN.GD.ZS


Debt and interest payments, Total debt GC.DOD.TOTL.GD.ZS


Debt and interest payments, Interest GC.XPN.INTP.RV.ZS


4.13 Central government expenditure


Goods and services GC.XPN.GSRV.ZS


Compensation of employees GC.XPN.COMP.ZS


Interest payments GC.XPN.INTP.ZS


Subsidies and other transfers GC.XPN.TRFT.ZS


Other expense GC.XPN.OTHR.ZS


4.14 Central government revenues


Taxes on income, profits and capital gains GC.TAX.YPKG.RV.ZS


Taxes on goods and services GC.TAX.GSRV.RV.ZS


Taxes on international trade GC.TAX.INTT.RV.ZS


Other taxes GC.TAX.OTHR.RV.ZS


Social contributions GC.REV.SOCL.ZS


Grants and other revenue GC.REV.GOTR.ZS


4.15 Monetary indicators


Broad money FM.LBL.BMNY.ZG


Claims on domestic economy FM.AST.DOMO.ZG.M3


Claims on central governments FM.AST.CGOV.ZG.M3


Interest rate, Deposit FR.INR.DPST


Interest rate, Lending FR.INR.LEND


Interest rate, Real FR.INR.RINR


4.16 Exchange rates and price


Official exchange rate PA.NUS.FCRF


Purchasing power parity (PPP) conversion
factor PA.NUS.PPP


Ratio of PPP conversion factor to market
exchange rate PA.NUS.PPPC.RF


Real effective exchange rate PX.REX.REER


GDP implicit deflator NY.GDP.DEFL.KD.ZG


Consumer price index FP.CPI.TOTL.ZG


Wholesale price index FP.WPI.TOTL


4.17 Balance of payments current account


Goods and services, Exports BX.GSR.GNFS.CD


Goods and services, Imports BM.GSR.GNFS.CD


Balance on primary income BN.GSR.FCTY.CD


Balance on secondary income BN.TRF.CURR.CD


Current account balance BN.CAB.XOKA.CD


Total reserves FI.RES.TOTL.CD




Front User guide World view People Environment?78 World Development Indicators 2013


STATES AND
MARKETS




World Development Indicators 2013 79Economy States and markets Global links Back


States and markets includes indicators of pri-
vate sector investment and performance, the
role of the public sector in nurturing investment
and growth, and the quality and availability of
infrastructure essential for growth and develop-
ment. These indicators measure the business
environment, the functions of government, finan-
cial system development, infrastructure, informa-
tion and communication technology, science and
technology, performance of governments and
their policies, and conditions in fragile countries
with weak institutions.


Measures of investment in infrastructure
projects with private participation show the
private sector’s contributions to providing pub-
lic services and easing fiscal constraints. For
example, private investment in the dynamic tele-
communications sector has increased more than
50 percent since 2006, reaching $158.5 billion
in 2011.


Data on access to finance, availability of
credit, cost of service, and stock markets help
improve understanding of the state of finan-
cial development. In 2011 people had greater
access to finance in Europe and Central Asia,
with 19 commercial bank branches and 47 auto-
mated teller machines per 100,000 people,
than in other developing regions. Stock market
measures show the effects of the global finan-
cial crises: the market capitalization of listed
companies dropped from $64.6 trillion in 2007
to $34.9  trillion in 2008, recovering to only
$46.8 trillion in 2011.


Economic health is measured not only in
macroeconomic terms but also by laws, regu-
lations, and institutional arrangements. Firms


evaluating investment options, governments
interested in improving business conditions,
and economists seeking to explain economic
performance have all grappled with defining and
measuring the business environment and how
constraints affect productivity and job creation.
The World Development Indicators database
includes results from enterprise surveys and
expert assessments of the business environ-
ment from the Doing Business project.


States and markets also includes data on the
size of the transportation and power sectors, as
well as the spread of new information technol-
ogy. To become competitive suppliers to the rest
of the world, many developing countries need to
improve their road, rail, port, and air transport
facilities. Expanding electricity supply to meet
the growing demand of increasingly urban and
industrialized economies without unacceptable
social, economic, and environmental costs is
one of the greatest challenges facing developed
and developing countries. Data on electric power
consumption per capita show that consumption
in developing countries has doubled since 1995,
to 1,660 kilowatt-hours in 2010.


With rapid growth of mobile telephony and
the global expansion of the Internet, informa-
tion and communication technologies are
increasingly recognized as essential for devel-
opment. World Development Indicators includes
data showing the rapid changes in this sec-
tor. For instance, mobile cellular subscriptions
increased from 16 percent of the global popula-
tion in 2001 to 85 percent in 2011, and Internet
users from 8 percent to 33 percent over the
same period.


5




80 World Development Indicators 2013


Highlights


Front User guide World view People Environment?


East Asia & Pacific: Patent applications are rising, especially in China


0


200


400


600


Patent applications, 2011 (thousands)


Ind
on


es
ia


Ne
w Z


ea
lan


d


Ma
lay


sia
Isr


ae
l


So
uth


Af
ric


a
Ita


ly


Sin
ga


po
re


Ho
ng


Ko
ng


SA
R,


Ch
ina


Me
xic


o
Fra


nc
e


Un
ite


d K
ing


do
m


Au
str


ali
a


Ca
na


da


Ru
ssi


an
Fe


de
rat


ionInd
ia


Ge
rm


an
y


Ko
rea


, R
ep


.
Jap


an


Un
ite


d S
tat


es
Ch


ina


While the global economy continued to underperform, intellectual


property filings worldwide grew strongly in 2011. Demand for patents


rose from 800,000 applications in the early 1980s to 2.14 million in


2011, topping 2 million for the first time. After dropping 4 percent in


2009, patent applications rebounded strongly in 2010 and 2011, aver-


aging 7 percent growth a year. In 2011 China’s patent office became


the world’s largest, measured by patent applications received, accord-


ing to World Intellectual Property Organization (2012). China received


526,412 applications, compared with 503,582 for the United States


and 342,610 for Japan. Of the 20 busiest patent offices, 7 were in


East Asia and Pacific, accounting for 58 percent of total applications


filed worldwide in 2011.


Source: Online table 5.13.


Europe & Central Asia: Container traffic picks up


0


2


4


6


8


UkraineTurkeyRussian
Federation


RomaniaLithuania


Container port trafc (millions of twenty-foot equivalent units)


2
0


0
9


2
0


1
1


Measures of port container traffic, much of it commodity shipments


of medium to high value added, give some indication of a country’s


economic status, though much of the economic benefit from trans-


shipments goes to the terminal operator and ancillary services for


ships and containers rather than to the country more broadly. After


the 2008 fiscal crisis, worldwide container shipments fell to


472 million in 2009, an almost 9 percent drop from 2008, affecting


all ports, operators, and countries. But shipments rebounded in


2010, growing 15 percent and reaching precrisis levels. Most of the


growth came from intercontinental shipments by developing coun-


tries. In 2011 the Russian Federation (3.8 million) and Turkey


(6.1 million) led the market in container shipments in Europe and


Central Asia.


Source: Online table 5.10.


Latin America & Caribbean: Homicide rates mount


0


25


50


75


100


Intentional homicides, 2011 or latest available (per 100,000 people)


So
uth


Af
ric


a


Trin
ida


d &
To


ba
go


Le
so


tho


Ma
law


i


Ug
an


da


Za
mb


ia


St.
Ki


tts
&


Ne
vis


Gu
ate


ma
la


Jam
aic


a
Be


lize


Ve
ne


zue
la,


RB



te


d’I
voi


re


El
Sa


lva
do


r


Ho
nd


ura
s


Homicide rates are very high in Latin America and the Caribbean and


Sub- Saharan Africa, where they add to the death toll caused by armed


conflicts. According to the Geneva Declaration on Armed Violence and


Development (2011), a quarter of all violent deaths occur in just 14


countries, averaging more than 30 violent deaths per 100,000 people


a year, half of them in Latin America and the Caribbean. In many of


these countries, homicides, not armed conflicts, account for the


majority of violent deaths. The links between violent death rates and


socioeconomic development show that homicide rates are higher


where income disparity, extreme poverty, and hunger are high. Coun-


tries that have strengthened their rule of law have seen a decline in


homicide rates.


Source: Online table 5.8.




World Development Indicators 2013 81Economy States and markets Global links Back


Middle East & North Africa: Military spending share continues to rise


Demanding open government and good governance from their head


of states, citizens of countries in the Middle East and North Africa


brought about the Arab Spring in late 2010 and 2011. Although it


is still too soon to estimate the effects of the turbulent years, there


are signs of higher military spending and arms imports. From 2010


to 2011 all regions except the Middle East and North Africa reduced


military spending as a share of gross domestic product (GDP). Algeria


increased military spending from 3.5 percent of GDP in 2010 to


4.6 percent in 2011, and Tunisia from 1.2 percent of GDP in 2010


to 1.3 percent in 2011. Saudi Arabia (8.4 percent of GDP) and Israel


(6.8 percent), both high-income economies, spend the most on the


military, followed by Iraq (5.1 percent), Jordan (4.7 percent), and Leba-


non (4.4 percent).


0


1


2


3


4


Sub-Saharan
Africa


South
Asia


Middle East
& North
Africa


Latin
America &
Caribbean


Europe
& Central


Asia


East Asia
& Pacic


Military spending (% of GDP)


2
0


1
0


2
0


1
1


Source: Online table 5.7.


South Asia: Mobile phone access growing rapidly


Mobile phone subscriptions have roughly doubled every two years


since 2002 and now exceed the number of fixed-line subscriptions


in 2002. By the end of 2011 there were 5.9 billion mobile phone


subscriptions worldwide, almost one for every person if distributed


equally. Developing economies have lagged behind, but they are catch-


ing up. Sub- Saharan Africa, where 53 per 100 people have mobile


phone subscriptions, started far behind but has reached the same


subscription rate as high-income economies did 11 years ago. South


Asia is only eight years behind. In recent years South Asia has had


the largest growth in mobile subscription coverage among developing


regions, with 69 mobile phone subscriptions per 100 people in 2011,


up from 8 in 2005.


0


50


100


150


2011201020082006200420022000


Europe & Central Asia


High income


Latin America & Caribbean


Middle East & North Africa


Mobile phone subscriptions (per 100 people)


East Asia & Pacic


South Asia


Sub-Saharan
Africa


Source: Online table 5.11.


Sub- Saharan Africa: Growth with good policies


The World Bank’s Country Policy and Institutional Assessment (CPIA)


score reflects country performance in promoting economic growth


and reducing poverty. Data for Sub- Saharan countries show a positive


association between average CPIA score and average GDP growth over


2006–11. In 2011 the region’s average CPIA score for International


Development Association countries was 3.2 on a scale of 1 (low) to


6 (high). The regional average masks the wide variation across coun-


tries, from 2.2 in Eritrea and Zimbabwe to 4.0 in Cape Verde. For


several countries the policy environment is the best in recent years.


Thirteen countries saw an improvement in the 2011 score by at least


0.1, 20 countries saw no change, and 5 saw a decline of 0.1 or more


(World Bank 2012).


2.0 2.5 3.0 3.5 4.0
0


5


10


15


Average GDP growth, 2006–11 (%)


Average CPIA score, 2006–11 (1, low, to 6, high)


Source: Online table 5.9.




82 World Development Indicators 2013 Front User guide World view People Environment?


Business
entry


density


Time
required
to start a
business


Stock market
capitalization


Domestic
credit


provided
by banking


sector


Tax revenue
collected
by central


government


Military
expenditures


Electric
power


consumption
per capita


Mobile
cellular


subscriptionsa


Individuals
using the
Interneta


High-technology
exports


per 1,000
people
ages


15–64 days % of GDP % of GDP % of GDP kilowatt-hours
per


100 people
% of


population
% of manufactured


exports% of GDP


2011 June 2012 2011 2011 2011 2011 2010 2011 2011 2011


Afghanistan 0.12 7 .. –2.9 8.7 4.7 .. 54 5 ..


Albania 0.96 4 .. 69.1 .. 1.5 1,770 96 49 0.5


Algeria 0.19 25 .. –4.7 39.4b 4.6 1,026 99 14 0.2


American Samoa .. .. .. .. .. .. .. .. .. ..


Andorra .. .. .. .. .. .. .. 75 81 ..


Angola .. 68 .. 17.9 .. 3.5 248 48 15 ..


Antigua and Barbuda .. 21 .. 101.4 .. .. .. 196 82 0.0


Argentina 0.46 26 9.8 31.3 .. 0.7 2,904 135 48 8.0


Armenia 1.12 8 0.4 36.0 17.0 4.0 1,606 104 32 2.6


Aruba .. .. .. .. .. .. .. 123 57 4.6


Australia 6.17 2 86.9 145.1 20.6b 1.9 10,286 108 79 13.0


Austria 0.56 25 19.7 135.3 18.4b 0.9 8,356 155 80 11.9


Azerbaijan 0.63 8 .. 20.0 12.7 4.9 1,603 109 50 1.3


Bahamas, The .. 31 .. 108.1 16.8 .. .. 86 65 0.0


Bahrain .. 9 89.0 75.2 .. 3.4 9,814 128 77 0.2


Bangladesh 0.10 19 21.0 70.4 10.0 1.3 279 56 5 ..


Barbados .. 18 124.1 136.3 27.2 .. .. 127 72 13.7


Belarus 0.91 5 .. 34.4 16.3 1.1 3,564 112 40 2.6


Belgium 3.00 4 44.8 116.9 24.6b 1.1 8,388 117 78 10.0


Belize 4.54 44 .. 66.9 22.3 1.1 .. 70 14 0.4


Benin .. 26 .. 21.7 15.7b 1.0 90 85 4 0.5


Bermuda .. .. 26.6 .. .. .. .. 136 88 ..


Bhutan 0.06 36 .. 49.2 9.2 .. .. 66 21 0.1


Bolivia 0.48 50 17.2 48.6 .. 1.5 616 83 30 13.7


Bosnia and Herzegovina 0.71 37 .. 57.7 20.9b 1.4 3,110 85 60 3.0


Botswana 9.44 61 23.7 7.1 20.8 2.1 1,586 143 7 0.9


Brazil 2.38 119 49.6 98.3 15.7 1.4 2,384 124 45 9.7


Brunei Darussalam .. 101 .. 8.1 .. 2.5 8,759 109 56 ..


Bulgaria 7.20 18 15.4 71.4 19.1 1.5 4,476 141 51 7.5


Burkina Faso 0.11 13 .. 17.6 14.2b 1.3 .. 45 3 5.9


Burundi .. 8 .. 28.3 .. 2.7 .. 22 1 8.5


Cambodia 0.22 85 .. 24.2 10.0b 1.5 146 96 3 0.1


Cameroon .. 15 .. 14.0 .. 1.4 271 52 5 4.9


Canada 7.56 5 109.8 177.6 11.9b 1.4 15,137 80 83 13.4


Cape Verde .. 11 .. 80.6 19.8 0.5 .. 79 32 0.6


Cayman Islands .. .. .. .. .. .. .. 168 69 ..


Central African Republic .. 22 .. 25.4 .. 2.6 .. 41 2 0.0


Chad .. 62 .. 7.0 .. 2.3 .. 32 2 ..


Channel Islands .. .. .. .. .. .. .. .. .. ..


Chile 4.13 8 108.7 71.2 19.1b 3.2 3,297 130 54 4.6


China .. 33 46.3 145.5 10.5 2.0c 2,944 73 38 25.8


Hong Kong SAR, China 27.67 3 357.8 207.1 13.5b .. 5,923 215 75 13.7


Macao SAR, China .. .. .. –28.1 37.9b .. .. 243 58 0.0


Colombia 1.80 13 60.4 65.6 13.9b 3.3 1,012 98 40 4.3


Comoros .. 20 .. 21.2 .. .. .. 29 6 ..


Congo, Dem. Rep. .. 58 .. 3.3 13.7 1.5 95 23 1 ..


Congo, Rep. .. 161 .. –16.1 .. 1.1 145 94 6 3.7


5 States and markets




World Development Indicators 2013 83Economy States and markets Global links Back


States and markets 5
Business


entry
density


Time
required
to start a
business


Stock market
capitalization


Domestic
credit


provided
by banking


sector


Tax revenue
collected
by central


government


Military
expenditures


Electric
power


consumption
per capita


Mobile
cellular


subscriptionsa


Individuals
using the
Interneta


High-technology
exports


per 1,000
people
ages


15–64 days % of GDP % of GDP % of GDP kilowatt-hours
per


100 people
% of


population
% of manufactured


exports% of GDP


2011 June 2012 2011 2011 2011 2011 2010 2011 2011 2011


Costa Rica 17.64 60 3.5 53.3 13.8b .. 1,855 92 42 40.8


Côte d’Ivoire .. 32 26.1 25.3 ..b 1.5 210 86 2 15.1


Croatia 2.39 9 34.9 90.3 18.4 1.7 3,813 116 71 7.6


Cuba .. .. .. .. .. 3.3 1,299 12 23 ..


Curaçao .. .. .. .. .. .. .. .. .. ..


Cyprus 24.73 8 11.6 330.1 26.1b 2.2 4,675 98 58 27.3


Czech Republic 2.84 20 17.7 67.4 13.7 1.1 6,321 123 73 16.0


Denmark 4.55 6 53.8 205.4 33.8b 1.5 6,327 128 90 13.9


Djibouti .. 37 .. 32.3 .. 3.7 .. 21 7 0.1


Dominica 3.30 13 .. 57.9 .. .. .. 164 51 0.0


Dominican Republic 0.96 19 .. 43.8 12.7b 0.6 1,442 87 36 2.2


Ecuador .. 56 8.8 28.3 .. 3.5 1,055 105 31 3.2


Egypt, Arab Rep. 0.13 7 21.2 74.9 14.1 1.9 1,608 101 39 0.7


El Salvador 0.46 17 23.7 66.5 13.5b 1.0 855 134 18 5.8


Equatorial Guinea .. 135 .. –3.0 .. .. .. 59 6 ..


Eritrea .. 84 .. 104.0 .. .. 52 4 6 ..


Estonia 8.10 7 7.3 85.8 16.0b 1.7 6,464 139 77 13.4


Ethiopia 0.03 15 .. 37.1 9.3 1.1 54 17 1 2.0


Faeroe Islands .. .. .. .. .. .. .. 122 81 0.6


Fiji .. 58 35.9 114.3 .. 1.6 .. 84 28 4.5


Finland 3.60 14 54.4 101.6 20.6b 1.5 16,483 166 89 9.3


France 3.13 7 56.6 133.5 21.3b 2.2 7,729 95 80 23.7


French Polynesia .. .. .. .. .. .. .. 81 49 15.8


Gabon 4.27 58 .. 12.2 .. 0.9 1,004 117 8 3.0


Gambia, The .. 27 .. 43.8 .. .. .. 79 11 3.3


Georgia 4.49 2 5.5 34.3 23.9 3.0 1,743 102 37 1.8


Germany 1.35 15 32.9 124.8 11.8b 1.3 7,215 132 83 15.0


Ghana 1.09 12 7.9 27.7 15.0 0.3 298 85 14 1.4


Greece 0.85 11 11.6 153.2 21.3b 2.8 5,242 106 53 9.7


Greenland .. .. .. .. .. .. .. 103 64 ..


Grenada .. 15 .. 91.3 18.3 .. .. 117 33 11.1


Guam .. .. .. .. .. .. .. .. 51 ..


Guatemala 0.64 40 .. 39.2 11.0 0.4 567 140 12 4.4


Guinea .. 35 .. 32.3 .. .. .. 44 1 0.1


Guinea-Bissau .. 9 .. 13.6 .. .. .. 56 3 ..


Guyana .. 20 17.1 51.0 .. 1.2 .. 70 32 0.1


Haiti .. 105 .. 17.4 .. .. 24 41 8 ..


Honduras .. 14 .. 53.3 15.0b 1.1 671 104 16 1.3


Hungary 7.63 5 13.4 75.7 21.0b 1.0 3,876 117 59 22.7


Iceland 7.94 5 14.4 148.2 22.3 0.1 51,440 106 95 20.9


India 0.09 27 54.2 74.1 10.4 2.5 616 72 10 6.9


Indonesia 0.27 47 46.1 38.5 11.8 0.7 641 103 18 8.3


Iran, Islamic Rep. .. 13 19.1 37.4 9.3 1.9 2,652 75 21 4.5


Iraq 0.11 74 .. –0.8 .. 5.1 1,183 78 5 ..


Ireland 4.78 10 16.3 225.7 23.1b 0.6 6,025 108 77 21.2


Isle of Man 11.65 .. .. .. .. .. .. .. .. ..


Israel 4.46 21 59.7 85.9 24.5b 6.8 6,856 122 70 14.0




84 World Development Indicators 2013 Front User guide World view People Environment?


5 States and markets
Business


entry
density


Time
required
to start a
business


Stock market
capitalization


Domestic
credit


provided
by banking


sector


Tax revenue
collected
by central


government


Military
expenditures


Electric
power


consumption
per capita


Mobile
cellular


subscriptionsa


Individuals
using the
Interneta


High-technology
exports


per 1,000
people
ages


15–64 days % of GDP % of GDP % of GDP kilowatt-hours
per


100 people
% of


population
% of manufactured


exports% of GDP


2011 June 2012 2011 2011 2011 2011 2010 2011 2011 2011


Italy 1.63 6 19.7 157.0 22.5b 1.6 5,384 158 57 7.4


Jamaica 1.10 7 50.0 49.6 25.6b 0.8 1,223 108 32 0.6


Japan 1.10 23 60.3 341.7 9.8b 1.0 8,394 105 80 17.5


Jordan 0.83 12 94.3 106.7 15.0 4.7 2,226 118 35 2.5


Kazakhstan 1.64 19 23.0 40.3 22.5 1.0 4,728 156 45 29.9


Kenya 0.85 32 30.3 52.0 19.9 1.5 156 67 28 5.7


Kiribati 0.11 31 .. .. .. .. .. 14 10 42.7


Korea, Dem. Rep. .. .. .. .. .. .. 749 4 0 ..


Korea, Rep. 1.83 7 89.1 102.7 15.6 2.8 9,744 109 84 25.7


Kosovo 0.92 52 .. 20.8 .. .. 2,650 .. .. ..


Kuwait .. 32 57.1 50.0 0.7 3.2 18,320 175 74 0.5


Kyrgyz Republic 0.95 10 2.7 .. 16.1 4.2 1,375 116 20 3.0


Lao PDR 0.10 92 .. 26.5 13.6 0.2 .. 87 9 ..


Latvia 11.18 16 3.8 79.3 13.3 1.0 3,026 103 72 8.2


Lebanon .. 9 25.3 173.7 17.0b 4.4 3,569 79 52 2.4


Lesotho 1.22 24 .. 0.7 58.9 2.4 .. 56 4 0.3


Liberia .. 6 .. 30.9 0.2 0.7 .. 49 3 ..


Libya .. .. .. –65.9 .. 1.2 4,270 156 17 ..


Liechtenstein 25.11 .. .. .. .. .. .. 102 85 ..


Lithuania 2.18 20 9.5 57.5 13.4b 1.0 3,271 151 65 10.3


Luxembourg 7.31 19 114.2 172.4 24.3 .. 16,834 148 91 9.7


Macedonia, FYR 4.12 2 24.0 45.5 19.1 1.3 3,591 107 57 3.9


Madagascar 0.08 8 .. 11.7 13.0b 0.7 .. 41 2 8.4


Malawi 0.08 39 24.6 38.0 .. .. .. 26 3 3.2


Malaysia 2.42 6 137.2 128.7 15.3 1.6 4,117 127 61 43.4


Maldives 3.09 9 .. 82.5 11.0 .. .. 166 34 ..


Mali .. 8 .. 16.6 14.6b 1.8 .. 68 2 2.4


Malta 9.52 40 38.5 159.2 27.7 0.7 4,151 125 69 47.2


Marshall Islands .. 17 .. .. .. .. .. .. 4 ..


Mauritania .. 19 .. 42.4 .. 3.8 .. 94 5 ..


Mauritius 7.88 6 58.1 110.0 18.4 0.1 .. 99 35 0.8


Mexico 0.87 9 35.4 45.5 .. 0.5 1,990 82 36 16.5


Micronesia, Fed. Sts. .. 16 .. –18.3 .. .. .. 25 20 ..


Moldova 1.32 9 .. 39.5 18.3 0.3 1,049 105 38 6.3


Monaco .. .. .. .. .. .. .. 90 75 ..


Mongolia .. 12 18.0 40.3 21.9 0.9 1,530 105 20 ..


Montenegro 10.44 10 73.9 61.8 .. 2.0 5,547 185 40 ..


Morocco 1.28 12 60.0 110.9 23.6b 3.3 781 113 51 7.7


Mozambique .. 13 .. 25.0 .. 0.9 444 33 4 26.5


Myanmar .. .. .. .. .. .. 131 3 1 0.0


Namibia .. 66 9.2 50.9 .. 3.4 1,479 96 12 1.6


Nepal .. 29 24.0 66.6 13.2 1.4 93 44 9 0.3


Netherlands 3.20 5 71.1 211.4 21.7b 1.4 7,010 115 92 19.8


New Caledonia .. .. .. .. .. .. .. 89 50 15.4


New Zealand 14.53 1 44.9 155.8 27.5b 1.1 9,566 109 86 9.3


Nicaragua .. 39 .. 46.1 15.2 0.6 473 82 11 5.3


Niger 0.00 17 .. 14.7 .. 0.9 .. 30 1 4.5




World Development Indicators 2013 85Economy States and markets Global links Back


States and markets 5
Business


entry
density


Time
required
to start a
business


Stock market
capitalization


Domestic
credit


provided
by banking


sector


Tax revenue
collected
by central


government


Military
expenditures


Electric
power


consumption
per capita


Mobile
cellular


subscriptionsa


Individuals
using the
Interneta


High-technology
exports


per 1,000
people
ages


15–64 days % of GDP % of GDP % of GDP kilowatt-hours
per


100 people
% of


population
% of manufactured


exports% of GDP


2011 June 2012 2011 2011 2011 2011 2010 2011 2011 2011


Nigeria 0.83 34 16.1 37.5 0.3 1.0 136 59 28 1.1


Northern Mariana Islands .. .. .. .. .. .. .. .. .. ..


Norway 4.94 7 45.1 .. 28.4b 1.6 25,175 116 94 18.5


Oman 1.67 8 27.5 32.3 2.2 6.0 5,933 169 68 2.6


Pakistan 0.03 21 15.6 43.3 9.3 3.0 457 62 9 1.8


Palau .. 28 .. .. .. .. .. 75 .. ..


Panama 0.08 7 39.9 104.9 .. .. 1,832 189 43 35.4


Papua New Guinea .. 51 69.6 27.8 .. 0.5 .. 34 2 ..


Paraguay .. 35 4.0 34.7 13.2 1.1 1,134 99 24 7.3


Peru 2.54 26 44.8 18.7 15.9 1.2 1,106 110 37 6.2


Philippines 0.19 36 73.6 51.8 12.3 1.1 643 99 29 46.4


Poland 0.52 32 26.9 66.2 17.0b 1.9 3,783 131 65 5.9


Portugal 3.92 5 26.0 204.1 21.5b 2.0 4,929 115 55 3.5


Puerto Rico .. 6 .. .. .. .. .. 83 48 ..


Qatar .. 9 72.5 70.2 14.4 2.2 14,997 123 86 0.0


Romania 4.41 10 11.2 52.1 17.2b 1.1 2,392 109 44 10.2


Russian Federation 0.83 18 42.9 39.5 15.4b 3.9 6,431 179 49 8.0


Rwanda 0.78 3 .. .. .. 1.2 .. 41 7 3.4


Samoa 1.37 9 .. 47.3 .. .. .. 91 7 0.2


San Marino .. .. .. .. 22.3 .. .. 112 50 ..


São Tomé and Príncipe 2.56 7 .. 39.5 .. .. .. 68 20 14.0


Saudi Arabia .. 21 58.7 –4.8 .. 8.4 7,967 191 48 0.7


Senegal 0.19 5 .. 31.5 .. 1.6 195 73 18 0.6


Serbia 1.66 12 18.3 54.1 20.5 2.1 4,359 125 42 ..


Seychelles .. 39 .. 45.8 31.7 0.9 .. 146 43 3.4


Sierra Leone 0.38 12 .. 12.9 11.1 0.9 .. 36 0 ..


Singapore 8.45 3 128.6 93.6 14.1 3.6 8,307 150 71 45.2


Sint Maarten .. .. .. .. .. .. .. .. .. ..


Slovak Republic 4.81 16 4.9 54.1 12.7b 1.1 5,164 109 74 7.1


Slovenia 4.04 6 12.8 94.7 17.9 1.4 6,521 107 72 5.9


Solomon Islands .. 9 .. 15.0 .. .. .. 50 6 ..


Somalia .. .. .. .. .. .. .. 7 1 ..


South Africa 0.77 19 209.6 175.0 25.7b 1.3 4,803 127 21 5.1


South Sudan 0.31 .. .. .. .. 2.9 .. .. .. ..


Spain 2.59 28 69.8 230.9 9.5b 1.0 6,155 113 68 6.4


Sri Lanka 0.58 7 32.8 46.2 12.4 2.6 449 87 15 1.0


St. Kitts and Nevis 5.69 19 85.8 123.3 19.7 .. .. 153 76 0.1


St. Lucia 3.00 15 .. 114.6 .. .. .. 123 42 16.9


St. Martin .. .. .. .. .. .. .. .. .. ..


St. Vincent and Grenadines .. 10 .. 56.6 22.2 .. .. 121 43 0.0


Sudan .. 36 .. 23.0 .. .. 141 56 19 29.4


Suriname 1.02 694 .. 24.1 .. .. .. 179 32 6.5


Swaziland .. 56 .. 25.8 .. 3.0 .. 64 18 ..


Sweden 7.17 16 87.1 142.3 21.9b 1.3 14,939 119 91 13.3


Switzerland 2.52 18 141.4 185.1 10.4 0.8 8,175 131 85 24.4


Syrian Arab Republic 0.05 13 .. 47.7 .. 3.9 1,905 63 23 1.3


Tajikistan 0.29 24 .. .. .. .. 2,004 91 13 ..




86 World Development Indicators 2013 Front User guide World view People Environment?


5 States and markets
Business


entry
density


Time
required
to start a
business


Stock market
capitalization


Domestic
credit


provided
by banking


sector


Tax revenue
collected
by central


government


Military
expenditures


Electric
power


consumption
per capita


Mobile
cellular


subscriptionsa


Individuals
using the
Interneta


High-technology
exports


per 1,000
people
ages


15–64 days % of GDP % of GDP % of GDP kilowatt-hours
per


100 people
% of


population
% of manufactured


exports% of GDP


2011 June 2012 2011 2011 2011 2011 2010 2011 2011 2011


Tanzania .. 26 6.4 24.2 .. 1.1 78 56 12 5.4


Thailand 0.59 29 77.7 159.0 17.6b 1.6 2,243 112 24 20.7


Timor-Leste .. 94 .. –26.5 .. 2.6 .. 53 1 ..


Togo 0.11 38 .. 35.4 17.1b 1.6 107 50 4 0.2


Tonga 1.96 16 .. 29.0 .. .. .. 53 25 0.0


Trinidad and Tobago .. 41 65.5 32.3 26.2 .. 5,894 136 55 0.1


Tunisia 0.63 11 20.8 82.1 20.9 1.3 1,350 117 39 5.6


Turkey 0.96 6 26.0 69.3 20.1b 2.3 2,477 89 42 1.8


Turkmenistan .. .. .. .. .. .. 2,403 69 5 ..


Turks and Caicos Islands .. .. .. .. .. .. .. .. .. 2.1


Tuvalu .. .. .. .. .. .. .. 22 30 ..


Uganda 0.72 33 46.0 18.7 16.1 1.6 .. 48 13 21.9


Ukraine 0.60 22 15.5 73.4 18.3 2.5 3,550 123 31 4.4


United Arab Emirates 1.37 8 26.0 81.4 .. 5.4 11,044 149 70 3.2


United Kingdom 10.41 13 118.7 212.6 27.4b 2.6 5,733 131 82 21.3


United States .. 6 104.3 234.9 10.1b 4.7 13,394 93 78 18.1


Uruguay 3.36 7 0.4 29.9 19.6 1.9 2,763 141 51 5.8


Uzbekistan 0.82 12 .. .. .. .. 1,648 92 30 ..


Vanuatu 2.18 35 .. 70.5 .. .. .. 56 8 ..


Venezuela, RB .. 144 1.6 29.3 .. 0.8 3,287 98 40 2.5


Vietnam .. 34 14.8 120.7 .. 2.2 1,035 143 35 8.6


Virgin Islands (U.S.) .. .. .. .. .. .. .. .. 27 ..


West Bank and Gaza .. 48 .. .. .. .. .. 46 41 ..


Yemen, Rep. .. 40 .. 22.7 .. 4.4 249 47 15 0.3


Zambia 1.26 17 20.9 18.0 16.6 1.6 623 61 12 24.8


Zimbabwe .. 90 112.9 .. .. 1.6 1,022 72 16 1.0


World 3.42 u 30 u 68.7 w 164.9 w 14.8 w 2.5 w 2,975 w 85 w 33 w 17.6 w


Low income 0.32 30 .. 40.4 11.6 1.6 242 42 6 ..


Middle income 2.30 35 47.7 92.4 13.2 2.0 1,823 86 27 17.1


Lower middle income 1.01 31 43.2 58.9 11.9 2.0 698 80 16 9.3


Upper middle income 3.50 40 48.8 101.3 13.6 2.0 2,942 92 38 18.6


Low & middle income 2.01 34 47.4 91.6 13.2 2.0 1,661 80 24 17.0


East Asia & Pacific 1.04 37d 50.6 132.5 10.9 1.8 2,337 81 34 26.0


Europe & Central Asia 2.82 15d 32.9 49.2 17.1 3.0 4,059 132 42 6.3


Latin America & Carib. 2.84 58d 42.0 68.5 .. 1.3 1,973 107 39 10.9


Middle East & N. Africa 0.36 25d .. 53.6 23.8 3.5 1,658 89 27 2.9


South Asia 0.16 19d 48.2 69.7 10.3 2.5 555 69 9 6.4


Sub- Saharan Africa 1.99 32d .. 74.7 .. 1.5 553 53 13 2.8


High income 6.38 18 78.6 203.2 14.6 2.8 9,415 114 76 17.9


Euro area 5.10 13 41.9 153.5 17.6 1.5 6,847 125 73 14.9


a. Data are from the International Telecommunication Union’s (ITU) World Telecommunication/ICT Indicators database. Please cite ITU for third party use of these data. b. Data were
reported on a cash basis and have been adjusted to the accrual framework of the International Monetary Fund’s Government Finance Statistics Manual 2001. c. Differs from the official
value published by the government of China (1.3 percent; see National Bureau of Statistics of China, www.stats.gov.cn). d. Differs from data reported on the Doing Business website
because the regional aggregates on the Doing Business website include developed economies.




World Development Indicators 2013 87Economy States and markets Global links Back


States and markets 5
Entrepreneurial activity


The rate new businesses are added to an economy is a measure of


its dynamism and entrepreneurial activity. Data on business entry


density are from the World Bank’s 2012 Entrepreneurship Data-


base, which includes indicators for more than 150 countries for


2004–11. Survey data are used to analyze firm creation, its relation-


ship to economic growth and poverty reduction, and the impact of


regulatory and institutional reforms. Data on total registered busi-


nesses were collected from national registrars of companies. For


cross-country comparability, only limited liability corporations that


operate in the formal sector are included. For additional information


on sources, methodology, calculation of entrepreneurship rates,


and data limitations see http://www.doingbusiness.org/data/


exploretopics/entrepreneurship.


Data on time required to start a business are from the Doing Busi-


ness database, whose indicators measure business regulation, gauge


regulatory outcomes, and measure the extent of legal protection of


property, the flexibility of employment regulation, and the tax burden


on businesses. The fundamental premise is that economic activity


requires good rules and regulations that are efficient, accessible,


and easy to implement. Some indicators give a higher score for more


regulation, such as stricter disclosure requirements in related-party


transactions, and others give a higher score for simplified regulations,


such as a one-stop shop for completing business startup formalities.


There are 11 sets of indicators covering starting a business, register-


ing property, dealing with construction permits, getting electricity,


enforcing contracts, getting credit, protecting investors, paying taxes,


trading across borders, resolving insolvency, and employing workers.


The indicators are available at www.doingbusiness.org.


Doing Business data are collected with a standardized survey


that uses a simple business case to ensure comparability across


economies and over time—with assumptions about the legal form


of the business, its size, its location, and nature of its operation.


Surveys in 185 countries are administered through more than 9,000


local experts, including lawyers, business consultants, accountants,


freight forwarders, government officials, and other professionals who


routinely administer or advise on legal and regulatory requirements.


The Doing Business methodology has limitations that should be


considered when interpreting the data. First, the data collected refer


to businesses in the economy’s largest city and may not represent


regulations in other locations of the economy. To address this limita-


tion, subnational indicators are being collected for selected econo-


mies; they point to significant differences in the speed of reform


and the ease of doing business across cities in the same economy.


Second, the data often focus on a specific business form—generally


a limited liability company of a specified size—and may not represent


regulation for other types of businesses such as sole proprietor-


ships. Third, transactions described in a standardized business case


refer to a specific set of issues and may not represent all the issues


a business encounters. Fourth, the time measures involve an ele-


ment of judgment by the expert respondents. When sources indicate


different estimates, the Doing Business time indicators represent


the median values of several responses given under the assumptions


of the standardized case. Fifth, the methodology assumes that a


business has full information on what is required and does not waste


time when completing procedures. In constructing the indicators, it is


assumed that entrepreneurs know about all regulations and comply


with them. In practice, entrepreneurs may not be aware of all required


procedures or may avoid legally required procedures altogether.


Financial systems


Stock markets and banking systems both enhance growth, the main


factor in poverty reduction. At low levels of economic development com-


mercial banks tend to dominate the financial system, while at higher


levels domestic stock markets become more active and efficient.


Open economies with sound macroeconomic policies, good legal


systems, and shareholder protection attract capital and thus have


larger financial markets. The table includes market capitalization


as a share of gross domestic product (GDP) as a measure of stock


market size. Market size can be measured in other ways that may


produce a different ranking of countries. Recent research on stock


market development shows that modern communications tech-


nology and increased financial integration have resulted in more


cross-border capital flows, a stronger presence of financial firms


around the world, and the migration of trading activities to interna-


tional exchanges. Many firms in emerging markets now cross-list


on international exchanges, which provides them with lower cost


capital and more liquidity-traded shares. However, this also means


that exchanges in emerging markets may not have enough financial


activity to sustain them. Comparability across countries may be lim-


ited by conceptual and statistical weaknesses, such as inaccurate


reporting and differences in accounting standards.


Standard & Poor’s (S&P) Indices provides regular updates on 21


emerging stock markets and 36 frontier markets. The S&P Global


Equity Indices, S&P Indices’s leading emerging markets index, is


designed to be sufficiently investable to support index tracking portfo-


lios in emerging market stocks that are legally and practically open to


foreign portfolio investment. The S&P Frontier Broad Market Index mea-


sures the performance of 36 smaller and less liquid markets. These


indexes are widely used benchmarks for international portfolio manage-


ment. See www.spindices.com for further information on the indexes.


Because markets included in S&P’s emerging markets category


vary widely in level of development, it is best to look at the entire


category to identify the most significant market trends. And it is


useful to remember that stock market trends may be distorted by


currency conversions, especially when a currency has registered


a significant devaluation (Demirgüç-Kunt and Levine 2006; Beck


and Levine 2001; and Claessens, Klingebiel, and Schmukler 2002).


Domestic credit provided by the banking sector as a share of GDP


measures banking sector depth and financial sector development in


terms of size. Data are taken from the banking survey of the Interna-


tional Monetary Fund’s (IMF) International Financial Statistics or, when


About the data




88 World Development Indicators 2013 Front User guide World view People Environment?


5 States and markets
unavailable, from its monetary survey. The monetary survey includes


monetary authorities (the central bank), deposit money banks, and


other banking institutions, such as finance companies, development


banks, and savings and loan institutions. In a few countries govern-


ments may hold international reserves as deposits in the banking


system rather than in the central bank. Claims on the central gov-


ernment are a net item (claims on the central government minus


central government deposits) and thus may be negative, resulting in


a negative value for domestic credit provided by the banking sector.


Tax revenues


Taxes are the main source of revenue for most governments. Tax


revenue as a share of GDP provides a quick overview of the fiscal


obligations and incentives facing the private sector across coun-


tries. The table shows only central government data, which may


significantly understate the total tax burden, particularly in countries


where provincial and municipal governments are large or have con-


siderable tax authority.


Low ratios of tax revenue to GDP may reflect weak administration and


large-scale tax avoidance or evasion. Low ratios may also reflect a


sizable parallel economy with unrecorded and undisclosed incomes.


Tax revenue ratios tend to rise with income, with higher income coun-


tries relying on taxes to finance a much broader range of social


services and social security than lower income countries are able to.


Military expenditures


Although national defense is an important function of government,


high expenditures for defense or civil conflicts burden the economy


and may impede growth. Military expenditures as a share of GDP


are a rough indicator of the portion of national resources used for


military activities. As an “input” measure, military expenditures are


not directly related to the “output” of military activities, capabilities,


or security. Comparisons across countries should take into account


many factors, including historical and cultural traditions, the length


of borders that need defending, the quality of relations with neigh-


bors, and the role of the armed forces in the body politic.


Data are from the Stockholm International Peace Research Institute


(SIPRI), whose primary source of military expenditure data is offi-


cial data provided by national governments. These data are derived


from budget documents, defense white papers, and other public


documents from official government agencies, including govern-


ment responses to questionnaires sent by SIPRI, the United Nations


Office for Disarmament Affairs, or the Organization for Security and


Co-operation in Europe. Secondary sources include international sta-


tistics, such as those of the North Atlantic Treaty Organization (NATO)


and the IMF’s Government Finance Statistics Yearbook. Other second-


ary sources include country reports of the Economist Intelligence Unit,


country reports by IMF staff, and specialist journals and newspapers.


In the many cases where SIPRI cannot make independent estimates,


it uses country-provided data. Because of differences in definitions


and the difficulty of verifying the accuracy and completeness of data,


data are not always comparable across countries. However, SIPRI puts


a high priority on ensuring that the data series for each country is com-


parable over time. More information on SIPRI’s military expenditure


project can be found at www.sipri.org/research/armaments/milex.


Infrastructure


The quality of an economy’s infrastructure, including power and com-


munications, is an important element in investment decisions and


economic development. The International Energy Agency (IEA) collects


data on electric power consumption from national energy agencies


and adjusts the values to meet international definitions. Consump-


tion by auxiliary stations, losses in transformers that are considered


integral parts of those stations, and electricity produced by pumping


installations are included. Where data are available, electricity gen-


erated by primary sources of energy—coal, oil, gas, nuclear, hydro,


geothermal, wind, tide and wave, and combustible renewables—are


included. Consumption data do not capture the reliability of supplies,


including breakdowns, load factors, and frequency of outages.


The International Telecommunication Union (ITU) estimates that


there were 5.9 billion mobile subscriptions globally in 2011. No


technology has ever spread faster around the world. Mobile com-


munications have a particularly important impact in rural areas.


The mobility, ease of use, flexible deployment, and relatively low


and declining rollout costs of wireless technologies enable them to


reach rural populations with low levels of income and literacy. The


next billion mobile subscribers will consist mainly of the rural poor.


Operating companies have traditionally been the main source of


telecommunications data, so information on subscriptions has been


widely available for most countries. This gives a general idea of access,


but a more precise measure is the penetration rate—the share of


households with access to telecommunications. During the past few


years more information on information and communication technology


use has become available from household and business surveys. Also


important are data on actual use of telecommunications services. The


quality of data varies among reporting countries as a result of differ-


ences in regulations covering data provision and availability.


High-technology exports


The method for determining high-technology exports was developed


by the Organisation for Economic Co-operation and Development in


collaboration with Eurostat. It takes a “product approach” (rather than


a “sectoral approach”) based on research and development intensity


(expenditure divided by total sales) for groups of products from Ger-


many, Italy, Japan, the Netherlands, Sweden, and the United States.


Because industrial sectors specializing in a few high-technology prod-


ucts may also produce low-technology products, the product approach


is more appropriate for international trade. The method takes only


research and development intensity into account, but other characteris-


tics of high technology are also important, such as knowhow, scientific


personnel, and technology embodied in patents. Considering these


characteristics would yield a different list (see Hatzichronoglou 1997).




World Development Indicators 2013 89Economy States and markets Global links Back


States and markets 5
Definitions


• Business entry density is the number of newly registered lim-


ited liability corporations per 1,000 people ages 15–64. • Time


required to start a business is the number of calendar days to com-


plete the procedures for legally operating a business using the fast-


est procedure, independent of cost. • Stock market capitalization


(also known as market value) is the share price times the number of


shares outstanding. • Domestic credit provided by banking sector


is all credit to various sectors on a gross basis, except to the central


government, which is net. The banking sector includes monetary


authorities, deposit money banks, and other banking institutions


for which data are available. • Tax revenue collected by central


government is compulsory transfers to the central government for


public purposes. Certain compulsory transfers such as fines, penal-


ties, and most social security contributions are excluded. Refunds


and corrections of erroneously collected tax revenue are treated as


negative revenue. The analytic framework of the IMF’s Government


Finance Statistics Manual 2001 (GFSM 2001) is based on accrual


accounting and balance sheets. For countries still reporting govern-


ment finance data on a cash basis, the IMF adjusts reported data to


the GFSM 2001 accrual framework. These countries are footnoted


in the table. • Military expenditures are SIPRI data derived from


NATO’s former definition (in use until 2002), which includes all cur-


rent and capital expenditures on the armed forces, including peace-


keeping forces; defense ministries and other government agencies


engaged in defense projects; paramilitary forces, if judged to be


trained and equipped for military operations; and military space


activities. Such expenditures include military and civil personnel,


including retirement pensions and social services for military per-


sonnel; operation and maintenance; procurement; military research


and development; and military aid (in the military expenditures of


the donor country). Excluded are civil defense and current expendi-


tures for previous military activities, such as for veterans benefits,


demobilization, and weapons conversion and destruction. This defi-


nition cannot be applied for all countries, however, since that would


require more detailed information than is available about military


budgets and off-budget military expenditures (for example, whether


military budgets cover civil defense, reserves and auxiliary forces,


police and paramilitary forces, and military pensions). • Electric


power consumption per capita is the production of power plants


and combined heat and power plants less transmission, distribu-


tion, and transformation losses and own use by heat and power


plants, divided by midyear population. • Mobile cellular subscrip-


tions are the number of subscriptions to a public mobile telephone


service that provides access to the public switched telephone net-


work using cellular technology. Postpaid subscriptions and active


prepaid accounts (that is, accounts that have been used during


the last three months) are included. The indicator applies to all


mobile cellular subscriptions that offer voice communications and


excludes subscriptions for data cards or USB modems, subscrip-


tions to public mobile data services, private-trunked mobile radio,


telepoint, radio paging, and telemetry services. • Individuals using


the Internet are the percentage of individuals who have used the


Internet (from any location) in the last 12 months. Internet can


be used via a computer, mobile phone, personal digital assistant,


games machine, digital television, or similar device. • High-tech-


nology exports are products with high research and development


intensity, such as in aerospace, computers, pharmaceuticals, sci-


entific instruments, and electrical machinery.


Data sources


Data on business entry density are from the World Bank’s Entrepre-


neurship Database (www.doingbusiness.org/data/exploretopics/


entrepreneurship). Data on time required to start a business are


from the World Bank’s Doing Business project (www.doingbusiness


.org). Data on stock market capitalization are from Standard &


Poor’s (2012). Data on domestic credit are from the IMF’s Inter-


national Financial Statistics. Data on central government tax rev-


enue are from the IMF’s Government Finance Statistics. Data on


military expenditures are from SIPRI’s Military Expenditure Database


(www.sipri.org/databases/milex). Data on electricity consumption


are from the IEA’s Energy Statistics of Non-OECD Countries, Energy


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


Countries and from the United Nations Statistics Division’s Energy


Statistics Yearbook. Data on mobile cellular phone subscriptions


and individuals using the Internet are from the ITU’s World Tele-


communication/ICT Indicators database and TeleGeography. Data


on high-technology exports are from the United Nations Statistics


Division’s Commodity Trade (Comtrade) database.


References


Beck, Thorsten, and Ross Levine. 2001. “Stock Markets, Banks, and


Growth: Correlation or Causality?” Policy Research Working Paper


2670, World Bank, Washington, DC.


Claessens, Stijn, Daniela Klingebiel, and Sergio L. Schmukler. 2002.


“Explaining the Migration of Stocks from Exchanges in Emerging


Economies to International Centers.” Policy Research Working Paper


2816, World Bank, Washington, DC.


Demirgüç-Kunt, Asli, and Ross Levine. 1996. “Stock Market Devel-


opment and Financial Intermediaries: Stylized Facts.” World Bank


Economic Review 10 (2): 291–321.


Geneva Declaration on Armed Violence and Development. 2011.


Global Burden of Armed Violence. Geneva.


Hatzichronoglou, Thomas. 1997. “Revision of the High-Technology


Sector and Product Classification.” STI Working Paper 1997/2.


Organisation for Economic Co-operation and Development, Direc-


torate for Science, Technology, and Industry, Paris.


Standard & Poors. 2012. Global Stock Markets Factbook 2012. New York.


WIPO (World Intellectual Property Organization). 2012. World Intel-


lectual Property Indicators 2012. Geneva.


World Bank. 2012. CPIA Africa: Accessing Africa’s Policies and Institu-


tions. Washington, DC.




90 World Development Indicators 2013 Front User guide World view People Environment?


5 States and markets


5.1 Private sector in the economy


Telecommunications investment IE.PPI.TELE.CD


Energy investment IE.PPI.ENGY.CD


Transport investment IE.PPI.TRAN.CD


Water and sanitation investment IE.PPI.WATR.CD


Domestic credit to private sector FS.AST.PRVT.GD.ZS


Businesses registered, New IC.BUS.NREG


Businesses registered, Entry density IC.BUS.NDNS.ZS


5.2 Business environment: enterprise surveys


Time dealing with officials IC.GOV.DURS.ZS


Average number of times meeting with tax
officials IC.TAX.METG


Time required to obtain operating license IC.FRM.DURS


Informal payments to public officials IC.FRM.CORR.ZS


Losses due to theft, robbery, vandalism,
and arson IC.FRM.CRIM.ZS


Firms competing against unregistered firms IC.FRM.CMPU.ZS


Firms with female top manager IC.FRM.FEMM.ZS


Firms using banks to finance investment IC.FRM.BNKS.ZS


Value lost due to electrical outages IC.FRM.OUTG.ZS


Internationally recognized quality
certification ownership IC.FRM.ISOC.ZS


Average time to clear exports through customs IC.CUS.DURS.EX


Firms offering formal training IC.FRM.TRNG.ZS


5.3 Business environment: Doing Business indicators


Number of procedures to start a business IC.REG.PROC


Time required to start a business IC.REG.DURS


Cost to start a business IC.REG.COST.PC.ZS


Number of procedures to register property IC.PRP.PROC


Time required to register property IC.PRP.DURS


Number of procedures to build a warehouse IC.WRH.PROC


Time required to build a warehouse IC.WRH.DURS


Time required to get electricity IC.ELC.TIME


Number of procedures to enforce a contract IC.LGL.PROC


Time required to enforce a contract IC.LGL.DURS


Business disclosure index IC.BUS.DISC.XQ


Time required to resolve insolvency IC.ISV.DURS


5.4 Stock markets


Market capitalization, $ CM.MKT.LCAP.CD


Market capitalization, % of GDP CM.MKT.LCAP.GD.ZS


Value of shares traded CM.MKT.TRAD.GD.ZS


Turnover ratio CM.MKT.TRNR


Listed domestic companies CM.MKT.LDOM.NO


S&P/Global Equity Indices CM.MKT.INDX.ZG


5.5 Financial access, stability, and efficiency


Strength of legal rights index IC.LGL.CRED.XQ


Depth of credit information index IC.CRD.INFO.XQ


Depositors with commercial banks FB.CBK.DPTR.P3


Borrowers from commercial banks FB.CBK.BRWR.P3


Commercial bank branches FB.CBK.BRCH.P5


Automated teller machines FB.ATM.TOTL.P5


Bank capital to assets ratio FB.BNK.CAPA.ZS


Ratio of bank non-performing loans to total
gross loans FB.AST.NPER.ZS


Domestic credit provided by banking sector FS.AST.DOMS.GD.ZS


Interest rate spread FR.INR.LNDP


Risk premium on lending FR.INR.RISK


5.6 Tax policies


Tax revenue collected by central government GC.TAX.TOTL.GD.ZS


Number of tax payments by businesses IC.TAX.PAYM


Time for businesses to prepare, file and
pay taxes IC.TAX.DURS


Business profit tax IC.TAX.PRFT.CP.ZS


Business labor tax and contributions IC.TAX.LABR.CP.ZS


Other business taxes IC.TAX.OTHR.CP.ZS


Total business tax rate IC.TAX.TOTL.CP.ZS


5.7 Military expenditures and arms transfers


Military expenditure, % of GDP MS.MIL.XPND.GD.ZS


Military expenditure, % of central
government expenditure MS.MIL.XPND.ZS


Arm forces personnel MS.MIL.TOTL.P1


Arm forces personnel, % of total labor force MS.MIL.TOTL.TF.ZS


Arms transfers, Exports MS.MIL.XPRT.KD


Arms transfers, Imports MS.MIL.MPRT.KD


5.8 Fragile situations


International Development Association
Resource Allocation Index IQ.CPA.IRAI.XQ


Peacekeeping troops, police, and military
observers VC.PKP.TOTL.UN


Battle related deaths VC.BTL.DETH


Intentional homicides VC.IHR.PSRC.P5


Military expenditures MS.MIL.XPND.GD.ZS


Losses due to theft, robbery, vandalism,
and arson IC.FRM.CRIM.ZS


Firms formally registered when operations
started IC.FRM.FREG.ZS


To access the World Development Indicators online tables, use


the URL http://wdi.worldbank.org/table/ and the table number (for


example, http://wdi.worldbank.org/table/5.1). To view a specific


indicator online, use the URL http://data.worldbank.org/indicator/


and the indicator code (for example, http://data.worldbank.org/


indicator/IE.PPI.TELE.CD).




World Development Indicators 2013 91Economy States and markets Global links Back


States and markets 5
Children in employment SL.TLF.0714.ZS


Refugees, By country of origin SM.POP.REFG.OR


Refugees, By country of asylum SM.POP.REFG


Internally displaced persons VC.IDP.TOTL.HE


Access to an improved water source SH.H2O.SAFE.ZS


Access to improved sanitation facilities SH.STA.ACSN


Maternal mortality ratio, National estimate SH.STA.MMRT.NE


Maternal mortality ratio, Modeled estimate SH.STA.MMRT


Under-five mortality rate SH.DYN.MORT


Depth of food deficit SN.ITK.DFCT


Primary gross enrollment ratio SE.PRM.ENRR


5.9 Public policies and institutions


International Development Association
Resource Allocation Index IQ.CPA.IRAI.XQ


Macroeconomic management IQ.CPA.MACR.XQ


Fiscal policy IQ.CPA.FISP.XQ


Debt policy IQ.CPA.DEBT.XQ


Economic management, Average IQ.CPA.ECON.XQ


Trade IQ.CPA.TRAD.XQ


Financial sector IQ.CPA.FINS.XQ


Business regulatory environment IQ.CPA.BREG.XQ


Structural policies, Average IQ.CPA.STRC.XQ


Gender equality IQ.CPA.GNDR.XQ


Equity of public resource use IQ.CPA.PRES.XQ


Building human resources IQ.CPA.HRES.XQ


Social protection and labor IQ.CPA.PROT.XQ


Policies and institutions for environmental
sustainability IQ.CPA.ENVR.XQ


Policies for social inclusion and equity, Average IQ.CPA.SOCI.XQ


Property rights and rule-based governance IQ.CPA.PROP.XQ


Quality of budgetary and financial management IQ.CPA.FINQ.XQ


Efficiency of revenue mobilization IQ.CPA.REVN.XQ


Quality of public administration IQ.CPA.PADM.XQ


Transparency, accountability, and
corruption in the public sector IQ.CPA.TRAN.XQ


Public sector management and institutions,
Average IQ.CPA.PUBS.XQ


5.10 Transport services


Total road network IS.ROD.TOTL.KM


Paved roads IS.ROD.PAVE.ZS


Road passengers carried IS.ROD.PSGR.K6


Road goods hauled IS.ROD.GOOD.MT.K6


Rail lines IS.RRS.TOTL.KM


Railway passengers carried IS.RRS.PASG.KM


Railway goods hauled IS.RRS.GOOD.MT.K6


Port container traffic IS.SHP.GOOD.TU


Registered air carrier departures worldwide IS.AIR.DPRT


Air passengers carried IS.AIR.PSGR


Air freight IS.AIR.GOOD.MT.K1


5.11 Power and communications


Electric power consumption per capita EG.USE.ELEC.KH.PC


Electric power transmission and
distribution losses EG.ELC.LOSS.ZS


Fixed telephone subscriptions IT.MLT.MAIN.P2


Mobile cellular subscriptions IT.CEL.SETS.P2


Fixed telephone international voice traffic ..a


Mobile cellular network international voice
traffic ..a


Population covered by mobile cellular network ..a


Fixed telephone sub-basket ..a


Mobile cellular sub-basket ..a


Telecommunications revenue ..a


Mobile cellular and fixed-line subscribers
per employee ..a


5.12 The information age


Households with television ..a


Households with a computer ..a


Individuals using the Internet ..a


Fixed (wired) broadband Internet
subscriptions IT.NET.BBND.P2


International Internet bandwidth ..a


Fixed broadband sub-basket ..a


Secure Internet servers IT.NET.SECR.P6


Information and communications
technology goods, Exports TX.VAL.ICTG.ZS.UN


Information and communications
technology goods, Imports TM.VAL.ICTG.ZS.UN


Information and communications
technology services, Exports BX.GSR.CCIS.ZS


5.13 Science and technology


Research and development (R&D), Researchers SP.POP.SCIE.RD.P6


Research and development (R&D), Technicians SP.POP.TECH.RD.P6


Scientific and technical journal articles IP.JRN.ARTC.SC


Expenditures for R&D GB.XPD.RSDV.GD.ZS


High-technology exports, $ TX.VAL.TECH.CD


High-technology exports, % of manufactured
exports TX.VAL.TECH.MF.ZS


Charges for the use of intellectual property,
Receipts BX.GSR.ROYL.CD


Charges for the use of intellectual property,
Payments BM.GSR.ROYL.CD


Patent applications filed, Residents IP.PAT.RESD


Patent applications filed, Nonresidents IP.PAT.NRES


Trademark applications filed, Total IP.TMK.TOTL


Data disaggregated by sex are available in
the World Development Indicators database.


a. Available online only as part of the table, not as an individual indicator.




92 World Development Indicators 2013 Front User guide World view People Environment?


GLOBAL
LINKS




World Development Indicators 2013 93Economy States and markets Global links Back


The world economy is bound together by trade
in goods and services, financial flows, and the
movement of people. As national economies
develop, their links expand and grow more com-
plex. The indicators in this section measure the
size and direction of these flows and document
the effects of policy interventions and aid flows
on the world economy.


The optimistic economic momentum at the
beginning of 2011 slowed over the course of the
year. The adverse effects of the tsunami in Japan
coupled with intensification of the sovereign debt
crisis in the euro area shook confidence at the
global level. The slowdown became more pro-
nounced in high-income economies, reducing the
growth in capital inflows to developing countries.


Net debt and equity inflows to developing econ-
omies in 2011 were $1.1 trillion—or 8 percent
lower than in 2010 and below the level reached
before the global financial crisis. The downturn
was driven by the collapse of portfolio equity,
again the most volatile of capital flows. Equity
flows to emerging markets with good growth pros-
pects, such as China, Brazil, and India, dropped
substantially. Low- and middle-income economies
recorded net inflows of only $8.3 billion in 2011,
compared with $130 billion in 2010.


Debt net inflows to developing countries in
2011 were $437 billion—down 9 percent from
2010. The slowdown was led by a drop in lending
by official creditors from $77 billion in 2010 to
$30 billion in 2011. However, commercial bank
financing tripled to $110 billion, and the pri-
vate sector created more liquidity through bond
issuances, reaching their highest stock level
of $1.5 trillion. Net inflows of short-term debt
shrank 27 percent in 2011 after being the fast-
est growing debt component the previous year.


Global foreign direct investment (FDI) rose
22.7 percent in 2011, to its pre-crisis level.
Some 40 percent of those investments were
directed to developing economies. In 2011
many developing countries continued to imple-
ment policy changes to further liberalize and
facilitate FDI entry and operations and to regu-
late FDI. The largest recipients of FDI inflows
were Brazil, China, India, and the Russian
Federation, accounting for more than half of
inflows to developing economies. China was
the largest recipient, with net FDI inflows of
$220 billion, a decline of 10 percent compared
with 2010.


In 2011 world merchandise exports to devel-
oping countries increased 27 percent from 2010,
while exports to high-income countries increased
20 percent. Brazil, China, India, and the Rus-
sian Federation were among the top traders, with
China accounting for 70 percent of East Asia and
Pacific’s merchandise trade.


Official development assistance, a stable
source of development financing and buffer
against the impact of several financial crises,
was $134 billion in 2011, or 0.58 percent of
developing countries’ combined gross national
income, down from 0.65 percent in 2010.


Worldwide tourism continues to grow and
has become one of the largest and fastest
growing economic sectors in the world. The
number of international tourist arrivals in 2011
reached a record high of over 1 billion, and
inbound tourism expenditures increased 12 per-
cent to $1.3 trillion from 2010. The only devel-
oping region that saw a drop in tourist arrivals
in 2011 was the Middle East and North Africa,
due to political instability and armed conflict in
the region.


6




94 World Development Indicators 2013


Highlights


Front User guide World view People Environment?


East Asia & Pacific: Equity investment drops


–50


0


50


100


150


200


250


300


2011201020082006200420022000


Foreign direct investment


Equity inows to East Asia and Pacic ($ billions)


Portfolio equity


After recovering from a 2009 low by 2010, equity investment in East


Asia and Pacific fell again in 2011. Turmoil in the euro area and the


natural disaster in Japan caused a large withdrawal of foreign equity.


Foreign direct investment (FDI) net inflows combined with portfolio


equity inflows in 2011 were 17 percent lower than in 2010. Net FDI


flows declined 5 percent, while portfolio equity inflows fell to a fifth


their 2010 level. China, the largest recipient of capital flows into the


region (with 80 percent), recorded a 10 percent decline of FDI inflows


compared with 2010 and portfolio equity inflows a sixth of their 2010


level. Investments in the service sector in China maintained their


growth rate.


Source: Online table 6.10.


Europe & Central Asia: Remittance flows diverge


0


5


10


15


20


25


30


201120102008200620042002


Commonwealth of Independent States


Remittance inows ($ billions)


Eastern Europe


High unemployment in the developed countries of the European Union


impaired employment prospects of existing migrants and limited their


ability to send money home. Remittance flows to developing countries


in Eastern Europe dropped sharply in 2009 and have yet to recover.


Romania, one of the largest recipients of remittances in the European


Union, experienced an annual average decrease of 30 percent over


the last three years. In contrast, remittances to almost all the member


countries of the Commonwealth of Independent States rebounded


strongly. Rising employment and higher real wages in the Russian


Federation gave migrants the opportunity to increase their remit-


tances. Tajikistan, with the most emigrants to the Russian Federation,


depends on remittances as a major source of external finance. In


2011 it received $3.2 billion in remittances, equivalent to 46 percent


of GDP.
Source: Online table 6.14.


Latin America & Caribbean: More trade with developing countries


0


20


40


60


80


2011201020082006200420022000


Developing countries to
high-income countries


Share of total merchandise exports (%)


Brazil to high-income countries


Brazil to developing countries


Developing countries to
developing countries


Trade between developing countries continues to grow. Between 2000


and 2010 merchandise exports between developing countries grew


more than 600 percent, while developing country exports to high-income


countries grew half that amount. By 2011 a third of developing country


trade went to other developing countries, while the share of merchan-


dise exports from developing countries to high-income countries fell


from 77 percent in 2000 to about 63 percent. In Latin America and the


Caribbean, Brazil is a leading example. Since 2009 Brazil’s merchandise


exports to developing countries have exceeded those to high-income


countries—largely due to Brazil’s increased natural resource exports to


Asia, particularly to China. In addition, intraregional trade has increased


since 2009, especially between Common Market of the South members


(Argentina, Brazil, Paraguay, Uruguay, and Venezuela), surpassing the


level reached before the global financial crisis.
Source: Online table 6.4.




World Development Indicators 2013 95Economy States and markets Global links Back


Middle East & North Africa: Tourist arrivals fall due to instability


Despite increased worldwide tourism, tourist arrivals fell in the


Middle East and North Africa in 2011 due to political instability and


armed conflict in the region (World Tourism Organization 2012b).


“The Arab Spring,” as it has come to be known, began in Tuni-


sia with mass demonstrations and riots at the end of 2010. As a


result, international tourist arrivals to Tunisia fell 31 percent, from


6.9 million in 2010 to 4.8 million in 2011, the lowest level since


1998, and tourist expenditures fell from $3.5 billion to $2.5 bil-


lion. In February 2011 Egypt’s government was overthrown. Egypt


experienced a 32 percent drop in tourist arrivals in 2011 after an


18 percent increase in 2010. Heavily dependent on tourism, it saw


tourist expenditures fall from $51.2 billion in 2010 to $43.1 billion


in 2011. 0


20


40


60


80


TunisiaEgyptMiddle East & North Africa


International tourist arrivals (millions)


2
0


0
9


2
0


1
0


2
0


1
1


Source: Online table 6.15.


South Asia: Encouraging investment in China and India


China and India, the two largest and fastest growing economies in


the developing world, have altered their regulatory systems to attract


and keep inflows of foreign direct investment (FDI). China started


first. It began opening its economy in 1979 and continued through to


its membership in the World Trade Organization in 2001, reassuring


investors of its increasing reliability. While FDI in China was driven by


export-oriented policies, India pursued an import substitution policy,


promoting domestic firms and limiting the rights of foreign investors.


It was not until the 1990s that India started significantly liberalizing


FDI, and it only recently liberalized the FDI policies of its retail sector.


Before the financial crisis net FDI inflows as a share of GDP to India


began to catch up with those to China. After bottoming out in 2010,


they began to recover, while net FDI inflows as a share of GDP to China


dropped again.


0


1


2


3


4


5


2011201020082006200420022000


China


Net inows of foreign direct investment (% of GDP)


India


Source: Online table 6.10.


Sub- Saharan Africa: Less official development assistance in 2011


Official development assistance to Sub- Saharan Africa as a share of


the region’s gross national income fell in 2011. But aid to three of the


five largest recipients, the Democratic Republic of Congo, São Tomé


and Príncipe, and Rwanda, rose. The Democratic Republic of Congo was


second among all countries receiving aid from Development Assistance


Committee (DAC) members (after Afghanistan). But this distinction is


likely to be short lived. Like Liberia in 2010, the Democratic Republic


of Congo benefited from debt forgiveness after reaching the completion


point under the enhanced Heavily Indebted Poor Countries initiative in


July 2010 and the subsequent Paris Club rescheduling agreement at


the end of 2010. As its two major Paris Club creditors, the United States


and France were also the top DAC donors to the Democratic Republic of


Congo, increasing their aid to the country from $278 million to $1.3 bil-


lion and from $135 million to $1.1 billion respectively from 2010.


0


50


100


150


LiberiaCongo,
Dem. Rep.


São Tomé
and Príncipe


BurundiRwanda


Net ofcial development assistance from all donors (% of GNI)


2
0


0
9


2
0


1
0


2
0


1
1


Source: Online table 6.12.




96 World Development Indicators 2013 Front User guide World view People Environment?


Merchandise
trade


Net barter
terms of


trade index


Inbound
tourism


expenditure


Net official
development
assistance


Net
migration


Personal
transfers and
compensation
of employees,


received


Foreign
direct


investment


Portfolio
equity


Total
external


debt stock


Total debt
service


% of exports
of goods,
services,


and primary
incomea% of GDP 2000 = 100 % of exports % of GNI thousands $ millions


Net inflow
$ millions


Net inflow
$ millions $ millions


2011 2011 2011 2011 2005–10 2011 2011 2011 2011 2011


Afghanistan 33.1 146.3 .. 35.0 –381 .. 83 .. 2,623 ..


Albania 56.6 94.1 41.7 2.4 –48 1,162 1,370 2 5,938 9.3


Algeria 63.5 199.1 0.4 0.1 –140 203 2,721 0 6,072 0.8


American Samoa .. 129.0 .. .. .. .. .. .. .. ..


Andorra .. .. .. .. .. .. .. .. .. ..


Angola 82.9 244.7 1.0 0.2 82 0 –3,024 0 21,115 4.3


Antigua and Barbuda 47.0 75.1 58.1 1.4 .. 20 58 .. .. ..


Argentina 35.5 135.2 6.2 0.0 –200 686 8,671 –174 114,704 15.3


Armenia 53.5 128.5 20.2 3.5 –75 1,994 663 0 7,383 25.4


Aruba .. 118.5 19.8 .. 4 5 544 0 .. ..


Australia 37.3 200.7 .. .. 1,125 1,871 67,638 –3,308 .. ..


Austria 88.8 89.0 9.6 .. 160 2,674 15,734 483 .. ..


Azerbaijan 71.3 187.6 4.0 0.5 53 1,893 4,485 0 8,427 4.9


Bahamas, The 46.4 108.6 66.0 .. 6 .. 595 .. .. ..


Bahrain .. 128.5 7.7 .. 448 .. 781 982 .. ..


Bangladesh 54.2 54.8 0.4 1.2 –2,908 12,068 798 –10 27,043 5.5


Barbados 62.7 111.0 .. .. 0 82 334 .. .. ..


Belarus 156.3 103.8 1.9 0.2 –50 814 4,002 0 29,120 4.5


Belgium 182.4 100.1 3.0 .. 200 10,912 102,000 –4,200 .. ..


Belize 86.4 104.4 26.7 2.3 –1 76 95 .. 1,278 13.9


Benin 61.7 125.1 .. 9.3 50 185 118 .. 1,423 ..


Bermuda .. 72.4 .. .. .. 1,253 111 –3 .. ..


Bhutan 90.6 151.5 .. 8.7 17 10 16 0 1,035 11.1


Bolivia 66.8 175.4 5.5 3.3 –165 1,043 859 0 6,474 4.9


Bosnia and Herzegovina 93.4 101.5 9.8 2.3 –10 1,958 380 .. 10,729 10.9


Botswana 74.7 82.4 .. 0.7 19 63 587 .. 2,396 ..


Brazil 19.9 135.8 2.3 0.0 –500 2,798 71,539 7,174 404,317 19.4


Brunei Darussalam 94.7 200.6 .. .. 4 .. 1,208 .. .. ..


Bulgaria 112.3 110.7 12.7 .. –50 1,483 2,588 –42 39,930 12.2


Burkina Faso 42.3 141.2 .. 9.5 –125 111 7 .. 2,420 ..


Burundi 36.1 172.6 1.6 24.8 370 45 3 .. 628 3.4


Cambodia 126.7 70.6 24.1 6.5 –255 160 902 0 4,336 1.0


Cameroon 44.0 149.6 .. 2.5 –19 115 360 .. 3,074 ..


Canada 52.7 122.5 3.7 .. 1,098 .. 39,510 21,313 .. ..


Cape Verde 53.7 106.0 55.5 13.3 –17 177 105 .. 1,025 5.0


Cayman Islands .. 77.6 .. .. .. .. 7,408 .. .. ..


Central African Republic 24.6 82.4 .. 12.4 5 .. 109 .. 573 ..


Chad 64.3 208.8 .. 4.9 –75 .. 1,855 .. 1,821 ..


Channel Islands .. .. .. .. 5 .. .. .. .. ..


Chile 62.3 213.3 .. 0.0 30 4 17,299 4,477 96,245 15.2


China 49.8 72.8 2.6 0.0 –1,884 40,483 220,143 5,308 685,418 3.6


Hong Kong SAR, China 388.9 95.9 6.0 .. 176 357 95,352 9,814 .. ..


Macao SAR, China 24.2 87.1 .. .. 51 48 2,116 0 .. ..


Colombia 33.5 142.4 4.9 0.4 –120 4,205 13,605 1,969 76,918 15.6


Comoros 49.5 76.1 .. 8.5 –10 .. 7 .. 278 ..


Congo, Dem. Rep. 77.3 146.7 .. 38.4 –24 115 1,596 .. 5,448 2.4


Congo, Rep. 110.2 214.5 .. 2.4 50 .. 2,931 .. 2,523 ..


6 Global links




World Development Indicators 2013 97Economy States and markets Global links Back


Global links 6
Merchandise


trade
Net barter
terms of


trade index


Inbound
tourism


expenditure


Net official
development
assistance


Net
migration


Personal
transfers and
compensation
of employees,


received


Foreign
direct


investment


Portfolio
equity


Total
external


debt stock


Total debt
service


% of exports
of goods,
services,


and primary
incomea% of GDP 2000 = 100 % of exports % of GNI thousands $ millions


Net inflow
$ millions


Net inflow
$ millions $ millions


2011 2011 2011 2011 2005–10 2011 2011 2011 2011 2011


Costa Rica 65.2 78.1 15.4 0.1 76 520 2,157 0 10,292 13.5


Côte d’Ivoire 74.0 159.0 .. 6.2 –360 373 344 .. 12,012 ..


Croatia 57.7 99.0 36.6 0.0 10 1,378 1,265 17 .. ..


Cuba .. 165.6 .. .. –190 .. 110 .. .. ..


Curaçao .. 99.9 .. .. .. 34 70 0 .. ..


Cyprus 42.1 103.1 25.5 .. 44 127 1,080 429 .. ..


Czech Republic 144.6 104.0 5.2 .. 240 1,815 5,380 –2 .. ..


Denmark 63.3 106.2 3.4 .. 90 1,273 13,106 –2,250 .. ..


Djibouti .. 77.8 4.6 .. 0 32 79 0 767 ..


Dominica 54.1 104.3 58.9 5.2 .. 23 34 .. 284 9.8


Dominican Republic 47.1 92.4 31.4 0.4 –140 3,650 2,295 0 15,395 10.4


Ecuador 70.7 130.4 3.4 0.3 –120 2,681 568 2 16,497 9.7


Egypt, Arab Rep. 39.0 159.4 19.8 0.2 –347 14,324 –483 –711 35,001 7.4


El Salvador 66.9 90.5 11.3 1.3 –292 3,665 247 0 11,995 21.7


Equatorial Guinea 98.5 226.4 .. 0.2 20 .. 737 .. .. ..


Eritrea 49.8 81.9 .. 6.3 55 .. 19 .. 1,055 ..


Estonia 155.0 141.9 7.6 .. 0 407 436 –112 .. ..


Ethiopia 38.1 136.5 34.4 11.8 –300 513 627 .. 8,597 6.1


Faeroe Islands .. 103.1 .. .. .. 146 .. .. .. ..


Fiji 82.8 107.8 .. 2.0 –29 158 204 .. 861 ..


Finland 61.8 74.8 5.3 .. 73 751 –5,758 –5 .. ..


France 47.3 95.6 8.0 .. 500 19,307 45,209 3,608 .. ..


French Polynesia .. 83.1 .. .. 0 700 40 .. .. ..


Gabon 95.6 218.6 .. 0.5 5 .. 728 .. 2,879 ..


Gambia, The 48.8 91.7 32.4 15.6 –14 91 36 .. 466 7.5


Georgia 64.4 133.8 20.2 3.9 –150 1,537 1,154 –7 11,124 26.9


Germany 75.8 99.5 3.0 .. 550 13,160 39,067 7,778 .. ..


Ghana 71.4 185.3 5.4 4.8 –51 152 3,222 1 11,289 2.4


Greece 30.9 93.9 22.0 .. 154 1,186 1,092 –354 .. ..


Greenland .. 72.4 .. .. .. .. .. .. .. ..


Grenada 42.4 101.9 57.0 1.6 –5 29 41 .. 567 13.3


Guam .. 85.0 .. .. 0 .. .. .. .. ..


Guatemala 57.7 90.9 10.5 0.9 –200 4,508 1,081 0 16,286 15.6


Guinea 73.7 109.9 0.1 4.5 –300 65 896 .. 3,139 11.2


Guinea-Bissau 54.8 80.6 .. 12.3 –10 46 19 .. 284 ..


Guyana 114.5 130.0 .. 6.2 –40 373 165 .. 1,846 ..


Haiti 49.8 64.5 15.9 23.2 –240 1,551 181 0 783 0.5


Honduras 97.2 89.8 8.5 3.8 –100 2,811 1,043 0 4,642 16.0


Hungary 152.9 94.1 5.5 .. 75 2,441 9,629 –203 .. ..


Iceland 72.5 90.3 9.0 .. 10 21 1,107 –11 .. ..


India 40.5 135.9 .. 0.2 –3,000 63,818 32,190 –4,137 334,331 6.5


Indonesia 44.6 134.1 4.1 0.1 –1,293 6,924 18,160 –326 213,541 14.5


Iran, Islamic Rep. .. 180.9 .. .. –186 1,330 4,150 .. 19,113 1.4


Iraq 119.0 211.8 1.9 1.7 –150 386 1,396 94 .. ..


Ireland 88.9 86.2 4.2 .. 100 755 11,506 86,184 .. ..


Isle of Man .. .. .. .. .. .. .. .. .. ..


Israel 58.7 95.0 6.2 .. 274 595 11,374 –821 .. ..




98 World Development Indicators 2013 Front User guide World view People Environment?


6 Global links
Merchandise


trade
Net barter
terms of


trade index


Inbound
tourism


expenditure


Net official
development
assistance


Net
migration


Personal
transfers and
compensation
of employees,


received


Foreign
direct


investment


Portfolio
equity


Total
external


debt stock


Total debt
service


% of exports
of goods,
services,


and primary
incomea% of GDP 2000 = 100 % of exports % of GNI thousands $ millions


Net inflow
$ millions


Net inflow
$ millions $ millions


2011 2011 2011 2011 2005–10 2011 2011 2011 2011 2011


Italy 49.2 95.6 7.2 .. 1,999 7,025 28,003 5,978 .. ..


Jamaica 55.4 68.6 48.1 0.4 –100 2,106 173 0 14,350 36.5


Japan 28.6 60.1 1.3 .. 270 2,132 79 5,645 .. ..


Jordan 91.1 76.5 29.4 3.4 203 3,453 1,469 109 17,634 6.7


Kazakhstan 67.1 219.9 1.6 0.1 7 180 13,227 39 124,437 34.6


Kenya 61.1 90.7 18.6 7.4 –189 934 335 20 10,258 4.2


Kiribati 78.0 90.1 .. 27.2 .. .. 4 .. .. ..


Korea, Dem. Rep. .. 79.6 .. .. 0 .. 55 .. .. ..


Korea, Rep. 96.7 63.1 2.7 .. –30 8,494 4,661 –7,479 .. ..


Kosovo .. .. .. 9.9 .. 1,122 546 0 1,531 8.9


Kuwait 69.9 219.2 .. .. 278 .. 400 1,099 .. ..


Kyrgyz Republic 105.1 109.4 20.3 9.7 –132 1,709 694 5 5,486 11.8


Lao PDR 60.9 125.0 17.2 5.2 –75 110 301 11 6,158 ..


Latvia 103.0 110.4 6.7 .. –10 695 1,502 38 38,255 47.0


Lebanon 65.9 98.1 27.5 1.1 –13 7,322 3,476 240 24,767 19.9


Lesotho 152.5 73.7 2.1 9.0 –20 649 132 0 792 ..


Liberia 90.7 162.0 18.6 53.6 300 360 1,313 .. 448 ..


Libya .. 185.4 .. .. –20 .. 200 0 .. ..


Liechtenstein .. .. .. .. .. .. .. .. .. ..


Lithuania 139.5 102.3 4.3 .. –36 1,956 1,443 9 29,988 20.1


Luxembourg 85.6 98.5 5.3 .. 42 1,740 18,366 32,486 .. ..


Macedonia, FYR 112.8 86.7 4.5 1.6 2 434 495 –8 6,286 18.9


Madagascar 45.3 74.7 .. 4.2 –5 .. 907 .. 2,769 2.1


Malawi 62.8 98.2 2.7 14.5 –20 17 92 –1 1,202 1.3


Malaysia 144.0 100.7 7.4 0.0 84 1,198 12,001 .. 94,468 3.9


Maldives 88.3 100.1 80.3 2.7 0 3 282 0 983 ..


Mali 52.7 177.3 .. 12.3 –101 473 178 .. 2,931 ..


Malta 120.2 53.6 16.1 .. 5 37 467 –7 .. ..


Marshall Islands 100.8 107.2 .. 38.2 .. .. 7 .. .. ..


Mauritania 125.1 131.5 .. 9.2 10 .. 45 .. 2,709 3.6


Mauritius 69.3 71.5 30.5 1.7 0 1 273 9,387 1,435 1.4


Mexico 61.6 107.9 3.4 0.1 –1,805 23,588 20,823 –6,244 287,037 11.2


Micronesia, Fed. Sts. 62.8 97.3 .. 41.2 –9 .. 8 .. .. ..


Moldova 105.9 105.9 8.3 6.0 –172 1,600 294 5 5,452 12.8


Monaco .. .. .. .. .. .. .. .. .. ..


Mongolia 129.1 226.8 4.7 4.3 –15 279 4,715 9 2,564 2.1


Montenegro 70.6 .. .. 1.6 –3 343 558 –15 2,093 10.2


Morocco 65.1 140.6 25.7 1.3 –675 7,256 2,521 166 29,049 9.9


Mozambique 77.6 106.8 7.0 16.3 –20 157 2,079 0 4,097 1.6


Myanmar .. 104.5 3.3 .. –500 127 1,001 .. 7,765 ..


Namibia 85.5 116.7 12.1 2.4 –1 15 969 4 .. ..


Nepal 35.5 77.9 22.3 4.7 –100 4,217 94 .. 3,956 9.5


Netherlands 150.7 .. .. .. 8 .. .. .. .. ..


New Caledonia .. 230.2 .. .. 6 519 1,745 0 .. ..


New Zealand 46.8 132.6 11.3 .. 65 875 4,285 1,594 .. ..


Nicaragua 80.4 82.8 8.0 7.6 –200 914 968 0 7,121 14.8


Niger 60.7 163.9 .. 10.9 –29 102 1,014 .. 1,408 ..




World Development Indicators 2013 99Economy States and markets Global links Back


Global links 6
Merchandise


trade
Net barter
terms of


trade index


Inbound
tourism


expenditure


Net official
development
assistance


Net
migration


Personal
transfers and
compensation
of employees,


received


Foreign
direct


investment


Portfolio
equity


Total
external


debt stock


Total debt
service


% of exports
of goods,
services,


and primary
incomea% of GDP 2000 = 100 % of exports % of GNI thousands $ millions


Net inflow
$ millions


Net inflow
$ millions $ millions


2011 2011 2011 2011 2005–10 2011 2011 2011 2011 2011


Nigeria 71.3 211.4 0.7 0.8 –300 20,619 8,842 2,571 13,108 0.4


Northern Mariana Islands .. 86.2 .. .. .. .. 0 .. .. ..


Norway 51.4 156.6 3.2 .. 171 765 7,281 1,708 .. ..


Oman 98.6 230.9 3.3 .. 153 39 788 –447 .. ..


Pakistan 33.2 52.4 3.6 1.6 –2,000 12,263 1,309 –37 60,182 9.2


Palau 76.1 104.2 .. 20.7 .. .. 2 .. .. ..


Panama 133.3 86.4 12.2 0.4 11 388 3,223 0 12,583 3.6


Papua New Guinea 92.4 166.0 .. 4.9 0 11 –309 .. 12,582 15.8


Paraguay 74.8 107.5 2.3 0.4 –40 893 412 0 6,011 3.6


Peru 47.6 159.0 5.8 0.4 –725 2,697 8,233 147 44,872 6.5


Philippines 49.9 64.7 6.0 –0.1 –1,233 22,973 1,869 1,038 76,043 17.6


Poland 76.8 99.7 5.0 .. 56 7,641 15,296 3,052 .. ..


Portugal 58.6 86.8 17.3 .. 150 3,778 13,074 –10,557 .. ..


Puerto Rico .. .. .. .. –145 .. .. .. .. ..


Qatar 83.4 213.3 .. .. 857 574 –87 –903 .. ..


Romania 77.2 98.3 2.9 .. –100 3,889 2,557 –37 129,822 27.5


Russian Federation 45.5 234.2 3.0 .. 1,136 4,951 52,878 –9,707 542,977 10.5


Rwanda 33.1 225.7 .. 20.2 15 103 106 .. 1,103 ..


Samoa 62.7 77.5 67.9 16.6 –16 139 15 .. 368 5.8


San Marino .. .. .. .. .. .. .. .. .. ..


São Tomé and Príncipe 57.6 120.3 54.2 30.2 –7 7 35 0 231 5.4


Saudi Arabia 82.6 215.8 2.5 .. 1,056 244 16,308 .. .. ..


Senegal 59.1 105.4 .. 7.5 –133 1,478 286 .. 4,320 ..


Serbia 69.7 .. 7.2 1.3 0 3,271 2,700 69 31,569 31.5


Seychelles 115.1 75.0 34.6 2.1 .. 26 139 0 1,780 3.2


Sierra Leone 68.6 61.7 8.1 14.7 60 59 715 .. 1,049 3.8


Singapore 323.4 81.2 .. .. 722 .. 64,003 –3,754 .. ..


Sint Maarten .. .. .. .. .. 11 –48 .. .. ..


Slovak Republic 163.0 87.4 3.0 .. 37 1,753 3,658 39 .. ..


Slovenia 141.6 86.1 8.0 .. 22 433 818 222 .. ..


Solomon Islands 104.3 87.9 .. 49.6 0 2 146 .. 256 2.0


Somalia .. 100.3 .. .. –300 .. 102 .. 3,050 ..


South Africa 53.5 154.1 9.1 0.3 700 1,158 5,889 –3,769 113,512 5.3


South Sudan .. .. .. .. .. .. .. .. .. ..


Spain 44.7 99.0 14.9 .. 2,250 9,907 31,419 2,978 .. ..


Sri Lanka 51.0 72.1 10.4 1.0 –250 5,153 956 –623 23,984 9.3


St. Kitts and Nevis 42.1 66.8 40.8 2.5 .. 48 114 .. .. ..


St. Lucia 69.9 94.8 56.7 3.0 –1 29 81 .. 448 7.8


St. Martin .. .. .. .. .. .. .. .. .. ..


St. Vincent and Grenadines 53.8 107.2 48.3 2.8 –5 29 110 .. 283 15.2


Sudan 28.3 229.4 .. 2.0 135 1,420 1,936 .. 21,169 ..


Suriname 96.3 141.8 2.6 .. –5 4 145 0 .. ..


Swaziland 97.8 105.3 .. 3.2 –6 55 95 .. 605 1.9


Sweden 67.1 87.2 6.2 .. 266 776 3,054 2,146 .. ..


Switzerland 67.1 79.8 5.0 .. 183 3,307 10,077 7,543 .. ..


Syrian Arab Republic .. 143.7 .. .. –56 2,079 1,060 .. 4,968 ..


Tajikistan 68.1 102.4 3.4 5.5 –296 3,060 11 0 3,323 ..




100 World Development Indicators 2013 Front User guide World view People Environment?


6 Global links
Merchandise


trade
Net barter
terms of


trade index


Inbound
tourism


expenditure


Net official
development
assistance


Net
migration


Personal
transfers and
compensation
of employees,


received


Foreign
direct


investment


Portfolio
equity


Total
external


debt stock


Total debt
service


% of exports
of goods,
services,


and primary
incomea% of GDP 2000 = 100 % of exports % of GNI thousands $ millions


Net inflow
$ millions


Net inflow
$ millions $ millions


2011 2011 2011 2011 2005–10 2011 2011 2011 2011 2011


Tanzania 66.3 151.4 19.9 10.4 –300 76 1,095 3 10,044 2.0


Thailand 132.3 93.9 .. –0.1 492 4,554 7,780 875 80,039 3.8


Timor-Leste 30.4 .. .. .. –50 131 47 .. .. ..


Togo 77.3 30.4 .. 15.5 –5 337 54 .. 643 ..


Tonga 44.7 81.2 .. 21.1 –8 72 10 .. 191 8.8


Trinidad and Tobago 103.2 152.6 .. .. –20 91 574 .. .. ..


Tunisia 91.2 95.0 11.2 1.5 –20 2,005 433 –44 22,335 10.7


Turkey 48.5 88.6 15.4 0.1 –50 1,087 16,049 –986 307,007 30.2


Turkmenistan 72.7 221.2 .. 0.2 –55 .. 3,186 .. 445 ..


Turks and Caicos Islands .. 73.4 .. .. .. .. 97 .. .. ..


Tuvalu 70.7 .. .. 76.9 .. .. 2 .. .. ..


Uganda 45.0 119.7 24.2 9.6 –135 949 797 106 3,858 1.7


Ukraine 91.4 124.0 6.1 0.5 –40 7,822 7,207 519 134,481 30.8


United Arab Emirates 136.0 178.3 .. .. 3,077 .. 7,679 .. .. ..


United Kingdom 45.4 101.4 5.9 .. 1,020 1,796 36,244 –16,894 .. ..


United States 25.0 94.6 8.8 .. 4,955 5,810 257,528 27,350 .. ..


Uruguay 40.1 100.9 18.7 0.0 –50 102 2,177 .. 14,350 11.1


Uzbekistan 51.2 178.0 .. 0.5 –518 .. 1,403 .. 8,382 ..


Vanuatu 47.4 91.9 71.2 12.4 0 22 58 0 202 1.6


Venezuela, RB 44.3 258.7 0.9 0.0 40 138 5,226 .. 67,908 6.4


Vietnam 164.8 99.9 5.3 3.0 –431 8,600 7,430 1,064 57,841 3.2


Virgin Islands (U.S.) .. .. .. .. –4 .. .. .. .. ..


West Bank and Gaza .. .. .. .. –90 1,545 .. .. .. ..


Yemen, Rep. 59.0 157.5 9.2 1.5 –135 1,404 –713 0 6,418 2.8


Zambia 83.3 192.8 1.6 6.1 –85 46 1,982 11 4,360 2.1


Zimbabwe 81.8 110.5 .. 7.4 –900 .. 387 .. 6,275 ..


World 51.8 .. 5.4b 0.2c .. 479,246 1,654,419 183,598 .. ..


Low income 58.1 .. 12.0b 8.8c –6,808 27,628 18,331 124 133,292 4.6


Middle income 51.4 .. 4.7b 0.2c –16,352 330,766 629,613 8,474 4,742,871 8.9


Lower middle income 53.2 .. 6.5b 0.8c –12,699 202,300 108,774 –349 1,170,790 9.5


Upper middle income 50.9 .. 4.3b 0.1c –3,653 128,466 520,838 8,822 3,572,081 8.8


Low & middle income 51.5 .. 4.8b 0.6c –23,160 358,394 647,943 8,598 4,876,163 8.8


East Asia & Pacific 57.2 .. 3.4b 0.1c –5,221 85,943 274,550 7,980 1,242,633 4.7


Europe & Central Asia 57.0 .. 5.7b 0.2c –595 42,960 119,394 –10,115 1,484,186 17.8


Latin America & Carib. 38.2 .. 4.7b 0.2c –5,088 59,532 161,618 7,351 1,233,484 13.4


Middle East & N. Africa 67.3 .. 11.1b .. –1,628 41,339 16,309 –145 166,124 5.1


South Asia 40.7 .. 6.5b 0.7c –8,622 97,532 35,728 –4,807 454,138 6.7


Sub- Saharan Africa 62.7 .. 6.5b 3.9c –2,006 31,088 40,345 8,334 295,598 3.4


High income 51.9 .. 5.7b 0.0c 22,906 120,853 1,006,476 175,000 .. ..


Euro area 70.8 .. 6.1b 0.0c 6,336 75,712 320,055 128,811 .. ..


a. The numerator refers to 2011, whereas the denominator is a three-year average of 2009–11 data. b. Calculated using the World Bank’s weighted aggregation methodology (see
Statistical methods) and thus may differ from data reported by the World Tourism Organization. c. Based on the World Bank classification of economies and thus may differ from data
reported by the Organisation for Economic Co-operation and Development.




Economy States and markets Global links Back World Development Indicators 2013 101


Global links 6
Starting with World Development Indicators 2013, the World Bank is


changing its presentation of balance of payments data to conform


to the International Monetary Fund’s (IMF) Balance of Payments


Manual, 6th edition (BPM6). The historical data series based on


BPM5 ends with data for 2005. Balance of payments data from


2005 forward have been presented in accord with the BPM6 meth-


odology, which can be accessed at www.imf.org/external/np/sta/


bop/bop.htm.


Trade in goods


Data on merchandise trade are from customs reports of goods


moving into or out of an economy or from reports of financial


transactions related to merchandise trade recorded in the balance


of payments. Because of differences in timing and definitions,


trade flow estimates from customs reports and balance of pay-


ments may differ. Several international agencies process trade


data, each correcting unreported or misreported data, leading to


other differences. The most detailed source of data on interna-


tional trade in goods is the United Nations Statistics Division’s


Commodity Trade Statistics (Comtrade) database. The IMF and


the World Trade Organization also collect customs-based data


on trade in goods.


The “terms of trade” index measures the relative prices of a coun-


try’s exports and imports. The most common way to calculate terms


of trade is the net barter (or commodity) terms of trade index, or


the ratio of the export price index to the import price index. When a


country’s net barter terms of trade index increases, its exports have


become more expensive or its imports cheaper.


Tourism


Tourism is defined as the activity of people traveling to and staying


in places outside their usual environment for no more than one year


for leisure, business, and other purposes not related to an activity


remunerated from within the place visited. Data on inbound and


outbound tourists refer to the number of arrivals and departures,


not to the number of unique individuals. Thus a person who makes


several trips to a country during a given period is counted each


time as a new arrival. Data on inbound tourism show the arrivals of


nonresident tourists (overnight visitors) at national borders. When


data on international tourists are unavailable or incomplete, the


table shows the arrivals of international visitors, which include tour-


ists, same-day visitors, cruise passengers, and crew members. The


aggregates are calculated using the World Bank’s weighted aggrega-


tion methodology (see Statistical methods) and differ from the World


Tourism Organization’s aggregates.


For tourism expenditure, the World Tourism Organization uses bal-


ance of payments data from the IMF supplemented by data from


individual countries. These data, shown in the table, include travel


and passenger transport items as defined by the Balance of Pay-


ments. When the IMF does not report data on passenger transport


items, expenditure data for travel items are shown.


Official development assistance


Data on official development assistance received refer to aid to


eligible countries from members of the Organisation of Economic


Co-operation and Development’s (OECD) Development Assistance


Committee (DAC), multilateral organizations, and non-DAC donors.


Data do not reflect aid given by recipient countries to other develop-


ing countries or distinguish among types of aid (program, project,


or food aid; emergency assistance; or postconflict peacekeeping


assistance), which may have different effects on the economy.


Ratios of aid to gross national income (GNI), gross capital for-


mation, imports, and government spending measure a country’s


dependency on aid. Care must be taken in drawing policy conclu-


sions. For foreign policy reasons some countries have traditionally


received large amounts of aid. Thus aid dependency ratios may


reveal as much about a donor’s interests as about a recipient’s


needs. Increases in aid dependency ratios can reflect events affect-


ing both the numerator (aid) and the denominator (GNI).


Data are based on information from donors and may not be con-


sistent with information recorded by recipients in the balance of


payments, which often excludes all or some technical assistance—


particularly payments to expatriates made directly by the donor.


Similarly, grant commodity aid may not always be recorded in trade


data or in the balance of payments. DAC statistics exclude aid for


military and antiterrorism purposes. The aggregates refer to World


Bank classifications of economies and therefore may differ from


those reported by the OECD.


Migration, personal transfers, and compensation of


employees


The movement of people, most often through migration, is a signifi-


cant part of global integration. Migrants contribute to the economies


of both their host country and their country of origin. Yet reliable sta-


tistics on migration are difficult to collect and are often incomplete,


making international comparisons a challenge.


Since data on emigrant stock is difficult for countries to collect,


the United Nations Population Division provides data on net migra-


tion, taking into account the past migration history of a country or


area, the migration policy of a country, and the influx of refugees


in recent periods to derive estimates of net migration. The data to


calculate these estimates come from various sources, including


border statistics, administrative records, surveys, and censuses.


When there are insufficient data, net migration is derived through


the difference between the growth rate of a country’s population


over a certain period and the rate of natural increase of that popu-


lation (itself being the difference between the birth rate and the


death rate).


Migrants often send funds back to their home countries, which are


recorded as personal transfers in the balance of payments. Personal


transfers thus include all current transfers between resident and


nonresident individuals, independent of the source of income of the


sender (irrespective of whether the sender receives income from


About the data




102 World Development Indicators 2013 Front User guide World view People Environment?


6 Global links
labor, entrepreneurial or property income, social benefits, or any


other types of transfers or disposes of assets) and the relationship


between the households (irrespective of whether they are related


or unrelated individuals).


Compensation of employees refers to the income of border,


seasonal, and other short-term workers who are employed in an


economy where they are not resident and of residents employed by


nonresident entities. Compensation of employees has three main


components: wages and salaries in cash, wages and salaries in


kind, and employers’ social contributions.


Equity flows


Equity flows comprise foreign direct investment (FDI) and portfolio


equity. The internationally accepted definition of FDI (from BPM6)


includes the following components: equity investment, including


investment associated with equity that gives rise to control or influ-


ence; investment in indirectly influenced or controlled enterprises;


investment in fellow enterprises; debt (except selected debt); and


reverse investment. The Framework for Direct Investment Relation-


ships provides criteria for determining whether cross-border owner-


ship results in a direct investment relationship, based on control


and influence.


Direct investments may take the form of greenfield investment,


where the investor starts a new venture in a foreign country by con-


structing new operational facilities; joint venture, where the inves-


tor enters into a partnership agreement with a company abroad to


establish a new enterprise; or merger and acquisition, where the


investor acquires an existing enterprise abroad. The IMF suggests


that investments should account for at least 10 percent of voting


stock to be counted as FDI. In practice many countries set a higher


threshold. Many countries fail to report reinvested earnings, and the


definition of long-term loans differs among countries.


Portfolio equity investment is defined as cross-border transac-


tions and positions involving equity securities, other than those


included in direct investment or reserve assets. Equity securities are


equity instruments that are negotiable and designed to be traded,


usually on organized exchanges or “over the counter.” The negotia-


bility of securities facilitates trading, allowing securities to be held


by different parties during their lives. Negotiability allows investors


to diversify their portfolios and to withdraw their investment read-


ily. Included in portfolio investment are investment fund shares or


units (that is, those issued by investment funds) that are evidenced


by securities and that are not reserve assets or direct investment.


Although they are negotiable instruments, exchange-traded financial


derivatives are not included in portfolio investment because they


are in their own category.


External debt


External indebtedness affects a country’s creditworthiness and


investor perceptions. Data on external debt are gathered through the


World Bank’s Debtor Reporting System (DRS). Indebtedness is cal-


culated using loan-by-loan reports submitted by countries on long-


term public and publicly guaranteed borrowing and using information


on short-term debt collected by the countries, from creditors through


the reporting systems of the Bank for International Settlements, or


based on national data from the World Bank’s Quarterly External


Debt Statistics. These data are supplemented by information from


major multilateral banks and official lending agencies in major credi-


tor countries. Currently, 128 developing countries report to the DRS.


Debt data are reported in the currency of repayment and compiled


and published in U.S. dollars. End-of-period exchange rates are used


for the compilation of stock figures (amount of debt outstanding),


and projected debt service and annual average exchange rates are


used for the flows. Exchange rates are taken from the IMF’s Inter-


national Financial Statistics. Debt repayable in multiple currencies,


goods, or services and debt with a provision for maintenance of the


value of the currency of repayment are shown at book value.


While data related to public and publicly guaranteed debt are


reported to the DRS on a loan-by-loan basis, data on long-term


private nonguaranteed debt are reported annually in aggregate by


the country or estimated by World Bank staff for countries. Private


nonguaranteed debt is estimated based on national data from the


World Bank’s Quarterly External Debt Statistics.


Total debt service as a share of exports of goods, services, and


primary income provides a measure of a country’s ability to service


its debt out of export earnings.




Economy States and markets Global links Back World Development Indicators 2013 103


Global links 6
Definitions


• Merchandise trade includes all trade in goods and excludes trade


in services. • Net barter terms of trade index is the percentage ratio


of the export unit value indexes to the import unit value indexes, mea-


sured relative to the base year 2000. • Inbound tourism expenditure


is expenditures by international inbound visitors, including payments


to national carriers for international transport and any other prepay-


ment made for goods or services received in the destination country.


They may include receipts from same-day visitors, except when these


are important enough to justify separate classification. Data include


travel and passenger transport items as defined by Balance of Pay-


ments. When passenger transport items are not reported, expendi-


ture data for travel items are shown. Exports refer to all transactions


between residents of a country and the rest of the world involving


a change of ownership from residents to nonresidents of general


merchandise, goods sent for processing and repairs, nonmonetary


gold, and services. • Net official development assistance is flows


(net of repayment of principal) that meet the DAC definition of official


development assistance and are made to countries and territories


on the DAC list of aid recipients, divided by World Bank estimates


of GNI. • Net migration is the net total of migrants (immigrants


less emigrants, including both citizens and noncitizens) during the


period. Data are five-year estimates. • Personal transfers and com-


pensation of employees, received, are the sum of personal transfers


(current transfers in cash or in kind made or received by resident


households to or from nonresident households) and compensation


of employees (remuneration for the labor input to the production


process contributed by an individual in an employer-employee rela-


tionship with the enterprise). • Foreign direct investment is cross-


border investment associated with a resident in one economy having


control or a significant degree of influence on the management of an


enterprise that is resident in another economy. • Portfolio equity is


net inflows from equity securities other than those recorded as direct


investment or reserve assets, including shares, stocks, depository


receipts, and direct purchases of shares in local stock markets


by foreign investors • Total external debt stock is debt owed to


nonresident creditors and repayable in foreign currency, goods, or


services by public and private entities in the country. It is the sum


of long-term external debt, short-term debt, and use of IMF credit.


• Total debt service is the sum of principal repayments and interest


actually paid in foreign currency, goods, or services on long-term


debt; interest paid on short-term debt; and repayments (repurchases


and charges) to the IMF. Exports of goods and services and primary


income are the total value of exports of goods and services, receipts


of compensation of nonresident workers, and primary investment


income from abroad.


Data sources


Data on merchandise trade are from the World Trade Organization.


Data on trade indexes are from the United Nations Conference


on Trade and Development’s (UNCTAD) annual Handbook of Sta-


tistics. Data on tourism expenditure are from the World Tourism


Organization’s Yearbook of Tourism Statistics and World Tourism


Organization (2012a) and updated from its electronic files. Data


on net official development assistance are compiled by the OECD


(http://stats.oecd.org). Data on net migration are from United


Nations Population Division (2011). Data on personal transfers


and compensation of employees are from the IMF’s Balance of


Payments Statistics Yearbook supplemented by World Bank staff


estimates. Data on FDI are World Bank staff estimates based


on IMF balance of payments statistics and UNCTAD data (http://


unctadstat.unctad.org/ReportFolders/reportFolders.aspx). Data


on portfolio equity are from the IMF’s Balance of Payments Sta-


tistics Yearbook. Data on external debt are mainly from reports


to the World Bank through its DRS from member countries that


have received International Bank for Reconstruction and Develop-


ment loans or International Development Assistance credits, with


additional information from the files of the World Bank, the IMF,


the African Development Bank and African Development Fund,


the Asian Development Bank and Asian Development Fund, and


the Inter-American Development Bank. Summary tables of the


external debt of developing countries are published annually in


the World Bank’s International Debt Statistics and International


Debt Statistics database.


References


IMF (International Monetary Fund). Various issues. International Finan-


cial Statistics. Washington, DC.


———. Various years. Balance of Payments Statistics Yearbook. Parts


1 and 2. Washington, DC.


UNCTAD (United Nations Conference on Trade and Development). Vari-


ous years. Handbook of Statistics. New York and Geneva.


United Nations Population Division. 2011. World Population Prospects:


The 2010 Revision. New York: United Nations, Department of Eco-


nomic and Social Affairs.


World Bank. Various years. International Debt Statistics. Washington,


DC.


World Tourism Organization. 2012a. Compendium of Tourism Statistics


2012. Madrid.


———. 2012b Tourism Highlights: 2012 Edition. Madrid.


———. Various years. Yearbook of Tourism Statistics. Vols. 1 and 2.


Madrid.




104 World Development Indicators 2013 Front User guide World view People Environment?


6 Global links


6.1 Growth of merchandise trade


Export volume TX.QTY.MRCH.XD.WD


Import volume TM.QTY.MRCH.XD.WD


Export value TX.VAL.MRCH.XD.WD


Import value TM.VAL.MRCH.XD.WD


Net barter terms of trade index TT.PRI.MRCH.XD.WD


6.2 Direction and growth of merchandise trade


This table provides estimates of the flow of
trade in goods between groups of economies. ..a


6.3 High-income economy trade with low- and


middle-income economies


This table illustrates the importance of
developing economies in the global trading
system. ..a


6.4 Direction of trade of developing economies


Exports to developing economies within region TX.VAL.MRCH.WR.ZS


Exports to developing economies outside region TX.VAL.MRCH.OR.ZS


Exports to high-income economies TX.VAL.MRCH.HI.ZS


Imports from developing economies within
region TM.VAL.MRCH.WR.ZS


Imports from developing economies outside
region TM.VAL.MRCH.OR.ZS


Imports from high-income economies TM.VAL.MRCH.HI.ZS


6.5 Primary commodity prices


This table provides historical commodity
price data. ..a


6.6 Tariff barriers


All products, Binding coverage TM.TAX.MRCH.BC.ZS


Simple mean bound rate TM.TAX.MRCH.BR.ZS


Simple mean tariff TM.TAX.MRCH.SM.AR.ZS


Weighted mean tariff TM.TAX.MRCH.WM.AR.ZS


Share of tariff lines with international peaks TM.TAX.MRCH.IP.ZS


Share of tariff lines with specific rates TM.TAX.MRCH.SR.ZS


Primary products, Simple mean tariff TM.TAX.TCOM.SM.AR.ZS


Primary products, Weighted mean tariff TM.TAX.TCOM.WM.AR.ZS


Manufactured products, Simple mean tariff TM.TAX.MANF.SM.AR.ZS


Manufactured products, Weighted mean
tariff TM.TAX.MANF.WM.AR.ZS


6.7 Trade facilitation


Logistics performance index LP.LPI.OVRL.XQ


Burden of customs procedures IQ.WEF.CUST.XQ


Lead time to export LP.EXP.DURS.MD


Lead time to import LP.IMP.DURS.MD


Documents to export IC.EXP.DOCS


Documents to import IC.IMP.DOCS


Liner shipping connectivity index IS.SHP.GCNW.XQ


Quality of port infrastructure IQ.WEF.PORT.XQ


6.8 External debt


Total external debt, $ DT.DOD.DECT.CD


Total external debt, % of GNI DT.DOD.DECT.GN.ZS


Long-term debt, Public and publicly
guaranteed DT.DOD.DPPG.CD


Long-term debt, Private nonguaranteed DT.DOD.DPNG.CD


Short-term debt, $ DT.DOD.DSTC.CD


Short-term debt, % of total debt DT.DOD.DSTC.ZS


Short-term debt, % of total reserves DT.DOD.DSTC.IR.ZS


Total debt service DT.TDS.DECT.EX.ZS


Present value of debt, % of GNI DT.DOD.PVLX.GN.ZS


Present value of debt, % of exports of
goods, services and primary income DT.DOD.PVLX.EX.ZS


6.9 Global private financial flows


Foreign direct investment net inflows, $ BX.KLT.DINV.CD.WD


Foreign direct investment net inflows, %
of GDP BX.KLT.DINV.WD.GD.ZS


Portfolio equity BX.PEF.TOTL.CD.WD


Bonds DT.NFL.BOND.CD


Commercial banks and other lendings DT.NFL.PCBO.CD


6.10 Net official financial flows


Net financial flows from bilateral sources DT.NFL.BLAT.CD


Net financial flows from multilateral
sources DT.NFL.MLAT.CD


World Bank, IDA DT.NFL.MIDA.CD


World Bank, IBRD DT.NFL.MIBR.CD


IMF, Concessional DT.NFL.IMFC.CD


IMF, Non concessional DT.NFL.IMFN.CD


Regional development banks, Concessional DT.NFL.RDBC.CD


Regional development banks,
Nonconcessional DT.NFL.RDBN.CD


Regional development banks, Other
institutions DT.NFL.MOTH.CD


6.11 Aid dependency


Net official development assistance (ODA) DT.ODA.ODAT.CD


Net ODA per capita DT.ODA.ODAT.PC.ZS


To access the World Development Indicators online tables, use


the URL http://wdi.worldbank.org/table/ and the table number (for


example, http://wdi.worldbank.org/table/6.1). To view a specific


indicator online, use the URL http://data.worldbank.org/indicator/


and the indicator code (for example, http://data.worldbank.org/


indicator/TX.QTY.MRCH.XD.WD).


Online tables and indicators




World Development Indicators 2013 105Economy States and markets Global links Back


Global links 6
Grants, excluding technical cooperation BX.GRT.EXTA.CD.WD


Technical cooperation grants BX.GRT.TECH.CD.WD


Net ODA, % of GNI DT.ODA.ODAT.GN.ZS


Net ODA, % of gross capital formation DT.ODA.ODAT.GI.ZS


Net ODA, % of imports of goods and
services and income DT.ODA.ODAT.MP.ZS


Net ODA, % of central government
expenditure DT.ODA.ODAT.XP.ZS


6.12 Distribution of net aid by Development Assistance


Committee members


Net bilateral aid flows from DAC donors DC.DAC.TOTL.CD


United States DC.DAC.USAL.CD


EU institutions DC.DAC.CECL.CD


Germany DC.DAC.DEUL.CD


France DC.DAC.FRAL.CD


United Kingdom DC.DAC.GBRL.CD


Japan DC.DAC.JPNL.CD


Netherlands DC.DAC.NLDL.CD


Australia DC.DAC.AUSL.CD


Canada DC.DAC.CANL.CD


Norway DC.DAC.NORL.CD


Other DAC donors ..a,b


6.13 Movement of people


Net migration SM.POP.NETM


International migrant stock SM.POP.TOTL


Emigration rate of tertiary educated to
OECD countries SM.EMI.TERT.ZS


Refugees by country of origin SM.POP.REFG.OR


Refugees by country of asylum SM.POP.REFG


Personal transfers and compensation of
employees, Received BX.TRF.PWKR.CD.DT


Personal transfers and compensation of
employees, Paid BM.TRF.PWKR.CD.DT


6.14 Travel and tourism


International inbound tourists ST.INT.ARVL


International outbound tourists ST.INT.DPRT


Inbound tourism expenditure, $ ST.INT.RCPT.CD


Inbound tourism expenditure, % of exports ST.INT.RCPT.XP.ZS


Outbound tourism expenditure, $ ST.INT.XPND.CD


Outbound tourism expenditure, % of
imports ST.INT.XPND.MP.ZS


a. Available online only as part of the table, not as an individual indicator.
b. Derived from data elsewhere in the World Development Indicators database.




106 World Development Indicators 2013 Front User guide World view People Environment?




World Development Indicators 2013 107Economy States and markets Global links Back


As a major user of socioeconomic data, the World
Bank recognizes the importance of data docu-
mentation to inform users of differences in the
methods and conventions used by primary data
collectors—usually national statistical agencies,
central banks, and customs services—and by
international organizations, which compile the
statistics that appear in the World Development
Indicators database. These differences may
give rise to significant discrepancies over time,
both within countries and across them. Delays in
reporting data and the use of old surveys as the
base for current estimates may further compro-
mise the quality of data reported here.


This section provides information on sources,
methods, and reporting standards of the princi-
pal demographic, economic, and environmental
indicators in World Development Indicators. Addi-
tional documentation is available from the World
Bank’s Bulletin Board on Statistical Capacity at
http://data.worldbank.org.


The demand for good-quality statistical
data is ever increasing. Statistics provide the
evidence needed to improve decisionmaking,


document results, and heighten public account-
ability. The need for improved statistics to moni-
tor the Millennium Development Goals and the
parallel effort to support a culture of results-
based management has stimulated a decade-
long effort to improve statistics. The results have
been impressive, but more needs to done.


The “Statistics for Transparency, Account-
ability, and Results: A Busan Action Plan for
Statistics” was endorsed by the Busan Partner-
ship for Effective Development Cooperation at
the Fourth High-level Forum for Aid Effective-
ness held November 29–December 1, 2011, in
Busan, Republic of Korea. This plan builds on
the progress made under the first global plan to
improve national and international statistics, the
2004 Marrakech Action Plan for Statistics, but
goes beyond it in many ways. The main objec-
tives of the plan are to integrate statistics into
decisionmaking, promote open access to statis-
tics within government and for all other uses, and
increase resources for statistical systems, both
for investment in new capacity and for maintain-
ing current operations.


Primary data documentation




108 World Development Indicators 2013


Currency National
accounts


Balance of payments
and trade


Government
finance


IMF data
dissem­
ination


standard


Latest
population


census


Latest demographic,
education, or health


household survey


Source of most
recent income


and expenditure data


Vital
registration
complete


Latest
agricultural


census


Latest
industrial


data


Latest
trade
data


Latest
water


withdrawal
data


Base
year


Reference
year


System of
National
Accounts


SNA
price


valuation


Alternative
conversion


factor


PPP
survey
year


Balance of
Payments
Manual
in use


External
debt


System
of trade


Accounting
concept


Front User guide World view People Environment?


Primary data documentation


Afghanistan Afghan afghani 2002/03 1993 B A G C G Afghanistan 1979 MICS, 2010/11 IHS, 2008 2013/14 2011 2000
Albania Albanian lek a 1996 1993 B Rolling 6 A G C G Albania 2011 DHS, 2009 LSMS, 2008 Yes 2009 2011 2006
Algeria Algerian dinar 1980 1968 B 6 A S B G Algeria 2008 MICS, 2012 IHS, 1995 2011 2001
American Samoa U.S. dollar 1968 S American Samoa 2010 Yes 2007
Andorra Euro 1990 1968 S Andorra 2011b Yes 2006
Angola Angolan kwanza 2002 1993 P 1991–96 2005 6 A S G Angola 1970 MIS, 2011; IBEP, 2008/09 IHS, 2000 1964/65 1991 2005
Antigua and Barbuda East Caribbean dollar 2006 1968 B 6 G G Antigua and Barbuda 2011 Yes 2007 2011 2005
Argentina Argentine peso 1993 1993 B 1971–84 2005 6 A S C S Argentina 2010 MICS, 2011 IHS, 2011 Yes 2013 2002 2011 2000
Armenia Armenian dram a 1996 1993 B 1990–95 2005 6 A S C S Armenia 2011 DHS, 2010 IHS, 2010 Yes 2013/14 2011 2007
Aruba Aruban florin 1995 1993 6 S Aruba 2010 Yes 2011
Australia Australian dollar a 2009 2008 B 2008 6 G C S Australia 2011 ES/BS, 1994 Yes 2011 2006 2011 2000
Austria Euro 2005 1993 B Rolling 6 S C S Austria 2011b IS, 2000 Yes 2010 2009 2011 2002
Azerbaijan New Azeri manat a 2003 1993 B 1992–95 2005 6 A G B G Azerbaijan 2009 DHS, 2006 ES/BS, 2008 Yes 2009 2011 2005
Bahamas, The Bahamian dollar 2006 1993 B 6 G B G Bahamas, The 2010 2011
Bahrain Bahraini dinar 1985 1968 P 2005 6 G B G Bahrain 2010 Yes 2011 2003
Bangladesh Bangladeshi taka 1995/96 1993 B 2005 6 A G C G Bangladesh 2011 DHS, 2011 IHS, 2010 2008 2007 2008
Barbados Barbados dollar 1974 1968 B 6 G B G Barbados 2010 MICS, 2012 Yes 2011 2005
Belarus Belarusian rubel a 2000 1993 B 1990–95 2005 6 A G C S Belarus 2009 MICS, 2012 ES/BS, 2009 Yes 2009 2011 2000
Belgium Euro 2005 1993 B Rolling 6 S C S Belgium 2011 IHS, 2000 Yes 2010 2009 2011 2007
Belize Belize dollar 2000 1993 B 6 A G B G Belize 2010 MICS, 2011 ES/BS, 2011 2011 2000
Benin CFA franc 1985 1968 P 1992 2005 6 A S B G Benin 2002 DHS, 2011/12 CWIQ, 2007 2011/12 2010 2001
Bermuda Bermuda dollar 2006 1993 B 6 G Bermuda 2010 Yes 2011
Bhutan Bhutanese ngultrum 2000 1993 B 2005 6 A G C G Bhutan 2005 MICS, 2010 IHS, 2010 2009 2011 2008
Bolivia Bolivian Boliviano 1990 1968 B 1960–85 2005 6 A G C G Bolivia 2012 DHS, 2008 IHS, 2009 2013 2001 2011 2000
Bosnia and Herzegovina Bosnia and Herzegovina


convertible mark


a 1996 1993 B Rolling 6 A S C Bosnia and Herzegovina 1991 MICS, 2011/12 LSMS, 2007 Yes 2011 2009


Botswana Botswana pula 1993/94 1993 B 2005 6 A G B G Botswana 2011 DHS, 1988 ES/BS, 2008 2009 2011 2000
Brazil Brazilian real 2000 1993 B 2005 6 A G C S Brazil 2010 LSMS, 1996/97 LFS, 2009 2006 2007 2011 2006
Brunei Darussalam Brunei dollar 2000 1993 P 2005 S G Brunei Darussalam 2011 Yes 2006 1994
Bulgaria Bulgarian lev a 2002 1993 B 1978–89,


1991–92
Rolling 6 A S C S Bulgaria 2011 LSMS, 2007 ES/BS, 2007 Yes 2010 2009 2011 2009


Burkina Faso CFA franc 1999 1993 B 1992–93 2005 6 A G B G Burkina Faso 2006 DHS, 2011 CWIQ, 2009 2010 2011 2001
Burundi Burundi franc 2008 1993 B 2005 6 A S C G Burundi 2008 DHS, 2010 CWIQ, 2006 2010 2000
Cambodia Cambodian riel 2000 1993 B 2005 6 A S C G Cambodia 2008 DHS, 2010 IHS, 2008 2013 2000 2011 2006
Cameroon CFA franc 2000 1993 B 2005 6 A S B G Cameroon 2005 DHS, 2011 PS, 2007 1984 2002 2011 2000
Canada Canadian dollar 2005 1993 B 2008 6 G C S Canada 2011 LFS, 2000 Yes 2011 2008 2011 1986
Cape Verde Cape Verde escudo 1980 1968 P 2005 6 A G C G Cape Verde 2010 DHS, 2005 ES/BS, 2007 Yes 2011 2001
Cayman Islands Cayman Islands dollar 1993 G Cayman Islands 2010 Yes
Central African Republic CFA franc 2000 1968 B 2005 6 P S B G Central African Republic 2003 MICS, 2010 PS, 2008 2011 2005
Chad CFA franc 1995 1993 B 2005 6 E S G Chad 2009 MICS, 2010 PS, 2002/03 2011 1995 2005
Channel Islands Pound sterling 2003 2007 1968 B Channel Islands 2009,


2011c


Chile Chilean peso 2003 1993 B 2008 6 A S C S Chile 2012 IHS, 2009 Yes 2007 2008 2011 2007
China Chinese yuan 2000 1993 P 1978–93 2005 6 P S B G China 2010 NSS, 2007 IHS, 2008 2007 2007 2011 2005
Hong Kong SAR, China Hong Kong dollar 2009 1993 B 2005 6 G C S Hong Kong SAR, China 2011d Yes 2009 2011
Macao SAR, China Macao pataca 2009 1993 B 2005 6 G C G Macao SAR, China 2011 Yes 2009 2007


Colombia Colombian peso 2005 1993 B 1992–94 2005 6 A G B S Colombia 2006 DHS, 2010 IHS, 2011 2013 2005 2011 2000
Comoros Comorian franc 1990 1968 P 2005 A S Comoros 2003 DHS, 2012 IHS, 2004 2007 1999
Congo, Dem. Rep. Congolese franc 2000 1968 B 1999–2001 2005 6 A S C G Congo, Dem. Rep. 1984 MICS, 2010 1-2-3, 2004/05 1990 1987 2005
Congo, Rep. CFA franc 1990 1968 P 1993 2005 6 P S C G Congo, Rep. 2007 DHS, 2011/12 CWIQ/PS, 2011 1985/86 2009 2010 2002
Costa Rica Costa Rican colon 1991 1993 B 6 A S C S Costa Rica 2011 MICS, 2011 LFS, 2011 Yes 1973 2009 2011 1997
Côte d’Ivoire CFA franc 1996 1968 P 2005 6 A S C G Côte d’Ivoire 1998 DHS, 2012 IHS, 2008 2001 2011 2005
Croatia Croatian kuna a 2005 1993 B Rolling 6 G C S Croatia 2011 ES/BS, 2008 Yes 2010 2011 2010
Cuba Cuban peso 2005 1993 B S Cuba 2012 MICS, 2010/11 Yes 2006 2007
Curaçao Netherlands Antilles


guilder
1993 Curaçao


Cyprus Euro a 2000 1993 B Rolling 6 G C S Cyprus 2011 Yes 2010 2009 2011 2009
Czech Republic Czech koruna 2005 1993 B Rolling 6 S C S Czech Republic 2011 RHS, 1993 IS, 1996 Yes 2010 2007 2011 2007




World Development Indicators 2013 109Economy States and markets Global links Back


Currency National
accounts


Balance of payments
and trade


Government
finance


IMF data
dissem­
ination


standard


Latest
population


census


Latest demographic,
education, or health


household survey


Source of most
recent income


and expenditure data


Vital
registration
complete


Latest
agricultural


census


Latest
industrial


data


Latest
trade
data


Latest
water


withdrawal
data


Base
year


Reference
year


System of
National
Accounts


SNA
price


valuation


Alternative
conversion


factor


PPP
survey
year


Balance of
Payments
Manual
in use


External
debt


System
of trade


Accounting
concept


Afghanistan Afghan afghani 2002/03 1993 B A G C G Afghanistan 1979 MICS, 2010/11 IHS, 2008 2013/14 2011 2000
Albania Albanian lek a 1996 1993 B Rolling 6 A G C G Albania 2011 DHS, 2009 LSMS, 2008 Yes 2009 2011 2006
Algeria Algerian dinar 1980 1968 B 6 A S B G Algeria 2008 MICS, 2012 IHS, 1995 2011 2001
American Samoa U.S. dollar 1968 S American Samoa 2010 Yes 2007
Andorra Euro 1990 1968 S Andorra 2011b Yes 2006
Angola Angolan kwanza 2002 1993 P 1991–96 2005 6 A S G Angola 1970 MIS, 2011; IBEP, 2008/09 IHS, 2000 1964/65 1991 2005
Antigua and Barbuda East Caribbean dollar 2006 1968 B 6 G G Antigua and Barbuda 2011 Yes 2007 2011 2005
Argentina Argentine peso 1993 1993 B 1971–84 2005 6 A S C S Argentina 2010 MICS, 2011 IHS, 2011 Yes 2013 2002 2011 2000
Armenia Armenian dram a 1996 1993 B 1990–95 2005 6 A S C S Armenia 2011 DHS, 2010 IHS, 2010 Yes 2013/14 2011 2007
Aruba Aruban florin 1995 1993 6 S Aruba 2010 Yes 2011
Australia Australian dollar a 2009 2008 B 2008 6 G C S Australia 2011 ES/BS, 1994 Yes 2011 2006 2011 2000
Austria Euro 2005 1993 B Rolling 6 S C S Austria 2011b IS, 2000 Yes 2010 2009 2011 2002
Azerbaijan New Azeri manat a 2003 1993 B 1992–95 2005 6 A G B G Azerbaijan 2009 DHS, 2006 ES/BS, 2008 Yes 2009 2011 2005
Bahamas, The Bahamian dollar 2006 1993 B 6 G B G Bahamas, The 2010 2011
Bahrain Bahraini dinar 1985 1968 P 2005 6 G B G Bahrain 2010 Yes 2011 2003
Bangladesh Bangladeshi taka 1995/96 1993 B 2005 6 A G C G Bangladesh 2011 DHS, 2011 IHS, 2010 2008 2007 2008
Barbados Barbados dollar 1974 1968 B 6 G B G Barbados 2010 MICS, 2012 Yes 2011 2005
Belarus Belarusian rubel a 2000 1993 B 1990–95 2005 6 A G C S Belarus 2009 MICS, 2012 ES/BS, 2009 Yes 2009 2011 2000
Belgium Euro 2005 1993 B Rolling 6 S C S Belgium 2011 IHS, 2000 Yes 2010 2009 2011 2007
Belize Belize dollar 2000 1993 B 6 A G B G Belize 2010 MICS, 2011 ES/BS, 2011 2011 2000
Benin CFA franc 1985 1968 P 1992 2005 6 A S B G Benin 2002 DHS, 2011/12 CWIQ, 2007 2011/12 2010 2001
Bermuda Bermuda dollar 2006 1993 B 6 G Bermuda 2010 Yes 2011
Bhutan Bhutanese ngultrum 2000 1993 B 2005 6 A G C G Bhutan 2005 MICS, 2010 IHS, 2010 2009 2011 2008
Bolivia Bolivian Boliviano 1990 1968 B 1960–85 2005 6 A G C G Bolivia 2012 DHS, 2008 IHS, 2009 2013 2001 2011 2000
Bosnia and Herzegovina Bosnia and Herzegovina


convertible mark


a 1996 1993 B Rolling 6 A S C Bosnia and Herzegovina 1991 MICS, 2011/12 LSMS, 2007 Yes 2011 2009


Botswana Botswana pula 1993/94 1993 B 2005 6 A G B G Botswana 2011 DHS, 1988 ES/BS, 2008 2009 2011 2000
Brazil Brazilian real 2000 1993 B 2005 6 A G C S Brazil 2010 LSMS, 1996/97 LFS, 2009 2006 2007 2011 2006
Brunei Darussalam Brunei dollar 2000 1993 P 2005 S G Brunei Darussalam 2011 Yes 2006 1994
Bulgaria Bulgarian lev a 2002 1993 B 1978–89,


1991–92
Rolling 6 A S C S Bulgaria 2011 LSMS, 2007 ES/BS, 2007 Yes 2010 2009 2011 2009


Burkina Faso CFA franc 1999 1993 B 1992–93 2005 6 A G B G Burkina Faso 2006 DHS, 2011 CWIQ, 2009 2010 2011 2001
Burundi Burundi franc 2008 1993 B 2005 6 A S C G Burundi 2008 DHS, 2010 CWIQ, 2006 2010 2000
Cambodia Cambodian riel 2000 1993 B 2005 6 A S C G Cambodia 2008 DHS, 2010 IHS, 2008 2013 2000 2011 2006
Cameroon CFA franc 2000 1993 B 2005 6 A S B G Cameroon 2005 DHS, 2011 PS, 2007 1984 2002 2011 2000
Canada Canadian dollar 2005 1993 B 2008 6 G C S Canada 2011 LFS, 2000 Yes 2011 2008 2011 1986
Cape Verde Cape Verde escudo 1980 1968 P 2005 6 A G C G Cape Verde 2010 DHS, 2005 ES/BS, 2007 Yes 2011 2001
Cayman Islands Cayman Islands dollar 1993 G Cayman Islands 2010 Yes
Central African Republic CFA franc 2000 1968 B 2005 6 P S B G Central African Republic 2003 MICS, 2010 PS, 2008 2011 2005
Chad CFA franc 1995 1993 B 2005 6 E S G Chad 2009 MICS, 2010 PS, 2002/03 2011 1995 2005
Channel Islands Pound sterling 2003 2007 1968 B Channel Islands 2009,


2011c


Chile Chilean peso 2003 1993 B 2008 6 A S C S Chile 2012 IHS, 2009 Yes 2007 2008 2011 2007
China Chinese yuan 2000 1993 P 1978–93 2005 6 P S B G China 2010 NSS, 2007 IHS, 2008 2007 2007 2011 2005
Hong Kong SAR, China Hong Kong dollar 2009 1993 B 2005 6 G C S Hong Kong SAR, China 2011d Yes 2009 2011
Macao SAR, China Macao pataca 2009 1993 B 2005 6 G C G Macao SAR, China 2011 Yes 2009 2007


Colombia Colombian peso 2005 1993 B 1992–94 2005 6 A G B S Colombia 2006 DHS, 2010 IHS, 2011 2013 2005 2011 2000
Comoros Comorian franc 1990 1968 P 2005 A S Comoros 2003 DHS, 2012 IHS, 2004 2007 1999
Congo, Dem. Rep. Congolese franc 2000 1968 B 1999–2001 2005 6 A S C G Congo, Dem. Rep. 1984 MICS, 2010 1-2-3, 2004/05 1990 1987 2005
Congo, Rep. CFA franc 1990 1968 P 1993 2005 6 P S C G Congo, Rep. 2007 DHS, 2011/12 CWIQ/PS, 2011 1985/86 2009 2010 2002
Costa Rica Costa Rican colon 1991 1993 B 6 A S C S Costa Rica 2011 MICS, 2011 LFS, 2011 Yes 1973 2009 2011 1997
Côte d’Ivoire CFA franc 1996 1968 P 2005 6 A S C G Côte d’Ivoire 1998 DHS, 2012 IHS, 2008 2001 2011 2005
Croatia Croatian kuna a 2005 1993 B Rolling 6 G C S Croatia 2011 ES/BS, 2008 Yes 2010 2011 2010
Cuba Cuban peso 2005 1993 B S Cuba 2012 MICS, 2010/11 Yes 2006 2007
Curaçao Netherlands Antilles


guilder
1993 Curaçao


Cyprus Euro a 2000 1993 B Rolling 6 G C S Cyprus 2011 Yes 2010 2009 2011 2009
Czech Republic Czech koruna 2005 1993 B Rolling 6 S C S Czech Republic 2011 RHS, 1993 IS, 1996 Yes 2010 2007 2011 2007




110 World Development Indicators 2013


Currency National
accounts


Balance of payments
and trade


Government
finance


IMF data
dissem­
ination


standard


Latest
population


census


Latest demographic,
education, or health


household survey


Source of most
recent income


and expenditure data


Vital
registration
complete


Latest
agricultural


census


Latest
industrial


data


Latest
trade
data


Latest
water


withdrawal
data


Base
year


Reference
year


System of
National
Accounts


SNA
price


valuation


Alternative
conversion


factor


PPP
survey
year


Balance of
Payments
Manual
in use


External
debt


System
of trade


Accounting
concept


Primary data documentation


Front User guide World view People Environment?


Denmark Danish krone 2005 1993 B Rolling 6 S C S Denmark 2011 ITR, 1997 Yes 2010 2008 2011 2009
Djibouti Djibouti franc 1990 1968 B 2005 6 A G G Djibouti 2009 MICS, 2006 PS, 2002 2009 2000
Dominica East Caribbean dollar 2006 1993 B 6 A S G Dominica 2011 Yes 2010 2004
Dominican Republic Dominican peso 1991 1993 B 6 A G C G Dominican Republic 2010 DHS, 2007 IHS, 2011 2012/13 2011 2005
Ecuador U.S. dollar 2000 1993 B 2005 6 A G B S Ecuador 2010 RHS, 2004 LFS, 2011 2013/15 2008 2011 2005
Egypt, Arab Rep. Egyptian pound 1991/92 1993 B 2005 6 A G C S Egypt, Arab Rep. 2006 DHS, 2008 ES/BS, 2008 Yes 2010 2006 2011 2000
El Salvador U.S. dollar 1990 1968 B 6 A S C S El Salvador 2007 RHS, 2008 IHS, 2010 Yes 2008 2011 2007
Equatorial Guinea CFA franc 2000 1968 B 1965–84 2005 G Equatorial Guinea 2002 2000
Eritrea Eritrean nakfa 2000 1968 B 6 E Eritrea 1984 DHS, 2002 2009 2003 2004
Estonia Euro 2005 1993 B 1987–95 Rolling 6 S C S Estonia 2012 ES/BS, 2004 Yes 2010 2009 2011 2007
Ethiopia Ethiopian birr 1999/2000 1993 B 2005 6 A G B G Ethiopia 2007 DHS, 2011 ES/BS, 2010/11 2009 2011 2002
Faeroe Islands Danish krone 1993 B 6 G Faeroe Islands 2011 Yes 2009
Fiji Fijian dollar 2005 1993 B 2005 6 A G B G Fiji 2007 ES/BS, 2009 Yes 2009 2008 2010 2000
Finland Euro 2005 1993 B Rolling 6 G C S Finland 2010 IS, 2000 Yes 2010 2009 2011 2005
France Euro a 2005 1993 B Rolling 6 S C S France 2006e ES/BS, 1994/95 Yes 2010 2009 2011 2007
French Polynesia CFP franc 1990/91 1993 S French Polynesia 2007 Yes 2011
Gabon CFA franc 1991 1993 P 1993 2005 6 A S G Gabon 2003 DHS, 2012 CWIQ/IHS, 2005 2009 2005
Gambia, The Gambian dalasi 2004 1993 P 2005 6 A G C G Gambia, The 2003 MICS, 2010 IHS, 2010 2001/02 2004 2011 2000
Georgia Georgian lari a 1996 1993 B 1990–95 2005 6 A G C S Georgia 2002 MICS, 2005 IHS, 2009 Yes 2009 2010 2005
Germany Euro 2005 1993 B Rolling 6 S C S Germany 2011e IHS, 2000 Yes 2010 2009 2011 2007
Ghana New Ghanaian cedi 2006 1993 B 1973–87 2005 6 A G B G Ghana 2010 MICS, 2011 LSMS, 2005/06 2013/14 2003 2011 2000
Greece Euro a 2005 1993 B Rolling 6 S C S Greece 2011 IHS, 2000 Yes 2009 2007 2011 2007
Greenland Danish krone 1990 1993 G Greenland 2010 Yes 2011
Grenada East Caribbean dollar 2006 1968 B 6 A S B G Grenada 2011 RHS, 1985 Yes 2012 2009 2005
Guam U.S. dollar 1993 G Guam 2010 Yes
Guatemala Guatemalan quetzal 2001 1993 B 6 A S B G Guatemala 2012 RHS, 2008/09 LSMS, 2011 Yes 2013 2011 2006
Guinea Guinean franc 2003 1993 B 2005 6 P S B G Guinea 1996 DHS, 2005 CWIQ, 2012 2008 2001
Guinea-Bissau CFA franc 2005 1993 B 2005 6 E G G Guinea-Bissau 2009 MICS, 2010 CWIQ, 2010 2005 2000
Guyana Guyana dollar 2006 1993 B 6 A S G Guyana 2012 DHS, 2009 IHS, 1998 2011 2000
Haiti Haitian gourde 1986/87 1968 B 1991 6 A G G Haiti 2003 DHS, 2012 IHS, 2001 2008 1997 2000
Honduras Honduran lempira 2000 1993 B 1988–89 6 A S C G Honduras 2001 DHS, 2011/12 IHS, 2010 2013 2009 2006
Hungary Hungarian forint a 2005 1993 B Rolling 6 S C S Hungary 2011 ES/BS, 2007 Yes 2010 2009 2011 2007
Iceland Iceland krona 2005 1993 B Rolling 6 G C S Iceland 2011 Yes 2010 2005 2011 2005
India Indian rupee 2004/05 1993 B 2005 6 A G C S India 2011 DHS, 2006 IHS, 2010 2011 2008 2011 2010
Indonesia Indonesian rupiah 2000 1993 P 2005 6 A S B S Indonesia 2010 DHS, 2012 IHS, 2012 2013 2008 2011 2000
Iran, Islamic Rep. Iranian rial 1997/98 1993 B 1980–2002 2005 6 A S C G Iran, Islamic Rep. 2011 DHS, 2000 ES/BS, 2005 2003 2008 2011 2004
Iraq Iraqi dinar 1997 1968 B 1997, 2004 2005 6 G Iraq 1997 MICS, 2011 IHS, 2007 2012 2009 2000
Ireland Euro 2005 1993 B Rolling 6 G C S Ireland 2011 IHS, 2000 Yes 2010 2009 2011 1979
Isle of Man Pound sterling 2003 1968 Isle of Man 2011 Yes
Israel Israeli new shekel 2005 1993 P 2008 6 S C S Israel 2009 ES/BS, 2001 Yes 2008 2011 2004
Italy Euro 2005 1993 B Rolling 6 S C S Italy 2012 ES/BS, 2000 Yes 2010 2009 2011 2000
Jamaica Jamaican dollar 2007 1993 B 6 A G C G Jamaica 2011 MICS, 2011 LSMS, 2010 2007 2010 1993
Japan Japanese yen 2005 1993 B 2008 6 G C S Japan 2010 IS, 1993 Yes 2010 2007 2011 2001
Jordan Jordanian dinar 1994 1968 B 2005 6 A G B S Jordan 2004 DHS, 2012 ES/BS, 2010 2007 2009 2011 2005
Kazakhstan Kazakh tenge a 2000 1993 B 1987–95 2005 6 A G C S Kazakhstan 2009 MICS, 2010/11 ES/BS, 2011 Yes 2011 2010
Kenya Kenyan shilling 2001 1993 B 2005 6 A G B G Kenya 2009 MIS, 2010;


HIV/MCH SPA, 2010
IHS, 2005/06 2007 2010 2003


Kiribati Australian dollar 2006 1993 B 6 G G Kiribati 2010 2011
Korea, Dem. Rep. Democratic People’s


Republic of Korean won
1968 6 Korea, Dem. Rep. 2008 MICS, 2009 2005


Korea, Rep. Korean won 2005 1993 B 2008 6 G C S Korea, Rep. 2010 ES/BS, 1998 Yes 2000 2008 2011 2002
Kosovo Euro 2008 1993 A G Kosovo 2011 IHS, 2009
Kuwait Kuwaiti dinar 1995 1968 P 2005 6 S B G Kuwait 2010 FHS, 1996 Yes 2009 2009 2002
Kyrgyz Republic Kyrgyz som a 1995 1993 B 1990–95 2005 6 A S B S Kyrgyz Republic 2009 DHS, 2012 ES/BS, 2010 Yes 2002 2009 2011 2006
Lao PDR Lao kip 2002 1993 B 2005 6 P S B Lao PDR 2005 MICS, 2011/12 ES/BS, 2008 2010/11 1974 2005
Latvia Latvian lats 2000 1993 B 1987–95 Rolling 6 A S C S Latvia 2011 IHS, 2008 Yes 2010 2009 2011 2002
Lebanon Lebanese pound 1997 1993 B 2005 6 A G B G Lebanon 1970 MICS, 2000 Yes 2011 2007 2011 2005
Lesotho Lesotho loti 1995 1993 B 2005 6 A G C G Lesotho 2006 DHS, 2009 ES/BS, 2002/03 2010 2009 2000




World Development Indicators 2013 111Economy States and markets Global links Back


Currency National
accounts


Balance of payments
and trade


Government
finance


IMF data
dissem­
ination


standard


Latest
population


census


Latest demographic,
education, or health


household survey


Source of most
recent income


and expenditure data


Vital
registration
complete


Latest
agricultural


census


Latest
industrial


data


Latest
trade
data


Latest
water


withdrawal
data


Base
year


Reference
year


System of
National
Accounts


SNA
price


valuation


Alternative
conversion


factor


PPP
survey
year


Balance of
Payments
Manual
in use


External
debt


System
of trade


Accounting
concept


Denmark Danish krone 2005 1993 B Rolling 6 S C S Denmark 2011 ITR, 1997 Yes 2010 2008 2011 2009
Djibouti Djibouti franc 1990 1968 B 2005 6 A G G Djibouti 2009 MICS, 2006 PS, 2002 2009 2000
Dominica East Caribbean dollar 2006 1993 B 6 A S G Dominica 2011 Yes 2010 2004
Dominican Republic Dominican peso 1991 1993 B 6 A G C G Dominican Republic 2010 DHS, 2007 IHS, 2011 2012/13 2011 2005
Ecuador U.S. dollar 2000 1993 B 2005 6 A G B S Ecuador 2010 RHS, 2004 LFS, 2011 2013/15 2008 2011 2005
Egypt, Arab Rep. Egyptian pound 1991/92 1993 B 2005 6 A G C S Egypt, Arab Rep. 2006 DHS, 2008 ES/BS, 2008 Yes 2010 2006 2011 2000
El Salvador U.S. dollar 1990 1968 B 6 A S C S El Salvador 2007 RHS, 2008 IHS, 2010 Yes 2008 2011 2007
Equatorial Guinea CFA franc 2000 1968 B 1965–84 2005 G Equatorial Guinea 2002 2000
Eritrea Eritrean nakfa 2000 1968 B 6 E Eritrea 1984 DHS, 2002 2009 2003 2004
Estonia Euro 2005 1993 B 1987–95 Rolling 6 S C S Estonia 2012 ES/BS, 2004 Yes 2010 2009 2011 2007
Ethiopia Ethiopian birr 1999/2000 1993 B 2005 6 A G B G Ethiopia 2007 DHS, 2011 ES/BS, 2010/11 2009 2011 2002
Faeroe Islands Danish krone 1993 B 6 G Faeroe Islands 2011 Yes 2009
Fiji Fijian dollar 2005 1993 B 2005 6 A G B G Fiji 2007 ES/BS, 2009 Yes 2009 2008 2010 2000
Finland Euro 2005 1993 B Rolling 6 G C S Finland 2010 IS, 2000 Yes 2010 2009 2011 2005
France Euro a 2005 1993 B Rolling 6 S C S France 2006e ES/BS, 1994/95 Yes 2010 2009 2011 2007
French Polynesia CFP franc 1990/91 1993 S French Polynesia 2007 Yes 2011
Gabon CFA franc 1991 1993 P 1993 2005 6 A S G Gabon 2003 DHS, 2012 CWIQ/IHS, 2005 2009 2005
Gambia, The Gambian dalasi 2004 1993 P 2005 6 A G C G Gambia, The 2003 MICS, 2010 IHS, 2010 2001/02 2004 2011 2000
Georgia Georgian lari a 1996 1993 B 1990–95 2005 6 A G C S Georgia 2002 MICS, 2005 IHS, 2009 Yes 2009 2010 2005
Germany Euro 2005 1993 B Rolling 6 S C S Germany 2011e IHS, 2000 Yes 2010 2009 2011 2007
Ghana New Ghanaian cedi 2006 1993 B 1973–87 2005 6 A G B G Ghana 2010 MICS, 2011 LSMS, 2005/06 2013/14 2003 2011 2000
Greece Euro a 2005 1993 B Rolling 6 S C S Greece 2011 IHS, 2000 Yes 2009 2007 2011 2007
Greenland Danish krone 1990 1993 G Greenland 2010 Yes 2011
Grenada East Caribbean dollar 2006 1968 B 6 A S B G Grenada 2011 RHS, 1985 Yes 2012 2009 2005
Guam U.S. dollar 1993 G Guam 2010 Yes
Guatemala Guatemalan quetzal 2001 1993 B 6 A S B G Guatemala 2012 RHS, 2008/09 LSMS, 2011 Yes 2013 2011 2006
Guinea Guinean franc 2003 1993 B 2005 6 P S B G Guinea 1996 DHS, 2005 CWIQ, 2012 2008 2001
Guinea-Bissau CFA franc 2005 1993 B 2005 6 E G G Guinea-Bissau 2009 MICS, 2010 CWIQ, 2010 2005 2000
Guyana Guyana dollar 2006 1993 B 6 A S G Guyana 2012 DHS, 2009 IHS, 1998 2011 2000
Haiti Haitian gourde 1986/87 1968 B 1991 6 A G G Haiti 2003 DHS, 2012 IHS, 2001 2008 1997 2000
Honduras Honduran lempira 2000 1993 B 1988–89 6 A S C G Honduras 2001 DHS, 2011/12 IHS, 2010 2013 2009 2006
Hungary Hungarian forint a 2005 1993 B Rolling 6 S C S Hungary 2011 ES/BS, 2007 Yes 2010 2009 2011 2007
Iceland Iceland krona 2005 1993 B Rolling 6 G C S Iceland 2011 Yes 2010 2005 2011 2005
India Indian rupee 2004/05 1993 B 2005 6 A G C S India 2011 DHS, 2006 IHS, 2010 2011 2008 2011 2010
Indonesia Indonesian rupiah 2000 1993 P 2005 6 A S B S Indonesia 2010 DHS, 2012 IHS, 2012 2013 2008 2011 2000
Iran, Islamic Rep. Iranian rial 1997/98 1993 B 1980–2002 2005 6 A S C G Iran, Islamic Rep. 2011 DHS, 2000 ES/BS, 2005 2003 2008 2011 2004
Iraq Iraqi dinar 1997 1968 B 1997, 2004 2005 6 G Iraq 1997 MICS, 2011 IHS, 2007 2012 2009 2000
Ireland Euro 2005 1993 B Rolling 6 G C S Ireland 2011 IHS, 2000 Yes 2010 2009 2011 1979
Isle of Man Pound sterling 2003 1968 Isle of Man 2011 Yes
Israel Israeli new shekel 2005 1993 P 2008 6 S C S Israel 2009 ES/BS, 2001 Yes 2008 2011 2004
Italy Euro 2005 1993 B Rolling 6 S C S Italy 2012 ES/BS, 2000 Yes 2010 2009 2011 2000
Jamaica Jamaican dollar 2007 1993 B 6 A G C G Jamaica 2011 MICS, 2011 LSMS, 2010 2007 2010 1993
Japan Japanese yen 2005 1993 B 2008 6 G C S Japan 2010 IS, 1993 Yes 2010 2007 2011 2001
Jordan Jordanian dinar 1994 1968 B 2005 6 A G B S Jordan 2004 DHS, 2012 ES/BS, 2010 2007 2009 2011 2005
Kazakhstan Kazakh tenge a 2000 1993 B 1987–95 2005 6 A G C S Kazakhstan 2009 MICS, 2010/11 ES/BS, 2011 Yes 2011 2010
Kenya Kenyan shilling 2001 1993 B 2005 6 A G B G Kenya 2009 MIS, 2010;


HIV/MCH SPA, 2010
IHS, 2005/06 2007 2010 2003


Kiribati Australian dollar 2006 1993 B 6 G G Kiribati 2010 2011
Korea, Dem. Rep. Democratic People’s


Republic of Korean won
1968 6 Korea, Dem. Rep. 2008 MICS, 2009 2005


Korea, Rep. Korean won 2005 1993 B 2008 6 G C S Korea, Rep. 2010 ES/BS, 1998 Yes 2000 2008 2011 2002
Kosovo Euro 2008 1993 A G Kosovo 2011 IHS, 2009
Kuwait Kuwaiti dinar 1995 1968 P 2005 6 S B G Kuwait 2010 FHS, 1996 Yes 2009 2009 2002
Kyrgyz Republic Kyrgyz som a 1995 1993 B 1990–95 2005 6 A S B S Kyrgyz Republic 2009 DHS, 2012 ES/BS, 2010 Yes 2002 2009 2011 2006
Lao PDR Lao kip 2002 1993 B 2005 6 P S B Lao PDR 2005 MICS, 2011/12 ES/BS, 2008 2010/11 1974 2005
Latvia Latvian lats 2000 1993 B 1987–95 Rolling 6 A S C S Latvia 2011 IHS, 2008 Yes 2010 2009 2011 2002
Lebanon Lebanese pound 1997 1993 B 2005 6 A G B G Lebanon 1970 MICS, 2000 Yes 2011 2007 2011 2005
Lesotho Lesotho loti 1995 1993 B 2005 6 A G C G Lesotho 2006 DHS, 2009 ES/BS, 2002/03 2010 2009 2000




112 World Development Indicators 2013


Currency National
accounts


Balance of payments
and trade


Government
finance


IMF data
dissem­
ination


standard


Latest
population


census


Latest demographic,
education, or health


household survey


Source of most
recent income


and expenditure data


Vital
registration
complete


Latest
agricultural


census


Latest
industrial


data


Latest
trade
data


Latest
water


withdrawal
data


Base
year


Reference
year


System of
National
Accounts


SNA
price


valuation


Alternative
conversion


factor


PPP
survey
year


Balance of
Payments
Manual
in use


External
debt


System
of trade


Accounting
concept


Primary data documentation


Front User guide World view People Environment?


Liberia Liberian dollar 2000 1968 P 2005 6 A S B G Liberia 2008 MIS, 2011 CWIQ, 2007 1984 2000
Libya Libyan dinar 1999 1993 B 1986 6 G G Libya 2006 2010 2000
Liechtenstein Swiss franc 1990 1993 B S Liechtenstein 2010 Yes
Lithuania Lithuanian litas 2000 1993 B 1990–95 Rolling 6 A G C S Lithuania 2011 ES/BS, 2008 Yes 2010 2009 2011 2007
Luxembourg Euro a 2005 1993 B Rolling 6 S C S Luxembourg 2011 Yes 2010 2009 2011 1999
Macedonia, FYR Macedonian denar 1995 1993 B Rolling 6 A S S Macedonia, FYR 2011 MICS, 2011 ES/BS, 2009 Yes 2007 2009 2011 2007
Madagascar Malagasy ariary 1984 1968 B 2005 6 A S C G Madagascar 1993 MIS, 2011 PS, 2010 2006 2011 2000
Malawi Malawi kwacha 2007 1993 B 2005 6 A G G Malawi 2008 MIS, 2012; LSMS, 2010/11 IHS, 2010/11 2007 2009 2011 2005
Malaysia Malaysian ringgit 2000 1993 P 2005 6 E G B S Malaysia 2010 ES/BS, 2009 Yes 2012 2008 2011 2005
Maldives Maldivian rufiyaa 2003 1993 B 2005 6 A G C G Maldives 2006 DHS, 2009 IHS, 2010 Yes 2011 2008
Mali CFA franc 1987 1968 B 2005 6 A S B G Mali 2009 DHS, 2012 IHS, 2009/10 2010 2000
Malta Euro 2005 1993 B Rolling 6 G C S Malta 2011 Yes 2010 2008 2011 2002
Marshall Islands U.S. dollar 2004 1968 B G Marshall Islands 2011
Mauritania Mauritanian ouguiya 2004 1993 B 2005 6 A S G Mauritania 2000 MICS, 2011 IHS, 2008 2011 2005
Mauritius Mauritian rupee 2006 1993 B 2005 6 A G C S Mauritius 2011 RHS, 1991 Yes 2007 2011 2003
Mexico Mexican peso 2003 1993 B 2008 6 A G C S Mexico 2010 ENPF, 1995 LFS, 2010 2007 2007 2011 2009
Micronesia, Fed. Sts. U.S. dollar 2004 1993 B Micronesia, Fed. Sts. 2010 IHS, 2000
Moldova Moldovan leu a 1996 1993 B 1990–95 2005 6 A G C S Moldova 2004 MICS, 2012 ES/BS, 2010 Yes 2011 2009 2011 2007
Monaco Euro 1990 1993 S Monaco 2008 Yes 2009
Mongolia Mongolian tugrik 2005 1993 B 2005 6 A G C G Mongolia 2010 MICS, 2010 LSMS, 2011 Yes 2012 2008 2007 2009
Montenegro Euro 2000 1993 B Rolling 6 A S G Montenegro 2011 MICS, 2005/06 ES/BS, 2011 Yes 2010 2011 2010
Morocco Moroccan dirham 1998 1993 B 2005 6 A S C S Morocco 2004 MICS/PAPFAM, 2006 ES/BS, 2007 2012 2009 2010 2000
Mozambique New Mozambican metical 2003 1993 B 1992–95 2005 6 A S G Mozambique 2007 DHS, 2011 ES/BS, 2008/09 2010 2011 2001
Myanmar Myanmar kyat 2005/06 1968 P 6 E G C Myanmar 1983 MICS, 2009/10 2011/12 2003 2010 2000
Namibia Namibian dollar 2004/05 1993 B 2005 6 G B G Namibia 2011 HIV/MCH, 2009 ES/BS, 2009 2011 2002
Nepal Nepalese rupee 2000/01 1993 B 2005 6 A G C G Nepal 2011 DHS, 2011 LSMS, 2011 2012 2008 2011 2006
Netherlands Euro a 2005 1993 B Rolling 6 S C S Netherlands 2011 IHS, 1999 Yes 2010 2008 2011 2008
New Caledonia CFP franc 1990 1993 S New Caledonia 2009 Yes 2011
New Zealand New Zealand dollar 2005/06 1993 B 2008 6 G C New Zealand 2006 IS, 1997 Yes 2012 2008 2011 2002
Nicaragua Nicaraguan gold cordoba 2006 1993 B 1965–95 6 A G B G Nicaragua 2005 RHS, 2006/07 LSMS, 2009 2011 2011 2001
Niger CFA franc 1987 1993 P 1993 2005 6 A S B G Niger 2012 DHS, 2012 CWIQ/PS, 2008 2008 2002 2011 2005
Nigeria Nigerian naira 2002 1993 B 1971–98 2005 6 A G B G Nigeria 2006 MICS, 2011 IHS, 2010 2007 2011 2005
Northern Mariana Islands U.S. dollar 1968 Northern Mariana Islands 2010
Norway Norwegian krone a 2005 1993 B Rolling 6 G C S Norway 2011 IS, 2000 Yes 2010 2008 2011 2006
Oman Rial Omani 1988 1993 P 2005 6 G B G Oman 2010 MICS, 2012 2007 2011 2003
Pakistan Pakistani rupee 1999/


2000
1993 B 2005 6 A G B G Pakistan 1998 DHS, 2012 IHS, 2008 2010 2006 2011 2008


Palau U.S. dollar 1995 1993 B S Palau 2010 Yes
Panama Panamanian balboa 1996 1993 B 6 A S C G Panama 2010 LSMS, 2008 LFS, 2010 2011 2001 2011 2000
Papua New Guinea Papua New Guinea kina 1998 1993 B 1989 6 A G B G Papua New Guinea 2011 LSMS, 1996 IHS, 1996 2004 2005
Paraguay Paraguayan guarani 1994 1993 P 2005 6 A S B G Paraguay 2012 RHS, 2008 IHS, 2011 2008 2002 2011 2000
Peru Peruvian new sol 1994 1993 B 1985–90 2005 6 A S C S Peru 2007 Continuous DHS, 2012 IHS, 2011 2012 2007 2011 2000
Philippines Philippine peso 2000 1993 P 2005 6 A G B S Philippines 2010 DHS, 2008 ES/BS, 2009 Yes 2012 2006 2011 2009
Poland Polish zloty a 2005 1993 B Rolling 6 S C S Poland 2011 ES/BS, 2010 Yes 2010 2009 2011 2009
Portugal Euro 2005 1993 B Rolling 6 S C S Portugal 2011 IS, 1997 Yes 2009 2009 2011 2002
Puerto Rico U.S. dollar 1954 1968 P G Puerto Rico 2010 RHS, 1995/96 Yes 2007 2006 2005
Qatar Qatari riyal 2001 1993 P 2005 S B G Qatar 2010 MICS, 2012 Yes 2006 2010 2005
Romania New Romanian leu a 2005 1993 B 1987–89,


1992
Rolling 6 A S C S Romania 2011 RHS, 1999 LFS, 2010 Yes 2010 2009 2011 2009


Russian Federation Russian ruble 2000 1993 B 1987–95 2008 6 P G C S Russian Federation 2010 LSMS, 1992 IHS, 2010 Yes 2006 2009 2011 2001
Rwanda Rwandan franc 2006 1993 P 1994 2005 6 A G C G Rwanda 2012 Special DHS, 2011 IHS, 2011 2008 2011 2000
Samoa Samoan tala 2002 1993 B 6 A S G Samoa 2011 DHS, 2009 2009 2011
San Marino Euro 1995 2000 1993 B C G San Marino 2010 Yes
São Tomé and Príncipe São Tomé and Príncipe


dobra
2001 1993 P 2005 6 A S G São Tomé and Príncipe 2012 DHS, 2008/09 PS, 2009/10 2011/12 2010 1993


Saudi Arabia Saudi Arabian riyal 1999 1993 P 2005 6 S G Saudi Arabia 2010 Demographic survey, 2007 2010 2006 2011 2006
Senegal CFA franc 1999 1987 1993 B 2005 6 A G B G Senegal 2002 Continuous DHS, 2012 PS, 2010/11 2011/12 2009 2011 2002
Serbia New Serbian dinar a 2002 1993 B Rolling 6 A S C G Serbia 2011 MICS, 2010 IHS, 2010 Yes 2012 2009 2011 2009




World Development Indicators 2013 113Economy States and markets Global links Back


Currency National
accounts


Balance of payments
and trade


Government
finance


IMF data
dissem­
ination


standard


Latest
population


census


Latest demographic,
education, or health


household survey


Source of most
recent income


and expenditure data


Vital
registration
complete


Latest
agricultural


census


Latest
industrial


data


Latest
trade
data


Latest
water


withdrawal
data


Base
year


Reference
year


System of
National
Accounts


SNA
price


valuation


Alternative
conversion


factor


PPP
survey
year


Balance of
Payments
Manual
in use


External
debt


System
of trade


Accounting
concept


Liberia Liberian dollar 2000 1968 P 2005 6 A S B G Liberia 2008 MIS, 2011 CWIQ, 2007 1984 2000
Libya Libyan dinar 1999 1993 B 1986 6 G G Libya 2006 2010 2000
Liechtenstein Swiss franc 1990 1993 B S Liechtenstein 2010 Yes
Lithuania Lithuanian litas 2000 1993 B 1990–95 Rolling 6 A G C S Lithuania 2011 ES/BS, 2008 Yes 2010 2009 2011 2007
Luxembourg Euro a 2005 1993 B Rolling 6 S C S Luxembourg 2011 Yes 2010 2009 2011 1999
Macedonia, FYR Macedonian denar 1995 1993 B Rolling 6 A S S Macedonia, FYR 2011 MICS, 2011 ES/BS, 2009 Yes 2007 2009 2011 2007
Madagascar Malagasy ariary 1984 1968 B 2005 6 A S C G Madagascar 1993 MIS, 2011 PS, 2010 2006 2011 2000
Malawi Malawi kwacha 2007 1993 B 2005 6 A G G Malawi 2008 MIS, 2012; LSMS, 2010/11 IHS, 2010/11 2007 2009 2011 2005
Malaysia Malaysian ringgit 2000 1993 P 2005 6 E G B S Malaysia 2010 ES/BS, 2009 Yes 2012 2008 2011 2005
Maldives Maldivian rufiyaa 2003 1993 B 2005 6 A G C G Maldives 2006 DHS, 2009 IHS, 2010 Yes 2011 2008
Mali CFA franc 1987 1968 B 2005 6 A S B G Mali 2009 DHS, 2012 IHS, 2009/10 2010 2000
Malta Euro 2005 1993 B Rolling 6 G C S Malta 2011 Yes 2010 2008 2011 2002
Marshall Islands U.S. dollar 2004 1968 B G Marshall Islands 2011
Mauritania Mauritanian ouguiya 2004 1993 B 2005 6 A S G Mauritania 2000 MICS, 2011 IHS, 2008 2011 2005
Mauritius Mauritian rupee 2006 1993 B 2005 6 A G C S Mauritius 2011 RHS, 1991 Yes 2007 2011 2003
Mexico Mexican peso 2003 1993 B 2008 6 A G C S Mexico 2010 ENPF, 1995 LFS, 2010 2007 2007 2011 2009
Micronesia, Fed. Sts. U.S. dollar 2004 1993 B Micronesia, Fed. Sts. 2010 IHS, 2000
Moldova Moldovan leu a 1996 1993 B 1990–95 2005 6 A G C S Moldova 2004 MICS, 2012 ES/BS, 2010 Yes 2011 2009 2011 2007
Monaco Euro 1990 1993 S Monaco 2008 Yes 2009
Mongolia Mongolian tugrik 2005 1993 B 2005 6 A G C G Mongolia 2010 MICS, 2010 LSMS, 2011 Yes 2012 2008 2007 2009
Montenegro Euro 2000 1993 B Rolling 6 A S G Montenegro 2011 MICS, 2005/06 ES/BS, 2011 Yes 2010 2011 2010
Morocco Moroccan dirham 1998 1993 B 2005 6 A S C S Morocco 2004 MICS/PAPFAM, 2006 ES/BS, 2007 2012 2009 2010 2000
Mozambique New Mozambican metical 2003 1993 B 1992–95 2005 6 A S G Mozambique 2007 DHS, 2011 ES/BS, 2008/09 2010 2011 2001
Myanmar Myanmar kyat 2005/06 1968 P 6 E G C Myanmar 1983 MICS, 2009/10 2011/12 2003 2010 2000
Namibia Namibian dollar 2004/05 1993 B 2005 6 G B G Namibia 2011 HIV/MCH, 2009 ES/BS, 2009 2011 2002
Nepal Nepalese rupee 2000/01 1993 B 2005 6 A G C G Nepal 2011 DHS, 2011 LSMS, 2011 2012 2008 2011 2006
Netherlands Euro a 2005 1993 B Rolling 6 S C S Netherlands 2011 IHS, 1999 Yes 2010 2008 2011 2008
New Caledonia CFP franc 1990 1993 S New Caledonia 2009 Yes 2011
New Zealand New Zealand dollar 2005/06 1993 B 2008 6 G C New Zealand 2006 IS, 1997 Yes 2012 2008 2011 2002
Nicaragua Nicaraguan gold cordoba 2006 1993 B 1965–95 6 A G B G Nicaragua 2005 RHS, 2006/07 LSMS, 2009 2011 2011 2001
Niger CFA franc 1987 1993 P 1993 2005 6 A S B G Niger 2012 DHS, 2012 CWIQ/PS, 2008 2008 2002 2011 2005
Nigeria Nigerian naira 2002 1993 B 1971–98 2005 6 A G B G Nigeria 2006 MICS, 2011 IHS, 2010 2007 2011 2005
Northern Mariana Islands U.S. dollar 1968 Northern Mariana Islands 2010
Norway Norwegian krone a 2005 1993 B Rolling 6 G C S Norway 2011 IS, 2000 Yes 2010 2008 2011 2006
Oman Rial Omani 1988 1993 P 2005 6 G B G Oman 2010 MICS, 2012 2007 2011 2003
Pakistan Pakistani rupee 1999/


2000
1993 B 2005 6 A G B G Pakistan 1998 DHS, 2012 IHS, 2008 2010 2006 2011 2008


Palau U.S. dollar 1995 1993 B S Palau 2010 Yes
Panama Panamanian balboa 1996 1993 B 6 A S C G Panama 2010 LSMS, 2008 LFS, 2010 2011 2001 2011 2000
Papua New Guinea Papua New Guinea kina 1998 1993 B 1989 6 A G B G Papua New Guinea 2011 LSMS, 1996 IHS, 1996 2004 2005
Paraguay Paraguayan guarani 1994 1993 P 2005 6 A S B G Paraguay 2012 RHS, 2008 IHS, 2011 2008 2002 2011 2000
Peru Peruvian new sol 1994 1993 B 1985–90 2005 6 A S C S Peru 2007 Continuous DHS, 2012 IHS, 2011 2012 2007 2011 2000
Philippines Philippine peso 2000 1993 P 2005 6 A G B S Philippines 2010 DHS, 2008 ES/BS, 2009 Yes 2012 2006 2011 2009
Poland Polish zloty a 2005 1993 B Rolling 6 S C S Poland 2011 ES/BS, 2010 Yes 2010 2009 2011 2009
Portugal Euro 2005 1993 B Rolling 6 S C S Portugal 2011 IS, 1997 Yes 2009 2009 2011 2002
Puerto Rico U.S. dollar 1954 1968 P G Puerto Rico 2010 RHS, 1995/96 Yes 2007 2006 2005
Qatar Qatari riyal 2001 1993 P 2005 S B G Qatar 2010 MICS, 2012 Yes 2006 2010 2005
Romania New Romanian leu a 2005 1993 B 1987–89,


1992
Rolling 6 A S C S Romania 2011 RHS, 1999 LFS, 2010 Yes 2010 2009 2011 2009


Russian Federation Russian ruble 2000 1993 B 1987–95 2008 6 P G C S Russian Federation 2010 LSMS, 1992 IHS, 2010 Yes 2006 2009 2011 2001
Rwanda Rwandan franc 2006 1993 P 1994 2005 6 A G C G Rwanda 2012 Special DHS, 2011 IHS, 2011 2008 2011 2000
Samoa Samoan tala 2002 1993 B 6 A S G Samoa 2011 DHS, 2009 2009 2011
San Marino Euro 1995 2000 1993 B C G San Marino 2010 Yes
São Tomé and Príncipe São Tomé and Príncipe


dobra
2001 1993 P 2005 6 A S G São Tomé and Príncipe 2012 DHS, 2008/09 PS, 2009/10 2011/12 2010 1993


Saudi Arabia Saudi Arabian riyal 1999 1993 P 2005 6 S G Saudi Arabia 2010 Demographic survey, 2007 2010 2006 2011 2006
Senegal CFA franc 1999 1987 1993 B 2005 6 A G B G Senegal 2002 Continuous DHS, 2012 PS, 2010/11 2011/12 2009 2011 2002
Serbia New Serbian dinar a 2002 1993 B Rolling 6 A S C G Serbia 2011 MICS, 2010 IHS, 2010 Yes 2012 2009 2011 2009




114 World Development Indicators 2013


Currency National
accounts


Balance of payments
and trade


Government
finance


IMF data
dissem­
ination


standard


Latest
population


census


Latest demographic,
education, or health


household survey


Source of most
recent income


and expenditure data


Vital
registration
complete


Latest
agricultural


census


Latest
industrial


data


Latest
trade
data


Latest
water


withdrawal
data


Base
year


Reference
year


System of
National
Accounts


SNA
price


valuation


Alternative
conversion


factor


PPP
survey
year


Balance of
Payments
Manual
in use


External
debt


System
of trade


Accounting
concept


Primary data documentation


Front User guide World view People Environment?


Seychelles Seychelles rupee 2006 1993 P 6 A G C G Seychelles 2010 IHS, 2007 Yes 2011 2008 2005
Sierra Leone Sierra Leonean leone 2006 1993 B 2005 6 A S B G Sierra Leone 2004 MICS, 2010 IHS, 2011 2002 2005
Singapore Singapore dollar 2005 1993 B 2005 6 G C S Singapore 2010 General household, 2005 Yes 2009 2011 1975
Sint Maarten Netherlands Antilles


guilder
1993 Sint Maarten 2001 Population Census, 2011


Slovak Republic Euro 2005 1993 B Rolling 6 S C S Slovak Republic 2011 IS, 2009 Yes 2001 2009 2011 2007
Slovenia Euro a 2005 1993 B Rolling 6 S C S Slovenia 2011b ES/BS, 2004 Yes 2010 2009 2011 2009
Solomon Islands Solomon Islands dollar 2004 1993 B 6 A S G Solomon Islands 2009 IHS, 2005/06 2012/13 2011
Somalia Somali shilling 1985 1968 B 1977–90 E Somalia 1987 MICS, 2006 1983 2003
South Africa South African rand 2005 1993 B 2005 6 P G C S South Africa 2011 DHS, 2003 ES/BS, 2010 2012 2009 2011 2000
South Sudan South Sudanese pound 1993 South Sudan 2008 ES/BS, 2009
Spain Euro 2005 1993 B Rolling 6 S C S Spain 2011 IHS, 2000 Yes 2010 2009 2011 2008
Sri Lanka Sri Lankan rupee 2002 1993 P 2005 6 A G B G Sri Lanka 2012 DHS, 2006/07 ES/BS, 2010 Yes 2013 2008 2011 2005
St. Kitts and Nevis East Caribbean dollar 2006 1993 B 6 S C G St. Kitts and Nevis 2011 Yes 2011
St. Lucia East Caribbean dollar 2006 1968 B 6 A S G St. Lucia 2010 MICS, 2012 IHS, 1995 Yes 2008 2005
St. Martin Euro 1993 St. Martin
St. Vincent and Grenadines East Caribbean dollar 2006 1993 B 6 A S B G St. Vincent and Grenadines 2011 Yes 2011 1995
Sudan Sudanese pound 1981/82f 1996 1968 B 2005 6 A G B G Sudan 2008 SHHS, 2010 ES/BS, 2009 2001 2009 2005g


Suriname Suriname dollar 1990 1993 B 6 G G Suriname 2012 MICS, 2010 ES/BS, 1999 Yes 2008 2004 2011 2000
Swaziland Swaziland lilangeni 2000 1993 B 2005 6 E G B G Swaziland 2007 MICS, 2010 ES/BS, 2009/10 2007 2000
Sweden Swedish krona a 2005 1993 B Rolling 6 G C S Sweden 2011 IS, 2000 Yes 2010 2009 2011 2007
Switzerland Swiss franc 2005 1993 B Rolling 6 S C S Switzerland 2010 ES/BS, 2000 Yes 2008 2007 2011 2000
Syrian Arab Republic Syrian pound 2000 1968 B 1970–2010 2005 6 E S C G Syrian Arab Republic 2004 MICS, 2006 ES/BS, 2004 1981 2009 2010 2005
Tajikistan Tajik somoni a 2000 1993 B 1990–95 2005 6 A G C G Tajikistan 2010 LSMS, 2009 LSMS, 2009 2013 2000 2006
Tanzania Tanzanian shilling a 2001 1993 B 2005 6 A G G Tanzania 2012 AIS, 2011/12;


LSMS, 2010/11
ES/BS, 2011 2007/08 2007 2011 2002


Thailand Thai baht 1988 1993 P 2005 6 A S C S Thailand 2010 MICS, 2012 IHS, 2010 2013 2006 2011 2007
Timor-Leste U.S. dollar 2000 2008 P G G Timor-Leste 2010 DHS, 2009/10 LSMS, 2007 2012 2005 2004
Togo CFA franc 2000 1968 P 2005 6 A S B G Togo 2010 MICS, 2010 CWIQ, 2011 2011/12 2011 2002
Tonga Tongan pa’anga 2010/11 1993 B 6 A G G Tonga 2006 2011
Trinidad and Tobago Trinidad and Tobago


dollar
2000 1993 B 6 S C G Trinidad and Tobago 2011 MICS, 2011 IHS, 1992 Yes 2006 2010 2000


Tunisia Tunisian dinar 1990 1993 B 2005 6 A G C S Tunisia 2004 MICS, 2011 IHS, 2010 2004 2006 2011 2001
Turkey New Turkish lira 1998 1993 B Rolling 6 A S B S Turkey 2011 DHS, 2003 LFS, 2009 2008 2011 2003
Turkmenistan New Turkmen manat a 2007 1993 B 1987–95,


1997–2007
6 E G Turkmenistan 2012 MICS, 2011 LSMS, 1998 Yes 2000 2004


Turks and Caicos Islands U.S. dollar 1993 G Turks and Caicos Islands 2012 Yes 2011
Tuvalu Australian dollar 2005 1968 P G Tuvalu 2012 2008
Uganda Ugandan shilling 2001/02 1968 B 2005 6 A G B G Uganda 2002 DHS, 2011 PS, 2009/10 2008 2000 2011 2002
Ukraine Ukrainian hryvnia a 2003 1993 B 1987–95 2005 6 A G C S Ukraine 2001 MICS, 2012 ES/BS, 2009 Yes 2012/13 2004 2011 2005
United Arab Emirates U.A.E. dirham 2007 1993 P 6 G B G United Arab Emirates 2010 2012 2008 2005
United Kingdom Pound sterling 2005 1993 B Rolling 6 G C S United Kingdom 2001 IS, 1999 Yes 2010 2009 2011 2007
United States U.S. dollar a 2005 1993 B 2008 6 G C S United States 2010 LFS, 2000 Yes 2007 2008 2011 2005
Uruguay Uruguayan peso 2005 1993 B 2005 6 A G C S Uruguay 2011 MICS, 2012 IHS, 2011 Yes 2011 2008 2009 2000
Uzbekistan Uzbek sum a 1997 1993 B 1990–95 6 A G Uzbekistan 1989 MICS, 2006 ES/BS, 2003 Yes 2005
Vanuatu Vanuatu vatu 2006 1993 P 6 E G C G Vanuatu 2009 MICS, 2007 2007 2011
Venezuela, R.B. Venezuelan bolivar fuerte 1997 1993 B 2005 6 A G C G Venezuela, R.B. 2011 MICS, 2000 IHS, 2011 Yes 2007 2011 2000
Vietnam Vietnamese dong 1994 1993 P 1991 2005 6 A G G Vietnam 2009 MICS, 2010/11 IHS, 2010 Yes 2011 2000 2010 2005
Virgin Islands (U.S.) U.S. dollar 1982 1968 G Virgin Islands (U.S.) 2010 Yes 2007
West Bank and Gaza Israeli new shekel 1997 1968 B 6 S B S West Bank and Gaza 2007 MICS, 2010 IHS, 2009 1971 2009 2011 2005
Yemen, Rep. Yemeni rial 1990 1993 P 1990–96 2005 6 A S B G Yemen, Rep. 2004 MICS, 2006 ES/BS, 2005 2006 2011 2005
Zambia Zambian kwacha 1994 1968 B 1990–92 2005 6 P S B G Zambia 2010 DHS, 2007 IHS, 2010 2011 2002
Zimbabwe U.S. dollar 2009 1993 B 1991, 1998 2005 6 A G C G Zimbabwe 2012 DHS, 2010/11 IHS, 2003/04 2011 2002


Note: For explanation of the abbreviations used in the table, see notes following the table.
a. Original chained constant price data are rescaled. b. Population data compiled from administrative registers. c. Latest population census: Guernsey, 2009; Jersey, 2011. d. The population
censuses for 1986 and 1996 were based on a one-in-seven sample of the population, while that for 2006 was based on a one-in-ten sample of the population. e. Rolling census based on
continuous sample survey. f. Reporting period switch from fiscal year to calendar year from 1996. Pre-1996 data converted to calendar year. g. Includes South Sudan.




World Development Indicators 2013 115Economy States and markets Global links Back


Currency National
accounts


Balance of payments
and trade


Government
finance


IMF data
dissem­
ination


standard


Latest
population


census


Latest demographic,
education, or health


household survey


Source of most
recent income


and expenditure data


Vital
registration
complete


Latest
agricultural


census


Latest
industrial


data


Latest
trade
data


Latest
water


withdrawal
data


Base
year


Reference
year


System of
National
Accounts


SNA
price


valuation


Alternative
conversion


factor


PPP
survey
year


Balance of
Payments
Manual
in use


External
debt


System
of trade


Accounting
concept


Seychelles Seychelles rupee 2006 1993 P 6 A G C G Seychelles 2010 IHS, 2007 Yes 2011 2008 2005
Sierra Leone Sierra Leonean leone 2006 1993 B 2005 6 A S B G Sierra Leone 2004 MICS, 2010 IHS, 2011 2002 2005
Singapore Singapore dollar 2005 1993 B 2005 6 G C S Singapore 2010 General household, 2005 Yes 2009 2011 1975
Sint Maarten Netherlands Antilles


guilder
1993 Sint Maarten 2001 Population Census, 2011


Slovak Republic Euro 2005 1993 B Rolling 6 S C S Slovak Republic 2011 IS, 2009 Yes 2001 2009 2011 2007
Slovenia Euro a 2005 1993 B Rolling 6 S C S Slovenia 2011b ES/BS, 2004 Yes 2010 2009 2011 2009
Solomon Islands Solomon Islands dollar 2004 1993 B 6 A S G Solomon Islands 2009 IHS, 2005/06 2012/13 2011
Somalia Somali shilling 1985 1968 B 1977–90 E Somalia 1987 MICS, 2006 1983 2003
South Africa South African rand 2005 1993 B 2005 6 P G C S South Africa 2011 DHS, 2003 ES/BS, 2010 2012 2009 2011 2000
South Sudan South Sudanese pound 1993 South Sudan 2008 ES/BS, 2009
Spain Euro 2005 1993 B Rolling 6 S C S Spain 2011 IHS, 2000 Yes 2010 2009 2011 2008
Sri Lanka Sri Lankan rupee 2002 1993 P 2005 6 A G B G Sri Lanka 2012 DHS, 2006/07 ES/BS, 2010 Yes 2013 2008 2011 2005
St. Kitts and Nevis East Caribbean dollar 2006 1993 B 6 S C G St. Kitts and Nevis 2011 Yes 2011
St. Lucia East Caribbean dollar 2006 1968 B 6 A S G St. Lucia 2010 MICS, 2012 IHS, 1995 Yes 2008 2005
St. Martin Euro 1993 St. Martin
St. Vincent and Grenadines East Caribbean dollar 2006 1993 B 6 A S B G St. Vincent and Grenadines 2011 Yes 2011 1995
Sudan Sudanese pound 1981/82f 1996 1968 B 2005 6 A G B G Sudan 2008 SHHS, 2010 ES/BS, 2009 2001 2009 2005g


Suriname Suriname dollar 1990 1993 B 6 G G Suriname 2012 MICS, 2010 ES/BS, 1999 Yes 2008 2004 2011 2000
Swaziland Swaziland lilangeni 2000 1993 B 2005 6 E G B G Swaziland 2007 MICS, 2010 ES/BS, 2009/10 2007 2000
Sweden Swedish krona a 2005 1993 B Rolling 6 G C S Sweden 2011 IS, 2000 Yes 2010 2009 2011 2007
Switzerland Swiss franc 2005 1993 B Rolling 6 S C S Switzerland 2010 ES/BS, 2000 Yes 2008 2007 2011 2000
Syrian Arab Republic Syrian pound 2000 1968 B 1970–2010 2005 6 E S C G Syrian Arab Republic 2004 MICS, 2006 ES/BS, 2004 1981 2009 2010 2005
Tajikistan Tajik somoni a 2000 1993 B 1990–95 2005 6 A G C G Tajikistan 2010 LSMS, 2009 LSMS, 2009 2013 2000 2006
Tanzania Tanzanian shilling a 2001 1993 B 2005 6 A G G Tanzania 2012 AIS, 2011/12;


LSMS, 2010/11
ES/BS, 2011 2007/08 2007 2011 2002


Thailand Thai baht 1988 1993 P 2005 6 A S C S Thailand 2010 MICS, 2012 IHS, 2010 2013 2006 2011 2007
Timor-Leste U.S. dollar 2000 2008 P G G Timor-Leste 2010 DHS, 2009/10 LSMS, 2007 2012 2005 2004
Togo CFA franc 2000 1968 P 2005 6 A S B G Togo 2010 MICS, 2010 CWIQ, 2011 2011/12 2011 2002
Tonga Tongan pa’anga 2010/11 1993 B 6 A G G Tonga 2006 2011
Trinidad and Tobago Trinidad and Tobago


dollar
2000 1993 B 6 S C G Trinidad and Tobago 2011 MICS, 2011 IHS, 1992 Yes 2006 2010 2000


Tunisia Tunisian dinar 1990 1993 B 2005 6 A G C S Tunisia 2004 MICS, 2011 IHS, 2010 2004 2006 2011 2001
Turkey New Turkish lira 1998 1993 B Rolling 6 A S B S Turkey 2011 DHS, 2003 LFS, 2009 2008 2011 2003
Turkmenistan New Turkmen manat a 2007 1993 B 1987–95,


1997–2007
6 E G Turkmenistan 2012 MICS, 2011 LSMS, 1998 Yes 2000 2004


Turks and Caicos Islands U.S. dollar 1993 G Turks and Caicos Islands 2012 Yes 2011
Tuvalu Australian dollar 2005 1968 P G Tuvalu 2012 2008
Uganda Ugandan shilling 2001/02 1968 B 2005 6 A G B G Uganda 2002 DHS, 2011 PS, 2009/10 2008 2000 2011 2002
Ukraine Ukrainian hryvnia a 2003 1993 B 1987–95 2005 6 A G C S Ukraine 2001 MICS, 2012 ES/BS, 2009 Yes 2012/13 2004 2011 2005
United Arab Emirates U.A.E. dirham 2007 1993 P 6 G B G United Arab Emirates 2010 2012 2008 2005
United Kingdom Pound sterling 2005 1993 B Rolling 6 G C S United Kingdom 2001 IS, 1999 Yes 2010 2009 2011 2007
United States U.S. dollar a 2005 1993 B 2008 6 G C S United States 2010 LFS, 2000 Yes 2007 2008 2011 2005
Uruguay Uruguayan peso 2005 1993 B 2005 6 A G C S Uruguay 2011 MICS, 2012 IHS, 2011 Yes 2011 2008 2009 2000
Uzbekistan Uzbek sum a 1997 1993 B 1990–95 6 A G Uzbekistan 1989 MICS, 2006 ES/BS, 2003 Yes 2005
Vanuatu Vanuatu vatu 2006 1993 P 6 E G C G Vanuatu 2009 MICS, 2007 2007 2011
Venezuela, R.B. Venezuelan bolivar fuerte 1997 1993 B 2005 6 A G C G Venezuela, R.B. 2011 MICS, 2000 IHS, 2011 Yes 2007 2011 2000
Vietnam Vietnamese dong 1994 1993 P 1991 2005 6 A G G Vietnam 2009 MICS, 2010/11 IHS, 2010 Yes 2011 2000 2010 2005
Virgin Islands (U.S.) U.S. dollar 1982 1968 G Virgin Islands (U.S.) 2010 Yes 2007
West Bank and Gaza Israeli new shekel 1997 1968 B 6 S B S West Bank and Gaza 2007 MICS, 2010 IHS, 2009 1971 2009 2011 2005
Yemen, Rep. Yemeni rial 1990 1993 P 1990–96 2005 6 A S B G Yemen, Rep. 2004 MICS, 2006 ES/BS, 2005 2006 2011 2005
Zambia Zambian kwacha 1994 1968 B 1990–92 2005 6 P S B G Zambia 2010 DHS, 2007 IHS, 2010 2011 2002
Zimbabwe U.S. dollar 2009 1993 B 1991, 1998 2005 6 A G C G Zimbabwe 2012 DHS, 2010/11 IHS, 2003/04 2011 2002


Note: For explanation of the abbreviations used in the table, see notes following the table.
a. Original chained constant price data are rescaled. b. Population data compiled from administrative registers. c. Latest population census: Guernsey, 2009; Jersey, 2011. d. The population
censuses for 1986 and 1996 were based on a one-in-seven sample of the population, while that for 2006 was based on a one-in-ten sample of the population. e. Rolling census based on
continuous sample survey. f. Reporting period switch from fiscal year to calendar year from 1996. Pre-1996 data converted to calendar year. g. Includes South Sudan.




116 World Development Indicators 2013 Front User guide World view People Environment?


Primary data documentation notes


• Base year is the base or pricing period used for con-


stant price calculations in the country’s national


accounts. Price indexes derived from national accounts


aggregates, such as the implicit deflator for gross


domestic product (GDP), express the price level rela-


tive to base year prices. • Reference year is the year


in which the local currency constant price series of a


country is valued. The reference year is usually the


same as the base year used to report the constant


price series. However, when the constant price data


are chain linked, the base year is changed annually, so


the data are rescaled to a specific reference year to


provide a consistent time series. When the country has


not rescaled following a change in base year, World


Bank staff rescale the data to maintain a longer histori-


cal series. To allow for cross-country comparison and


data aggregation, constant price data reported in


World Development Indicators are rescaled to a com-


mon reference year (2000) and currency (U.S. dollars).


• System of National Accounts identifies whether a


country uses the 1968, 1993, or 2008 System of


National Accounts (SNA). • SNA price valuation shows


whether value added in the national accounts is


reported at basic prices (B) or producer prices (P). Pro-


ducer prices include taxes paid by producers and thus


tend to overstate the actual value added in production.


However, value added can be higher at basic prices


than at producer prices in countries with high agricul-


tural subsidies. • Alternative conversion factor identi-


fies the countries and years for which a World Bank–


estimated conversion factor has been used in place of


the official exchange rate (line rf in the International


Monetary Fund’s [IMF] International Financial Statis-


tics). See Statistical methods for further discussion of


alternative conversion factors. • Purchasing power


parity (PPP) survey year is the latest available survey


year for the International Comparison Program’s esti-


mates of PPPs. • Balance of Payments Manual in use


refers to the classification system used to compile and


report data on balance of payments. 6 refers to the 6th


edition of the IMF’s Balance of Payments Manual


(2009). • External debt shows debt reporting status


for 2011 data. A indicates that data are as reported,


P that data are based on reported or collected informa-


tion but include an element of staff estimation, and E


that data are World Bank staff estimates. • System of


trade refers to the United Nations general trade system


(G) or special trade system (S). Under the general trade


system goods entering directly for domestic consump-


tion and goods entered into customs storage are


recorded as imports at arrival. Under the special trade


system goods are recorded as imports when declared


for domestic consumption whether at time of entry or


on withdrawal from customs storage. Exports under


the general system comprise outward-moving goods:


(a) national goods wholly or partly produced in the coun-


try; (b) foreign goods, neither transformed nor declared


for domestic consumption in the country, that move


outward from customs storage; and (c) nationalized


goods that have been declared for domestic consump-


tion and move outward without being transformed.


Under the special system of trade, exports are catego-


ries a and c. In some compilations categories b and c


are classified as re-exports. Direct transit trade—


goods entering or leaving for transport only—is


excluded from both import and export statistics. • Gov-


ernment finance accounting concept is the account-


ing basis for reporting central government financial


data. For most countries government finance data have


been consolidated (C) into one set of accounts captur-


ing all central government fiscal activities. Budgetary


central government accounts (B) exclude some central


government units. • IMF data dissemination standard


shows the countries that subscribe to the IMF’s Spe-


cial Data Dissemination Standard (SDDS) or General


Data Dissemination System (GDDS). S refers to coun-


tries that subscribe to the SDDS and have posted data


on the Dissemination Standards Bulletin Board at


http://dsbb.imf.org. G refers to countries that sub-


scribe to the GDDS. The SDDS was established for


member countries that have or might seek access to


international capital markets to guide them in providing


their economic and financial data to the public. The


GDDS helps countries disseminate comprehensive,


timely, accessible, and reliable economic, financial,


and sociodemographic statistics. IMF member coun-


tries elect to participate in either the SDDS or the


GDDS. Both standards enhance the availability of


timely and comprehensive data and therefore contrib-


ute to the pursuit of sound macroeconomic policies.


The SDDS is also expected to improve the functioning


of financial markets. •  Latest population census


shows the most recent year in which a census was


conducted and in which at least preliminary results


have been released. The preliminary results from the


very recent censuses could be reflected in timely revi-


sions if basic data are available, such as population


by age and sex, as well as the detailed definition of


counting, coverage, and completeness. Countries that


hold register-based censuses produce similar census


tables every 5 or 10 years. Germany’s 2001 census is


a register-based test census using a sample of 1.2


percent of the population. A rare case, France has been


conducting a rolling census every year since 2004; the


1999 general population census was the last to cover


the entire population simultaneously. •  Latest


demographic, education, or health household survey


indicates the household surveys used to compile the


demographic, education, and health data in section 2.


AIS is HIV/AIDS Indicator Survey, DHS is Demographic


and Health Survey, ENPF is National Family Planning


Survey, FHS is Family Health Survey, HIV/MCH is


HIV/Maternal and Child Health, IBEP is Integrated Sur-


vey on Population Welfare, LSMS is Living Standards


Measurement Study Survey, MICS is Multiple Indicator


Cluster Survey, MIS is Malaria Indicator Survey, NSS


is National Sample Survey on Population Change,


PAPFAM is Pan Arab Project for Family Health, RHS is


Reproductive Health Survey, SHHS is Sudan House-


hold Health Survey, and SPA is Service Provision


Assessments. Detailed information for AIS, DHS, MIS,


and SPA are available at www.measuredhs.com; for


MICS at www.childinfo.org; and for RHS at www.cdc


.gov/reproductivehealth. • Source of most recent


income and expenditure data shows household sur-


veys that collect income and expenditure data. Names


and detailed information on household surveys can be


found on the website of the International Household


Survey Network (www.surveynetwork.org). Core Wel-


fare Indicator Questionnaire Surveys (CWIQ), devel-


oped by the World Bank, measure changes in key social


indicators for different population groups—specifically


indicators of access, utilization, and satisfaction with


core social and economic services. Expenditure


survey/ budget surveys (ES/BS) collect detailed infor-


mation on household consumption as well as on gen-


eral demographic, social, and economic characteris-


tics. Integrated household surveys (IHS) collect


detailed information on a wide variety of topics, includ-


ing health, education, economic activities, housing,


and utilities. Income surveys (IS) collect information


on the income and wealth of households as well as


various social and economic characteristics. Income


tax registers (ITR) provide information on a population’s


income and allowance, such as gross income, taxable


income, and taxes by socioeconomic group. Labor


force surveys (LFS) collect information on employment,


unemployment, hours of work, income, and wages.


Living Standards Measurement Study Surveys (LSMS),


developed by the World Bank, provide a comprehensive


picture of household welfare and the factors that affect


it; they typically incorporate data collection at the indi-


vidual, household, and community levels. Priority sur-


veys (PS) are a light monitoring survey, designed by the


World Bank, that collect data from a large number of


households cost-effectively and quickly. 1-2-3 (1-2-3)


surveys are implemented in three phases and collect


socio demographic and employment data, data on the


informal sector, and information on living conditions




World Development Indicators 2013 117Economy States and markets Global links Back


Primary data documentation notes


and household consumption. • Vital registration com-


plete identifies countries that report at least 90 per-


cent complete registries of vital (birth and death) sta-


tistics to the United Nations Statistics Division and are


reported in its Population and Vital Statistics Reports.


Countries with complete vital statistics registries may


have more accurate and more timely demographic


indicators than other countries. • Latest agricultural


census shows the most recent year in which an agri-


cultural census was conducted and reported to the


Food and Agriculture Organization of the United


Nations. • Latest industrial data show the most recent


year for which manufacturing value added data at the


three-digit level of the International Standard Industrial


Classification (revision 2 or 3) are available in the


United Nations Industrial Development Organization


database. • Latest trade data show the most recent


year for which structure of merchandise trade data


from the United Nations Statistics Division’s Commod-


ity Trade (Comtrade) database are available. • Latest


water withdrawal data show the most recent year for


which data on freshwater withdrawals have been com-


piled from a variety of sources.


Exceptional reporting periods


In most economies the fiscal year is concurrent with


the calendar year. Exceptions are shown in the table


at right. The ending date reported here is for the fis-


cal year of the central government. Fiscal years for


other levels of government and reporting years for


statistical surveys may differ.


The reporting period for national accounts data is


designated as either calendar year basis (CY) or fiscal


year basis (FY). Most economies report their national


accounts and balance of payments data using calen-


dar years, but some use fiscal years. In World Devel-


opment Indicators fiscal year data are assigned to


the calendar year that contains the larger share of


the fiscal year. If a country’s fiscal year ends before


June 30, data are shown in the first year of the fiscal


period; if the fiscal year ends on or after June 30, data


are shown in the second year of the period. Balance


of payments data are reported in World Development


Indicators by calendar year.


Revisions to national accounts data


National accounts data are revised by national sta-


tistical offices when methodologies change or data


sources improve. National accounts data in World


Development Indicators are also revised when data


sources change. The following notes, while not com-


prehensive, provide information on revisions from pre-


vious data. • Afghanistan. National accounts data are


sourced from the IMF and differ from the Central Sta-


tistics Organization numbers due to exclusion of the


opium economy. • Angola. Based on IMF data, national


accounts data have been revised for 2000 onward;


the new base year is 2002. • Australia. Value added


series data are taken from the United Nations National


Accounts Main Aggregates, and gross national income


is computed using Australian Bureau of Statistics


data. • Bhutan. Data were updated recently using the


government of Bhutan macroeconomic framework.


• China. National accounts historical data for expendi-


ture series in constant prices have been revised based


on National Statistics Bureau data not previously avail-


able. • Democratic Republic of Congo. Based on IMF


data, national accounts data have been revised for


2000 onward; the new base year is 2000. • Republic


of Congo. Based on IMF data, national accounts data


have been revised for 1990 onward; the new base year


is 1990. • Croatia. Based on official government sta-


tistics, the new base year for constant price series is


2005. • Eritrea. Based on IMF data, national accounts


data have been revised for 2000 onward; the new base


year is 2000. • The Gambia. Based on official gov-


ernment statistics, national accounts data have been


revised for 2004 onward; the new base year is 2004.


• Guinea. Based on IMF data, national accounts data


have been revised for 2000 onward; the new base


year is 2003. • Hong Kong SAR, China. Agriculture


value added includes mining and quarrying. • India.


The India Central Statistical Office revised historical


data series both current and constant going back to


1960 with 2004–05 as the base. • Jamaica. Based


on official government statistics, national accounts


data have been revised for 2002 onward; the new


base year is 2007. • Kiribati. Based on data from the


Asian Development Bank, national accounts data have


been revised for 2005 onward. • Liberia. Based on IMF


data, national accounts data have been revised for


2000 onward; the new base year is 2000. • Malawi.


Based on IMF data, national accounts data have been


revised for 2003 onward; the new base year is 2007.


• Malaysia. Based on data from the National Statistics


Office, national accounts data in current prices have


been revised for 2005 onward. • Nicaragua. Based on


official government statistics, national accounts data


have been revised for 1994 onward; the new base


year is 2006. • Palau. Based on IMF data, national


accounts data have been revised for 2007 onward.


• Rwanda. Based on official government statistics,


national accounts data have been revised for 1999


onward; the new base year is 2006. • Samoa. Based


on IMF data, national accounts data have been revised


for 2007 onward. • Seychelles. Based on official gov-


ernment statistics, national accounts data have been


revised for 1976 onward; the new base year is 2006.


• Sierra Leone. Based on official government statis-


tics, national accounts data have been revised for


1990 onward; the new base year is 2006. • Syrian


Arab Republic. Based on data from the Central Bureau


of Statistics, national accounts data have been revised


for 2003 onward. • Togo. Based on IMF data, national


accounts data have been revised for 2000; the new


base year is 2000. • Tonga. Based on data from the


National Bureau of Statistics, national accounts data


have been revised; the new base year is 2010/11.


• Tuvalu. Based on IMF data, national accounts data


for 2000 onward have been revised. • United Arab


Emirates. Based on data from the National Bureau of


Statistics, national accounts data have been revised


for 2001 onward; the new base year is 2007.


Economies with exceptional reporting periods


Economy
Fiscal


year end


Reporting period
for national


accounts data


Afghanistan Mar. 20 FY


Australia Jun. 30 FY


Bangladesh Jun. 30 FY


Botswana Jun. 30 FY


Canada Mar. 31 CY


Egypt, Arab Rep. Jun. 30 FY


Ethiopia Jul. 7 FY


Gambia, The Jun. 30 CY


Haiti Sep. 30 FY


India Mar. 31 FY


Indonesia Mar. 31 CY


Iran, Islamic Rep. Mar. 20 FY


Japan Mar. 31 CY


Kenya Jun. 30 CY


Kuwait Jun. 30 CY


Lesotho Mar. 31 CY


Malawi Mar. 31 CY


Myanmar Mar. 31 FY


Namibia Mar. 31 CY


Nepal Jul. 14 FY


New Zealand Mar. 31 FY


Pakistan Jun. 30 FY


Puerto Rico Jun. 30 FY


Sierra Leone Jun. 30 CY


Singapore Mar. 31 CY


South Africa Mar. 31 CY


Swaziland Mar. 31 CY


Sweden Jun. 30 CY


Thailand Sep. 30 CY


Uganda Jun. 30 FY


United States Sep. 30 CY


Zimbabwe Jun. 30 CY




118 World Development Indicators 2013 Front User guide World view People Environment?


Statistical methods
This section describes some of the statistical proce-
dures used in preparing World Development Indica-
tors. It covers the methods employed for calculating
regional and income group aggregates and for calcu-
lating growth rates, and it describes the World Bank
Atlas method for deriving the conversion factor used
to estimate gross national income (GNI) and GNI per
capita in U.S. dollars. Other statistical procedures
and calculations are described in the About the data
sections following each table.


Aggregation rules
Aggregates based on the World Bank’s regional and
income classifications of economies appear at the
end of the tables, including most of those available
online. The 214 economies included in these classifi-
cations are shown on the flaps on the front and back
covers of the book. Aggregates also contain data for
Taiwan, China. Most tables also include the aggregate
for the euro area, which includes the member states
of the Economic and Monetary Union (EMU) of the
European Union that have adopted the euro as their
currency: Austria, Belgium, Cyprus, Estonia, Finland,
France, Germany, Greece, Ireland, Italy, Luxembourg,
Malta, Netherlands, Portugal, Slovak Republic, Slo-
venia, and Spain. Other classifications, such as the
European Union, are documented in About the data
for the online tables in which they appear.


Because of missing data, aggregates for groups
of economies should be treated as approximations
of unknown totals or average values. The aggregation
rules are intended to yield estimates for a consistent
set of economies from one period to the next and for
all indicators. Small differences between sums of sub-
group aggregates and overall totals and averages may
occur because of the approximations used. In addi-
tion, compilation errors and data reporting practices
may cause discrepancies in theoretically identical
aggregates such as world exports and world imports.


Five methods of aggregation are used in World
Development Indicators:
• For group and world totals denoted in the tables by


a t, missing data are imputed based on the rela-
tionship of the sum of available data to the total in


the year of the previous estimate. The imputation
process works forward and backward from 2000.
Missing values in 2000 are imputed using one of
several proxy variables for which complete data
are available in that year. The imputed value is
calculated so that it (or its proxy) bears the same
relationship to the total of available data. Imputed
values are usually not calculated if missing data
account for more than a third of the total in the
benchmark year. The variables used as proxies are
GNI in U.S. dollars; total population; exports and
imports of goods and services in U.S. dollars; and
value added in agriculture, industry, manufactur-
ing, and services in U.S. dollars.


• Aggregates marked by an s are sums of available
data. Missing values are not imputed. Sums are
not computed if more than a third of the observa-
tions in the series or a proxy for the series are
missing in a given year.


• Aggregates of ratios are denoted by a w when cal-
culated as weighted averages of the ratios (using
the value of the denominator or, in some cases,
another indicator as a weight) and denoted by a
u when calculated as unweighted averages. The
aggregate ratios are based on available data. Miss-
ing values are assumed to have the same average
value as the available data. No aggregate is calcu-
lated if missing data account for more than a third
of the value of weights in the benchmark year. In
a few cases the aggregate ratio may be computed
as the ratio of group totals after imputing values
for missing data according to the above rules for
computing totals.


• Aggregate growth rates are denoted by a w when
calculated as a weighted average of growth rates.
In a few cases growth rates may be computed from
time series of group totals. Growth rates are not
calculated if more than half the observations in a
period are missing. For further discussion of meth-
ods of computing growth rates see below.


• Aggregates denoted by an m are medians of the
values shown in the table. No value is shown if
more than half the observations for countries with
a population of more than 1 million are missing.




World Development Indicators 2013 119Economy States and markets Global links Back


Exceptions to the rules may occur. Depending on
the judgment of World Bank analysts, the aggregates
may be based on as little as 50 percent of the avail-
able data. In other cases, where missing or excluded
values are judged to be small or irrelevant, aggregates
are based only on the data shown in the tables.


Growth rates
Growth rates are calculated as annual averages and
represented as percentages. Except where noted,
growth rates of values are computed from constant
price series. Three principal methods are used to cal-
culate growth rates: least squares, exponential end-
point, and geometric endpoint. Rates of change from
one period to the next are calculated as proportional
changes from the earlier period.


Least squares growth rate. Least squares growth
rates are used wherever there is a sufficiently long
time series to permit a reliable calculation. No growth
rate is calculated if more than half the observations in
a period are missing. The least squares growth rate, r,
is estimated by fitting a linear regression trend line to
the logarithmic annual values of the variable in the rel-
evant period. The regression equation takes the form


ln Xt = a + bt


which is the logarithmic transformation of the com-
pound growth equation,


Xt = Xo (1 + r )
t.


In this equation X is the variable, t is time, and a = ln Xo
and b = ln (1 + r) are parameters to be estimated. If
b* is the least squares estimate of b, then the aver-
age annual growth rate, r, is obtained as [exp(b*) – 1]
and is multiplied by 100 for expression as a percent-
age. The calculated growth rate is an average rate that
is representative of the available observations over
the entire period. It does not necessarily match the
actual growth rate between any two periods.


Exponential growth rate. The growth rate between
two points in time for certain demographic indicators,


notably labor force and population, is calculated from
the equation


r = ln(pn/p0)/n


where pn and p0 are the last and first observations
in the period, n is the number of years in the period,
and ln is the natural logarithm operator. This growth
rate is based on a model of continuous, exponential
growth between two points in time. It does not take into
account the intermediate values of the series. Nor does
it correspond to the annual rate of change measured
at a one-year interval, which is given by (pn – pn–1)/pn–1.


Geometric growth rate. The geometric growth
rate is applicable to compound growth over discrete
periods, such as the payment and reinvestment of
interest or dividends. Although continuous growth, as
modeled by the exponential growth rate, may be more
realistic, most economic phenomena are measured
only at intervals, in which case the compound growth
model is appropriate. The average growth rate over n
periods is calculated as


r = exp[ln(pn/p0)/n] – 1.


World Bank Atlas method
In calculating GNI and GNI per capita in U.S. dollars
for certain operational purposes, the World Bank uses
the Atlas conversion factor. The purpose of the Atlas
conversion factor is to reduce the impact of exchange
rate fluctuations in the cross-country comparison of
national incomes.


The Atlas conversion factor for any year is the aver-
age of a country’s exchange rate (or alternative conver-
sion factor) for that year and its exchange rates for
the two preceding years, adjusted for the difference
between the rate of inflation in the country and that
in Japan, the United Kingdom, the United States, and
the euro area. A country’s inflation rate is measured
by the change in its GDP deflator.


The inflation rate for Japan, the United Kingdom,
the United States, and the euro area, representing




120 World Development Indicators 2013 Front User guide World view People Environment?


Statistical methods
international inflation, is measured by the change in
the “SDR deflator.” (Special drawing rights, or SDRs,
are the International Monetary Fund’s unit of account.)
The SDR deflator is calculated as a weighted average
of these countries’ GDP deflators in SDR terms, the
weights being the amount of each country’s currency
in one SDR unit. Weights vary over time because both
the composition of the SDR and the relative exchange
rates for each currency change. The SDR deflator is
calculated in SDR terms first and then converted to
U.S. dollars using the SDR to dollar Atlas conversion
factor. The Atlas conversion factor is then applied to
a country’s GNI. The resulting GNI in U.S. dollars is
divided by the midyear population to derive GNI per
capita.


When official exchange rates are deemed to
be unreliable or unrepresentative of the effective
exchange rate during a period, an alternative esti-
mate of the exchange rate is used in the Atlas formula
(see below).


The following formulas describe the calculation of
the Atlas conversion factor for year t:


and the calculation of GNI per capita in U.S. dollars
for year t:


Yt
$ = (Yt/Nt)/et


*


where et
* is the Atlas conversion factor (national cur-


rency to the U.S. dollar) for year t, et is the average
annual exchange rate (national currency to the U.S.
dollar) for year t, pt is the GDP deflator for year t, pt


S$
is the SDR deflator in U.S. dollar terms for year t, Yt


$
is the Atlas GNI per capita in U.S. dollars in year t, Yt
is current GNI (local currency) for year t, and Nt is the
midyear population for year t.


Alternative conversion factors
The World Bank systematically assesses the appro-
priateness of official exchange rates as conversion
factors. An alternative conversion factor is used when
the official exchange rate is judged to diverge by an
exceptionally large margin from the rate effectively
applied to domestic transactions of foreign curren-
cies and traded products. This applies to only a small
number of countries, as shown in Primary data docu-
mentation. Alternative conversion factors are used in
the Atlas methodology and elsewhere in World Devel-
opment Indicators as single-year conversion factors.




World Development Indicators 2013 121Economy States and markets Global links Back


1. World view
Section 1 was prepared by a team led by Eric Swan-
son. Eric Swanson wrote the introduction with input
from Neil Fantom, Juan Feng, Masako Hiraga, Wendy
Huang, Hiroko Maeda, Johan Mistiaen, Vanessa
Moreira, Esther Naikal, William Prince, Evis Rucaj,
Rubena Sakaj, and Emi Suzuki. Bala Bhaskar Naidu
Kalimili coordinated tables 1.1 and 1.6. Masako
Hiraga, Hiroko Maeda, Johan Mistiaen, Vanessa
Moreira, and Emi Suzuki prepared tables 1.2 and
1.5. Mahyar Eshragh-Tabary, Masako Hiraga, Buyant
Erdene Khaltarkhuu, Hiroko Maeda, Vanessa Moreira,
and Emi Suzuki prepared table 1.3. Wendy Huang
prepared table 1.4 with input from Azita Amjadi. Signe
Zeikate of the World Bank’s Economic Policy and Debt
Department provided the estimates of debt relief for
the Heavily Indebted Poor Countries Debt Initiative
and Multilateral Debt Relief Initiative.


2. People
Section 2 was prepared by Juan Feng, Masako Hiraga,
Hiroko Maeda, Johan Mistiaen, Vanessa Moreira, Emi
Suzuki, and Eric Swanson in partnership with the
World Bank’s Human Development Network and the
Development Research Group in the Development
Economics Vice Presidency. Emi Suzuki prepared the
demographic estimates and projections. The poverty
estimates at national poverty lines were compiled
by the Global Poverty Working Group, a team of pov-
erty experts from the Poverty Reduction and Equality
Network, the Development Research Group, and the
Development Data Group. Shaohua Chen and Prem
Sangraula of the World Bank’s Development Research
Group prepared the poverty estimates at international
poverty lines. Lorenzo Guarcello and Furio Rosati of
the Understanding Children’s Work project prepared
the data on children at work. Other contributions
were provided by Samuel Mills (health); Maddalena
Honorati, Montserrat Pallares-Miralles, and Claudia
Rodríguez (vulnerability and security); Theodoor Spar-
reboom and Alan Wittrup of the International Labour
Organization (labor force); Amélie Gagnon, Said Ould
Voffal, and Weixin Lu of the United Nations Educa-
tional, Scientific and Cultural Organization Institute for


Statistics (education and literacy); the World Health
Organization Chandika Indikadahena (health expendi-
ture), Monika Bloessner and Mercedes de Onis (mal-
nutrition and overweight), Teena Kunjumen (health
workers), Jessica Ho (hospital beds), Rifat Hossain
(water and sanitation), Luz Maria de Regil (anemia),
Hazim Timimi (tuberculosis), and Lori Marie Newman
(syphilis); Leonor Guariguata of the International Dia-
betes Federation (diabetes); Mary Mahy of the Joint
United Nations Programme on HIV/AIDS (HIV/AIDS);
and Colleen Murray of the United Nations Children’s
Fund (health). Eric Swanson provided comments and
suggestions on the introduction and at all stages of
production.


3. Environment
Section 3 was prepared by Mahyar Eshragh-Tabary
in partnership with the Agriculture and Environmen-
tal Services Department of the Sustainable Devel-
opment Network Vice Presidency of the World Bank.
Mahyar Eshragh-Tabary wrote the introduction with
suggestions from Eric Swanson. Other contributors
include Esther G. Naikal and Karen Treanton of the
International Energy Agency, Gerhard Metchies and
Armin Wagner of German International Cooperation,
Craig Hilton-Taylor and Caroline Pollock of the Interna-
tional Union for Conservation of Nature, and Cristian
Gonzalez of the International Road Federation. The
World Bank’s Agriculture and Environmental Services
Department devoted generous staff resources.


4. Economy
Section 4 was prepared by Bala Bhaskar Naidu Kali-
mili in close collaboration with the Sustainable Devel-
opment and Economic Data Team of the World Bank’s
Development Data Group and with suggestions from
Liu Cui and William Prince. Bala Bhaskar Naidu Kali-
mili wrote the introduction with suggestions from Eric
Swanson. The highlights section was prepared by Bala
Bhaskar Naidu Kalimili, Maurice Nsabimana, and Olga
Victorovna Vybornaia. The national accounts data for
low- and middle-income economies were gathered by
the World Bank’s regional staff through the annual
Unified Survey. Federico M. Escaler, Mahyar Eshragh-


Credits




122 World Development Indicators 2013


Credits


Front User guide World view People Environment?


Tabary, Bala Bhaskar Naidu Kalimili, Buyant Erdene
Khaltarkhuu, Maurice Nsabimana, and Olga Victoro-
vna Vybornaia updated, estimated, and validated the
databases for national accounts. Esther G. Naikal
prepared adjusted savings and adjusted income data.
Azita Amjadi contributed trade data from the World
Integrated Trade Solution. The team is grateful to
Eurostat, the International Monetary Fund, the Organ-
isation for Economic Co-operation and Development,
the United Nations Industrial Development Organiza-
tion, and the World Trade Organization for access to
their databases.


5. States and markets
Section 5 was prepared by Federico Escaler and
Buyant Erdene Khaltarkhuu in partnership with the
World Bank’s Financial and Private Sector Develop-
ment Network, Poverty Reduction and Economic
Management Network, and Sustainable Development
Network; the International Finance Corporation; and
external partners. Buyant Erdene Khaltarkhuu wrote
the introduction with input from Eric Swanson. Other
contributors include Alexander Nicholas Jett (priva-
tization and infrastructure projects); Leora Klapper
(business registration); Federica Saliola and Joshua
Wimpey (Enterprise Surveys); Carolin Geginat and
Frederic Meunier (Doing Business); Alka Banerjee,
Trisha Malinky, and Michael Orzano (Standard &
Poor’s global stock market indexes); Gary Milante
and Kenneth Anya (fragile situations); Satish Man-
nan (public policies and institutions); James Hackett
of the International Institute for Strategic Studies
(military personnel); Sam Perlo-Freeman of the Stock-
holm International Peace Research Institute (military
expenditures and arms transfers); Christian Gonzalez
of the International Road Federation, Zubair Anwar
and Narjess Teyssier of the International Civil Aviation
Organization, and Marc Juhel and Hélène Stephan
(transport); Vincent Valentine of the United Nations
Conference on Trade and Development (ports); Azita
Amjadi (high-tech exports); Vanessa Grey, Esperanza
Magpantay, and Susan Teltscher of the International
Telecommunication Union; Torbjörn Fredriksson and
Diana Korka of the United Nations Conference on


Trade and Development (information and communi-
cation technology goods trade); Martin Schaaper of
the United Nations Educational, Scientific and Cul-
tural Organization Institute for Statistics (research
and development, researchers, and technicians); and
Ryan Lamb of the World Intellectual Property Organi-
zation (patents and trademarks).


6. Global links
Section 6 was prepared by Wendy Huang with input
from Evis Rucaj and Rubena Sukaj and in partner-
ship with the Financial Data Team of the World Bank’s
Development Data Group, Development Research
Group (trade), Development Prospects Group (com-
modity prices and remittances), International Trade
Department (trade facilitation), and external part-
ners. Wendy Huang and Evis Rucaj wrote the intro-
duction, with substantial input from Eric Swanson.
Azita Amjadi (trade and tariffs) and Rubena Sukaj
(external debt and financial data) provided substan-
tial input on the data and tables. Other contributors
include Frédéric Docquier (emigration rates); Flavine
Creppy and Yumiko Mochizuki of the United Nations
Conference on Trade and Development and Mondher
Mimouni of the International Trade Centre (trade); Cris-
tina Savescu (commodity prices); Jeff Reynolds and
Joseph Siegel of DHL (freight costs); Yasmin Ahmad
and Elena Bernaldo of the Organisation for Economic
Co-operation and Development (aid); Ibrahim Levent
and Maryna Taran (external debt); Gemechu Ayana
Aga and Ani Rudra Silwal (remittances); and Teresa
Ciller of the World Tourism Organization (tourism).
Ramgopal Erabelly, Shelley Fu, and William Prince
provided technical assistance.


Other parts of the book
Jeff Lecksell of the World Bank’s Map Design Unit
coordinated preparation of the maps on the inside cov-
ers. Alison Kwong and William Prince prepared User
guide and the lists of online tables and indicators for
each section. Eric Swanson wrote Statistical methods,
with input from William Prince. Federico Escaler and
Leila Rafei prepared Primary data documentation. Part-
ners was prepared by Alison Kwong.




World Development Indicators 2013 123Economy States and markets Global links Back


Database management
William Prince coordinated management of the World
Development Indicators database, with assistance
from Liu Cui and Shelley Fu in the Data Administration
and Quality Team. Operation of the database man-
agement system was made possible by Ramgopal
Erabelly in the Data and Information Systems Team
under the leadership of Reza Farivari.


Design, production, and editing
Azita Amjadi and Alison Kwong coordinated all stages
of production with Communications Development
Incorporated, which provided overall design direction,
editing, and layout, led by Meta de Coquereaumont,
Jack Harlow, Bruce Ross-Larson, and Christopher
Trott. Elaine Wilson created the cover and graphics
and typeset the book. Peter Grundy, of Peter Grundy
Art & Design, and Diane Broadley, of Broadley Design,
designed the report.


Administrative assistance, office technology,
and systems development support
Elysee Kiti provided administrative assistance.
Jean-Pierre Djomalieu, Gytis Kanchas, and Nacer
Megherbi provided information technology support.
Ugendran Machakkalai, Shanmugam Natarajan,
Atsushi Shimo, and Malarvizhi Veerappan provided
software support on the Development Data Platform
application.


Publishing and dissemination
The Office of the Publisher, under the direction of
Carlos Rossel, provided assistance throughout the
production process. Denise Bergeron, Stephen
McGroarty, Nora Ridolfi, and Janice Tuten coordinated
printing, marketing, and distribution. Merrell Tuck-
Primdahl of the Development Economics Vice Presi-
dent’s Office managed the communications strategy.


World Development Indicators mobile applications
Software preparation and testing were managed by
Shelley Fu with assistance from Prashant Chaudhari,
Ying Chi, Liu Cui, Ghislaine Delaine, Neil Fantom,
Ramgopal Erabelly, Federico Escaler, Buyant Erdene
Khaltarkhuu, Sup Lee, Maurice Nsabimana, Parastoo
Oloumi, Beatriz Prieto Oramas, William Prince, Virginia
Romand, Jomo Tariku, Malarvizhi Veerappan, and Vera
Wen. Systems development was undertaken in the
Data and Information Systems Team led by Reza Fari-
vari. William Prince provided data quality assurance.


Online access
Coordination of the presentation of the WDI online,
through the Open Data website, the World Databank
application, the new table browser application, and
the Application Programming Interface, was provided
by Neil Fantom and Soong Sup Lee. Development and
maintenance of the website were managed by a team
led by Azita Amjadi and including Alison Kwong, George
Gongadze, Timothy Herzog, Jeffrey McCoy, and Jomo
Tariku. Systems development was managed by a team
led by Reza Farivari, with project management provided
by Malarvizhi Veerappan. Design, programming, and
testing were carried out by Ying Chi, Shelley Fu, Sid-
dhesh Kaushik, Ugendran Machakkalai, Nacer Meghe-
rbi, Shanmugam Natarajan, Parastoo Oloumi, Man-
ish Rathore, Ashish B. Shah, Atsushi Shimo, Maryna
Taran, and Jomo Tariku. Liu Cui and William Prince
coordinated production and provided data quality
assurance. Multilingual translations of online applica-
tions were provided by a team led by Jim Rosenberg in
the World Bank’s External Affairs department.


Client feedback
The team is grateful to the many people who have
taken the time to provide feedback and suggestions,
which have helped improve this year’s edition. Please
contact us at data@worldbank.org.






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Burkina
Faso


Dominican
Republic Puerto


Rico (US)


U.S. Virgin
Islands (US)


St. Kitts
and Nevis


Antigua and Barbuda


Dominica


St. Lucia


Barbados


Grenada


Trinidad
and Tobago


R.B. de Venezuela


Martinique (Fr)


Guadeloupe (Fr)


Poland


Czech Republic
Slovak Republic


Ukraine


Austria


Germany


San
Marino


Italy


Slovenia
Croatia


Bosnia and
Herzegovina


Hungary


Romania


Bulgaria


Albania
Greece


FYR
Macedonia


Samoa


American
Samoa (US)


Tonga
Fiji


Kiribati


French Polynesia (Fr)


N. Mariana Islands (US)


Guam (US)


Palau


Federated States of Micronesia Marshall Islands


Nauru Kiribati


Solomon
Islands


Tuvalu


Vanuatu Fiji


New
Caledonia


(Fr)


Haiti
Jamaica


Cuba
Cayman Is.(UK)


The Bahamas


Bermuda
(UK)


United States


Canada


Mexico


PanamaCosta Rica


Nicaragua
Honduras


El Salvador
Guatemala


Belize


Colombia
French Guiana (Fr)


Guyana
Suriname


R.B. de
Venezuela


Ecuador


Peru Brazil


Bolivia


Paraguay


Chile
Argentina


Uruguay


Greenland
(Den)


NorwayIceland


Isle of Man (UK)


Ireland
United


Kingdom


Faeroe
Islands
(Den) Sweden Finland


Denmark


Estonia
Latvia


Lithuania


Poland


Russian
Fed.


Belarus


Ukraine
Moldova


Romania


Bulgaria


Greece


Italy


Germany
Belgium


The Netherlands


Luxembourg


Channel Islands (UK)


Switzerland
Liechtenstein France


Andorra


Portugal
Spain


Monaco


Gibraltar (UK)
Malta


Morocco


Tunisia


Algeria


Mauritania


Mali


Senegal
The Gambia


Guinea-Bissau Guinea


Cape Verde


Sierra Leone


Liberia


Côte
d’Ivoire


Ghana


Togo


Benin


Niger


Nigeria


Libya Arab Rep.
of Egypt


Chad


Cameroon


Central
African


Republic


Equatorial Guinea
São Tomé and Príncipe


Gabon Congo


Angola


Dem.Rep.of
Congo


Eritrea


Djibouti


Ethiopia


Somalia


Kenya
Uganda


Rwanda


Burundi
Tanzania


Zambia Malawi


MozambiqueZimbabwe


BotswanaNamibia


Swaziland


LesothoSouth
Africa


Madagascar Mauritius


Seychelles


Comoros


Mayotte
(Fr)


Réunion (Fr)


Rep. of Yemen


Oman


United Arab
Emirates


Qatar
Bahrain


Saudi
Arabia


KuwaitIsrael
West Bank and Gaza Jordan


Lebanon
Syrian
Arab
Rep.


Cyprus


Iraq
Islamic Rep.


of Iran


Turkey
Azer-
baijanArmenia


Georgia


Turkmenistan


Uzbekistan


Kazakhstan


Afghanistan


Tajikistan


Kyrgyz
Rep.


Pakistan


India


Bhutan
Nepal


Bangladesh


Myanmar


Sri
Lanka


Maldives


Thailand


Lao
P.D.R.


Vietnam
Cambodia


Singapore


Malaysia


Philippines


Papua New GuineaIndonesia


Australia


New
Zealand


Japan
Rep.of
Korea


Dem.People’s
Rep.of Korea


Mongolia


China


Russian Federation


Antarctica


Timor-Leste


Vatican
City


Serbia


Brunei Darussalam


IBRD 39818 MARCH 2013


Kosovo


Turks and Caicos Is. (UK)


Sudan


South
Sudan


Curaçao (Neth)


Aruba (Neth) St. Vincent and
the Grenadines


St. Martin (Fr)


St. Maarten (Neth)


Western
Sahara


Montenegro


Classified according to
World Bank analytical
grouping


The world by region


Low- and middle-income economies


East Asia and Pacific


Europe and Central Asia


Latin America and the Caribbean


Middle East and North Africa


South Asia


Sub-Saharan Africa


High-income economies


OECD


Other No data






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