World Development Indicators 2015
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The document is the 2015 edition of the World Development Indicators published by the World Bank, providing comprehensive data on global development issues, including progress on Millennium Development Goals. It highlights successes such as the halving of extreme poverty and challenges that remain, and sets the stage for the transition to the Sustainable Development Goals. The report advocates for a data revolution to enhance monitoring and analysis of development progress.
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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.
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imputed using a statistical model. More information on the results
of the 2011 ICP is available at http://icp.worldbank.org.
Growth rates of GDP and GDP per capita are calculated using con-
stant price data in local currency. Constant price U.S. dollar series
are used to calculate regional and income group growth rates. Growth
rates in the table are annual averages (see Statistical 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 asylum, who
are generally considered part of the population of their country of
origin. The values shown are midyear estimates. • Surface 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 obtained by the United
Nations Population Division. • 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 con-
verted 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 subsi-
dies) 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 Health Global
Practice, operational staff, and country offices. The United Nations
Population Division (2013) 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 publica-
tions from national statistical offices, Eurostat’s Population database,
the United Nations Statistics Division’s Population and Vital Statistics
Report, 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 (2014).
GNI, GNI per capita, GDP growth, and GDP per capita growth are
estimated by World Bank staff based on national accounts data
collected 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 Euro-
stat/OECD and by World Bank staff based on data collected by
the ICP.
References
Eurostat. n.d. Population database. [http://ec.europa.eu/eurostat/].
Luxembourg.
FAO (Food and Agriculture Organization), IFAD (International Fund for
Agricultural Development), and WFP (World Food Programme). 2014.
The State of Food Insecurity in the World 2014: Strengthening the
Enabling Environment for Food Security and Nutrition. Rome. [www
.fao.org/3/a-i4030e.pdf].
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). 2014. The
Gap Report. [www.unaids.org/en/resources/campaigns/2014
/2014gapreport/gapreport/]. Geneva.
UNESCO (United Nations Educational, Scientific and Cultural Organi-
zation). 2004. Education for All Global Monitoring Report 2003/4:
Gender and Education for All—The Leap to Equality. Paris.
UNFPA (United Nations Population Fund) and Guttmacher Institute.
2014. Adding It Up 2014: The Costs and Benefits of Investing in
Sexual and Reproductive Health. [www.unfpa.org/sites/default
/files/pub-pdf/Adding%20It%20Up-Final-11.18.14.pdf]. New York.
UNICEF (United Nations Children’s Fund). 2014. Committing to Child Sur-
vival: A Promise Renewed—Progress Report 2014. [http://files.unicef
.org/publications/files/APR_2014_web_15Sept14.pdf]. New York.
UNICEF (United Nations Children’s Fund), WHO (World Health Orga-
nization), and World Bank. 2014. 2013 Joint Child Malnutrition
Estimates—Levels and Trends. New York: UNICEF. [www.who.int
/nutgrowthdb/estimates2013/].
United Nations. 2014. A World That Counts: Mobilising the Data Revolu-
tion for Sustainable Development. New York. [www.undatarevolution
.org/wp-content/uploads/2014/12/A-World-That-Counts2.pdf].
United Nations Population Division. 2013. World Population Prospects:
The 2012 Revision. [http://esa.un.org/unpd/wpp/Documentation
/publications.htm]. New York.
United Nations Statistics Division. Various years. Population and Vital
Statistics Report. New York.
———. 2014. World Urbanization Prospects: The 2014 Revision.
[http://esa.un.org/unpd/wup/]. New York.
WHO (World Health Organization). 2014a. Global Tuberculosis Report
2014. [http://who.int/tb/publications/global_report/]. Geneva.
———. 2014b. “Maternal Mortality.” Fact sheet 348. [www.who.int
/mediacentre/factsheets/fs348/]. Geneva.
———. 2014c. World Malaria Report 2014. [www.who.int/malaria
/publications/world_malaria_report_2014/]. Geneva.
World Bank. 2015. Global Monitoring Report 2014/2015: Ending Pov-
erty and Sharing Prosperity. Washington, DC.](https://image.slidesharecdn.com/banquemondiale2-150716122016-lva1-app6892/85/World-Development-Indicators-2015-54-320.jpg)
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the time period as closely as possible across all countries, including
the most recent data, and ensuring the widest possible coverage of
countries, across regions and income levels. In practice, this means
that time periods will not match perfectly across countries. This is
a compromise: While it introduces a degree of incomparability, it
also creates a database that includes a larger set of countries than
would be possible otherwise.
Data quality
Like poverty rate estimates, estimates of annualized growth of mean
per capita real income or consumption of the bottom 40 percent
are based on income or consumption data collected in household
surveys, and the same quality issues apply. See the discussion in
the Poverty rates section.
Definitions
• Period is the period of reference of a survey. For surveys in which
the period of reference covers multiple years, it is the year with the
majority of the survey respondents. For surveys in which the period
of reference is half in one year and half in another, it is the first year.
• Annualized growth of survey mean per capita real income or con-
sumption is the annualized growth in mean per capita real income
consumption from household surveys over a roughly five-year period.
It is calculated for the bottom 40 percent of a country’s population
and for the total population of a country. • Survey mean per capita
real consumption or income is the mean income or consumption per
capita from household surveys used in calculating the welfare growth
rate, expressed in purchasing power parity (PPP)–adjusted dollars
per day at 2005 prices. It is calculated for the bottom 40 percent
of a country’s population and for the total population of a country.
Data sources
The Global Database of Shared Prosperity was prepared by the Global
Poverty Working Group, which comprises poverty measurement spe-
cialists of different departments of the World Bank Group. The data-
base’s primary source of data is the World Bank Group’s PovcalNet
database, an interactive computational tool that allows users to rep-
licate the World Bank Group’s official poverty estimates measured at
international poverty lines ($1.25 or $2 per day per capita). The data-
sets included in PovcalNet are provided and reviewed by the members
of the Global Poverty Working Group. The choice of consumption or
income to measure shared prosperity for a country is consistent with
the welfare aggregate used to estimate extreme poverty rates in Pov-
calNet, unless there are strong arguments for using a different welfare
aggregate. The practice adopted by the World Bank Group for estimat-
ing global and regional poverty rates is, in principle, to use per capita
consumption expenditure as the welfare measure wherever available
and to use income as the welfare measure for countries for which
consumption data are unavailable. However, in some cases data on
consumption may be available but are outdated or not shared with the
World Bank Group for recent survey years. In these cases, if data on
income are available, income is used for estimating shared prosperity.
References
Ambar, Narayan, Jaime Saavedra-Chanduvi, and Sailesh Tiwari. 2013.
“Shared Prosperity: Links to Growth, Inequality and Inequality of
Opportunity.” Policy Research Working Paper 6649. World Bank,
Washington, DC.
World Bank. 2014a. “A Measured Approach to Ending Poverty and
Boosting Shared Prosperity: Concepts, Data, and the Twin Goals.”
Washington, DC.
———. 2014b. Global Database of Shared Prosperity. [http://www
.worldbank.org/en/topic/poverty/brief/global-database-of-shared
-prosperity]. Washington, DC.
———. Various years. PovcalNet. [http://iresearch.worldbank.org
/PovcalNet/]. Washington, DC.
Shared prosperity
About the data](https://image.slidesharecdn.com/banquemondiale2-150716122016-lva1-app6892/85/World-Development-Indicators-2015-65-320.jpg)
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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 develop-
ment. 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 child-
birth, and their children set off on firmer developmental paths, both
physically and mentally. Undernourished children have lower resis-
tance to infection and are more likely to die from common childhood
ailments such as diarrheal diseases and respiratory infections. Fre-
quent 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 2013 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 dis-
eases are unavailable, mortality rates may be used to identify vulner-
able 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 systems
and direct or indirect estimates based on sample surveys or cen-
suses. 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 registration 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 UN Inter-agency Group for Child
Mortality Estimation, which comprises UNICEF, the 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 [2014]; 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, the United Nations Population
Fund (UNFPA), the World Bank, and the United Nations Population
Division 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 estimated with
a multilevel regression model using available national maternal
mortality data and socioeconomic information, including fertility,
birth attendants, and gross domestic product. The methodology dif-
fers from that used for previous estimates, so data presented here
should not be compared across editions (WHO and others 2014).
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
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
About the data](https://image.slidesharecdn.com/banquemondiale2-150716122016-lva1-app6892/85/World-Development-Indicators-2015-77-320.jpg)
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Occupations 1988. Data are drawn mostly from labor force surveys,
supplemented in limited cases with other household surveys, popu-
lation censuses, and official estimates. Countries could apply differ-
ent practice whether or where the armed forces are included. Armed
forces constitute a separate major group, but in some countries they
are included in the most closely matching civilian occupation or in
nonclassifiable workers. For country-level information on classifica-
tion, source, reference period, or definition, consult the footnotes in
the World Development Indicators database or the ILO’s Key Indica-
tors of the Labour Market, 8th edition, database.
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 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 comple-
tion rate, or gross intake ratio to the last grade of primary educa-
tion, is the number of new entrants (enrollments minus repeaters)
in the last grade of primary education, 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 people ages 15–24 who can both read
and write with understanding 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. Data are modeled ILO estimates. • 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. Data are
modeled ILO estimates. • 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). Data on under-five mortality rates are from
the UN Inter-agency Group for Child Mortality Estimation (www
.childmortality.org) and are based mainly on household surveys,
censuses, and vital registration data. Modeled estimates of mater-
nal mortality ratios are from the UN Maternal Mortality Estimation
Inter-agency Group (www.who.int/reproductivehealth/publications
/monitoring/maternal-mortality-2013/). Data on adolescent fertil-
ity rates are from United Nations Population Division (2013), with
annual data linearly interpolated by the World Bank’s Development
Data Group. Data on HIV prevalence are from UNAIDS (2014).
Data on primary completion rates and youth literacy rates are from
the UNESCO Institute for Statistics (www.uis.unesco.org). Data on
labor force participation rates, vulnerable employment, unemploy-
ment, and female legislators, senior officials, and managers are
from the ILO’s Key Indicators of the Labour Market, 8th edition,
database.
References
Amin, Mohammad, and Asif Islam. 2014. “Are There More Female
Managers in the Retail Sector? Evidence from Survey Data in Devel-
oping Countries.” Policy Research Working Paper 6843. World Bank,
Washington, DC.
ILO (International Labour Organization).Various years. Key Indicators of
the Labour Market. Geneva: International Labour Office.
UNAIDS (Joint United Nations Programme on HIV/AIDS). 2014. The
Gap Report. [www.unaids.org/en/resources/campaigns/2014
/2014gapreport/gapreport/]. Geneva.
UNICEF (United Nations Children’s Fund), WHO (World Health Orga-
nization), and the World Bank. 2014. 2013 Joint Child Malnutrition
Estimates - Levels and Trends. New York: UNICEF. [www.who.int
/nutgrowthdb/estimates2013/].
UN Inter-agency Group for Child Mortality Estimation. 2014. Levels and
Trends in Child Mortality: Report 2014. [www.unicef.org/media/files
/Levels_and_Trends_in_Child_Mortality_2014.pdf]. New York.
United Nations Population Division. 2013. World Population Prospects:
The 2012 Revision. [http://esa.un.org/unpd/wpp/Documentation
/publications.htm]. 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), World Bank, and
United Nations Population Division. 2014. Trends in Maternal Mor-
tality: 1990 to 2013. [www.who.int/reproductivehealth/publications
/monitoring/maternal-mortality-2013/]. Geneva: WHO.
World Bank. 2014. Global Database of Shared Prosperity. [http://
www.worldbank.org/en/topic/poverty/brief/global-database-of
-shared-prosperity]. Washington, DC.](https://image.slidesharecdn.com/banquemondiale2-150716122016-lva1-app6892/85/World-Development-Indicators-2015-80-320.jpg)
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Environment 3
acid precipitation, or forest fires. • Nationally protected areas are
terrestrial and marine protected areas as a percentage of total terri-
torial area and include all nationally designated protected areas with
known location and extent. All overlaps between different designa-
tions 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 using an improved drinking water source. An improved
drinking water source includes piped water on premises (piped
household water connection located inside the user’s dwelling, plot
or yard), public taps or standpipes, tube wells or boreholes, protected
dug wells, protected springs, and rainwater collection. • Access to
improved sanitation facilities is the percentage of the population
using improved sanitation facilities. Improved sanitation facilities are
likely to ensure hygienic separation of human excreta from human
contact. They include flush/pour flush toilets (to piped sewer system,
septic tank, or pit latrine), ventilated improved pit latrines, pit latrines
with slab, and composting toilets. • Urban population growth is the
annual rate of change of urban population assuming exponential
change. Urban population is the proportion of midyear population
of areas defined as urban in each country, which is obtained by the
United Nations, multiplied by the World Bank estimate of total popu-
lation. • Population-weighted exposure to ambient PM2.5 pollution
is defined as exposure to fine suspended particulates of less than
2.5 microns in diameter that are capable of penetrating deep into
the respiratory tract and causing severe health damage. Data are
aggregated at the national level and include both rural and urban
areas. Exposure is calculated by weighting mean annual concen-
trations of PM2.5 by population. • Carbon dioxide emissions are
emissions from the burning of fossil fuels and the manufacture of
cement and include carbon dioxide produced during consumption 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. Production 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 website.
Data on protected areas, derived from the UNEP and WCMC online
databases, are based on data from national authorities, 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 the WHO/UNICEF Joint Monitor-
ing Programme for Water Supply and Sanitation (www.wssinfo.org).
Data on urban population are from the United Nations Population
Division (2014). Data on particulate matter concentrations are from
the Global Burden of Disease 2010 study (www.healthdata.org/gbd
/data) by the Institute for Health Metrics and Evaluation (see Lim
and others 2012). See Brauer and others (2012) for the data and
methods used to estimate ambient PM2.5 exposure. Data on carbon
dioxide emissions are from CDIAC online databases. Data on energy
use and electricity production are from IEA online databases and its
annual Energy Statistics of Non-OECD Countries, Energy Balances of
Non-OECD Countries, Energy Statistics of OECD Countries, and Energy
Balances of OECD Countries.
References
Brauer, M., M. Amman, R.T. Burnett, A. Cohen, F. Dentener, et al. 2012.
“Exposure Assessment for Estimation of the Global Burden of Dis-
ease Attributable to Outdoor Air Pollution.” Environmental Science
& Technology 46: 652–60.
CDIAC (Carbon Dioxide Information Analysis Center). n.d. Online data-
base. [http://cdiac.ornl.gov/home.html]. Oak Ridge National Labo-
ratory, 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.
Lim, S.S., T. Vos, A.D. Flaxman, G. Danaei, K. Shibuya, et al. 2012.
“A Comparative Risk Assessment of Burden of Disease and Injury
Attributable to 67 Risk Factors and Risk Factor Clusters in 21
Regions, 1990–2010: A Systematic Analysis for the Global Burden
of Disease Study 2010.” Lancet 380(9859): 2224–60.
UNEP (United Nations Environment Programme) and WCMC (World
Conservation Monitoring Centre). 2013. Online databases. [www
.unep-wcmc.org/datasets-tools--reports_15.html?&types=Data,We
bsite,Tool&ctops=]. Cambridge, UK.
United Nations Population Division. 2014. World Urbanization Pros-
pects: The 2014 Revision. [http://esa.un.org/unpd/wup/]. New
York: United Nations, Department of Economic and Social Affairs.
WHO (World Health Organization). 2006. WHO Air Quality Guidelines for
Particulate Matter, Ozone, Nitrogen Dioxide, and Sulfur Dioxide: Global
Update 2005, Summary of Risk Assessment. [http://whqlibdoc.who
.int/hq/2006/WHO_SDE_PHE_OEH_06.02_eng.pdf].](https://image.slidesharecdn.com/banquemondiale2-150716122016-lva1-app6892/85/World-Development-Indicators-2015-97-320.jpg)
![World Development Indicators 2015 89Economy States and markets Global links Back
Economy 4
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 rev-
enue 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 particular 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). Data
on consumption of fixed capital are from the United Nations Statis-
tics Division’s National Accounts Statistics: Main Aggregates and
Detailed Tables, the Organization for Economic Co-operation and
Development’s National Accounts Statistics database, and the Penn
World Table (Feenstra, Inklaar, and Timmler 2013), with missing
data estimated by World Bank staff. Data on education expenditure
are from the United Nations Educational, Scientific and Cultural
Organization Institute for Statistics, with missing data estimated
by World Bank staff. Data on forest, energy, and mineral deple-
tion are based on the sources and methods described in World
Bank (2011). Additional data on energy commodity production and
reserves are from the United States Energy Information Administra-
tion. Estimates of damages from carbon dioxide emissions follow
the method of Fankhauser (1994) using data from the International
Energy Agency’s CO2 Emissions from Fuel Combustion Statistics
database. Data on exposure to household air pollution and ambient
particulate matter pollution are from the Institute for Health Metrics
and Evaluation’s Global Burden of Disease 2010 study. Data on
current account balances 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, Eduardo Levy Yeyati, Samuel Pienknagura. 2013.
Latin America’s Deceleration and the Exchange Rate Buffer. Semian-
nual Report, Office of the Chief Economist. Washington, DC: World
Bank.
Fankhauser, Samuel. 1994. “The Social Costs of Greenhouse Gas
Emissions: An Expected Value Approach.” Energy Journal 15 (2):
157–84.
Feenstra, Robert C., Robert Inklaar, and Marcel P. Timmer. 2013. “The
Next Generation of the Penn World Table.” [www.ggdc.net/pwt].
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.
Institute for Health Metrics and Evaluation. 2010. Global Burden
of Disease data. University of Washington, Seattle. [https://www
.healthdata.org/gbd/data].
International Energy Agency. Various years. IEA CO2 Emissions from
Fuel Combustion Statistics database. [http://dx.doi.org/10.1787
/co2-data-en]. Paris.
Organisation for Economic Co-operation and Development. Vari-
ous years. National Accounts Statistics database. [http://dx.doi
.org/10.1787/na-data-en]. Paris.
United Nations Statistics Division. Various years. National Accounts
Statistics: Main Aggregates and Detailed Tables. Parts 1 and 2. New
York: United Nations.
United States Energy Information Administration. Various years. Inter-
national Energy Statistics database. [http://www.eia.gov/cfapps
/ipdbproject/IEDIndex3.cfm]. Washington, DC.
World Bank. 2011. The Changing Wealth of Nations: Measuring Sustain-
able Development for the New Millennium. Washington, DC.
———. 2015. Global Economic Prospects: Having Fiscal Space and
Using It. Washington, DC.
———. Various years. World Development Indicators. Washington, DC.](https://image.slidesharecdn.com/banquemondiale2-150716122016-lva1-app6892/85/World-Development-Indicators-2015-113-320.jpg)
![134 World Development Indicators 2015 Front User guide World view People Environment?
Primary data documentation notes
• Base year is the base or pricing period used for
constant 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 relative 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 refer-
ence 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 historical series. To allow for
cross-country comparison and data aggregation,
constant price data reported in World Development
Indicators are rescaled to a common 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). The 2008 SNA is an update of the
1993 SNA and retains its basic theoretical frame-
work. • SNA price valuation shows whether value
added in the national accounts is reported at basic
prices (B) or producer prices (P). Producer prices
include taxes paid by producers and thus tend to
overstate the actual value added in production. How-
ever, value added can be higher at basic prices than
at producer prices in countries with high agricultural
subsidies. • Alternative conversion factor identifies
the countries and years for which a World Bank–esti-
mated 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 avail-
able survey year for the International Comparison
Program’s estimates of PPPs. • Balance of Pay-
ments 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 2013 data. A
indicates that data are as reported, P that data are
based on reported or collected information 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 sys-
tem (G) or special trade system (S). Under the gen-
eral trade system goods entering directly for
domestic consumption and goods entered into cus-
toms 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 cus-
toms storage. Exports under the general system
comprise outward-moving goods: (a) national goods
wholly or partly produced in the country; (b) foreign
goods, neither transformed nor declared for domes-
tic consumption in the country, that move outward
from customs storage; and (c) nationalized goods
that have been declared for domestic consumption
and move outward without being transformed. Under
the special system of trade, exports are categories
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. • Govern-
ment finance accounting concept is the accounting
basis for reporting central government financial
data. For most countries government finance data
have been consolidated (C) into one set of accounts
capturing all central government fiscal activities.
Budgetary central government accounts (B) exclude
some central government units. • IMF data dissemi-
nation standard shows the countries that subscribe
to the IMF’s Special Data Dissemination Standard
(SDDS) or General Data Dissemination System
(GDDS). S refers to countries 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 subscribe to the GDDS.
The SDDS was established for member countries
that have or might seek access to international capi-
tal markets to guide them in providing their eco-
nomic and financial data to the public. The GDDS
helps countries disseminate comprehensive, timely,
accessible, and reliable economic, financial, and
sociodemographic statistics. IMF member countries
elect to participate in either the SDDS or the GDDS.
Both standards enhance the availability of timely
and comprehensive data and therefore contribute
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
revisions if basic data are available, such as popula-
tion 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. A rare case,
France conducts a rolling census every year; the
1999 general population census was the last to
cover the entire population simultaneously. • Latest
demographic, education, or health household sur-
vey indicates the household surveys used to com-
pile the demographic, education, and health data in
section 2. DHS is Demographic and Health Survey,
ENADID is National Survey of Demographic Dynam-
ics, FHS is Family Health Survey, HIV/MCH is
HIV/Maternal and Child Health, IrMIDHS is Iran’s
Multiple Indicator Demographic and Health Survey,
KDHS is Kiribati Demographic and Health Survey,
LSMS is Living Standards Measurement Study,
MICS is Multiple Indicator Cluster Survey, MIS is
Malaria Indicator Survey, NHS is National Health
Survey, NSS is National Sample Survey on Popula-
tion Changes, PAPFAM is Pan Arab Project for Family
Health, RHS is Reproductive Health Survey, RMIDHS
is Republic of the Marshall Islands Demographic and
Health Survey, SPA is Service Provision Assess-
ments, TDHS is Turkey Demographic and Health
Survey, and WHS is World Health Survey. Detailed
information for DHS, HIV/MCH, MIS, and SPA are
available at www.dhsprogram.com; for MICS at www
.childinfo.org; for RHS at www.cdc.gov
/reproductivehealth; and for WHS at www.who.int
/healthinfo/survey/en. • Source of most recent
income and expenditure data shows household sur-
veys that collect income and expenditure data.
Names and detailed information on household sur-
veys can be found on the website of the International
Household Survey Network (www.surveynetwork
.org). Core Welfare Indicator Questionnaire Surveys
(CWIQ), developed by the World Bank, measure
changes in key social indicators for different popula-
tion groups—specifically indicators of access, utili-
zation, and satisfaction with core social and eco-
nomic services. Expenditure survey/budget surveys
(ES/BS) collect detailed information on household
consumption as well as on general demographic,
social, and economic characteristics. Integrated
household surveys (IHS) collect detailed information
on a wide variety of topics, including health, educa-
tion, 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, tax-
able income, and taxes by socioeconomic group.
Labor force surveys (LFS) collect information on
employment, unemployment, hours of work, income,](https://image.slidesharecdn.com/banquemondiale2-150716122016-lva1-app6892/85/World-Development-Indicators-2015-158-320.jpg)
![World Development Indicators 2015 137Economy States and markets Global links Back
• Aggregates marked by an s are sums of available
data. Missing values are not imputed. Sums are not
computed if more than a third of the observations
in the series or a proxy for the series are missing
in a given year.
• 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.
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 in real terms computed
from constant price series. Three principal methods
are used to calculate growth rates: least squares,
exponential endpoint, 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
.](https://image.slidesharecdn.com/banquemondiale2-150716122016-lva1-app6892/85/World-Development-Indicators-2015-161-320.jpg)
![138 World Development Indicators 2015 Front User guide World view People Environment?
Statistical methods
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 and analytical purposes, the
World Bank uses the Atlas conversion factor instead
of simple exchange rates. 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 the
rate of international inflation.
The objective of the adjustment is to reduce any
changes to the exchange rate caused by inflation.
A country’s inflation rate between year t and year t–n
(rt–n
) is measured by the change in its GDP deflator (pt
):
pt
rt–n
= pt–n
International inflation between year t and year t–n
(rt–n
SDR$) is measured using the change in a deflator
based on the International Monetary Fund’s unit of
account, special drawing rights (or SDRs). Known as
the “SDR deflator,” it is a weighted average of the GDP
deflators (in SDR terms) of Japan, the United Kingdom,
the United States, and the euro area, converted to
U.S. dollar terms; weights are the amount of each
currency in one SDR unit.
pt
SDR$
rt–n
SDR$
=
pt–n
SDR$
The Atlas conversion factor (local currency to the
U.S. dollar) for year t (et
atlas
) is given by:
where et is the average annual exchange rate (local
currency to the U.S. dollar) for year t.
GNI in U.S. dollars (Atlas method) for year t (Yt
atlas$
)
is calculated by applying the Atlas conversion factor
to a country’s GNI in current prices (local currency)
(Yt) as follows:
Yt
atlas$
= Yt /et
atlas
The resulting Atlas GNI in U.S. dollars can then be
divided by a country’s midyear population to yield its
GNI per capita (Atlas method).
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 deemed to be
unreliable or unrepresentative of 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 documentation. Alternative conversion factors
are used in the Atlas methodology and elsewhere in
World Development Indicators as single-year conver-
sion factors.](https://image.slidesharecdn.com/banquemondiale2-150716122016-lva1-app6892/85/World-Development-Indicators-2015-162-320.jpg)
World Development Indicators 2015
- 3. 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 Cabo 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 Mozambique Zimbabwe Botswana Namibia 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- baijan Armenia 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 41313 NOVEMBER 2014 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
- 6. ©2015 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 18 17 16 15 This work is a product of the staff of The World Bank with external contributions. 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 govern- ments 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 judgment 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. Rights and Permissions This work is available under the Creative Commons Attribution 3.0 IGO license (CC BY 3.0 IGO) http://creativecommons.org /licenses/by/3.0/igo. Under the Creative Commons Attribution license, you are free to copy, distribute, transmit, and adapt this work, including for commercial purposes, under the following conditions: Attribution—Please cite the work as follows: World Bank. 2015. World Development Indicators 2015. Washington, DC: World Bank. doi:10.1596/978–1-4648–0440–3. License: Creative Commons Attribution CC BY 3.0 IGO Translations—If you create a translation of this work, please add the following disclaimer along with the attribution: This transla- tion was not created by The World Bank and should not be considered an official World Bank translation. The World Bank shall not be liable for any content or error in this translation. Adaptations—If you create an adaptation of this work, please add the following disclaimer along with the attribution: This is an adaptation of an original work by The World Bank. Views and opinions expressed in the adaptation are the sole responsibility of the author or authors of the adaptation and are not endorsed by The World Bank. Third-party content—The World Bank does not necessarily own each component of the content contained within the work. The World Bank therefore does not warrant that the use of any third-party-owned individual component or part contained in the work will not infringe on the rights of those third parties. The risk of claims resulting from such infringement rests solely with you. If you wish to re-use a component of the work, it is your responsibility to determine whether permission is needed for that re-use and to obtain permission from the copyright owner. Examples of components can include, but are not limited to, tables, figures, or images. All queries on rights and licenses should be addressed to the Publishing and Knowledge Division, The World Bank, 1818 H Street NW, Washington, DC 20433, USA; fax: 202–522–2625; e-mail: pubrights@worldbank.org. ISBN (paper): 978-1-4648-0440-3 ISBN (electronic): 978–1-4648–0441–0 DOI: 10.1596/978–1-4648–0440–3 Cover design: Communications Development Incorporated. Cover photo: © Arne Hoel/World Bank. Further permission required for reuse. Other photos: pages xx and 42, © Arne Hoel/World Bank; page 60, © Givi Pirtskhalava/World Bank; page 76, © Curt Carnemark/ World Bank; page 92, © Tom Perry/World Bank; page 108, © Gerardo Pesantez/World Bank. Further permission required for reuse.
- 7. World Development Indicators 2015 iii The year 2015 is when the world aimed to achieve many of the targets set out in the Millennium Devel- opment Goals. Some have been met. The rate of extreme poverty and the proportion of people with- out access to safe drinking water were both halved between 1990 and 2010, five years ahead of sched- ule. But some targets have not been achieved, and the aggregates used to measure global trends can mask the uneven progress in some regions and countries. This edition of World Development Indi- cators uses the latest available data and forecasts to show whether the goals have been achieved and highlights some of the differences between countries and regions that underlie the trends. Figures and data are also available online at http://data.worldbank .org/mdgs. But this will be the last edition of World Devel- opment Indicators that reports on the Millennium Development Goals in this way. A new and ambi- tious set of goals and targets for development—the Sustainable Development Goals—will be agreed at the UN General Assembly in September 2015. Like the Millennium Development Goals before them, the Sustainable Development Goals will require more and better data to monitor progress and to design and adjust the policies and programs that will be needed to achieve them. Policymakers and citizens need data and, equally important, the ability to analyze them and understand their meaning. The need for a data revolution has been recognized during the framing of the Sustainable Development Goals by the UN Secretary-General’s High-Level Panel on the Post-2015 Development Agenda. In response, a group of independent advisors—of which I was privileged to have been part—has called for action in several areas. A global consensus is needed on prin- ciples and standards for interoperable data. Emerging technology innovations need to be shared, especially in low-capacity countries and institutions. National capacities among data producers and users need to be strengthened with new and sustained investment. And new forms of public–private partnerships are needed to promote innovation, knowledge and data sharing, advocacy, and technology transfer. The World Bank Group is addressing all four of these action areas, especially developing new funding streams and forging public–private partnerships for innovation and capacity development. This edition of World Development Indicators retains the structure of previous editions: World view, People, Environment, Economy, States and markets, and Global links. New data include the average growth in income of the bottom 40 percent of the population, an indi- cator of shared prosperity presented in World View, and an indicator of statistical capacity in States and markets. World view also includes a new snapshot of progress toward the Millennium Development Goals, and each section includes a map highlighting an indi- cator of special interest. World Development Indicators is the result of a collaborative effort of many partners, including the UN family, the International Monetary Fund, the Inter- national Telecommunication Union, the Organisation for Economic Co-operation and Development, the statistical offices of more than 200 economies, and countless others. I wish to thank them all. Their work is at the very heart of development and the fight to eradicate poverty and promote shared prosperity. Haishan Fu Director Development Economics Data Group Preface
- 8. iv World Development Indicators 2015 Acknowledgments This book was prepared by a team led by Masako Hiraga under the management of Neil Fantom and com- prising Azita Amjadi, Maja Bresslauer, Tamirat Chulta, Liu Cui, Federico Escaler, Mahyar Eshragh-Tabary, Juan Feng, Saulo Teodoro Ferreira, Wendy Huang, Bala Bhaskar Naidu Kalimili, Haruna Kashiwase, Buyant Erdene Khaltarkhuu, Tariq Khokhar, Elysee Kiti, Hiroko Maeda, Malvina Pollock, William Prince, Leila Rafei, Evis Rucaj, Umar Serajuddin, Rubena Sukaj, Emi Suzuki, Jomo Tariku, and Dereje Wolde, working closely with other teams in the Development Econom- ics Vice Presidency’s Development Data Group. World Development Indicators electronic products were prepared by a team led by Soong Sup Lee and comprising Ying Chi, Jean-Pierre Djomalieu, Ramgopal Erabelly, Shelley Fu, Omar Hadi, Gytis Kanchas, Siddhesh Kaushik, Ugendran Machakkalai, Nacer Megherbi, Parastoo Oloumi, Atsushi Shimo, and Malarvizhi Veerappan. All work was carried out under the direction of Haishan Fu. Valuable advice was provided by Poonam Gupta, Zia M. Qureshi, and David Rosenblatt. The choice of indicators and text content was shaped through close consultation with and substan- tial contributions from staff in the World Bank’s vari- ous Global Practices and Cross-Cutting Solution Areas and staff of the International Finance Corporation and the Multilateral Investment Guarantee Agency. Most important, the team received substantial help, guid- ance, and data from external partners. For individual acknowledgments of contributions to the book’s con- tent, see Credits. For a listing of our key partners, see Partners. Communications Development Incorporated pro- vided overall design direction, editing, and layout, led by 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. Staff from the World Bank’s Pub- lishing and Knowledge Division oversaw printing and dissemination of the book.
- 9. World Development Indicators 2015 v Table of contents Preface iii Acknowledgments iv Partners vi User guide xii 1. World view 1 2. People 43 3. Environment 61 4. Economy 77 5. States and markets 93 6. Global links 109 Primary data documentation 125 Statistical methods 136 Credits 139 Introduction Millennium Development Goals snapshot MDG 1 Eradicate extreme poverty MDG 2 Achieve universal primary education MDG 3 Promote gender equality and empower women MDG 4 Reduce child mortality MDG 5 Improve maternal health MDG 6 Combat HIV/AIDS, malaria, and other diseases MDG 7 Ensure environmental sustainability MDG 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 About the data Shared prosperity indicators About the data Map Introduction Highlights Map Table of indicators About the data Online tables and indicators
- 10. vi World Development Indicators 2015 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 Development Indicators are the fruit of decades of work at many levels, from the field workers who administer censuses and household surveys to the committees and working parties of the national and international statistical agencies that develop the nomenclature, classifications, and standards funda- mental to an international statistical system. Non- governmental organizations and the private sector have also made important contributions, both in gath- ering primary data and in organizing and publishing their results. And academic researchers have played a crucial role in developing statistical methods and carrying on a continuing dialogue about the quality and interpretation of statistical indicators. All these contributors have a strong belief that available, accu- rate data will improve the quality of public and private decisionmaking. The organizations listed here have made World Development Indicators possible by sharing their data and their expertise with us. More important, their 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, 2015.
- 11. World Development Indicators 2015 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 Institute for Health Metrics and Evaluation www.healthdata.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
- 12. viii World Development Indicators 2015 Front User guide World view People Environment? Partners International Monetary Fund www.imf.org International Telecommunication Union www.itu.int Joint United Nations Programme on HIV/AIDS www.unaids.org National Science Foundation www.nsf.gov The Office of U.S. Foreign Disaster Assistance www.usaid.gov 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
- 13. World Development Indicators 2015 ixEconomy States and markets Global links Back 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 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
- 14. x World Development Indicators 2015 Front User guide World view People Environment? Partners 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
- 15. World Development Indicators 2015 xiEconomy States and markets Global links Back 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.ch Netcraft http://news.netcraft.com 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
- 16. xii World Development Indicators 2015 Front User guide World view People Environment? User guide to tables 66 World Development Indicators 2015 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 Mean annual exposure to PM2.5 pollution micrograms per cubic meter average annual % Per capita cubic meters % of total population % of total population % growth million metric tons Per capita kilograms of oil equivalent billion kilowatt hours 2000–10 2012 2013 2012 2012 2012–13 2010 2010 2011 2011 Afghanistan 0.00 0.4 1,543 64 29 4.0 24 8.2 .. .. Albania –0.10 9.5 9,284 96 91 1.8 14 4.3 748 4.2 Algeria 0.57 7.4 287 84 95 2.8 22 123.5 1,108 51.2 American Samoa 0.19 16.8 .. 100 63 0.0 .. .. .. .. Andorra 0.00 9.8 3,984 100 100 0.5 13 0.5 .. .. Angola 0.21 12.1 6,893 54 60 5.0 11 30.4 673 5.7 Antigua and Barbuda 0.20 1.2 578 98 91 –1.0 17 0.5 .. .. Argentina 0.81 6.6 7,045 99 97 1.0 5 180.5 1,967 129.6 Armenia 1.48 8.1 2,304 100 91 0.0 19 4.2 916 7.4 Aruba 0.00 0.0 .. 98 98 –0.2 .. 2.3 .. .. Australia 0.37 15.0 21,272 100 100 1.9 6 373.1 5,501 252.6 Austria –0.13 23.6 6,486 100 100 0.6 13 66.9 3,935 62.2 Azerbaijan 0.00 7.4 862 80 82 1.7 17 45.7 1,369 20.3 Bahamas, The 0.00 1.0 53 98 92 1.5 13 2.5 .. .. Bahrain –3.55 6.8 3 100 99 1.1 49 24.2 7,353 13.8 Bangladesh 0.18 4.2 671 85 57 3.6 31 56.2 205 44.1 Barbados 0.00 0.1 281 100 .. 0.1 19 1.5 .. .. Belarus –0.43 8.3 3,930 100 94 0.6 11 62.2 3,114 32.2 Belgium –0.16 24.5 1,073 100 100 0.5 19 108.9 5,349 89.0 Belize 0.67 26.4 45,978 99 91 1.9 6 0.4 .. .. Benin 1.04 25.5 998 76 14 3.7 22 5.2 385 0.2 Bermuda 0.00 5.1 .. .. .. 0.3 .. 0.5 .. .. Bhutan –0.34 28.4 103,456 98 47 3.7 22 0.5 .. .. Bolivia 0.50 20.8 28,441 88 46 2.3 6 15.5 746 7.2 Bosnia and Herzegovina 0.00 1.5 9,271 100 95 0.2 12 31.1 1,848 15.3 Botswana 0.99 37.2 1,187 97 64 1.3 5 5.2 1,115 0.4 Brazil 0.50 26.0 28,254 98 81 1.2 5 419.8 1,371 531.8 Brunei Darussalam 0.44 29.6 20,345 .. .. 1.8 5 9.2 9,427 3.7 Bulgaria –1.53 35.4 2,891 100 100 –0.1 17 44.7 2,615 50.0 Burkina Faso 1.01 15.2 738 82 19 5.9 27 1.7 .. .. Burundi 1.40 4.9 990 75 48 5.6 11 0.3 .. .. Cabo Verde –0.36 0.2 601 89 65 2.1 43 0.4 .. .. Cambodia 1.34 23.8 7,968 71 37 2.7 17 4.2 365 1.1 Cameroon 1.05 10.9 12,267 74 45 3.6 22 7.2 318 6.0 Canada 0.00 7.0 81,071 100 100 1.4 10 499.1 7,333 636.9 Cayman Islands 0.00 1.5 .. 96 96 1.5 .. 0.6 .. .. Central African Republic 0.13 18.0 30,543 68 22 2.6 19 0.3 .. .. Chad 0.66 16.6 1,170 51 12 3.4 33 0.5 .. .. Channel Islands .. 0.5 .. .. .. 0.7 .. .. .. .. Chile –0.25 15.0 50,228 99 99 1.1 8 72.3 1,940 65.7 China –1.57 16.1 2,072 92 65 2.9 73 8,286.9 2,029 4,715.7 Hong Kong SAR, China .. 41.9 .. .. .. 0.5 .. 36.3 2,106 39.0 Macao SAR, China .. .. .. .. .. 1.7 .. 1.0 .. .. Colombia 0.17 20.8 46,977 91 80 1.7 5 75.7 671 61.8 Comoros 9.34 4.0 1,633 95 35 2.7 5 0.1 .. .. Congo, Dem. Rep. 0.20 12.0 13,331 47 31 4.0 15 3.0 383 7.9 Congo, Rep. 0.07 30.4 49,914 75 15 3.2 14 2.0 393 1.3 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 1,300 time series indicators for 214 economies and more than 30 country groups, with data for many indicators going back more than 50 years. The 2015 edition of World Development Indicators offers a condensed presentation of the principal indica- tors, arranged in their traditional sections, along with regional and topical highlights and maps. 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 purple 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, 2015.
- 17. World Development Indicators 2015 xiiiEconomy States and markets Global links Back World Development Indicators 2015 67Economy 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 Mean annual exposure to PM2.5 pollution micrograms per cubic meter average annual % Per capita cubic meters % of total population % of total population % growth million metric tons Per capita kilograms of oil equivalent billion kilowatt hours 2000–10 2012 2013 2012 2012 2012–13 2010 2010 2011 2011 Costa Rica –0.93 22.6 23,193 97 94 2.7 8 7.8 983 9.8 Côte d’Ivoire –0.15 22.2 3,782 80 22 3.8 15 5.8 579 6.1 Croatia –0.19 10.3 8,859 99 98 0.2 14 20.9 1,971 10.7 Cuba –1.66 9.9 3,384 94 93 0.1 7 38.4 992 17.8 Curaçao .. .. .. .. .. 1.0 .. .. .. .. Cyprus –0.09 17.1 684 100 100 0.9 19 7.7 2,121 4.9 Czech Republic –0.08 22.4 1,251 100 100 0.0 16 111.8 4,138 86.8 Denmark –1.14 23.6 1,069 100 100 0.6 12 46.3 3,231 35.2 Djibouti 0.00 0.2 344 92 61 1.6 27 0.5 .. .. Dominica 0.58 3.7 .. .. .. 0.9 18 0.1 .. .. Dominican Republic 0.00 20.8 2,019 81 82 2.6 9 21.0 727 13.0 Ecuador 1.81 37.0 28,111 86 83 1.9 6 32.6 849 20.3 Egypt, Arab Rep. –1.73 11.3 22 99 96 1.7 33 204.8 978 156.6 El Salvador 1.45 8.7 2,465 90 71 1.4 5 6.2 690 5.8 Equatorial Guinea 0.69 15.1 34,345 .. .. 3.1 7 4.7 .. .. Eritrea 0.28 3.8 442 .. .. 5.2 25 0.5 129 0.3 Estonia 0.12 23.2 9,643 99 95 –0.5 7 18.3 4,221 12.9 Ethiopia 1.08 18.4 1,296 52 24 4.9 15 6.5 381 5.2 Faeroe Islands 0.00 1.0 .. .. .. 0.4 .. 0.7 .. .. Fiji –0.34 6.0 32,404 96 87 1.4 5 1.3 .. .. Finland 0.14 15.2 19,673 100 100 0.6 5 61.8 6,449 73.5 France –0.39 28.7 3,033 100 100 0.7 14 361.3 3,869 556.9 French Polynesia –3.97 0.1 .. 100 97 0.9 .. 0.9 .. .. Gabon 0.00 19.1 98,103 92 41 2.7 6 2.6 1,253 1.8 Gambia, The –0.41 4.4 1,622 90 60 4.3 36 0.5 .. .. Georgia 0.09 3.7 12,955 99 93 0.2 12 6.2 790 10.2 Germany 0.00 49.0 1,327 100 100 0.6 16 745.4 3,811 602.4 Ghana 2.08 14.4 1,170 87 14 3.4 18 9.0 425 11.2 Greece –0.81 21.5 5,260 100 99 –0.1 17 86.7 2,402 59.2 Greenland 0.00 40.6 .. 100 100 –0.1 .. 0.6 .. .. Grenada 0.00 0.3 .. 97 98 0.3 15 0.3 .. .. Guam 0.00 5.3 .. 100 90 1.5 .. .. .. .. Guatemala 1.40 29.8 7,060 94 80 3.4 12 11.1 691 8.1 Guinea 0.54 26.8 19,242 75 19 3.8 22 1.2 .. .. Guinea-Bissau 0.48 27.1 9,388 74 20 4.2 31 0.2 .. .. Guyana 0.00 5.0 301,396 98 84 0.8 6 1.7 .. .. Haiti 0.76 0.1 1,261 62 24 3.8 11 2.1 320 0.7 Honduras 2.06 16.2 11,196 90 80 3.2 7 8.1 609 7.1 Hungary –0.62 23.1 606 100 100 0.4 16 50.6 2,503 36.0 Iceland –4.99 13.3 525,074 100 100 1.1 6 2.0 17,964 17.2 India –0.46 5.0 1,155 93 36 2.4 32 2,008.8 614 1,052.3 Indonesia 0.51 9.1 8,080 85 59 2.7 14 434.0 857 182.4 Iran, Islamic Rep. 0.00 7.0 1,659 96 89 2.1 30 571.6 2,813 239.7 Iraq –0.09 0.4 1,053 85 85 2.7 30 114.7 1,266 54.2 Ireland –1.53 12.8 10,658 100 99 0.7 9 40.0 2,888 27.7 Isle of Man 0.00 .. .. .. .. 0.8 .. .. .. .. Israel –0.07 14.7 93 100 100 1.9 26 70.7 2,994 59.6 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 (2013 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 $1,045 or less in 2013. Middle-income economies are those with a GNI per capita of more than $1,045 but less than $12,746. Lower middle-income and upper middle- income economies are separated at a GNI per capita of $4,125. High-income economies are those with a GNI per capita of $12,746 or more. The 19 participating member countries of the euro area are presented as a subgroup under high income economies. Statistics Data are shown for economies as they were constituted in 2013, and historical data have been revised to reflect current political arrangements. Exceptions are noted in the tables. Additional information about the data is provided in Primary 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 concepts used, and other background information. Statistical methods provides technical information on calculations 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 Serbia do not include data for Kosovo or Montenegro. • Data for Sudan exclude 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 2012/13, 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.
- 18. xiv World Development Indicators 2015 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 available online. Using an automated query process, these refer- ence tables are consistently updated based on 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 section). Each section of this book also lists the indicators included by table and by code. 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 to view a page for total population).
- 19. World Development Indicators 2015 xvEconomy States and markets Global links Back How to use DataBank DataBank (http://databank.worldbank.org) is a web resource that provides simple and quick access to col- lections 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, saved, shared, and embed- ded 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 download corresponding indicator metadata Click to export the table to Excel Click to export the table and corresponding indicator metadata to PDF Click to print the table and corresponding indicator metadata 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
- 20. xvi World Development Indicators 2015 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.
- 21. World Development Indicators 2015 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.
- 22. xviii World Development Indicators 2015 Front User guide World view People Environment? User guide to MDG Data Dashboards The World Development Indicators database provides data on trends in Millennium Development Goals (MDG) indica- tors for developing countries and other country groups. Each year the World Bank’s Global Monitoring Report uses these data to assess progress toward achieving the MDGs. Six online interactive MDG Data Dashboards, available at http://data.worldbank.org/mdgs, provide an opportunity to learn more about the assessments. The MDG progress charts presented in the World view section of this book correspond to the Global Monitoring Report assessments (except MDG 6). Sufficient progress indicates that the MDG will be attained by 2015 based on an extrapolation of the last observed data point using the growth rate over the last observable five-year period (or three-year period in the case of MDGs 1 and 5). Insuffi- cient progress indicates that the MDG will be met between 2016 and 2020. Moderately off target indicates that the MDG will be met between 2020 and 2030. Seriously off target indicates that the MDG will not be met by 2030. Insufficient data indicates an inadequate number of data points to estimate progress or that the MDG’s starting value is missing. View progress status for regions, income classifications, and other groups by number or percentage of countries.
- 23. World Development Indicators 2015 xixEconomy States and markets Global links Back View details of a country’s progress toward each MDG tar- get, including trends from 1990 to the latest year of avail- able data, and projected trends toward the 2015 target and 2030. Compare trends and targets of each MDG indicator for selected groups and countries. Compare the progress status of all MDG indicators across selected groups.
- 24. xx World Development Indicators 2015 Front User guide World view People Environment? WORLD VIEW
- 25. World Development Indicators 2015 1Economy States and markets Global links Back 1 The United Nations set 2015 as the year by which the world should achieve many of the targets set out in the eight Millennium Develop- ment Goals. World view presents the progress made toward these goals and complements the detailed analysis in the World Bank Group’s Global Monitoring Report and the online progress charts at http://data.worldbank.org/mdgs. This section also includes indicators that measure progress toward the World Bank Group’s two new goals of ending extreme poverty by 2030 and enhancing shared prosperity in every country. Indicators of shared prosperity, based on mea- suring the growth rates of the average income of the bottom 40 percent of the population, are new for this edition of World Development Indica- tors and have been calculated for 72 countries. A final verdict on the Millennium Develop- ment Goals is close, and the focus of the inter- national community continues to be on achieving them, especially in areas that have been lag- ging. Attention is also turning to a new sustain- able development agenda for the next genera- tion, to help respond to the global challenges of the 21st century. An important step was taken on September 8, 2014, when the UN General Assembly decided that the proposal of the UN Open Working Group on Sustainable Develop- ment Goals, with 17 candidate goals and 169 associated targets, will be the basis for integrat- ing sustainable development goals into the post- 2015 development agenda. Final negotiations will be concluded at the 69th General Assembly in September 2015, with implementation likely to begin in January 2016. This is thus the last edition of World Development Indicators to report on the Millennium Development Goals in their current form. One important aspect of the Millennium Development Goals has been their focus on measuring and monitoring progress, which has presented a clear challenge in improving the quality, frequency, and availability of relevant sta- tistics. In the last few years much has been done by both countries and international partners to invest in the national statistical systems where most data originate. But weaknesses remain in the coverage and quality of many indicators in the poorest countries, where resources are scarce and careful measurement of progress may matter the most. With a new, broader set of goals, targets, and indicators, the data challenge will become even greater. The recent report, A World That Counts (United Nations 2014), discusses the actions and strategies needed to mobilize a data revolu- tion for sustainable development—by exploiting advances in knowledge and technology, using resources for capacity development, and improv- ing coordination among key actors. Both govern- ments and development partners still need to invest in national statistical systems and other relevant public institutions, where much of the data will continue to originate. At the same time serious efforts need to be made to better use data and techniques from the private sector, especially so-called “big data” and other new sources.
- 26. 2 World Development Indicators 2015 Front User guide World view People Environment? Millennium Development Goals snapshot MDG 1: Eradicate extreme poverty and hunger People living on less than $1.25 a day (% of population) Developing countries as a whole met the Millennium Development Goal target of halving extreme poverty rates five years ahead of the 2015 deadline. Fore- casts indicate that the extreme poverty rate will fall to 13.4 percent by 2015, a drop of more than two-thirds from the 1990 estimate of 43.6 percent. East Asia and Pacific has had the most astound- ing record of poverty alleviation; despite improve- ments, Sub-Saharan Africa still lags behind and is not forecast to meet the target by 2015. Source: World Bank PovcalNet (http://iresearch.worldbank.org/PovcalNet/). MDG 2: Achieve universal primary education Primary completion rate (% of relevant age group) The primary school completion rate for develop- ing countries reached 91 percent in 2012 but appears to fall short of the MDG 2 target. While substantial progress was made in the 2000s, par- ticularly in Sub-Saharan Africa and South Asia, only East Asia and Pacific and Europe and Central Asia have achieved or are close to achieving uni- versal primary education. Source: United Nations Educational, Scientific and Cultural Organization Institute for Statistics. MDG 3: Promote gender equality and empower women Ratio of girls’ to boys’ primary and secondary gross enrollment rate (%) Developing countries have made substantial gains in closing gender gaps in education and will likely reach gender parity in primary and secondary education. In particular, the ratio of girls’ to boys’ primary and secondary gross enrollment rate in South Asia was the lowest of all regions in 1990, at 68 percent, but improved dramatically to reach gender parity in 2012, surpassing other regions that were making slower progress. Source: United Nations Educational, Scientific and Cultural Organization Institute for Statistics. MDG 4: Reduce child mortality Under-five mortality rate (per 1,000 live births) The under-five mortality rate in developing coun- tries declined by half, from 99 deaths per 1,000 live births in 1990 to 50 in 2013. Despite this tremendous progress, developing countries as a whole are likely to fall short of the MDG 4 target of reducing under-five mortality rate by two-thirds between 1990 and 2015. However, East Asia and Pacific and Latin America and the Caribbean have already achieved the target. Source: United Nations Inter-agency Group for Child Mortality Estimation. 0 50 100 150 200 2015 target 20102005200019951990 Developing countries 60 70 80 90 100 110 2015 target 20102005200019951990 Developing countries 0 25 50 75 100 125 2015 target 20102005200019951990 Developing countries 0 25 50 75 2015 target 20102005200019951990 2015 target Forecast Developing countries 0 25 50 75 2015 target 20102005200019951990 South Asia Sub-Saharan Africa Middle East & North Africa Europe & Central Asia Latin America & Caribbean East Asia & Pacific Forecast 0 25 50 75 100 125 2015 target 20102005200019951990 Sub-Saharan Africa East Asia & PacificEurope & Central Asia Middle East & North Africa South Asia Latin America & Caribbean 60 70 80 90 100 110 2015 target 20102005200019951990 South Asia Sub-Saharan Africa Latin America & CaribbeanEast Asia & Pacific Europe & Central Asia Middle East & North Africa 0 50 100 150 200 2015 target 20102005200019951990 South Asia Sub-Saharan Africa Latin America & Caribbean Middle East & North Africa Europe & Central Asia East Asia & Pacific
- 27. World Development Indicators 2015 3Economy States and markets Global links Back Millennium Development Goals snapshot MDG 5: Improve maternal health Maternal mortality ratio, modeled estimate (per 100,000 live births) The maternal mortality ratio has steadily decreased in developing countries as a whole, from 430 in 1990 to 230 in 2013. While substan- tial, the decline is not enough to achieve the MDG 5 target of reducing the maternal mortality ratio by 75 percent between 1990 and 2015. Regional data also indicate large declines, though no region is likely to achieve the target on time. Despite considerable drops, the maternal mortality ratio in Sub-Saharan Africa and South Asia remains high. Source: United Nations Maternal Mortality Estimation Inter-agency Group. MDG 6: Combat HIV/AIDS, malaria, and other diseases The prevalence of HIV is highest in Sub-Saharan Africa. The spread of HIV/AIDS there has slowed, and the proportion of adults living with HIV has begun to fall while the survival rate of those with access to antiretroviral drugs has increased. Global prevalence has remained flat since 2000. Tuberculosis prevalence, incidence, and death rates have fallen since 1990. Globally, the target of halting and reversing tuberculosis incidence by 2015 has been achieved. Source: Joint United Nations Programme on HIV/AIDS. Source: World Health Organization. MDG 7: Ensure environmental sustainability In developing countries the proportion of people with access to an improved water source rose from 70 percent in 1990 to 87 percent in 2012, achieving the target. The proportion with access to improved sanitation facilities rose from 35 per- cent to 57 percent, but 2.5 billion people still lack access. The large urban-rural disparity, especially in South Asia and Sub-Saharan Africa, is the prin- cipal reason the sanitation target is unlikely to be met on time. Source: World Health Organization–United Nations Children’s Fund Joint Monitoring Programme for Water Supply and Sanitation. MDG 8: Develop a global partnership for development In 2000 Internet use was rapidly increasing in high-income economies but barely under way in developing countries. Now developing countries are catching up. Internet users per 100 people have grown 27 percent a year since 2000. The debt service–to-export ratio averaged 11 percent in 2013 for developing countries, half its 2000 level but with wide disparity across regions. It will likely rise, considering the 33 percent increase in their combined external debt stock since 2010. Source: International Telecommunications Union. Source: World Development Indicators database. For a more detailed assessment of each MDG, see the spreads on the following pages. 0 250 500 750 1,000 2015 target 20102005200019951990 South Asia Sub-Saharan Africa East Asia & Pacific Middle East & North Africa Latin America & Caribbean Europe & Central Asia0 250 500 750 1,000 2015 target 20102005200019951990 Developing countries 0 100 200 300 400 201320102005200019951990 Prevalence Incidence Death rate Tuberculosis prevalence, incidence, and deaths in developing countries (per 100,000 people) 0 25 50 75 100 2015 target 20102005200019951990 South Asia Latin America & Caribbean Middle East & North Africa Europe & Central Asia East Asia & Pacific Sub-Saharan Africa Share of population with access to improved sanitation facilities (%) 0 25 50 75 100 2015 target 20102005200019951990 Access to improved sanitation facilities, developing countries Access to improved water sources, developing countries Share of population with access (%) 0 10 20 30 40 50 201320102005200019951990 South Asia Latin America & Caribbean Europe & Central Asia Sub-Saharan Africa Developing countries Middle East & North Africa East Asia & Pacific Total debt service (% of exports of goods, services, and primary income) 0 2 4 6 201320102005200019951990 Sub-Saharan Africa South Asia WorldMiddle East & North Africa HIV prevalence (% of population ages 15–49) 0 25 50 75 100 2013201020052000 South Asia Latin America & Caribbean High income Europe & Central Asia East Asia & Pacific Middle East & North Africa Sub-Saharan Africa Internet users (per 100 people)
- 28. 4 World Development Indicators 2015 Front User guide World view People Environment? MDG 1 Eradicate extreme poverty Developing countries as a whole (as classified in 1990) met the Mil- lennium Development Goal target of halving the proportion of the pop- ulation in extreme poverty five years ahead of the 2015 deadline. The latest estimates show that the proportion of people living on less than $1.25 a day fell from 43.6 percent in 1990 to 17.0 percent in 2011. Forecasts based on country-specific growth rates in the past 10 years indicate that the extreme poverty rate will fall to 13.4 percent by 2015 (figure 1a), a drop of more than two-thirds from the baseline. Despite the remarkable achievement in developing countries as a whole, progress in reducing poverty has been uneven across regions. East Asia and Pacific has had an astounding record of alle- viating long-term poverty, with the share of people living on less than $1.25 a day declining from 58.2 percent in 1990 to 7.9 percent in 2011. Relatively affluent regions such as Europe and Central Asia, Latin America and the Caribbean, and the Middle East and North Africa started with very low extreme poverty rates and sustained pov- erty reduction in the mid-1990s to reach the target by 2010. South Asia has also witnessed a steady decline of poverty in the past 25 years, with a strong acceleration since 2008 that enabled the region to achieve the Millennium Development Goal target by 2011. By con- trast, the extreme poverty rate in Sub-Saharan Africa did not begin to fall below its 1990 level until after 2002. Even with the acceleration in the past decade, Sub-Saharan Africa still lags behind and is not forecast to meet the target by 2015 (see figure 1a). The number of people worldwide living on less than $1.25 a day is forecast to be halved by 2015 from its 1990 level as well. Between 1990 and 2011 the number of extremely poor people fell from 1.9 billion to 1 billion, and according to forecasts, another 175 million people will be lifted out of extreme poverty by 2015. Compared with 1990, the number of extremely poor people has fallen in all regions except Sub-Saharan Africa, where population growth exceeded the rate of poverty reduction, increasing the number of extremely poor people from 290 million in 1990 to 415 million in 2011. South Asia has the second largest number of extremely poor people: In 2011 close to 400 million people lived on less than $1.25 a day (figure 1b). 0 25 50 75 100 Countries making progress toward eradicating extreme poverty (% of countries in region) Target met Sufficient progress Insufficient progress Moderately off target Seriously off target Insufficient data Sub-Saharan Africa (47 countries) South Asia (8 countries) Middle East & North Africa (13 countries) Latin America & Caribbean (26 countries) Europe & Central Asia (21 countries) East Asia & Pacific (24 countries) Developing countries (139 countries) Progress in reaching the poverty target by region 1c Source: World Bank (2015) and World Bank MDG Data Dashboards (http://data.worldbank.org/mdgs). 0.0 0.5 1.0 1.5 2.0 201520112008200520021999199619931990 Number of people living on less than 2005 PPP $1.25 a day (billions) South Asia Sub-Saharan Africa Middle East & North Africa Europe & Central Asia Latin America & Caribbean East Asia & Pacific Forecast A billion people were lifted out of extreme poverty between 1990 and 2015 1b Source: World Bank PovcalNet (http://iresearch.worldbank.org /PovcalNet/). 0 25 50 75 2015 target 20102005200019951990 Proportion of the population living on less than 2005 PPP $1.25 a day (%) South Asia Developing countries Sub-Saharan Africa Forecast Middle East & North Africa Latin America & Caribbean Europe & Central Asia East Asia & Pacific The poverty target has been met in nearly all developing country regions 1a Source: World Bank PovcalNet (http://iresearch.worldbank.org/PovcalNet/).
- 29. World Development Indicators 2015 5Economy States and markets Global links Back 0 20 40 60 2015 target 20102005200019951990 Prevalence of malnutrition, weight for age (% of children under age 5) South Asia Sub-Saharan Africa Europe & Central Asia Latin America & Caribbean East Asia & Pacific Middle East & North Africa Developing countries The prevalence of child malnutrition has fallen in every region 1f Source: UNICEF, WHO, and World Bank 2014. 0 10 20 30 40 2015 target 20102005200019951991 Prevalence of undernourishment, three-year moving average (% of population) South Asia Sub-Saharan Africa Middle East & North Africa Latin America & Caribbean East Asia & Pacific Undernourishment has fallen in most regions 1e Note: Insufficient country data are available for Europe and Central Asia. Source: FAO, IFAD, and WFP (2014). 0 25 50 75 100 Countries making progress toward eradicating extreme poverty (% of countries in group) Target met Sufficient progress Insufficient progress Moderately off target Seriously off target Insufficient data Small states (36 countries) Fragile & conflict situations (36 countries) International Bank for Recon- struction and Development (56 countries) Blend (18 countries) International Development Association (64 countries) Upper middle income (55 countries) Lower middle income (48 countries) Low income (36 countries) Progress in reaching the poverty target by income and lending group 1d Source: World Bank (2015) and World Bank MDG Data Dashboards (http://data.worldbank.org/mdgs). Based on current trends, nearly half of developing countries have already achieved the poverty target of Millennium Develop- ment Goal 1. However, 20 percent are seriously off track, meaning that at the current pace of progress they will not be able to halve their 1990 extreme poverty rates even by 2030 (World Bank 2015). Progress is most sluggish among countries in Sub-Saharan Africa, where about 45 percent of countries are seriously off track (fig- ure 1c). A large proportion of countries classified as International Development Association–eligible and defined by the World Bank as being in fragile and conflict situations are also among those seri- ously off track (figure 1d). Millennium Development Goal 1 also addresses hunger and malnutrition. On average, developing countries saw the prevalence of undernourishment drop from 24 percent in 1990–92 to 13 per- cent in 2012–14. The decline has been steady in most developing country regions in the past decade, although the situation appears to have worsened in the Middle East and North Africa, albeit from a low base. The 2013 estimates show that East Asia and Pacific and Latin America and the Caribbean have met the target of halv- ing the prevalence of undernourishment from its 1990 level by 2012–14. By crude linear growth prediction, developing countries as a whole will meet the target by 2015, whereas the Middle East and North Africa, South Asia, and Sub-Saharan Africa likely will not (figure 1e). Another measure of hunger is the prevalence of underweight chil- dren (child malnutrition). Prevalence of malnutrition in developing countries has dropped substantially, from 28 percent of children under age 5 in 1990 to 17 percent in 2013. Despite considerable progress, in 2013 South Asia still had the highest prevalence, 32 percent. By 2013 East Asia and Pacific, Europe and Central Asia, and Latin America and the Caribbean met the target of halv- ing the prevalence of underweight children under age 5 from its 1990 level. The Middle East and North Africa is predicted to be on track to meet the target by 2015. However, developing countries as a whole may not be able to meet the target by 2015, nor will South Asia or Sub-Saharan Africa (figure 1f).
- 30. 6 World Development Indicators 2015 Front User guide World view People Environment? 0 25 50 75 100 125 201220102005200019951990 Primary school–age children not attending school (millions) South Asia Sub-Saharan Africa Middle East & North Africa Europe & Central Asia Latin America & Caribbean East Asia & Pacific Some 55 million children remain out of school 2c Source: United Nations Educational, Scientific and Cultural Organization Institute for Statistics. 0 25 50 75 100 Countries making progress toward universal primary education (% of countries in region) Target met Sufficient progress Insufficient progress Moderately off target Seriously off target Insufficient data Sub-Saharan Africa (47 countries) South Asia (8 countries) Middle East & North Africa (13 countries) Latin America & Caribbean (26 countries) Europe & Central Asia (21 countries) East Asia & Pacific (24 countries) Developing countries (139 countries) Universal primary education remains elusive in many countries 2b Source: World Bank (2015) and World Bank MDG Data Dashboards (http://data.worldbank.org/mdgs). 0 25 50 75 100 125 2015 target 20102005200019951990 Primary completion rate (% of relevant age group) Middle East & North Africa Sub-Saharan Africa Latin America & Caribbean South Asia Europe & Central Asia East Asia & Pacific Developing countries More children are completing primary school 2a Source: United Nations Educational, Scientific and Cultural Organization Institute for Statistics. After modest movement toward universal primary education in the poorest countries during the 1990s, progress has accelerated con- siderably since 2000, particularly for South Asia and Sub-Saharan Africa. But achieving full enrollment remains daunting. Moreover, enrollment by itself is not enough. Many children start school but drop out before completion, discouraged by cost, distance, physi- cal danger, and failure to advance. An added challenge is that even as countries approach the target and the education demands of modern economies expand, primary education will increasingly be of value only as a stepping stone toward secondary and higher education. Achieving the target of everyone, boys and girls alike, completing a full course of primary education by 2015 appeared within reach only a few years ago. But the primary school completion rate— the number of new entrants in the last grade of primary education divided by the population at the entrance age for the last grade of primary education—has been stalled at 91 percent for develop- ing countries since 2009. Only two regions, East Asia and Pacific and Europe and Central Asia, have reached or are close to reach- ing universal primary education. The Middle East and North Africa has steadily improved, to 95 percent in 2012, the same rate as Latin America and the Caribbean. South Asia reached 91 percent in 2009, but progress since has been slow. The real challenge is in Sub-Saharan Africa, which lags behind at 70 percent in 2012 (figure 2a). When country-level performance is considered, a more nuanced picture emerges: 35 percent of developing countries have achieved or are on track to achieve the target of the Millennium Development Goal, while 28 percent are seriously off track and unlikely to achieve the target even by 2030 (figure 2b). Data gaps continue to hinder monitoring efforts: In 24 countries, or 17 percent of developing countries, data availability remains inadequate to assess progress. In developing countries the number of children of primary school age not attending school has been almost halved since 1996. A large MDG 2 Achieve universal primary education
- 31. World Development Indicators 2015 7Economy States and markets Global links Back 2010 2000 1990 2010 2000 1990 2010 2000 1990 2010 2000 1990 2010 2000 1990 2010 2000 1990 Youth literacy rate (% of population ages 15–24) 0 25 50 75 100 Male Female Sub-Saharan Africa South Asia Middle East & North Africa Latin America & Caribbean Europe & Central Asia East Asia & Pacific Progress in youth literacy varies by region and gender 2e Source: United Nations Educational, Scientific and Cultural Organization Institute for Statistics. reduction was made in South Asia in the early 2000s, driven by progress in India. Still, many children never attend school or start school but attend intermittently or drop out entirely; as many as 55 million children remained out of school in 2012. About 80 per- cent of out-of-school children live in South Asia and Sub-Saharan Africa (figure 2c). Obstacles such as the need for boys and girls to participate in the planting and harvesting of staple crops, the lack of suitable school facilities, the absence of teachers, and school fees may discourage parents from sending their children to school. Not all children have the same opportunities to enroll in school or remain in school, and children from poorer households are par- ticularly disadvantaged. For example, in Niger two-thirds of children not attending primary school are from the poorest 20 percent of households; children from wealthier households are three times more likely than children from poorer households to complete pri- mary education (figure 2d). The country also faces an urban-rural divide: In 2012 more than 90 percent of children in urban areas completed primary education, compared with 51 percent of chil- dren in rural areas. And boys were more likely than girls to enroll and stay in school. Girls from poor households in rural areas are the most disadvantaged and the least likely to acquire the human capital that could be their strongest asset to escape poverty. Many countries face similar wealth, urban-rural, and gender gaps in education. A positive development is that demand is growing for measur- ing and monitoring education quality and learning achievements. However, measures of quality that assess learning outcomes are still not fully developed for use in many countries. Achieving basic literacy is one indicator that can measure the quality of education outcomes, though estimates of even this variable can be flawed. Still, the best available data show that nearly 90 percent of young people in developing countries had acquired basic literacy by 2012, but the level and speed of this achievement vary across regions and by gender (figure 2e). 0 25 50 75 100 FemaleMaleRuralUrbanPoorest quintile Richest quintile Primary completion rate by income, area, and gender, Niger, 2012 (% of relevant age group) Access to education is inequitably distributed by income, area, and gender 2d Source: United Nations Educational, Scientific and Cultural Organization Institute for Statistics and World Bank EdStats database.
- 32. 8 World Development Indicators 2015 Front User guide World view People Environment? Millennium Development Goal 3 is concerned with boosting wom- en’s social, economic, and political participation to build gender- equitable societies. Expanding women’s opportunities in the public and private sectors is a core development strategy that not only benefits girls and women, but also improves society more broadly. By enrolling and staying in school, girls gain the skills they need to enter the labor market, care for families, and make informed decisions for themselves and others. The target of Millennium Development Goal 3 is to eliminate gender disparity in all levels of education by 2015. Over the past 25 years, girls have made sub- stantial gains in school enrollment across all developing country regions. In 1990 the average enrollment rate of girls in primary and secondary schools in developing countries was 83 percent of that of boys; by 2012 it had increased to 97 percent (figure 3a). The ratio of girls to boys in tertiary education has also increased con- siderably, from 74 percent to 101 percent. Developing countries as a whole are likely to reach gender parity in primary and secondary enrollment (defined as having a ratio of 97–103 percent, according to UNESCO 2004). However, these averages disguise large differences across regions and countries. South Asia made remarkable progress, clos- ing the gender gap in primary and secondary enrollment more than 40 percent between 1990 and 2012. Sub-Saharan Africa and the Middle East and North Africa saw fast progress but continue to have the largest gender disparities in primary and secondary enroll- ment rates among developing country regions. Given past rates of change, the two regions are unlikely to meet the target of elimi- nating disparities in education by 2015. Furthermore, about half the countries in the Middle East and North Africa are seriously off track to achieve the target (figure 3b). Disparities across regions are larger in tertiary education: The ratio of girls’ to boys’ enroll- ment in tertiary education is 64 percent in Sub-Saharan Africa, compared with 128 percent in Latin America and the Caribbean. These high estimates tend to drive up the aggregate estimates for MDG 3 Promotegenderequalityandempowerwomen 60 70 80 90 100 110 2015 target 20102005200019951990 Ratio of girls’ to boys’ primary and secondary gross enrollment rate (%) Middle East & North Africa Sub-Saharan Africa Latin America & Caribbean South Asia Europe & Central Asia East Asia & Pacific Developing countries Gender gaps in access to education have narrowed 3a Source: United Nations Educational, Scientific and Cultural Organization Institute for Statistics. Countries making progress toward gender equity in education (% of countries in region) 0 25 50 75 100 Sub-Saharan Africa (47 countries) South Asia (8 countries) Middle East & North Africa (13 countries) Latin America & Caribbean (26 countries) Europe & Central Asia (21 countries) East Asia & Pacific (24 countries) Developing countries (139 countries) Target met Sufficient progress Insufficient progress Moderately off target Seriously off target Insufficient data Gender disparities in primary and secondary education vary within regions 3b Source: World Bank (2015) and World Bank MDG Data Dashboards (http://data.worldbank.org/mdgs). 0 25 50 75 100 9 years8 years7 years6 years5 years4 years3 years2 years1 year Education completion by wealth quintile, Nigeria, 2013 (% of population ages 15–19) Richest quintile, boys Poorest quintile, girls Poorest quintile, boys Richest quintile, girls In Nigeria poor girls are often the worst off in completing education 3c Source: Demographic and Health Surveys and World Bank EdStats database.
- 33. World Development Indicators 2015 9Economy States and markets Global links Back 0 5 10 15 20 25 30 201420102005200019951990 Proportion of seats held by women in national parliament (%) Middle East & North Africa Latin America & Caribbean South Asia East Asia & Pacific Sub-Saharan Africa Europe & Central Asia More women are in decisionmaking positions 3f Source: Inter-Parliamentary Union. 0 10 20 30 40 50 Middle East & North Africa South Asia Sub-Saharan Africa East Asia & Pacific Latin America & Caribbean Europe & Central Asia Female employees in nonagricultural wage employment, median value, 2008–12 (% of total nonagricultural wage employment) Fewer women than men are employed in nonagricultural wage employment 3e Source: International Labour Organization Key Indicators of the Labour Market 8th edition database. 0 25 50 75 Unpaid family workers, national estimates, most recent year available during 2009–13 (% of employment) Female Male Timor-Leste Bolivia Azerbaijan India Georgia Egypt,ArabRep. Cameroon Tanzania Albania Madagascar In many countries more women than men work as unpaid family workers 3d Source: International Labour Organization Key Indicators of the Labour Market 8th edition database. all developing countries, disguising some of the large disparities in other regions and countries. There are also large differences within countries. Poor house- holds are often less likely than wealthy households to enroll and keep children in school, and girls from poor households tend to be the worst off. In Nigeria only 4 percent of girls in the poorest quintile stay in school until grade 9, compared with 85 percent of girls in the richest quintile. Within the poorest quintile, 15 percent of boys complete nine years of schooling, compared with 4 percent for the poorest girls. (figure 3c). Women work long hours and contribute considerably to their fam- ilies’ economic well-being, but many are unpaid for their labor or work in the informal sector. These precarious forms of work, often not properly counted as economic activity, tend to lack formal work arrangements, social protection, and safety nets and leave work- ers vulnerable to poverty. In many countries a far larger proportion of women than men work for free in establishments operated by families (according to the International Labour Organization’s Key Indicators of the Labour Market 8th edition database; figure 3d). The share of women’s paid employment in the nonagricultural sec- tor is less than 20 percent in South Asia and the Middle East and North Africa and has risen only marginally over the years. The share of women’s employment in the nonagricultural sector is highest in Europe and Central Asia, where it almost equals men’s (figure 3e). More women are participating in public life and decisionmaking at the highest levels than in 1990, based on the proportion of par- liamentary seats held by women. Latin America and the Caribbean leads developing country regions in 2014, at 27 percent, followed closely by Sub-Saharan Africa at 23 percent. The biggest change has occurred in the Middle East and North Africa, where the pro- portion of seats held by women more than quadrupled between 1990 and 2014 (figure 3f). At the country level Rwanda leads the way with 64 percent in 2014, higher than the percentage for high- income countries, at 26 percent.
- 34. 10 World Development Indicators 2015 Front User guide World view People Environment? In the last two decades the world has witnessed a dramatic decline in child mortality, enough to almost halve the number of children who die each year before their fifth birthday. In 1990 that number was 13 million, by 1999 it was less than 10 million, and by 2013 it had fallen to just over 6 million. This means that at least 17,000 fewer children now die each day compared with 1990. The target of Millennium Development Goal 4 was to reduce the under-five mortality rate by two-thirds between 1990 and 2015. In 1990 the average rate for all developing countries was 99 deaths per 1,000 live births; in 2013 it had fallen to 50—or about half the 1990 rate. This is tremendous progress. But based on the current trend, developing countries as a whole are likely to fall short of the Millennium Development Goal target. Despite rapid improve- ments since 2000, child mortality rates in Sub-Saharan Africa and South Asia remain considerably higher than in the rest of the world (figure 4a). While 53 developing countries (38 percent) have already met or are likely to meet the target individually, 84 countries (61 per- cent) are unlikely to achieve it based on recent trends (figure 4b). Still, the average annual rate of decline of global under-five mortal- ity rates accelerated from 1.2 percent over 1990–95 to 4 percent over 2005–13. If the more recent rate of decline had started in 1990, the target for Millennium Development Goal 4 would likely have been achieved by 2015. And if this recent rate of decline con- tinues, the target will be achieved in 2026 (UNICEF 2014). Although there has been a dramatic decline in deaths, most chil- dren still die from causes that are readily preventable or curable with existing interventions. Pneumonia, diarrhea, and malaria are the leading causes, accounting for 30 percent under-five deaths. MDG 4 Reduce child mortality 0 50 100 150 200 2015 target 20102005200019951990 Under-five mortality rate (deaths per 1,000 live births) Middle East & North Africa Sub-Saharan Africa South Asia Europe & Central Asia Latin America & Caribbean East Asia & Pacific Developing countries Under-five mortality rates continue to fall 4a Source: United Nations Inter-agency Group for Child Mortality Estimation. 0 25 50 75 100 Countries making progress toward reducing child mortality (% of countries in region) Target met Sufficient progress Insufficient progress Moderately off target Seriously off target Insufficient data Sub-Saharan Africa (47 countries) South Asia (8 countries) Middle East & North Africa (13 countries) Latin America & Caribbean (26 countries) Europe & Central Asia (21 countries) East Asia & Pacific (24 countries) Developing countries (139 countries) Progress toward Millennium Development Goal 4 4b Source: World Bank (2015) and World Bank MDG Data Dashboards (http://data.worldbank.org/mdgs). 0 1 2 3 4 Europe & Central Asia Latin America & Caribbean Middle East & North Africa East Asia & Pacific South Asia Sub-Saharan Africa Under-five deaths, 2013 (millions) Deaths (1–4 years) Deaths (1–11 months) Deaths in the first month after birth Most deaths occur in the first year of life 4c Source: United Nations Inter-agency Group for Child Mortality Estimation.
- 35. World Development Indicators 2015 11Economy States and markets Global links Back 0 25 50 75 100 201320102005200019951990 Children ages 12–23 months immunized against measles (%) Middle East & North Africa Sub-Saharan Africa East Asia & Pacific South Asia Developing countries Latin America & Caribbean Europe & Central Asia Measles immunization rates are stagnating 4e Source: World Health Organization and United Nations Children’s Fund. Deaths of children under age 5, 2013 (millions) 0 1 2 3 4 Kenya Malawi Sudan Egypt,Arab Rep. Mali Afghanistan Brazil Angola Mozambique Niger Uganda Tanzania Indonesia Bangladesh Congo, Dem. Rep. Ethiopia Pakistan China Nigeria India At 2013 mortality rate Deaths averted based on 1990 mortality rate More than 6 million deaths averted in 20 countries 4d Source: World Bank staff calculations. Almost 74 percent of deaths of children under age 5 occur in the first year of life, and 60 percent of those occur in the neonatal period (the first month; figure 4c). Preterm birth (before 37 weeks of pregnancy) complications account for 35 percent of neonatal deaths, and complications during birth another 24 percent (UNICEF 2014). Because declines in the neonatal mortality rate are slower than declines in the postneonatal mortality rate, the share of neo- natal deaths among all under-five deaths increased from 37 per- cent in 1990 to 44 percent in 2013. Tackling neonatal mortality will have a major impact in reducing under-five mortality rate. Twenty developing countries accounted for around 4.6 million under-five deaths in 2013, or around 73 percent of all such deaths worldwide. These countries are mostly large, often with high birth rates, but many have substantially reduced mortality rates over the past two decades. Of these 20 countries, Bangladesh, Bra- zil, China, the Arab Republic of Egypt, Ethiopia, Indonesia, Malawi, Niger, and Tanzania achieved or are likely to achieve a two-thirds reduction in their under-five mortality rate by 2015. Had the mortal- ity rates of 1990 prevailed in 2013, 2.5 million more children would have died in these 9 countries, and 3.6 million more would have died in the remaining 11 (figure 4d). Measles vaccination coverage is one indicator used to monitor the progress toward achieving Millennium Development Goal 4. In developing countries measles vaccinations of one-year-old chil- dren reached about 83 percent in 2013. Both Sub-Saharan Africa and South Asia have seen the coverage of measles vaccinations increase since 1990, but the trend has recently slowed in both regions. This is concerning, as it might make further reductions in under-five mortality more challenging (figure 4e).
- 36. 12 World Development Indicators 2015 Front User guide World view People Environment? While many maternal deaths are avoidable, pregnancy and delivery are not completely risk free. Every day, around 800 women lose their lives before, during, or after child delivery (WHO 2014b). In 2013 an estimated 289,000 maternal deaths occurred worldwide, 99 percent of them in developing countries. More than half of maternal deaths occurred in Sub-Saharan Africa, and about a quar- ter occurred in South Asia. However, countries in both South Asia and Sub-Saharan Africa have made great progress in reducing the maternal mortality ratio. In South Asia it fell from 550 per 100,000 live births in 1990 to 190 in 2013, a drop of 65 percent. In Sub-Saharan Africa, where maternal deaths are more than twice as prevalent as in South Asia, the maternal mortality ratio dropped almost 50 percent. And East Asia and Pacific, Europe and Central Asia, and the Middle East and North Africa have all reduced their maternal morality ratio by more than 50 percent (figure 5a). These achievements are impressive, but progress in reducing maternal mortality ratios has been slower than the 75 percent reduction between 1990 and 2015 targeted by the Millennium Development Goals. No developing regions on average are likely to achieve the target. But the average annual rate of decline has accelerated from 1.1 percent over 1990–95 to 3.1 percent over 2005–13. This recent rate of progress is getting closer to the 5.5 percent that would have been needed since 1990 to achieve the Millennium Development Goal 5 target. According to recent data, a handful of developing countries (15 or about 11 percent) have already achieved or are likely to achieve the target (figure 5b). The maternal mortality ratio is an estimate of the risk of a mater- nal death at each birth, a risk that is compounded with each preg- nancy. And because women in poor countries have more children under riskier conditions, their lifetime risk of maternal death may be 100 or more times greater than that of women in high-income MDG 5 Improve maternal health 0 250 500 750 1,000 2015 target 201320102005200019951990 Maternal mortality ratio, modeled estimate (per 100,000 live births) South Asia East Asia & Pacific Europe & Central Asia Sub-Saharan Africa Developing countries Middle East & North Africa Latin America & Caribbean Maternal deaths are more likely in South Asia and Sub-Saharan Africa 5a Source: United Nations Maternal Mortality Estimation Inter-agency Group. 0 25 50 75 100 Countries making progress toward reducing maternal mortality (% of countries in region) Target met Sufficient progress Insufficient progress Moderately off target Seriously off target Insufficient data Sub-Saharan Africa (47 countries) South Asia (8 countries) Middle East & North Africa (13 countries) Latin America & Caribbean (26 countries) Europe & Central Asia (21 countries) East Asia & Pacific (24 countries) Developing countries (139 countries) Progress toward reducing maternal mortality 5b Source: World Bank (2015) and World Bank MDG Data Dashboards (http://data.worldbank.org/mdgs). 0 2 4 6 8 Europe & Central Asia East Asia & Pacific Latin America & Caribbean Middle East & North Africa South Asia Sub-Saharan Africa Lifetime risk of maternal death (%) 1990 2013 Reducing the risk to mothers 5c Source: United Nations Maternal Mortality Estimation Inter-agency Group.
- 37. World Development Indicators 2015 13Economy States and markets Global links Back 0 25 50 75 100 Europe & Central Asia East Asia & Pacific Latin America & Caribbean Middle East & North Africa South Asia Sub-Saharan Africa Births attended by skilled health staff, most recent year available, 2008–14 (%) Every mother needs care 5f Source: United Nations Children’s Fund and household surveys (including Demographic and Health Surveys and Multiple Indicator Cluster Surveys). 0 50 100 150 201320112009200720052003200119991997 Adolescent fertility rate (births per 1,000 women ages 15–19) Europe & Central Asia Latin America & Caribbean South Asia Sub-Saharan Africa East Asia & Pacific Middle East & North Africa Fewer young women are giving birth 5e Source: United Nations Population Division. 0 10 20 30 40 50 Unmet need for contraception, most recent year available during 2007–14 (% of married women ages 15–49) Regional median Sub-Saharan Africa (38 countries) South Asia (9 countries) Middle East & North Africa (5 countries) Latin America & Caribbean (17 countries) Europe & Central Asia (12 countries) East Asia & Pacific (15 countries) A wide range of contraception needs 5d Source: United Nations Population Division and household surveys (including Demographic and Health Surveys and Multiple Indicator Cluster Surveys). countries. Improved health care and lower fertility rates have reduced the lifetime risk in all regions, but in 2013 women ages 15–49 in Sub-Saharan Africa still faced a 2.6 percent chance of dying in childbirth, down from more than 6 percent in 1990 (fig- ure 5c). In Chad and Somalia, both fragile states, lifetime risk is still more than 5 percent, meaning more than 1 woman in 20 will die in childbirth, on average. Reducing maternal mortality requires a comprehensive approach to women’s reproductive health, starting with family planning and access to contraception. In countries with data, more than half of women who are married or in union use some method of contra- ception. However, around 225 million women want to delay or con- clude childbearing, but they are not using effective family planning methods (UNFPA and Guttmacher Institute 2014). There are wide differences across regions in the share of women of childbearing age who say they need but are not using contraception (figure 5d). 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 chil- dren and are at greater risk of death or serious complications from pregnancy. The adolescent birth rate is highest in Sub-Saharan Africa, and though it has been declining, the pace is slow (fig- ure 5e). By contrast, South Asia has experienced a rapid decrease. Many health problems among pregnant women are preventable or treatable through visits with trained health workers before child- birth. One of the keys to reducing maternal mortality is to provide skilled attendants at delivery and access to hospital treatments, required for treating life-threatening emergencies such as severe bleeding and hypertensive disorders. In South Asia and Sub- Saharan Africa only half of births are attended by doctors, nurses, or trained midwives (figure 5f).
- 38. 14 World Development Indicators 2015 Front User guide World view People Environment? HIV/AIDS, malaria, and tuberculosis are among the world’s dead- liest communicable diseases. In Africa the spread of HIV/AIDS has reversed decades of improvement in life expectancy and left millions of children orphaned. Malaria takes a large toll on young children and weakens adults at great cost to their productivity. Tuberculosis killed 1.1 million people in 2013, most of them ages 15–45, and sickened millions more. Millennium Development Goal 6 targets are to halt and begin to reverse the spread and incidence of these diseases by 2015. Some 35 million people were living with HIV/AIDS in 2013. The number of people who are newly infected with HIV is continuing to decline in most parts of the world: 2.1 million people contracted the disease in 2013, down 38 percent from 2001 and 13 percent from 2011. The spread of new HIV infections has slowed, in line with the target of halting and reversing the spread of HIV/AIDS by 2015. However, the proportion of adults living with HIV worldwide has not fallen; it has stayed around 0.8 percent since 2000. Sub-Saharan Africa remains the center of the HIV/AIDS epidemic, but the propor- tion of adults living with AIDS has begun to drop while the survival rate of those with access to antiretroviral drugs has increased (fig- ures 6a and 6b). At the end of 2013, 12.9 million people worldwide were receiving antiretroviral drugs. The percentage of people living with HIV who are not receiving antiretroviral therapy has fallen from 90 percent in 2006 to 63 percent in 2013 (UNAIDS 2014). Altering the course of the HIV epidemic requires changes in behavior by those already infected with the virus and those at risk of becoming infected. Knowledge of the cause of the disease, its transmission, and what can be done to avoid it is the starting point. The ability to reject false information is another important kind of knowledge. But wide gaps in knowledge remain. Many young people do not know enough about HIV and continue with risky behavior. In MDG 6 CombatHIV/AIDS,malaria,andotherdiseases 0 1 2 3 4 5 6 201320102005200019951990 HIV prevalence (% of population ages 15–49) Middle East & North Africa Sub-Saharan Africa World South Asia HIV prevalence in Sub-Saharan Africa continues to fall 6a Source: Joint United Nations Programme on HIV/AIDS. Countries making progress toward halting and reversing the HIV epidemic (% of countries in region) 0 25 50 75 100 Halted and reversed Halted or reversed Stable low prevalence Not improving Insufficient data Sub-Saharan Africa (47 countries) South Asia (8 countries) Middle East & North Africa (13 countries) Latin America & Caribbean (26 countries) Europe & Central Asia (21 countries) East Asia & Pacific (24 countries) Developing countries (139 countries) Progress toward halting and reversing the HIV epidemic 6b Source: World Bank staff calculations. Share of population ages 15–24 with comprehensive and correct knowledge about HIV, most recent year available during 2007–12 (%) 0 20 40 60 South Africa Lesotho Uganda Zambia Malawi Zimbabwe Mozambique Namibia Swaziland Kenya Men Women Knowledge helps control the spread of HIV/AIDS 6c Source: Household surveys (including Demographic and Health Surveys and Multiple Indicator Cluster Surveys).
- 39. World Development Indicators 2015 15Economy States and markets Global links Back 0 20 40 60 80 Madagascar Rwanda Tanzania Togo Zambia Malawi São Tomé and Príncipe Burundi Sierra Leone Kenya Senegal Suriname Comoros Côte d’Ivoire Central African Republic Guinea-Bissau Namibia Gambia,The Sudan Guyana Equatorial Guinea Cameroon Niger Chad Swaziland Use of insecticide-treated nets (% of population under age 5) First observation (2000 or earlier) Most recent observation (2007 or later) Use of insecticide-treated nets is increasing in Sub-Saharan Africa 6e Source: Household surveys (including Demographic and Health Surveys, Malaria Indicator Surveys, and Multiple Indicator Cluster Surveys). 0 100 200 300 400 201320102005200019951990 Incidence of, prevalence of, and death rate from tuberculosis in developing countries (per 100,000 people) Incidence Death rate Prevalence Fewer people are contracting, living with, and dying from tuberculosis 6d Source: World Health Organization. only 2 of the 10 countries (Namibia and Swaziland) with the high- est HIV prevalence rates in 2013 did more than half the men and women ages 15–24 tested demonstrate knowledge of two ways to prevent HIV and reject three misconceptions about HIV (figure 6c). In Kenya and Mozambique men scored above 50 percent, but women fell short; the reverse was true in Zimbabwe. In 2013 there were 9 million new tuberculosis cases and 1.5 mil- lion tuberculosis-related deaths, but incidence of, prevalence of, and death rates from tuberculosis are falling (figure 6d). Tubercu- losis incidence fell an average rate of 1.5 percent a year between 2000 and 2013. By 2013 tuberculosis prevalence had fallen 41 percent since 1990, and the tuberculosis mortality rate had fallen 45 percent (WHO 2014a). Globally, the target of halting and reversing tuberculosis incidence by 2015 has been achieved. An estimated 200 million cases of malaria occurred globally in 2013, which led to 600,000 deaths. An estimated 3.2 billion people are at risk of being infected with malaria and developing the disease, and 1.2 billion of them are at high risk. But there has been progress. In 2013, 2 countries reported zero indigenous cases for the first time (Azerbaijan and Sri Lanka) and 11 countries maintained zero cases (Argentina, Armenia, Egypt, Iraq, Georgia, Kyrgyz Republic, Morocco, Oman, Paraguay, Turkmenistan, and Uzbekistan; WHO 2014c). Although malaria occurs in all regions, the most lethal form of the malaria parasite is most abundant in Sub-Saharan Africa. Insecticide-treated nets have proven an effec- tive preventative, and their use by children in the region is growing (figure 6e). Better testing and the use of combination therapies with artemisinin-based drugs are improving the treatment of at-risk populations. But malaria is difficult to control. There is evidence of emerging resistance to artemisinins and to pyrethroid insecticides used to treat mosquito nets.
- 40. 16 World Development Indicators 2015 Front User guide World view People Environment? Millennium Development Goal 7 has far-reaching implications for the planet’s current and future inhabitants. It addresses the con- dition of the natural and built environments: reversing the loss of natural resources, preserving biodiversity, increasing access to safe water and sanitation, and improving the living conditions 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 continued rise in greenhouse gas emissions leaves billions of people vulnerable to the impacts of climate change, with devel- oping countries hit hardest. Higher temperatures, changes in pre- cipitation patterns, rising sea levels, and more frequent weather- related disasters pose risks for agriculture, food, and water supplies. Annual emissions of carbon dioxide reached 33.6 billion metric tons in 2010, a considerable 51 percent rise since 1990, the baseline for Kyoto Protocol requirements (figure 7a). Carbon dioxide emissions were estimated at an unprecedented 36 billion metric tons in 2013, with an annual growth rate of 2 percent— slightly lower than the average growth of 3 percent since 2000. One target of Millennium Development Goal 7 calls for halving the proportion of the population without access to improved water sources and sanitation facilities by 2015. In 1990 almost 1.3 bil- lion people worldwide lacked access to drinking water from a con- venient, protected source. By 2012 that had dropped to 752 million people—a 41 percent reduction. In developing countries the propor- tion of people with access to an improved water source rose from 70 percent in 1990 to 87 percent in 2012, achieving the target of 85 percent of people with access by 2015. Despite such major gains, almost 28 percent of countries are seriously off track toward meeting the water target. Some 52 countries have not made enough progress to reach the target, and 18 countries do not have enough data to determine whether they will reach the target by 2015. Sub- Saharan Africa is lagging the most, with 36 percent of its population lacking access (figure 7b). East Asia and Pacific made impressive improvements from a starting position of only 68 percent in 1990, MDG 7 Ensure environmental sustainability 0 10 20 30 40 20102005200019951990 Carbon dioxide emissions from fossil fuel (billions of metric tons) High income Upper middle income Lower middle incomeLow income Carbon dioxide emissions are at unprecedented levels 7a Source: Carbon Dioxide Information Analysis Center. 0 25 50 75 100 2015 target 20102005200019951990 Share of population with access to an improved source of drinking water (%) Latin America & Caribbean Sub-Saharan Africa South Asia East Asia & Pacific Europe & Central Asia Middle East & North Africa Progress has been made in access to safe drinking water 7b Source: World Health Organization/United Nations Children’s Fund Joint Monitoring Programme for Water Supply and Sanitation. 0 25 50 75 100 2015 target 20102005200019951990 Share of population with access to improved sanitation facilities (%) South Asia East Asia & Pacific Europe & Central Asia Latin America & Caribbean Middle East & North Africa Sub-Saharan Africa South Asia and Sub-Saharan Africa are lagging in access to basic sanitation 7c Source: World Health Organization/United Nations Children’s Fund Joint Monitoring Programme for Water Supply and Sanitation.
- 41. World Development Indicators 2015 17Economy States and markets Global links Back 0 1,000 2,000 3,000 4,000 5,000 Latin America & Caribbean East Asia & Pacific Sub-Saharan Africa Europe & Central Asia South Asia Middle East & North Africa Threatened species, by taxonomic group, 2014 Mammals Birds Fish Plants The number of threatened species is an important measure of biodiversity loss 7f Source: International Union for the Conservation of Nature Red List of Threatened Species. 0 5 10 15 20 25 WorldHigh income Europe & Central Asia South Asia Middle East & North Africa East Asia & Pacific Sub- Saharan Africa Latin America & Caribbean Territorial and marine protected areas (% of terrestrial area and territorial waters) 1990 2012 The world’s nationally protected areas have increased substantially 7e Source: United Nations Environment Programme–World Conservation Monitoring Centre. Average annual change in forest area, 1990–2012 (millions of hectares) High income Sub-Saharan Africa South Asia Middle East & North Africa Latin America & Caribbean Europe & Central Asia East Asia & Pacific –7 –6 –5 –4 –3 –2 –1 0 1 Forest losses and gains vary by region 7d Source: Food and Agriculture Organization. to 91 percent in 2012. In general, the other regions have managed to reach access rates of more than 89 percent. In 1990 only 35 percent of the people in developing economies had access to a flush toilet or other form of improved sanitation. By 2012, 57 percent did. But 2.5 billion people in developing countries still lack access to improved sanitation. The situation is worse in rural areas, where only 43 percent of the population has access to improved sanitation, compared with 73 percent in urban areas. This large disparity, especially in South Asia and Sub-Saharan Africa, is the principal reason the sanitation target of the Millennium Devel- opment Goals is unlikely to be met on time (figure 7c). The loss of forests threatens the livelihood of poor people, destroys the habitat that harbors biodiversity, 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 have been only partly compensated for by gains elsewhere (figure 7d). The rate of deforestation slowed over 2002–12, but on current trends zero net losses will not be reached for another two decades. Protecting forests and other terrestrial and marine areas helps protect plant and animal habitats and preserve the diversity of spe- cies. By 2012 over 14 percent of the world’s land and over 12 per- cent of its oceans had been protected, an improvement of 6 per- cent for both since 1990 (figure 7e). Deforestation is a major cause of loss of biodiversity, and habi- tat conservation is vital for stemming this loss. Many species are under threat due to climate change, overfishing, pollution, and habi- tat degradation. As conservation efforts focus on protecting areas of high biodiversity, the number of threatened species becomes an important measure of the immediate need for conservation in an area. Among assessed species, the highest number of threatened plant species are in Latin America and the Caribbean, the highest number of threatened fish species are in Sub-Saharan Africa, and the highest number of threatened mammal and bird species are in East Asia and Pacific (figure 7f).
- 42. 18 World Development Indicators 2015 Front User guide World view People Environment? 0 25 50 75 100 201220102008200620042002200019981996 Goods (excluding arms) admitted free of tariffs from developing countries (% of total merchandise imports, excluding arms) Norway Japan Australia European Union United States More opportunities for exporters in developing countries 8c Source: World Trade Organization, International Trade Center, and United Nations Conference on Trade and Development. 0 50 100 150 200 201320102005200019951990 Agricultural support ($ billions) European Union Korea, Rep. Turkey United States Japan Domestic subsidies to agriculture exceed aid flows 8b Source: Organisation for Economic Co-operation and Development StatExtracts. 0 30 60 90 120 150 201320102005200019951990 Official development assistance from Development Assistance Committee members (2012 $ billions) Multilateral net official development assistance Bilateral net official development assistance Aid flows have increased 8a Source: Organisation for Economic Co-operation and Development StatExtracts. Millennium Development Goal 8 focuses on the multidimensional nature of development and the need for wealthy countries and developing countries to work together to create an environment in which rapid, sustainable development is possible. It recognizes that development challenges differ for large and small countries and for those that are landlocked or isolated by large expanses of ocean and that building and sustaining partnership are ongoing pro- cesses that do not stop on a given date or when a specific target is reached. Increased aid flows and debt relief for the poorest, highly indebted countries are only part of what is required. In parallel, Mil- lennium Development Goal 8 underscores the need to reduce bar- riers to trade, to support infrastructure development, and to share the benefits of new communications technology. In 2013 members of the Organisation for Economic Co-operation and Development’s (OECD) Development Assistance Committee (DAC) provided $135 billion in official development assistance (ODA), an increase of 6.1 percent in real terms over 2012. After fall- ing through much of the 1990s, ODA grew steadily from $71 billion in 1997 to $134 billion in 2010. The financial crisis that began in 2008 forced many governments to implement austerity measures and trim aid budgets, and ODA fell in 2011 and 2012. The rebound in 2013 resulted from several members stepping up spending on foreign aid, despite continued budget pressure, and from an expan- sion of the DAC by five new member countries: the Czech Republic, Iceland, Poland, the Slovak Republic, and Slovenia (figure 8a). Collectively OECD members, mostly high-income economies but also some upper middle-income economies such as Mexico and Turkey, spend almost 2.5 times as much on support to domestic agricultural producers as they do on ODA. In 2013 the OECD esti- mate of total support to agriculture was $344 billion, 62 percent of which went to EU and US producers (figure 8b). Many rich countries are committed to opening their markets to exports from developing countries, and pledges to facilitate trade and reform border procedures were reiterated at the December 2013 World Trade Organization Ministerial Meeting in Bali. The share of goods (excluding arms) admitted duty free by OECD econo- mies continues to rise, albeit it moderately. However, arcane rules of origin and phytosanitary standards prevent many developing MDG 8 Developaglobalpartnershipfordevelopment
- 43. World Development Indicators 2015 19Economy States and markets Global links Back countries from qualifying for duty-free access and, in turn, inhibit development of export-oriented industries (figure 8c). Since 2000, developing countries have seen much improvement in their external debt servicing capacity thanks to increased export earnings, improved debt management, debt restructuring, and— more recently—attractive borrowing conditions in international capital markets. The poorest, most indebted countries have also benefitted from extensive debt relief: 35 of the 39 countries eli- gible for the Heavily Indebted Poor Country Initiative and the Multi- lateral Debt Relief Initiative have completed the process. The debt service–to-export ratio averaged 11 percent in 2013, half its 2000 level, but with wide disparity across regions (figure 8d). Going for- ward the ratio is likely to be on an upward trajectory in light of the fragile global outlook, soft commodity prices, and projected 20 per- cent rise in developing countries’ external debt service over the next two to three years, following the 33 percent increase in their combined external debt stock since 2010. Telecommunications is an essential tool for development, and new technologies are creating 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. Mobile cellular subscriptions topped 6.7 billion in 2013 worldwide, and early estimates show close to 7 billion for 2014. High-income economies had 121 subscriptions per 100 people in 2013—more than one per person—and upper middle-income economies have reached 100 subscriptions per 100 people. Lower middle-income economies had 85, and low-income economies had 55 (figure 8e). Mobile phones are one of several ways to access the Internet. In 2000 Internet use was spreading rapidly in high-income economies but was barely under way in developing country regions. Now develop- ing countries are beginning to catch up. Since 2000, Internet users per 100 people in developing economies has grown 27 percent a year. For instance, the percentage of the population with access to the Internet has doubled in South Asia since 2010, reaching 14 per- cent in 2013. Like telephones, Internet use is strongly correlated with income. The low-income economies of South Asia and Sub- Saharan Africa lag behind, accounting for 50 percent of the more than 4 billion people who are not yet using the Internet (figure 8f). 0 10 20 30 40 50 201320102005200019951990 Total debt service (% of exports of goods, services, and income) Europe & Central Asia Latin America & Caribbean South Asia Sub-Saharan Africa East Asia & Pacific Middle East & North Africa Debt service burdens beginning to rise 8d Source: World Development Indicators database. 0 50 100 150 201320102005200019951990 Mobile cellular subscriptions (per 100 people) High income Upper middle income Lower middle income Low income Mobile phone access growing rapidly 8e Source: International Telecommunications Union. 0 20 40 60 80 2013201020082006200420022000 Internet users (per 100 people) High income South Asia Middle East & North Africa Latin America & Caribbean Sub-Saharan Africa Europe & Central Asia East Asia & Pacific Gap in Internet access still large 8f Source: International Telecommunications Union.
- 44. 20 World Development Indicators 2015 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?
- 45. World Development Indicators 2015 21Economy 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.
- 46. Dominican Republic Trinidad and Tobago Grenada St. Vincent and the Grenadines Dominica Puerto Rico, US St. Kitts and Nevis Antigua and Barbuda St. Lucia Barbados R.B. de Venezuela U.S. Virgin Islands (US) Martinique (Fr) Guadeloupe (Fr) Curaçao (Neth) St. Martin (Fr) Anguilla (UK) St. Maarten (Neth) Caribbean inset Samoa Tonga Fiji Kiribati Haiti Jamaica Cuba The Bahamas United States Canada Panama Costa Rica Nicaragua Honduras El Salvador Guatemala Mexico Belize Colombia Guyana SurinameR.B. de Venezuela Ecuador Peru Brazil Bolivia Paraguay Chile Argentina Uruguay American Samoa (US) French Polynesia (Fr) French Guiana (Fr) Greenland (Den) Turks and Caicos Is. (UK) IBRD 41450 50.0 or more 25.0–49.9 10.0–24.9 2.0–9.9 Less than 2.0 No data Poverty SHARE OF POPULATION LIVING ON LESS THAN $1.25 A DAY, 2011 (%) Bermuda (UK) 22 World Development Indicators 2015 The poverty headcount ratio at $1.25 a day is the share of the population living on less than $1.25 a day in 2005 purchasing power parity (PPP) terms. It is also referred as extreme poverty. The PPP 2005 $1.25 a day poverty line is the average poverty line of the 15 poorest countries in the world, estimated from household surveys conducted by national statisti- cal offices or by private agencies under the supervi- sion of government or international agencies. Income and consumption data used for estimating poverty are also collected from household surveys. The latest com- prehensive update is the 2011 estimates, which draw on more than 2 million randomly sampled households, representing 85 percent of the population in develop- ing countries. It covers 128 developing countries (as defined in 1990). This map shows the country-level poverty estimates for generating the 2011 regional and global poverty numbers. Front User guide World view People Environment?
- 47. Economy States and markets Global links Back Romania Serbia Greece San Marino BulgariaUkraine Germany FYR Macedonia Croatia Bosnia and Herzegovina Czech Republic Poland Hungary Italy Austria Slovenia Slovak Republic Kosovo Montenegro Albania Europe inset Burkina Faso Palau Federated States of Micronesia Marshall Islands Nauru Kiribati Solomon Islands Tuvalu Vanuatu Fiji Norway Iceland Ireland United Kingdom Sweden Finland Denmark Estonia Latvia Lithuania Poland Belarus Ukraine Moldova Romania Bulgaria Greece Italy Germany Belgium The Netherlands Luxembourg Switzerland Liechtenstein France AndorraPortugal Spain Monaco Malta Morocco Tunisia Algeria Mauritania Mali Senegal The Gambia Guinea- Bissau Guinea Cabo 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 Mozambique Zimbabwe Botswana Namibia Swaziland LesothoSouth Africa Madagascar Mauritius Seychelles Comoros Rep. of Yemen Oman United Arab Emirates Qatar Bahrain Saudi Arabia Kuwait Israel 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 Guinea Indonesia Australia New Zealand JapanRep.of Korea Dem.People’s Rep.of Korea Mongolia China Russian Federation Brunei Darussalam Sudan South Sudan Timor-Leste N. Mariana Islands (US) Guam (US) New Caledonia (Fr) Greenland (Den) West Bank and Gaza Western Sahara Réunion (Fr) Mayotte (Fr) World Development Indicators 2015 23 Developing countries as a whole met the Millennium Development Goal target of halving extreme poverty rates five years ahead of the 2015 deadline. The share of people living on less than $1.25 a day in developing countries fell from 43.6 percent in 1990 to 17.0 percent in 2011. Between 1990 and 2011 the number of people living on less than $1.25 a day in the world fell from 1.9 billion to 1 billion, and it is forecast to be halved by 2015 from its 1990 level. In 2011 nearly 60 percent of the world’s 1 billion extremely poor people lived in just five countries: India, Nigeria, China, Bangladesh, and the Democratic Republic of the Congo.
- 48. 24 World Development Indicators 2015 Front User guide World view People Environment? 1 World view Population Surface area Population density Urban population Gross national income Gross domestic product Atlas 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 2013 2013 2013 2013 2013 2013 2013 2013 2012–13 2012–13 Afghanistan 30.6 652.9 47 26 21.0 690 59.9a 1,960a 1.9 –0.5 Albania 2.9 28.8 106 55 13.1 4,510 28.8 9,950 1.4 1.5 Algeria 39.2 2,381.7 17 70 208.8 5,330 512.5 13,070 2.8 0.9 American Samoa 0.1 0.2 276 87 .. ..b .. .. .. .. Andorra 0.1 0.5 169 86 .. ..c .. .. .. .. Angola 21.5 1,246.7 17 42 110.9 5,170 150.2 7,000 6.8 3.6 Antigua and Barbuda 0.1 0.4 205 25 1.2 13,050 1.8 20,490 –0.1 –1.1 Argentina 41.4 2,780.4 15 91 ..d ..b,d ..d ..d 2.9e ..d Armenia 3.0 29.7 105 63 11.3 3,800 24.3 8,180 3.5 3.2 Aruba 0.1 0.2 572 42 .. ..c .. .. .. .. Australia 23.1 7,741.2 3 89 1,512.6 65,400 974.1 42,110 2.5 0.7 Austria 8.5 83.9 103 66 427.3 50,390 381.9 45,040 0.2 –0.4 Azerbaijan 9.4 86.6 114 54 69.2 7,350 152.4 16,180 5.8 4.4 Bahamas, The 0.4 13.9 38 83 8.1 21,570 8.6 22,700 0.7 –0.8 Bahrain 1.3 0.8 1,753 89 26.0 19,700 47.8 36,290 5.3 4.2 Bangladesh 156.6 148.5 1,203 33 158.8 1,010 498.8 3,190 6.0 4.7 Barbados 0.3 0.4 662 32 4.3 15,080 4.3 15,090 0.0 –0.5 Belarus 9.5 207.6 47 76 63.7 6,730 160.5 16,950 0.9 0.9 Belgium 11.2 30.5 369 98 518.2 46,340 460.2 41,160 0.3 –0.2 Belize 0.3 23.0 15 44 1.5 4,510 2.6 7,870 1.5 –0.9 Benin 10.3 114.8 92 43 8.2 790 18.4 1,780 5.6 2.8 Bermuda 0.1 0.1 1,301 100 6.8 104,610 4.3 66,430 –4.9 –5.2 Bhutan 0.8 38.4 20 37 1.8 2,330 5.2 6,920 2.0 0.4 Bolivia 10.7 1,098.6 10 68 27.2 2,550 61.3 5,750 6.8 5.0 Bosnia and Herzegovina 3.8 51.2 75 39 18.3 4,780 37.0 9,660 2.5 2.6 Botswana 2.0 581.7 4 57 15.7 7,770 31.6 15,640 5.8 4.9 Brazil 200.4 8,515.8 24 85 2,342.6 11,690 2,956.0 14,750 2.5 1.6 Brunei Darussalam 0.4 5.8 79 77 .. ..c .. .. –1.8 –3.1 Bulgaria 7.3 111.0 67 73 53.5 7,360 110.5 15,210 1.1 1.6 Burkina Faso 16.9 274.2 62 28 12.7 750 28.5 1,680 6.6 3.7 Burundi 10.2 27.8 396 11 2.6 260 7.8 770 4.6 1.4 Cabo Verde 0.5 4.0 124 64 1.8 3,620 3.1 6,210 0.5 –0.4 Cambodia 15.1 181.0 86 20 14.4 950 43.8 2,890 7.4 5.5 Cameroon 22.3 475.4 47 53 28.6 1,290 61.7 2,770 5.6 2.9 Canada 35.2 9,984.7 4 81 1,835.4 52,210 1,480.8 42,120 2.0 0.9 Cayman Islands 0.1 0.3 244 100 .. ..c .. .. .. .. Central African Republic 4.6 623.0 7 40 1.5 320 2.8 600 –36.0 –37.3 Chad 12.8 1,284.0 10 22 13.2 1,030 25.7 2,010 4.0 0.9 Channel Islands 0.2 0.2 853 31 .. ..c .. .. .. .. Chile 17.6 756.1 24 89 268.3 15,230 371.1 21,060 4.1 3.2 China 1,357.4 9,562.9 145 53 8,905.3 6,560 16,084.5 11,850 7.7 7.1 Hong Kong SAR, China 7.2 1.1 6,845 100 276.1 38,420 390.1 54,270 2.9 2.5 Macao SAR, China 0.6 0.0f 18,942 100 35.7 64,050 62.5 112,230 11.9 10.0 Colombia 48.3 1,141.7 44 76 366.6 7,590 577.8 11,960 4.7 3.3 Comoros 0.7 1.9 395 28 0.6 840 1.1 1,490 3.5 1.0 Congo, Dem. Rep. 67.5 2,344.9 30 41 29.1 430 49.9 740 8.5 5.6 Congo, Rep. 4.4 342.0 13 65 11.5 2,590 20.5 4,600 3.4 0.9
- 49. World Development Indicators 2015 25Economy States and markets Global links Back World view 1 Population Surface area Population density Urban population Gross national income Gross domestic product Atlas 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 2013 2013 2013 2013 2013 2013 2013 2013 2012–13 2012–13 Costa Rica 4.9 51.1 95 75 46.5 9,550 66.1 13,570 3.5 2.1 Côte d’Ivoire 20.3 322.5 64 53 29.5 1,450 62.7 3,090 8.7 6.2 Croatia 4.3 56.6 76 58 57.1 13,420 88.6 20,810 –0.9 –0.7 Cuba 11.3 109.9 106 77 66.4 5,890 208.9 18,520 2.7 2.8 Curaçao 0.2 0.4 346 90 .. ..c .. .. .. .. Cyprus 1.1 9.3 124 67 21.9g 25,210g 24.0g 27,630g –5.4g –5.8g Czech Republic 10.5 78.9 136 73 199.4 18,970 283.6 26,970 –0.7 –0.7 Denmark 5.6 43.1 132 87 346.3 61,670 254.3 45,300 –0.5 –0.9 Djibouti 0.9 23.2 38 77 .. ..h .. .. 5.0 3.4 Dominica 0.1 0.8 96 69 0.5 6,930 0.7 10,060 –0.9 –1.4 Dominican Republic 10.4 48.7 215 77 60.0 5,770 121.0 11,630 4.6 3.3 Ecuador 15.7 256.4 63 63 90.6 5,760 168.8 10,720 4.6 3.0 Egypt, Arab Rep. 82.1 1,001.5 82 43 257.4 3,140 885.1 10,790 2.1 0.4 El Salvador 6.3 21.0 306 66 23.6 3,720 47.5 7,490 1.7 1.0 Equatorial Guinea 0.8 28.1 27 40 10.8 14,320 17.6 23,270 –4.8 –7.4 Eritrea 6.3 117.6 63 22 3.1 490 7.5a 1,180a 1.3 –1.9 Estonia 1.3 45.2 31 68 23.4 17,780 32.8 24,920 1.6 2.0 Ethiopia 94.1 1,104.3 94 19 44.5 470 129.6 1,380 10.5 7.7 Faeroe Islands 0.0i 1.4 35 42 .. ..c .. .. .. .. Fiji 0.9 18.3 48 53 3.9 4,370 6.7 7,590 3.5 2.7 Finland 5.4 338.4 18 84 265.5 48,820 216.8 39,860 –1.2 –1.7 France 65.9 549.1 120 79 2,869.8 43,520 2,517.8 38,180 0.3 –0.2 French Polynesia 0.3 4.0 76 56 .. ..c .. .. .. .. Gabon 1.7 267.7 7 87 17.8 10,650 28.8 17,230 5.9 3.4 Gambia, The 1.8 11.3 183 58 0.9 500 3.0 1,610 4.8 1.5 Georgia 4.5j 69.7 78j 53 16.0j 3,560j 31.5j 7,020j 3.3j 3.4j Germany 80.7 357.2 231 75 3,810.6 47,250 3,630.5 45,010 0.1 –0.2 Ghana 25.9 238.5 114 53 45.8 1,770 101.0 3,900 7.6 5.4 Greece 11.0 132.0 86 77 250.3 22,690 283.0 25,660 –3.3 –2.7 Greenland 0.1 410.5k 0l 86 .. ..c .. .. .. .. Grenada 0.1 0.3 311 36 0.8 7,490 1.2 11,230 2.4 2.0 Guam 0.2 0.5 306 94 .. ..c .. .. .. .. Guatemala 15.5 108.9 144 51 51.6 3,340 110.3 7,130 3.7 1.1 Guinea 11.7 245.9 48 36 5.4 460 13.6 1,160 2.3 –0.3 Guinea-Bissau 1.7 36.1 61 48 1.0 590 2.4 1,410 0.3 –2.1 Guyana 0.8 215.0 4 28 3.0 3,750 5.3a 6,610a 5.2 4.7 Haiti 10.3 27.8 374 56 8.4 810 17.7 1,720 4.3 2.8 Honduras 8.1 112.5 72 54 17.7 2,180 34.6 4,270 2.6 0.5 Hungary 9.9 93.0 109 70 131.2 13,260m 224.2 22,660 1.5 1.8 Iceland 0.3 103.0 3 94 15.0 46,290 13.3 41,090 3.5 2.5 India 1,252.1 3,287.3 421 32 1,961.6 1,570 6,700.1 5,350 6.9 5.6 Indonesia 249.9 1,910.9 138 52 895.0 3,580 2,315.1 9,270 5.8 4.5 Iran, Islamic Rep. 77.4 1,745.2 48 72 447.5 5,780 1,208.6 15,610 –5.8 –7.0 Iraq 33.4 435.2 77 69 224.6 6,720 499.0 14,930 4.2 1.6 Ireland 4.6 70.3 67 63 198.1 43,090 178.7 38,870 0.2 –0.1 Isle of Man 0.1 0.6 151 52 .. ..c .. .. .. .. Israel 8.1 22.1 372 92 273.5 33,930 256.2 31,780 3.2 1.3
- 50. 26 World Development Indicators 2015 Front User guide World view People Environment? 1 World view Population Surface area Population density Urban population Gross national income Gross domestic product Atlas 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 2013 2013 2013 2013 2013 2013 2013 2013 2012–13 2012–13 Italy 60.2 301.3 205 69 2,145.3 35,620 2,121.5 35,220 –1.9 –3.1 Jamaica 2.7 11.0 251 54 14.2 5,220 23.0 8,490 1.3 1.0 Japan 127.3 378.0 349 92 5,899.9 46,330 4,782.2 37,550 1.6 1.8 Jordan 6.5 89.3 73 83 32.0 4,950 75.3 11,660 2.8 0.6 Kazakhstan 17.0 2,724.9 6 53 196.8 11,550 352.3 20,680 6.0 4.5 Kenya 44.4 580.4 78 25 51.6 1,160n 123.3 2,780 5.7 2.9 Kiribati 0.1 0.8 126 44 0.3 2,620 0.3a 2,780a 3.0 1.4 Korea, Dem. People’s Rep. 24.9 120.5 207 61 .. ..o .. .. .. .. Korea, Rep. 50.2 100.2 516 82 1,301.6 25,920 1,675.2 33,360 3.0 2.5 Kosovo 1.8 10.9 168 .. 7.2 3,940 16.6a 9,090a 3.0 2.0 Kuwait 3.4 17.8 189 98 141.0 45,130 265.0 84,800 8.3 4.1 Kyrgyz Republic 5.7 199.9 30 35 6.9 1,210 17.6 3,080 10.5 8.4 Lao PDR 6.8 236.8 29 36 9.8 1,450 30.8 4,550 8.5 6.5 Latvia 2.0 64.5 32 67 30.8 15,290 45.3 22,510 4.1 5.2 Lebanon 4.5 10.5 437 88 44.1 9,870 77.7a 17,400a 0.9 –0.1 Lesotho 2.1 30.4 68 26 3.1 1,500 6.5 3,160 5.5 4.3 Liberia 4.3 111.4 45 49 1.7 410 3.4 790 11.3 8.6 Libya 6.2 1,759.5 4 78 .. ..b .. .. –10.9 –11.6 Liechtenstein 0.0i 0.2 231 14 .. ..c .. .. .. .. Lithuania 3.0 65.3 47 67 44.1 14,900 72.6 24,530 3.3 4.3 Luxembourg 0.5 2.6 210 90 38.0 69,880 31.4 57,830 2.0 –0.3 Macedonia, FYR 2.1 25.7 84 57 10.3 4,870 24.3 11,520 3.1 3.0 Madagascar 22.9 587.3 39 34 10.2 440 31.4 1,370 2.4 –0.4 Malawi 16.4 118.5 174 16 4.4 270 12.3 750 5.0 2.0 Malaysia 29.7 330.8 90 73 309.9 10,430 669.5 22,530 4.7 3.1 Maldives 0.3 0.3 1,150 43 1.9 5,600 3.4 9,900 3.7 1.7 Mali 15.3 1,240.2 13 38 10.2 670 23.6 1,540 2.1 –0.8 Malta 0.4 0.3 1,323 95 8.9 20,980 11.4 27,020 2.9 1.9 Marshall Islands 0.1 0.2 292 72 0.2 4,310 0.2a 4,630a 3.0 2.8 Mauritania 3.9 1,030.7 4 59 4.1 1,060 11.1 2,850 6.7 4.1 Mauritius 1.3 2.0 620 40 12.0 9,570 22.3 17,730 3.2 3.0 Mexico 122.3 1,964.4 63 79 1,216.1 9,940 1,960.0 16,020 1.1 –0.2 Micronesia, Fed. Sts. 0.1 0.7 148 22 0.3 3,280 0.4a 3,680a –4.0 –4.1 Moldova 3.6p 33.9 124p 45 8.8p 2,470p 18.5p 5,180p 8.9p 8.9p Monaco 0.0i 0.0f 18,916 100 .. ..c .. .. .. .. Mongolia 2.8 1,564.1 2 70 10.7 3,770 25.0 8,810 11.7 10.1 Montenegro 0.6 13.8 46 64 4.5 7,250 9.0 14,410 3.3 3.3 Morocco 33.0 446.6 74 59 101.6q 3,020q 235.0q 7,000q 4.4q 2.8q Mozambique 25.8 799.4 33 32 15.8 610 28.5 1,100 7.4 4.8 Myanmar 53.3 676.6 82 33 .. ..o .. .. .. .. Namibia 2.3 824.3 3 45 13.5 5,870 21.9 9,490 5.1 3.1 Nepal 27.8 147.2 194 18 20.3 730 62.9 2,260 3.8 2.6 Netherlands 16.8 41.5 498 89 858.0 51,060 777.4 46,260 –0.7 –1.0 New Caledonia 0.3 18.6 14 69 .. ..c .. .. .. .. New Zealand 4.4 267.7 17 86 157.6 35,760 136.5 30,970 2.5 1.7 Nicaragua 6.1 130.4 51 58 10.9 1,790 27.4 4,510 4.6 3.1 Niger 17.8 1,267.0 14 18 7.1 400 15.9 890 4.1 0.2
- 51. World Development Indicators 2015 27Economy States and markets Global links Back World view 1 Population Surface area Population density Urban population Gross national income Gross domestic product Atlas 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 2013 2013 2013 2013 2013 2013 2013 2013 2012–13 2012–13 Nigeria 173.6 923.8 191 46 469.7 2,710 930.2 5,360 5.4 2.5 Northern Mariana Islands 0.1 0.5 117 89 .. ..c .. .. .. .. Norway 5.1 385.2 14 80 521.7 102,700 332.5 65,450 0.6 –0.6 Oman 3.6 309.5 12 77 83.4 25,150 174.9 52,780 5.8 –3.5 Pakistan 182.1 796.1 236 38 247.0 1,360 881.4 4,840 4.4 2.7 Palau 0.0i 0.5 45 86 0.2 10,970 0.3a 14,540a –0.3 –1.1 Panama 3.9 75.4 52 66 41.3 10,700 74.6 19,300 8.4 6.6 Papua New Guinea 7.3 462.8 16 13 14.8 2,020 18.4a 2,510a 5.5 3.3 Paraguay 6.8 406.8 17 59 27.3 4,010 52.2 7,670 14.2 12.3 Peru 30.4 1,285.2 24 78 190.5 6,270 338.9 11,160 5.8 4.4 Philippines 98.4 300.0 330 45 321.8 3,270 771.3 7,840 7.2 5.3 Poland 38.5 312.7 126 61 510.0 13,240 879.2 22,830 1.7 1.7 Portugal 10.5 92.2 114 62 222.4 21,270 284.4 27,190 –1.4 –0.8 Puerto Rico 3.6 8.9 408 94 69.4 19,210 86.2a 23,840a –0.6 0.4 Qatar 2.2 11.6 187 99 188.2 86,790 278.8 128,530 6.3 –0.2 Romania 20.0 238.4 87 54 180.8 9,050 367.5 18,390 3.5 3.9 Russian Federation 143.5 17,098.2 9 74 1,987.7 13,850 3,484.5 24,280 1.3 1.1 Rwanda 11.8 26.3 477 27 7.4 630 17.1 1,450 4.7 1.9 Samoa 0.2 2.8 67 19 0.8 3,970 1.1a 5,560a –1.1 –1.9 San Marino 0.0i 0.1 524 94 .. ..c .. .. .. .. São Tomé and Príncipe 0.2 1.0 201 64 0.3 1,470 0.6 2,950 4.0 1.4 Saudi Arabia 28.8 2,149.7r 13 83 757.1 26,260 1,546.5 53,640 4.0 1.9 Senegal 14.1 196.7 73 43 14.8 1,050 31.3 2,210 2.8 –0.2 Serbia 7.2 88.4 82 55 43.3 6,050 89.4 12,480 2.6 3.1 Seychelles 0.1 0.5 194 53 1.2 13,210j 2.1 23,730 5.3 4.2 Sierra Leone 6.1 72.3 84 39 4.1 660 10.3 1,690 5.5 3.6 Singapore 5.4 0.7 7,713 100 291.8 54,040 415.0 76,860 3.9 2.2 Sint Maarten 0.0i 0.0f 1,167 100 .. ..c .. .. .. .. Slovak Republic 5.4 49.0 113 54 96.4 17,810 140.6 25,970 1.4 1.3 Slovenia 2.1 20.3 102 50 47.8 23,220 59.0 28,650 –1.0 –1.1 Solomon Islands 0.6 28.9 20 21 0.9 1,600 1.0a 1,810a 3.0 0.8 Somalia 10.5 637.7 17 39 .. ..o .. .. .. .. South Africa 53.2 1,219.1 44 64 393.8 7,410 666.0 12,530 2.2 0.6 South Sudan 11.3 644.3 .. 18 10.8 950s 21.0a 1,860a 13.1 8.5 Spain 46.6 505.6 93 79 1,395.9 29,940 1,532.1 32,870 –1.2 –0.9 Sri Lanka 20.5 65.6 327 18 65.0 3,170 194.1 9,470 7.3 6.4 St. Kitts and Nevis 0.1 0.3 208 32 0.8 13,890 1.1 20,990 4.2 3.0 St. Lucia 0.2 0.6 299 18 1.3 7,060 1.9 10,290 –0.4 –1.2 St. Martin 0.0i 0.1 575 .. .. ..c .. .. .. .. St. Vincent & the Grenadines 0.1 0.4 280 50 0.7 6,460 1.1 10,440 1.7 1.7 Sudan 38.0 1,879.4 21t 33 58.8 1,550 122.7 3,230 –6.0 –7.9 Suriname 0.5 163.8 3 66 5.1 9,370 8.6 15,960 2.9 2.0 Swaziland 1.2 17.4 73 21 3.7 2,990 7.6 6,060 2.8 1.3 Sweden 9.6 447.4 24 86 592.4 61,710 443.3 46,170 1.5 0.6 Switzerland 8.1 41.3 205 74 733.4 90,680 482.1 59,610 1.9 0.8 Syrian Arab Republic 22.8 185.2 124 57 .. ..h .. .. .. .. Tajikistan 8.2 142.6 59 27 8.1 990 20.5 2,500 7.4 4.8
- 52. 28 World Development Indicators 2015 Front User guide World view People Environment? 1 World view Population Surface area Population density Urban population Gross national income Gross domestic product Atlas 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 2013 2013 2013 2013 2013 2013 2013 2013 2012–13 2012–13 Tanzania 49.3 947.3 56 30 41.0u 860u 116.3u 2,430u 7.3u 3.8u Thailand 67.0 513.1 131 48 357.7 5,340 899.7 13,430 1.8 1.4 Timor-Leste 1.2 14.9 79 31 4.5 3,940 8.8a 7,670a 7.8 5.2 Togo 6.8 56.8 125 39 3.6 530 8.1 1,180 5.1 2.4 Tonga 0.1 0.8 146 24 0.5 4,490 0.6a 5,450a 0.5 0.1 Trinidad and Tobago 1.3 5.1 261 9 21.1 15,760 35.2 26,220 1.6 1.3 Tunisia 10.9 163.6 70 66 45.8 4,200 115.5 10,610 2.5 1.5 Turkey 74.9 783.6 97 72 821.7 10,970 1,391.4 18,570 4.1 2.8 Turkmenistan 5.2 488.1 11 49 36.1 6,880 67.7a 12,920a 10.2 8.8 Turks and Caicos Islands 0.0i 1.0 35 91 .. ..c .. .. .. .. Tuvalu 0.0i 0.0f 329 58 0.1 5,840 0.1a 5,260a 1.3 1.1 Uganda 37.6 241.6 188 15 22.5 600 61.2 1,630 3.3 –0.1 Ukraine 45.5 603.6 79 69 179.9 3,960 407.8 8,970 1.9 2.1 United Arab Emirates 9.3 83.6 112 85 353.1 38,360 551.3 59,890 5.2 1.2 United Kingdom 64.1 243.6 265 82 2,671.7 41,680 2,433.9 37,970 1.7 1.1 United States 316.1 9,831.5 35 81 16,903.0 53,470 16,992.4 53,750 2.2 1.5 Uruguay 3.4 176.2 19 95 51.7 15,180 64.5 18,940 4.4 4.0 Uzbekistan 30.2 447.4 71 36 56.9 1,880 159.9a 5,290a 8.0 6.3 Vanuatu 0.3 12.2 21 26 0.8 3,130 0.7a 2,870a 2.0 –0.3 Venezuela, RB 30.4 912.1 34 89 381.6 12,550 544.2 17,900 1.3 –0.2 Vietnam 89.7 331.0 289 32 156.4 1,740 455.0 5,070 5.4 4.3 Virgin Islands (U.S.) 0.1 0.4 299 95 .. ..c .. .. .. .. West Bank and Gaza 4.2 6.0 693 75 12.4 3,070 21.4 5,300 –4.4 –7.2 Yemen, Rep. 24.4 528.0 46 33 32.6 1,330 93.3 3,820 4.2 1.8 Zambia 14.5 752.6 20 40 26.3 1,810 55.4 3,810 6.7 3.3 Zimbabwe 14.1 390.8 37 33 12.2 860 24.0 1,690 4.5 1.3 World 7,125.1 s 134,324.7 s 55 w 53 w 76,119.3 t 10,683 w 102,197.6 t 14,343 w 2.3 w 1.1 w Low income 848.7 15,359.5 57 30 617.7 728 1,662.6 1,959 5.6 3.3 Middle income 4,970.0 65,026.4 78 50 23,628.9 4,754 47,504.2 9,558 4.9 3.8 Lower middle income 2,561.1 21,590.5 123 39 5,312.2 2,074 15,280.5 5,966 5.8 4.3 Upper middle income 2,408.9 43,436.0 56 62 18,316.9 7,604 32,292.8 13,405 4.7 3.9 Low & middle income 5,818.7 80,385.9 74 47 24,252.8 4,168 49,134.9 8,444 5.0 3.6 East Asia & Pacific 2,005.8 16,270.8 126 51 11,104.7 5,536 21,519.5 10,729 7.1 6.4 Europe & Central Asia 272.4 6,478.6 43 60 1,937.5 7,114 3,711.8 13,628 3.7 3.0 Latin America & Carib. 588.0 19,461.7 31 79 5,610.9 9,542 8,340.8 14,185 2.5 1.3 Middle East & N. Africa 345.4 8,775.4 40 60 .. .. .. .. –0.5 –2.2 South Asia 1,670.8 5,136.2 350 32 2,477.5 1,483 8,405.8 5,031 6.6 5.2 Sub-Saharan Africa 936.3 24,263.1 40 37 1,578.8 1,686 3,103.1 3,314 4.1 1.4 High income 1,306.4 53,938.8 25 80 52,009.9 39,812 53,285.4 40,788 1.4 0.9 Euro area 337.3 2,758.5 126 75 13,272.8 39,350 12,801.4 37,953 –0.5 –0.8 a. Based on regression; others are extrapolated from the 2011 International Comparison Program benchmark estimates. b. Estimated to be upper middle income ($4,126–$12,745). c. Estimated to be high income ($12,746 or more). d. Data series will be calculated once ongoing revisions to official statistics reported by the National Statistics and Censuses Institute of Argentina have been finalized. e. Data for Argentina are officially reported by the National Statistics and Censuses Institute of Argentina. The International Monetary Fund has, however, issued a declaration of censure and called on Argentina to adopt remedial measures to address the quality of official GDP and consumer price index data. Alternative data sources have shown significantly lower real growth and higher inflation than the official data since 2008. In this context, the World Bank is also using alternative data sources and estimates for the surveillance of macroeconomic developments in Argentina. f. Greater than 0 but less than 50. g. Data are for the area controlled by the government of Cyprus. h. Estimated to be lower middle income ($1,046–$4,125). i. Greater than 0 but less than 50,000. j. Excludes Abkhazia and South Ossetia. k. Refers to area free from ice. l. Greater than 0 but less than 0.5. m. Included in the aggregates for upper middle-income economies based on earlier data. n. Included in the aggregates for low-income economies based on earlier data. o. Estimated to be low income ($1,045 or less). p. Excludes Transnistria. q. Includes Former Spanish Sahara. r. Provisional estimate. s. Included in the aggregates for lower middle-income economies based on earlier data. t. Includes South Sudan. u. Covers mainland Tanzania only.
- 53. World Development Indicators 2015 29Economy 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 protection against misuse of census data, and census agencies’ independence from political influence. Moreover, comparability of population indi- cators is limited by differences in the concepts, definitions, collec- tion procedures, and estimation methods used by national statisti- cal agencies and other organizations that collect the data. More countries conducted a census in the 2010 census round (2005–14) than in previous rounds. As of December 2014 (the end of the 2010 census round), about 93 percent of the estimated world population has been enumerated in a census. 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. Current population estimates for developing countries that lack recent census data and pre- and post-census estimates for coun- tries with census data are provided by the United Nations Popula- tion Division and other agencies. The cohort component method—a standard method for estimating and projecting population—requires fertility, mortality, and net migration data, often collected from sam- ple 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 Surface area includes inland bodies of water and some coastal waterways and thus differs from land area, which excludes bod- ies of water, and from gross area, which may include offshore territorial waters. It 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 distin- guishing urban from rural areas, in part because of the wide variety of situations across countries. Most countries use an urban classi- fication related to the size or characteristics of settlements. Some define urban areas based on the presence 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 definitions of what constitutes a city or metropoli- tan area, cross-country comparisons should be made with caution. 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 intermediate inputs. The World Bank uses GNI per capita in U.S. dollars to clas- sify 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 further 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. 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 by the International Com- parison Program (ICP) in 2011, 177 countries and territories fully participated and 22 partially participated. PPP rates for 47 high- and upper middle-income countries are from Eurostat and the Organ- isation for Economic Co-operation and Development (OECD); PPP estimates incorporate new price data collected since 2011. For the remaining 2011 ICP economies PPP rates are extrapolated from the 2011 ICP benchmark results, which account for relative price changes between each economy and the United States. For coun- tries that did not participate in the 2011 ICP round, PPP rates are About the data
- 54. 30 World Development Indicators 2015 Front User guide World view People Environment? 1 World view imputed using a statistical model. More information on the results of the 2011 ICP is available at http://icp.worldbank.org. Growth rates of GDP and GDP per capita are calculated using con- stant price data in local currency. Constant price U.S. dollar series are used to calculate regional and income group growth rates. Growth rates in the table are annual averages (see Statistical 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 asylum, who are generally considered part of the population of their country of origin. The values shown are midyear estimates. • Surface 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 obtained by the United Nations Population Division. • 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 con- verted 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 subsi- dies) 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 Health Global Practice, operational staff, and country offices. The United Nations Population Division (2013) 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 publica- tions from national statistical offices, Eurostat’s Population database, the United Nations Statistics Division’s Population and Vital Statistics Report, 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 (2014). GNI, GNI per capita, GDP growth, and GDP per capita growth are estimated by World Bank staff based on national accounts data collected 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 Euro- stat/OECD and by World Bank staff based on data collected by the ICP. References Eurostat. n.d. Population database. [http://ec.europa.eu/eurostat/]. Luxembourg. FAO (Food and Agriculture Organization), IFAD (International Fund for Agricultural Development), and WFP (World Food Programme). 2014. The State of Food Insecurity in the World 2014: Strengthening the Enabling Environment for Food Security and Nutrition. Rome. [www .fao.org/3/a-i4030e.pdf]. 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). 2014. The Gap Report. [www.unaids.org/en/resources/campaigns/2014 /2014gapreport/gapreport/]. Geneva. UNESCO (United Nations Educational, Scientific and Cultural Organi- zation). 2004. Education for All Global Monitoring Report 2003/4: Gender and Education for All—The Leap to Equality. Paris. UNFPA (United Nations Population Fund) and Guttmacher Institute. 2014. Adding It Up 2014: The Costs and Benefits of Investing in Sexual and Reproductive Health. [www.unfpa.org/sites/default /files/pub-pdf/Adding%20It%20Up-Final-11.18.14.pdf]. New York. UNICEF (United Nations Children’s Fund). 2014. Committing to Child Sur- vival: A Promise Renewed—Progress Report 2014. [http://files.unicef .org/publications/files/APR_2014_web_15Sept14.pdf]. New York. UNICEF (United Nations Children’s Fund), WHO (World Health Orga- nization), and World Bank. 2014. 2013 Joint Child Malnutrition Estimates—Levels and Trends. New York: UNICEF. [www.who.int /nutgrowthdb/estimates2013/]. United Nations. 2014. A World That Counts: Mobilising the Data Revolu- tion for Sustainable Development. New York. [www.undatarevolution .org/wp-content/uploads/2014/12/A-World-That-Counts2.pdf]. United Nations Population Division. 2013. World Population Prospects: The 2012 Revision. [http://esa.un.org/unpd/wpp/Documentation /publications.htm]. New York. United Nations Statistics Division. Various years. Population and Vital Statistics Report. New York. ———. 2014. World Urbanization Prospects: The 2014 Revision. [http://esa.un.org/unpd/wup/]. New York. WHO (World Health Organization). 2014a. Global Tuberculosis Report 2014. [http://who.int/tb/publications/global_report/]. Geneva. ———. 2014b. “Maternal Mortality.” Fact sheet 348. [www.who.int /mediacentre/factsheets/fs348/]. Geneva. ———. 2014c. World Malaria Report 2014. [www.who.int/malaria /publications/world_malaria_report_2014/]. Geneva. World Bank. 2015. Global Monitoring Report 2014/2015: Ending Pov- erty and Sharing Prosperity. Washington, DC.
- 55. World Development Indicators 2015 31Economy 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 Prevalence of HIV 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 Internet users IT.NET.USER.PZ 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 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). Online tables and indicators
- 56. 32 World Development Indicators 2015 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 Reference 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 % Reference 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 2008c <2 <0.5 <2 <0.5 2012c <2 <0.5 3.0 0.6 Algeria 48.4d 77.5d 1988 7.1 1.1 23.7 6.4 1995 6.4 1.3 22.8 6.2 Angola 88.1 141.0 .. .. .. .. 2009 43.4 16.5 67.4 31.5 Argentina 1.7 2.7 2010e,f <2 0.9 4.0 1.6 2011e,f <2 0.8 2.9 1.3 Armenia 245.2 392.4 2011c 2.5 <0.5 17.6 3.5 2012c <2 <0.5 15.5 3.1 Azerbaijan 2,170.9 3,473.5 2005c <2 <0.5 <2 <0.5 2008c <2 <0.5 2.4 0.5 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 2010c <2 <0.5 <2 <0.5 2011c <2 <0.5 <2 <0.5 Belize 1.8d 2.9d 1998f 11.3 4.8 26.4 10.3 1999f 12.2 5.5 22.0 9.9 Benin 344.0 550.4 2003 47.3 15.7 75.3 33.5 2011 51.6 18.8 74.3 35.9 Bhutan 23.1 36.9 2007 10.2 1.8 29.8 8.5 2012 2.4 <0.5 15.2 3.3 Bolivia 3.2 5.1 2011f 7.0 3.1 12.0 5.5 2012f 8.0 4.2 12.7 6.5 Bosnia and Herzegovina 1.1 1.7 2004c <2 <0.5 <2 <0.5 2007c <2 <0.5 <2 <0.5 Botswana 4.2 6.8 2003c 24.4 8.5 41.6 17.9 2009c 13.4 4.0 27.8 10.2 Brazil 2.0 3.1 2011f 4.5 2.5 8.2 3.9 2012f 3.8 2.1 6.8 3.3 Bulgaria 0.9 1.5 2010f <2 0.6 3.3 1.2 2011f <2 0.8 3.9 1.6 Burkina Faso 303.0 484.8 2003 48.9 18.3 72.5 34.7 2009 44.5 14.6 72.4 31.6 Burundi 558.8 894.1 1998 86.4 47.3 95.4 64.1 2006 81.3 36.4 93.5 56.1 Cabo Verde 97.7 156.3 2002 21.0 6.1 40.9 15.2 2007 13.7 3.2 34.7 11.1 Cambodia 2,019.1 3,230.6 2010 11.3 1.7 40.9 10.6 2011 10.1 1.4 41.3 10.3 Cameroon 368.1 589.0 2001 24.9 6.7 50.7 18.5 2007 27.6 7.2 53.2 20.0 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 2002 61.9 25.6 83.3 43.9 2011 36.5 14.2 60.5 27.3 Chile 484.2 774.7 2009f <2 0.7 2.6 1.1 2011f <2 <0.5 <2 0.8 China 5.1g 8.2g 2010h 9.2 2.0 23.2 7.3 2011h 6.3 1.3 18.6 5.5 Colombia 1,489.7 2,383.5 2011f 5.0 2.0 11.3 4.3 2012f 5.6 2.3 12.0 4.7 Comoros 368.0 588.8 .. .. .. .. 2004 46.1 20.8 65.0 34.2 Congo, Dem. Rep. 395.3 632.5 .. .. .. .. 2005 87.7 52.8 95.2 67.6 Congo, Rep. 469.5 751.1 2005 54.1 22.8 74.4 38.8 2011 32.8 11.5 57.3 24.2 Costa Rica 348.7d 557.9d 2011f <2 0.6 3.2 1.2 2012f <2 0.6 3.1 1.2 Côte d’Ivoire 5.6 8.9 2004c <2 <0.5 <2 <0.5 2008c <2 <0.5 <2 <0.5 Croatia 19.0 30.4 2010f <2 <0.5 <2 <0.5 2011f <2 <0.5 <2 <0.5 Czech Republic 407.3 651.6 2002 29.7 9.1 56.9 22.0 2008 35.0 12.7 59.1 25.9 Djibouti 134.8 215.6 .. .. .. .. 2002 18.8 5.3 41.2 14.6 Dominican Republic 25.5d 40.8d 2011f 2.5 0.6 8.5 2.4 2012f 2.3 0.6 8.8 2.4 Ecuador 0.6 1.0 2011f 4.0 1.9 9.0 3.6 2012f 4.0 1.8 8.4 3.4 Egypt, Arab Rep. 2.5 4.0 2004 2.3 <0.5 20.1 3.8 2008 <2 <0.5 15.4 2.8 El Salvador 6.0d 9.6d 2011f 2.8 0.6 10.3 2.7 2012f 2.5 0.6 8.8 2.4 Estonia 11.0 17.7 2010f <2 1.0 <2 1.0 2011f <2 1.2 <2 1.2 Ethiopia 3.4 5.5 2005 39.0 9.6 77.6 28.9 2010 36.8 10.4 72.2 27.6 Fiji 1.9 3.1 2002 29.2 11.3 48.7 21.8 2008 5.9 1.1 22.9 6.0 Gabon 554.7 887.5 .. .. .. .. 2005 6.1 1.3 20.9 5.8 Gambia, The 12.9 20.7 1998 65.6 33.8 81.2 49.1 2003 33.6 11.7 55.9 24.4 Georgia 1.0 1.6 2011c 16.1 5.6 33.5 12.8 2012c 14.1 4.5 31.3 11.4 Ghana 5,594.8 8,951.6 1998 39.1 14.4 63.3 28.5 2005 28.6 9.9 51.8 21.3 Guatemala 5.7d 9.1d 2006f 13.5 4.7 26.0 10.4 2011f 13.7 4.8 29.8 11.2 Guinea 1,849.5 2,959.1 2007 39.3 13.0 65.9 28.3 2012 40.9 12.7 72.7 29.8 Poverty rates
- 57. World Development Indicators 2015 33Economy States and markets Global links Back International poverty line in local currency Population below international poverty linesa $1.25 a day $2 a day Reference 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 % Reference 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 65.3 29.0 84.6 46.8 2002 48.9 16.6 78.0 34.9 Guyana 131.5d 210.3d 1992i 6.9 1.5 17.1 5.4 1998i 8.7 2.8 18.0 6.7 Haiti 24.2d 38.7d .. .. .. .. 2001f 61.7 32.3 77.5 46.7 Honduras 12.1d 19.3d 2010f 13.4 4.8 26.3 10.5 2011f 16.5 7.2 29.2 13.2 Hungary 171.9 275.0 2010f <2 <0.5 <2 <0.5 2011f <2 <0.5 <2 <0.5 India 19.5j 31.2j 2009h 32.7 7.5 68.8 24.5 2011h 23.6 4.8 59.2 19.0 Indonesia 5,241.0j 8,385.7j 2010h 18.0 3.3 46.3 14.3 2011h 16.2 2.7 43.3 13.0 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 2007c 3.4 0.6 22.4 4.7 2012c 3.9 0.6 21.2 4.7 Jamaica 54.2d 86.7d 2002 <2 <0.5 8.5 1.5 2004 <2 <0.5 5.9 0.9 Jordan 0.6 1.0 2008 <2 <0.5 2.0 <0.5 2010 <2 <0.5 <2 <0.5 Kazakhstan 81.2 129.9 2008c <2 <0.5 <2 <0.5 2010c <2 <0.5 <2 <0.5 Kenya 40.9 65.4 1997 31.8 9.8 56.2 22.9 2005 43.4 16.9 67.2 31.8 Kyrgyz Republic 16.2 26.0 2010c 6.0 1.4 21.1 5.8 2011c 5.1 1.2 21.1 5.3 Lao PDR 4,677.0 7,483.2 2007 35.1 9.2 68.3 25.7 2012 30.3 7.7 62.0 22.4 Latvia 0.4 0.7 2010f <2 1.3 2.9 1.6 2011f <2 1.0 2.0 1.2 Lesotho 4.3 6.9 2002 55.2 28.0 73.7 42.0 2010 56.2 29.2 73.4 42.9 Liberia 0.6 1.0 .. .. .. .. 2007 83.8 40.9 94.9 59.6 Lithuania 2.1 3.3 2010f <2 1.3 2.5 1.5 2011f <2 0.8 <2 0.9 Macedonia, FYR 29.5 47.2 2006 <2 <0.5 4.6 1.1 2008 <2 <0.5 4.2 0.7 Madagascar 945.5 1,512.8 2005 82.4 40.4 93.1 58.6 2010 87.7 48.6 95.1 64.9 Malawi 71.2 113.8 2004 75.0 33.2 90.8 52.6 2010 72.2 34.3 88.1 52.1 Malaysia 2.6 4.2 2007i <2 <0.5 2.9 <0.5 2009i <2 <0.5 2.3 <0.5 Maldives 12.2 19.5 1998 25.6 13.1 37.0 20.0 2004 <2 <0.5 12.2 2.5 Mali 362.1 579.4 2006 51.4 18.8 77.1 36.5 2010 50.6 16.5 78.8 35.3 Mauritania 157.1 251.3 2004 25.4 7.0 52.6 19.2 2008 23.4 6.8 47.7 17.7 Mauritius 22.2 35.5 2006 <2 <0.5 <2 <0.5 2012 <2 <0.5 <2 <0.5 Mexico 9.6 15.3 2010f 4.0 1.8 8.3 3.4 2012f 3.3 1.4 7.5 2.9 Micronesia, Fed. Sts. 0.8d 1.3d .. .. .. .. 2000e 31.2 16.3 44.7 24.5 Moldova 6.0 9.7 2010c <2 <0.5 4.0 0.7 2011c <2 <0.5 2.8 <0.5 Montenegro 0.6 1.0 2010c <2 <0.5 <2 <0.5 2011c <2 <0.5 <2 <0.5 Morocco 6.9 11.0 2001 6.3 0.9 24.4 6.3 2007 2.6 0.6 14.2 3.2 Mozambique 14,532.1 23,251.4 2002 74.7 35.4 90.0 53.6 2009 60.7 25.8 82.5 43.7 Namibia 6.3 10.1 2004i 31.9 9.5 51.1 21.8 2009i 23.5 5.7 43.2 16.4 Nepal 33.1 52.9 2003 53.1 18.4 77.3 36.6 2010 23.7 5.2 56.0 18.4 Nicaragua 9.1d 14.6d 2005f 12.1 4.2 28.3 10.2 2009f 8.5 2.9 20.8 7.2 Niger 334.2 534.7 2007 42.1 11.8 74.1 29.9 2011 40.8 10.4 76.1 29.3 Nigeria 98.2 157.2 2004 61.8 26.9 83.3 44.7 2010 62.0 27.5 82.2 44.8 Pakistan 25.9 41.4 2007 17.2 2.6 55.8 15.7 2010 12.7 1.9 50.7 13.3 Panama 0.8d 1.2d 2011f 3.6 1.1 8.4 3.0 2012f 4.0 1.3 8.9 3.2 Papua New Guinea 2.1d 3.4d .. .. .. .. 1996 35.8 12.3 57.4 25.5 Paraguay 2,659.7 4,255.6 2011f 4.4 1.7 11.0 4.0 2012f 3.0 1.0 7.7 2.6 Peru 2.1 3.3 2011f 3.0 0.8 8.7 2.6 2012f 2.9 0.8 8.0 2.5 Philippines 30.2 48.4 2009 18.1 3.6 41.1 13.6 2012 19.0 4.0 41.7 14.1 Poland 2.7 4.3 2010c <2 <0.5 <2 <0.5 2011c <2 <0.5 <2 <0.5 Romania 2.1 3.4 2010f 3.0 1.3 7.7 2.8 2011f 4.0 1.8 8.8 3.5 Russian Federation 16.7 26.8 2008c <2 <0.5 <2 <0.5 2009c <2 <0.5 <2 <0.5 Poverty rates
- 58. 34 World Development Indicators 2015 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 Reference 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 % Reference 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 Rwanda 295.9 473.5 2006 72.0 34.7 87.4 52.1 2011 63.0 26.5 82.3 44.5 São Tomé and Príncipe 7,953.9 12,726.3 2000 28.2 7.9 54.2 20.6 2010 43.5 13.9 73.1 31.2 Senegal 372.8 596.5 2005 33.5 10.8 60.4 24.7 2011 34.1 11.1 60.3 25.0 Serbia 42.9 68.6 2009c <2 <0.5 <2 <0.5 2010c <2 <0.5 <2 <0.5 Seychelles 5.6d 9.0d 1999 <2 <0.5 <2 <0.5 2006 <2 <0.5 <2 <0.5 Sierra Leone 1,745.3 2,792.4 2003 59.4 22.7 82.0 41.4 2011 56.6 19.2 82.5 39.0 Slovak Republic 23.5 37.7 2010f <2 <0.5 <2 <0.5 2011f <2 <0.5 <2 <0.5 Slovenia 198.2 317.2 2010f <2 <0.5 <2 <0.5 2011f <2 <0.5 <2 <0.5 South Africa 5.7 9.1 2009 13.7 2.3 31.2 10.1 2011 9.4 1.2 26.2 7.7 Sri Lanka 50.0 80.1 2006 7.0 1.0 29.1 7.4 2009 4.1 0.7 23.9 5.4 St. Lucia 2.4d 3.8d .. .. .. .. 1995i 21.0 7.2 40.6 15.6 Sudan 154.4 247.0 .. .. .. .. 2009 19.8 5.5 44.1 15.4 Suriname 2.3d 3.7d .. .. .. .. 1999i 15.5 5.9 27.2 11.7 Swaziland 4.7 7.5 2000 43.0 14.9 64.1 29.8 2009 39.3 15.2 59.1 28.3 Syrian Arab Republic 30.8 49.3 .. .. .. .. 2004 <2 <0.5 16.9 3.3 Tajikistan 1.2 1.9 2007c 12.2 4.4 36.9 11.5 2009c 6.5 1.3 27.4 6.7 Tanzania 603.1 964.9 2007 67.9 28.1 87.9 47.5 2012 43.5 13.0 73.0 30.6 Thailand 21.8 34.9 2008c <2 <0.5 4.6 0.7 2010c <2 <0.5 3.5 0.6 Timor-Leste 0.6d 1.0d .. .. .. .. 2007 34.9 8.1 71.1 25.7 Togo 352.8 564.5 2006 53.2 20.3 75.3 37.3 2011 52.5 22.5 72.8 38.0 Trinidad and Tobago 5.8d 9.2d 1988i <2 <0.5 8.6 1.9 1992i 4.2 1.1 13.5 3.9 Tunisia 0.9 1.4 2005 <2 <0.5 7.6 1.7 2010 <2 <0.5 4.5 1.0 Turkey 1.3 2.0 2010c <2 <0.5 3.1 0.7 2011c <2 <0.5 2.6 <0.5 Turkmenistan 5,961.1d 9,537.7d .. .. .. .. 1998 24.8 7.0 49.7 18.4 Uganda 930.8 1,489.2 2009 37.9 12.2 64.7 27.3 2012 37.8 12.0 62.9 26.8 Ukraine 2.1 3.4 2009 <2 <0.5 <2 <0.5 2010c <2 <0.5 <2 <0.5 Uruguay 19.1 30.6 2011f <2 <0.5 <2 <0.5 2012f <2 <0.5 <2 <0.5 Venezuela, RB 1,563.9 2,502.2 2005f 13.2 8.0 20.9 11.3 2006f 6.6 3.7 12.9 5.9 Vietnam 7,399.9 11,839.8 2010 3.9 0.8 16.8 4.2 2012 2.4 0.6 12.5 2.9 West Bank and Gaza 2.7d 4.3d 2007c <2 <0.5 3.5 0.7 2009c <2 <0.5 <2 <0.5 Yemen, Rep. 113.8 182.1 1998 10.5 2.4 32.1 9.4 2005 9.8 1.9 37.3 9.9 Zambia 3,537.9 5,660.7 2006 68.5 37.0 82.6 51.8 2010 74.3 41.8 86.6 56.6 a. Based on nominal per capita consumption averages and distributions estimated parametrically from grouped household survey data, unless otherwise noted. b. Refers to the period of reference of a survey. For surveys in which the period of reference covers multiple years, it is the year with the majority of the survey respondents. For surveys in which the period of reference is half in one year and half in another, it is the first year. c. Estimated nonparametrically from nominal consumption per capita distributions based on unit-record household survey data. d. Based on purchasing power parity (PPP) dollars imputed using regression. e. Covers urban areas only. 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 per capita income averages and distribution data estimated parametrically from grouped household survey data. j. Based on benchmark national PPP estimate rescaled to account for cost-of-living differences in urban and rural areas. Poverty rates
- 59. World Development Indicators 2015 35Economy States and markets Global links Back Poverty rates Trends in poverty indicators by region, 1990–2015 Region 1990 1993 1996 1999 2002 2005 2008 2011 2015 forecast Trend, 1990–2011 Share of population living on less than 2005 PPP $1.25 a day (%) East Asia & Pacific 57.0 51.7 38.3 35.9 27.3 16.7 13.7 7.9 4.1 Europe & Central Asia 1.5 2.9 4.3 3.8 2.1 1.3 0.5 0.5 0.3 Latin America & Caribbean 12.2 11.9 10.5 11.0 10.2 7.3 5.4 4.6 4.3 Middle East & North Africa 5.8 5.3 4.8 4.8 3.8 3.0 2.1 1.7 2.0 South Asia 54.1 52.1 48.6 45.0 44.1 39.3 34.1 24.5 18.1 Sub-Saharan Africa 56.6 60.9 59.7 59.3 57.1 52.8 49.7 46.8 40.9 Developing countries 43.4 41.6 35.9 34.2 30.6 24.8 21.9 17.0 13.4 World 36.4 35.1 30.4 29.1 26.1 21.1 18.6 14.5 11.5 People living on less than 2005 PPP $1.25 a day (millions) East Asia & Pacific 939 887 682 661 518 324 272 161 86 Europe & Central Asia 7 13 20 18 10 6 2 2 1 Latin America & Caribbean 53 55 51 55 54 40 31 28 27 Middle East & North Africa 13 13 12 13 11 9 7 6 7 South Asia 620 636 630 617 638 596 540 399 311 Sub-Saharan Africa 290 338 359 385 400 398 403 415 403 Developing countries 1,923 1,942 1,754 1,751 1,631 1,374 1,255 1,011 836 World 1,923 1,942 1,754 1,751 1,631 1,374 1,255 1,011 836 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 48.8 45.7 38.9 37.7 31.8 23.6 21.7 15.9 10.3 Europe & Central Asia 0.4 0.7 1.1 1.0 0.6 0.4 0.2 0.2 0.2 Latin America & Caribbean 2.8 2.8 2.9 3.1 3.3 2.9 2.5 2.8 3.2 Middle East & North Africa 0.7 0.7 0.7 0.7 0.7 0.7 0.6 0.6 0.9 South Asia 32.2 32.7 35.9 35.2 39.1 43.4 43.0 39.5 37.2 Sub-Saharan Africa 15.1 17.4 20.5 22.0 24.5 29.0 32.1 41.0 48.3 Survey coverage (% of total population represented by surveys conducted within five years of the reference year) East Asia & Pacific 92.4 93.3 93.7 93.4 93.5 93.2 93.6 92.9 .. Europe & Central Asia 81.5 87.3 97.1 93.9 96.3 94.7 89.9 89.0 .. Latin America & Caribbean 94.9 91.8 95.9 97.7 97.5 95.9 94.5 99.1 .. Middle East & North Africa 76.8 65.3 81.7 70.0 21.5 85.7 46.7 15.7 .. South Asia 96.5 98.2 98.1 20.1 98.0 98.0 97.9 98.2 .. Sub-Saharan Africa 46.0 68.8 68.0 53.1 65.7 82.7 81.7 67.5 .. Developing countries 86.4 89.4 91.6 68.2 87.8 93.0 90.2 86.5 .. Source: World Bank PovcalNet (http://iresearch.worldbank.org/PovcalNet/).
- 60. 36 World Development Indicators 2015 Front User guide World view People Environment? The World Bank produced its first global poverty estimates for devel- oping countries for World Development Report 1990: Poverty (World Bank 1990) using household survey data for 22 countries (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 incorporated into the table) and conducts a major reas- sessment of progress against poverty about every three years. The most recent comprehensive reassessment was completed in October 2014, when the 2011 extreme poverty estimates for developing coun- try regions, developing countries as a whole (that is, countries classi- fied as low or middle income in 1990), and the world were released. The revised and updated poverty data are also available in the World Development Indicators online tables and database. As in previous rounds, the new poverty estimates combine purchas- ing power parity (PPP) exchange rates for household consumption from the 2005 International Comparison Program with income and consumption data from primary household surveys. The 2015 projec- tions use the newly released 2011 estimates as the baseline and assumes that mean household income or consumption will grow in line with the aggregate economic projections reported in Global Eco- nomic Prospects 2014 (World Bank 2014) and that inequality within countries will remain unchanged. Estimates of the number of people living in extreme poverty use population projections in the World Bank’s HealthStats database (http://datatopics.worldbank.org/hnp). PovcalNet (http://iresearch.worldbank.org/PovcalNet) is an inter- active computational tool that allows users to replicate these inter- nationally comparable $1.25 and $2 a day poverty estimates for countries, developing country regions, and the developing world as a whole and to compute poverty measures for custom country group- ings 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 interac- tive maps that visualize trends in key poverty and inequality indicators for different regions and countries. 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 data- base draws on income or detailed consumption data from more than 1,000 household surveys across 128 developing countries and 21 high-income countries (as defined in 1990). For high-income countries, estimates are available for inequality and income distribution only. The 2011 estimates use more than million randomly sampled households, representing 85 percent of the population in developing countries. Despite progress in the last decade, the challenges of measuring poverty remain. The timeliness, frequency, accessibility, 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 countries and even in 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 per- centage of the regional and global population represented by house- hold 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 coverage in Sub- Saharan Africa and the Middle East and North Africa remains low and variable. The need to improve household survey programs for monitor- ing 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 person- nel. Income is generally more difficult to measure accurately, and consumption comes closer to the notion of living standards. More- over, 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 differences in the relative importance of the consumption of nonmarket goods. The local market value of all consumption in kind (including own pro- duction, particularly important in underdeveloped rural economies) should be included in total consumption expenditure, but in practice are often not. 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 consumption and income data were available from the same surveys found income to yield a higher mean than consumption but also higher inequality. When poverty measures based on con- sumption and income were compared, the two effects roughly can- celled 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 Poverty rates About the data
- 61. World Development Indicators 2015 37Economy States and markets Global links Back the population. However, households with different income might not be equally likely to respond. Richer households may be less likely to participate because of the high opportunity cost of their time or con- cerns because of privacy concerns. It is conceivable that the poorest can likewise be underrepresented; some are homeless 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 inter- viewed. 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 dif- ficult. National 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 cur- rencies. The commonly used $1 a day standard, measured in 1985 international prices and adjusted to local currency using 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 revised following the release of PPPs compiled in the 2005 round of the International Comparison Program, along with data from an expanded set of household income and expenditure sur- veys. The current 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 (Ravallion, Chen, and Sangraula 2009). This 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. The international poverty line will be updated again later this year using the PPP estimates from the 2011 round of the International Comparison Program. 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 inter- national poverty line measures the same degree of need or depriva- tion 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. • Reference year is the period of reference of a survey. For surveys in which the period of reference covers multiple years, it is the year with the majority of the survey respondents. For surveys in which the period of reference is half in one year and half in another, it is the first year. • 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 (http:// iresearch.worldbank.org/PovcalNet) 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 Development Research Group. The international poverty lines are based on nation- ally representative primary household surveys conducted by national statistical offices or by private agencies under the supervision of government or international agencies and obtained from government statistical offices and World Bank Group country departments. For details on data sources and methods used in deriving the World Bank’s latest estimates, see http://iresearch.worldbank.org/povcalnet. 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. ———. 2014. Global Economic Prospects: Coping with Policy Normaliza- tion in High-income Countries. Volume 8, January 14. Washington, DC. Poverty rates
- 62. 38 World Development Indicators 2015 Front User guide World view People Environment? Period Annualized growth of survey mean income or consumption per capita % Survey mean income or consumption per capita 2005 PPP $ a day Bottom 40% of the population Total population Bottom 40% of the population Total population Baseline year Most recent year Baseline Most recent Baseline Most recent Albania 2008 2012 –1.2 –1.3 3.5 3.3 6.3 6.0 Argentina 2006 2011 6.5 3.4 4.0 5.4 13.0 15.3 Armenia 2006 2011 0.5 0.0 2.0 2.0 3.8 3.8 Bangladesh 2005 2010 1.8 1.4 0.8 0.9 1.6 1.7 Belarus 2006 2011 9.1 8.1 6.3 9.8 11.3 16.7 Bhutan 2007 2012 6.5 6.4 1.6 2.2 3.7 5.1 Bolivia 2006 2011 12.8 4.0 1.6 2.9 7.3 8.9 Botswana 2003 2009 5.3 2.1 1.1 1.6 6.4 7.4 Brazil 2006 2011 5.8 3.6 2.6 3.5 10.7 12.7 Bulgaria 2007 2011 1.4 0.5 4.9 5.2 10.7 10.8 Cambodia 2007 2011 9.2 3.0 1.1 1.5 2.5 2.8 Chile 2006 2011 3.9 2.8 4.4 5.4 14.7 16.9 China 2005 2010 7.2 7.9 1.3 1.9 3.6 5.3 Colombia 2008 2011 8.8 5.6 2.1 2.7 8.8 10.4 Congo, Rep. 2005 2011 7.3 4.3 0.6 0.9 1.8 2.3 Costa Rica 2004 2009 5.5 6.3 3.2 4.2 10.6 14.4 Czech Republic 2006 2011 1.8 1.8 12.2 13.4 20.5 22.4 Dominican Republic 2006 2011 2.3 –0.6 2.6 2.9 8.8 8.6 Ecuador 2006 2011 4.4 0.5 2.5 3.1 8.8 9.0 El Salvador 2006 2011 1.1 –0.6 2.5 2.7 7.1 6.9 Estonia 2005 2010 4.1 3.7 7.1 8.7 14.3 17.1 Ethiopia 2005 2010 –0.4 1.4 1.0 0.9 1.7 1.8 Georgia 2007 2012 0.7 1.5 1.4 1.5 3.5 3.8 Guatemala 2006 2011 –1.9 –4.6 1.7 1.5 6.5 5.2 Honduras 2006 2011 4.1 2.2 1.2 1.4 5.6 6.2 Hungary 2006 2011 –0.5 –0.2 8.3 8.0 14.7 14.6 India 2004 2011 3.3 3.8 0.9 1.2 1.8 2.3 Iraq 2007 2012 0.3 1.0 1.9 1.9 3.3 3.5 Jordan 2006 2010 2.8 2.6 3.2 3.6 6.4 7.1 Kazakhstan 2006 2010 6.2 5.4 3.2 4.0 5.7 7.1 Kyrgyz Republic 2006 2011 5.8 2.5 1.4 1.9 3.4 3.8 Lao PDR 2007 2012 1.4 2.0 1.0 1.0 2.0 2.2 Latvia 2006 2011 0.4 0.4 6.3 6.4 13.7 14.0 Lithuania 2006 2011 1.1 0.7 6.9 7.3 14.2 14.7 Madagascar 2005 2010 –4.5 –3.5 0.4 0.3 0.9 0.8 Malawi 2004 2010 –1.5 1.8 0.5 0.5 1.1 1.2 Mali 2006 2010 2.3 –1.5 0.7 0.8 1.6 1.5 Mauritius 2007 2012 0.0 0.0 3.9 3.9 8.1 8.2 Mexico 2006 2010 0.4 –0.3 3.6 3.6 10.8 10.6 Moldova 2006 2011 5.7 2.9 2.6 3.4 5.4 6.3 Montenegro 2006 2011 2.5 2.8 4.5 5.1 8.3 9.6 Mozambique 2002 2009 3.8 3.7 0.4 0.6 1.2 1.5 Namibia 2004 2009 3.4 1.9 1.0 1.2 4.8 5.4 Nepal 2003 2010 7.3 3.7 0.7 1.2 1.8 2.3 Nicaragua 2005 2009 4.8 1.0 1.6 1.9 5.3 5.5 Nigeria 2004 2010 –0.3 0.8 0.5 0.5 1.3 1.4 Pakistan 2005 2010 3.0 1.8 1.2 1.4 2.2 2.4 Shared prosperity
- 63. World Development Indicators 2015 39Economy States and markets Global links Back Period Annualized growth of survey mean income or consumption per capita % Survey mean income or consumption per capita 2005 PPP $ a day Bottom 40% of the population Total population Bottom 40% of the population Total population Baseline year Most recent year Baseline Most recent Baseline Most recent Panama 2008 2011 5.4 4.3 3.2 3.8 12.0 13.6 Paraguay 2006 2011 7.5 7.3 2.1 3.0 7.8 11.1 Peru 2006 2011 8.0 6.1 2.3 3.3 7.4 10.0 Philippines 2006 2012 1.4 0.7 1.2 1.3 3.3 3.4 Poland 2006 2011 3.3 2.8 5.3 6.2 10.7 12.3 Romania 2006 2011 5.8 4.3 3.0 4.0 5.6 7.0 Russian Federation 2004 2009 9.6 8.2 4.0 6.2 9.9 14.6 Rwanda 2006 2011 4.6 3.4 0.5 0.6 1.5 1.7 Senegal 2006 2011 –0.2 0.3 0.9 0.9 2.2 2.2 Serbia 2007 2010 –1.7 –1.3 5.7 5.4 10.4 10.0 Slovak Republic 2006 2011 8.4 9.3 8.9 13.4 14.8 23.1 Slovenia 2006 2011 1.5 1.6 17.1 18.4 27.9 30.2 South Africa 2006 2011 4.3 3.6 1.4 1.8 8.7 10.5 Sri Lanka 2006 2009 3.0 –0.4 1.7 1.9 3.9 3.9 Tajikistan 2004 2009 6.1 4.9 1.2 1.6 2.5 3.1 Tanzania 2007 2012 9.8 9.1 0.5 0.9 1.2 1.8 Thailand 2006 2010 4.3 2.2 2.7 3.2 6.9 7.5 Togo 2006 2011 –2.1 1.0 0.7 0.6 1.7 1.8 Tunisia 2005 2010 3.5 2.6 2.9 3.4 6.6 7.5 Turkey 2006 2011 5.4 5.1 3.6 4.6 8.8 11.3 Uganda 2005 2012 3.5 4.4 0.7 0.9 1.7 2.3 Ukraine 2005 2010 5.2 3.1 5.0 6.5 9.0 10.5 Uruguay 2006 2011 8.4 6.1 3.9 5.9 12.0 16.1 Vietnam 2004 2010 6.2 7.8 1.4 2.0 3.3 5.1 West Bank and Gaza 2004 2009 2.3 2.3 4.4 4.9 9.0 10.0 Shared prosperity
- 64. 40 World Development Indicators 2015 Front User guide World view People Environment? The World Bank Group released the Global Database of Shared Prosperity in October 2014, a year and half after announcing its new twin goals of ending extreme poverty and promoting shared pros- perity around the world. It contains data for monitoring the goal of promoting shared prosperity that have been published in the World Development Indicators online tables and database and are now featured in this edition of World Development Indicators. Promoting shared prosperity is defined as fostering income growth of the bottom 40 percent of the welfare distribution in every country and is measured by calculating the annualized growth of mean per capita real income or consumption of the bottom 40 percent. The choice of the bottom 40 percent as the target population is one of practical compromise. The bottom 40 percent differs across countries depending on the welfare distribution, and it can change over time within a country. Because boosting shared prosperity is a country-specific goal, there is no numerical target defined globally. And at the country level the shared prosperity goal is unbounded. Improvements in shared prosperity require both a growing econ- omy and a consideration for equity. Shared prosperity explicitly recognizes that while growth is necessary for improving economic welfare in a society, progress is measured by how those gains are shared with its poorest members. It also recognizes that for prosper- ity to be truly shared in a society, it is not sufficient to raise everyone above an absolute minimum standard of living. Rather, for a society that seeks to become more inclusive, the goal is to ensure that economic progress increases prosperity among the poorer members of society over time. The decision to measure shared prosperity based on income or consumption was not taken to ignore the many other dimensions of welfare. It is motivated by the need for an indicator that is easy to understand, communicate, and measure—though measurement challenges exist. Indeed, shared prosperity comprises many dimen- sions of well-being of the less well-off, and when analyzing shared prosperity in the context of a country, it is important to consider a wide range of indicators of welfare. To generate measures of shared prosperity that are reasonably comparable across countries, the World Bank Group has a standard- ized approach for choosing time periods, data sources, and other relevant parameters. The Global Database of Shared Prosperity is the result of these efforts. Its purpose is to allow for cross-country comparison and benchmarking, but users should consider alter- native choices for surveys and time periods when cross-country comparison is not the primary consideration. The indicators from the database in this edition of World Develop- ment Indicators are survey mean per capita real income or consump- tion of the bottom 40 percent, survey mean per capita real income or consumption of the total population, annualized growth of survey mean per capita real income or consumption of the bottom 40 per- cent, and annualized growth of survey mean per capita real income or consumption of the total population. Related information, such as survey years defining the growth period and the type of welfare aggregate used to calculate the growth rates, are provided in the footnotes. The World Bank Group is committed to updating the shared pros- perity indicators every year. Given that new household surveys are not available every year for most countries, updated estimates will be reported only for a subset of countries each year. Calculation of growth rates Growth rates are calculated as annualized average growth rates over a roughly five-year period. Since many countries do not conduct surveys on a precise five-year schedule, the following rules guide selection of the survey years used to calculate the growth rates: the final year of the growth period (T1) is the most recent year of a survey but no earlier than 2009, and the initial year (T0) is as close to T1 – 5 as possible, within a two-year band. Thus the gap between initial and final survey years ranges from three to seven years. If two surveys are equidistant from T1 – 5, other things being equal, the more recent survey year is selected as T0 . The comparability of welfare aggregates (income or consumption) for the years chosen for T0 and T1 is assessed for every country. If comparability across the two surveys is a major concern, the selection criteria are re-applied to select the next best survey year. Once two surveys are selected for a country, the annualized growth of mean per capita real income or consumption is computed by first estimating the mean per capita real income or consumption of the bottom 40 percent of the welfare distribution in years T0 and T1 and then computing the annual average growth rate between those years using a compound growth formula. Growth of mean per capita real income or consumption of the total population is computed in the same way using data for the total population. Data availability This edition of World Development Indicators includes estimates of shared prosperity for 72 developing countries. While all countries are encouraged to estimate the annualized growth of mean per cap- ita real income or consumption of the bottom 40 percent, the Global Database of Shared Prosperity includes only a subset of countries that meet certain criteria. The first important consideration is com- parability across time and across countries. Household surveys are infrequent in most countries and are rarely aligned across countries in terms of timing. Consequently, comparisons across countries or over time should be made with a high degree of caution. The second consideration is the coverage of countries, with data that are as recent as possible. Since shared prosperity must be estimated and used at the country level, there are good reasons for obtaining a wide coverage of countries, regardless of the size of their population. Moreover, for policy purposes it is important to have indicators for the most recent period possible for each coun- try. The selection of survey years and countries needs to be made consistently and transparently, achieving a balance among matching Shared prosperity About the data
- 65. World Development Indicators 2015 41Economy States and markets Global links Back the time period as closely as possible across all countries, including the most recent data, and ensuring the widest possible coverage of countries, across regions and income levels. In practice, this means that time periods will not match perfectly across countries. This is a compromise: While it introduces a degree of incomparability, it also creates a database that includes a larger set of countries than would be possible otherwise. Data quality Like poverty rate estimates, estimates of annualized growth of mean per capita real income or consumption of the bottom 40 percent are based on income or consumption data collected in household surveys, and the same quality issues apply. See the discussion in the Poverty rates section. Definitions • Period is the period of reference of a survey. For surveys in which the period of reference covers multiple years, it is the year with the majority of the survey respondents. For surveys in which the period of reference is half in one year and half in another, it is the first year. • Annualized growth of survey mean per capita real income or con- sumption is the annualized growth in mean per capita real income consumption from household surveys over a roughly five-year period. It is calculated for the bottom 40 percent of a country’s population and for the total population of a country. • Survey mean per capita real consumption or income is the mean income or consumption per capita from household surveys used in calculating the welfare growth rate, expressed in purchasing power parity (PPP)–adjusted dollars per day at 2005 prices. It is calculated for the bottom 40 percent of a country’s population and for the total population of a country. Data sources The Global Database of Shared Prosperity was prepared by the Global Poverty Working Group, which comprises poverty measurement spe- cialists of different departments of the World Bank Group. The data- base’s primary source of data is the World Bank Group’s PovcalNet database, an interactive computational tool that allows users to rep- licate the World Bank Group’s official poverty estimates measured at international poverty lines ($1.25 or $2 per day per capita). The data- sets included in PovcalNet are provided and reviewed by the members of the Global Poverty Working Group. The choice of consumption or income to measure shared prosperity for a country is consistent with the welfare aggregate used to estimate extreme poverty rates in Pov- calNet, unless there are strong arguments for using a different welfare aggregate. The practice adopted by the World Bank Group for estimat- ing global and regional poverty rates is, in principle, to use per capita consumption expenditure as the welfare measure wherever available and to use income as the welfare measure for countries for which consumption data are unavailable. However, in some cases data on consumption may be available but are outdated or not shared with the World Bank Group for recent survey years. In these cases, if data on income are available, income is used for estimating shared prosperity. References Ambar, Narayan, Jaime Saavedra-Chanduvi, and Sailesh Tiwari. 2013. “Shared Prosperity: Links to Growth, Inequality and Inequality of Opportunity.” Policy Research Working Paper 6649. World Bank, Washington, DC. World Bank. 2014a. “A Measured Approach to Ending Poverty and Boosting Shared Prosperity: Concepts, Data, and the Twin Goals.” Washington, DC. ———. 2014b. Global Database of Shared Prosperity. [http://www .worldbank.org/en/topic/poverty/brief/global-database-of-shared -prosperity]. Washington, DC. ———. Various years. PovcalNet. [http://iresearch.worldbank.org /PovcalNet/]. Washington, DC. Shared prosperity About the data
- 66. 42 World Development Indicators 2015 Front User guide World view People Environment? PEOPLE
- 67. World Development Indicators 2015 43Economy States and markets Global links Back The People section presents indicators of edu- cation, health, jobs, social protection, and gen- der, complementing other important indicators of human development presented in World view, such as population, poverty, and shared pros- perity. Together, they provide a multidimensional portrait of societal progress. Many of these indicators are also used for monitoring the Millennium Development Goals. Over the last 15 years data for estimating these indicators have been collected and compiled through the efforts of national authorities and various international development agencies, including the World Bank, working together in the Inter-agency and Expert Group organized by the United Nations Statistics Division and in several thematic interagency groups. These groups have made international development statistics more readily available and consistent, over time and between coun- tries. For example, estimates of child mortality used to vary by data source and by methodol- ogy, making their interpretation for global mon- itoring purposes difficult. The United Nations Inter-agency Group for Child Mortality Estima- tion, established in 2004, has addressed this issue by compiling all available data, assess- ing data quality, and fitting an appropriate statistical model to generate a smooth trend curve. This effort has produced harmonized and good quality estimates of neonatal, infant, and under-five mortality rates that span more than 50 years. Similar interagency efforts have also been made to improve maternal mortality estimates. In gender statistics, the World Bank is contributing to the work to obtain better esti- mates of female asset ownership and entrepre- neurship, and a minimum set of gender indica- tors has been endorsed by the United Nations Statistics Commission to help focus national efforts to produce, compile, and disseminate relevant data. People includes indicators disaggregated by socioeconomic and demographic variables, such as sex, age, and wealth. This year, some indicators such as malnutrition and poverty are available disaggregated by subnational location at http://data.worldbank.org/data-catalog/sub -national-poverty-data. These data provide impor- tant perspectives on disparities within countries, and World Development Indicators will continue to expand coverage in this direction, wherever data sources permit. An important new addition this year is an indicator for monitoring the World Bank Group’s new goal of promoting shared prosperity. This is detailed further in World view and available at www.worldbank.org/en/topic/poverty/brief /global-database-of-shared-prosperity. Other new indicators include the share of the youth population that is not in education, employ- ment, or training and the share of students who obtained the lowest levels of proficiency on the Organisation for Economic Co-operation and Development’s Program for International Student Assessment scores in mathematics, reading, and science, which serves to improve coverage of the outcomes of education systems. 2
- 68. 44 World Development Indicators 2015 Highlights Front User guide World view People Environment? Pupil–teacher ratios in primary education are improving very slowly 0 10 20 30 40 50 20122005200019951990 Pupil–teacher ratio, primary education Sub-Saharan Africa South Asia World Middle East & North Africa Latin America & Caribbean East Asia & Pacific Europe & Central Asia While substantial progress has been made in achieving universal pri- mary education, pupil–teacher ratios, an important indicator of the quality of education, have shown only slight improvement, declining from a global average of 26 in 1990 to 24 in 2012. In Sub-Saharan Africa the average pupil–teacher ratio rose from 36 in 1990 to 41 in 2012, indicating that the increase in the number of teachers is not keeping pace with the increase in primary enrollment. South Asia’s average pupil–teacher ratio (36) also remains far above the world aver- age. However, there has been a steady improvement in both regions in recent years. Although East Asia and Pacific has reduced its pupil– teacher ratio remarkably since 2000, there was an increasing trend in 2012, due mainly to an increase in the ratio in China. Source: United Nations Educational, Scientific and Cultural Organization Institute for Statistics and online table 2.10. The adolescent fertility rate declines as more women attend secondary education 0 25 50 75 100 0 25 50 75 100 125 150 175 Adolescent fertility rate (births per 1,000 women ages 15–19) Secondary school enrollment, gross, female (%) 1970 1970 1970 1970 1970 1970 1970 1970 2012 2012 2012 2012 2012 2012 2012 2012 East Asia & Pacific Europe & Central Asia High income Latin America & Caribbean World Sub-Saharan Africa South Asia Middle East & North Africa Teenage women are less likely to become mothers when they attend secondary school. Globally, the adolescent fertility rate declined from 77 per 1,000 women ages 15–19 in 1970 to 45 in 2012, while female secondary school enrollment increased from 35 percent to 72 percent. The relationship between the two tends to be similar across regions, except for Latin America and the Caribbean and East Asia and Pacific, where the correlation is much weaker. Both the Middle East and North Africa and South Asia saw large drops in adolescent fertility rates as secondary education has expanded. The rates in the Middle East and North Africa and South Asia in 2012 are similar to those in high-income countries in 1970. Sub-Saharan Africa has the highest adolescent fertility rate and the lowest female secondary gross enrollment ratio. Source: United Nations Population Division, United Nations Educational, Scientific and Cultural Organization Institute for Statistics, and online tables 2.11 and 2.17. Growth in many countries between 2006 and 2011 seems to be inclusive –5 0 5 10 Annualized growth of mean per capita income or consumption of the bottom 40 percent of the population (%) Annualized growth of mean per capita income or consumption of the total population (%) –5 0 5 10 15 Low income Lower middle income Upper middle income High income Many countries have seen growth in income or consumption among the bottom 40 percent of the population in their welfare distribution between 2006 and 2011. The bottom 40 percent fared better in middle- and high-income countries than in low-income countries. The median annualized growth of mean per capita income or consumption of the bottom 40 percent was 3.9 percent in middle- and high-income countries, 0.4 percentage point higher than in low-income countries. Furthermore, growth was more inclusive in richer countries. In particu- lar, the annualized growth of mean per capita income or consumption was faster for the bottom 40 percent than for the total population in 7 of 9 high-income countries (78 percent), 20 of 26 upper middle-income countries (77 percent), 16 of 22 lower middle-income countries (73 per- cent), and 8 of 13 low-income countries (62 percent). Source: World Bank Global Database of Shared Prosperity and online table 2.9.2.
- 69. World Development Indicators 2015 45Economy States and markets Global links Back Large rich-poor gap in contraceptive use in Sub-Saharan Africa The contraceptive prevalence rate is an important indicator of the suc- cess of family planning programs. While most regions have attained a contraceptive prevalence rate of more than 50 percent (80 percent in East Asia and Pacific and 64 percent in the Middle East and North Africa), Sub-Saharan Africa’s rate remains at less than 25 percent, with a wide gap between the rich and the poor. Nine of the ten countries with the widest rich-poor gap are in Sub-Saharan Africa. In Cameroon and Nigeria the contraceptive prevalence rate is less than 3 percent among women in the poorest quintile and over 36 percent among women in the richest quintile. Contraceptive use among women in poor families is low in nearly all countries across Sub-Saharan Africa. Source: United Nations Children’s Fund, household surveys (including Demographic and Health Surveys and Multiple Indicator Cluster Surveys), and online table 2.22.3. Labor force participation is lowest in the Middle East and North Africa Labor force participation rates—the proportion of the population ages 15 and older that engages actively in the labor market, by either work- ing or looking for work—are higher in East Asia and Pacific and Sub- Saharan Africa than in other regions. In contrast, in the Middle East and North Africa less than 50 percent of the working-age population is in the labor force, lower than in any other region. This is driven largely by low female participation. A low labor force participation rate typically results from a host of obstacles that prevent people from entering the labor market. The region has a large number of unemployed people, and high unemployment rates could be another reason that discour- ages people from seeking work. Only 41 percent of the working-age population in the Middle East and North Africa is employed. Labor force status, 2013 (% of population ages 15 and older) Employed Unemployed Not in the labor force 0 25 50 75 100 Middle East & North Africa South Asia Europe & Central Asia Latin America & Caribbean Sub-Saharan Africa East Asia & Pacific Source: International Labour Organization’ Key Indicators of the Labour Market, 8th edition, database and online tables 2.2, 2.4, and 2.5. Women occupy few top management positions in developing countries Women’s participation in economic activities, particularly in business leadership roles as the top managers in firms, highlights their eco- nomic empowerment and advancement. Globally the share of firms with female top managers is low, at about 20 percent. The highest share is in East Asia and Pacific (almost 30 percent); the lowest is in the Middle East and North Africa (less than 5 percent) and South Asia (almost 9 percent). These statistics do not fully describe women-led firms, which tend to be smaller than male-led firms and concentrated in such areas as retail businesses (Amin and Islam 2014). These statistics are based on World Bank Enterprise Surveys, which col- lect data from registered firms with five or more employees and thus exclude small informal firms, which are believed to be important for women. Share of firms with a female top manager (%) 0 10 20 30 Middle East & North Africa South Asia Sub-Saharan Africa Europe & Central Asia Latin America & Caribbean East Asia & Pacific Source: World Bank Enterprise Surveys and online table 5.2. 0 10 20 30 40 50 60 Pakistan Tanzania Kenya Madagascar Uganda Ethiopia Burkina Faso Mozambique Cameroon Nigeria Contraceptive prevalence rate among countries with the widest rich-poor gaps, most recent year available during 2008–14 (%) Richest quintile Poorest quintile
- 70. Dominican Republic Trinidad and Tobago Grenada St. Vincent and the Grenadines Dominica Puerto Rico, US St. Kitts and Nevis Antigua and Barbuda St. Lucia Barbados R.B. de Venezuela U.S. Virgin Islands (US) Martinique (Fr) Guadeloupe (Fr) Curaçao (Neth) St. Martin (Fr) Anguilla (UK) St. Maarten (Neth) Samoa Tonga Fiji Kiribati Haiti Jamaica Cuba The Bahamas United States Canada Panama Costa Rica Nicaragua Honduras El Salvador Guatemala Mexico Belize Colombia Guyana Suriname R.B. de Venezuela Ecuador Peru Brazil Bolivia Paraguay Chile Argentina Uruguay American Samoa (US) French Polynesia (Fr) French Guiana (Fr) Greenland (Den) Turks and Caicos Is. (UK) IBRD 41451 80 or more 40–79 20–39 10–19 Fewer than 10 No data Child mortality UNDER-FIVE MORTALITY RATE PER 1,000 LIVE BIRTHS, 2013 Caribbean inset Bermuda (UK) 46 World Development Indicators 2015 The under-five mortality rate is the probability of dying between birth and exactly 5 years of age, expressed per 1,000 live births. It is a key indicator of child well-being, including health and nutrition sta- tus. Also, it is among the indicators most frequently used to compare socioeconomic development across countries. The world has made substantial progress, reducing the rate from 183 deaths per 1,000 live births in 1960 to 90 deaths in 1990 to 46 deaths in 2013. Despite this progress, 6.3 million children still died before their fifth birthday in 2013—roughly 17,000 a day—mostly from preventable causes and treatable diseases. The number of child deaths has been falling in every region, but the reduction is slow- est in Sub-Saharan Africa. In 2013 around 44 percent of under-five deaths occurred during the first 28 days of life—the neonatal period—which is the most vulner- able time for a child. Front User guide World view People Environment?
- 71. Romania Serbia Greece San Marino BulgariaUkraine Germany FYR Macedonia Croatia Bosnia and Herzegovina Czech Republic Poland Hungary Italy Austria Slovenia Slovak Republic Kosovo Montenegro Albania Burkina Faso Palau Federated States of Micronesia Marshall Islands Nauru Kiribati Solomon Islands Tuvalu Vanuatu Fiji Norway Iceland Ireland United Kingdom Sweden Finland Denmark Estonia Latvia Lithuania Poland Belarus Ukraine Moldova Romania Bulgaria Greece Italy Germany Belgium The Netherlands Luxembourg Switzerland Liechtenstein France AndorraPortugal Spain Monaco Malta Morocco Tunisia Algeria Mauritania Mali Senegal The Gambia Guinea- Bissau Guinea Cabo 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 Mozambique Zimbabwe Botswana Namibia Swaziland LesothoSouth Africa Mauritius Seychelles Comoros Rep. of Yemen Oman United Arab Emirates Qatar Bahrain Saudi Arabia Kuwait Israel 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 Guinea Indonesia Australia New Zealand JapanRep.of Korea Dem.People’s Rep.of Korea Mongolia China Russian Federation Brunei Darussalam Sudan South Sudan Timor-Leste Madagascar N. Mariana Islands (US) Guam (US) New Caledonia (Fr) Greenland (Den) West Bank and Gaza Western Sahara Réunion (Fr) Mayotte (Fr) Europe inset World Development Indicators 2015 47Economy States and markets Global links Back Twelve countries have an under-five mortality rate above 100 deaths per 1,000 live births: Angola, Sierra Leone, Chad, Somalia, Central African Republic, Guinea-Bissau, Mali, the Democratic Republic of the Congo, Nigeria, Niger, Guinea, and Côte d’Ivoire. The highest under-five mortality rates are in Sub-Saharan Africa (92 deaths per 1,000 live births) and South Asia (57), compared with 20 in East Asia and Pacific, 23 in Europe and Central Asia, 18 in Latin America and the Caribbean, 26 in the Middle East and North Africa, and 6 in high-income countries. About half of under-five deaths worldwide occur in only five countries: India, Nigeria, Pakistan, the Democratic Republic of the Congo, and China. On average, 1 in 11 children born in Sub-Saharan Africa dies before age 5.
- 72. 48 World Development Indicators 2015 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 population ages 15–24 Modeled ILO estimate % of population ages 15 and older Modeled estimate per 100,000 live births births per 1,000 women ages 15–19 % of children under age 5 per 1,000 live births % of population ages 15–49 % of relevant age group Modeled ILO estimate % of total labor force % of total 2007–13a 2013 2013 2013 2013 2009–13a 2005–13a 2013 2009–13a 2013 2009–13a Afghanistan .. 97 400 83 <0.1 .. 47 48 .. 8 .. Albania 6.3 15 21 14 <0.1 .. 99 55 58 16 22 Algeria .. 25 89 10 0.1 100 92 44 27 10 11 American Samoa .. .. .. .. .. .. .. .. .. .. .. Andorra .. 3 .. .. .. .. .. .. .. .. .. Angola 15.6 167 460 167 2.4 54 73 70 .. 7 .. Antigua and Barbuda .. 9 .. 48 .. 100 .. .. .. .. .. Argentina .. 13 69 54 .. 110 99 61 19 8 .. Armenia 5.3 16 29 27 0.2 .. 100 63 30 16 .. Aruba .. .. .. 25 .. 95 99 .. .. .. 43 Australia 0.2 4 6 11 0.2 .. .. 65 .. 6 .. Austria .. 4 4 3 .. 97 .. 61 9 5 27 Azerbaijan .. 34 26 39 0.2 92 100 66 56 6 .. Bahamas, The .. 13 37 28 3.2 93 .. 74 .. 14 52 Bahrain .. 6 22 14 .. .. 98 70 2 7 .. Bangladesh 31.9 41 170 79 <0.1 75 80 71 .. 4 5 Barbados 3.5 14 52 48 0.9 104 .. 71 .. 12 48 Belarus .. 5 1 20 0.5 100 100 56 2 6 46 Belgium .. 4 6 6 .. 90 .. 53 11 8 30 Belize 6.2 17 45 70 1.5 109 .. 66 .. 15 .. Benin .. 85 340 88 1.1 76 42 73 .. 1 .. Bermuda .. .. .. .. .. 88 .. .. .. .. 44 Bhutan 12.8 36 120 40 0.1 98 74 73 53 2 17 Bolivia 4.5 39 200 71 0.3 89 99 73 55 3 35 Bosnia and Herzegovina 1.5 7 8 15 .. .. 100 45 25 28 .. Botswana 11.2 47 170 43 21.9 95 96 77 13 18 39 Brazil 2.2 14 69 70 0.6 .. 99 70 25 6 .. Brunei Darussalam .. 10 27 23 .. 98 100 64 .. 4 .. Bulgaria .. 12 5 34 .. 98 98 53 8 13 37 Burkina Faso 26.2 98 400 112 0.9 63 39 83 .. 3 .. Burundi 29.1 83 740 30 1.0 70 89 83 .. 7 .. Cabo Verde .. 26 53 69 0.5 95 98 68 .. 7 .. Cambodia 29.0 38 170 44 0.7 97 87 83 64 0 .. Cameroon 15.1 95 590 113 4.3 73 81 70 76 4 .. Canada .. 5 11 14 .. .. .. 66 .. 7 .. Cayman Islands .. .. .. .. .. .. 99 .. .. .. .. Central African Republic 23.5 139 880 97 3.8 45 36 79 .. 8 .. Chad 30.3 148 980 147 2.5 39 49 72 .. 7 .. Channel Islands .. .. .. 8 .. .. .. .. .. .. .. Chile 0.5 8 22 55 0.3 97 99 62 .. 6 .. China 3.4 13 32 9 .. .. 100 71 .. 5 .. Hong Kong SAR, China .. .. .. 3 .. 96 .. 59 7 3 32 Macao SAR, China .. .. .. 4 .. .. 100 72 4 2 32 Colombia 3.4 17 83 68 0.5 113 98 67 49 11 53 Comoros 16.9 78 350 50 .. 74 86 58 .. 7 .. Congo, Dem. Rep. 23.4 119 730 134 1.1 73 66 72 .. 8 .. Congo, Rep. 11.8 49 410 125 2.5 73 81 71 .. 7 .. 2 People
- 73. World Development Indicators 2015 49Economy 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 population ages 15–24 Modeled ILO estimate % of population ages 15 and older Modeled estimate per 100,000 live births births per 1,000 women ages 15–19 % of children under age 5 per 1,000 live births % of population ages 15–49 % of relevant age group Modeled ILO estimate % of total labor force % of total 2007–13a 2013 2013 2013 2013 2009–13a 2005–13a 2013 2009–13a 2013 2009–13a Costa Rica 1.1 10 38 60 0.2 90 99 63 20 8 35 Côte d’Ivoire 15.7 100 720 126 2.7 60 48 67 .. 4 .. Croatia .. 5 13 13 .. 93 100 51 14 18 25 Cuba .. 6 80 43 0.2 93 100 57 .. 3 .. Curaçao .. .. .. 27 .. .. .. .. .. .. .. Cyprus .. 4 10 5 <0.1 100 100 64 14 16 14 Czech Republic .. 4 5 5 <0.1 102 .. 60 15 7 26 Denmark .. 4 5 5 0.2 99 .. 63 6 7 28 Djibouti 29.8 70 230 18 0.9 61b .. 52 .. .. .. Dominica .. 11 .. .. .. 103 .. .. .. .. .. Dominican Republic 4.0 28 100 98 0.7 90 97 65 37 15 37 Ecuador 6.4 23 87 76 0.4 111 99 69 51 4 40 Egypt, Arab Rep. 6.8 22 45 42 <0.1 107 89 49 26 13 7 El Salvador 6.6 16 69 75 0.5 101 97 62 38 6 37 Equatorial Guinea 5.6 96 290 111 .. 55 98 87 .. 8 .. Eritrea 38.8 50 380 63 0.6 .. 91 85 .. 7 .. Estonia .. 3 11 16 1.3 96 100 62 5 9 36 Ethiopia 25.2b 64 420 76 1.2 .. 55 84 .. 6 22 Faeroe Islands .. .. .. .. .. .. .. .. .. .. .. Fiji .. 24 59 42 0.1 104 .. 55 .. 8 .. Finland .. 3 4 9 .. 99 .. 60 9 8 32 France .. 4 12 6 .. .. .. 56 7 10 39 French Polynesia .. .. .. 38 .. .. .. 56 .. .. .. Gabon 6.5 56 240 99 3.9 .. 89 61 .. 20 .. Gambia, The 17.4 74 430 114 1.2 71 69 77 .. 7 .. Georgia 1.1 13 41 46 0.3 109 100 65 61 14 .. Germany .. 4 7 3 0.2 98 .. 60 7 5 30 Ghana 13.4 78 380 57 1.3 97b 86 69 77 5 .. Greece .. 4 5 11 .. 101 99 53 30 27 23 Greenland .. .. .. .. .. .. .. .. .. .. .. Grenada .. 12 23 34 .. 112 .. .. .. .. .. Guam .. .. .. 50 .. .. .. 63 .. .. .. Guatemala 13.0 31 140 95 0.6 86 94 68 .. 3 .. Guinea 16.3 101 650 127 1.7 61 31 72 .. 2 .. Guinea-Bissau 18.1 124 560 97 3.7 64 74 73 .. 7 .. Guyana 11.1 37 250 87 1.4 85 93 61 .. 11 .. Haiti 11.6 73 380 41 2.0 .. 72 66 .. 7 .. Honduras 7.1 22 120 82 0.5 93 95 63 53 4 .. Hungary .. 6 14 12 .. 99 99 52 6 10 40 Iceland .. 2 4 11 .. 97 .. 74 8 6 40 India .. 53 190 32 0.3 96 81 54 81 4 14 Indonesia 19.9 29 190 48 0.5 105 99 68 33 6 23 Iran, Islamic Rep. .. 17 23 31 0.1 104 98 45 .. 13 .. Iraq 8.5 34 67 68 .. .. 82 42 .. 16 .. Ireland .. 4 9 8 .. .. .. 61 13 13 33 Isle of Man .. .. .. .. .. .. .. .. .. .. .. Israel .. 4 2 7 .. 106 100 63 .. 6 ..
- 74. 50 World Development Indicators 2015 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 population ages 15–24 Modeled ILO estimate % of population ages 15 and older Modeled estimate per 100,000 live births births per 1,000 women ages 15–19 % of children under age 5 per 1,000 live births % of population ages 15–49 % of relevant age group Modeled ILO estimate % of total labor force % of total 2007–13a 2013 2013 2013 2013 2009–13a 2005–13a 2013 2009–13a 2013 2009–13a Italy .. 4 4 4 0.3 99 100 49 18 12 25 Jamaica 3.2 17 80 69 1.8 86 96 63 38 15 .. Japan .. 3 6 5 .. 102 .. 59 .. 4 .. Jordan 3.0 19 50 26 .. 93 99 42 10 13 .. Kazakhstan 3.7 16 26 29 .. 102 100 73 29 5 .. Kenya 16.4 71 400 92 6.0 .. 82 67 .. 9 .. Kiribati 14.9 58 130 16 .. .. .. .. .. .. 36 Korea, Dem. People’s Rep. 15.2 27 87 1 .. .. 100 78 .. 5 .. Korea, Rep. 0.6 4 27 2 .. 111 .. 61 .. 3 .. Kosovo .. .. .. .. .. .. .. .. 17 .. 15 Kuwait 2.2 10 14 14 .. .. 99 68 2 3 .. Kyrgyz Republic 2.8b 24 75 28 0.2 98 100 68 .. 8 .. Lao PDR 26.5 71 220 64 0.2 101 84 78 .. 1 .. Latvia .. 8 13 13 .. 103 100 61 7 11 45 Lebanon .. 9 16 12 .. 89 99 48 .. 7 .. Lesotho 13.5 98 490 86 22.9 74 83 66 .. 25 .. Liberia 15.3 71 640 114 1.1 59b 49 62 79 4 .. Libya 5.6 15 15 2 .. .. 100 53 .. 20 .. Liechtenstein .. .. .. .. .. 102 .. .. .. .. .. Lithuania .. 5 11 10 .. 98 100 61 10 12 38 Luxembourg .. 2 11 8 .. 85 .. 58 6 6 24 Macedonia, FYR 1.3 7 7 18 <0.1 94 99 55 23 29 28 Madagascar .. 56 440 121 0.4 68 65 89 88 4 25 Malawi 16.7b 68 510 143 10.3 75 72 83 .. 8 .. Malaysia .. 9 29 6 0.4 .. 98 59 22 3 25 Maldives 17.8 10 31 4 <0.1 110 99 67 .. 12 .. Mali .. 123 550 174 0.9 59 47 66 .. 8 .. Malta .. 6 9 18 .. 88 98 52 9 7 23 Marshall Islands .. 38 .. .. .. 100 .. .. .. .. .. Mauritania 19.5 90 320 72 .. 71 56 54 .. 31 .. Mauritius .. 14 73 31 1.1 102 98 59 17 8 .. Mexico 2.8 15 49 62 0.2 99 99 62 .. 5 .. Micronesia, Fed. Sts. .. 36 96 17 .. .. .. .. .. .. .. Moldova 2.2 15 21 29 0.6 93 100 41 31 5 44 Monaco .. 4 .. .. .. .. .. .. .. .. .. Mongolia 1.6 32 68 18 <0.1 .. 98 63 51 5 .. Montenegro 1.0 5 7 15 .. 101 99 50 .. 20 30 Morocco 3.1 30 120 35 0.2 101b 82 51 51 9 .. Mozambique 15.6 87 480 133 10.8 49 67 84 .. 8 .. Myanmar 22.6 51 200 11 0.6 95 96 79 .. 3 .. Namibia 13.2 50 130 52 14.3 85 87 59 8 17 43 Nepal 29.1 40 190 72 0.2 102b 82 83 .. 3 .. Netherlands .. 4 6 6 .. .. .. 64 12 7 30 New Caledonia .. .. .. 21 .. .. 100 57 .. .. .. New Zealand .. 6 8 24 .. .. .. 68 .. 6 .. Nicaragua .. 24 100 99 0.2 80 87 63 47 7 .. Niger 37.9 104 630 205 0.4 49 24 65 .. 5 ..
- 75. World Development Indicators 2015 51Economy 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 population ages 15–24 Modeled ILO estimate % of population ages 15 and older Modeled estimate per 100,000 live births births per 1,000 women ages 15–19 % of children under age 5 per 1,000 live births % of population ages 15–49 % of relevant age group Modeled ILO estimate % of total labor force % of total 2007–13a 2013 2013 2013 2013 2009–13a 2005–13a 2013 2009–13a 2013 2009–13a Nigeria 31.0 117 560 118 3.2 76 66 56 .. 8 .. Northern Mariana Islands .. .. .. .. .. .. .. .. .. .. .. Norway .. 3 4 7 .. 99 .. 65 5 4 31 Oman 8.6 11 11 10 .. 104 98 65 .. 8 .. Pakistan 31.6 86 170 27 <0.1 73 71 54 .. 5 .. Palau .. 18 .. .. .. 83b 100 .. .. .. .. Panama 3.9 18 85 77 0.7 96b 98 66 29 4 46 Papua New Guinea 27.9 61 220 61 0.7 78 71 72 .. 2 .. Paraguay .. 22 110 66 0.4 86 99 70 43 5 32 Peru 3.5 17 89 50 0.4 93 99 76 46 4 30 Philippines 20.2 30 120 46 .. 91 98 65 40 7 .. Poland .. 5 3 12 .. 95 100 57 18 10 38 Portugal .. 4 8 12 .. .. 99 60 17 17 33 Puerto Rico .. .. 20 47 .. .. 99 43 .. 14 .. Qatar .. 8 6 9 .. .. 99 87 0 1 12 Romania .. 12 33 31 0.1 94 99 57 31 7 31 Russian Federation .. 10 24 26 .. 97 100 64 .. 6 .. Rwanda 11.7 52 320 32 2.9 59 77 86 .. 1 .. Samoa .. 18 58 28 .. 102 100 42 38 .. 36 San Marino .. 3 .. .. .. 93 .. .. .. .. .. São Tomé and Príncipe 14.4 51 210 63 0.6 104 80 61 .. .. 24 Saudi Arabia .. 16 16 10 .. 108 99 55 .. 6 7 Senegal 16.8 55 320 92 0.5 61b 66 77 58 10 .. Serbia 1.8b 7 16 17 <0.1 99 99 52 29 22 33 Seychelles .. 14 .. 56 .. .. 99 .. .. .. .. Sierra Leone 18.1 161 1,100 98 1.6 71 63 67 .. 3 .. Singapore .. 3 6 6 .. .. 100 68 9 3 34 Sint Maarten .. .. .. .. .. .. .. .. .. .. .. Slovak Republic .. 7 7 15 .. 95 .. 60 12 14 31 Slovenia .. 3 7 1 .. 101 100 58 14 10 38 Solomon Islands 11.5 30 130 64 .. 86 .. 66 .. 4 .. Somalia .. 146 850 107 0.5 .. .. 56 .. 7 .. South Africa 8.7 44 140 49 19.1 .. 99 52 10 25 31 South Sudan 27.6 99 730 72 2.2 37 .. .. .. .. .. Spain .. 4 4 10 0.4 102 100 59 13 27 30 Sri Lanka 26.3 10 29 17 <0.1 97 98 55 43 4 28 St. Kitts and Nevis .. 10 .. .. .. 90 .. .. .. .. .. St. Lucia 2.8 15 34 55 .. .. .. 69 .. .. .. St. Martin .. .. .. .. .. .. .. .. .. .. .. St. Vincent & the Grenadines .. 19 45 54 .. 107 .. 67 .. .. .. Sudan .. 77 360 80 0.2 57 88 54 .. 15 .. Suriname 5.8 23 130 34 0.9 85 98 55 .. 8 36 Swaziland 5.8 80 310 69 27.4 78 94 57 .. 23 .. Sweden .. 3 4 6 .. 102 .. 64 7 8 35 Switzerland .. 4 6 2 0.4 97 .. 68 9 4 33 Syrian Arab Republic 10.1 15 49 41 .. 64 96 44 33 11 9 Tajikistan 13.3 48 44 41 0.3 98b 100 68 47 11 ..
- 76. 52 World Development Indicators 2015 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 population ages 15–24 Modeled ILO estimate % of population ages 15 and older Modeled estimate per 100,000 live births births per 1,000 women ages 15–19 % of children under age 5 per 1,000 live births % of population ages 15–49 % of relevant age group Modeled ILO estimate % of total labor force % of total 2007–13a 2013 2013 2013 2013 2009–13a 2005–13a 2013 2009–13a 2013 2009–13a Tanzania 13.6 52 410 121 5.0 76 75 89 74 4 .. Thailand 9.2 13 26 40 1.1 .. 97 72 56 1 25 Timor-Leste 45.3 55 270 50 .. 71 80 38 70 4 10 Togo 16.5 85 450 89 2.3 81 80 81 .. 7 .. Tonga .. 12 120 17 .. 100 99 64 .. .. .. Trinidad and Tobago .. 21 84 34 1.7 95 100 64 .. 6 .. Tunisia 2.3 15 46 4 <0.1 98 97 48 29 13 .. Turkey 1.9 19 20 29 .. 101 99 49 31 10 10 Turkmenistan .. 55 61 17 .. .. 100 62 .. 11 .. Turks and Caicos Islands .. .. .. .. .. .. .. .. .. .. .. Tuvalu 1.6 29 .. .. .. 80 .. .. .. .. .. Uganda 14.1 66 360 122 7.4 54 87 78 .. 4 .. Ukraine .. 10 23 25 0.8 110 100 59 18 8 38 United Arab Emirates .. 8 8 27 .. 111 95 80 1 4 .. United Kingdom .. 5 8 26 0.3 .. .. 62 12 8 34 United States 0.5 7 28 30 .. .. .. 63 .. 7 .. Uruguay 4.5 11 14 58 0.7 104 99 66 22 7 44 Uzbekistan .. 43 36 37 0.2 92 100 62 .. 11 .. Vanuatu 11.7 17 86 44 .. 84 95 71 70 .. 29 Venezuela, RB 2.9 15 110 82 0.6 96 99 65 30 8 .. Vietnam 12.0 24 49 29 0.4 97 97 78 63 2 .. Virgin Islands (U.S.) .. .. .. 48 .. .. .. 63 .. .. .. West Bank and Gaza 1.4b 22 47 45 .. 93 99 41 26 23 .. Yemen, Rep. 35.5 51 270 46 <0.1 70 87 49 30 17 5 Zambia 14.9 87 280 122 12.5 84 64 79 .. 13 .. Zimbabwe 11.2b 89 470 58 15.0 92 91 87 .. 5 .. World 15.0 w 46 w 210 w 45 w 0.8 w 92 w 89 w 63 w .. w 6 w Low income 21.4 76 440 92 2.3 71 72 76 .. 5 Middle income 15.8 43 170 40 .. 96 91 63 .. 6 Lower middle income 24.4 59 240 46 0.7 92 83 58 68 5 Upper middle income 2.7 20 57 32 .. 102 99 67 .. 6 Low & middle income 17.0 50 230 49 1.2 91 88 64 .. 6 East Asia & Pacific 5.2 20 75 20 .. 105 99 71 .. 5 Europe & Central Asia 1.6 23 28 29 .. 99 99 57 28 10 Latin America & Carib. 2.8 18 87 68 0.5 95 98 67 32 6 Middle East & N. Africa 6.0 26 78 37 0.1 95 91 47 .. 13 South Asia 32.5 57 190 38 0.3 91 79 56 80 4 Sub-Saharan Africa 21.0 92 510 106 4.5 70 70 70 .. 8 High income 0.9 6 17 18 .. 99 .. 61 .. 8 Euro area .. 4 7 6 .. 98 100 57 11 12 a. Data are for the most recent year available. b. Data are for 2014.
- 77. World Development Indicators 2015 53Economy 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 develop- ment. 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 child- birth, and their children set off on firmer developmental paths, both physically and mentally. Undernourished children have lower resis- tance to infection and are more likely to die from common childhood ailments such as diarrheal diseases and respiratory infections. Fre- quent 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 2013 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 dis- eases are unavailable, mortality rates may be used to identify vulner- able 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 systems and direct or indirect estimates based on sample surveys or cen- suses. 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 registration 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 UN Inter-agency Group for Child Mortality Estimation, which comprises UNICEF, the 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 [2014]; 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, the United Nations Population Fund (UNFPA), the World Bank, and the United Nations Population Division 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 estimated with a multilevel regression model using available national maternal mortality data and socioeconomic information, including fertility, birth attendants, and gross domestic product. The methodology dif- fers from that used for previous estimates, so data presented here should not be compared across editions (WHO and others 2014). 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 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 About the data
- 78. 54 World Development Indicators 2015 Front User guide World view People Environment? 2 People 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 country’s population. Low national prevalence rates can be misleading, how- ever. They often disguise epidemics that are initially concentrated in certain localities or population groups and threaten to spill over into the wider population. In many developing countries most new infec- tions 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. The models, which are routinely updated, track the course of HIV epidemics and their impacts, mak- ing full use of information on HIV prevalence trends from surveil- lance data as well as survey data. The models take into account reduced infectivity among people receiving antiretroviral 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 in generalized epidemics (important because prevalence is higher in urban areas and because many countries have seen rapid urbanization over the past two decades). The estimates include plausibility 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, graduates, financial and human resources, and efficiency indicators such as repetition rates, pupil–teacher ratios, and cohort progres- sion. Primary completion, measured by 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 Educa- tion for All initiative. The indicator reflects the primary cycle, which typically lasts six years (with a range of four to seven years), as defined by the Inter- national Standard Classification of Education (ISCED2011). 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 primary 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 and secondary education by indicating the proportion of the population that has acquired basic literacy and numeracy skills over the previous 10 years or so. Conventional literacy statistics that divide the population into two groups—literate and illiterate—are widely available and useful for tracking global progress toward universal literacy. In practice, however, literacy is difficult to measure. Estimating literacy rates requires census or survey measurements under controlled con- ditions. Many countries report the number of literate or illiterate people from self-reported data. Some use educational attainment data as a proxy but apply different lengths of school attendance or levels of completion. And there is a trend among recent national and international surveys toward using a direct reading test of lit- eracy skills. Because definitions and methods of data collection differ across countries, data should be used cautiously. Generally, literacy encompasses numeracy, the ability to make simple arith- metic calculations. Data on youth literacy are compiled by the United Nations Edu- cational, Scientific and Cultural Organization (UNESCO) Institute for Statistics based on national censuses and household surveys during 1975–2012 and, for countries without recent literacy data, using the Global Age-Specific Literacy Projection Model. For detailed information, see www.uis.unesco.org. 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
- 79. World Development Indicators 2015 55Economy 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, 8th edition, database. The labor force participation rates in the table are modeled esti- mates from the ILO’s Key Indicators of the Labour Market, 8th 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 men- tioned above. Estimates are based mainly on labor force surveys, with other sources (population censuses and nationally reported estimates) used only when no survey data are available. National estimates of labor force participation rates are available in the World Development Indicators online database. Because other employ- ment data are mostly national estimates, caution should be used when comparing the modeled 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 surveys and general household surveys, censuses, and official estimates. 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. The unemployment rates in the table are modeled estimates from the ILO’s Key Indicators of the Labour Market, 8th edition, database. National estimates of unemployment are available in the World Development Indicators online database. Female legislators, senior officials, and managers Despite much progress in recent decades, gender inequalities remain pervasive in many dimensions of life. While gender inequali- ties exist throughout the world, they are most prevalent in develop- ing 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 usually receiving a larger share of education and health spending than girls, for example. The share of women in high-skilled occupations such as legislators, senior officials, and managers indicates women’s status and role in the labor force and society at large. Women are vastly underrepresented in decisionmaking positions in government, although there is some evidence of recent improvement. Data on female legislators, senior officials, and managers are based on the employment by occupation estimates, clas- sified according to the International Standard Classification of
- 80. 56 World Development Indicators 2015 Front User guide World view People Environment? 2 People Occupations 1988. Data are drawn mostly from labor force surveys, supplemented in limited cases with other household surveys, popu- lation censuses, and official estimates. Countries could apply differ- ent practice whether or where the armed forces are included. Armed forces constitute a separate major group, but in some countries they are included in the most closely matching civilian occupation or in nonclassifiable workers. For country-level information on classifica- tion, source, reference period, or definition, consult the footnotes in the World Development Indicators database or the ILO’s Key Indica- tors of the Labour Market, 8th edition, database. 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 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 comple- tion rate, or gross intake ratio to the last grade of primary educa- tion, is the number of new entrants (enrollments minus repeaters) in the last grade of primary education, 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 people ages 15–24 who can both read and write with understanding 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. Data are modeled ILO estimates. • 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. Data are modeled ILO estimates. • 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). Data on under-five mortality rates are from the UN Inter-agency Group for Child Mortality Estimation (www .childmortality.org) and are based mainly on household surveys, censuses, and vital registration data. Modeled estimates of mater- nal mortality ratios are from the UN Maternal Mortality Estimation Inter-agency Group (www.who.int/reproductivehealth/publications /monitoring/maternal-mortality-2013/). Data on adolescent fertil- ity rates are from United Nations Population Division (2013), with annual data linearly interpolated by the World Bank’s Development Data Group. Data on HIV prevalence are from UNAIDS (2014). Data on primary completion rates and youth literacy rates are from the UNESCO Institute for Statistics (www.uis.unesco.org). Data on labor force participation rates, vulnerable employment, unemploy- ment, and female legislators, senior officials, and managers are from the ILO’s Key Indicators of the Labour Market, 8th edition, database. References Amin, Mohammad, and Asif Islam. 2014. “Are There More Female Managers in the Retail Sector? Evidence from Survey Data in Devel- oping Countries.” Policy Research Working Paper 6843. World Bank, Washington, DC. ILO (International Labour Organization).Various years. Key Indicators of the Labour Market. Geneva: International Labour Office. UNAIDS (Joint United Nations Programme on HIV/AIDS). 2014. The Gap Report. [www.unaids.org/en/resources/campaigns/2014 /2014gapreport/gapreport/]. Geneva. UNICEF (United Nations Children’s Fund), WHO (World Health Orga- nization), and the World Bank. 2014. 2013 Joint Child Malnutrition Estimates - Levels and Trends. New York: UNICEF. [www.who.int /nutgrowthdb/estimates2013/]. UN Inter-agency Group for Child Mortality Estimation. 2014. Levels and Trends in Child Mortality: Report 2014. [www.unicef.org/media/files /Levels_and_Trends_in_Child_Mortality_2014.pdf]. New York. United Nations Population Division. 2013. World Population Prospects: The 2012 Revision. [http://esa.un.org/unpd/wpp/Documentation /publications.htm]. 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), World Bank, and United Nations Population Division. 2014. Trends in Maternal Mor- tality: 1990 to 2013. [www.who.int/reproductivehealth/publications /monitoring/maternal-mortality-2013/]. Geneva: WHO. World Bank. 2014. Global Database of Shared Prosperity. [http:// www.worldbank.org/en/topic/poverty/brief/global-database-of -shared-prosperity]. Washington, DC.
- 81. World Development Indicators 2015 57Economy 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.WAG.0714.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
- 82. 58 World Development Indicators 2015 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 2.9.2 Shared prosperity Annualized growth in mean consumption or income per capita, bottom 40% SI.SPR.PC40.ZG Annualized growth in mean consumption or income per capita, total population SI.SPR.PCAP.ZG Mean consumption or income per capita, bottom 40% SI.SPR.PC40 Mean consumption or income per capita, total population SI.SPR.PCAP 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 Gross enrollment ratio, Preprimary SE.PRE.ENRR Gross enrollment ratio, Primary SE.PRM.ENRR Gross enrollment ratio, Secondary SE.SEC.ENRR Gross enrollment ratio, Tertiary SE.TER.ENRR Net enrollment rate, Primary SE.PRM.NENR Net enrollment rate, Secondary SE.SEC.NENR Adjusted net enrollment rate, Primary, Male SE.PRM.TENR.MA Adjustednetenrollmentrate,Primary,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 Cohort survival rate, Reaching grade 5, Male SE.PRM.PRS5.MA.ZS Cohort survival rate, Reaching grade 5, Female SE.PRM.PRS5.FE.ZS Cohort survival rate, Reaching last grade of primary education, Male SE.PRM.PRSL.MA.ZS Cohort survival rate, 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 Transitionratetosecondaryeducation,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 Students at lowest proficiency on PISA, Mathematics ..b Students at lowest proficiency on PISA, Reading ..b Students at lowest proficiency on PISA, Science ..b 2.14 Education gaps by income, gender, and area 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.TO.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
- 83. World Development Indicators 2015 59Economy States and markets Global links Back People 2 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 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 severe wasting, Male SH.SVR.WAST.MA.ZS Prevalence of severe wasting, Female SH.SVR.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.
- 84. 60 World Development Indicators 2015 Front User guide World view People Environment? ENVIRONMENT
- 85. World Development Indicators 2015 61Economy States and markets Global links Back The World Bank Group’s twin goals of elimi- nating extreme poverty and boosting shared prosperity to promote sustainable develop- ment require the efficient use of environmental resources. Whether the world can sustain itself depends largely on properly managing its natu- ral resources. The indicators in the Environment section measure the use of resources and the way human activities affect the natural and built environment. They include measures of envi- ronmental goods (forest, water, and cultivable 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 resources. Economic growth and greater energy use are positively correlated. Access to electricity and the use of energy are vital in raising people’s standard of living. But economic growth often has negative environmental consequences with disproportionate impacts on poor people. Rec- ognizing this, the World Bank Group has joined the UN Sustainable Energy for All initiative, which calls on governments, businesses, and civil soci- eties to achieve three goals by 2030: providing universal access to electricity and clean cooking fuels, doubling the share of the world’s energy supply from renewable sources, and doubling the rate of improvement in energy efficiency. Several energy- and emissions-related indicators are pre- sented in this section, covering data on access to electricity, energy use and efficiency, elec- tricity production and use, and greenhouse gas emissions from various international sources. Household and ambient air pollution place a major burden on people’s health. About 40 percent of the world’s population relies on dung, wood, crop waste, coal, or other solid fuels to meet basic energy needs. Previous assessments of global disease burden attributable to air pollution have been limited to urban areas or by coarse spatial resolution of concentration estimates. Recent developments in remote sensing and global chemical transport models and improvements in coverage of surface measurements facilitate vir- tually complete spatially resolved global air pollut- ant concentration estimates. This year’s Environ- ment section introduces the new global estimates of exposure to ambient air pollution, including population-weighted exposure to mean annual concentrations of fine particulate matter (PM2.5) and the proportion of people who are exposed to ambient PM2.5 concentrations that exceed World Health Organization guidelines. Produced by the Global Burden of Disease team at the Institute for Health Metrics and Evaluation, these improved estimates replace data on PM10 pollution in urban areas. Other indicators in this section cover land use, agriculture and food production, forests and biodiversity, threatened species, water resources, climate variability, exposure to impact, resilience, urbanization, traffic and congestion, and natural resource rents. Where possible, the indicators come from international sources and have been standardized to facili- tate comparison across countries. But ecosys- tems 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. Greenhouse gas emissions and climate change are measured globally, but their effects are experienced locally. 3
- 86. 62 World Development Indicators 2015 Highlights Front User guide World view People Environment? Agricultural output has grown faster than the population since 1990 100 125 150 175 200 225 201420102005200019951990 Population growth and food production (Index, 1990 = 100) Population, high-income countries Food production, high-income countries Population, world Population, developing countries Food production, world Food production, developing countries Since 1990, food production has outpaced population growth in every region and income group. The pace has been considerably faster in developing economies, particularly those in Sub-Saharan Africa and East Asia and Pacific, than in high-income economies. Over the same period developing countries have boosted the area of land under cereal production 21 percent. Sub-Saharan African countries increased the area of land under cereal production 49 percent, to just under 100 mil- lion hectares in 2013. According to World Bank projections, there will likely be almost 9.5 billion people living on Earth by 2050, about 2 bil- lion more than today. Most will live in cities, and the majority will depend on rural areas to feed them. Meeting the growing demand for food will require using agricultural inputs more efficiently and bringing more land into production. But intensive use of land and cultivation may cause further environmental degradation. Source: Online table 3.3. The number of threatened species is highest in Latin America and the Caribbean and Sub-Saharan Africa 0 1,000 2,000 3,000 4,000 5,000 Mammals Birds Fish Plants Threatened species, by taxonomic group, 2014 (number of species) East Asia & Pacific Europe & Central Asia Latin America & Caribbean Middle East & North Africa South Asia Sub-Saharan Africa As threats to biodiversity mount, the international community is increas- ingly focusing on conserving diversity, making the number of threat- ened species an important measure of the immediate need for con- servation in an area. More than 74,000 species are on the International Union for Conservation of Nature Red List, but global analyses of the status of threatened species have been carried out for only a few groups of organisms: The status of virtually all known species has been assessed only for mammals (excluding whales and porpoises), birds (as listed for the area where their breeding or wintering ranges are located), and amphibians. East Asia and Pacific has the largest number of threatened mammal and bird species, Sub-Saharan Africa has the largest number of threatened fish species, and Latin America and the Caribbean has the most threatened plant species. Source: International Union for the Conservation of Nature Red List of Threatened Species and online table 3.4. Agriculture accounts for 90 percent of water use in low-income countries Share of freshwater withdrawals, most recent year available (%) Industrial Domestic Agricultural 0 25 50 75 100 High income Europe & Central Asia Latin America & Caribbean East Asia & Pacific Sub- Saharan Africa Middle East & North Africa South Asia Water is crucial to economic growth and development and to the survival of both terrestrial and aquatic systems. Agriculture accounts for more than 70 percent of freshwater drawn from lakes, rivers, and under- ground sources and about 90 percent in low-income countries, where most of the water is used for irrigation. The volume of water on Earth is about 1,400 million cubic kilometers, only 3.1 percent of which, or about 43 million cubic kilometers, is freshwater. Due to increased demand, global per capita freshwater supplies have declined by nearly half over the past 45 years. As demand for water increases, more people will face water stress (having less than 1,700 cubic meters of water a year per person). Most of the people living in countries facing chronic and widespread water shortages are in developing country regions. Source: Online table 3.5.
- 87. World Development Indicators 2015 63Economy States and markets Global links Back Air pollution exceeds World Health Organization guidelines for 84 percent of the population In many parts of the world exposure to air pollution is increasing at an alarming rate and has become the main environmental threat to health. In 2010 almost 84 percent of the world’s population lived in areas where ambient concentrations of fine particulates with a diameter of fewer than 2.5 microns (PM2.5) exceeded the World Health Organiza- tion’s air quality guideline of 10 micrograms per cubic meter (annual average; WHO 2006). Exposure to ambient PM2.5 pollution in 2010 resulted in more than 3.2 million premature deaths globally, accord- ing to the Global Burden of Disease 2010. Air pollution also carries substantial economic costs and represents a drag on development, particularly for developing countries, where average exposure to pol- lution has worsened since 1990, due largely to increases in East Asia and Pacific and South Asia. Globally, population-weighted exposure to PM2.5 increased as much as 10 percent between 1990 and 2010. 0 10 20 30 40 50 60 Latin America & Caribbean Europe & Central Asia High income Sub-Saharan Africa Middle East & North Africa South Asia East Asia & Pacific World Ambient population-weighted exposure to PM2.5 pollution (micrograms per cubic meter) 1990 2010 Source: Online table 3.13. Some 2.5 billion people still lack access to improved sanitation facilities Sanitation services in developing countries have improved over the last two decades. In 1990 only 35 percent of the people in develop- ing countries had access to flush toilets or other forms of improved sanitation. By 2012, 57 percent did. But 2.5 billion people still lack access to improved sanitation, and the situation is worst in rural areas, where only 43 percent of the population in developing countries has access. East Asia and Pacific has made the most improvement, more than doubling access to improved sanitation since 1990—an impres- sive achievement, bringing access to basic sanitation facilities to more than 850 million additional people, mostly in China. But in the region more that 42 percent of people in rural areas still lack access to acceptable sanitation facilities, and there is wide variation within and across countries. 0 25 50 75 100 20122005200019951990 Share of population with access to improved sanitation facilities (%) Palau Papua New Guinea Thailand Cambodia China East Asia & Pacific Source: Online table 3.12. Natural resource rents account for 17 percent of Sub-Saharan Africa’s GDP In some countries earnings from natural resources, especially from fossil fuels and minerals, account for a sizable share of GDP, much of it in the form of economic rents—revenues above the cost of extract- ing natural resources. Natural resources give rise to economic rents because they are not produced. Rents from nonrenewable resources and from overharvesting forests indicate the liquidation of a country’s capital stock. When countries use these rents to support current consumption rather than to invest in new capital to replace what is being used, they are, in effect, borrowing against their future. The Middle East and North Africa (more than 27 percent of GDP) and Sub-Saharan Africa (nearly 17 percent) are the most dependent on these revenues. 0 5 10 15 20 25 30 High income South Asia East Asia & Pacific Europe & Central Asia Latin America & Caribbean Sub- Saharan Africa Middle East & North Africa Natural resource rents, 2013 (% of GDP) Oil rents Natural gas rents Mineral rents Forest rent Coal rents Source: Online table 3.15.
- 88. Dominican Republic Trinidad and Tobago Grenada St. Vincent and the Grenadines Dominica Puerto Rico, US St. Kitts and Nevis Antigua and Barbuda St. Lucia Barbados R.B. de Venezuela U.S. Virgin Islands (US) Martinique (Fr) Guadeloupe (Fr) Curaçao (Neth) St. Martin (Fr) Anguilla (UK) St. Maarten (Neth) Samoa Tonga Fiji Kiribati Haiti Jamaica Cuba The Bahamas United States Canada Panama Costa Rica Nicaragua Honduras El Salvador Guatemala Mexico Belize Colombia Guyana SurinameR.B. de Venezuela Ecuador Peru Brazil Bolivia Paraguay Chile Argentina Uruguay American Samoa (US) French Polynesia (Fr) French Guiana (Fr) Greenland (Den) Turks and Caicos Is. (UK) IBRD 41452 Less than 1.0 1.0–4.9 5.0–9.9 10.0–19.9 20.0 or more No data Protected areas NATIONALLY PROTECTED TERRESTRIAL AND MARINE AREAS AS A SHARE OF T0TAL TERRITORIAL AREA, 2012 (%) Caribbean inset Bermuda (UK) 64 World Development Indicators 2015 Biodiversity refers to the variety of life on Earth, Including the variety of plant and animal species, the genetic variability within each species, and the vari- ety of different ecosystems. The Earth’s biodiversity is the result of millions of years of evolution of life on the planet. The two most species-rich ecosystems are tropical forests and coral reefs. Tropical forests are under threat largely from conversion to other land uses, while coral reefs are experiencing increasing overexploitation and pollution. The pressure on biodi- versity is driven largely by economic development and related demands. Several international conventions have been developed to conserve threatened species. One of the most widely used approaches for conserv- ing habitat is to designate protected areas, such as national parks. The total area of protected sites has increased steadily in the past three decades. Front User guide World view People Environment?
- 89. Romania Serbia Greece San Marino BulgariaUkraine Germany FYR Macedonia Croatia Bosnia and Herzegovina Czech Republic Poland Hungary Italy Austria Slovenia Slovak Republic Kosovo Montenegro Albania Burkina Faso Palau Federated States of Micronesia Marshall Islands Nauru Kiribati Solomon Islands Tuvalu Vanuatu Fiji Norway Iceland Ireland United Kingdom Sweden Finland Denmark Estonia Latvia Lithuania Poland Belarus Ukraine Moldova Romania Bulgaria Greece Italy Germany Belgium The Netherlands Luxembourg Switzerland Liechtenstein France AndorraPortugal Spain Monaco Malta Morocco Tunisia Algeria Mauritania Mali Senegal The Gambia Guinea- Bissau Guinea Cabo 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 Mozambique Zimbabwe Botswana Namibia Swaziland LesothoSouth Africa Mauritius Seychelles Comoros Rep. of Yemen Oman United Arab Emirates Qatar Bahrain Saudi Arabia Kuwait Israel 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 Guinea Indonesia Australia New Zealand JapanRep.of Korea Dem.People’s Rep.of Korea Mongolia China Russian Federation Brunei Darussalam Sudan South Sudan Timor-Leste Madagascar N. Mariana Islands (US) Guam (US) New Caledonia (Fr) Greenland (Den) West Bank and Gaza Western Sahara Réunion (Fr) Mayotte (Fr) Europe inset World Development Indicators 2015 65Economy States and markets Global links Back Over the last two decades the world’s forests have shrunk by 142 million hectares—equivalent to more than 172 million soccer fields. Protecting forests and other terrestrial and marine areas helps protect plant and animal habitats and preserve the diversity of species. By 2012 more than 14 percent of the world’s land and more than 12 percent of its marine areas had been protected, an increase of almost 6 percentage points in both categories since 1990. Latin America and the Caribbean and Sub-Saharan Africa have the highest share of protected areas among developing country regions.
- 90. 66 World Development Indicators 2015 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 Mean annual exposure to PM2.5 pollution micrograms per cubic meter average annual % Per capita cubic meters % of total population % of total population % growth million metric tons Per capita kilograms of oil equivalent billion kilowatt hours 2000–10 2012 2013 2012 2012 2012–13 2010 2010 2011 2011 Afghanistan 0.00 0.4 1,543 64 29 4.0 24 8.2 .. .. Albania –0.10 9.5 9,284 96 91 1.8 14 4.3 748 4.2 Algeria 0.57 7.4 287 84 95 2.8 22 123.5 1,108 51.2 American Samoa 0.19 16.8 .. 100 63 0.0 .. .. .. .. Andorra 0.00 9.8 3,984 100 100 0.5 13 0.5 .. .. Angola 0.21 12.1 6,893 54 60 5.0 11 30.4 673 5.7 Antigua and Barbuda 0.20 1.2 578 98 91 –1.0 17 0.5 .. .. Argentina 0.81 6.6 7,045 99 97 1.0 5 180.5 1,967 129.6 Armenia 1.48 8.1 2,304 100 91 0.0 19 4.2 916 7.4 Aruba 0.00 0.0 .. 98 98 –0.2 .. 2.3 .. .. Australia 0.37 15.0 21,272 100 100 1.9 6 373.1 5,501 252.6 Austria –0.13 23.6 6,486 100 100 0.6 13 66.9 3,935 62.2 Azerbaijan 0.00 7.4 862 80 82 1.7 17 45.7 1,369 20.3 Bahamas, The 0.00 1.0 53 98 92 1.5 13 2.5 .. .. Bahrain –3.55 6.8 3 100 99 1.1 49 24.2 7,353 13.8 Bangladesh 0.18 4.2 671 85 57 3.6 31 56.2 205 44.1 Barbados 0.00 0.1 281 100 .. 0.1 19 1.5 .. .. Belarus –0.43 8.3 3,930 100 94 0.6 11 62.2 3,114 32.2 Belgium –0.16 24.5 1,073 100 100 0.5 19 108.9 5,349 89.0 Belize 0.67 26.4 45,978 99 91 1.9 6 0.4 .. .. Benin 1.04 25.5 998 76 14 3.7 22 5.2 385 0.2 Bermuda 0.00 5.1 .. .. .. 0.3 .. 0.5 .. .. Bhutan –0.34 28.4 103,456 98 47 3.7 22 0.5 .. .. Bolivia 0.50 20.8 28,441 88 46 2.3 6 15.5 746 7.2 Bosnia and Herzegovina 0.00 1.5 9,271 100 95 0.2 12 31.1 1,848 15.3 Botswana 0.99 37.2 1,187 97 64 1.3 5 5.2 1,115 0.4 Brazil 0.50 26.0 28,254 98 81 1.2 5 419.8 1,371 531.8 Brunei Darussalam 0.44 29.6 20,345 .. .. 1.8 5 9.2 9,427 3.7 Bulgaria –1.53 35.4 2,891 100 100 –0.1 17 44.7 2,615 50.0 Burkina Faso 1.01 15.2 738 82 19 5.9 27 1.7 .. .. Burundi 1.40 4.9 990 75 48 5.6 11 0.3 .. .. Cabo Verde –0.36 0.2 601 89 65 2.1 43 0.4 .. .. Cambodia 1.34 23.8 7,968 71 37 2.7 17 4.2 365 1.1 Cameroon 1.05 10.9 12,267 74 45 3.6 22 7.2 318 6.0 Canada 0.00 7.0 81,071 100 100 1.4 10 499.1 7,333 636.9 Cayman Islands 0.00 1.5 .. 96 96 1.5 .. 0.6 .. .. Central African Republic 0.13 18.0 30,543 68 22 2.6 19 0.3 .. .. Chad 0.66 16.6 1,170 51 12 3.4 33 0.5 .. .. Channel Islands .. 0.5 .. .. .. 0.7 .. .. .. .. Chile –0.25 15.0 50,228 99 99 1.1 8 72.3 1,940 65.7 China –1.57 16.1 2,072 92 65 2.9 73 8,286.9 2,029 4,715.7 Hong Kong SAR, China .. 41.9 .. .. .. 0.5 .. 36.3 2,106 39.0 Macao SAR, China .. .. .. .. .. 1.7 .. 1.0 .. .. Colombia 0.17 20.8 46,977 91 80 1.7 5 75.7 671 61.8 Comoros 9.34 4.0 1,633 95 35 2.7 5 0.1 .. .. Congo, Dem. Rep. 0.20 12.0 13,331 47 31 4.0 15 3.0 383 7.9 Congo, Rep. 0.07 30.4 49,914 75 15 3.2 14 2.0 393 1.3 3 Environment
- 91. World Development Indicators 2015 67Economy 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 Mean annual exposure to PM2.5 pollution micrograms per cubic meter average annual % Per capita cubic meters % of total population % of total population % growth million metric tons Per capita kilograms of oil equivalent billion kilowatt hours 2000–10 2012 2013 2012 2012 2012–13 2010 2010 2011 2011 Costa Rica –0.93 22.6 23,193 97 94 2.7 8 7.8 983 9.8 Côte d’Ivoire –0.15 22.2 3,782 80 22 3.8 15 5.8 579 6.1 Croatia –0.19 10.3 8,859 99 98 0.2 14 20.9 1,971 10.7 Cuba –1.66 9.9 3,384 94 93 0.1 7 38.4 992 17.8 Curaçao .. .. .. .. .. 1.0 .. .. .. .. Cyprus –0.09 17.1 684 100 100 0.9 19 7.7 2,121 4.9 Czech Republic –0.08 22.4 1,251 100 100 0.0 16 111.8 4,138 86.8 Denmark –1.14 23.6 1,069 100 100 0.6 12 46.3 3,231 35.2 Djibouti 0.00 0.2 344 92 61 1.6 27 0.5 .. .. Dominica 0.58 3.7 .. .. .. 0.9 18 0.1 .. .. Dominican Republic 0.00 20.8 2,019 81 82 2.6 9 21.0 727 13.0 Ecuador 1.81 37.0 28,111 86 83 1.9 6 32.6 849 20.3 Egypt, Arab Rep. –1.73 11.3 22 99 96 1.7 33 204.8 978 156.6 El Salvador 1.45 8.7 2,465 90 71 1.4 5 6.2 690 5.8 Equatorial Guinea 0.69 15.1 34,345 .. .. 3.1 7 4.7 .. .. Eritrea 0.28 3.8 442 .. .. 5.2 25 0.5 129 0.3 Estonia 0.12 23.2 9,643 99 95 –0.5 7 18.3 4,221 12.9 Ethiopia 1.08 18.4 1,296 52 24 4.9 15 6.5 381 5.2 Faeroe Islands 0.00 1.0 .. .. .. 0.4 .. 0.7 .. .. Fiji –0.34 6.0 32,404 96 87 1.4 5 1.3 .. .. Finland 0.14 15.2 19,673 100 100 0.6 5 61.8 6,449 73.5 France –0.39 28.7 3,033 100 100 0.7 14 361.3 3,869 556.9 French Polynesia –3.97 0.1 .. 100 97 0.9 .. 0.9 .. .. Gabon 0.00 19.1 98,103 92 41 2.7 6 2.6 1,253 1.8 Gambia, The –0.41 4.4 1,622 90 60 4.3 36 0.5 .. .. Georgia 0.09 3.7 12,955 99 93 0.2 12 6.2 790 10.2 Germany 0.00 49.0 1,327 100 100 0.6 16 745.4 3,811 602.4 Ghana 2.08 14.4 1,170 87 14 3.4 18 9.0 425 11.2 Greece –0.81 21.5 5,260 100 99 –0.1 17 86.7 2,402 59.2 Greenland 0.00 40.6 .. 100 100 –0.1 .. 0.6 .. .. Grenada 0.00 0.3 .. 97 98 0.3 15 0.3 .. .. Guam 0.00 5.3 .. 100 90 1.5 .. .. .. .. Guatemala 1.40 29.8 7,060 94 80 3.4 12 11.1 691 8.1 Guinea 0.54 26.8 19,242 75 19 3.8 22 1.2 .. .. Guinea-Bissau 0.48 27.1 9,388 74 20 4.2 31 0.2 .. .. Guyana 0.00 5.0 301,396 98 84 0.8 6 1.7 .. .. Haiti 0.76 0.1 1,261 62 24 3.8 11 2.1 320 0.7 Honduras 2.06 16.2 11,196 90 80 3.2 7 8.1 609 7.1 Hungary –0.62 23.1 606 100 100 0.4 16 50.6 2,503 36.0 Iceland –4.99 13.3 525,074 100 100 1.1 6 2.0 17,964 17.2 India –0.46 5.0 1,155 93 36 2.4 32 2,008.8 614 1,052.3 Indonesia 0.51 9.1 8,080 85 59 2.7 14 434.0 857 182.4 Iran, Islamic Rep. 0.00 7.0 1,659 96 89 2.1 30 571.6 2,813 239.7 Iraq –0.09 0.4 1,053 85 85 2.7 30 114.7 1,266 54.2 Ireland –1.53 12.8 10,658 100 99 0.7 9 40.0 2,888 27.7 Isle of Man 0.00 .. .. .. .. 0.8 .. .. .. .. Israel –0.07 14.7 93 100 100 1.9 26 70.7 2,994 59.6
- 92. 68 World Development Indicators 2015 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 Mean annual exposure to PM2.5 pollution micrograms per cubic meter average annual % Per capita cubic meters % of total population % of total population % growth million metric tons Per capita kilograms of oil equivalent billion kilowatt hours 2000–10 2012 2013 2012 2012 2012–13 2010 2010 2011 2011 Italy –0.90 21.0 3,030 100 .. 1.3 19 406.3 2,819 300.6 Jamaica 0.11 7.1 3,464 93 80 0.6 12 7.2 1,135 5.1 Japan –0.05 11.0 3,377 100 100 0.5 22 1,170.7 3,610 1,042.7 Jordan 0.00 0.0 106 96 98 2.5 29 20.8 1,143 14.6 Kazakhstan 0.17 3.3 3,777 93 98 1.3 13 248.7 4,717 86.6 Kenya 0.33 11.6 467 62 30 4.4 6 12.4 480 7.8 Kiribati 0.00 20.2 .. 67 40 1.8 6 0.1 .. .. Korea, Dem. People’s Rep. 2.00 1.7 2,691 98 82 0.8 32 71.6 773 21.6 Korea, Rep. 0.11 5.3 1,291 98 100 0.6 38 567.6 5,232 520.1 Kosovo .. .. .. .. .. .. .. .. 1,411 5.8 Kuwait –2.57 12.9 0 99 100 3.6 50 93.7 10,408 57.5 Kyrgyz Republic –1.07 6.3 8,555 88 92 2.2 16 6.4 562 15.2 Lao PDR 0.49 16.7 28,125 72 65 4.9 22 1.9 .. .. Latvia –0.34 17.6 8,317 98 79 –1.2 9 7.6 2,122 6.1 Lebanon –0.45 0.5 1,074 100 .. 1.1 24 20.4 1,449 16.4 Lesotho –0.47 0.5 2,521 81 30 3.1 6 0.0 .. .. Liberia 0.67 2.4 46,576 75 17 3.2 9 0.8 .. .. Libya 0.00 0.1 113 .. 97 1.0 37 59.0 2,186 27.6 Liechtenstein 0.00 43.1 .. .. .. 0.5 .. .. .. .. Lithuania –0.68 17.2 5,261 96 94 –1.1 10 13.6 2,406 4.2 Luxembourg 0.00 39.7 1,840 100 100 2.7 13 10.8 8,046 2.6 Macedonia, FYR –0.41 7.3 2,563 99 91 0.1 17 10.9 1,484 6.9 Madagascar 0.45 4.7 14,700 50 14 4.7 5 2.0 .. .. Malawi 0.97 18.3 986 85 10 3.7 5 1.2 .. .. Malaysia 0.54 13.9 19,517 100 96 2.7 13 216.8 2,639 130.1 Maldives 0.00 .. 87 99 99 4.5 16 1.1 .. .. Mali 0.61 6.0 3,921 67 22 5.0 34 0.6 .. .. Malta 0.00 2.2 119 100 100 1.1 21 2.6 2,060 2.2 Marshall Islands 0.00 0.7 .. 95 76 0.5 8 0.1 .. .. Mauritania 2.66 1.2 103 50 27 3.5 65 2.2 .. .. Mauritius 1.00 0.7 2,186 100 91 –0.2 5 4.1 .. .. Mexico 0.30 13.7 3,343 95 85 1.6 17 443.7 1,560 295.8 Micronesia, Fed. Sets. –0.04 0.1 .. 89 57 0.3 5 0.1 .. .. Moldova –1.77 3.8 281 97 87 0.0 14 4.9 936 5.8 Monaco 0.00 98.4 .. 100 100 0.7 .. .. .. .. Mongolia 0.73 13.8 12,258 85 56 2.8 9 11.5 1,310 4.8 Montenegro 0.00 12.8 .. 98 90 0.3 16 2.6 1,900 2.7 Morocco –0.23 19.9 879 84 75 2.3 20 50.6 539 24.9 Mozambique 0.54 16.4 3,883 49 21 3.3 5 2.9 415 16.8 Myanmar 0.93 6.0 18,832 86 77 2.5 22 9.0 268 7.3 Namibia 0.97 42.6 2,674 92 32 4.2 4 3.2 717 1.4 Nepal 0.70 16.4 7,130 88 37 3.2 33 3.8 383 3.3 Netherlands –0.14 31.5 655 100 100 1.1 19 182.1 4,638 113.0 New Caledonia 0.00 30.5 .. 99 100 2.4 .. 3.9 .. .. New Zealand –0.01 21.3 73,614 100 .. 0.8 6 31.6 4,144 44.5 Nicaragua 2.01 32.5 25,689 85 52 2.0 5 4.5 515 3.8 Niger 0.98 16.7 196 52 9 5.1 37 1.4 .. ..
- 93. World Development Indicators 2015 69Economy 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 Mean annual exposure to PM2.5 pollution micrograms per cubic meter average annual % Per capita cubic meters % of total population % of total population % growth million metric tons Per capita kilograms of oil equivalent billion kilowatt hours 2000–10 2012 2013 2012 2012 2012–13 2010 2010 2011 2011 Nigeria 3.67 13.8 1,273 64 28 4.7 27 78.9 721 27.0 Northern Mariana Islands 0.53 19.9 .. 98 80 1.0 .. .. .. .. Norway –0.80 12.2 75,194 100 100 1.6 4 57.2 5,681 126.9 Oman 0.00 9.3 385 93 97 9.8 35 57.2 8,356 21.9 Pakistan 2.24 10.6 302 91 48 2.8 38 161.4 482 95.3 Palau –0.18 28.2 .. 95 100 1.7 .. 0.2 .. .. Panama 0.36 14.1 35,350 94 73 2.1 5 9.6 1,085 7.9 Papua New Guinea 0.48 1.4 109,407 40 19 2.1 5 3.1 .. .. Paraguay 0.97 6.4 17,200 94 80 2.1 4 5.1 739 57.6 Peru 0.18 18.3 54,024 87 73 1.7 10 57.6 695 39.2 Philippines –0.75 5.1 4,868 92 74 1.3 7 81.6 426 69.2 Poland –0.31 34.8 1,392 .. .. –0.2 16 317.3 2,629 163.1 Portugal –0.11 14.7 3,634 100 100 0.4 13 52.4 2,187 51.9 Puerto Rico –1.76 4.6 1,964 .. 99 –1.1 .. .. .. .. Qatar 0.00 2.4 26 100 100 5.7 69 70.5 17,419 30.7 Romania –0.32 19.2 2,117 .. .. –0.1 17 78.7 1,778 62.0 Russian Federation 0.00 11.3 30,056 97 71 0.3 10 1,740.8 5,113 1,053.0 Rwanda –2.38 10.5 807 71 64 6.4 14 0.6 .. .. Samoa 0.00 2.3 .. 99 92 –0.2 5 0.2 .. .. San Marino 0.00 .. .. .. .. 0.7 .. .. .. .. São Tomé and Príncipe 0.00 0.0 11,296 97 34 3.6 5 0.1 .. .. Saudi Arabia 0.00 29.9 83 97 100 2.1 62 464.5 6,738 250.1 Senegal 0.49 24.2 1,825 74 52 3.6 41 7.1 264 3.0 Serbia –0.99 6.3 1,173 99 97 –0.4 16 46.0 2,237 38.0 Seychelles 0.00 1.3 .. 96 97 1.6 5 0.7 .. .. Sierra Leone 0.69 10.3 26,264 60 13 2.8 18 0.7 .. .. Singapore 0.00 3.4 111 100 100 1.6 20 13.5 6,452 46.0 Sint Maarten .. .. .. .. .. 1.5 .. .. .. .. Slovak Republic –0.06 36.1 2,328 100 100 –0.3 15 36.1 3,214 28.3 Slovenia –0.16 54.9 9,063 100 100 0.0 15 15.3 3,531 15.9 Solomon Islands 0.25 1.1 79,646 81 29 4.3 6 0.2 .. .. Somalia 1.07 0.5 572 32 24 4.1 8 0.6 .. .. South Africa 0.00 6.6 843 95 74 2.4 8 460.1 2,742 259.6 South Sudan .. .. 2,302 57 9 5.2 .. .. .. .. Spain –0.68 25.3 2,385 100 100 0.0 14 269.7 2,686 289.0 Sri Lanka 1.12 15.4 2,578 94 92 0.8 9 12.7 499 11.6 St. Kitts and Nevis 0.00 0.8 443 98 .. 1.3 .. 0.2 .. .. St. Lucia –0.07 2.5 .. 94 65 0.8 18 0.4 .. .. St. Martin 0.00 .. .. .. .. .. .. .. .. .. St. Vincent & the Grenadines –0.27 1.2 .. 95 .. 0.7 17 0.2 .. .. Sudan 0.08c 7.1c 81 56 24 2.5 26c 14.2c 355 8.6 Suriname 0.01 15.2 183,579 95 80 0.8 5 2.4 .. .. Swaziland –0.84 3.0 2,113 74 58 1.3 5 1.0 .. .. Sweden –0.30 13.9 17,812 100 100 1.0 6 52.5 5,190 150.3 Switzerland –0.38 26.3 4,995 100 100 1.2 14 38.8 3,207 62.9 Syrian Arab Republic –1.29 0.7 312 90 96 2.7 26 61.9 910 41.1 Tajikistan 0.00 4.8 7,732 72 94 2.7 17 2.9 306 16.2
- 94. 70 World Development Indicators 2015 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 Mean annual exposure to PM2.5 pollution micrograms per cubic meter average annual % Per capita cubic meters % of total population % of total population % growth million metric tons Per capita kilograms of oil equivalent billion kilowatt hours 2000–10 2012 2013 2012 2012 2012–13 2010 2010 2011 2011 Tanzania 1.13 31.7 1,705 53 12 5.4 5 6.8 448 5.3 Thailand 0.02 16.4 3,350 96 93 3.0 21 295.3 1,790 156.0 Timor-Leste 1.40 6.2 6,961 71 39 4.8 5 0.2 .. .. Togo 5.13 24.2 1,687 60 11 3.8 21 1.5 427 0.1 Tonga 0.00 9.5 .. 99 91 0.6 5 0.2 .. .. Trinidad and Tobago 0.32 10.1 2,863 94 92 –1.2 4 50.7 15,691 8.9 Tunisia –1.86 4.8 385 97 90 1.3 19 25.9 890 16.1 Turkey –1.11 2.1 3,029 100 91 2.0 17 298.0 1,539 229.4 Turkmenistan 0.00 3.2 268 71 99 2.0 48 53.1 4,839 17.2 Turks and Caicos Islands 0.00 3.6 .. .. .. 2.5 .. 0.2 .. .. Tuvalu 0.00 0.3 .. 98 83 1.9 .. .. .. .. Uganda 2.56 11.5 1,038 75 34 5.4 10 3.8 .. .. Ukraine –0.21 4.5 1,167 98 94 0.1 13 304.8 2,766 194.9 United Arab Emirates –0.24 15.5 16 100 98 1.9 80 167.6 7,407 99.1 United Kingdom –0.31 23.4 2,262 100 100 1.0 14 493.5 2,973 364.9 United States –0.13 15.1 8,914 99 100 0.9 13 5,433.1 7,032 4,326.6 Uruguay –2.14 2.6 27,061 100 96 0.5 6 6.6 1,309 10.3 Uzbekistan –0.20 3.4 540 87 100 1.7 22 104.4 1,628 52.4 Vanuatu 0.00 0.5 .. 91 58 3.4 5 0.1 .. .. Venezuela, RB 0.60 49.5 26,476 .. .. 1.5 8 201.7 2,380 122.1 Vietnam –1.65 4.7 4,006 95 75 3.1 30 150.2 697 99.2 Virgin Islands (U.S.) 0.80 2.8 .. 100 96 –0.4 .. .. .. .. West Bank and Gaza –0.10 0.6 195 82 94 3.3 25 2.4 .. .. Yemen, Rep. 0.00 1.1 86 55 53 4.0 30 21.9 312 6.2 Zambia 0.33 37.8 5,516 63 43 4.3 6 2.4 621 11.5 Zimbabwe 1.88 27.2 866 80 40 2.5 5 9.4 697 8.9 World 0.11 w 14.0 w 6,055 s 89 w 64 w 2.1 w 31 w 33,615.4d w 1,890 w 22,158.5 w Low income 0.61 13.6 4,875 69 37 3.9 19 222.9 359 190.6 Middle income 0.13 14.3 4,920 90 60 2.4 37 16,554.9 1,280 9,794.1 Lower middle income 0.31 11.0 3,047 88 47 2.6 27 3,833.4 686 2,226.3 Upper middle income 0.04 15.8 6,910 93 74 2.3 47 12,721.1 1,893 7,566.7 Low & middle income 0.22 14.2 4,913 87 57 2.6 34 16,777.5 1,179 10,005.1 East Asia & Pacific –0.44 13.7 4,376 91 67 2.8 55 9,570.5 1,671 5,410.8 Europe & Central Asia –0.48 5.2 2,710 95 94 1.1 17 1,416.7 2,080 908.6 Latin America & Carib. 0.46 21.2 22,124 94 81 1.5 8 1,553.7 1,292 1,348.0 Middle East & N. Africa –0.15 5.9 656 90 88 2.3 28 1,277.9 1,376 654.4 South Asia –0.29 5.9 1,186 91 40 2.6 32 2,252.6 555 1,215.8 Sub-Saharan Africa 0.48 16.3 4,120 64 30 4.1 17 703.8 681 445.2 High income –0.03 13.8 11,269 99 96 0.8 17 14,901.7 4,877 12,198.4 Euro area –0.31 26.7 2,991 100 100 0.6 16 2,480.0 3,485 2,298.3 a. Negative values indicate an increase in forest area. b. River flows from other countries are not included because of data unreliability. c. Includes South Sudan. d. Includes emissions not allocated to specific countries.
- 95. World Development Indicators 2015 71Economy 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 man- age and use forest resources. FAO (2010) provides information on forest cover in 2010 and adjusted estimates of forest cover in 1990 and 2000. Data presented 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 disappearing 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 col- lected intermittently and may hide substantial year-to-year variations in total renewable water resources. Data do not distinguish between seasonal and geographic variations in water availability within coun- tries. 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 per- centage of the population with ready access to water for domes- tic purposes and are estimated by the World Health Organization (WHO)/United Nations Children’s Fund (UNICEF) Joint Monitoring Programme for Water Supply and Sanitation based on surveys and censuses. 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 Air pollution places a major burden on world health. More than 40 percent of the world’s people rely on wood, charcoal, dung, crop waste, or coal to meet basic energy needs. Cooking with solid fuels creates harmful smoke and particulates that fill homes and the surrounding environment. Household air pollution from cooking with solid fuels is responsible for 3.9 million premature deaths a year—about one every 8 seconds. In many places, including cities but also nearby rural areas, exposure to air pollution exposure is the main environmental threat to health. Long-term exposure to high levels of fine particulates in the air contributes to a range of health About the data
- 96. 72 World Development Indicators 2015 Front User guide World view People Environment? 3 Environment effects, including respiratory diseases, lung cancer, and heart dis- ease, resulting in 3.2 million premature deaths annually. Not only does exposure to air pollution endanger the health of the world’s people, it also carries huge economic costs and represents a drag on development, particularly for low- and middle-income countries and vulnerable segments of the population such as children and the elderly. Data on exposure to ambient air pollution are derived from esti- mates of annual concentrations of very fine particulates produced for the Global Burden of Disease. Estimates of annual concentra- tions are generated by combining data from atmospheric chemistry transport models and satellite observations of aerosols in the atmo- sphere. Modeled concentrations are calibrated against observa- tions from ground-level monitoring of particulates in more than 460 locations around the world. Exposure to concentrations of particu- lates in both urban and rural areas is weighted by population and is aggregated at the national level. Pollutant concentrations are sensitive to local conditions, and even monitoring sites in the same city may register different levels. Direct monitoring of ambient PM2.5 is still rare in many parts of the world, and measurement protocols and standards are not the same for all countries. These data should be considered only a general indication of air quality, intended for cross-country comparisons of the relative risk of particulate matter pollution. 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 more than four 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 liq- uid from biomass, and industrial and municipal waste. Biomass is any plant matter used directly as fuel or converted into fuel, heat, or electricity. Data for combustible renewables and waste are often based on small surveys or other incomplete information and thus give only a broad impression of developments and are not strictly comparable across countries. The International Energy Agency (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 IEA compiles data and data on energy inputs used to gen- erate electricity. Data for countries that are not members of the Organisation for Economic Co-operation and Development (OECD) are based on national energy data adjusted to conform to annual questionnaires completed by OECD member governments. In addi- tion, estimates are sometimes made to complete major aggregates from which key data are missing, and adjustments are made to compensate for differences in definitions. The IEA makes these estimates in consultation with national statistical offices, oil com- panies, electric utilities, and national energy experts. It occasionally revises its time series to reflect political changes. For example, the IEA has constructed historical energy statistics for countries of the former Soviet Union. In addition, energy statistics for other countries have undergone continuous changes in coverage or methodology in recent years as more detailed energy accounts have become avail- able. Breaks in series are therefore unavoidable. 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,
- 97. World Development Indicators 2015 73Economy States and markets Global links Back Environment 3 acid precipitation, or forest fires. • Nationally protected areas are terrestrial and marine protected areas as a percentage of total terri- torial area and include all nationally designated protected areas with known location and extent. All overlaps between different designa- tions 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 using an improved drinking water source. An improved drinking water source includes piped water on premises (piped household water connection located inside the user’s dwelling, plot or yard), public taps or standpipes, tube wells or boreholes, protected dug wells, protected springs, and rainwater collection. • Access to improved sanitation facilities is the percentage of the population using improved sanitation facilities. Improved sanitation facilities are likely to ensure hygienic separation of human excreta from human contact. They include flush/pour flush toilets (to piped sewer system, septic tank, or pit latrine), ventilated improved pit latrines, pit latrines with slab, and composting toilets. • Urban population growth is the annual rate of change of urban population assuming exponential change. Urban population is the proportion of midyear population of areas defined as urban in each country, which is obtained by the United Nations, multiplied by the World Bank estimate of total popu- lation. • Population-weighted exposure to ambient PM2.5 pollution is defined as exposure to fine suspended particulates of less than 2.5 microns in diameter that are capable of penetrating deep into the respiratory tract and causing severe health damage. Data are aggregated at the national level and include both rural and urban areas. Exposure is calculated by weighting mean annual concen- trations of PM2.5 by population. • Carbon dioxide emissions are emissions from the burning of fossil fuels and the manufacture of cement and include carbon dioxide produced during consumption 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. Production 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 website. Data on protected areas, derived from the UNEP and WCMC online databases, are based on data from national authorities, 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 the WHO/UNICEF Joint Monitor- ing Programme for Water Supply and Sanitation (www.wssinfo.org). Data on urban population are from the United Nations Population Division (2014). Data on particulate matter concentrations are from the Global Burden of Disease 2010 study (www.healthdata.org/gbd /data) by the Institute for Health Metrics and Evaluation (see Lim and others 2012). See Brauer and others (2012) for the data and methods used to estimate ambient PM2.5 exposure. Data on carbon dioxide emissions are from CDIAC online databases. Data on energy use and electricity production are from IEA online databases and its annual Energy Statistics of Non-OECD Countries, Energy Balances of Non-OECD Countries, Energy Statistics of OECD Countries, and Energy Balances of OECD Countries. References Brauer, M., M. Amman, R.T. Burnett, A. Cohen, F. Dentener, et al. 2012. “Exposure Assessment for Estimation of the Global Burden of Dis- ease Attributable to Outdoor Air Pollution.” Environmental Science & Technology 46: 652–60. CDIAC (Carbon Dioxide Information Analysis Center). n.d. Online data- base. [http://cdiac.ornl.gov/home.html]. Oak Ridge National Labo- ratory, 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. Lim, S.S., T. Vos, A.D. Flaxman, G. Danaei, K. Shibuya, et al. 2012. “A Comparative Risk Assessment of Burden of Disease and Injury Attributable to 67 Risk Factors and Risk Factor Clusters in 21 Regions, 1990–2010: A Systematic Analysis for the Global Burden of Disease Study 2010.” Lancet 380(9859): 2224–60. UNEP (United Nations Environment Programme) and WCMC (World Conservation Monitoring Centre). 2013. Online databases. [www .unep-wcmc.org/datasets-tools--reports_15.html?&types=Data,We bsite,Tool&ctops=]. Cambridge, UK. United Nations Population Division. 2014. World Urbanization Pros- pects: The 2014 Revision. [http://esa.un.org/unpd/wup/]. New York: United Nations, Department of Economic and Social Affairs. WHO (World Health Organization). 2006. WHO Air Quality Guidelines for Particulate Matter, Ozone, Nitrogen Dioxide, and Sulfur Dioxide: Global Update 2005, Summary of Risk Assessment. [http://whqlibdoc.who .int/hq/2006/WHO_SDE_PHE_OEH_06.02_eng.pdf].
- 98. 74 World Development Indicators 2015 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 2011 PPP $ of GDP EN.ATM.CO2E.PP.GD.KD 3.9 Trends in greenhouse gas emissions Carbon dioxide emissions, Total EN.ATM.CO2E.KT Carbon dioxide emissions, % change ..a,b Methane emissions, Total EN.ATM.METH.KT.CE Methane emissions, % change ..a,b 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
- 99. World Development Indicators 2015 75Economy States and markets Global links Back Environment 3 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 Averagedailyminimum/maximumtemperature ..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 PM2.5 pollution EN.ATM.PM25.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.
- 100. 76 World Development Indicators 2015 Front User guide World view People Environment? ECONOMYECONOMY
- 101. World Development Indicators 2015 77Economy States and markets Global links Back The Economy section provides a picture of the global economy and the economic activity of more than 200 countries and territories. It includes measures of macroeconomic perfor- mance and stability as well as broader measures of income and savings adjusted for pollution, depreciation, and resource depletion. The world economy grew 2.6 percent in 2014 to reach $77 trillion in current prices, and growth is projected to accelerate to 3 percent in 2015. The share that developing economies account for increased to 32.9 percent in 2014, from 32.1 percent in 2013 in current prices. Develop- ing economies grew an estimated 4.4 percent in 2014 and are projected to grow 4.8 percent in 2015. Growth in high-income economies has been updated from earlier forecasts to 1.8 per- cent in 2014 and 2.2 percent in 2015. The structures of economies change over time. GDP is a well recognized and frequently quoted indicator of an economy’s size and strength. To measure changes over time, or growth, it is necessary to strip out any effect of price changes and look at changes in the volume of output. This is done by valuing the production at an earlier year’s (base year) prices, referred to as constant price estimates. Countries con- duct a periodic statistical re-evaluation, known as a national accounts revision exercise, that assesses the importance of different sectors to the aggregate economy and prices. These exer- cises are a recommended practice to ensure that official GDP estimates use an accurate pic- ture of the economy’s structure. In 2014 several African countries revised their national accounts estimates by incorpo- rating new data sources to ensure coverage of economic activities, including new activities, new standards and methods (such as the 2008 Sys- tem of National Accounts), and a new base year for constant price estimates. In general, African economies tend to have large informal sectors and economic activities that are not always well captured by existing statistics. As census and survey data for these activities have become available, estimates for economic activities pre- viously not covered in national accounts have been included to better reflect the true size and structure of the economies. For many countries, incorporating new activities has led to upward adjustments to GDP. Adjusted net savings has been included in World Development Indicators since 1999. It measures the change in a country’s real wealth, including manufactured, natural, and human capital. Years of negative adjusted net sav- ings suggest that a country’s economy is on an unsustainable path. This year the methodology has been adjusted to improve accounting of the economic costs of air pollution. In previous edi- tions the scope of pollution damages included in adjusted net savings was limited to outdoor air pollution in urban areas with more than 100,000 people, but it now covers outdoor air pollution and household air pollution in urban and rural areas. Health costs previously estimated for exposure to airborne particles with a diameter of 10 micrometers or less (PM10 ) are now mea- sured for exposure to finer particles that are more closely associated with health effects (PM2.5). And pollution damages are now calcu- lated as productivity losses in the workforce due to premature death and illness. These costs rep- resent only a part of the total welfare losses from pollution, but they are more amenable to the standard national accounting framework. 4
- 102. 78 World Development Indicators 2015 Highlights Front User guide World view People Environment? Economic growth slowed in developing countries –5 0 5 10 15 201320122011201020092008200720062005 GDP growth (%) China Low income India Brazil South Africa Middle income Lower middle income In recent years GDP growth has decelerated considerably in almost all developing countries. The average GDP growth rate of developing economies declined 1.8 percentage points between 2010 and 2013 thanks mostly to large middle-income countries such as Brazil, China, India, and South Africa, where growth fell an average of 3 percentage points. Low-income countries performed better than middle-income countries, whose growth rates fell around 1 percentage point. Latin America and the Caribbean saw GDP growth drop significantly (3.4 per- centage points), as did South Asia (2.5 percentage points). Source: Online table 4.1. Inflation remains high across most of South Asia 0 5 10 15 201320122011201020092008200720062005 Inflation (%) South Asia Europe & Central Asia Latin America & Caribbean Middle East & North Africa Sub-Saharan Africa Developing countries In 2013 South Asia’s median inflation rate, 7.6 percent as measured by the consumer price index, was the highest of all regions and 5 per- centage points above the world median, even after falling from the 2012 rate. Even in countries where inflation is falling, the rate remains higher than in other countries. India’s average inflation rate was 10.9 percent, followed closely by Nepal at 9 percent. In all other South Asian countries inflation hovered between 7 and 8 percent, except the Maldives (2.3 percent). Source: Online table 4.16. Many economies in Africa are larger than previously thought –25 0 25 50 75 100 Equatorial Guinea Rwanda Mozambique South Africa Namibia Uganda Zambia Kenya Tanzania Congo, Dem. Rep. Nigeria Revisions in 2013 nominal GDP, selected countries (%) Nigeria, Africa’s most populous country, is also its largest economy. Last year, as part of a statistical review of national accounts, it adjusted its estimate of 2013 GDP up 91 percent, from $273 billion to $521 billion. This was the first major revision of Nigeria’s GDP estimate in almost two decades, changing the base year from 1990 to 2010. The most notable improvements include incorporating small business activity and fast-growing industries (such as mobile telecoms, real estate, and the film industry). Several other countries in Sub-Saharan Africa also improved the quality of their GDP estimates, including the Democratic Republic of the Congo (up 62 percent), Tanzania (up 31 per- cent), Kenya (up 25 percent, to become the region’s fourth largest economy), Zambia (up 20 percent), Uganda (up 15 percent), and Namibia (up 14 percent). Two countries revised their GDP estimates down: Rwanda (3 percent) and Equatorial Guinea (9 percent). Source: Online table 4.2.
- 103. World Development Indicators 2015 79Economy States and markets Global links Back How Mercosur and the Pacific Alliance compare The Pacific Alliance is a Latin American trade bloc that officially launched in 2012 among Chile, Colombia, Mexico, and Peru. Together the four Pacific Alliance countries have a combined population of 218.6 million and GDP of $2.1 trillion. The Southern Common Mar- ket (Mercosur), another bloc in the region, was created in 1991 and includes Argentina, Brazil, Paraguay, Uruguay, and Venezuela. Together the five Mercosur countries have 282.4 million inhabitants and GDP of $3.3 trillion. The Pacific Alliance saw average GDP growth of 3.3 per- cent over 2011–13, surpassing the overall GDP growth of 2.7 percent in Latin America and the 2.0 percent growth of Mercosur. In addition, Pacific Alliance exports increased an average of 3.5 percent, compared with constant exports in Mercosur. –5 0 5 10 20132012201120102009200820072006 Annual GDP growth (%) Latin America & Caribbean Mercosur (Argentina, Brazil, Paraguay, Uruguay and Venezuela) Pacific Alliance (Chile, Colombia, Mexico and Peru) Source: Online table 4.1. Developing countries have a higher share of world GDP Purchasing power parity (PPP) estimates based on the 2011 round of the International Comparison Program were incorporated into World Development Indicators in 2014, replacing the extrapolated PPP esti- mates based on the 2005 round. When comparing the 2011 results to the 2005 results, high-income countries’ share in the world economy is about 4.5 percentage points smaller, lower middle-income coun- tries’ share is 3.4 percentage points larger, and upper middle-income countries’ share is 0.9 percentage point larger. Compared with esti- mates based on market exchange rates, lower middle-income and low-income countries’ PPP-based shares are more than double, upper middle-income countries’ share is more than 30 percent greater, and high-income countries’ share decreases to half of the world economy from two-thirds. Source: International Comparison Program and World Development Indicators database. Different starting points but similarly low levels of sustainability in Sub-Saharan Africa Gross national savings, a measure of natural resources available for investment, averaged about 16 percent of gross national income for upper middle-income countries in Sub-Saharan Africa, compared with 3–6 percent in the region’s low- and lower middle-income countries. Upper middle-income countries are investing substantially more in human capital, with much higher current public expenditure on educa- tion. These countries depend heavily on extractive industries, which are both capital and resource intensive, so their savings were nearly zero after adjusting for natural resource depletion and the depreciation of manufactured capital. In the region’s low-income countries overharvest of timber resources accounted for the largest downward adjustment in savings for 2013. Much of this was due to harvesting wood fuel, as the majority of people in these countries rely on solid fuels for cooking, with the resulting emissions causing the majority of pollution damage. a. Data are for 2010, the most recent year available. Source: Online table 4.11. GDP as a share of the world economy, 2011 (%) PPP based (2011 benchmark) PPP based (extrapolation from 2005 benchmark) Exchange rate based (2011 benchmark) 0 20 40 60 80 Low income (32 countries) Lower middle income (48 countries) Upper middle income (48 countries) High income (50 countries) –5 0 5 10 15 20 Adjusted net savings Less pollution damagea Less forest depletion Less mineral depletion Less energy depletion Plus education spending Less consump- tion of fixed capital Gross savings Share of gross national income, Sub-Saharan Africa, 2013 (%) Upper middle income Lower middle income Low income
- 104. Dominican Republic Trinidad and Tobago Grenada St. Vincent and the Grenadines Dominica Puerto Rico, US St. Kitts and Nevis Antigua and Barbuda St. Lucia Barbados R.B. de Venezuela U.S. Virgin Islands (US) Martinique (Fr) Guadeloupe (Fr) St. Martin (Fr) Anguilla (UK) St. Maarten (Neth) Curaçao (Neth) Samoa Tonga Fiji Kiribati Haiti Jamaica Cuba The Bahamas United States Canada Panama Costa Rica Nicaragua Honduras El Salvador Guatemala Mexico Belize Colombia Guyana Suriname R.B. de Venezuela Ecuador Peru Brazil Bolivia Paraguay Chile Argentina Uruguay American Samoa (US) French Polynesia (Fr) Bermuda (UK) French Guiana (Fr) Greenland (Den) Turks and Caicos Is. (UK) IBRD 41453 Less than 0.0 0.0–1.9 2.0–3.9 4.0–5.9 6.0 or more No data Economic growth AVERAGE ANNUAL GROWTH OF GDP PER CAPITA, 2009–13 (%) Caribbean inset 80 World Development Indicators 2015 Economic growth reduces poverty. As a result, fast-growing developing countries are closing the income gap with high-income economies. But growth must be sustained over the long term, and the gains from growth must be shared to make lasting improve- ments to the well-being of all people. In 2009 the financial crisis, which began in 2007 and spread from high-income to low-income economies in 2008, became the most severe global recession in 50 years and affected sustained development around the world. The average annual growth of gross domes- tic product (GDP) per capita in developing countries, while still faster than in high-income countries, slowed from 5 percent in 2000–09 (the pre-crisis period) to 4.5 percent in 2009–13 (the post-crisis period). High- income countries grew an average of 1.3 percent after the crisis, down from 1.5 percent before crisis. The Middle East and North Africa saw the largest drop: Average annual GDP growth fell 2.6 percentage points from before the pre-crisis period. Front User guide World view People Environment?
- 105. Romania Serbia Greece San Marino BulgariaUkraine Germany FYR Macedonia Croatia Bosnia and Herzegovina Czech Republic Poland Hungary Italy Austria Slovenia Slovak Republic Kosovo Montenegro Albania Burkina Faso Palau Federated States of Micronesia Marshall Islands Nauru Kiribati Solomon Islands Tuvalu Vanuatu Fiji Norway Iceland Ireland United Kingdom Sweden Finland Denmark Estonia Latvia Lithuania Poland Belarus Ukraine Moldova Romania Bulgaria Greece Italy Germany Belgium The Netherlands Luxembourg Switzerland Liechtenstein France AndorraPortugal Spain Monaco Malta Morocco Tunisia Algeria Mauritania Mali Senegal The Gambia Guinea- Bissau Guinea Cabo 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 Mozambique Madagascar Zimbabwe Botswana Namibia Swaziland LesothoSouth Africa Mauritius Seychelles Comoros Rep. of Yemen Oman United Arab Emirates Qatar Bahrain Saudi Arabia Kuwait Israel 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 Guinea Indonesia Australia New Zealand JapanRep.of Korea Dem.People’s Rep.of Korea Mongolia China Russian Federation Brunei Darussalam Sudan South Sudan Timor-Leste N. Mariana Islands (US) Guam (US) New Caledonia (Fr) Greenland (Den) West Bank and Gaza Western Sahara Réunion (Fr) Mayotte (Fr) Europe inset World Development Indicators 2015 81 Mongolia recorded the highest average GDP per capita growth in 2009–13 among developing countries at 10.8 percent, thanks to stronger mineral production led by copper and gold in the Oyu Tolgoi mine. Turkmenistan’s average GDP per capita growth of 10.2 percent over 2009–13 was sustained by vast hydrocarbon resources and considerable government infrastructure spending. Panama is the fastest growing country in Latin America and the Caribbean, driven by a steady rise in investments, including the large Panama Canal expansion, and business-friendly regulations. After a decade of economic decline and hyperinflation, Zimbabwe has seen a recovery since 2009, supported by better economic policies, which have moved the country from a 7.5 percent annual average decrease in GDP per capita pre-crisis to 7.3 percent growth post-crisis. Economy States and markets Global links Back
- 106. 82 World Development Indicators 2015 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 % of GDP % of GNI % of GDP % of GDP % of GDP % growth % of GDP 1990–2000 2000–09 2009–13 2013 2013a 2013 2012 2012 2013 2013 Afghanistan .. 8.5 8.1 –21.2 –34.8 –33.0 –0.6 .. 7.6 33.0 Albania 3.8 5.5 2.3 18.2 4.4 –10.7 .. .. 1.9 84.1 Algeria 1.9 4.2 3.1 45.3 24.7 0.4 –0.3 .. 3.3 62.7 American Samoa .. .. .. .. .. .. .. .. .. .. Andorra 3.2 5.9 .. .. .. .. .. .. .. .. Angola 1.6 13.8 4.8 18.5 –20.1 6.7 6.7 .. 8.8 36.7 Antigua and Barbuda 3.5 4.9 –0.9 7.8 .. –17.0 –1.3 .. 1.1 98.2 Argentina 4.3 4.9b 5.2b 16.2 8.3 –0.8 .. .. ..b 27.2 Armenia –1.9 10.6 4.7 13.7 1.6 –8.0 –1.4 .. 5.8 36.2 Aruba 3.9 –0.1 .. .. .. –10.1 .. .. –2.4 68.3 Australia 3.6 3.3 2.7 24.6 9.3 –3.2 –3.0 40.5 2.4 106.4 Austriac 2.5 1.9 1.6 25.6 12.9 1.0 –2.4 78.5 2.0 .. Azerbaijan –6.3 17.9 2.8 40.9 14.1 16.6 6.1 6.4 2.4 33.4 Bahamas, The 2.6 1.0 1.1 11.3 8.4 –19.2 –4.1 47.5 0.4 74.8 Bahrain 5.0 6.0 3.6 27.6 17.6 7.8 –0.5 35.6 3.2 74.3 Bangladesh 4.8 5.9 6.2 38.8 26.8 1.6 –0.8 .. 7.5 61.3 Barbados 2.1 1.8 0.4 .. .. .. –8.0 96.8 1.8 .. Belarus –1.6 8.2 3.9 28.5 21.5 –10.5 0.1 25.2 18.3 30.4 Belgiumc 2.2 1.8 1.1 20.9 7.0 –3.5 –3.5 89.4 1.1 .. Belize 4.5 4.2 2.7 9.9 –6.5 –4.4 –0.2 74.5 0.7 76.2 Benin 4.6 3.9 4.2 13.8 –1.6 –7.6 1.7 .. 1.0 41.8 Bermuda 2.9 2.3 –3.4 .. .. 16.9 .. .. .. .. Bhutan 5.2 8.4 6.6 25.5 9.4 –28.6 .. .. 7.0 57.0 Bolivia 4.0 4.0 5.3 23.9 7.3 3.8 .. .. 5.7 76.7 Bosnia and Herzegovina .. 5.0 0.6 12.3 .. –5.9 –1.6 .. –0.1 61.2 Botswana 4.9 4.4 6.0 39.4 29.0 12.0 1.4 19.0 5.9 40.9 Brazil 2.7 3.6 3.1 13.7 3.1 –3.6 –2.0 .. 6.2 79.9 Brunei Darussalam 2.1 1.4 1.5 .. .. 33.5 .. .. 0.4 70.3 Bulgaria –0.3 5.3 1.1 23.4 10.6 1.8 –0.8 17.5 0.9 83.8 Burkina Faso 5.5 5.9 7.7 .. .. .. –3.0 .. 0.5 28.9 Burundi –2.9 3.3 4.1 17.8 –18.4 –9.3 .. .. 8.0 21.8 Cabo Verde 12.1 7.3 2.0 29.7 21.5 –3.9 –10.1 .. 1.5 88.1 Cambodia 7.0 9.2 7.0 8.5 –3.8 –10.5 –4.4 .. 2.9 53.6 Cameroon 1.8 3.3 4.4 10.2 –6.0 –3.8 .. .. 1.9 20.9 Canada 3.1 2.1 2.3 21.0 6.0 –3.0 –0.2 53.5 0.9 .. Cayman Islands .. .. .. .. .. .. .. .. .. .. Central African Republic 1.8 3.8 –5.3 .. .. .. 0.7 .. 1.5 28.1 Chad 2.2 11.4 6.1 .. .. .. .. .. 0.1 12.8 Channel Islands .. 0.5 .. .. .. .. .. .. .. .. Chile 6.6 4.2 5.3 20.4 4.2 –3.4 0.5 .. 1.8 82.2 China 10.6 10.9 8.7 51.3 29.5 2.0 .. .. 2.6 194.5 Hong Kong SAR, China 3.6 4.8 3.8 25.6 .. 1.9 .. .. 4.4 352.7 Macao SAR, China 2.2 11.9 16.8 58.2 .. 43.2 24.1 .. 5.5 106.7 Colombia 2.8 4.6 4.9 19.7 2.1 –3.2 2.8 65.3 2.0 45.8 Comoros 1.2 2.5 2.8 14.6 –3.2 –7.5 .. .. 2.3 40.5 Congo, Dem. Rep. –4.9 5.1 7.3 9.5 –28.1 –8.8 2.3 .. 1.6 11.4 Congo, Rep. 1.0 4.0 4.6 .. .. .. .. .. 6.0 32.0 4 Economy
- 107. World Development Indicators 2015 83Economy 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 % of GDP % of GNI % of GDP % of GDP % of GDP % growth % of GDP 1990–2000 2000–09 2009–13 2013 2013a 2013 2012 2012 2013 2013 Costa Rica 5.3 5.1 4.6 16.1 15.9 –5.1 –4.0 .. 5.2 49.2 Côte d’Ivoire 3.2 1.0 3.8 .. .. .. –2.8 .. 2.6 35.7 Croatia 3.1 3.7 –1.3 19.3 4.8 1.2 –3.4 .. 2.2 69.8 Cuba –0.7 6.4 2.5 .. .. .. .. .. .. .. Curaçao .. .. .. .. .. .. .. .. .. .. Cyprusc 4.2 3.4d –1.5 .. .. –1.9 –6.4 131.0 –0.4 .. Czech Republic 1.4 4.1 0.7 23.6 4.8 –1.4 –2.3 40.8 1.4 77.0 Denmark 2.8 1.2 0.4 25.9 14.2 7.1 –3.8 47.2 0.8 72.1 Djibouti –2.0 4.0 4.4 .. .. –21.2 .. .. 2.4 85.2 Dominica 2.0 3.4 –0.4 –1.9 .. –14.0 –11.1 .. 0.0 93.2 Dominican Republic 6.3 5.1 4.2 18.8 15.5 –4.0 –2.5 .. 4.8 34.9 Ecuador 2.2 4.5 5.5 27.2 9.4 –1.4 .. .. 2.7 32.0 Egypt, Arab Rep. 4.4 4.9 2.6 13.0 2.2 –2.7 –10.6 .. 9.5 79.1 El Salvador 4.8 2.4 1.8 9.1 4.7 –6.5 –0.8 47.8 0.8 44.8 Equatorial Guinea 36.7 15.7 1.2 .. .. .. .. .. 6.4 23.5 Eritrea 6.5 0.2 5.4 .. .. .. .. .. .. 110.8 Estoniac 6.5 5.2 4.7 25.1 13.0 –1.2 –0.1 10.4 2.8 .. Ethiopia 3.8 8.5 10.5 31.1 9.9 –6.9 –1.3 .. 8.1 .. Faeroe Islands .. .. .. .. .. .. .. .. .. .. Fiji 2.7 1.6 2.6 .. .. –14.5 .. .. 2.9 80.6 Finlandc 2.9 2.4 0.7 19.7 6.2 –0.9 –1.0 51.0 1.5 .. Francec 2.0 1.5 1.2 20.1 6.8 –1.4 –4.6 100.9 0.9 .. French Polynesia .. .. .. .. .. .. .. .. .. .. Gabon 2.3 1.9 6.3 .. .. .. .. .. 0.5 22.7 Gambia, The 3.0 3.2 2.6 25.8 2.0 6.4 .. .. 5.7 55.8 Georgia –7.1e 7.4e 5.9e 19.0e 8.7e –5.7 –0.5 32.5 –0.5 36.6 Germanyc 1.7 1.0 2.0 25.8 12.1 6.9 0.1 55.2 1.5 .. Ghana 4.3 5.8 10.2 20.7 10.1 –11.8 –3.9 .. 11.6 29.1 Greecec 2.4 3.2 –6.4 11.2 –5.0 0.6 –9.4 163.6 –0.9 .. Greenland 1.9 1.7 .. .. .. .. .. .. .. .. Grenada 3.2 3.1 0.3 –5.9 .. –25.5 –5.5 .. 0.0 90.8 Guam .. .. .. .. .. .. .. .. .. .. Guatemala 4.2 3.7 3.5 11.8 4.2 –2.7 –2.3 24.3 4.3 47.1 Guinea 4.2 2.7 3.2 –17.0 –50.4 –18.9 .. .. 11.9 36.4 Guinea-Bissau 0.6 2.4 2.9 .. .. –8.7 .. .. 0.7 39.4 Guyana 5.4 0.7 5.0 .. –0.3 –14.2 .. .. 1.8 67.1 Haiti .. 0.7 2.2 23.1 17.8 –6.4 .. .. 5.9 44.4 Honduras 3.2 4.9 3.6 13.4 8.7 –8.9 –3.2 .. 5.2 52.9 Hungary 1.9 2.8 0.6 23.9 9.3 4.1 –2.6 84.7 1.7 61.5 Iceland 2.8 4.3 1.1 20.4 12.4 8.9 –3.3 112.6 3.9 84.8 India 6.0 7.6 6.9 31.8 19.6 –2.6 –3.8 50.3 10.9 77.4 Indonesia 4.2 5.3 6.2 29.0 22.1 –3.4 .. .. 6.4 41.1 Iran, Islamic Rep. 3.1 5.4 1.7 .. .. .. .. .. 39.3 .. Iraq 10.3 3.8 8.1 30.4 .. 13.7 .. .. 1.9 33.4 Irelandc 7.5 3.5 0.7 20.7 18.2 6.2 –7.6 120.5 0.5 .. Isle of Man 6.4 6.2 .. .. .. .. .. .. .. .. Israel 7.6 3.6 4.0 20.9 12.6 2.4 –5.4 .. 1.5 ..
- 108. 84 World Development Indicators 2015 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 % of GDP % of GNI % of GDP % of GDP % of GDP % growth % of GDP 1990–2000 2000–09 2009–13 2013 2013a 2013 2012 2012 2013 2013 Italyc 1.6 0.6 –0.6 19.0 4.2 1.0 –3.0 126.2 1.2 .. Jamaica .. .. .. 8.7 .. –9.2 –4.0 .. 9.3 50.3 Japan 1.0 0.9 1.6 21.8 2.8 0.7 –8.0 196.0 0.4 247.8 Jordan 5.0 7.1 2.6 18.0 13.4 –10.0 –8.3 66.8 5.5 124.5 Kazakhstan –4.1 8.8 6.4 23.9 –1.9 –0.1 .. .. 5.8 32.9 Kenya 2.2 4.3 6.0 11.3 6.0 –8.4 –3.9 .. 5.7 41.3 Kiribati 4.0 1.5 2.2 .. .. –8.7 14.8 .. .. .. Korea, Dem. People’s Rep. .. .. .. .. .. .. .. .. .. .. Korea, Rep. 6.2 4.4 3.7 34.6 19.0 6.1 1.7 .. 1.3 134.5 Kosovo .. 5.3 3.3 21.3 .. –6.4 .. .. 1.8 44.8 Kuwait 4.9 7.2 5.7 59.5 .. 39.7 27.9 .. 2.7 57.6 Kyrgyz Republic –4.1 4.6 3.7 12.5 –2.1 –23.3 –6.5 .. 6.6 .. Lao PDR 6.4 7.0 8.2 16.7 –4.1 –3.3 –0.8 .. 6.4 .. Latvia –1.5 6.2 3.8 25.9 14.2 –0.8 0.5 41.1 0.0 43.0 Lebanon 5.3 5.3 3.0 20.7 6.1 –24.8 –8.8 .. .. 250.1 Lesotho 3.8 3.6 5.3 36.5 .. –3.3 .. .. 4.9 38.4 Liberia 4.1 4.3 10.3 24.5 –14.7 –27.5 –2.6 32.7 7.6 38.2 Libya .. 5.4 –8.6 .. .. –0.1 .. .. 2.6 70.9 Liechtenstein 6.2 2.5 .. .. .. .. .. .. .. .. Lithuania –2.5 6.3 3.8 16.9 8.2 1.5 –3.1 49.4 1.1 47.3 Luxembourgc 4.4 3.2 2.1 14.4 6.4 5.3 –0.6 20.0 1.7 .. Macedonia, FYR –0.8 3.4 1.9 30.7 15.8 –1.9 –4.0 .. 2.8 59.7 Madagascar 2.0 3.6 1.9 .. .. .. –1.7 .. 5.8 23.8 Malawi 3.7 4.5 4.2 7.9 –15.0 –18.9 .. .. 27.3 38.7 Malaysia 7.0 5.1 5.7 30.4 15.4 3.7 –4.5 53.3 2.1 143.8 Maldives .. 8.1 4.5 .. .. –7.7 –8.7 73.5 2.3 67.0 Mali 4.1 5.7 2.3 18.1 0.4 –6.2 0.0 .. –0.6 33.6 Maltac 5.2 1.8 2.2 12.1 .. 0.9 –3.2 85.9 1.4 .. Marshall Islands 0.4 1.4 3.2 .. .. .. .. .. .. .. Mauritania –1.3 4.6 5.5 34.7 –15.9 –30.3 .. .. 4.1 35.4 Mauritius 5.2 3.8 3.6 12.7 1.7 –9.9 –0.6 37.2 3.5 99.8 Mexico 3.3 2.2 3.6 20.6 6.5 –2.1 .. .. 3.8 33.3 Micronesia, Fed. Sts. 1.8 –0.3 0.4 .. .. .. .. .. .. 46.1 Moldova –9.6f 5.6f 5.0f 19.3f 15.2f –5.0 –2.0 24.3 4.6 62.4 Monaco 1.9 4.2 .. .. .. .. .. .. .. .. Mongolia 1.0 7.5 12.5 34.1 13.9 –27.7 –8.4 .. 8.6 53.9 Montenegro .. 4.7 1.3 4.5 .. –14.7 .. .. 2.2 52.2 Morocco 2.9g 4.9g 3.9g 26.6g 13.8g –7.6 –6.0 59.7 1.9 112.3 Mozambique 6.1 7.6 7.3 17.9 7.1 –37.7 –2.7 .. 4.3 46.0 Myanmar .. .. .. .. .. .. .. .. 5.5 .. Namibia 3.3 5.3 5.3 17.5 14.3 –4.1 –11.9 35.5 5.6 54.5 Nepal 4.9 3.7 4.2 43.1 36.7 6.0 –0.6 33.9 9.0 85.6 Netherlandsc 3.2 1.8 0.1 26.7 14.4 10.2 –3.3 67.9 2.5 .. New Caledonia .. .. .. .. .. .. .. .. .. .. New Zealand 3.5 2.9 2.1 16.3 8.3 –3.2 –0.5 69.0 1.3 .. Nicaragua 3.7 3.4 4.8 18.2 13.1 –11.4 0.5 .. 7.1 35.4 Niger 2.4 4.1 6.4 21.0 0.4 –16.6 .. .. 2.3 24.1
- 109. World Development Indicators 2015 85Economy 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 % of GDP % of GNI % of GDP % of GDP % of GDP % growth % of GDP 1990–2000 2000–09 2009–13 2013 2013a 2013 2012 2012 2013 2013 Nigeria 1.9 10.0 5.4 33.3 19.4 4.4 –1.3 10.4 8.5 21.5 Northern Mariana Islands .. .. .. .. .. .. .. .. .. .. Norway 3.9 1.9 1.5 37.5 19.9 11.2 14.6 20.9 2.1 .. Oman 4.5 2.8 3.5 .. .. 6.4 –0.4 5.0 1.2 38.2 Pakistan 3.8 5.1 3.1 21.0 10.7 –1.9 –8.0 .. 7.7 40.9 Palau 2.4 0.7 3.9 .. .. .. .. .. .. .. Panama 4.7 6.8 9.1 25.2 23.8 –11.5 .. .. 4.0 60.5 Papua New Guinea 3.8 3.8 8.3 .. .. –14.9 .. .. 5.0 52.3 Paraguay 3.0 3.2 6.2 17.3 8.5 2.1 –1.0 .. 2.7 48.6 Peru 4.5 5.9 6.6 23.8 11.3 –4.5 2.0 19.2 2.8 43.0 Philippines 3.3 4.9 6.1 43.2 26.9 3.8 –1.9 51.5 3.0 69.7 Poland 4.7 4.3 3.0 18.3 10.3 –1.4 –3.6 .. 1.0 59.0 Portugalc 2.8 0.8 –1.5 16.5 3.5 0.5 –6.8 122.8 0.3 .. Puerto Rico 3.6 0.3 –2.0 .. .. .. .. .. .. .. Qatar 11.1 13.5 10.2 61.8 30.1 30.8 2.9 .. 3.1 61.8 Romania –0.6 5.8 1.3 21.8 20.9 –0.9 –2.5 .. 4.0 38.3 Russian Federation –4.7 6.0 3.5 24.2 10.6 1.6 2.7 9.4 6.8 55.8 Rwanda –0.2 7.7 7.4 19.6 5.3 –7.5 –4.0 .. 8.0 .. Samoa 2.6 3.6 1.8 .. .. –5.7 0.0 .. 0.6 40.8 San Marino 5.8 3.2 .. .. .. .. .. .. 1.6 .. São Tomé and Príncipe .. 5.3 4.4 18.0 .. –25.8 –12.2 .. 7.1 37.5 Saudi Arabia 2.1 5.9 6.6 43.6 21.2 17.7 .. .. 3.5 55.9 Senegal 3.0 4.3 3.1 21.8 12.9 –7.9 –5.3 .. 0.7 42.8 Serbia 0.7 5.5 0.7 10.7 .. –6.1 –6.1 .. 7.7 44.3 Seychelles 4.4 2.4 5.4 19.7 .. –15.8 5.3 80.2 4.3 53.7 Sierra Leone –3.0 7.3 5.5 28.1 13.2 –9.3 –5.6 .. 10.3 20.8 Singapore 7.2 6.0 6.3 47.4 .. 18.3 8.7 110.9 2.4 133.0 Sint Maarten .. .. .. .. .. .. .. .. .. .. Slovak Republicc 4.5 5.8 2.5 21.8 3.6 2.1 –4.5 53.5 1.4 .. Sloveniac 4.3 3.7 –0.6 24.9 8.9 6.1 –3.5 .. 1.8 .. Solomon Islands 3.4 3.9 6.8 .. .. –4.5 .. .. 5.4 43.0 Somalia .. .. .. .. .. .. .. .. .. .. South Africa 2.1 4.0 2.7 14.4 1.2 –5.6 –4.5 .. 3.3 71.1 South Sudan .. .. .. .. .. .. .. .. 47.3 .. Spainc 2.7 2.9 –1.1 21.1 8.0 0.8 –8.8 65.9 1.4 .. Sri Lanka 5.3 5.5 7.4 25.7 21.1 –3.9 –6.1 79.2 6.9 39.4 St. Kitts and Nevis 4.6 3.4 0.3 20.5 .. –8.2 11.2 .. 0.7 156.5 St. Lucia 3.5 2.8 –0.4 16.8 .. –7.5 –6.5 .. 1.5 91.5 St. Martin .. .. .. .. .. .. .. .. .. .. St. Vincent & the Grenadines 3.1 4.2 –0.2 –4.7 .. –29.6 –2.1 .. 0.8 73.6 Sudan 5.5h 7.0h –4.6i 13.6 8.6 –6.7 .. .. 30.0 21.0 Suriname 0.8 5.2 4.1 .. .. –3.7 –1.2 .. 1.9 51.5 Swaziland 3.2 2.5 1.3 19.9 12.6 6.3 .. .. 5.6 30.6 Sweden 2.3 2.4 2.2 28.8 17.9 6.0 –0.3 35.3 0.0 85.7 Switzerland 1.2 2.2 1.8 37.5 20.7 14.2 0.6 24.3 –0.2 182.3 Syrian Arab Republic 5.1 5.0 .. .. .. .. .. .. 36.7 .. Tajikistan –10.4 8.5 7.2 16.6 13.0 –3.2 .. .. 5.0 21.0
- 110. 86 World Development Indicators 2015 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 % of GDP % of GNI % of GDP % of GDP % of GDP % growth % of GDP 1990–2000 2000–09 2009–13 2013 2013a 2013 2012 2012 2013 2013 Tanzaniaj 3.0 6.9 6.6 17.3 11.7 –10.8 –5.3 .. 7.9 23.1 Thailand 4.2 4.6 4.2 28.5 11.8 –0.7 –2.2 .. 2.2 134.5 Timor-Leste .. 3.4 11.0 249.0 .. 216.3 .. .. 11.2 32.0 Togo 3.5 2.2 5.1 .. .. .. –6.1 .. 1.8 45.2 Tonga 2.6 0.8 1.9 18.1 .. –9.6 .. .. 0.7 44.0 Trinidad and Tobago 3.2 7.4 0.3 .. .. 12.2 –1.6 .. 5.2 60.7 Tunisia 4.7 4.7 2.4 13.0 –2.7 –8.3 –5.0 44.5 6.1 66.7 Turkey 3.9 4.9 5.9 13.1 9.4 –7.9 –0.6 45.1 7.5 60.7 Turkmenistan –3.2 8.0 11.6 .. .. .. .. .. .. .. Turks and Caicos Islands .. .. .. .. .. .. .. .. .. .. Tuvalu 3.2 1.2 2.2 .. .. .. .. .. .. .. Uganda 7.0 7.8 5.9 21.5 4.7 –8.1 –2.1 33.2 5.5 20.8 Ukraine –9.3 5.7 2.8 10.4 –5.4 –9.3 –4.0 33.5 –0.3 62.5 United Arab Emirates 4.8 5.3 4.2 .. .. .. –0.2 .. 1.1 61.2 United Kingdom 2.6 2.2 1.4 12.8 4.0 –4.3 –5.5 97.2 2.6 150.9 United States 3.6 2.1 2.1 17.4 5.0 –2.4 –7.6 94.3 1.5 88.4 Uruguay 3.9 3.1 5.8 17.2 9.0 –5.4 –2.1 44.5 8.6 46.2 Uzbekistan –0.2 6.9 8.2 .. .. .. .. .. .. .. Vanuatu 3.4 3.9 1.6 20.5 .. –3.7 –2.3 .. 1.4 70.9 Venezuela, RB 1.6 5.1 2.9 25.6 13.4 2.9 .. .. 40.6 44.8 Vietnam 7.9 6.8 5.8 32.0 16.3 5.5 .. .. 6.6 117.0 Virgin Islands (U.S.) .. .. .. .. .. .. .. .. .. .. West Bank and Gaza 14.3 2.7 6.0 5.6 .. –20.3 .. .. .. 15.6 Yemen, Rep. 5.6 4.0 –2.7 .. .. –4.3 .. .. 11.0 39.1 Zambia 1.6 7.2 7.3 .. .. 0.7 4.1 .. 7.0 21.4 Zimbabwe 2.5 –7.2 9.9 .. .. .. .. .. 1.6 .. World 2.9 w 2.9 w 2.8 w 22.5 w 10.9 w Low income 2.7 5.4 6.3 24.9 9.4 Middle income 4.3 6.4 5.8 31.0 18.7 Lower middle income 3.5 6.4 5.7 28.6 17.2 Upper middle income 4.6 6.4 5.9 31.8 18.9 Low & middle income 4.3 6.4 5.8 31.0 18.6 East Asia & Pacific 8.5 9.4 8.1 46.3 27.7 Europe & Central Asia 0.2 5.4 4.3 17.0 7.6 Latin America & Carib. 3.1 3.6 3.8 17.7 5.5 Middle East & N. Africa 3.9 4.9 2.3 .. 8.1 South Asia 5.6 7.2 6.6 30.7 18.9 Sub-Saharan Africa 2.3 5.7 4.2 19.4 6.3 High income 2.6 2.1 1.8 20.8 7.7 Euro area 2.1 1.5 0.6 22.0 8.7 a. Includes data on pollution damage for 2010, the most recent year available. b. Data for Argentina are officially reported by the National Statistics and Censuses Institute of Argentina. The International Monetary Fund has, however, issued a declaration of censure and called on Argentina to adopt remedial measures to address the quality of official GDP and consumer price index data. Alternative data sources have shown significantly lower real growth and higher inflation than the official data since 2008. In this context, the World Bank is also using alternative data sources and estimates for the surveillance of macroeconomic developments in Argentina. c. As members of the European Monetary Union, these countries share a single currency, the euro. d. Refers to the area controlled by the government of the Republic of Cyprus. e. Excludes Abkhazia and South Ossetia. f. Excludes Transnistria. g. Includes Former Spanish Sahara. h. Includes South Sudan. i. Includes South Sudan until July 9, 2011. j. Covers mainland Tanzania only.
- 111. World Development Indicators 2015 87Economy States and markets Global links Back Economy 4 Economic data are organized by several different accounting conven- tions: the system of national accounts, the balance of payments, 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 implemen- tation 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 pay- ments methodology (BPM6), but many countries are still using the previous version. Similarly, the standards and definitions for govern- ment finance statistics were updated in 2001, but several countries still report using the 1986 version. For individual country informa- tion 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 (2014). 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 developing 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 switches the reference year to 2005. 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 editions 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 posi- tive 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, sug- gesting 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 damages from carbon dioxide emissions and local air pollution. Damages from local air pollution include damages from exposure to household air pollution and ambient concentrations of very fine particulate matter in urban and rural areas. By accounting for the depletion of natural resources and the degradation of the environ- ment, adjusted net savings go 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 external 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 same extent as the foreign exchange earned by a goods exporter. About the data
- 112. 88 World Development Indicators 2015 Front User guide World view People Environment? 4 Economy 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 implemented BPM6 in its online databases and publications from April 2013. Balance 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 Indi- cators 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. 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 out- put. 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 out- puts and subtracting intermediate inputs. • Gross savings are the difference between gross national income and public and private consumption, 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 expenditure 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
- 113. World Development Indicators 2015 89Economy States and markets Global links Back Economy 4 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 rev- enue 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 particular 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). Data on consumption of fixed capital are from the United Nations Statis- tics Division’s National Accounts Statistics: Main Aggregates and Detailed Tables, the Organization for Economic Co-operation and Development’s National Accounts Statistics database, and the Penn World Table (Feenstra, Inklaar, and Timmler 2013), with missing data estimated by World Bank staff. Data on education expenditure are from the United Nations Educational, Scientific and Cultural Organization Institute for Statistics, with missing data estimated by World Bank staff. Data on forest, energy, and mineral deple- tion are based on the sources and methods described in World Bank (2011). Additional data on energy commodity production and reserves are from the United States Energy Information Administra- tion. Estimates of damages from carbon dioxide emissions follow the method of Fankhauser (1994) using data from the International Energy Agency’s CO2 Emissions from Fuel Combustion Statistics database. Data on exposure to household air pollution and ambient particulate matter pollution are from the Institute for Health Metrics and Evaluation’s Global Burden of Disease 2010 study. Data on current account balances 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, Eduardo Levy Yeyati, Samuel Pienknagura. 2013. Latin America’s Deceleration and the Exchange Rate Buffer. Semian- nual Report, Office of the Chief Economist. Washington, DC: World Bank. Fankhauser, Samuel. 1994. “The Social Costs of Greenhouse Gas Emissions: An Expected Value Approach.” Energy Journal 15 (2): 157–84. Feenstra, Robert C., Robert Inklaar, and Marcel P. Timmer. 2013. “The Next Generation of the Penn World Table.” [www.ggdc.net/pwt]. 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. Institute for Health Metrics and Evaluation. 2010. Global Burden of Disease data. University of Washington, Seattle. [https://www .healthdata.org/gbd/data]. International Energy Agency. Various years. IEA CO2 Emissions from Fuel Combustion Statistics database. [http://dx.doi.org/10.1787 /co2-data-en]. Paris. Organisation for Economic Co-operation and Development. Vari- ous years. National Accounts Statistics database. [http://dx.doi .org/10.1787/na-data-en]. Paris. United Nations Statistics Division. Various years. National Accounts Statistics: Main Aggregates and Detailed Tables. Parts 1 and 2. New York: United Nations. United States Energy Information Administration. Various years. Inter- national Energy Statistics database. [http://www.eia.gov/cfapps /ipdbproject/IEDIndex3.cfm]. Washington, DC. World Bank. 2011. The Changing Wealth of Nations: Measuring Sustain- able Development for the New Millennium. Washington, DC. ———. 2015. Global Economic Prospects: Having Fiscal Space and Using It. Washington, DC. ———. Various years. World Development Indicators. Washington, DC.
- 114. 90 World Development Indicators 2015 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 domestic product, % growth NY.GDP.MKTP.KD.ZG Gross national income, $ NY.GNP.MKTP.CD Gross national income, % growth NY.GNP.MKTP.KD.ZG 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 Adjusted net national income, % growth 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
- 115. World Development Indicators 2015 91Economy 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
- 116. 92 World Development Indicators 2015 Front User guide World view People Environment? STATES AND MARKETS
- 117. World Development Indicators 2015 93Economy States and markets Global links Back States and markets includes indicators of private investment and performance, the public sector’s role in nurturing investment and growth, and the quality and availability of infrastructure essen- tial for growth. These indicators measure the business environment, government functions, financial system development, infrastructure, information and communication technology, science and technology, government and policy performance, and conditions in fragile countries with weak institutions. Doing Business measures business regula- tions that affect domestic small and medium- size firms in 11 areas across 189 economies. It provides quantitative measures of regulations for starting a business, dealing with construc- tion permits, getting electricity, registering prop- erty, getting credit, protecting minority investors, paying taxes, trading across borders, enforcing contracts, and resolving insolvency. It also mea- sures labor market regulations. Since 2004, Doing Business has captured more than 2,400 regulatory reforms that make it easier to do business. From June 1, 2013, to June 1, 2014, 123 economies implemented at least one reform in measured areas—230 in total. More than 63 percent of these reforms reduced the complexity and cost of regulatory pro- cesses; the rest strengthened legal institutions. More than 80 percent of the economies covered by Doing Business saw their distance to frontier score improve—it is now easier to do business in most parts of the world. Singapore continues to have the most business-friendly regulations. Doing Business 2015 introduces three improvements: a revised calculation of the ease of doing business ranking, an expanded sam- ple of cities covered in large economies, and a broader scope of indicator sets. First, the report changes the basis for the rank- ing, from the percentile rank to the distance to frontier score, which benchmarks economies with respect to a measure of regulatory best practice— showing the gap between each economy’s perfor- mance and the best performance on each indica- tor. This measure captures more information than the percentile rank because it shows not only how economies are ordered on their performance on the indicators, but also how far apart they are. Second, the report extends its coverage to include the second largest business city in econ- omies with a population of more than 100 mil- lion (Bangladesh, Brazil, China, India, Indonesia, Japan, Mexico, Nigeria, Pakistan, the Russian Federation, and the United States). Third, the report expands the data in 3 of the 11 topics covered, with plans to expand on 5 top- ics next year. These improvements provide a new conceptual framework in which the emphasis on regulatory efficiency is complemented by greater emphasis on regulatory quality. Doing Business 2015 introduces a new measure of quality in the resolving insolvency indicator set and expands the measures of quality in the getting credit and protecting minority investors’ indicator sets. Doing Business 2016 will add measures of regu- latory quality to the indicator sets for dealing with construction permits, getting electricity, register- ing property, paying taxes, and enforcing con- tracts. The results so far suggest that efficiency and quality go hand in hand. This year States and markets contains a new table, table 5.14 on statistical capacity. The main Statistical Capacity Indicator and its sub- categories assess the changes in national sta- tistical capacity, thus helping national statistics offices and governments identify gaps in their capability to collect, produce, and use data. 5
- 118. 94 World Development Indicators 2015 Highlights Front User guide World view People Environment? Asia dominates the information and communications technology goods trade 0 5 10 15 20 25 Middle East & North Africa Sub-Saharan Africa South Asia Europe & Central Asia Latin America & Caribbean East Asia & Pacific Information and communication technology goods as a share of goods exported and imported, 2012 (%) Exports Imports Information and communications technology (ICT) goods—products such as mobile phones, smartphones, laptops, tablets, integrated circuits, and various other parts and components—now account for more than 10 percent of merchandise trade worldwide. Seven of the top ten export economies in 2012 and six of the top ten import econo- mies were in East Asia and Pacific. According to the United Nations Conference on Trade and Development, Asia’s rising share in the manufacture and trade of ICT goods has been fueled by the cross- border transport of intermediate goods within intraregional production networks, which resulted in considerable flows between developing countries. In monetary terms China led the ICT goods trade in 2012 with exports of $508 billion and imports of $356 billion, followed by the United States with exports of $139 billion and imports of $299 billion. Source: Online table 5.12. Private investment goes primarily to energy and telecommunications Private investment in developing countries, by sector ($ billions) Water Transport Telecommunications Energy 0 25 50 75 100 201320122011201020092008200720062005 Infrastructure is a key element in the enabling environment for economic growth. The continuing global recession will curtail maintenance and new investment in infrastructure as governments face shrinking bud- gets and declining private financial flows. In 2013 private participation in infrastructure in developing countries fell 23 percent from 2012, to $150.3 billion. Investment in the energy sector dropped 23 percent from $73.6 billion in 2012 to $56.4 billion in 2013, and investment in the telecom sector dropped 6 percent to $57.3 billion. In 2013 the transport and water sectors both saw a 40 percent decline in private investment. Between 2005 and 2013 the transport sector accounted for an average of 20 percent of total private investment ($34.0 billion in 2013). The water and sanitation sector remained low at average of 2 percent, or $3.2 billion a year. Source: Online table 5.1. Research and development expenditures are rising steadily in selected economies 0 1 2 3 4 2011201020092008200720062005200420032002 Research and development expenditures (% of GDP) Japan United States European Union China Brazil India Research and development (R&D) intensity, measured by the resources spent on R&D activities as a share of GDP, has risen gradually since 2002. In 2011 high-income countries spent 2.5 percent of GDP on R&D activities, compared with developing countries’ 1.2 percent. In some developing countries the rise in gross domestic expenditure on R&D has been related to strong economic growth—for example, climb- ing more than 70 percent since 2002 to 1.84 percent in 2011 in China. The United Nations Educational, Scientific and Cultural Organization reported that developing countries, including Brazil, China, and India, are witnessing sustained domestic growth and moving upstream in the value chain (UNESCO 2010). These economies once served as a repository for the outsourcing of manufacturing activities and now undertake autonomous technology development, product develop- ment, design, and applied research. Source: Online table 5.13.
- 119. World Development Indicators 2015 95Economy States and markets Global links Back Regulation places a heavy burden on businesses in Latin America and the Caribbean Firms in Latin America and the Caribbean report that their senior man- agers spend more time dealing with the requirements of government regulations than firms in other regions. According to Enterprise Sur- veys, in Latin America and the Caribbean 14 percent of senior manage- ment’s time is spent dealing with regulation, double the 7 percent in Sub-Saharan Africa and 6 percent in East Asia and Pacific and close to triple the less than 5 percent in the Middle East and North Africa and South Asia. However, the time varies greatly within regions. Firms in smaller Caribbean countries spend 6 percent of management time on regulations, compared with 16 percent for firms in the rest of the region. Smaller economies tend to rely on trade, and their efforts focus on maintaining a business-friendly environment. Senior management time spent dealing with the requirements of government regulation (%) Exports Imports 0 5 10 15 South Asia Middle East & North Africa East Asia & Pacific Sub-Saharan Africa Europe & Central Asia Latin America & Caribbean Source: Online table 5.2. Managing the public sector effectively and adopting good policy are not easy The links among weak institutions, poor development outcomes, and the risk of conflict are often evident in countries with fragile situations. A capable and accountable state creates opportunities for poor peo- ple, provides better services, and improves development outcomes. A total of 39 Sub-Saharan African countries have been part of the World Bank’s Country Policy and Institutional Assessment exercise, which determines eligibility for the World Bank’s International Development Association lending. In 2013, 7 countries showed improvement in the public sector and institutions cluster score from 2012, 9 countries were downgraded, and 23 remained unchanged. Cabo Verde (4.1 on a scale of 1, low, to 6, high) and Tanzania (3.4) were the top perform- ers, and Chad and the Democratic Republic of the Congo improved the most, with both increasing their scores 0.2 point, from 2.2 to 2.4. Source: Online table 5.9. The statistical capacity of developing countries has improved steadily over the last 10 years The Statistical Capacity Indicator is a useful monitoring and tracking tool for assessing changes in national statistical capacity, as well as for helping governments identify gaps in their capability to collect, produce, and use data. The combined Statistical Capacity Indicator of all developing countries has improved since assessment began in 2004, from 65 to 68 (on a scale of 0, low, to 100, high). The average scores increased from 58 to 62 for countries eligible for International Development Association funding (see http://data.worldbank.org/ about/country-and-lending-groups) and from 73 to 75 for those eligible for International Bank for Reconstruction and Development funding. However, continued efforts are needed to help countries adhere to international statistical standards and methods and to improve data availability and periodicity. Source: Online table 5.14. 1 2 3 4 5 6 Chad Congo, Dem. Rep. Guinea Liberia Côte d’Ivoire Tanzania Cabo Verde Public sector and institutions cluster score (1, low, to 6, high) 2012 2013 55 60 65 70 75 80 20142013201220112010200920082007200620052004 Statistical Capacity Indicator (0, low, to 100, high) International Bank for Reconstruction and Development–eligible countries All developing countries International Development Association–eligible countries
- 120. Dominican Republic Trinidad and Tobago Grenada St. Vincent and the Grenadines Dominica Puerto Rico, US St. Kitts and Nevis Antigua and Barbuda St. Lucia Barbados R.B. de Venezuela U.S. Virgin Islands (US) Martinique (Fr) Guadeloupe (Fr) Curaçao (Neth) St. Martin (Fr) Anguilla (UK) St. Maarten (Neth) Samoa Tonga Fiji Kiribati Haiti Jamaica Cuba The Bahamas United States Canada Panama Costa Rica Nicaragua Honduras El Salvador Guatemala Mexico Belize Colombia Guyana Suriname R.B. de Venezuela Ecuador Peru Brazil Bolivia Paraguay Chile Argentina Uruguay American Samoa (US) French Polynesia (Fr) French Guiana (Fr) Greenland (Den) Turks and Caicos Is. (UK) IBRD 41454 Fewer than 20 20–39 40–59 60–79 80 or more No data Internet users INDIVIDUALS USING THE INTERNET AS A SHARE OF POPULATION, 2013 Caribbean inset Bermuda (UK) 96 World Development Indicators 2015 The digital and information revolution has changed the way the world learns, communicates, does busi- ness, and treats illnesses. Information and communi- cation technologies offer vast opportunities for prog- ress in all walks of life in all countries—opportunities for economic growth, improved health, better service delivery, learning through distance education, and social and cultural advances. The Internet delivers information to schools and hospitals, improves public and private services, and increases productivity and participation. Through mobile phones, Internet access is expanding in developing countries. The mobility, ease of use, flexible deployment, and declining rollout costs of wireless technologies enable mobile commu- nications to reach rural populations. According to the International Telecommunication Union, by the end of 2014 the number of Internet users worldwide will have reached 3 billion. Front User guide World view People Environment?
- 121. Romania Serbia Greece San Marino BulgariaUkraine Germany FYR Macedonia Croatia Bosnia and Herzegovina Czech Republic Poland Hungary Italy Austria Slovenia Slovak Republic Kosovo Montenegro Albania Burkina Faso Palau Federated States of Micronesia Marshall Islands Nauru Kiribati Solomon Islands Tuvalu Vanuatu Fiji Norway Iceland Ireland United Kingdom Sweden Finland Denmark Estonia Latvia Lithuania Poland Belarus Ukraine Moldova Romania Bulgaria Greece Italy Germany Belgium The Netherlands Luxembourg Switzerland Liechtenstein France AndorraPortugal Spain Monaco Malta Morocco Tunisia Algeria Mauritania Mali Senegal The Gambia Guinea- Bissau Guinea Cabo 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 Mozambique Madagascar Zimbabwe Botswana Namibia Swaziland LesothoSouth Africa Mauritius Seychelles Comoros Rep. of Yemen Oman United Arab Emirates Qatar Bahrain Saudi Arabia Kuwait Israel 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 Guinea Indonesia Australia New Zealand JapanRep.of Korea Dem.People’s Rep.of Korea Mongolia China Russian Federation Brunei Darussalam Sudan South Sudan Timor-Leste N. Mariana Islands (US) Guam (US) New Caledonia (Fr) Greenland (Den) West Bank and Gaza Western Sahara Réunion (Fr) Mayotte (Fr) Europe inset World Development Indicators 2015 97 Latin America and the Caribbean and Europe and Central Asia have the highest Internet user penetration rate among developing country regions: 46 percent in 2013. In Sub-Saharan Africa 17 percent of the population was online at the end of 2013, up from 10 percent in 2010. The number of people using the Internet continues to grow worldwide. Some 2.7 billion people—38 percent of the population—were online in 2013. The number of Internet users in developing countries tripled from 440 million in 2006 to 1.7 billion in 2013. Economy States and markets Global links Back
- 122. 98 World Development Indicators 2015 Front User guide World view People Environment? Business entry density Time required to start a business Domestic credit provided by financial sector Tax revenue collected by central government Military expenditures Electric power consumption per capita Mobile cellular subscriptionsa Individuals using the Interneta High-technology exports Statistical Capacity Indicator per 1,000 people ages 15–64 days % of GDP % of GDP kilowatt-hours per 100 people % of population % of manufactured exports (0, low, to 100, high)% of GDP 2012 June 2014 2013 2012 2013 2011 2013 2013 2013 2014 Afghanistan 0.15 7 –3.9 7.5b 6.4 .. 71 6 .. 54.4 Albania 0.88 5 66.9 .. 1.3 2,195 116 60 0.5 75.6 Algeria 0.53 22 3.0 37.4 5.0 1,091 101 17 0.2 52.2 American Samoa .. .. .. .. .. .. .. .. .. .. Andorra .. .. .. .. .. .. 81 94 .. .. Angola .. 66 18.9 18.8b 4.9 248 62 19 .. 48.9 Antigua and Barbuda .. 21 90.0 18.6b .. .. 127 63 0.0 58.9 Argentina 0.47 25 33.3 .. 0.7 2,967 163 60 9.8 83.0 Armenia 1.55 3 46.0 18.7b 4.1 1,755 112 46 2.9 87.8 Aruba .. .. 56.0 .. .. .. 135 79 10.2 .. Australia 12.16 3 159.1 21.4 1.6 10,712 107 83 12.9 .. Austria 0.50 22 127.9 18.3 0.8 8,388 156 81 13.7 .. Azerbaijan 0.70 5 25.5 13.0b 4.7 1,705 108 59 13.4 70.0 Bahamas, The .. 24 104.9 15.5b .. .. 76 72 0.0 .. Bahrain .. 9 78.6 1.1 3.8 10,018 166 90 0.2 .. Bangladesh 0.09 20 57.9 8.7b 1.2 259 74 7 0.2 80.0 Barbados .. 18 .. 25.2 .. .. 108 75 15.3 .. Belarus 1.14 9 39.9 15.1b 1.3 3,628 119 54 4.4 87.8 Belgium 2.48 4 111.2 24.9 1.0 8,021 111 82 11.4 .. Belize 4.31 43 58.3 22.6b 1.0 .. 53 32 0.0 55.6 Benin .. 12 21.5 15.6 1.0 .. 93 5 1.2 65.6 Bermuda .. .. .. .. .. .. 144 95 12.4 .. Bhutan 0.20 17 50.2 .. .. .. 72 30 0.0 78.9 Bolivia 0.56 49 50.4 .. 1.5 623 98 40 9.4 76.7 Bosnia and Herzegovina 0.70 37 67.7 20.9 1.1 3,189 91 68 2.3 72.2 Botswana 12.30 60 13.6 27.1b 2.0 1,603 161 15 0.4 51.1 Brazil 2.17 84 110.1 15.4b 1.4 2,438 135 52 9.6 75.6 Brunei Darussalam .. 101 20.8 .. 2.6 8,507 112 65 15.2 .. Bulgaria 9.03 18 71.1 19.0b 1.5 4,864 145 53 8.0 84.4 Burkina Faso 0.15 13 22.8 15.0 1.3 .. 66 4 13.7 71.1 Burundi .. 5 23.9 .. 2.2 .. 25 1 2.7 54.4 Cabo Verde .. 10 82.8 17.8b 0.5 .. 100 38 0.6 68.9 Cambodia .. 101 40.3 11.6 1.6 164 134 6 0.2 76.7 Cameroon .. 15 15.5 .. 1.3 256 70 6 3.7 56.7 Canada 1.07 5 .. 11.7 1.0 16,473 81 86 14.0 .. Cayman Islands .. .. .. .. .. .. 168 74 .. .. Central African Republic .. 22 36.7 9.5 .. .. 29 4 0.0 58.9 Chad .. 60 7.0 .. 2.0 .. 36 2 .. 63.3 Channel Islands .. .. .. .. .. .. .. .. .. .. Chile 5.69 6 115.5 19.0 2.0 3,568 134 67 4.8 95.6 China .. 31 163.0 10.6b 2.1c 3,298 89 46 27.0 70.0 Hong Kong SAR, China 28.12 3 224.0 .. .. 5,949 237 74 16.2 .. Macao SAR, China .. .. –10.7 37.0b .. .. 304 66 0.0 .. Colombia 2.00 11 70.1 13.2 3.4 1,123 104 52 7.4 81.1 Comoros .. 15 26.9 .. .. .. 47 7 .. 40.0 Congo, Dem. Rep. 0.02 16 7.3 8.4b 1.3 105 42 2 .. 57.0 Congo, Rep. .. 53 –7.2 .. .. 172 105 7 1.6 47.8 5 States and markets
- 123. World Development Indicators 2015 99Economy States and markets Global links Back States and markets 5 Business entry density Time required to start a business Domestic credit provided by financial sector Tax revenue collected by central government Military expenditures Electric power consumption per capita Mobile cellular subscriptionsa Individuals using the Interneta High-technology exports Statistical Capacity Indicator per 1,000 people ages 15–64 days % of GDP % of GDP kilowatt-hours per 100 people % of population % of manufactured exports (0, low, to 100, high)% of GDP 2012 June 2014 2013 2012 2013 2011 2013 2013 2013 2014 Costa Rica 3.55 24 56.5 13.6 .. 1,844 146 46 43.3 77.8 Côte d’Ivoire .. 7 26.9 14.2 1.5 212 95 3 1.3 46.7 Croatia 2.82 15 94.1 19.6b 1.7 3,901 115 67 8.6 83.3 Cuba .. .. .. .. 3.3 1,327 18 26 .. .. Curaçao .. .. .. .. .. .. 128 .. .. .. Cyprus 22.51 8 335.8 25.5 2.1 4,271 96 65 7.2 .. Czech Republic 2.96 19 67.0 13.4b 1.0 6,289 128 74 14.8 .. Denmark 4.36 6 199.6 33.4 1.4 6,122 127 95 14.3 .. Djibouti .. 14 33.9 .. .. .. 28 10 .. 45.6 Dominica .. 12 61.9 21.8b .. .. 130 59 8.8 55.6 Dominican Republic 1.05 20 47.7 12.2 0.6 893 88 46 2.7 78.9 Ecuador .. 56 29.6 .. 3.0 1,192 111 40 4.4 70.0 Egypt, Arab Rep. .. 8 86.2 13.2b 1.7 1,743 122 50 0.5 90.0 El Salvador 0.48 17 72.1 14.5 1.1 830 136 23 4.4 91.1 Equatorial Guinea .. 135 –3.5 .. .. .. 67 16 .. 34.0 Eritrea .. 84 98.3 .. .. 49 6 1 .. 31.1 Estonia .. 5 71.6 16.3 1.9 6,314 160 80 10.6 86.7 Ethiopia .. 15 .. 9.2b 0.8 52 27 2 2.4 61.1 Faeroe Islands .. .. .. .. .. .. 121 90 .. .. Fiji .. 59 121.8 .. 1.4 .. 106 37 2.2 71.1 Finland 2.32 14 104.9 20.0 1.2 15,738 172 92 7.2 .. France 2.88 5 130.8 21.4 2.2 7,292 98 82 25.9 .. French Polynesia .. .. .. .. .. .. 86 57 7.8 .. Gabon .. 50 11.7 .. 1.3 907 215 9 .. 42.2 Gambia, The .. 26 50.1 .. .. .. 100 14 7.3 66.7 Georgia 4.86 2 42.9 24.1b 2.7 1,918 115 43 2.5 82.2 Germany 1.29 15 113.5 11.5 1.3 7,081 121 84 16.1 .. Ghana 0.90 14 34.8 14.9b 0.5 344 108 12 6.1 62.2 Greece 0.77 13 134.3 22.4 2.5 5,380 117 60 7.5 .. Greenland .. .. .. .. .. .. 106 66 8.0 .. Grenada .. 15 80.0 18.7b .. .. 126 35 .. 44.4 Guam .. .. .. .. .. .. .. 65 .. .. Guatemala 0.52 19 40.6 10.8b 0.5 539 140 20 4.7 68.9 Guinea 0.23 8 32.2 .. .. .. 63 2 .. 52.2 Guinea-Bissau .. 9 18.6 .. 1.7 .. 74 3 .. 43.3 Guyana .. 19 55.3 .. 1.1 .. 69 33 0.0 58.9 Haiti 0.06 97 20.4 .. .. 32 69 11 .. 47.8 Honduras .. 14 57.3 14.7 1.2 708 96 18 2.4 73.3 Hungary 4.75 5 64.7 22.9 0.9 3,895 116 73 16.3 85.6 Iceland 8.17 4 130.9 22.3 0.1 52,374 108 97 15.5 .. India 0.12 28 77.2 10.8b 2.4 684 71 15 8.1 81.1 Indonesia 0.29 53 45.6 .. 0.9 680 125 16 7.1 83.3 Iran, Islamic Rep. .. 12 .. .. 2.1 2,649 84 31 4.1 73.3 Iraq 0.13 29 –1.4 .. 3.4 1,343 96 9 .. 46.7 Ireland 4.50 6 186.1 22.0 0.5 5,701 103 78 22.4 .. Isle of Man 45.27 .. .. .. .. .. .. .. .. .. Israel 2.96 13 .. 22.1 5.6 6,926 123 71 15.6 ..
- 124. 100 World Development Indicators 2015 Front User guide World view People Environment? 5 States and markets Business entry density Time required to start a business Domestic credit provided by financial sector Tax revenue collected by central government Military expenditures Electric power consumption per capita Mobile cellular subscriptionsa Individuals using the Interneta High-technology exports Statistical Capacity Indicator per 1,000 people ages 15–64 days % of GDP % of GDP kilowatt-hours per 100 people % of population % of manufactured exports (0, low, to 100, high)% of GDP 2012 June 2014 2013 2012 2013 2011 2013 2013 2013 2014 Italy 1.91 5 161.8 22.4 1.5 5,515 159 58 7.3 .. Jamaica 1.11 15 51.4 27.1 0.8 1,553 102 38 0.7 78.9 Japan 1.15 11 366.5 10.1 1.0 7,848 118 86 16.8 .. Jordan 0.98 12 111.9 15.3 3.6 2,289 142 44 1.6 74.4 Kazakhstan 1.71 10 39.1 .. 1.2 4,893 185 54 36.9 88.9 Kenya .. 30 42.8 15.9b 1.6 155 72 39 .. 54.4 Kiribati 0.11 31 .. 16.1b .. .. 17 12 38.5 35.6 Korea, Dem. People’s Rep. .. .. .. .. .. 739 10 0 .. .. Korea, Rep. 2.03 4 155.9 14.4b 2.6 10,162 111 85 27.1 .. Kosovo 1.22 11 23.3 .. .. 2,947 .. .. .. 33.3 Kuwait .. 31 47.9 0.7b 3.2 16,122 190 75 1.4 .. Kyrgyz Republic 0.92 8 .. 18.1b 3.2 1,642 121 23 5.3 86.7 Lao PDR 0.10 92 .. 14.8b 0.2 .. 68 13 .. 73.3 Latvia 11.63 13 58.6 13.8b 1.0 3,264 228 75 13.0 86.7 Lebanon .. 9 187.6 15.5 4.4 3,499 81 71 2.2 62.2 Lesotho 1.49 29 1.7 .. 2.1 .. 86 5 .. 72.2 Liberia .. 5 38.7 20.9b 0.7 .. 59 5 .. 46.7 Libya .. 35 –51.1 .. 3.6 3,926 165 17 .. 28.9 Liechtenstein .. .. .. .. .. .. 104 94 .. .. Lithuania 4.71 4 51.0 13.4 0.8 3,530 151 68 10.3 83.3 Luxembourg 20.98 19 163.9 25.5 0.5 15,530 149 94 8.1 .. Macedonia, FYR 3.60 2 52.4 16.7b 1.2 3,881 106 61 3.7 84.4 Madagascar 0.05 8 15.6 10.1 0.5 .. 37 2 0.6 62.2 Malawi .. 38 31.2 .. 1.4 .. 32 5 6.0 75.6 Malaysia 2.28 6 142.6 16.1b 1.5 4,246 145 67 43.5 74.4 Maldives .. 9 86.9 15.5b .. .. 181 44 .. 66.7 Mali .. 11 20.9 15.6 1.4 .. 129 2 1.2 66.7 Malta 13.61 35 146.7 27.0 0.6 4,689 130 69 38.6 .. Marshall Islands .. 17 .. .. .. .. .. 12 .. 46.7 Mauritania .. 9 39.1 .. 3.6 .. 103 6 .. 59.0 Mauritius 7.40 6 122.4 19.0 0.2 .. 123 39 0.6 85.6 Mexico 0.88 6 49.5 .. 0.6 2,092 86 43 15.9 85.6 Micronesia, Fed. Sts. .. 16 –27.2 .. .. .. 30 28 .. 36.7 Moldova .. 6 44.0 18.6b 0.3 1,470 106 49 2.4 94.4 Monaco .. .. .. .. .. .. 94 91 .. .. Mongolia .. 11 63.6 18.2b 1.1 1,577 124 18 15.9 83.3 Montenegro 10.66 10 61.0 .. 1.6 5,747 160 57 .. 75.6 Morocco .. 11 115.5 24.5 3.9 826 129 56 6.4 78.9 Mozambique .. 13 29.3 20.8b .. 447 48 5 13.4 74.4 Myanmar .. 72 .. .. .. 110 13 1 .. 46.7 Namibia 0.85 66 49.7 23.1 3.0 1,549 118 14 1.7 48.9 Nepal 0.66 17 69.1 13.9b 1.4 106 77 13 0.3 65.6 Netherlands 4.44 4 193.0 19.7 1.2 7,036 114 94 20.4 .. New Caledonia .. .. .. .. .. .. 94 66 10.6 .. New Zealand 15.07 1 .. 29.3 1.0 9,444 106 83 10.3 .. Nicaragua .. 13 44.8 14.8b 0.8 522 112 16 0.4 65.6 Niger .. 15 11.8 .. 1.1 .. 39 2 52.4 67.8
- 125. World Development Indicators 2015 101Economy States and markets Global links Back States and markets 5 Business entry density Time required to start a business Domestic credit provided by financial sector Tax revenue collected by central government Military expenditures Electric power consumption per capita Mobile cellular subscriptionsa Individuals using the Interneta High-technology exports Statistical Capacity Indicator per 1,000 people ages 15–64 days % of GDP % of GDP kilowatt-hours per 100 people % of population % of manufactured exports (0, low, to 100, high)% of GDP 2012 June 2014 2013 2012 2013 2011 2013 2013 2013 2014 Nigeria 0.91 31 22.3 1.6 0.5 149 73 38 2.7 72.2 Northern Mariana Islands .. .. .. .. .. .. .. .. .. .. Norway 7.83 5 .. 27.3 1.4 23,174 116 95 19.1 .. Oman .. 7 35.7 2.5b 11.5 6,292 155 66 3.4 .. Pakistan 0.04 19 49.0 10.1b 3.5 449 70 11 1.9 74.4 Palau .. 28 .. .. .. .. 86 .. .. 36.7 Panama 14.10 6 67.6 .. .. 1,829 163 43 0.0 82.0 Papua New Guinea .. 53 48.8 .. 0.6 .. 41 7 3.5 46.7 Paraguay .. 35 38.3 12.8b 1.6 1,228 104 37 7.5 71.1 Peru 3.83 26 22.0 16.5b 1.4 1,248 98 39 3.6 99.0 Philippines 0.27 34 51.9 12.9b 1.3 647 105 37 47.1 77.8 Poland .. 30 65.8 16.0 1.8 3,832 149 63 7.9 78.9 Portugal 3.62 3 183.3 20.3 2.1 4,848 113 62 4.3 .. Puerto Rico .. 6 .. .. .. .. 84 74 .. .. Qatar 1.74 9 73.9 14.7b .. 15,755 153 85 0.0 .. Romania 4.12 8 52.0 18.8 1.3 2,639 106 50 5.7 87.8 Russian Federation 4.30 11 48.3 15.1 4.2 6,486 153 61 10.0 84.0 Rwanda 1.07 7 .. 13.7b 1.1 .. 57 9 4.4 78.9 Samoa 1.04 9 40.8 0.0b .. .. .. 15 0.6 53.3 San Marino .. 40 .. .. .. .. 117 51 .. .. São Tomé and Príncipe 3.75 4 28.8 14.0 .. .. 65 23 14.1 68.9 Saudi Arabia .. 21 –7.9 .. 9.0 8,161 184 61 0.7 .. Senegal 0.27 6 35.1 19.2 0.0 187 93 21 0.7 73.3 Serbia 1.68 12 49.5 19.7b 2.0 4,490 119 52 .. 92.3 Seychelles .. 38 35.2 31.2b 0.9 .. 147 50 .. 62.2 Sierra Leone 0.32 12 14.5 11.7b 0.0 .. 66 2 .. 58.9 Singapore 8.04 3 112.6 14.0b 3.3 8,404 156 73 47.0 .. Sint Maarten .. .. .. .. .. .. .. .. .. .. Slovak Republic 5.11 12 .. 12.2 1.0 5,348 114 78 10.3 83.3 Slovenia 4.36 6 82.8 17.5b 1.1 6,806 110 73 6.2 .. Solomon Islands .. 9 20.3 .. .. .. 58 8 12.6 53.3 Somalia .. .. .. .. .. .. 49 2 .. 20.0 South Africa 6.54 19 182.2 25.5 1.1 4,606 146 49 5.5 74.4 South Sudan 0.73 14 .. .. 9.3 .. 25 .. .. 29.4 Spain 2.71 13 205.1 7.1 0.9 5,530 107 72 7.7 .. Sri Lanka 0.51 11 47.4 12.0b 2.7 490 95 22 1.0 78.9 St. Kitts and Nevis 5.69 19 65.9 20.2b .. .. 142 80 0.1 52.2 St. Lucia 3.00 15 123.1 23.0b .. .. 116 35 .. 66.7 St. Martin .. .. .. .. .. .. .. .. .. .. St. Vincent & the Grenadines 1.37 10 58.4 23.0b .. .. 115 52 0.1 55.6 Sudan .. 36 24.0 .. .. 143 73 23 0.7 43.3 Suriname 1.63 84 31.5 19.4b .. .. 161 37 6.5 63.3 Swaziland .. 30 18.4 .. 3.0 .. 71 25 .. 60.0 Sweden 6.41 16 138.1 20.7 1.1 14,030 124 95 14.0 .. Switzerland 2.53 10 173.4 9.8 0.7 7,928 137 87 26.5 .. Syrian Arab Republic 0.04 13 .. .. .. 1,715 56 26 .. 44.4 Tajikistan 0.26 39 19.0 .. .. 1,714 92 16 .. 75.6
- 126. 102 World Development Indicators 2015 Front User guide World view People Environment? 5 States and markets Business entry density Time required to start a business Domestic credit provided by financial sector Tax revenue collected by central government Military expenditures Electric power consumption per capita Mobile cellular subscriptionsa Individuals using the Interneta High-technology exports Statistical Capacity Indicator per 1,000 people ages 15–64 days % of GDP % of GDP kilowatt-hours per 100 people % of population % of manufactured exports (0, low, to 100, high)% of GDP 2012 June 2014 2013 2012 2013 2011 2013 2013 2013 2014 Tanzania .. 26 24.3 16.1b 1.1 92 56 4 5.4 72.2 Thailand 0.86 28 173.3 16.5 1.5 2,316 140 29 20.1 83.3 Timor-Leste 2.76 10 –53.6 .. 2.3 .. 57 1 9.8 64.4 Togo 0.12 10 36.0 16.4 1.6 .. 63 5 0.2 64.4 Tonga 1.91 16 27.1 .. .. .. 55 35 6.5 50.0 Trinidad and Tobago .. 12 33.7 28.3b .. 6,332 145 64 .. 62.2 Tunisia 1.52 11 83.4 21.0b 2.0 1,297 116 44 4.9 72.0 Turkey 0.79 7 84.3 20.4 2.3 2,709 93 46 1.9 84.4 Turkmenistan .. .. .. .. .. 2,444 117 10 .. 43.3 Turks and Caicos Islands .. .. .. .. .. .. .. .. 1.9 .. Tuvalu .. .. .. .. .. .. 34 37 .. 33.3 Uganda 1.17 32 14.2 11.0b 1.9 .. 44 16 1.9 64.4 Ukraine 0.92 21 95.7 18.2b 2.9 3,662 138 42 5.9 91.1 United Arab Emirates 1.38 8 76.5 0.4 5.0 9,389 172 88 .. .. United Kingdom 11.04 6 184.1 25.3 2.2 5,472 125 90 16.3 .. United States .. 6 240.5 10.2 3.8 13,246 96 84 17.8 .. Uruguay 2.98 7 36.3 19.3b 1.9 2,810 155 58 8.7 90.0 Uzbekistan 0.64 8 .. .. .. 1,626 74 38 .. 54.4 Vanuatu .. 35 68.7 16.0b .. .. 50 11 54.0 43.3 Venezuela, RB .. 144 52.5 .. 1.2 3,313 102 55 2.3 81.1 Vietnam .. 34 108.2 .. 2.2 1,073 131 44 28.2 76.7 Virgin Islands (U.S.) .. .. .. .. .. .. .. 45 .. .. West Bank and Gaza .. 44 .. .. .. .. 74 47 .. 82.0 Yemen, Rep. .. 40 33.9 .. 3.9 193 69 20 0.4 56.0 Zambia 1.36 7 27.5 16.0b 1.4 599 72 15 2.4 60.0 Zimbabwe .. 90 .. .. 2.6 757 96 19 3.6 57.8 World 3.83 u 22 u 166.5 w 14.3 w 2.3 w 3,045 w 93 w 38 w 17.8 w .. u Low income 0.33 29 35.8 11.8 1.5 219 55 7 4.1 60.5 Middle income 2.20 24 108.4 13.2 1.9 1,816 92 33 19.1 70.8 Lower middle income 1.10 22 61.9 10.9 1.9 736 85 21 11.1 68.8 Upper middle income 3.01 26 121.3 14.0 1.9 2,932 100 45 21.2 72.8 Low & middle income 1.86 25 106.9 13.1 1.9 1,646 87 29 18.9 67.8 East Asia & Pacific 1.34 35d 149.8 11.2 1.9 2,582 96 39 26.8 71.4 Europe & Central Asia 2.19 11d 68.3 19.6 2.1 2,954 112 46 10.4 78.1 Latin America & Carib. 2.38 34d 72.5 .. 1.3 1,985 114 46 12.0 77.1 Middle East & N. Africa 0.55 20d 46.8 .. 3.3 1,696 101 34 2.0 63.4 South Asia 0.25 16d 71.6 10.7 2.5 605 71 14 7.5 72.4 Sub-Saharan Africa 2.09 25d 61.0 14.0 1.3 535 66 17 4.3 58.7 High income 7.47 15 196.6 14.2 2.5 8,906 121 78 17.2 .. Euro area 6.62 11 143.2 17.1 1.5 6,599 123 76 15.9 .. 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.
- 127. World Development Indicators 2015 103Economy 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 2013 Entrepreneurship Database, which includes indicators for more than 150 countries for 2004–12. Survey data are used to analyze firm creation, its relationship to eco- nomic growth and poverty reduction, and the impact of regulatory and institutional reforms. Data on total registered businesses were collected from national registrars of companies. For cross-country comparability, only limited liability corporations that operate in the for- mal sector are included. For additional information on sources, meth- odology, calculation of entrepreneurship rates, and data limitations see 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 189 countries are administered through more than 10,700 local experts, including lawyers, business consultants, accountants, freight forwarders, government officials, and other professionals who routinely administer or advise on legal and regulatory requirements. Over the next two years Doing Business will introduce important improvements in 8 of the 10 sets of Doing Business indicators to provide a new conceptual framework in which the emphasis on efficiency of regulation is complemented by increased emphasis on quality of regulation. Moreover, Doing Business will change the basis for the ease of doing business ranking, from the percentile rank to the distance to frontier score. The distance to frontier score benchmarks economies with respect to a measure of regulatory best practice—showing the gap between each economy’s performance and the best performance on each indicator. This measure captures more information than the simple rankings previously used as the basis because it shows not only how economies are ordered on their performance on the indicators, but also how far apart they are. 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 business city and may not represent regulations in other locations of the economy. To address this limitation, subnational indicators are being collected for selected economies, and coverage has been extended to the second largest business city in economies with a population of more than 100 million. Subnational indicators point to significant differences in the speed of reform and the ease of doing busi- ness 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 proprietorships. Third, transactions described in a standardized business case refer to a specific set of issues and may not represent all the issues a busi- ness encounters. Fourth, the time measures involve an element 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 assump- tions 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 regula- tions and comply with them. In practice, entrepreneurs may not be aware of all required procedures or may avoid legally required procedures altogether. Financial systems The development of an economy’s financial markets is closely related to its overall development. Well functioning financial sys- tems provide good and easily accessible information. That lowers transaction costs, which in turn improves resource allocation and boosts economic growth. Data on the access to finance, availability of credit, and cost of service improve understanding of the state of financial development. Credit is an important link in money transmis- sion; it finances production, consumption, and capital formation, which in turn affect economic activity. The availability of credit to households, private companies, and public entities shows the depth of banking and financial sector development in the economy. Domestic credit provided by the financial sector as a share of GDP measures banking sector depth and financial sector development in terms of size. Data are taken from the financial corporation survey of the International Monetary Fund’s (IMF) International Financial Sta- tistics or, when unavailable, from its depository corporation survey. The financial corporation 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 governments may hold inter- national reserves as deposits in the banking system rather than in the central bank. Claims on the central government 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 financial sector. About the data
- 128. 104 World Development Indicators 2015 Front User guide World view People Environment? 5 States and markets 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 ser- vices 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 6.7 billion mobile subscriptions globally in 2013. 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). Statistical capacity Statistical capacity is a country’s ability to collect, analyze, and dis- seminate high-quality data about its population and economy. When statistical capacity improves and policymakers use accurate sta- tistics to inform their decisions, this results in better development policy design and outcomes. The Statistical Capacity Indicator is an essential tool for monitoring and tracking the statistical capacity of developing countries and helps national statistics offices worldwide identify gaps in their capabilities to collect, produce, and use data.
- 129. World Development Indicators 2015 105Economy States and markets Global links Back States and markets 5 Definitions • Business entry density is the number of newly registered limited liability corporations per 1,000 people ages 15–64. • Time required to start a business is the number of calendar days to complete the procedures for legally operating a business using the fastest procedure, independent of cost. • Domestic credit provided by financial sector is all credit to various sectors on a gross basis, except to the central government, which is net. The financial sec- tor 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 trans- fers such as fines, penalties, 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 government 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 current and capital expenditures on the armed forces, including peacekeeping 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 personnel; operation and maintenance; procurement; military research and development; and military aid (in the mili- tary expenditures of the donor country). Excluded are civil defense and current expenditures for previous military activities, such as for veterans benefits, demobilization, and weapons conversion and destruction. This definition cannot be applied for all countries, how- ever, 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 pen- sions). • Electric power consumption per capita is the production of power plants and combined heat and power plants less transmis- sion, distribution, and transformation losses and own use by heat and power plants, divided by midyear population. • Mobile cellular subscriptions are the number of subscriptions to a public mobile telephone service that provides access to the public switched tele- phone network 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, sub- scriptions 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. • Statistical Capac- ity Indicator is the composite score assessing the capacity of a country’s statistical system. It is based on a diagnostic framework that assesses methodology, data sources, and periodicity and time- liness. Countries are scored against 25 criteria in these areas, using publicly available information and country input. The overall statisti- cal capacity score is then calculated as simple average of all three area scores on a scale of 0–100. 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 domestic credit are from the IMF’s International Financial Statistics. Data on central government tax revenue are from the IMF’s Government Finance Statistics. Data on military expenditures are from SIPRI’s Military Expenditure Database (www .sipri.org/research/armaments/milex/milex_database/milex_ database). 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 Telecommunication/ICT Indicators database. Data on high-technology exports are from the United Nations Statistics Division’s Commodity Trade (Com- trade) database. Data on Statistical Capacity Indicator are from the World Bank’s Bulletin Board on Statistical Capacity (http:// bbsc.worldbank.org). References 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. 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. UNESCO (United Nations Educational, Scientific and Cultural Organiza- tion). 2010. Science Report. Paris.
- 130. 106 World Development Indicators 2015 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 government regulations IC.GOV.DURS.ZS Averagenumberoftimesmeetingwithtaxofficials IC.TAX.METG Time required to obtain operating license IC.FRM.DURS Bribery incidence IC.FRM.BRIB.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 working capital IC.FRM.BKWC.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 nonperforming loans to total gross loans FB.AST.NPER.ZS Domestic credit to private sector by banks FD.AST.PRVT.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 Children in employment SL.TLF.0714.ZS Refugees, By country of origin SM.POP.REFG.OR Refugees, By country of asylum SM.POP.REFG 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).
- 131. World Development Indicators 2015 107Economy States and markets Global links Back States and markets 5 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 Mobilecellularnetworkinternationalvoicetraffic ..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 5.14 Statistical capacity Overall level of statistical capacity IQ.SCI.OVRL Methodology assessment IQ.SCI.MTHD Source data assessment IQ.SCI.SRCE Periodicity and timeliness assessment IQ.SCI.PRDC 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.
- 132. 108 World Development Indicators 2015 Front User guide World view People Environment? GLOBAL LINKS
- 133. World Development Indicators 2015 109Economy States and markets Global links Back The world economy is bound together by trade in goods and services, financial flows, and movements of people. As national economies develop, their links expand and grow more com- plex. The indicators in Global links measure the size and direction of these flows and document the effects of policy interventions, such as tar- iffs, trade facilitation, and aid flows, on the devel- opment of the world economy. Despite signs that international financial markets started to regain confidence in 2013, concerns in capital markets caused international investment to fluctuate, mainly in emerging mar- ket economies. Real exchange rates depreci- ated, causing the withdrawal of capital and mak- ing capital flows more volatile. Global portfolio equity flows declined sharply in the second and third quarters, resulting in an overall decline of 11 percent by the end of 2013 and a decline of 33 percent in middle-income economies and 8 percent in high-income economies. The value of stock markets in low-income economies grew faster than expected, resulting in equity inflows that were twice as high as in 2012. Foreign direct investment (FDI) flows were less volatile than portfolio equity investment. Global FDI inflows increased 10.5 percent in 2013, to $1.7 trillion. FDI flows to high-income economies increased 11 percent, compared with a 22 percent decrease in 2012. FDI flows to developing economies were around $734 billion in 2012, some 42 percent of world inflows. Although many economies receive FDI, the flows remain highly concentrated among the 10 largest recipients, with Brazil, China, and India account- ing for more than half. The important economic role of the private sector in developing countries has led to a major shift in borrowing patterns in recent years and in the composition of external debt stocks and flows. Net debt flows to developing countries increased 28 percent from 2012, to $542 bil- lion in 2013. There has also been an evolution in the composition of these flows. Bond issuance by private sector entities has grown to account for 45 percent of medium-term debt inflows of private nonguaranteed debt since 2009. And bond issuance by public and private entities in developing countries reached a record $233 bil- lion in 2013. Growth in international trade showed signs of recovery after the major slowdown from the sov- ereign debt crisis in the euro area. While demand for goods from high-income economies remains low, annual growth in merchandise imports increased slightly, from 0.6 percent in 2012 to 1.5 percent in 2013. Growth of merchandise exports also showed improvement, from 0.4 per- cent to 2.3 percent, with merchandise exports to developing countries rising 3 percent from 2012 and merchandise exports to high-income coun- tries rising 1.3 percent. 6
- 134. 110 World Development Indicators 2015 Highlights Front User guide World view People Environment? The Middle East and North Africa’s merchandise exports to high-income countries decreased –40 –20 0 20 40 60 80 100 All developing countries Sub-Saharan Africa South Asia Latin America & Caribbean Europe & Central Asia East Asia & Pacific Middle East & North Africa Change in merchandise exports between 2008 and 2013 (%) To developing economies To high-income economies While the volume of merchandise trade continues to increase, following a fall in 2009 as a result of the 2008 financial crisis, the growth of trade has declined over the last two years. This is due mainly to mer- chandise exports between high-income economies falling below pre-crisis levels ($8,673 billion in 2008) for the last two years, though exports to developing economies increased. The trend is most evident in the Middle East and North Africa, where merchandise exports to high-income economies fell to $201 billion in 2013, 27 percent below their 2008 peak of $276 billion. Even though merchandise exports to developing economies have decreased since 2012, they are 22 per- cent higher than in 2008. Source: Online table 6.4. Aid to Sub-Saharan Africa is not keeping pace with economic growth 0 2 4 6 2013201020082006200420022000 Official development assistance (% of GNI) Sub-Saharan Africa Developing countries Official development assistance (ODA) increased to $150 billion in 2013, 0.62 percent of the combined gross national income (GNI) of developing countries. Donor governments increased their spending on foreign aid, after a decline in 2012. Despite the increases in total ODA, aid as a share of GNI to Sub-Saharan Africa continues to decline. The biggest drop was for Côte d’Ivoire—from 10 percent in 2012 to 4 per- cent in 2013, though the 2012 figure was unusually high because of increased debt relief from reaching the completion point under the Heavily Indebted Poor Countries (HIPC) initiative in June 2012. Liberia also registered close to a 6 percentage point drop, while Mauritania, Niger, Sierra Leone, and Gambia all had 3 percentage point decreases. Total bilateral aid from Development Assistance Committee donors to the region also fell 5 percent from the previous year, to $31.9 billion in 2013. Source: Online table 6.12. Foreign direct investment and private sector borrowing drive financial flows to Mexico –10 0 10 20 30 40 50 2013201220112010200920082007 Debt and foreign direct investment inflows ($ billions) Private nonguaranteed Public and publicly guaranteed Foreign direct investment Foreign direct investment (FDI) inflows in Mexico amounted to $30 bil- lion in 2013, more than double the 2012 level, making Mexico the third largest developing country recipient behind China and Brazil. Net finan- cial flows to private sector borrowers exceeded net debt flows to public borrowers through FDI and long-term private nonguaranteed debt inflows. The large increase in FDI inflows was due to investment in acquisitions and is usually an important indication of improved investor confidence, especially in the private sector. Further evidence can be found in the steady increase in net debt inflows to private nonguaran- teed borrowers, up 77 percent in 2013, to $42 billion, and accounting for almost half of total net debt inflows. But net debt flows to public borrowers, the main component of the country’s financial flows until 2012, declined 41 percent, to $23 billion in 2013. Source: Online table 6.9 and World Bank Debtor Reporting System.
- 135. World Development Indicators 2015 111 Bond issuance in Sub-Saharan Africa increased sharply Total bond issuance by public and private entities in developing coun- tries continued to increase in 2013, reaching a record $233 billion. The rapid growth was led by Sub-Saharan Africa, which registered an increase of 109 percent in 2013, to $13.5 billion, with debut issues from Mozambique, Rwanda, and Tanzania. Even though the region’s international bond market remains small, bond issuance continues to increase substantially: Bond issuance by public sector borrowers increased 155 percent, to $8.4 billion in 2013, and bond issuance by private sector borrowers increased 62 percent, to $5.1 billion. The region’s high return potential and considerable development needs have facilitated access to markets. Bond issuance continues to rely mainly on public and government bodies to finance development in infrastructure and manage debt, as corporate bond issuance is not fully open to international markets. Note: Bond issuance in 2008 was zero. Source: Online table 6.9. India saw a downturn in net capital flows in 2013 The depreciation of the rupee increased the vulnerability of capital inflows into India’s economy. Net short-term capital flows saw an out- flow of $642 million in 2013, compared with an inflow of $15.3 billion in 2012. In addition to a 13 percent decline in net portfolio equity inflows, net flows to holders of Indian bonds fell from an inflow of $4.5 billion in 2012 to an outflow of $3 billion in 2013. This was partly offset by the surge in long-term bank lending to $36.5 billion, an increase of 33 percent from 2012, directed almost entirely to the private sector. Despite the volatility of capital flows, foreign direct investment was more resilient, rising 17 percent in 2013, resulting in overall net flows of $28 billion. Source: Online tables 6.8 and 6.9. Private sector borrowing has accelerated in Europe and Central Asia In Europe and Central Asia net inflows from official creditors doubled in 2009, to $49 billion, while inflows from private creditors fell to $7.7 bil- lion, from $130 billion in 2008. This was driven by the 2008 financial crisis, which resulted in costly cross-border borrowing from the private sector and caused official creditors, mainly multilateral organizations, to lend money to the public sector. The situation has now reversed: Net medium- and long-term borrowing from foreign private creditors has rapidly increased, from –$11.5 billion in 2010 to $80.7 billion in 2013, its highest level. More than half of those net flows came from borrowing by commercial banks and other sectors, while official creditors recorded an outflow of $19 billion. Hungary, Kazakhstan, and Turkey received 81 percent of those net inflows. Source: World Bank Debtor Reporting System. Bond issuance in Sub-Saharan Africa ($ billions) Public and publicly guaranteed Private nonguaranteed 0 5 10 15 201320122011201020092007 0 10 20 30 40 50 External debt Foreign direct investment Portfolio equity Net capital inflows to India ($ billions) 2012 2013 –25 0 25 50 75 100 20132012201120102009 Net medium- and long-term debt inflows to Europe and Central Asia, by creditor type ($ billions) Official creditors Private creditors Economy States and markets Global links Back
- 136. Dominican Republic Trinidad and Tobago Grenada St. Vincent and the Grenadines Dominica Puerto Rico, US St. Kitts and Nevis Antigua and Barbuda St. Lucia Barbados R.B. de Venezuela U.S. Virgin Islands (US) Martinique (Fr) Guadeloupe (Fr) Curaçao (Neth) St. Martin (Fr) Anguilla (UK) St. Maarten (Neth) Samoa Tonga Fiji Kiribati Haiti Jamaica Cuba The Bahamas United States Canada Panama Costa Rica Nicaragua Honduras El Salvador Guatemala Mexico Belize Colombia Guyana Suriname R.B. de Venezuela Ecuador Peru Brazil Bolivia Paraguay Chile Argentina Uruguay American Samoa (US) French Polynesia (Fr) French Guiana (Fr) Greenland (Den) Turks and Caicos Is. (UK) IBRD 41455 Less than 1.0 1.0–1.9 2.0–3.9 4.0–5.9 6.0 or more No data Foreign direct investment FOREIGN DIRECT INVESTMENT NET INFLOWS AS A SHARE OF GDP, 2013 (%) Caribbean inset Bermuda (UK) 112 World Development Indicators 2015 Over the past decade flows of foreign direct investment (FDI) toward developing economies have increased sub- stantially. It has long been recognized that FDI flows can carry with them benefits of knowledge and technology transfer to domestic firms and the labor force, produc- tivity spillover, enhanced competition, and improved access for exports abroad. Moreover, they are the pre- ferred source of capital for financing a current account deficit because FDI is non-debt-creating. Although slowed by the financial crisis, FDI inflows to developing economies recovered considerably, from $418 billion in 2009 to $739 billion in 2013, an increase of 76 percent. Front User guide World view People Environment?
- 137. Romania Serbia Greece San Marino BulgariaUkraine Germany FYR Macedonia Croatia Bosnia and Herzegovina Czech Republic Poland Hungary Italy Austria Slovenia Slovak Republic Kosovo Montenegro Albania Burkina Faso Palau Federated States of Micronesia Marshall Islands Nauru Kiribati Solomon Islands Tuvalu Vanuatu Fiji Norway Iceland Ireland United Kingdom Sweden Finland Denmark Estonia Latvia Lithuania Poland Belarus Ukraine Moldova Romania Bulgaria Greece Italy Germany Belgium The Netherlands Luxembourg Switzerland Liechtenstein France AndorraPortugal Spain Monaco Malta Morocco Tunisia Algeria Mauritania Mali Senegal The Gambia Guinea- Bissau Guinea Cabo 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 Mozambique Madagascar Zimbabwe Botswana Namibia Swaziland LesothoSouth Africa Mauritius Seychelles Comoros Rep. of Yemen Oman United Arab Emirates Qatar Bahrain Saudi Arabia Kuwait Israel 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 Guinea Indonesia Australia New Zealand JapanRep.of Korea Dem.People’s Rep.of Korea Mongolia China Russian Federation Brunei Darussalam Sudan South Sudan Timor-Leste N. Mariana Islands (US) Guam (US) New Caledonia (Fr) Greenland (Den) West Bank and Gaza Western Sahara Réunion (Fr) Mayotte (Fr) Europe inset World Development Indicators 2015 113 Brazil ($81 billion), Mexico ($42 billion), and Colombia ($16 billion) are the top three recipients of foreign direct investment among developing countries in Latin America and the Caribbean. A large portion of Mozambique’s GDP is from foreign direct investment inflows: 42 percent in 2013. China received the most foreign direct investment (FDI) among all countries in East Asia and Pacific (84 percent) and commanded almost half of all FDI inflows in developing countries. Foreign direct investment in Djibouti more than doubled in 2013, increasing from 8 percent of GDP in 2012 to 20 percent in 2013. Economy States and markets Global links Back
- 138. 114 World Development Indicators 2015 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 remittances, received Foreign direct investment Portfolio equity Total external debt stock Total debt service % of exports of goods, services, and primary income% of GDP 2000 = 100 % of exports % of GNI thousands $ millions Net inflow $ millions Net inflow $ millions $ millions 2013 2013 2013 2013 2010–15 2013 2013 2013 2013 2013 Afghanistan 45.5 136.1 2.5 25.7 –400 538 60 0 2,577 0.6 Albania 55.8 94.4 43.4 2.3 –50 1,094 1,254 2 7,776 10.2 Algeria 57.1 215.7 0.5 0.1 –50 210 1,689 .. 5,231 0.7 American Samoa .. 138.5 .. .. .. .. .. .. .. .. Andorra .. .. .. .. .. .. .. .. .. .. Angola 75.1 257.4 1.8 0.3 66 .. –7,120 .. 24,004 6.9 Antigua and Barbuda 47.7 62.1 56.5 0.1 0 21 134 .. .. .. Argentina 25.5 131.2 5.2 0.0 –100 532 11,392 462 136,272 13.7 Armenia 57.1 114.4 17.4 2.7 –50 2,192 370 –2 8,677 50.8 Aruba .. 113.2 69.8 .. 1 6 169 .. .. .. Australia 31.7 177.0 10.9 .. 750 2,465 51,967 15,433 .. .. Austria 83.3 86.7 9.9 .. 150 2,810 15,608 2,348 .. .. Azerbaijan 58.4 194.8 7.3 –0.1 0 1,733 2,619 30 9,219 6.8 Bahamas, The 48.3 90.3 63.5 .. 10 .. 382 .. .. .. Bahrain 107.3 122.0 7.7 .. 22 .. 989 1,386 .. .. Bangladesh 43.7 57.4 0.4 1.6 –2,041 13,857 1,502 270 27,804 5.2 Barbados .. 113.5 .. .. 2 .. 376 .. .. .. Belarus 111.9 104.4 2.6 0.2 –10 1,214 2,246 2 39,108 10.3 Belgium 175.3 94.4 3.4 .. 150 11,126 –3,269 12,633 .. .. Belize 94.6 99.5 33.2 3.3 8 74 89 .. 1,249 12.7 Benin 46.9 117.0 .. 7.9 –10 .. 320 .. 2,367 .. Bermuda .. 96.9 32.1 .. .. 1,225 55 –10 .. .. Bhutan 87.0 122.8 17.6 8.1 10 12 50 .. 1,480 11.0 Bolivia 68.1 174.2 5.0 2.4 –125 1,201 1,750 .. 7,895 4.3 Bosnia and Herzegovina 89.5 97.6 13.2 3.0 –5 1,929 315 .. 11,078 17.8 Botswana 102.5 82.3 1.4 0.7 20 36 189 2 2,430 2.2 Brazil 21.9 126.2 2.5 0.1 –190 2,537 80,843 11,636 482,470 28.6 Brunei Darussalam 93.5 216.9 .. .. 2 .. 895 .. .. .. Bulgaria 117.2 107.0 12.5 .. –50 1,667 1,888 –19 52,995 13.0 Burkina Faso 44.7 118.0 .. 8.1 –125 .. 374 .. 2,564 .. Burundi 33.5 130.7 1.4 20.1 –20 49 7 .. 683 14.1 Cabo Verde .. 100.2 59.9 13.4 –17 176 41 .. 1,484 4.6 Cambodia 146.3 69.6 28.9 5.6 –175 176 1,345 .. 6,427 1.5 Cameroon 37.9 154.8 7.6 2.5 –50 244 325 .. 4,922 2.6 Canada 51.1 124.7 3.2 .. 1,100 1,199 70,753 17,902 .. .. Cayman Islands .. 69.6 .. .. .. .. 10,577 .. .. .. Central African Republic 26.0 68.1 .. 12.3 10 .. 1 .. 574 .. Chad 51.8 213.7 .. 3.1 –120 .. 538 .. 2,216 .. Channel Islands .. .. .. .. 4 .. .. .. .. .. Chile 56.2 187.5 3.6 0.0 30 0 20,258 6,027 .. .. China 45.0 74.8 2.4 0.0 –1,500 38,819 347,849 32,595 874,463 1.5 Hong Kong SAR, China 422.5 96.4 6.9 .. 150 360 76,639 11,916 .. .. Macao SAR, China 22.0 85.0 94.7 .. 35 49 3,708 .. .. .. Colombia 31.2 144.1 7.1 0.2 –120 4,119 16,198 1,926 91,978 14.1 Comoros 50.8 83.2 .. 13.3 –10 .. 14 .. 146 .. Congo, Dem. Rep. 38.5 128.9 0.0 8.6 –75 33 1,698 .. 6,082 3.0 Congo, Rep. 108.6 226.8 .. 1.4 –45 .. 2,038 .. 3,452 .. 6 Global links
- 139. World Development Indicators 2015 115Economy 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 remittances, received Foreign direct investment Portfolio equity Total external debt stock Total debt service % of exports of goods, services, and primary income% of GDP 2000 = 100 % of exports % of GNI thousands $ millions Net inflow $ millions Net inflow $ millions $ millions 2013 2013 2013 2013 2010–15 2013 2013 2013 2013 2013 Costa Rica 59.7 77.8 21.2 0.1 64 596 3,234 .. 17,443 22.3 Côte d’Ivoire 83.6 141.9 .. 4.2 50 .. 371 .. 11,288 .. Croatia 56.6 97.7 39.1 .. –20 1,497 588 –98 .. .. Cuba .. 140.1 .. .. –140 .. .. .. .. .. Curaçao .. .. .. .. 14 33 17 .. .. .. Cyprus 38.0 92.2 31.2 .. 35 83 607 –2 .. .. Czech Republic 146.1 101.6 5.1 .. 200 2,270 5,007 110 .. .. Denmark 61.6 100.0 3.6 .. 75 1,459 1,597 5,800 .. .. Djibouti 57.6 85.7 4.6 .. –16 36 286 .. 833 8.2 Dominica 46.6 100.6 48.3 4.0 .. 24 18 .. 293 10.7 Dominican Republic 43.4 93.2 31.6 0.3 –140 4,486 1,600 .. 23,831 16.8 Ecuador 55.3 134.5 4.5 0.2 –30 2,459 725 2 20,280 11.2 Egypt, Arab Rep. 31.9 153.0 .. 2.1 –216 .. 5,553 .. 44,430 .. El Salvador 67.0 87.6 16.5 0.7 –225 3,971 197 .. 13,372 17.1 Equatorial Guinea 138.0 230.6 .. 0.1 20 .. 1,914 .. .. .. Eritrea 39.5 84.8 .. 2.5 55 .. 44 .. 946 .. Estonia 138.5 94.1 8.4 .. 0 429 965 53 .. .. Ethiopia 31.4 124.4 .. 8.1 –60 .. 953 .. 12,557 .. Faeroe Islands .. 95.6 .. .. .. .. .. .. .. .. Fiji 102.7 108.2 42.8 2.4 –29 204 158 .. 797 1.9 Finland 56.8 87.9 5.5 .. 50 1,066 –5,297 2,447 .. .. France 44.9 88.3 7.9 .. 650 23,336 6,480 35,019 .. .. French Polynesia .. 78.6 .. .. –1 .. 119 .. .. .. Gabon 69.3 226.3 .. 0.5 5 .. 856 .. 4,316 .. Gambia, The 48.7 96.5 .. 12.7 –13 .. 25 .. 523 .. Georgia 66.8 132.6 26.7 4.1 –125 1,945 956 1 13,694 22.0 Germany 70.8 96.3 3.2 .. 550 15,792 51,267 15,345 .. .. Ghana 65.1 178.1 6.2 2.8 –100 119 3,227 .. 15,832 5.6 Greece 40.8 88.3 24.2 .. 50 805 2,945 3,135 .. .. Greenland .. 76.2 .. .. .. .. .. .. .. .. Grenada 48.6 85.3 57.2 1.2 –4 30 75 .. 586 16.5 Guam .. 76.8 .. .. 0 .. .. .. .. .. Guatemala 51.2 83.8 11.6 0.9 –75 5,371 1,350 .. 16,823 9.5 Guinea 55.3 98.1 .. 8.8 –10 93 135 .. 1,198 3.0 Guinea-Bissau 44.8 79.8 .. 10.8 –10 .. 15 .. 277 .. Guyana 104.8 114.4 5.0 3.4 –33 328 201 .. 2,303 4.9 Haiti 54.4 71.7 37.0 13.7 –175 1,781 186 .. 1,271 0.6 Honduras 101.7 72.4 11.1 3.6 –50 3,136 1,069 .. 6,831 14.4 Hungary 156.0 95.2 5.5 .. 75 4,325 –4,302 25 196,739 95.5 Iceland 63.8 84.6 13.1 .. 5 176 469 –19 .. .. India 41.5 131.1 4.1 0.1 –2,294 69,970 28,153 19,892 427,562 8.6 Indonesia 42.7 121.8 5.0 0.0 –700 7,614 23,344 –1,827 259,069 19.4 Iran, Islamic Rep. 35.5 190.3 .. 0.0 –300 .. 3,050 .. 7,647 0.4 Iraq 65.6 222.0 .. 0.7 450 .. 2,852 .. .. .. Ireland 77.4 94.8 4.1 .. 50 718 49,960 109,126 .. .. Isle of Man .. .. .. .. .. .. .. .. .. .. Israel 48.8 100.6 6.7 .. –76 765 11,804 2,712 .. ..
- 140. 116 World Development Indicators 2015 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 remittances, received Foreign direct investment Portfolio equity Total external debt stock Total debt service % of exports of goods, services, and primary income% of GDP 2000 = 100 % of exports % of GNI thousands $ millions Net inflow $ millions Net inflow $ millions $ millions 2013 2013 2013 2013 2010–15 2013 2013 2013 2013 2013 Italy 46.3 97.7 7.5 .. 900 7,471 13,126 17,454 .. .. Jamaica 54.4 81.8 48.2 0.5 –80 2,161 666 103 13,790 25.9 Japan 31.5 59.0 2.0 .. 350 2,364 3,715 169,753 .. .. Jordan 88.4 75.9 36.1 4.2 400 3,643 1,798 158 23,970 6.7 Kazakhstan 56.7 229.6 1.9 0.0 0 207 9,739 65 148,456 34.0 Kenya 40.2 88.3 .. 5.9 –50 .. 514 .. 13,471 5.7 Kiribati 70.7 84.8 .. 25.5 –1 .. 9 .. .. .. Korea, Dem. People’s Rep. .. 71.2 .. .. 0 .. 227 .. .. .. Korea, Rep. 82.4 61.4 2.7 .. 300 6,425 12,221 4,243 .. .. Kosovo .. .. .. 7.4 .. 1,122 343 –1 2,199 3.7 Kuwait 82.1 222.8 0.5 .. 300 4 1,843 509 .. .. Kyrgyz Republic 108.8 108.8 18.9 7.7 –175 2,278 758 –2 6,804 12.4 Lao PDR 47.0 107.4 20.1 4.0 –75 60 427 7 8,615 9.7 Latvia 104.4 104.2 6.6 .. –10 762 881 41 .. .. Lebanon .. 98.1 33.6 1.4 500 7,864 3,029 –134 30,947 16.7 Lesotho 130.5 72.2 4.3 11.2 –20 462 45 .. 885 2.8 Liberia .. 149.1 .. 30.5 –20 383 700 .. 542 0.7 Libya 95.0 199.5 .. .. –239 .. 702 .. .. .. Liechtenstein .. .. .. .. .. .. .. .. .. .. Lithuania 147.6 93.4 4.1 .. –28 2,060 712 –18 .. .. Luxembourg 75.0 77.7 5.0 .. 26 1,818 30,075 225,929 .. .. Macedonia, FYR 106.6 89.0 5.8 2.5 –5 376 413 –1 6,934 18.9 Madagascar 48.1 81.0 .. 4.9 –5 .. 838 .. 2,849 .. Malawi 109.4 97.6 .. 31.5 0 .. 118 .. 1,558 .. Malaysia 138.7 100.5 8.1 0.0 450 1,396 11,583 .. 213,129 3.5 Maldives 89.8 88.9 82.3 1.2 0 3 361 .. 821 2.5 Mali 57.0 148.9 .. 13.5 –302 .. 410 .. 3,423 .. Malta 96.8 124.8 18.1 .. 5 34 –1,869 0 .. .. Marshall Islands 104.8 98.4 .. 41.4 .. .. 23 .. .. .. Mauritania 142.5 156.1 1.8 7.5 –20 .. 1,126 .. 3,570 5.6 Mauritius 69.3 67.5 25.4 1.2 0 1 259 706 10,919 42.0 Mexico 61.2 104.4 3.6 0.0 –1,200 23,022 42,093 –943 443,012 10.3 Micronesia, Fed. Sts. 72.7 85.4 .. 41.7 –8 22 2 .. .. .. Moldova 99.0 102.0 10.5 4.2 –103 1,985 249 10 6,613 16.1 Monaco .. .. .. .. .. .. .. .. .. .. Mongolia 92.3 190.3 4.6 4.0 –15 256 2,151 3 18,921 27.9 Montenegro 64.4 .. 50.3 2.8 –3 423 446 14 2,956 17.2 Morocco 64.4 112.8 25.1 1.9 –450 6,882 3,361 43 39,261 15.3 Mozambique 83.8 94.8 5.2 14.9 –25 217 6,697 0 6,890 2.6 Myanmar .. 112.5 8.3 .. –100 229 2,255 .. 7,367 8.2 Namibia 93.0 119.9 9.5 2.0 –3 11 904 12 .. .. Nepal 38.8 74.8 21.0 4.5 –401 5,552 74 .. 3,833 8.7 Netherlands 147.8 92.7 3.4 .. 50 1,565 32,110 14,174 .. .. New Caledonia .. 174.7 .. .. 6 .. 2,065 .. .. .. New Zealand 42.6 123.9 14.1 .. 75 459 –510 3,506 .. .. Nicaragua 71.5 80.9 8.3 4.5 –120 1,081 845 .. 9,601 12.6 Niger 49.3 172.7 .. 10.7 –28 .. 631 .. 2,656 ..
- 141. World Development Indicators 2015 117Economy 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 remittances, received Foreign direct investment Portfolio equity Total external debt stock Total debt service % of exports of goods, services, and primary income% of GDP 2000 = 100 % of exports % of GNI thousands $ millions Net inflow $ millions Net inflow $ millions $ millions 2013 2013 2013 2013 2010–15 2013 2013 2013 2013 2013 Nigeria 30.5 222.1 .. 0.5 –300 .. 5,609 .. 13,792 0.5 Northern Mariana Islands .. 73.4 .. .. .. .. 6 .. .. .. Norway 47.6 159.7 3.2 .. 150 791 2,627 2,678 .. .. Oman 114.7 240.4 3.2 .. 1,030 39 1,626 1,361 .. .. Pakistan 30.1 59.1 3.1 0.9 –1,634 14,626 1,307 118 56,461 26.3 Palau 63.6 92.0 .. 14.8 .. .. 6 .. .. .. Panama 86.6 88.9 19.0 0.0 29 452 5,053 .. 16,471 5.7 Papua New Guinea 74.2 191.1 .. 4.5 0 .. 18 .. 21,733 .. Paraguay 74.4 105.2 2.1 0.5 –40 591 346 .. 13,430 12.9 Peru 42.4 153.8 8.3 0.2 –300 2,707 9,298 585 56,661 14.0 Philippines 44.8 62.4 8.3 0.1 –700 26,700 3,664 –34 60,609 7.7 Poland 77.4 97.9 5.0 .. –38 6,984 –4,586 2,602 .. .. Portugal 60.8 92.6 17.9 .. 100 4,372 7,882 584 .. .. Puerto Rico .. .. .. .. –104 .. .. .. .. .. Qatar 84.5 219.7 5.7 .. 500 574 –840 616 .. .. Romania 73.4 109.7 2.5 .. –45 3,518 4,108 1,053 133,996 39.7 Russian Federation 41.3 244.8 3.4 .. 1,100 6,751 70,654 –7,625 .. .. Rwanda 39.9 200.6 29.1 14.6 –45 170 111 0 1,690 3.5 Samoa 53.5 79.9 60.9 15.3 –13 158 24 .. 447 6.1 San Marino .. .. .. .. .. .. .. .. .. .. São Tomé and Príncipe 54.1 111.9 62.7 16.8 –2 27 11 0 214 11.0 Saudi Arabia 72.7 214.7 2.2 .. 300 269 9,298 .. .. .. Senegal 63.3 109.1 .. 6.7 –100 .. 298 .. 5,223 .. Serbia 77.2 103.1 6.6 1.8 –100 4,023 1,974 –41 36,397 43.6 Seychelles 115.1 88.1 37.1 1.8 –2 13 178 .. 2,714 5.7 Sierra Leone 89.4 60.2 3.0 9.8 –21 68 144 9 1,395 1.2 Singapore 262.9 80.6 3.4 .. 400 .. 63,772 –90 .. .. Sint Maarten .. .. .. .. .. 23 34 .. .. .. Slovak Republic 171.8 91.6 2.8 .. 15 2,072 2,148 86 .. .. Slovenia 140.9 94.6 8.2 .. 22 686 –419 154 .. .. Solomon Islands 87.5 90.1 12.1 30.0 –12 17 45 .. 204 7.4 Somalia .. 115.7 .. .. –150 .. 107 .. 3,054 .. South Africa 60.7 96.5 9.6 0.4 –100 971 8,118 1,011 139,845 8.3 South Sudan .. .. .. 13.4 865 .. .. .. .. .. Spain 47.1 89.3 14.8 .. 600 9,584 44,917 9,649 .. .. Sri Lanka 41.6 68.8 16.6 0.6 –317 6,422 916 263 25,168 11.9 St. Kitts and Nevis 38.0 68.2 34.3 3.9 .. 52 111 .. .. .. St. Lucia 55.2 91.4 57.6 1.9 0 30 84 .. 486 5.9 St. Martin .. .. .. .. .. .. .. .. .. .. St. Vincent & the Grenadines 60.1 94.5 47.4 1.1 –5 32 127 .. 293 13.5 Sudan 25.5a .. 9.3a 1.8 –800 424a 2,179a 0a 22,416a 3.5a Suriname 86.2 127.1 3.6 0.6 –5 7 137 .. .. .. Swaziland 97.9 108.9 0.6 3.4 –6 30 24 .. 464 1.3 Sweden 56.5 92.9 5.6 .. 200 1,167 –5,119 5,100 .. .. Switzerland 62.7 78.8 4.4 .. 320 3,149 –8,179 3,026 .. .. Syrian Arab Republic .. 148.4 .. .. –1,500 .. .. .. 4,753 .. Tajikistan 62.3 92.4 .. 4.5 –100 .. 108 .. 3,538 ..
- 142. 118 World Development Indicators 2015 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 remittances, received Foreign direct investment Portfolio equity Total external debt stock Total debt service % of exports of goods, services, and primary income% of GDP 2000 = 100 % of exports % of GNI thousands $ millions Net inflow $ millions Net inflow $ millions $ millions 2013 2013 2013 2013 2010–15 2013 2013 2013 2013 2013 Tanzania 51.7 135.9 22.9 10.4 –150 59 1,872 4 13,024 1.9 Thailand 123.8 91.6 16.2 0.0 100 5,690 12,650 –6,487 135,379 4.4 Timor-Leste .. .. 33.0 .. –75 34 52 2 .. .. Togo 84.1 28.9 .. 6.0 –10 .. 84 .. 903 .. Tonga 48.3 83.1 .. 16.8 –8 .. 12 .. 199 .. Trinidad and Tobago 87.8 147.7 .. .. –15 .. 1,713 .. .. .. Tunisia 87.9 96.3 13.0 1.6 –33 2,291 1,059 80 25,827 11.8 Turkey 49.1 90.4 16.6 0.3 350 1,135 12,823 841 388,243 28.9 Turkmenistan 66.9 231.0 .. 0.1 –25 .. 3,061 .. 502 .. Turks and Caicos Islands .. 71.1 .. .. .. .. .. .. .. .. Tuvalu 42.5 .. .. 48.3 .. 4 0 .. .. .. Uganda 33.3 106.1 23.4 7.0 –150 932 1,194 95 4,361 1.6 Ukraine 79.1 116.8 7.3 0.4 –40 9,667 4,509 1,180 147,712 42.3 United Arab Emirates 156.6 185.4 .. .. 514 .. 10,488 .. .. .. United Kingdom 44.7 102.2 6.4 .. 900 1,712 48,314 27,517 .. .. United States 23.3 95.3 9.4 .. 5,000 6,695 294,971 –85,407 .. .. Uruguay 37.2 107.8 14.8 0.1 –30 123 2,789 0 .. .. Uzbekistan 45.1 171.1 .. 0.5 –200 .. 1,077 .. 10,605 .. Vanuatu 42.5 89.9 77.9 11.4 0 24 33 .. 132 1.9 Venezuela, RB 32.5 254.6 .. 0.0 40 .. 7,040 .. 118,758 .. Vietnam 154.1 98.6 5.3 2.5 –200 .. 8,900 1,389 65,461 3.5 Virgin Islands (U.S.) .. .. .. .. –4 .. .. .. .. .. West Bank and Gaza .. 74.2 17.3 19.1 –44 1,748 177 –14 .. .. Yemen, Rep. 60.4 165.5 9.8 2.9 –135 3,343 –134 .. 7,671 2.8 Zambia 77.4 177.1 2.0 4.4 –40 54 1,811 5 5,596 2.8 Zimbabwe 57.9 104.7 .. 6.5 400 .. 400 .. 8,193 .. World 49.4 w .. 6.1b w 0.2c w 0 s 460,224 s 1,756,575 s 702,202 s .. s .. w Low income 48.6 .. 9.5 7.1 –4,337 24,136 23,702 378 146,957 5.8 Middle income 48.6 .. 5.6 0.3 –12,655 300,393 714,923 64,721 5,359,415 10.6 Lower middle income 47.7 .. 6.2 0.9 –10,340 174,327 109,463 21,034 1,398,505 11.8 Upper middle income 48.9 .. 5.5 0.1 –2,314 126,066 605,460 43,687 3,960,910 10.3 Low & middle income 48.6 .. 5.7 0.6 –16,991 324,529 738,625 65,099 5,506,372 10.5 East Asia & Pacific 52.0 .. 4.6 0.1 –3,061 81,401 414,775 25,648 1,672,953 3.3 Europe & Central Asia 68.9 .. 9.1 0.5 –661 40,833 44,955 3,158 1,234,241 39.5 Latin America & Carib. 36.6 .. 5.5 0.2 –3,017 60,729 184,616 13,771 1,495,399 16.5 Middle East & N. Africa 52.3 .. 14.4 .. –1,632 26,015 23,423 134 190,569 4.9 South Asia 40.6 .. 4.6 0.6 –7,076 110,980 32,421 20,543 545,704 9.4 Sub-Saharan Africa 50.1 .. 7.6 3.0 –1,545 4,572 38,435 1,845 367,507 6.2 High income 49.8 .. 6.2 0.0 16,941 135,695 1,017,950 637,104 .. .. Euro area 68.9 .. 6.3 0.0 3,364 86,590 248,832 448,156 .. .. a. Includes South Sudan. 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.
- 143. World Development Indicators 2015 119 Global links 6 Economy States and markets Global links Back Starting with World Development Indicators 2013, the World Bank changed 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 BPM6. 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 and personal remittances 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 labor, entrepreneurial or property income, social benefits, or any About the data
- 144. 120 World Development Indicators 2015 Front User guide World view People Environment? 6 Global links 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. Personal remittances are the sum of personal transfers and compensation of employees. 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, 124 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.
- 145. World Development Indicators 2015 121 Global links 6 Economy States and markets Global links Back Definitions • Merchandise trade includes all trade in goods and excludes trade in services. • Net barter terms of trade index is the percent- age ratio of the export unit value indexes to the import unit value indexes, measured relative to the base year 2000. • Inbound tour- ism expenditure is expenditures by international inbound visitors, including payments to national carriers for international transport and any other prepayment 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 sepa- rate classification. Data include travel and passenger transport items as defined by BPM6. When passenger transport items are not reported, expenditure 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 non- residents 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 coun- tries 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 remit- tances, received, are the sum of personal transfers (current trans- fers 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 contrib- uted by an individual in an employer-employee relationship with the enterprise). • Foreign direct investment is cross-border investment associated with a resident in one economy having control or a signifi- cant 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 inves- tors • Total external debt stock is debt owed to nonresident credi- tors 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 Statistics. Data on tourism expenditure are from the World Tourism Organiza- tion’s Yearbook of Tourism Statistics and World Tourism Organization (2015) 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 (2013). Data on personal remittances 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 Statistics 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. 2013. World Population Prospects: The 2012 Revision. New York: United Nations, Department of Eco- nomic and Social Affairs. World Bank. Various years. International Debt Statistics. Washington, DC. World Tourism Organization. 2015. Compendium of Tourism Statistics 2015. Madrid. ———. Various years. Yearbook of Tourism Statistics. Vols. 1 and 2. Madrid.
- 146. 122 World Development Indicators 2015 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 Exportstodevelopingeconomiesoutsideregion 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, Nonconcessional 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
- 147. World Development Indicators 2015 123 Global links 6 Economy States and markets Global links Back 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 Norway DC.DAC.NORL.CD Sweden DC.DAC.SWEL.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 remittances, Received BX.TRF.PWKR.CD.DT Personal remittances, 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.
- 148. 124 World Development Indicators 2015 Front User guide World view People Environment?
- 149. World Development Indicators 2015 125 As a major user of development data, the World Bank recognizes the importance of data docu- mentation to inform users of the methods and conventions used by primary data collectors— usually national statistical agencies, central banks, and customs services—and by interna- tional organizations, which compile the statistics that appear in the World Development Indicators database. 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 in the World Development Indicators database and from the World Bank’s Bulletin Board on Statistical Capac- ity at http://data.worldbank.org. The demand for good-quality statistical data is ever increasing. Statistics provide the evi- dence needed to improve decisionmaking, docu- ment results, and heighten public accountability. However, differences among data collectors may give rise to large discrepancies over time, both within and across countries. Data relevant at the national level may not be suitable for standard- ized international use due to methodological con- cerns or the lack of clear documentation. Delays in reporting data and the use of old surveys as the base for current estimates may further com- promise the quality of data reported. To meet these challenges and improve the quality of data disseminated, the World Bank works closely with other international agencies, regional development banks, donors, and other partners to: • Develop appropriate frameworks, guidance, and standards of good practice for statistics. • Build consensus and define internationally agreed indicators, such as those for the Mil- lennium Development Goals and the post- 2015 development agenda. • Establish data exchange processes and methods. • Help countries improve their statistical capacity. More information on these activities and other data programs is available at http://data .worldbank.org. Primary data documentation Economy States and markets Global links Back
- 150. 126 World Development Indicators 2015 Currency National accounts Balance of payments and trade Government finance IMF data dissem- ination standard 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 Albania Albanian lek a 1996 1993 B Rolling 6 A G B G Algeria Algerian dinar 1980 1968 B 2011 6 A S B G American Samoa U.S. dollar 1968 2011b S Andorra Euro 1990 1968 S Angola Angolan kwanza 2002 1993 P 1991–96 2011 6 A S B G Antigua and Barbuda East Caribbean dollar 2006 1968 B 2011 6 G B G Argentina Argentine peso 2004 2008 B 1971–84 6 A S C S Armenia Armenian dram a 1996 1993 B 1990–95 2011 6 A S C S Aruba Aruban florin 2000 1993 B 2011 6 S Australia Australian dollar a 2012/13 2008 B 2011 6 G C S Austria Euro 2005 2008 B Rolling 6 S C S Azerbaijan New Azeri manat 2000 1993 B 1992–95 2011 6 A G C G Bahamas, The Bahamian dollar 2006 1993 B 2011 6 G B G Bahrain Bahraini dinar 2010 1968 P 2011 6 G B G Bangladesh Bangladeshi taka 2005/06 1993 B 2011 6 E G C G Barbados Barbados dollar 1974 1968 B 2011 6 G B G Belarus Belarusian rubel a 2000 1993 B 1990–95 2011 6 A G C S Belgium Euro 2005 2008 B Rolling 6 S C S Belize Belize dollar 2000 1993 B 2011 6 A G B G Benin CFA franc 1985 1968 P 1992 2011 6 A S B G Bermuda Bermuda dollar 2006 1993 B 2011 6 G Bhutan Bhutanese ngultrum 2000 1993 B 2011 6 A G C G Bolivia Bolivian Boliviano 1990 1968 B 1960–85 2011 6 A G C G Bosnia and Herzegovina Bosnia and Herzegovina convertible mark a 2010 1993 B Rolling 6 A S C G Botswana Botswana pula 2006 1993 B 2011 6 A G B G Brazil Brazilian real 2000 1993 B 2011 6 A G C S Brunei Darussalam Brunei dollar 2000 1993 P 2011 S G Bulgaria Bulgarian lev a 2010 1993 B 1978–89, 1991–92 Rolling 6 A S C S Burkina Faso CFA franc 1999 1993 B 1992–93 2011 6 A G B G Burundi Burundi franc 2005 1993 B 2011 6 A S C G Cabo Verde Cabo Verde escudo 2007 1993 P 2011 6 A G B G Cambodia Cambodian riel 2000 1993 B 2011 6 A S B G Cameroon CFA franc 2000 1993 B 2011 6 A S B G Canada Canadian dollar 2005 2008 B 2011 6 G C S Cayman Islands Cayman Islands dollar 2007 1993 2011 G Central African Republic CFA franc 2000 1968 B 2011 6 A S B G Chad CFA franc 2005 1993 B 2011 6 P S G Channel Islands Pound sterling 2003 2007 1968 B Chile Chilean peso 2008 1993 B 2011 6 S C S China Chinese yuan 2000 1993 P 1978–93 2011 6 P S C G Hong Kong SAR, China Hong Kong dollar a 2012 2008 B 2011 6 G C S Macao SAR, China Macao pataca 2012 1993 B 2011 6 G C G Colombia Colombian peso 2005 1993 B 1992–94 2011 6 A G C S Comoros Comorian franc 1990 1968 P 2011 A S G Congo, Dem. Rep. Congolese franc 2005 1968 B 1999–2001 2011 6 P S C G Congo, Rep. CFA franc 1990 1968 P 1993 2011 6 A S C G Costa Rica Costa Rican colon 1991 1993 B 2011 6 A S C S Côte d’Ivoire CFA franc 2009 1968 P 2011 6 A S B G Croatia Croatian kuna a 2010 1993 B Rolling 6 G C S Cuba Cuban peso 2005 1993 B 2011 S Curaçao Netherlands Antillean guilder 1993 2011 Cyprus Euro a 2000 1993 B Rolling 6 G C S
- 151. World Development Indicators 2015 127Economy States and markets Global links Back 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 Afghanistan 1979 MICS, 2010/11 IHS, 2008 2013/14 2013 2000 Albania 2011 DHS, 2008/09 LSMS, 2011/12 Yes 2012 2011 2013 2006 Algeria 2008 MICS, 2012 IHS, 1995 2010 2013 2001 American Samoa 2010 Yes 2007 Andorra 2011c Yes 2006 Angola 2014 MIS, 2011 IHS, 2008/09 2015 2005 Antigua and Barbuda 2011 Yes 2007 2013 2005 Argentina 2010 MICS, 2011/12 IHS, 2012 Yes 2013 2002 2013 2011 Armenia 2011 DHS, 2010 IHS, 2012 Yes 2013/14 2008 2013 2012 Aruba 2010 Yes 2012 Australia 2011 ES/BS, 2003 Yes 2011 2011 2013 2000 Austria 2011c IHS, 2004 Yes 2010 2010 2013 2002 Azerbaijan 2009 DHS, 2006 LSMS, 2011/12 Yes 2015 2011 2013 2012 Bahamas, The 2010 2013 Bahrain 2010 Yes 2010 2011 2003 Bangladesh 2011 DHS, 2014; HIV/MCH SPA, 2014 IHS, 2010 2008 2007 2008 Barbados 2010 MICS, 2012 Yes 2010d 2013 2005 Belarus 2009 MICS, 2012 IHS, 2013 Yes 2011 2013 2000 Belgium 2011 IHS, 2000 Yes 2010 2010 2013 2007 Belize 2010 MICS, 2011 LFS, 1999 2013 2000 Benin 2013 MICS, 2014 CWIQ, 2011/12 2011/12 2013 2001 Bermuda 2010 Yes 2013 Bhutan 2005 MICS, 2010 IHS, 2012 2009 2011 2008 Bolivia 2012 DHS, 2008 IHS, 2012 2013 2013 2000 Bosnia and Herzegovina 2013 MICS, 2011/12 LSMS, 2007 Yes 2013 2012 Botswana 2011 MICS, 2000 ES/BS, 2009/10 2011d 2011 2013 2000 Brazil 2010 WHS, 2003 IHS, 2012 2006 2011 2013 2010 Brunei Darussalam 2011 Yes 2013 1994 Bulgaria 2011 LSMS, 2007 ES/BS, 2012 Yes 2010 2011 2013 2009 Burkina Faso 2006 MIS, 2014 CWIQ, 2009 2010 2013 2005 Burundi 2008 MIS, 2012 CWIQ, 2006 2010 2012 2000 Cabo Verde 2010 DHS, 2005 CWIQ, 2007 Yes 2014 2013 2001 Cambodia 2008 DHS, 2014 IHS, 2011 2013 2013 2006 Cameroon 2005 MICS, 2014 PS, 2007 2012 2000 Canada 2011 LFS, 2010 Yes 2011 2011 2013 1986 Cayman Islands 2010 Yes Central African Republic 2003 MICS, 2010 PS, 2008 2011 2005 Chad 2009 DHS, 2014 PS, 2011 2010/11 1995 2005 Channel Islands 2009/11e Yesf Chile 2012 IHS, 2011 Yes 2007 2013 2006 China 2010 NSS, 2013 IHS, 2013 2007 2007 2013 2005 Hong Kong SAR, China 2011 Yes 2011 2012 Macao SAR, China 2011 Yes 2011 2012 Colombia 2006 DHS, 2010 IHS, 2012 2013 2011 2013 2008 Comoros 2003 DHS, 2012 IHS, 2004 2009 1999 Congo, Dem. Rep. 1984 DHS, 2013/14 1-2-3, 2005/06 2005 Congo, Rep. 2007 DHS, 2011/12 CWIQ/PS, 2011 2013 2009 2013 2002 Costa Rica 2011 MICS, 2011 IHS, 2012 Yes 2014 2011 2013 2013 Côte d’Ivoire 2014 DHS, 2011/12 IHS, 2008 2014 2013 2005 Croatia 2011 WHS, 2003 IHS, 2012 Yes 2010 2013 2010 Cuba 2012 MICS, 2014 Yes 2006 2007 Curaçao 2011 Yes 2010 Cyprus 2011 Yes 2010 2011 2013 2009
- 152. 128 World Development Indicators 2015 Currency National accounts Balance of payments and trade Government finance IMF data dissem- ination standard 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? Czech Republic Czech koruna 2005 2008 B Rolling 6 S C S Denmark Danish krone 2005 2008 B Rolling 6 S C S Djibouti Djibouti franc 1990 1968 B 2011 6 A G G Dominica East Caribbean dollar 2006 1993 B 2011 6 A S B G Dominican Republic Dominican peso 1991 1993 B 2011 6 A G C G Ecuador U.S. dollar 2007 2008 B 2011 6 A G B S Egypt, Arab Rep. Egyptian pound 2001/02 1993 B 2011 6 A G C S El Salvador U.S. dollar 1990 1968 B 2011 6 A S C S Equatorial Guinea CFA franc 2006 1968 B 1965–84 2011 G B Eritrea Eritrean nakfa 2000 1968 B 6 E Estonia Euro 2005 2008 B 1987–95 Rolling 6 S C S Ethiopia Ethiopian birr 2010/11 1993 B 2011 6 A G B G Faeroe Islands Danish krone 1993 B 6 G Fiji Fijian dollar 2005 1993 B 2011 6 A G B G Finland Euro 2005 2008 B Rolling 6 G C S France Euro a 2005 2008 B Rolling 6 S C S French Polynesia CFP franc 1990 1993 2011b S Gabon CFA franc 2001 1993 P 1993 2011 6 A S G Gambia, The Gambian dalasi 2004 1993 P 2011 6 A G B G Georgia Georgian lari a 1996 1993 B 1990–95 2011 6 A G C S Germany Euro 2005 2008 B Rolling 6 S C S Ghana New Ghanaian cedi 2006 1993 B 1973–87 2011 6 A G B G Greece Euro a 2005 2008 B Rolling 6 S C S Greenland Danish krone 1990 1993 G Grenada East Caribbean dollar 2006 1968 B 2011 6 A S B G Guam U.S. dollar 1993 2011b G Guatemala Guatemalan quetzal 2001 1993 B 2011 6 A S B G Guinea Guinean franc 2003 1993 B 2011 6 E S B G Guinea-Bissau CFA franc 2005 1993 B 2011 6 E G G Guyana Guyana dollar 2006 1993 B 6 A S G Haiti Haitian gourde 1986/87 1968 B 1991 2011 6 A G G Honduras Honduran lempira 2000 1993 B 1988–89 2011 6 A S C G Hungary Hungarian forint a 2005 2008 B Rolling 6 A S C S Iceland Iceland krona 2005 2008 B Rolling 6 G C S India Indian rupee 2011/12 2008 B 2011 6 A G C S Indonesia Indonesian rupiah 2000 1993 P 2011 6 A S B S Iran, Islamic Rep. Iranian rial 1997/98 1993 B 1980–2002 2011 6 A S C G Iraq Iraqi dinar 1988 1968 P 1997, 2004 2011 6 G Ireland Euro 2005 2008 B Rolling 6 G C S Isle of Man Pound sterling 2003 1968 Israel Israeli new shekel a 2010 1993 P 2011 6 S C S Italy Euro 2005 2008 B Rolling 6 S C S Jamaica Jamaican dollar 2007 1993 B 2011 6 A G C G Japan Japanese yen 2005 1993 B 2011 6 G C S Jordan Jordanian dinar 1994 1968 B 2011 6 A G S Kazakhstan Kazakh tenge a 2005 1993 B 1987–95 2011 6 A G C S Kenya Kenyan shilling 2009 1993 B 2011 6 A G B G Kiribati Australian dollar 2006 1993 B 2011b 6 G B G Korea, Dem. People’s Rep. Democratic People's Republic of Korean won 1968 6 Korea, Rep. Korean won 2010 2008 B 2011 6 G C S Kosovo Euro 2008 1993 B A G Kuwait Kuwaiti dinar 2010 1968 P 2011 6 S B G Kyrgyz Republic Kyrgyz som a 1995 1993 B 1990–95 2011 6 A S B S Lao PDR Lao kip 2002 1993 B 2011 6 A S B Latvia Latvian lats 2000 1993 B 1987–95 Rolling 6 S C S Lebanon Lebanese pound 1997 1993 B 2011 6 A G B G
- 153. World Development Indicators 2015 129Economy States and markets Global links Back 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 Czech Republic 2011 WHS, 2003 IHS, 2012 Yes 2010 2010 2013 2007 Denmark 2011 ITR, 2010 Yes 2010 2013 2009 Djibouti 2009 MICS, 2006 PS, 2002 2009 2000 Dominica 2011 Yes 2012 2004 Dominican Republic 2010 MICS, 2014 IHS, 2012 2012/13 2008 2012 2005 Ecuador 2010 RHS, 2004 IHS, 2013 2013/15 2013 2005 Egypt, Arab Rep. 2006 DHS, 2014 ES/BS, 2011 Yes 2009/10 2010 2013 2000 El Salvador 2007 MICS, 2014 IHS, 2012 Yes 2007/08 2013 2005 Equatorial Guinea 2002 DHS, 2011 PS, 2006 2000 Eritrea 1984 DHS, 2002 PS, 1993 2011 2003 2004 Estonia 2012 WHS, 2003 IHS, 2011 Yes 2010 2011 2013 2007 Ethiopia 2007 HIV/MCH SPA, 2014 ES/BS, 2010/11 2009 2013 2002 Faeroe Islands 2011 Yes 2009 Fiji 2007 ES/BS, 2008/09 Yes 2009 2010 2013 2000 Finland 2010 IHS, 2010 Yes 2010 2010 2013 2005 France 2006g ES/BS, 2005 Yes 2010 2010 2013 2007 French Polynesia 2007 Yes 2013 Gabon 2013 DHS, 2012 CWIQ/IHS, 2005 2009 2005 Gambia, The 2013 DHS, 2013 IHS, 2010 2004 2013 2000 Georgia 2002 MICS, 2005; RHS, 2005 IHS, 2012 Yes 2011 2013 2008 Germany 2011 IHS, 2010 Yes 2010 2010 2013 2007 Ghana 2010 DHS, 2014 LSMS, 2012 2013/14 2003 2013 2000 Greece 2011 IHS, 2010 Yes 2009 2007 2013 2007 Greenland 2010 Yes 2013 Grenada 2011 RHS, 1985 Yes 2012 2009 2005 Guam 2010 Yes 2007 Guatemala 2002 RHS, 2008/09 LSMS, 2011 Yes 2013 2013 2006 Guinea 2014 DHS, 2012 CWIQ, 2012 2008 2001 Guinea-Bissau 2009 MICS, 2014 CWIQ, 2010 2005 2000 Guyana 2012 MICS, 2014 IHS, 1998 2013 2010 Haiti 2003 HIV/MCH SPA, 2013 IHS, 2012 2008/09 1997 2000 Honduras 2013 DHS, 2011/12 IHS, 2013 2013 2012 2003 Hungary 2011 WHS, 2003 IHS, 2012 Yes 2010 2010 2013 2007 Iceland 2011 IHS, 2010 Yes 2010 2005 2013 2005 India 2011 DHS, 2005/06 IHS, 2011/12 2011 2010 2013 2010 Indonesia 2010 DHS, 2012 IHS, 2013 2013 2011 2013 2000 Iran, Islamic Rep. 2011 IrMIDHS, 2010 ES/BS, 2005 Yes 2013 2010 2011 2004 Iraq 1997 MICS, 2011 IHS, 2012 2011/12 2011 2000 Ireland 2011 IHS, 2010 Yes 2010 2010 2013 1979 Isle of Man 2011 Yes Israel 2009 ES/BS, 2010 Yes 2010 2013 2004 Italy 2012 IS, 2010 Yes 2010 2010 2013 2000 Jamaica 2011 MICS, 2011 LSMS, 2010 2007 2013 1993 Japan 2010 IHS, 2008 Yes 2010 2010 2013 2001 Jordan 2004 DHS, 2012 ES/BS, 2010 2007 2011 2013 2005 Kazakhstan 2009 MICS, 2010/11 ES/BS, 2013 Yes 2013 2010 Kenya 2009 DHS, 2014 IHS, 2005/06 2009d 2011 2010 2003 Kiribati 2010 KDHS, 2009 2012 Korea, Dem. People’s Rep. 2008 MICS, 2009 2005 Korea, Rep. 2010 ES/BS, 1998 Yes 2010 2009 2013 2002 Kosovo 2011 MICS, 2013/14 IHS, 2011 Kuwait 2011 FHS, 1996 Yes 2011 2013 2002 Kyrgyz Republic 2009 MICS, 2014 ES/BS, 2013 Yes 2014 2010 2013 2006 Lao PDR 2005 MICS, 2011/12 ES/BS, 2012 2010/11 2005 Latvia 2011 WHS, 2003 IHS, 2012 Yes 2010 2011 2013 2002 Lebanon 1970 FHS, 2004 Yes 2011 2007 2013 2005
- 154. 130 World Development Indicators 2015 Currency National accounts Balance of payments and trade Government finance IMF data dissem- ination standard 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? Lesotho Lesotho loti 2004 1993 B 2011 6 A G C G Liberia Liberian dollar 2000 1968 P 2011 6 A S B G Libya Libyan dinar 1999 1993 B 1986 6 G G Liechtenstein Swiss franc 1990 1993 B S Lithuania Lithuanian litas 2000 1993 B 1990–95 Rolling 6 G C S Luxembourg Euro a 2005 2008 B Rolling 6 S C S Macedonia, FYR Macedonian denar 1995 1993 B Rolling 6 A S C S Madagascar Malagasy ariary 1984 1968 B 2011 6 A S C G Malawi Malawi kwacha 2009 1993 B 2011 6 A G G Malaysia Malaysian ringgit 2005 1993 P 2011 6 E G B S Maldives Maldivian rufiyaa 2003 1993 B 2011 6 A G C G Mali CFA franc 1987 1968 B 2011 6 A S B G Malta Euro 2005 1993 B Rolling 6 G C S Marshall Islands U.S. dollar 2003/04 1968 B 2011b G G Mauritania Mauritanian ouguiya 1998 1993 B 2011 6 A S G Mauritius Mauritian rupee 2006 1993 B 2011 6 A G S Mexico Mexican peso 2008 2008 B 2011 6 A G C S Micronesia, Fed. Sts. U.S. dollar 2003/04 1993 B 2011b G Moldova Moldovan leu a 1996 1993 B 1990–95 2011 6 A G C S Monaco Euro 1990 1993 S Mongolia Mongolian tugrik 2005 1993 B 2011 6 A G C G Montenegro Euro 2000 1993 B Rolling 6 A S G Morocco Moroccan dirham 1998 1993 B 2011 6 A S C S Mozambique NewMozambicanmetical 2009 1993 B 1992–95 2011 6 A S B G Myanmar Myanmar kyat 2005/06 1968 P 2011 6 E G C G Namibia Namibian dollar 2010 1993 B 2011 6 G B G Nepal Nepalese rupee 2000/01 1993 B 2011 6 A G B G Netherlands Euro a 2005 2008 B Rolling 6 S C S New Caledonia CFP franc 1990 1993 2011b S New Zealand New Zealand dollar 2005/06 1993 B 2011 6 G C Nicaragua Nicaraguan gold cordoba 2006 1993 B 1965–95 2011 6 A G B G Niger CFA franc 2006 1993 P 1993 2011 6 A S B G Nigeria Nigerian naira 2010 2008 B 1971–98 2011 6 A G B G Northern Mariana Islands U.S. dollar 1968 2011b Norway Norwegian krone a 2005 1993 B Rolling 6 G C S Oman Rial Omani 2010 1993 P 2011 6 G B G Pakistan Pakistani rupee 2005/06 1993 B 2011 6 A G B G Palau U.S. dollar 2004/05 1993 B 2011b S G Panama Panamanian balboa 2007 1993 B 2011 6 A S C G Papua New Guinea Papua New Guinea kina 1998 1993 B 1989 2011b 6 A G B G Paraguay Paraguayan guarani 1994 1993 B 2011 6 A S C G Peru Peruvian new sol 2007 1993 B 1985–90 2011 6 A S C S Philippines Philippine peso 2000 1993 P 2011 6 A G B S Poland Polish zloty a 2005 2008 B Rolling 6 S C S Portugal Euro 2005 2008 B Rolling 6 S C S Puerto Rico U.S. dollar 1953/54 1968 P G Qatar Qatari riyal 2001 1993 P 2011 S B G Romania New Romanian leu 2000 1993 B 1987–89, 1992 Rolling 6 A S C S Russian Federation Russian ruble 2000 1993 B 1987–95 2011 6 G C S Rwanda Rwandan franc 2011 2008 P 1994 2011 6 A G B G Samoa Samoan tala 2008/09 1993 B 2011b 6 A S B G San Marino Euro 1995 2000 1993 B C G São Tomé and Príncipe São Tomé and Príncipe dobra 2000 1993 P 2011 6 A S B G Saudi Arabia Saudi Arabian riyal 1999 1993 P 2011 6 S G Senegal CFA franc 1999 1993 B 2011 6 A G B G
- 155. World Development Indicators 2015 131Economy States and markets Global links Back 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 Lesotho 2006 DHS, 2014 ES/BS, 2010 2010 2009 2000 Liberia 2008 DHS, 2013 CWIQ, 2007 2008d 2000 Libya 2006 FHS, 2007 2013/14 2010 2000 Liechtenstein 2010 Yes Lithuania 2011 ES/BS, 2012 Yes 2010 2011 2013 2007 Luxembourg 2011 Yes 2010 2010 2013 1999 Macedonia, FYR 2002 MICS, 2011 ES/BS, 2010 Yes 2007 2010 2013 2007 Madagascar 1993 MIS, 2013 PS, 2010 2006 2013 2000 Malawi 2008 MIS, 2014 IHS, 2010/11 2006/07 2010 2013 2005 Malaysia 2010 WHS, 2003 IS, 2012 Yes 2015 2010 2013 2005 Maldives 2014 DHS, 2009 IHS, 2010 Yes 2013 2008 Mali 2009 DHS, 2012/13 IHS, 2009/10 2012 2006 Malta 2011 Yes 2010 2009 2013 2002 Marshall Islands 2011 RMIDHS, 2007 IHS, 1999 2011d Mauritania 2013 MICS, 2011 IHS, 2008 2013 2005 Mauritius 2011 WHS, 2003 IHS, 2012 Yes 2013/14 2011 2013 2003 Mexico 2010 ENADID, 2009 IHS, 2012 2007 2010 2013 2011 Micronesia, Fed. Sts. 2010 IHS, 2000 Moldova 2014 MICS, 2012 ES/BS, 2012 Yes 2011 2011 2013 2007 Monaco 2008 Yes 2009 Mongolia 2010 MICS, 2013 LSMS, 2012 Yes 2012 2011 2013 2009 Montenegro 2011 MICS, 2013 ES/BS, 2013 Yes 2010 2013 2010 Morocco 2014 MICS/PAPFAM, 2006 ES/BS, 2007 2012 2010 2012 2000 Mozambique 2007 DHS, 2011 ES/BS, 2008/09 2009/10 2013 2001 Myanmar 2014 MICS, 2009/10 2010 2010 2000 Namibia 2011 DHS, 2013 ES/BS, 2009/10 2014 2013 2002 Nepal 2011 MICS, 2014 LSMS, 2010/11 2011/12 2008 2013 2006 Netherlands 2011 IHS, 2010 Yes 2010 2010 2013 2008 New Caledonia 2009 Yes 2012 New Zealand 2013 Yes 2012 2010 2013 2002 Nicaragua 2005 RHS, 2006/07 LSMS, 2009 2011 2013 2011 Niger 2012 DHS, 2012 CWIQ/PS, 2011 2004-08 2002 2013 2005 Nigeria 2006 DHS, 2013 IHS, 2009/10 2013 2013 2005 Northern Mariana Islands 2010 2007 Norway 2011 IS, 2010 Yes 2010 2010 2013 2006 Oman 2010 MICS, 2014 2012/13 2010 2013 2003 Pakistan 1998 DHS, 2012/13 IHS, 2010/11 2010 2006 2013 2008 Palau 2010 Yes 2012 Panama 2010 MICS, 2013 IHS, 2012 2011 2001 2013 2010 Papua New Guinea 2011 LSMS, 1996 IHS, 2009/10 2001 2012 2005 Paraguay 2012 RHS, 2008 IHS, 2013 2008 2002 2013 2012 Peru 2007 Continuous DHS, 2013 IHS, 2013 2012 2011 2013 2008 Philippines 2010 DHS, 2013 ES/BS, 2012 Yes 2012 2008 2013 2009 Poland 2011 ES/BS, 2012 Yes 2010 2011 2013 2009 Portugal 2011 Yes 2009 2010 2013 2002 Puerto Rico 2010 RHS, 1995/96 Yes 2007 2006 2005 Qatar 2010 MICS, 2012 Yes 2010 2013 2005 Romania 2011 RHS, 2004 ES/BS, 2012 Yes 2010 2011 2013 2009 Russian Federation 2010 WHS, 2003 IHS, 2013 Yes 2014 2011 2013 2001 Rwanda 2012 MIS, 2013 IHS, 2010/11 2008 2013 2000 Samoa 2011 DHS, 2009 2009 2013 San Marino 2010 Yes São Tomé and Príncipe 2012 MICS, 2014 PS, 2010 2011/12 2013 1993 Saudi Arabia 2010 Demographic survey, 2007 2010 2006 2013 2006 Senegal 2013 HIV/MCH SPA, 2014 PS, 2011 2013 2010 2012 2002
- 156. 132 World Development Indicators 2015 Currency National accounts Balance of payments and trade Government finance IMF data dissem- ination standard 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? Serbia New Serbian dinar a 2010 1993 B Rolling 6 A S C G Seychelles Seychelles rupee 2006 1993 P 2011 6 A G C G Sierra Leone Sierra Leonean leone 2006 1993 B 2011 6 A S B G Singapore Singapore dollar 2010 2008 B 2011 6 G C S Sint Maarten Netherlands Antillean guilder 1993 2011 Slovak Republic Euro 2005 2008 B Rolling 6 S C S Slovenia Euro a 2005 2008 B Rolling 6 S C S Solomon Islands Solomon Islands dollar 2004 1993 B 2011b 6 A S G Somalia Somali shilling 1985 1968 B 1977–90 E South Africa South African rand 2010 2008 B 2011 6 P G C S South Sudan South Sudanese pound 2009 1993 Spain Euro 2005 2008 B Rolling 6 S C S Sri Lanka Sri Lankan rupee 2002 1993 P 2011 6 A G B G St. Kitts and Nevis East Caribbean dollar 2006 1993 B 2011 6 S B G St. Lucia East Caribbean dollar 2006 1968 B 2011 6 A S B G St. Martin Euro 1993 St. Vincent and the Grenadines East Caribbean dollar 2006 1993 B 2011 6 A S B G Sudan Sudanese pound 1981/82h 1996 1968 B 2011 6 P G B G Suriname Suriname dollar 2007 1993 B 2011 6 G B G Swaziland Swaziland lilangeni 2000 1993 B 2011 6 A G C G Sweden Swedish krona a 2005 2008 B Rolling 6 G C S Switzerland Swiss franc 2005 2008 B Rolling 6 S C S Syrian Arab Republic Syrian pound 2000 1968 B 1970–2010 2011 6 E S B G Tajikistan Tajik somoni a 2000 1993 B 1990–95 2011 6 A G C G Tanzania Tanzanian shilling 2007 2008 B 2011 6 A G B G Thailand Thai baht 1988 1993 P 2011 6 A S C S Timor-Leste U.S. dollar 2010 2008 B S G Togo CFA franc 2000 1968 P 2011 6 A S B G Tonga Tongan pa'anga 2010/11 1993 B 2011b 6 A G G Trinidad and Tobago Trinidad and Tobago dollar 2000 1993 B 2011 6 S C G Tunisia Tunisian dinar 2005 1993 B 2011 6 A G C S Turkey New Turkish lira 1998 1993 B Rolling 6 A S C S Turkmenistan New Turkmen manat 2005 1993 B 1987–95, 1997–2007 6 E G Turks and Caicos Islands U.S. dollar 1993 2011 G Tuvalu Australian dollar 2005 1968 B 2011b G G Uganda Ugandan shilling 2009/10 2008 P 2011 6 A G B G Ukraine Ukrainian hryvnia a 2003 1993 B 1987–95 2011 6 A G C S United Arab Emirates U.A.E. dirham 2007 1993 P 2011 6 G C G United Kingdom Pound sterling 2005 1993 B Rolling 6 G C S United States U.S. dollar a 2005 2008 B 2011 6 G C S Uruguay Uruguayan peso 2005 1993 B 2011 6 G C S Uzbekistan Uzbek sum a 1997 1993 B 1990–95 6 A G Vanuatu Vanuatu vatu 2006 1993 B 2011b 6 E G B G Venezuela, RB Venezuelan bolivar fuerte 1997 1993 B 2011 6 A G C G Vietnam Vietnamese dong 2010 1993 P 1991 2011 6 A G G Virgin Islands (U.S.) U.S. dollar 1982 1968 G West Bank and Gaza Israeli new shekel 2004 1968 B 2011 6 S B S Yemen, Rep. Yemeni rial 2007 1993 P 1990–96 2011 6 A S B G Zambia New Zambian kwacha 2010 2008 B 1990–92 2011 6 A S B G Zimbabwe U.S. dollar 2009 1993 B 1991, 1998 2011 6 A G C G
- 157. World Development Indicators 2015 133Economy States and markets Global links Back 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 Serbia 2011 MICS, 2014 IHS, 2011 Yes 2012 2011 2009 Seychelles 2010 BS, 2006/07 Yes 2011 2008 2005 Sierra Leone 2004 DHS, 2013; MIS, 2013 IHS, 2011 2008 2002 2005 Singapore 2010 NHS, 2010 Yes 2011 2013 1975 Sint Maarten 2011 Yes Slovak Republic 2011 WHS, 2003 IS, 2012 Yes 2010 2010 2013 2007 Slovenia 2011 c WHS, 2003 ES/BS, 2012 Yes 2010 2011 2013 2009 Solomon Islands 2009 IHS, 2005/06 2012/13 2013 Somalia 1987 MICS, 2006 2003 South Africa 2011 DHS, 2003; WHS, 2003 ES/BS, 2010/11 2007 2010 2013 2000 South Sudan 2008 MICS, 2010 ES/BS, 2009 2012 2011 Spain 2011 IHS, 2010 Yes 2010 2010 2013 2008 Sri Lanka 2012 DHS, 2006/07 ES/BS, 2013 Yes 2013/14 2010 2013 2005 St. Kitts and Nevis 2011 Yes 2011 St. Lucia 2010 MICS, 2012 IHS, 1995 Yes 2007 2008 2005 St. Martin St. Vincent and the Grenadines 2011 Yes 2012 1995 Sudan 2008 MICS, 2014 ES/BS, 2009 2013/14 2001 2011 2011 Suriname 2012 MICS, 2010 ES/BS, 1999 Yes 2008 2004 2011 2006 Swaziland 2007 MICS, 2014 ES/BS, 2009/10 2007d 2007 2000 Sweden 2011 IS, 2005 Yes 2010 2010 2013 2007 Switzerland 2010 ES/BS, 2004 Yes 2008 2010 2013 2000 Syrian Arab Republic 2004 MICS, 2006 ES/BS, 2004 2014 2005 2010 2005 Tajikistan 2010 DHS, 2012 LSMS, 2009 2013 2000 2006 Tanzania 2012 HIV/MCH SPA, 2014/15 ES/BS, 2011/12 2007/08 2010 2013 2002 Thailand 2010 MICS, 2012 IHS, 2011 2013 2006 2013 2007 Timor-Leste 2010 DHS, 2009/10 LSMS, 2007 2010d 2013 2004 Togo 2010 DHS, 2013/14 CWIQ, 2011 2011/12 2013 2002 Tonga 2006 2012 Trinidad and Tobago 2011 MICS, 2011 IHS, 1992 Yes 2006 2010 2000 Tunisia 2014 MICS, 2011/12 IHS, 2010 2014/15 2010 2013 2001 Turkey 2011 TDHS, 2008 ES/BS, 2011 Yes 2009 2013 2003 Turkmenistan 2012 MICS, 2006 LSMS, 1998 2000 2004 Turks and Caicos Islands 2012 Yes 2012 Tuvalu 2012 2008 Uganda 2014 MIS, 2014 IHS, 2012/13 2008/09 2013 2002 Ukraine 2001 MICS, 2012 ES/BS, 2013 Yes 201213 2004 2013 2005 United Arab Emirates 2010 WHS, 2003 2012 2010 2011 2005 United Kingdom 2011 IS, 2010 Yes 2010 2010 2013 2007 United States 2010 LFS, 2010 Yes 2012 2008 2013 2005 Uruguay 2011 MICS, 2012/13 IHS, 2013 Yes 2011 2009 2013 2000 Uzbekistan 1989 MICS, 2006 ES/BS, 2011 Yes 2005 Vanuatu 2009 MICS, 2007 2007 2011 Venezuela, RB 2011 MICS, 2000 IHS, 2012 Yes 2007 2011 2000 Vietnam 2009 MICS, 2013/14 IHS, 2012 Yes 2011/12 2011 2013 2005 Virgin Islands (U.S.) 2010 Yes 2007 West Bank and Gaza 2007 MICS, 2014 IHS, 2011 2010 2005 Yemen, Rep. 2004 DHS, 2013 ES/BS, 2005 2009 2013 2005 Zambia 2010 DHS, 2013/14 IHS, 2010 2010d 2013 2002 Zimbabwe 2012 MICS, 2014 IHS, 2011/12 2013 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. Household consumption only. c. Population data compiled from administrative registers. d. Population and Housing Census. e. Latest population census: Guernsey, 2009; Jersey, 2011 f. Vital registration for Guernsey and Jersey. g. Rolling census based on continuous sample survey. h. Reporting period switch from fiscal year to calendar year from 1996. Pre-1996 data converted to calendar year.
- 158. 134 World Development Indicators 2015 Front User guide World view People Environment? Primary data documentation notes • Base year is the base or pricing period used for constant 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 relative 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 refer- ence 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 historical series. To allow for cross-country comparison and data aggregation, constant price data reported in World Development Indicators are rescaled to a common 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). The 2008 SNA is an update of the 1993 SNA and retains its basic theoretical frame- work. • SNA price valuation shows whether value added in the national accounts is reported at basic prices (B) or producer prices (P). Producer prices include taxes paid by producers and thus tend to overstate the actual value added in production. How- ever, value added can be higher at basic prices than at producer prices in countries with high agricultural subsidies. • Alternative conversion factor identifies the countries and years for which a World Bank–esti- mated 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 avail- able survey year for the International Comparison Program’s estimates of PPPs. • Balance of Pay- ments 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 2013 data. A indicates that data are as reported, P that data are based on reported or collected information 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 sys- tem (G) or special trade system (S). Under the gen- eral trade system goods entering directly for domestic consumption and goods entered into cus- toms 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 cus- toms storage. Exports under the general system comprise outward-moving goods: (a) national goods wholly or partly produced in the country; (b) foreign goods, neither transformed nor declared for domes- tic consumption in the country, that move outward from customs storage; and (c) nationalized goods that have been declared for domestic consumption and move outward without being transformed. Under the special system of trade, exports are categories 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. • Govern- ment finance accounting concept is the accounting basis for reporting central government financial data. For most countries government finance data have been consolidated (C) into one set of accounts capturing all central government fiscal activities. Budgetary central government accounts (B) exclude some central government units. • IMF data dissemi- nation standard shows the countries that subscribe to the IMF’s Special Data Dissemination Standard (SDDS) or General Data Dissemination System (GDDS). S refers to countries 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 subscribe to the GDDS. The SDDS was established for member countries that have or might seek access to international capi- tal markets to guide them in providing their eco- nomic and financial data to the public. The GDDS helps countries disseminate comprehensive, timely, accessible, and reliable economic, financial, and sociodemographic statistics. IMF member countries elect to participate in either the SDDS or the GDDS. Both standards enhance the availability of timely and comprehensive data and therefore contribute 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 revisions if basic data are available, such as popula- tion 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. A rare case, France conducts a rolling census every year; the 1999 general population census was the last to cover the entire population simultaneously. • Latest demographic, education, or health household sur- vey indicates the household surveys used to com- pile the demographic, education, and health data in section 2. DHS is Demographic and Health Survey, ENADID is National Survey of Demographic Dynam- ics, FHS is Family Health Survey, HIV/MCH is HIV/Maternal and Child Health, IrMIDHS is Iran’s Multiple Indicator Demographic and Health Survey, KDHS is Kiribati Demographic and Health Survey, LSMS is Living Standards Measurement Study, MICS is Multiple Indicator Cluster Survey, MIS is Malaria Indicator Survey, NHS is National Health Survey, NSS is National Sample Survey on Popula- tion Changes, PAPFAM is Pan Arab Project for Family Health, RHS is Reproductive Health Survey, RMIDHS is Republic of the Marshall Islands Demographic and Health Survey, SPA is Service Provision Assess- ments, TDHS is Turkey Demographic and Health Survey, and WHS is World Health Survey. Detailed information for DHS, HIV/MCH, MIS, and SPA are available at www.dhsprogram.com; for MICS at www .childinfo.org; for RHS at www.cdc.gov /reproductivehealth; and for WHS at www.who.int /healthinfo/survey/en. • Source of most recent income and expenditure data shows household sur- veys that collect income and expenditure data. Names and detailed information on household sur- veys can be found on the website of the International Household Survey Network (www.surveynetwork .org). Core Welfare Indicator Questionnaire Surveys (CWIQ), developed by the World Bank, measure changes in key social indicators for different popula- tion groups—specifically indicators of access, utili- zation, and satisfaction with core social and eco- nomic services. Expenditure survey/budget surveys (ES/BS) collect detailed information on household consumption as well as on general demographic, social, and economic characteristics. Integrated household surveys (IHS) collect detailed information on a wide variety of topics, including health, educa- tion, 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, tax- able income, and taxes by socioeconomic group. Labor force surveys (LFS) collect information on employment, unemployment, hours of work, income,
- 159. World Development Indicators 2015 135Economy States and markets Global links Back Primary data documentation notes and wages. Living Standards Measurement Study Surveys (LSMS), developed by the World Bank, pro- vide a comprehensive picture of household welfare and the factors that affect it; they typically incorpo- rate data collection at the individual, household, and community levels. Priority surveys (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 and household consumption. • Vital registration complete identifies countries that report at least 90 percent complete registries of vital (birth and death) statistics 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. • Lat- est agricultural census shows the most recent year in which an agricultural census was conducted or planned to be conducted, as 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 Indus- trial Classification (revision 2 or 3) are available in the United Nations Industrial Development Organiza- tion database. • Latest trade data show the most recent year for which structure of merchandise trade data from the United Nations Statistics Division’s Commodity Trade (Comtrade) database are avail- able. • Latest water withdrawal data show the most recent year for which data on freshwater withdrawals have been compiled 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 fiscal year of the central government. Fiscal years for other levels of government and reporting years for statisti- cal 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 statistical 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 comprehensive, provide information on revisions from previous data. • Argentina. The base year has changed to 2004. • Bahrain. Based on official government statistics, the new base year is 2010. • Bangladesh. The new base year is 2005/06. • Bosnia and Herzegovina. Based on official government statistics for chain-linked series, the new reference year is 2010. • Bulgaria. The new reference year for chain-linked series is 2010. • Congo, Dem. Rep. Based on official govern- ment statistics, the new base year 2005. • Côte d’Ivoire. The new base year is 2009. • Croatia. The new reference year for chain-linked series is 2010. • Egypt, Arab Rep. The new base year is 2001/02. • Equatorial Guinea. Based on IMF data and official government statistics, the new base year is 2006. • Gabon. Based on IMF data and official government statistics, the new base year is 2001. • India. Based on official government statistics, the new base year is 2011/12. India reports using SNA 2008. • Israel. Based on official government statistics for chain-linked series, the new reference year is 2010. • Kazakhstan. The new reference year for chain-linked series is 2005. • Kenya. Based on official government statistics, the new base year is 2009. • Korea, Rep. The new base year is 2010. • Kuwait. Based on official government statistics, the new base year is 2010. • Mauritania. Based on official statistics from the Ministry of Economic Affairs and Development, the base year has changed from 2004 to 1998. • Mozambique. Based on offi- cial government statistics, the new base year is 2009. • Namibia. Based on official government sta- tistics, the new base year is 2010. • Nigeria. Based on official government statistics, the new base year is 2010. Nigeria reports using SNA 2008. • Oman. Based on official government statistics, the new base year is 2010. • Panama. The new base year is 2007. • Peru. The new base year is 2007. • Rwanda. Based on official government statistics, the new base year is 2011. Rwanda reports using SNA 2008. • Samoa. The new base year is 2008/09. Other methodological changes include increased reliance on summary data from the country’s Value Added Goods and Services Tax system, incorporation of more recent benchmarks, and use of improved data sources. • São Tomé and Príncipe. The base year has changed from 2001 to 2000. • Serbia. The new reference year for chain-linked series is 2010. • South Africa. The new base year is 2010. South Africa reports using SNA 2008. • Tanzania. The new base year is 2007. Tanzania reports using a blend of SNA 1993 and SNA 2008. • Uganda. Based on official government statistics, the new base year is 2009/10. Uganda reports using SNA 2008. Price valuation is in producer prices. • West Bank and Gaza. The new base year is 2004. • Yemen, Rep. The new base year is 2007. • Zambia. The new base year is 2010. Zambia reports using SNA 2008. 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 Mar. 31 CY 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 Marshall Islands Sep. 30 FY Micronesia, Fed. Sts. Sep. 30 FY Myanmar Mar. 31 FY Namibia Mar. 31 CY Nepal Jul. 14 FY New Zealand Mar. 31 FY Pakistan Jun. 30 FY Palau Sep. 30 FY Puerto Rico Jun. 30 FY Samoa 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 Tonga Jun. 30 FY Uganda Jun. 30 FY United States Sep. 30 CY Zimbabwe Jun. 30 CY
- 160. 136 World Development Indicators 2015 Front User guide World view People Environment? Statistical methods This section describes some of the statistical prac- tices and procedures used in preparing World Develop- ment Indicators. It covers data consistency, reliability, and comparability as well as the methods employed for calculating regional and income group aggregates and for calculating growth rates. It also 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. Data consistency, reliability, and comparability Considerable effort has been made to standardize the data, but full comparability cannot be assured, so care must be taken in interpreting the indicators. Many factors affect data availability, comparability, and reliability: statistical systems in many developing economies are still weak; statistical methods, cov- erage, practices, and definitions differ widely; and cross-country and intertemporal comparisons involve complex technical and conceptual problems that can- not be resolved unequivocally. Data coverage may not be complete because of special circumstances affecting the collection and reporting of data, such as problems stemming from conflicts. Thus, although drawn from sources thought to be the most authoritative, data should be construed only as indicating trends and characterizing major dif- ferences among economies rather than as offering precise quantitative measures of those differences. Discrepancies in data presented in different editions of World Development Indicators reflect updates by countries as well as revisions to historical series and changes in methodology. Therefore readers are advised not to compare data series between editions of World Development Indicators or between differ- ent World Bank publications. Consistent time-series data for 1960–2013 are available at http://data .worldbank.org. 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 classifications 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, Latvia, Lithuania, Luxembourg, Malta, Netherlands, Portugal, Slovak Republic, Slovenia, 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 2005. Missing values in 2005 are imputed using one of several proxy variables for which complete data are available in that year. The imputed value is calcu- lated so that it (or its proxy) bears the same relation- ship 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, manufacturing, and services in U.S. dollars.
- 161. World Development Indicators 2015 137Economy States and markets Global links Back • Aggregates marked by an s are sums of available data. Missing values are not imputed. Sums are not computed if more than a third of the observations in the series or a proxy for the series are missing in a given year. • 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. 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 in real terms computed from constant price series. Three principal methods are used to calculate growth rates: least squares, exponential endpoint, 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 .
- 162. 138 World Development Indicators 2015 Front User guide World view People Environment? Statistical methods 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 and analytical purposes, the World Bank uses the Atlas conversion factor instead of simple exchange rates. 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 the rate of international inflation. The objective of the adjustment is to reduce any changes to the exchange rate caused by inflation. A country’s inflation rate between year t and year t–n (rt–n ) is measured by the change in its GDP deflator (pt ): pt rt–n = pt–n International inflation between year t and year t–n (rt–n SDR$) is measured using the change in a deflator based on the International Monetary Fund’s unit of account, special drawing rights (or SDRs). Known as the “SDR deflator,” it is a weighted average of the GDP deflators (in SDR terms) of Japan, the United Kingdom, the United States, and the euro area, converted to U.S. dollar terms; weights are the amount of each currency in one SDR unit. pt SDR$ rt–n SDR$ = pt–n SDR$ The Atlas conversion factor (local currency to the U.S. dollar) for year t (et atlas ) is given by: where et is the average annual exchange rate (local currency to the U.S. dollar) for year t. GNI in U.S. dollars (Atlas method) for year t (Yt atlas$ ) is calculated by applying the Atlas conversion factor to a country’s GNI in current prices (local currency) (Yt) as follows: Yt atlas$ = Yt /et atlas The resulting Atlas GNI in U.S. dollars can then be divided by a country’s midyear population to yield its GNI per capita (Atlas method). 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 deemed to be unreliable or unrepresentative of 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 documentation. Alternative conversion factors are used in the Atlas methodology and elsewhere in World Development Indicators as single-year conver- sion factors.
- 163. World Development Indicators 2015 139Economy States and markets Global links Back 1. World view Section 1 was prepared by a team led by Neil Fantom. Juan Feng and Umar Serajuddin wrote the introduc- tion, and the Millennium Development Goal spreads were produced by Mahyar Eshragh-Tabary, Juan Feng, Masako Hiraga, Wendy Huang, Haruna Kashiwase, Buyant Erdene Khaltarkhuu, Tariq Khokhar, Hiroko Maeda, Malvina Pollock, Umar Serajuddin, Emi Suzuki, and Dereje Wolde. The tables were produced by Mahyar Eshragh-Tabary, Juan Feng, Masako Hiraga, Wendy Huang, Bala Bhaskar Naidu Kalimili, Haruna Kashiwase, Buyant Erdene Khaltarkhuu, Hiroko Maeda, Umar Serajuddin, Emi Suzuki, and Dereje Wolde. 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 Relief Initiative and Multilateral Debt Relief Ini- tiative. The map was produced by Liu Cui, Juan Feng, William Prince, and Umar Serajuddin. 2. People Section 2 was prepared by Juan Feng, Masako Hiraga, Haruna Kashiwase, Hiroko Maeda, Umar Serajuddin, Emi Suzuki, and Dereje Wolde in partnership with the World Bank’s various Global Practices and Cross- Cutting Solutions Areas—Education, Gender, Health, Jobs, Poverty, and Social Protection and Labor. Emi Suzuki prepared the demographic estimates and pro- jections. The new indicators on shared prosperity were prepared by the Global Poverty Working Group, a team of poverty experts from the Poverty Global Practice, the Development Research Group, and the Develop- ment Data Group coordinated by Andrew Dabalen, Umar Serajuddin, and Nobuo Yoshida. Poverty esti- mates at national poverty lines were compiled by the Global Poverty Working Group. Shaohua Chen and Prem Sangraula of the World Bank’s Development Research Group and the Global Poverty Working Group prepared the poverty estimates at international pov- erty 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 Isis Gaddis (gender) and Samuel Mills (health); Salwa Haidar, Maddalena Honorati, Theodoor Sparreboom, and Alan Wittrup of the International Labour Organization (labor force); Colleen Murray (health), Julia Krasevec (malnutrition and overweight), and Rolf Luyendijk and Andrew Trevett (water and sani- tation) of the United Nations Children’s Fund; Amé- lie Gagnon, Friedrich Huebler, and Weixin Lu of the United Nations Educational, Scientific and Cultural Organization Institute for Statistics (education and literacy); Patrick Gerland and François Pelletier of the United Nations Population Division; Callum Brindley and Chandika Indikadahena (health expenditure), Monika Bloessner, Elaine Borghi, Mercedes de Onis, and Leanne Riley (malnutrition and overweight), Teena Kunjumen (health workers), Jessica Ho (hospital beds), Rifat Hossain (water and sanitation), Luz Maria de Regil and Gretchen Stevens (anemia), Hazim Timimi (tuberculosis), Colin Mathers and Wahyu Mahanani (cause of death), and Lori Marie Newman (syphilis), all of the World Health Organization; Juliana Daher and Mary Mahy of the Joint United Nations Programme on HIV/AIDS (HIV/AIDS); and Leonor Guariguata of the International Diabetes Federation (diabetes). The map was produced by Liu Cui, William Prince, and Emi Suzuki. 3. Environment Section 3 was prepared by Mahyar Eshragh-Tabary in partnership with the World Bank’s Environment and Natural Resources Global Practices and Energy and Extractives Global Practices. Mahyar Eshragh-Tabary wrote the introduction and highlights with editorial help and comments from Neil Fantom and Tariq Khokhar. Christopher Sall helped prepare the intro- duction, highlights, and about the data sections on air pollution with valuable comments from Esther G. Naikal and Urvashi Narain. Esther G. Naikal, Urvashi Narain, and Christopher Sall prepared the data and metadata on population-weighted exposure to ambi- ent PM2.5 pollution and natural resources rents. Sudeshna Ghosh Banerjee and Elisa Portale prepared the data and metadata on access to electricity. Neil Fantom, Masako Hiraga, and William Prince provided instrumental comments, suggestions, and support at all stages of production. Several other staff members Credits
- 164. 140 World Development Indicators 2015 Front User guide World view People Environment? Credits from the World Bank made valuable contributions: Gabriela Elizondo Azuela, Marianne Fay, Vivien Foster, Glenn-Marie Lange, and Ulf Gerrit Narloch. Contribu- tors from other institutions included Michael Brauer, Aaron Cohen, Mohammad H. Forouzanfar, and Peter Speyer from the Institute for Health Metrics and Evalu- ation; Pierre Boileau and Maureen Cropper from the University of Maryland; Sharon Burghgraeve and Jean- Yves Garnier of the International Energy Agency; Armin Wagner of German International Cooperation; Craig Hilton-Taylor and Caroline Pollock of the International Union for Conservation of Nature; and Cristian Gonza- lez of the International Road Federation. The team is grateful to the Food and Agriculture Organization, the Global Burden of Disease of the Institute for Health Metrics and Evaluation, the International Energy Agency, the International Union for Conservation of Nature, the United Nations Environment Programme and World Conservation Monitoring Centre, the U.S. Agency for International Development’s Office of For- eign Disaster Assistance, and the U.S. Department of Energy’s Carbon Dioxide Information Analysis Center for access to their online databases. The World Bank’s Environment and Natural Resources Global Practices also devoted generous staff resources. 4. Economy Section 4 was prepared by Bala Bhaskar Naidu Kali- mili in close collaboration with the Environment and Natural Resources Global Practice and Economic Data Team of the World Bank’s Development Data Group. Bala Bhaskar Naidu Kalimili wrote the introduction, with inputs from Christopher Sall and Tamirat Yacob. The highlights were prepared by Bala Bhaskar Naidu Kalimili, Marko Olavi Rissanen, Christopher Sall, Saulo Teodoro Ferreira, and Tamirat Yacob, with invaluable comments and editorial help from Neil Fantom and Tariq Khokhar. The national accounts data for low- and middle-income economies were gathered by the World Bank’s regional staff through the annual Unified Survey. Maja Bresslauer, Liu Cui, Federico Escaler, Mahyar Eshragh-Tabary, Bala Bhaskar Naidu Kalimili, Buyant Erdene Khaltarkhuu, Saulo Teodoro Ferreira, and Tamirat Yacob updated, estimated, and validated the databases for national accounts. Esther G. Naikal and Christopher Sall prepared the data on adjusted savings and adjusted income. Azita Amjadi contrib- uted data on trade from the World Integrated Trade Solution. The team is grateful to Eurostat, the Interna- tional Monetary Fund, the Organisation for Economic Co-operation and Development, the United Nations Industrial Development Organization, and the World Trade Organization for access to their databases. 5. States and markets Section 5 was prepared by Federico Escaler and Buy- ant Erdene Khaltarkhuu in partnership with the World Bank Group’s Finance and Markets, Macroeconomics and Fiscal Management, Transport and Information, Communication Technologies Global Practices and its Public–Private Partnerships and Fragility, Conflict, and Violence Cross-Cutting Solution Areas; the Inter- national Finance Corporation; and external partners. Buyant Erdene Khaltarkhuu wrote the introduction and highlights with substantial input from Frederic Meunier (Doing Business) and Annette Kinitz (statisti- cal capacity). Neil Fantom, Tariq Khokhar, and William Prince provided valuable comments. Other contribu- tors include Alexander Nicholas Jett (privatization and infrastructure projects); Leora Klapper and Frederic Meunier (business registration); Jorge Luis Rodriguez Meza, Valeria Perotti, and Joshua Wimpey (Enter- prise Surveys); Frederic Meunier and Rita Ramalho (Doing Business); Michael Orzano (Standard & Poor’s global stock market indexes); James Hackett of the International Institute for Strategic Studies (military personnel); Sam Perlo-Freeman of the Stockholm International Peace Research Institute (military expenditures and arms transfers); Therese Petterson (battle-related deaths); Clare Spurrell (internally dis- placed persons); Cristian Gonzalez of the International Road Federation, Cyrille Martin of the International Civil Aviation Organization, and Andreas Dietrich Kopp (transport); Vincent Valentine of the United Nations Conference on Trade and Development (ports); Azita Amjadi (high-tech exports); Naman Khandelwal and Renato Perez of the International Monetary Fund (financial soundness indicators); Vanessa Grey,
- 165. World Development Indicators 2015 141Economy States and markets Global links Back Esperanza Magpantay, Susan Teltscher, and Ivan Vallejo Vall of the International Telecommunication Union and Torbjörn Fredriksson, Scarlett Fondeur Gil, and Diana Korka of the United Nations Conference on Trade and Development (information and communica- tion technology goods trade); Martin Schaaper and Rohan Pathirage of the United Nations Educational, Scientific and Cultural Organization Institute for Sta- tistics (research and development, researchers, and technicians); and Ryan Lamb of the World Intellectual Property Organization (patents and trademarks). 6. Global links Section 6 was prepared by Wendy Huang with sub- stantial input from Evis Rucaj and Rubena Sukaj and in partnership with the Financial Data Team of the World Bank’s Development Data Group, Development Research Group (trade), Development Prospects Group (commodity prices and remittances), International Trade Department (trade facilitation), and external part- ners. Evis Rucaj wrote the introduction. Azita Amjadi and Molly Fahey Watts (trade and tariffs) and Rubena Sukaj (external debt and financial data) provided input on the data and table. Other contributors include Fré- déric 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); Cristina 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); Tarek Abou Chabake of the Office of the UN High Commissioner for Refugees (refugees); and Teresa Ciller and Leandry Moreno of the World Tour- ism Organization (tourism). Ramgopal Erabelly, Shelley Fu, and William Prince provided technical assistance. Other parts of the book Jeff Lecksell and Bruno Bonansea of the World Bank’s Map Design Unit coordinated preparation of the maps on the inside covers and within each section. William Prince prepared User guide and the lists of online tables and indicators for each section and wrote Sta- tistical methods, with input from Neil Fantom. Federico Escaler prepared Primary data documentation. Leila Rafei prepared Partners. Database management William Prince coordinated management of the World Development Indicators database, with assistance from Liu Cui and Shelley Fu in the Sustainable Devel- opment and Data Quality Team. Operation of the database management system was made possible by Ramgopal Erabelly working with the Data and Infor- mation Systems Team under the leadership of Soong Sup Lee. Design, production, and editing Azita Amjadi and Leila Rafei coordinated all stages of production with Communications Development Incorporated, which provided overall design direction, editing, and layout, led by 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, Atsushi Shimo, and Malarvizhi Veer- appan provided software support on the DataBank application. Publishing and dissemination The World Bank’s Publishing and Knowledge Division, under the direction of Carlos Rossel, provided assis- tance throughout the production process. Denise Bergeron, Stephen McGroarty, Nora Ridolfi, Paola Scalabrin, and Janice Tuten coordinated printing, marketing, and distribution. World Development Indicators mobile applications Software preparation and testing were managed by Shelley Fu with assistance from Prashant Chaudhari,
- 166. 142 World Development Indicators 2015 Front User guide World view People Environment? Credits Neil Fantom, Mohammed Omar Hadi, Soong Sup Lee, Parastoo Oloumi, William Prince, Jomo Tariku, and Malarvizhi Veerappan. Systems development was undertaken in the Data and Information Systems Team led by Soong Sup Lee. Liu Cui and William Prince provided data quality assurance. Online access Coordination of the presentation of the WDI online, through the Open Data website, the DataBank appli- cation, the table browser application, and the Appli- cation Programming Interface, was provided by Neil Fantom and Soong Sup Lee. Development and main- tenance of the website were managed by a team led by Azita Amjadi and comprising George Gongadze, Timothy Herzog, Jeffrey McCoy, Paige Morency- Notario, Leila Rafei, and Jomo Tariku. Systems development was managed by a team led by Soong Sup Lee, with project management provided by Malar- vizhi Veerappan. Design, programming, and testing were carried out by Ying Chi, Rajesh Danda, Shel- ley Fu, Mohammed Omar Hadi, Siddhesh Kaushik, Ugendran Machakkalai, Nacer Megherbi, Parastoo Oloumi, Atsushi Shimo, and Jomo Tariku. Liu Cui and William Prince coordinated production and provided data quality assurance. Multilingual translations of online content were provided by a team in the General Services 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.
- 168. E C O - A U D I T Environmental Benefits Statement The World Bank is committed to preserving endangered forests and natural resources. World Development Indicators 2015 is printed on recycled paper with 30 percent post- consumer fiber in accordance with the rec- ommended standards for paper usage set by the Green Press Initiative, a nonprofit program supporting publishers in using fiber that is not sourced from endangered forests. For more information, visit www .greenpressinitiative.org. Saved: • 13 trees • 6 million British thermal units of total energy • 1,086 pounds of net greenhouse gases (CO2 equivalent) • 5,890 gallons of waste water • 394 pounds of solid waste
- 169. 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 Cabo 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 Mozambique Zimbabwe Botswana Namibia 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- baijan Armenia 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 41312 NOVEMBER 2014 Kosovo Montenegro Classified according to World Bank estimates of 2013 GNI per capita The world by income Low ($1,045 or less) Lower middle ($1,046–$4,125) Upper middle ($4,126–$12,745) High ($12,746 or more) No data