Countries from Natural Earth 50M scale data with a Human Development Index attribute, repeated for each of the following years: 1980, 1985, 1990, 1995, 2000, 2005, 2010, & 2013, to enable time-series display using the YEAR attribute. The Human Development Index measures achievement in 3 areas of human development: long life, good education and income. Specifically, the index is computed using life expectancy at birth, Mean years of schooling, expected years of schooling, and gross national income (GNI) per capita (PPP $). The United Nations categorizes the HDI values into 4 groups. In 2013 these groups were defined by the following HDI values: Very High: 0.736 and higher High: 0.615 to 0.735 Medium: 0.494 to 0.614 Low: 0.493 and lower

Human Development Index attributes are from The World Bank: HDRO calculations based on data from UNDESA (2013a), Barro and Lee (2013), UNESCO Institute for Statistics (2013), UN Statistics Division (2014), World Bank (2014) and IMF (2014).

The average for 2023 based on 184 countries was 0.744 points. The highest value was in Iceland: 0.972 points and the lowest value was in South Africa: 0.388 points. The indicator is available from 1980 to 2023. Below is a chart for all countries where data are available.

Countries from Natural Earth 50M scale data with a Human Development Index attribute for each of the following years: 1980, 1985, 1990, 1995, 2000, 2005, 2010, 2013, 2015, & 2017. The Human Development Index measures achievement in 3 areas of human development: long life, good education and income. Specifically, the index is computed using life expectancy at birth, Mean years of schooling, expected years of schooling, and gross national income (GNI) per capita (PPP $). The United Nations categorizes the HDI values into 4 groups. In 2013 these groups were defined by the following HDI values: Very High: 0.736 and higher High: 0.615 to 0.735 Medium: 0.494 to 0.614 Low: 0.493 and lower

In 2015 & 2017 these groups were defined by the following HDI values: Very High: 0.800 and higher High: 0.700 to 0.799 Medium: 0.550 to 0.699 Low: 0.549 and lower

Human Development Index attributes are from The World Bank: HDRO calculations based on data from UNDESA (2013a), Barro and Lee (2013), UNESCO Institute for Statistics (2013), UN Statistics Division(2014), World Bank (2014) and IMF (2014). 2015 & 2017 values source: HDRO calculations based on data from UNDESA (2017a), UNESCO Institute for Statistics (2018), United Nations Statistics Division (2018b), World Bank (2018b), Barro and Lee (2016) and IMF (2018).

Population data are from (1) United Nations Population Division. World Population Prospects, (2) United Nations Statistical Division. Population and Vital Statistics Report (various years), (3) Census reports and other statistical publications from national statistical offices, (4) Eurostat: Demographic Statistics, (5) Secretariat of the Pacific Community: Statistics and Demography Programme, and (6) U.S. Census Bureau: International Database.

Human Development Index by country for 2013. This is a filtered layer based on the "Human Development Index by country, 1980-2010 time-series" layer.The Human Development Index measures achievement in 3 areas of human development: long life, good education and income. Specifically, the index is computed using life expectancy at birth, Mean years of schooling, expected years of schooling, and gross national income (GNI) per capita (PPP $).The United Nations categorizes the HDI values into 4 groups. In 2013 these groups were defined by the following HDI values:

Very High Human Development: 0.736 and higher High Human Development: 0.615 to 0.735 Medium Human Development: 0.494 to 0.614 Low Human Development: 0.493 and lower

Country shapes from Natural Earth 50M scale data. Human Development Index attributes are from The World Bank: HDRO calculations based on data from UNDESA (2013a), Barro and Lee (2013), UNESCO Institute for Statistics (2013), UN Statistics Division (2014), World Bank (2014) and IMF (2014).

Is the Gini Coefficient Enough? A Microeconomic Data Decomposition StudyIvan Skliarov, Lukasz Goczek (2023).List of data files:1. theil_raw.csv - data obtained from LISSY using the lis_theil.R script.*2. scv_raw.csv - data obtained from LISSY using the scv_theil.R script.*3. hdi.csv - Human Development Index and its components.4. gini.csv - Gini coefficient from SWIID 9.4.5. wdi.csv - World Development Indicators from the World Bank.6. wgi.csv - World Governance Indicators from the World Bank.7. govcon.csv - government consumption (% of GDP) from UNCTAD.8. theil_fin.csv - final dataset (1, 3-7 combined), which is used in lis_analysis.do.9. scv_fin.csv - final dataset (2-7 combined), which is used in lis_analysis.do.10. indexes.csv - only within and between-cohort components of the Theil index and SCV with imputed values (see lis_analysis.do) for Georgia and Lithuania, which is used in lis_plot.R. * LISSY is the remote-execution system allowing access to the Luxembourg Income Study database: https://www.lisdatacenter.org/data-access/lissy/.For questions about this research please contact:Ivan Skliarov, MA: Faculty of Economic Sciences, University of Warsaw, Poland, Długa 44/50, Warsaw 00-241, Poland, i.skliarov@student.uw.edu.pl.Lukasz Goczek, PhD: Faculty of Economic Sciences, University of Warsaw, Poland, Długa 44/50, Warsaw 00-241, Poland, lgoczek@wne.uw.edu.pl.

The E-Government Development Index presents the state of E-Government Development of the United Nations Member States. Along with an assessment of the website development patterns in a country, the E-Government Development index incorporates the access characteristics, such as the infrastructure and educational levels, to reflect how a country is using information technologies to promote access and inclusion of its people. The EGDI is a composite measure of three important dimensions of e-government, namely: provision of online services, telecommunication connectivity and human capacity.

School life expectancy, primary, gender parity index (GPI) in Uzbekistan was reported at 0.98764 GPI in 2019, according to the World Bank collection of development indicators, compiled from officially recognized sources. Uzbekistan - School life expectancy, primary, gender parity index - actual values, historical data, forecasts and projections were sourced from the World Bank on June of 2025.

This study explores the relationship between human development and market institutions and tests the performance of three alternative economic perspectives that each assign a different role to governments. Based on a sample of 34 OECD countries plus Russia across a time frame spanning 1990 to 2018, the results demonstrate that economic freedom and small size of government do not significantly affect human development as measured by the Human Development Index. Hence, we find no support for the free-market ideal. Conversely, it is found that human development is positively related to governmental interventions that aim to reduce externalities (public expenditure on education and environmental regulation). These results support the perfect-market perspective. With respect to the welfare-state perspective, the findings are mixed. On the one hand, we found that (some) labor market regulations (particularly hiring and firing regulations, hours regulations and mandated cost of worker dismissal) have a negative impact upon human development. On the other hand, human development is shown to be positively affected by governmental intervention seeking to reduce gender stratification in the labor market.

School life expectancy, primary to tertiary, gender parity index (GPI) in Russia was reported at 1.027 GPI in 2018, according to the World Bank collection of development indicators, compiled from officially recognized sources. Russia - School life expectancy, primary to tertiary, gender parity index - actual values, historical data, forecasts and projections were sourced from the World Bank on July of 2025.

Freedom in the World, produced by Freedom House, is an annual report assessing political rights and civil liberties in 195 countries and 13 territories. It uses numerical ratings and descriptive texts to evaluate real-world rights and freedoms, emphasizing implementation over legal guarantees. The methodology is based on the Universal Declaration of Human Rights, applying universal standards regardless of geography, ethnicity, or level of economic development.

The HCI calculates the contributions of health and education to worker productivity. The final index score ranges from zero to one and measures the productivity as a future worker of child born today relative to the benchmark of full health and complete education. For more information, consult the Human Capital Index website: http://www.worldbank.org/en/publication/human-capital

Maximum and minimum values for the indicators of the GNI from 1990–2015.

Liberia is one of the poorest countries, ranking 180th out of the 190 countries in the World Bank’s development database. Based on the national poverty line, 59 percent of Liberians were poor in 2016, the latest year for which household survey data is available. According to World Bank estimations, about 6 out of 10 Liberians continue to live in poverty. Broader welfare measures tell a similar story: Liberia ranked 177th out of 193 countries on the UN Human Development Index and the UN Gender Inequality Index in 2022. Low human development is exemplified by Liberia’s score of 0.32 on the World Bank’s measure of human capital, suggesting that a newborn child will only reach 32 percent of their potential productivity as an adult under current conditions of healthcare and education. Poverty is more prevalent in rural areas, and its incidence increases with distance from the capital, Monrovia, highlighting Liberia’s severe spatial challenges. Rapid population growth, deforestation, and the accelerating impacts of climate change are degrading the country’s abundant natural capital, a dynamic which, in turn, is increasingly tied to the persistence of poverty. Pervasive food insecurity contributes to the high rate of child stunting and to malnutrition more generally. Inadequate sanitation heightens the risk of communicable disease.

Multidimensional Poverty Headcount Ratio: UNDP: % of total population data was reported at 3.800 % in 2015. Multidimensional Poverty Headcount Ratio: UNDP: % of total population data is updated yearly, averaging 3.800 % from Dec 2015 (Median) to 2015, with 1 observations. The data reached an all-time high of 3.800 % in 2015 and a record low of 3.800 % in 2015. Multidimensional Poverty Headcount Ratio: UNDP: % of total population data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Brazil – Table BR.World Bank.WDI: Social: Poverty and Inequality. The multidimensional poverty headcount ratio (UNDP) is the percentage of a population living in poverty according to UNDPs multidimensional poverty index. The index includes three dimensions -- health, education, and living standards.;Alkire, S., Kanagaratnam, U., and Suppa, N. (2023). ‘The global Multidimensional Poverty Index (MPI) 2023 country results and methodological note’, OPHI MPI Methodological Note 55, Oxford Poverty and Human Development Initiative (OPHI), University of Oxford. (https://ophi.org.uk/mpi-methodological-note-55-2/);;

Abstract

The objectives of the survey were to provide information regarding the following: a. Household use of, and expenditure patterns for, social services; b. Reasons for low levels of household investment in education and health services for children; c. The distribution of the benefits of public spending for social services and how to improve targeting; d. Households' evaluation of the social services available to them; e. The potential for demand-side interventions to increase human capital investment directly (especially for girls and the poor); and f. The feasibility of repeated national monitoring surveys to assess the impact of future Bank and government projects in the social sectors, and to increase Tanzania's capacity to perform household survey work.

Geographic coverage

National coverage

Kind of data

Sample survey data [ssd]

Sampling procedure

Sample size is 5,184 households

The HRDS is national in scope and uses all the 222 clusters of the National Master Sample (NMS) maintained by the Bureau of Statistics as its sampling frame.4 Two NMS clusters were not surveyed because of weather conditions. For example, Nyamburi village in the Mara region was inaccessible. Heavy rains had washed away a bridge 8 kms (14 miles) from the village. All household surveys conducted by the Bureau of Statistics (e.g. Agricultural Sample Survey since 1986/87, Labor Force Survey in 1990/91) have used the framework of the NMS. This permits obtaining estimates at the national level and by area: rural, Dar es Salaam (DSM), and other urban towns. The current NMS covers 222 clusters: 100 rural villages representing the rural areas, and 122 Enumeration Areas (EAs) representing the urban areas. Fifty-two EAs are from the capital city, itself, 40 EAs are from the nine municipalities (Arusha, Dodoma, Moshi, Tanga, Morogoro, Iringa, Mbeya, Tabora, and Mwanza), and 10 EAs are from the remaining regional headquarters.

Selection of households and non-response.

Household selection was done in the field. In each cluster the team supervisor would first obtain the list of ten-cell leaders from the local authorities, and then, from each ten cell-leader, the list of households belonging to his/her cell. Each household was assigned a unique number, and then, using a table of random numbers, randomly selected. In each cluster, a list of about 30 households was then obtained, the last households in the list being alternates. With the collaboration of local authorities, the field workers were able to have an almost 100 percent reponse rate, except for the cases in which no member of the household was present for intervieing, and returning to the household was not feasible. Refusals to cooperate were rare. In those cases--absent households or refusals--, new households were drawn from the list of alternates.

The survey covered a total of 4,953 households in the 20 regions of Mainland Tanzania: 2,135 rural and 2,818 urban (see Table 1). In a second stage, the survey was extended to Zanzibar, where 230 households, in 24 clusters, were interviewed.

Region / Rural / Urban / Total Dodoma / 100 / 80 / 180 Arusha / 118 / 121 / 239 Kilimanjaro / 124 / 154 / 278 Tanga / 132 / 167 / 299 Morogoro / 88 / 120 / 208 Coast / 79 / 88 / 167 Dar es Salaam / 0 / 1127 / 1127 Lindi / 84 / 50 / 134 Mtwara / 114 / 44 / 158 Ruvuma / 69 / 49 / 118 Iringa / 124 / 128 / 252 Mbeya / 174 / 153 / 327 Singida / 82 / 41 / 123 Tabora / 99 / 72 / 171 Rukwa / 59 / 56 / 115 Kigoma / 83 / 35 / 118 Shinyanga / 153 / 54 / 207 Kagera / 193 / 24 / 217 Mwanza / 163 / 192 / 355 Mara / 97 / 63 / 160 Mainland Tanzania / 2135 / 2818 / 4953 Zanzibar / 127 / 104 / 231

Mode of data collection

Face-to-face [f2f]

Research instrument

Development of Survey Instrument.

The first draft of the household survey was developed in English in July, 1993. Training of enumerators, based on this draft, began on August 2, 1993. The month of August was devoted to training the enumerators and pre-testing the questionnaire. The first pre-test of the questionnaire took place in mid-August. The household questionnaire was almost completely precoded to eliminate coding errors and time delays. A category labeled "other: specify" was added to several questions. For those questions for which answers were not mutually exclusive, we precoded them with letters, rather than numbers, to allow for unambiguously coding of multiple answers. To minimize nonsampling errors, the questionnaire was in a form that reduced to a minimum the number of decisions required of interviewers while in the field. In anticipation of pages becoming detached from the questionnaire, every page contained a space for the household number and the last digit of the cluster code. Despite the fact that questions were written exactly as they were supposed to be asked by the interviewer, interviewers were granted some flexibility to give the interview greater semblance to a conversation, rather than an inquisition.

Pre-Test of Questionnaire.

The "pre-pre-test" of the questionnaire (August 16, 1993) was done only to discern whether the questions were understood, how long the administration of the survey required, whether all responses had been anticipated, which sections needed to be stressed during the training, etc. In this pre-pre-test, each questionnaire required an average of 4 hours to complete, far longer than the planned 1.5 hour maximum. The survey was consequently shortened and streamlined.

The true pre-test was conducted in two different types of clusters: Ubungo ward in DSM (urban) and Kibaha in the Coast Region (rural) over a period of two days. We chose these clusters because they are representative of two distinct groups, so a broader spectrum of answers and problems with the instrument could be anticipated. In the pre-test each questionnaire required an average of 2.5 hours. After a couple weeks of interviewing, the enumerators became more familiar with the instrument, resulting in their spending an average of 1.5 to 2 hours per questionnaire.

During the pre-test, each supervisor was asked to comment on each interview. The supervisor was asked to pay special attention to questions that seemed to make the respondent uncomfortable, that the respondent had difficulty understanding, or that the respondent seemed to dislike. The supervisor also evaluated which sections seemed to go slowly, had the most difficult questions, or provided insufficient opportunity for a complete response.

Revision of questionnaire.

Given the results of the two pre-tests, several areas for improvement in the questionnaire were identified. Perhaps most importantly, the willingness-to-pay amounts were adjusted. The sample distributions of the maximum willingness-to-pay questions were analyzed, and, based on that analysis, we decided to change some of the values. For example, in the child spacing question, the "pay Tsh 1,000" responses unexpectedly accounted for a large share of the bids. Thus, we provided the option of paying more by introducing "pay Tsh 50,000" and "pay Tsh 25,000" as answer choices. For the other contigent valuation sections--health and education--the first pre-test determined that there was also a large lumping of responses at the high end of the scale. We adjusted the ranges accordingly, although there remains some lumping at the high end in the final data.

We also changed the order of the sections. Based on the pre-test and judgment of the field workers, we decided to first ask the questions in the individual section, then the contigent valuation questions, then the household questions. Because the respondents enjoyed the contigent valuation questions so much, this decision helped increase interest in the questionnaire and re-energized the respondent before proceeding with the household questions--the last part of the questionnaire. The final survey instrument, incorporating all of the changes dictated by the pre-tests and other expert advice, was completed on September 12, 1993.

Translation.

Translation of the survey instrument was a joint effort of the enumerators and supervisors. Given the specific characteristics of the Kswahili language, this was a much better approach than asking one translator to translate from English to Kswahili, and another one to translate from Kswahili to English. The "group" translation, involving those who would ask the questions, was intended to avoid different interpretations of the same question and achieve uniformity. In this way the enumerators were able to better convey the message/objective of each question.

The majority of the interviews were conducted in swahili. In very few cases, because no one in the selected household could speak swahili, the need arose to use interpreters.

Our initial plan called for the field work to start no later than August 29. However, unforeseen circumstances, including both financial and logistical problems, delayed the first field trip. Both the money and the materials were available by September 6, and five of the six teams left for Tanga region on that day. Initially we had planned to have the sixth team based full-time in Dar es Salaam; however, tighter time constraints imposed by the above and subsequent delays eventually made it necessary to send the sixth team into the field as well, as detailed below.

Description of questionnaires

The main objective of the survey was to obtain data on the use of, and spending on, the social sectors. The primary emphasis was on education and health--the areas in which the major gaps in availability of data were identified. The survey was divided into five major components, each of which was further subdivided, as described below:

I. Individual Questionnaire A. Household Roster; B. Information on

The HCI Lower Bound reflects uncertainty in the measurement of the components and the overall index. It is obtained by recalculating the HCI using estimates of the lower bounds of each of the components of the HCI. The range between the upper and lower bound is the uncertainty interval. While the uncertainty intervals constructed here do not have a rigorous statistical interpretation, a rule of thumb is that if for two countries they overlap substantially, the differences between their HCI values are not likely to be practically meaningful. For more information, consult the Human Capital Index website: http://www.worldbank.org/en/publication/human-capital

This document includes a table showing the recorded number of deaths due to five different diseases (respiratory tuberculosis, other tuberculosis, hepatitis, malaria and human immunodeficiency virus), classified by countries and sex. It also includes the population of each country by sex and the calculated mortality. Each country is classified and ranked by life expectancy and the human development index. Data sourced from WHO, World Bank Population, UNDP and Barcelona's city council websites.

The HCI Upper Bound reflects uncertainty in the measurement of the components and the overall index. It is obtained by recalculating the HCI using estimates of the upper bounds of each of the components of the HCI. The range between the upper and lower bound is the uncertainty interval. While the uncertainty intervals constructed here do not have a rigorous statistical interpretation, a rule of thumb is that if for two countries they overlap substantially, the differences between their HCI values are not likely to be practically meaningful. For more information, consult the Human Capital Index website: http://www.worldbank.org/en/publication/human-capital

Correlation between rural comprehensive development level and income type, per capita GDP, HDI, and urbanization rate.

This map is adapted from the outstanding work of Dr. Joseph Kerski at ESRI. A map of political, social, and economic indicators for 2010. Created at the Data Analysis and Social Inquiry Lab at Grinnell College by Megan Schlabaugh, April Chen, and Adam Lauretig.Data from Freedom House, the Center for Systemic Peace, and the World Bank.Shapefile:Weidmann, Nils B., Doreen Kuse, and Kristian Skrede Gleditsch. 2010. The Geography of the International System: The CShapes Dataset. International Interactions 36 (1).Field Descriptions:

Variable Name Variable Description Years Available Further Description Source

TotPop Total Population 2011 Population of the country/region World Bank

GDPpcap GDP per capita (current USD) 2011 A measure of the total output of a country that takes the gross domestic product (GDP) and divides it by the number of people in the country. The per capita GDP is especially useful when comparing one country to another because it shows the relative performance of the countries. World Bank

GDPpcapPPP GDP per capita based on purchasing power parity (PPP) 2011

World Bank

HDI Human Development Index (HDI) 2011 A tool developed by the United Nations to measure and rank countries' levels of social and economic development based on four criteria: Life expectancy at birth, mean years of schooling, expected years of schooling and gross national income per capita. The HDI makes it possible to track changes in development levels over time and to compare development levels in different countries. World Bank

LifeExpct Life expectancy at birth 2011 The probable number of years a person will live after a given age, as determined by mortality in a specific geographic area. World Bank

MyrSchool Mean years of schooling 2011 Years that a 25-year-old person or older has spent in schools World Bank

ExpctSch Expected years of schooling 2011 Number of years of schooling that a child of school entrance age can expect to receive if prevailing patterns of age-specific enrolment rates persist throughout the child’s life. World Bank

GNIpcap Gross National Income (GNI) per capita 2011 Gross national income (GNI) 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. GNI per capita is gross national income divided by mid-year population. World Bank

GNIpcapHDI GNI per capita rank minus HDI rank 2011

World Bank

NaIncHDI Nonincome HDI
2011

World Bank

15+LitRate Adult (15+) literacy rate (%). Total 2010

UNESCO

EmplyAgr Employment in Agriculture 2009

World Bank

GDPenergy GDP per unit of energy use 2010 The PPP GDP per kilogram of oil equivalent of energy use. World Bank

GDPgrowth GDP growth (annual %) 2011

World Bank

GDP GDP (current USD) 2011

World Bank

ExptGDP Exports of Goods and Service (% GDP) 2011 The value of all goods and other market services provided to the rest of the world World Bank

ImprtGDP Imports of Goods and Service (% GDP) 2011 The value of all goods and other market services received from the rest of the world. World Bank

AgrGDP Agriculture, Value added (% GDP) 2011 Agriculture corresponds to ISIC divisions 1-5 and includes forestry, hunting, and fishing, as well as cultivation of crops and livestock production. Value added is the net output of a sector after adding up all outputs and subtracting intermediate inputs. World Bank

FDI Foreign Direct Investment, net (current USD) 2011 Foreign direct investment are the net inflows of investment to acquire a lasting management interest (10 percent or more of voting stock) in an enterprise operating in an economy other than that of the investor. It is the sum of equity capital, reinvestment of earnings, other long-term capital, and short-term capital as shown in the balance of payments. World Bank

GNIpcap GNI per capita PP 2011 GNI per capita based on purchasing power parity (PPP). PPP GNI is gross national income (GNI) converted to international dollars using purchasing power parity rates. An international dollar has the same purchasing power over GNI as a U.S. dollar has in the United States. GNI 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. World Bank

Inflatn Inflation, Consumer Prices (annual %) 2011 Inflation as measured by the consumer price index reflects the annual percentage change in the cost to the average consumer of acquiring a basket of goods and services that may be fixed or changed at specified intervals, such as yearly. World Bank

InfltnGDP Inflation, GDP deflator (annual %) 2011 Inflation as measured by the annual growth rate of the GDP implicit deflator shows the rate of price change in the economy as a whole. The GDP implicit deflator is the ratio of GDP in current local currency to GDP in constant local currency. World Bank

PctWomParl % women in national parliament 2010

United Nations

IntnetUser Internet Users, per 100 peple 2011 Internet users are people with access to the worldwide network. World Bank

HIVPrevlnc Estimated HIV Prevalence% - (Ages 15-49) 2009 Prevalence of HIV refers to the percentage of people ages 15-49 who are infected with HIV. UNAIDS estimates. UNAIDS

AgrLand Agricultural land (% of land area) 2009 Agricultural land refers to the share of land area that is arable, under permanent crops, and under permanent pastures. World Bank

AidRecPP Aid received per person (current US$) 2010 Net official development assistance (ODA) per capita consists of disbursements of loans made on concessional terms (net of repayments of principal) and grants by official agencies of the members of the Development Assistance Committee (DAC), by multilateral institutions, and by non-DAC countries to promote economic development and welfare in countries and territories in the DAC list of ODA recipients; and is calculated by dividing net ODA received by the midyear population estimate. It includes loans with a grant element of at least 25 percent (calculated at a rate of discount of 10 percent). World Bank

AlcohAdul Alcohol consumption per adult (15+) in litres 2008 Liters of pure alcohol, computed as the sum of alcohol production and imports, less alcohol exports, divided by the adult population (aged 15 years and older). World Health Organization

ArmyPct Military expenditure (% of central government expenditure) 2008 Military expenditures data from SIPRI are derived from the NATO definition, 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 these are judged to be trained and equipped for military operations; and military space activities. Such expenditures include military and civil personnel, including retirement pensions of military personnel and social services for personnel; operation and maintenance; procurement; military research and development; and military aid (in the military expenditures of the donor country). World Development Indicators (World Bank)

TFR Total Fertility Rate 2011 The average number of children that would be born per woman if all women lived to the end of their childbearing years and bore children according to a given fertility rate at each age. This indicator shows the potential for population change in a country. World Bank

CO2perUSD CO2 kg per USD 2008 Carbon dioxide emissions are those stemming from the burning of fossil fuels and the manufacture of cement. They include carbon dioxide produced during consumption of solid, liquid, and gas fuels and gas flaring. World Bank

ExpdtrPrim Expenditure per student, primary (% of GDP per capita) 2008 Public expenditure per pupil as a % of GDP per capita. Primary is the total public expenditure per student in primary education as a percentage of GDP per capita. Public expenditure (current and capital) includes government spending on educational institutions (both public and private), education administration as well as subsidies for private entities (students/households and other privates entities). World Bank

ExpdtrSecd Expenditure per student, secondary (% of GDP per capita) 2008 Public expenditure per pupil as a % of GDP per capita. Secondary is the total public expenditure per student in secondary education as a percentage of GDP per capita. World Bank

ExpdtrTert Expenditure per student, tertiary (% of GDP per capita) 2008 Public expenditure per pupil as a % of GDP per capita. Tertiary is the total public expenditure per student in tertiary education as a percentage of GDP per capita. World Bank

FDIoutf Foreign direct investment, net outflows (% of GDP) 2010 Foreign direct investment are the net inflows of investment to acquire a lasting management interest (10 percent or more of voting stock) in an enterprise operating in an economy other than that of the investor. It is the sum of equity capital, reinvestment of earnings, other long-term capital, and short-term capital as shown in the balance of payments. This series shows net outflows of investment from the