(1) The Human Development Index (HDI) is compiled by the United Nations Development Programme (UNDP) to measure a country's comprehensive development in the areas of health, education, and economy according to the UNDP's calculation formula.(2) Explanation: (1) The HDI value ranges from 0 to 1, with higher values being better. (2) Due to our country's non-membership in the United Nations and its special international situation, the index is calculated by our department according to the UNDP formula using our country's data. The calculation of the comprehensive index for each year is mainly based on the data of various indicators adopted by the UNDP. (3) In order to have the same baseline for international comparison, the comprehensive index and rankings are not retroactively adjusted after being published.(3) Notes: (1) The old indicators included life expectancy at birth, adult literacy rate, gross enrollment ratio, and average annual income per person calculated by purchasing power parity. (2) The indicators were updated to include life expectancy at birth, mean years of schooling, expected years of schooling, and nominal gross national income (GNI) calculated by purchasing power parity. Starting in 2011, the GNI per capita was adjusted from nominal value to real value to exclude the impact of price changes. Additionally, the HDI calculation method has changed from arithmetic mean to geometric mean. (3) The calculation method for indicators in the education domain changed from geometric mean to simple average due to retrospective adjustments in the 2014 Human Development Report for the years 2005, 2008, and 2010-2012. Since 2016, the education domain has adopted data compiled by the Ministry of Education according to definitions from the United Nations Educational, Scientific and Cultural Organization (UNESCO) and the Organization for Economic Co-operation and Development (OECD).

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).

This Subnational Human Development Index Database contains for the period 1990-2017 for 1625 regions within 161 countries the national and subnational values of the Subnational Human Development Index (SHDI), for the three dimension indices on the basis of which the SHDI is constructed – education, health and standard of living --, and for the four indicators needed to create the dimension indices -- expected years of schooling, mean years of schooling, life expectancy and gross national income per capita.

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).

In 2021, Ghana scored 0.63 on the Human Development Index (HDI), which indicated a medium level of development. The country experienced a steady increase in the index from 2000 onwards. However, it remained between the medium and low indicators of human development.

Switzerland had the highest level of the Human Development Index (HDI) worldwide in 2022 with a value of 0.967. With a score of 0.966, Norway followed closely behind Switzerland and had the second highest level of human development in that year. The rise of the Asian tigers In the decades after the Cold War, the four so-called Asian tigers, South Korea, Singapore, Taiwan, and Hong Kong (now a Special Administrative Region of China) experienced rapid economic growth and increasing human development. At number four and number nine of the HDI, respectively, Hong Kong and Singapore are the only Asian locations within the top 10 highest HDI scores. Both locations have experienced tremendous economic growth since the 1980’s and 1990’s. In 1980, the per capita GDP of Hong Kong was 5,703 U.S. dollars, increasing throughout the decades until reaching 50,029 in 2023, which is expected to continue to increase in the future. Meanwhile, in 1989, Singapore had a GDP of nearly 31 billion U.S. dollars, which has risen to nearly 501 billion U.S. dollars today and is also expected to keep increasing. Growth of the UAE The United Arab Emirates (UAE) is the only Middle Eastern country besides Israel within the highest ranking HDI scores globally. Within the Middle East and North Africa (MENA) region, the UAE has the third largest GDP behind Saudi Arabia and Israel, reaching nearly 507 billion U.S. dollars by 2022. Per capita, the UAE GDP was around 21,142 U.S. dollars in 1989, and has nearly doubled to 43,438 U.S. dollars by 2021. Moreover, this is expected to reach over 67,538 U.S. dollars by 2029. On top of being a major oil producer, the UAE has become a hub for finance and business and attracts millions of tourists annually.

Turkey: Human Development Index (0 - 1): The latest value from 2022 is 0.855 points, an increase from 0.838 points in 2021. In comparison, the world average is 0.727 points, based on data from 185 countries. Historically, the average for Turkey from 1980 to 2022 is 0.701 points. The minimum value, 0.496 points, was reached in 1980 while the maximum of 0.855 points was recorded in 2022.

The Human development index (HDI) of Switzerland from 1990 to 2021 shows that throughout this period Switzerland has consistently had very high levels of human development which has increased year-on-year. The HDI itself is a statistic that combines life-expectancy, education levels and GDP per capita. Countries with scores over 0.800 are considered to have very high levels of development, compared with countries that score lower.

Ghana achieved a gender development score of 0.95 in the 2019 Gender Development Index. The country registered lower values in the preceding years studied. In 2013, it stood at 0.88 points. The indicator measures differences in male and female achievements in three basic dimensions of human development, comprising health, education, and command over economic resources.

South Sudan and Somalia had the lowest levels of human development based on the Human Development Index (HDI). Many of the countries at the bottom of the list are located in Sub-Saharan Africa, underlining the prevalence of poverty and low levels of education in the region. Meanwhile, Switzerland had the highest HDI worldwide.

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.

The Gross Domestic Product per capita (gross domestic product divided by mid-year population converted to international dollars, using purchasing power parity rates) has been identified as an important determinant of susceptibility and vulnerability by different authors and used in the Disaster Risk Index 2004 (Peduzzi et al. 2009, Schneiderbauer 2007, UNDP 2004) and is commonly used as an indicator for a country's economic development (e.g. Human Development Index). Despite some criticisms (Brooks et al. 2005) it is still considered useful to estimate a population's susceptibility to harm, as limited monetary resources are seen as an important factor of vulnerability. However, collection of data on economic variables, especially sub-national income levels, is problematic, due to various shortcomings in the data collection process. Additionally, the informal economy is often excluded from official statistics. Night time lights satellite imagery of NOAA grid provides an alternative means for measuring economic activity. NOAA scientists developed a model for creating a world map of estimated total (formal plus informal) economic activity. Regression models were developed to calibrate the sum of lights to official measures of economic activity at the sub-national level for some target Country and at the national level for other countries of the world, and subsequently regression coefficients were derived. Multiplying the regression coefficients with the sum of lights provided estimates of total economic activity, which were spatially distributed to generate a 30 arc-second map of total economic activity (see Ghosh, T., Powell, R., Elvidge, C. D., Baugh, K. E., Sutton, P. C., & Anderson, S. (2010).Shedding light on the global distribution of economic activity. The Open Geography Journal (3), 148-161). We adjusted the GDP to the total national GDPppp amount as recorded by IMF (International Monetary Fund) for 2010 and we divided it by the population layer from Worldpop Project. Further, we ran a focal statistics analysis to determine mean values within 10 cell (5 arc-minute, about 10 Km) of each grid cell. This had a smoothing effect and represents some of the extended influence of intense economic activity for local people. Finally we apply a mask to remove the area with population below 1 people per square Km.

This dataset has been produced in the framework of the "Climate change predictions in Sub-Saharan Africa: impacts and adaptations (ClimAfrica)" project, Work Package 4 (WP4). More information on ClimAfrica project is provided in the Supplemental Information section of this metadata.

Data publication: 2014-06-01

Supplemental Information:

ClimAfrica was an international project funded by European Commission under the 7th Framework Programme (FP7) for the period 2010-2014. The ClimAfrica consortium was formed by 18 institutions, 9 from Europe, 8 from Africa, and the Food and Agriculture Organization of United Nations (FAO).

ClimAfrica was conceived to respond to the urgent international need for the most appropriate and up-to-date tools and methodologies to better understand and predict climate change, assess its impact on African ecosystems and population, and develop the correct adaptation strategies. Africa is probably the most vulnerable continent to climate change and climate variability and shows diverse range of agro-ecological and geographical features. Thus the impacts of climate change can be very high and can greatly differ across the continent, and even within countries.

The project focused on the following specific objectives:

1. Develop improved climate predictions on seasonal to decadal climatic scales, especially relevant to SSA;

2. Assess climate impacts in key sectors of SSA livelihood and economy, especially water resources and agriculture;

3. Evaluate the vulnerability of ecosystems and civil population to inter-annual variations and longer trends (10 years) in climate;

4. Suggest and analyse new suited adaptation strategies, focused on local needs;

5. Develop a new concept of 10 years monitoring and forecasting warning system, useful for food security, risk management and civil protection in SSA;

6. Analyse the economic impacts of climate change on agriculture and water resources in SSA and the cost-effectiveness of potential adaptation measures.

The work of ClimAfrica project was broken down into the following work packages (WPs) closely connected. All the activities described in WP1, WP2, WP3, WP4, WP5 consider the domain of the entire South Sahara Africa region. Only WP6 has a country specific (watershed) spatial scale where models validation and detailed processes analysis are carried out.

Contact points:

Metadata Contact: FAO-Data

Resource Contact: Selvaraju Ramasamy

Resource constraints:

copyright

Online resources:

GDP per capita

Project deliverable D4.1 - Scenarios of major production systems in Africa

Climafrica Website - Climate Change Predictions In Sub-Saharan Africa: Impacts And Adaptations

Resource recovery from human excreta can advance circular economies while improving access to sanitation and renewable agricultural inputs. While national projections of nutrient recovery potential provide motivation for resource recovery sanitation, elucidating generalizable strategies for sustainable implementation requires a deeper understanding of country-specific overlap between supply and demand. For 107 countries, we analyze the colocation of human-derived nutrients (in urine) and crop demands for nitrogen, phosphorus, and potassium. To characterize colocation patterns, we fit data for each country to a generalized logistic function. Using fitted logistic curve parameters, three typologies were identified: (i) dislocated nutrient supply and demand resulting from high density agriculture (with low population density) and nutrient islands (e.g., dense cities) motivating nutrient concentration and transport; (ii) colocated nutrient supply and demand enabling local reuse; and (iii) diverse nutrient supply–demand proximities, with countries spanning the continuum between (i) and (ii). Finally, we explored connections between these typologies and country-specific contextual characteristics via principal component analysis and found that the Human Development Index was clustered by typology. By providing a generalizable, quantitative framework for characterizing the colocation of human-derived nutrient supply and agricultural nutrient demand, these typologies can advance resource recovery by informing resource management strategies, policy, and investment.

The Human Development Index (HDI) of Ireland from 1990 to 2022 shows that after 1995, Ireland's HDI score increased quite rapidly, so that by 2022 it's score of 0.950 gave it the status of a very highly developed country. The HDI itself is a statistic that combines life-expectancy, education levels and GDP per capita. Countries with scores over 0.700 are considered to have high levels of development, compared with countries that score lower.

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 March of 2025.

In sub-Saharan Africa, a score of 0.55 was achieved on the Human Development Index (HDI) in 2021. This represented a low level of human development. Throughout the periods under study, the sub-region remained within the index scores of 0.42 and 0.56, an indication of low human development.

The Human Development Index (HDI) of Germany has increased from 0.829 in 1990 to 0.942 by 2021, indicating that Germany has reached very high levels of human development. The HDI itself is a statistic that combines life-expectancy, education levels and GDP per capita. Countries with scores over 0.800 are considered to have very high levels of development, compared with countries that score lower. Germany's HDI score has increased from 0.801 in 1990 to 0.947 by 2019, implying that Germany has consistently had a very high level of human development.

The Human development index (HDI) of Greece from 1990 to 2021 shows that Greece's HDI score has increased from 0.759 in 1990 to 0.887 by 2021, implying that the country has reached very high levels of development. The HDI itself is a statistic that combines life-expectancy, education levels and GDP per capita. Countries with scores over 0.700 are considered to have high levels of development, compared with countries that score lower.

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 system of social indicators for the Federal Republic of Germany - developed in its original version as part of the SPES project under the direction of Wolfgang Zapf - provides quantitative information on levels, distributions and changes in quality of life, social progress and social change in Germany from 1950 to 2013, i.e. over a period of more than sixty years. With the approximately 400 objective and subjective indicators that the indicator system comprises in total, it claims to measure welfare and quality of life in Germany in a differentiated way across various areas of life and to observe them over time. In addition to the indicators for 13 areas of life, including income, education and health, a selection of cross-cutting global welfare measures were also included in the dashboard, i.e. general welfare indicators such as life satisfaction, social isolation or the Human Development Index. Based on available data from official statistics and survey data, time series were compiled for all indicators, ideally with annual values from 1950 to 2013. Around 90 of the indicators were marked as "key indicators" in order to highlight central dimensions of welfare and quality of life across the various areas of life. The further development and expansion, regular maintenance and updating as well as the provision of the data of the system of social indicators for the Federal Republic of Germany have been among the tasks of the Center for Social Indicator Research, which is based at GESIS, since 1987. For a detailed description of the system of social indicators for the Federal Republic of Germany, see the study description under "Other documents".
The data on the area of life ´Application of Income and Supply´ is composed as follows: The system of social indicators for the Federal Republic of Germany - developed in its original version as part of the SPES project under the leadership of Wolfgang Zapf - provides quantitative information on levels, distributions and changes in quality of life, social progress and social change in Germany from 1950 to 2013 , i.e. over a period of more than sixty years. The approximately 400 objective and subjective indicators that the indicator system includes as a whole aim to measure welfare and quality of life in Germany in a differentiated manner across different areas of life and to monitor them over time. In addition to the indicators for 13 areas of life, including income, education and health, a selection of cross-sectional global welfare measures were also included in the indicator system, i.e. general welfare indicators, such as life satisfaction, social isolation or the Human Development Index. Based on available data from official statistics and survey data, time series were compiled for all indicators, ideally with annual values from 1950 to 2013. Of the indicators, around 90 were marked as “key indicators” to identify central dimensions of welfare and quality of life across different areas of life to highlight. The further development and expansion, regular maintenance and updating as well as the provision of data from the system of social indicators for the Federal Republic of Germany have been among the tasks of the Center for Social Indicators Research at GESIS since 1987. For a detailed description of the system of social indicators for the Federal Republic of Germany, see the study description under “Other documents”.