The Human development index (HDI) for European countries in 2022 shows that although all of the countries in this statistic have scores which imply high levels of development, Switzerland's score of 0.962 was the highest in this year. The HDI 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.

The average for 2022 based on 27 countries was 0.903 points. The highest value was in Denmark: 0.952 points and the lowest value was in Bulgaria: 0.799 points. The indicator is available from 1980 to 2022. Below is a chart for all countries where data are available.

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.

Europe and Central Asia was the region with the highest Human Development Index (HDI) worldwide at 0.8. Meanwhile, the lowest HDI was found in Sub-Saharan Africa, underlining the high prevalence of poverty in the region. The difference between the regions was even stronger after adjusting for inequality.

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

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 for the area of life ´population´ is made up as follows:

Agglomeration and migration: external migration, number of immigration, net migration, share of immigration from the EU in total immigration, number of asylum seekers per 10,000 inhabitants. Population density: population density, population density in independent cities, population density in large cities, population density in communities with less than 5000 inhabitants. Regional mobility: internal migration. Burden on the working population: total burden of support (inactive population ratio), burden of supporting children (children´s quotient), burden of supporting students (education quotient), burden of supporting older people (old-age quotient). Population size, growth and structure: Population size (resident population (end of year), population growth rate, natural population growth), generative behavior (net production rate, combined birth rate, mean age at first child), population structure (proportion of the population under 15 years, proportion of the population between 15 and 15). y. and 65 y., proportion of the population over 65 years of age), ethnic structure and integration (proportion of foreigners, proportion of foreigners from the European Union, proportion of marriages between Germans and foreigners, consent for foreigners to remain). Forms of cohabitation: propensity to marry (marriage rate of 35 to 45 year olds, marriage age of single people, combined first marriage rate (= total marriage rate)), importance of stability of marriage and family (out-of-wedlock birth rate, divorce rate, combined divorce rate, remarriage rate), lifestyles and family types (Proportion of single-person households, proportion of incomplete families, proportion of non-marital partnerships, families with children, families with one child, families with two children, families with three children, families with four or more children), widowhood disparity (gender ratio of widowed people aged 65 and over). year of life), subjective evaluation of the family (ideal number of children, importance of the family, family satisfaction). Household structure: contraction tendency (proportion of 3- and 4-generation households, proportion of the population in large households (5 or more people)), solitarization (proportion of the population in single-person households).

In 2023 Zurich was both the leading smart city based on the IMD smart city index as well as the city with the highest human development index score, making it one of the premier places on earth to live in. Notable exceptions to the HDI to IMD index score were Beijing, Dubai, and Abu Dhabi. Beijing is a notable outlier because although it ranked 12th on the digital smart cities ranking it was nearly 90 points lower than Zurich on the HDI score. This is compared to Munich, Germany, which was the 20th digital city but had a HDI score of 950.

Smart tech is watching.

CCTV cameras powered by artificial intelligence have become a significant growing market in the modern city. These are predominantly residential, with half the market catering to residential applications of CCTV cameras. However, commercial and business-related CCTV cameras have also seen significant growth, with the market reaching over 800 million U.S. dollars in 2023.

Digital cities need data and data needs infrastructure.

The leading issue with AI infrastructure is data management. AI is a strong influence on how digital cities work and requires a considerable amount of infrastructure to be effective. Storage of AI software is a minor concern, accounting for less than ten percent of challenges globally in 2023.

Europe and Central Asia were the regions with the highest Human Development Index (HDI) when adjusting for inequality. The lowest inequality-adjusted HDI was found in Sub-Saharan Africa, underlining the high prevalence of poverty in the region. Meanwhile, Switzerland topped the HDI not adjusted for inequality.

Abstract

The principal objective of this survey is to collect basic data reflecting the actual living conditions of the population in Tajikistan. These data will then be used for evaluating socio-economic development and formulating policies to improve living conditions.

The first assessment of living standards in Tajikistan was conducted in 1999. This assessment is bringing about data in order to update the 1999 assessment.

The survey collects information on education, health, employment and other productive activities, demographic characteristics, migration, housing conditions, expenditures and assets.

The information gathered is intended to improve economic and social policy in Tajikistan. It should enable decision-makers to 1) identify target groups for government assistance, 2) inform programs of socio-economic development, and 3) analyse the impact of decisions already made and the current economic conditions on households.

Geographic coverage

National coverage. The 2003 data are representative at the regional level (4 regions) and urban/rural.

Analysis unit

• Households
• Individuals
• Communites

Kind of data

Sample survey data [ssd]

Sampling procedure

The Tajikistan Living Standards Survey (TLSS) for 2003 was based on a stratified random probability sample, with the sample stratified according to oblast and urban/rural settlements and with the share of each strata in the overall sample being in proportion to its share in the total number of households as recorded in the 2000 Census. The same approach was used in the TLSS 1999 although there were some differences in the sampling. First the share of each strata in the overall sample in 1999 was determined according to ‘best estimates’, as it was conducted prior to the 2000 Census. Second the TLSS 2003 over-sampled by 40 percent in Dushanbe, 300 percent in rural Gorno-Badakhshan Administrative Oblast (GBAO) and 600 percent in urban GBAO. Third the sample size was increased in 2003 in comparison with 1999 in order to reduce sampling error. In 2003, the overall sample size was 4,156 households compared with 2,000 households in 1999. [Note: Taken from “Republic of Tajikistan: Poverty Assessment Update”, Report No. 30853, Human Development Sector Unit, Central Asia Country Unit, Europe and Central Asia Region, World Bank, January 2005.]

In addition to the capital city of Dushanbe, the country has several oblasts (regions): (i) Khatlon (comprising Kurban-Tube and Khulyab), which is an agricultural area with most of the country’s cotton growing districts; (ii) the Rayons of Republican Subordination (RRS) with the massive aluminum smelter in the west and agricultural valleys in the east growing crops other than cotton; (iii) Sugd which is the most industrialized oblast; and (iv) Gorno-Badakhshan Administrative Oblast which is mountainous and remote with a small population.

The 2003 data are representative at the regional level (4 regions) and urban/rural.

Mode of data collection

Face-to-face [f2f]

According to the 2024 survey of United Nations (UN) member states, Europe was ranked highest with an E-government development index (EGDI) rating of 0.8493. The EGDI is based on three components: the online service index, the telecommunication infrastructure index, and the human capital index.

In 2020, the number of immigrants to Iceland from non-EU, EFTA, and candidate countries who held a citizenship from a country with a high or very high Human Development Index (HDI) level increased sharply. On the other hand, the number of people from countries with a medium HDI level dropped from over 600 to less than 340. From 2015 to 2018, the number of immigrants from these countries more than doubled due to the influx of immigrants to Europe in 2015 and 2016. Citizens of countries with a low HDI level was the smallest group of immigrants to Iceland, with only 160 in 2021.

In 2025, Luxembourg was the country with the highest gross domestic product per capita in the world. Of the 20 listed countries, 13 are in Europe and four are in Asia, alongside the U.S., Canada, and Australia. There are no African or Latin American countries among the top 20. Correlation with high living standards While GDP is a useful indicator for measuring the size or strength of an economy, GDP per capita is much more reflective of living standards. For example, when compared to life expectancy or indices such as the Human Development Index or the World Happiness Report, there is a strong overlap - 14 of the 20 countries on this list are also ranked among the 20 happiest countries in 2024, and all 20 have "very high" HDIs. Misleading metrics? GDP per capita figures, however, can be misleading, and to paint a fuller picture of a country's living standards then one must look at multiple metrics. GDP per capita figures can be skewed by inequalities in wealth distribution, and in countries such as those in the Middle East, a relatively large share of the population lives in poverty while a smaller number live affluent lifestyles.

In 2022, Finland was the European country with the highest score on the Digital Economy and Society Index (DESI), ranking first in the human capital component thanks to the advanced digital skills of its citizens. Denmark ranked first for its connectivity. Estonia was the first in digital public services. Greece, Bulgaria, and Romania were the member states with the lowest scores in the digitalization of their economy and society.

Die Schweiz erreichte beim Index der menschlichen Entwicklung (Human Development Index, HDI) im Jahr 2022 einen Wert von 0,967 Punkten und war damit das Land auf Platz eins des Rankings. Dahinter folgen Norwegen, Island, Hongkong und Dänemark. Deutschland erreichte mit 0,950 Punkten Platz sieben.

Was ist der Human Development Index?

Der Human Development Index, abgekürzt HDI, ist ein Index der menschlichen Entwicklung in den Ländern der Welt. Er wird von den Vereinten Nationen veröffentlicht und gilt als Wohlstandsindikator. Der HDI kann Werte zwischen 0 und 1 annehmen (zur besseren Darstellung wurden die ursprünglichen Werte in dieser Statistik mit 1.000 multipliziert). Je höher der Wert, desto weiter ist die menschliche Entwicklung in den jeweiligen Ländern vorangeschritten. Hochentwickelte Länder weisen einen HDI von mindestens 0,8 auf.

Die Zusammensetzung des HDI Der Human Development Index (HDI) ist ein zusammengesetzter Index, der auf drei grundlegenden Dimensionen der menschlichen Entwicklung beruht:

die Fähigkeit, ein langes und gesundes Leben zu führen, gemessen an der Lebenserwartung bei der Geburt die Fähigkeit, Wissen zu erwerben, gemessen an durchschnittlichen Schuljahren und erwarteten Schuljahren die Fähigkeit, einen angemessenen Lebensstandard zu erreichen, gemessen am Bruttonationaleinkommen pro Kopf

Siehe zum Thema auch die entsprechenden Statistiken zum Gender Development Index sowie zum Gender Inequality Index.