In 2022, South Africa scored 0.72 points in the Human Development Index (HDI), which indicated a high level of development. Moreover, this was the highest score achieved in the Southern African region. Botswana followed closely behind, with an HDI of 0.71 points. Conversely, Mozambique recorded the lowest in the region with 0.46 points, which signifies low human development.

In 2021, South Africa scored 0.71 in the Human Development Index (HDI), which indicated a high level of development. The country experienced a drop in the HDI score compared to the previous year, which was 0.73. However, an improvement was recorded from 2005 onwards. At that year, South Africa's score was 0.63, meaning that the country had a medium human development. The categorization changed from medium to high in 2013.

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.

Compared to other African countries, Seychelles scored the highest in the Human Development Index (HDI) in 2022. The country also ranked 67th globally, as one of the countries with a very high human development. This was followed by Mauritius, Libya, Egypt, and Tunisia, with scores ranging from 0.80 to 0.73 points. On the other hand, Central African Republic, South Sudan, and Somalia were among the countries in the region with the lowest index scores, indicating a low level of human development.

The average for 2023 based on 52 countries was 0.585 points. The highest value was in the Seychelles: 0.848 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.

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.

In 2022, Mauritius and the Seychelles scored just over 0.8 points on the Human Development Index (HDI), which indicated a very high level of development. Moreover, this was the highest score achieved in the East African region. Kenya followed, with an HDI of 0.6 points. Conversely, Somalia and South Sudan recorded the lowest in the region with 0.38 points, which signifies low human development.

The average for 2023 based on 46 countries was 0.569 points. The highest value was in the Seychelles: 0.848 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.

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

******* had the highest level of the Human Development Index (HDI) worldwide in 2023 with a value of *****. With a score of ****, ****** followed closely behind *********** 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 eight and number 13 of the HDI, respectively, *********************** are the only Asian locations within the top-15 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 ***** U.S. dollars, increasing throughout the decades until reaching ****** in 2023, which is expected to continue to increase in the future. Meanwhile, in 1989, Singapore had a GDP of nearly ** billion U.S. dollars, which has risen to nearly *** 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 *** billion U.S. dollars by 2022. Per capita, the UAE GDP was around ****** U.S. dollars in 1989, and has nearly doubled to ****** U.S. dollars by 2021. Moreover, this is expected to reach over ****** 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.

South Sudan and Somalia had the ****** 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 ******* HDI worldwide.

This data set contains information on 155 countries, including geographic, climatologic, demographic, psychological, genetic and economic data.

The index provides the only comprehensive measure available for non-income poverty, which has become a critical underpinning of the SDGs. Critically the MPI comprises variables that are already reported under the Demographic Health Surveys (DHS) and Multi-Indicator Cluster Surveys (MICS) The resources subnational multidimensional poverty data from the data tables published by the Oxford Poverty and Human Development Initiative (OPHI), University of Oxford. The global Multidimensional Poverty Index (MPI) measures multidimensional poverty in over 100 developing countries, using internationally comparable datasets and is updated annually. The measure captures the severe deprivations that each person faces at the same time using information from 10 indicators, which are grouped into three equally weighted dimensions: health, education, and living standards. The global MPI methodology is detailed in Alkire, Kanagaratnam & Suppa (2023)

Explore The Human Capital Report dataset for insights into Human Capital Index, Development, and World Rankings. Find data on Probability of Survival to Age 5, Expected Years of School, Harmonized Test Scores, and more.

Low income, Upper middle income, Lower middle income, High income, Human Capital Index (Lower Bound), Human Capital Index, Human Capital Index (Upper Bound), Probability of Survival to Age 5, Expected Years of School, Harmonized Test Scores, Learning-Adjusted Years of School, Fraction of Children Under 5 Not Stunted, Adult Survival Rate, Development, Human Capital, World Rankings

Afghanistan, Albania, Algeria, Angola, Antigua and Barbuda, Argentina, Armenia, Australia, Austria, Azerbaijan, Bahrain, Bangladesh, Belarus, Belgium, Benin, Bhutan, Bosnia and Herzegovina, Botswana, Brazil, Brunei, Bulgaria, Burkina Faso, Burundi, Côte d'Ivoire, Cambodia, Cameroon, Canada, Central African Republic, Chad, Chile, China, Colombia, Comoros, Congo, Costa Rica, Croatia, Cyprus, Denmark, Dominica, Dominican Republic, Ecuador, Egypt, El Salvador, Estonia, Eswatini, Ethiopia, Fiji, Finland, France, Gabon, Gambia, Georgia, Germany, Ghana, Greece, Grenada, Guatemala, Guinea, Guyana, Haiti, Honduras, Hungary, Iceland, India, Indonesia, Iran, Iraq, Ireland, Israel, Italy, Jamaica, Japan, Jordan, Kazakhstan, Kenya, Kiribati, Kuwait, Latvia, Lebanon, Lesotho, Liberia, Lithuania, Luxembourg, Madagascar, Malawi, Malaysia, Mali, Malta, Marshall Islands, Mauritania, Mauritius, Mexico, Micronesia, Moldova, Mongolia, Montenegro, Morocco, Mozambique, Myanmar, Namibia, Nauru, Nepal, Netherlands, New Zealand, Nicaragua, Niger, Nigeria, North Macedonia, Norway, Oman, Pakistan, Palau, Panama, Papua New Guinea, Paraguay, Peru, Philippines, Poland, Portugal, Qatar, Romania, Russia, Rwanda, Samoa, Saudi Arabia, Senegal, Serbia, Seychelles, Sierra Leone, Singapore, Slovenia, Solomon Islands, South Africa, South Sudan, Spain, Sri Lanka, Sudan, Sweden, Switzerland, Tajikistan, Tanzania, Thailand, Timor-Leste, Togo, Tonga, Trinidad and Tobago, Tunisia, Turkey, Tuvalu, Uganda, Ukraine, United Arab Emirates, United Kingdom, Uruguay, Uzbekistan, Vanuatu, Vietnam, Yemen, Zambia, Zimbabwe, WORLD

Follow data.kapsarc.org for timely data to advance energy economics research.

Last year edition of the World Economic Forum Human Capital Report explored the factors contributing to the development of an educated, productive and healthy workforce. This year edition deepens the analysis by focusing on a number of key issues that can support better design of education policy and future workforce planning.

Spearman rank correlation coefficients of index values when dropping one indicator at a time to compute index.

South Sudan had the lowest level of the Human Development Index (HDI) worldwide in 2022 after adjusting for inequality, with a value of 0.22. Its neighbors Chad and the Central African Republic followed behind. Meanwhile, Switzerland topped the HDI not adjusted for inequality.

South Africa Multidimensional Poverty Headcount Ratio: UNDP: % of total population data was reported at 6.300 % in 2016. South Africa Multidimensional Poverty Headcount Ratio: UNDP: % of total population data is updated yearly, averaging 6.300 % from Dec 2016 (Median) to 2016, with 1 observations. The data reached an all-time high of 6.300 % in 2016 and a record low of 6.300 % in 2016. South Africa 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 South Africa – Table ZA.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/);;

This project commissioned by the KwaZulu-Natal Provincial Government was designed to obtain baseline data on subjective and objective development indicators. The project comprised a household survey conducted during November and December 1996. The complete survey covered at least 6 500 households across the province of KwaZulu-Natal. It followed a pilot study of perceptions of development conducted among 678 adults in October 1995. As one of the most comprehensive contributions on development indicators in the history of South Africa, it is the first large survey covering the usual “hard” indicators – such as service delivery levels – and peoples’ comments and perceptions of these services and of their governments’ development programmes and priorities. The study/project was motivated by the need to establish an information database for the preparation and monitoring of the province’s RDP business and development plans, to synthesise subjectively articulated (bottom-up) and objectively defined (top-down) approaches to the determination of needs, to modify and improve on the usefulness of the Human Development Index (HDI), to provide an opportunity for research capacity building among civil servants and thereby providing a means to effect good governance practices and, to provide a basis for the development of objective matrices, objectives-by-time-scales and, a semi-rational budgeting and planning tool. 1 data file with 6,606 cases.

Afrique du Sud: Human Development Index (0 - 1): Pour cet indicateur, The United Nations fournit des données pour la Afrique du Sud de 1980 à 2023. La valeur moyenne pour Afrique du Sud pendant cette période était de 0.638 points avec un minimum de 0.381 points en 2022 et un maximum de 0.713 points en 2021.

BackgroundDespite rising incidence and mortality rates in Africa, cancer has been given low priority in the research field and in healthcare services. Indeed, 57% of all new cancer cases around the world occur in low income countries exacerbated by lack of awareness, lack of preventive strategies, and increased life expectancies. Despite recent efforts devoted to cancer epidemiology, statistics on cancer rates in Africa are often dispersed across different registries. In this study our goal included identifying the most promising prevention and treatment approaches available in Africa. To do this, we collated and analyzed the incidence and fatality rates for the 10 most common and fatal cancers in 56 African countries grouped into 5 different regions (North, West, East, Central and South) over 16-years (2002–2018). We examined temporal and regional trends by investigating the most important risk factors associated to each cancer type. Data were analyzed by cancer type, African region, gender, measures of socioeconomic status and the availability of medical devices.ResultsWe observed that Northern and Southern Africa were most similar in their cancer incidences and fatality rates compared to other African regions. The most prevalent cancers are breast, bladder and liver cancers in Northern Africa; prostate, lung and colorectal cancers in Southern Africa; and esophageal and cervical cancer in East Africa. In Southern Africa, fatality rates from prostate cancer and cervical cancer have increased. In addition, these three cancers are less fatal in Northern and Southern Africa compared to other regions, which correlates with the Human Development Index and the availability of medical devices. With the exception of thyroid cancer, all other cancers have higher incidences in males than females.ConclusionOur results show that the African continent suffers from a shortage of medical equipment, research resources and epidemiological expertise. While recognizing that risk factors are interconnected, we focused on risk factors more or less specific to each cancer type. This helps identify specific preventive and therapeutic options in Africa. We see a need for implementing more accurate preventive strategies to tackle this disease as many cases are likely preventable. Opportunities exist for vaccination programs for cervical and liver cancer, genetic testing and use of new targeted therapies for breast and prostate cancer, and positive changes in lifestyle for lung, colorectal and bladder cancers. Such recommendations should be tailored for the different African regions depending on their disease profiles and specific needs.