In 2021, Southern Africa's richest ** percent held around ** percent of the total wealth. Furthermore, the richest one percent in the region held over ** percent. The other African regions had a slightly smaller share of wealth with the wealthiest people. For instance, in West Africa, the richest ** percent held close to ** percent of the wealth, while the richest one percent held ** percent. On the other hand. The poorest ** percent in all the regions held lower than ***** percent of the wealth.

Temporal Neighborhood-Level Material Wealth Maps of Africa (1990–2019)

This repository provides neighborhood-level material wealth estimates across Africa for the period 1990–2019. The data are stored in a single GeoTIFF file (wealth_map.tif), where each band corresponds to a three-year interval. These estimates were generated using a deep-learning model trained on Demographic and Health Surveys (DHS) data, as described in Pettersson et al. (2023).

  Overview

Data… See the full description on the dataset page: https://huggingface.co/datasets/cjerzak/TemporalNeighborhoodMaterialWealthAfrica.

Seychelles recorded the highest Gross National Income (GNI) per capita in Africa as of 2023, at 16,940 U.S. dollars. The African island was, therefore, the only high-income country on the continent, according to the source's classification. Mauritius, Gabon, Botswana, Libya, South Africa, Equatorial Guinea, Algeria, and Namibia were defined as upper-middle-income economies, those with a GNI per capita between 4,516 U.S. dollars and 14,005 U.S. dollars. On the opposite, 20 African countries recorded a GNI per capita below 1,145 U.S. dollars, being thus classified as low-income economies. Among them, Burundi presented the lowest income per capita, some 230 U.S. dollars. Poverty and population growth in Africa Despite a few countries being in the high income and upper-middle countries classification, Africa had a significant number of people living under extreme poverty. However, this number is expected to decline gradually in the upcoming years, with experts forecasting that this number will decrease to almost 400 million individuals by 2030 from nearly 430 million in 2023, despite the continent currently having the highest population growth rate globally. African economic growth and prosperity In recent years, Africa showed significant growth in various industries, such as natural gas production, clean energy generation, and services exports. Furthermore, it is forecast that the GDP growth rate would reach 4.5 percent by 2027, keeping the overall positive trend of economic growth in the continent.

Time Series of Satellite Imagery Improve Deep Learning Estimates of Neighborhood-Level Poverty in Africa These maps contain estimates of material wealth at the neighborhood-level across Africa. These estimates were made using a novel deep-learning model for predicting material wealth based on satellite images from Landsat, DMSP and VIIRS. The model was trained on DHS survey data as described in the corresponding paper (Pettersson et al. 2023). The maps cover all populated areas according to the Global Human Settlement Layer. The spatial resolution of the maps is 6.72 x 6.72 km and the unit of measurement is the International Wealth Index (IWI), scaled from 0 to 1. Each map represents a three-year time span between 1990 to 2019. In the tif file these maps are stored as bands in the image, resulting in the following configuration: Band 1: IWI estimates for 1990-1992 Band 2: IWI estimates for 1993-1995 Band 3: IWI estimates for 1996-1998 Band 4: IWI estimates for 1999-2001 Band 5: IWI estimates for 2002-2004 Band 6: IWI estimates for 2005-2007 Band 7: IWI estimates for 2008-2010 Band 8: IWI estimates for 2011-2013 Band 9: IWI estimates for 2014-2016 Band 10: IWI estimates for 2017-2019 For a full explanation of the map-generating process and estimates, see the corresponding paper.

The purpose of this dataset is to provide village-level wealth estimates for places where up-to-date information about geographic wealth distribution is needed. This dataset contains information on buildings, roads, points of interest (POIs), night-time luminosity, population density, and estimated wealth index for 1-mi² inhabited places identified by the underlying datasets. The wealth level is an estimated value of the International Wealth Index which is a comparable asset based wealth index covering the complete developing world.

In 2023, the Middle East and North Africa, and Latin America were the regions with the lowest level of distribution of wealth worldwide, with the richest ten percent holding around ** percent of the total wealth. On the other hand, in Europe, the richest ten percent held around ** percent of the wealth. East and South Asia were the regions where the poorest half of the population held the highest share of the wealth, but still only around **** percent, underlining the high levels of wealth inequalities worldwide.

This statistic shows the distribution of annual household income in South Africa in 2010. In 2010, 15.5 percent of households in South Africa had no income.

On this map I used three different layers. On one of them you can see the most populated places of west Africa, on another one you can see the places of Africa with the lowest income per day and the highlighted places on the first layer are the same in the second one because the places with the most population tend to have lower incomes per day. The third layer is also related to the two others in a way that the places that were highlighted in previous layers are again the ones shown on the third layer showing the poorest countries worldwide.

South Africa ZA: Income Share Held by Third 20% data was reported at 8.200 % in 2014. This records an increase from the previous number of 8.000 % for 2010. South Africa ZA: Income Share Held by Third 20% data is updated yearly, averaging 8.200 % from Dec 1993 (Median) to 2014, with 7 observations. The data reached an all-time high of 9.900 % in 2000 and a record low of 7.500 % in 2005. South Africa ZA: Income Share Held by Third 20% 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: Poverty. Percentage share of income or consumption is the share that accrues to subgroups of population indicated by deciles or quintiles. Percentage shares by quintile may not sum to 100 because of rounding.; ; World Bank, Development Research Group. Data are based on primary household survey data obtained from government statistical agencies and World Bank country departments. Data for high-income economies are from the Luxembourg Income Study database. For more information and methodology, please see PovcalNet (http://iresearch.worldbank.org/PovcalNet/index.htm).; ; The World Bank’s internationally comparable poverty monitoring database now draws on income or detailed consumption data from more than one thousand six hundred household surveys across 164 countries in six regions and 25 other high income countries (industrialized economies). While income distribution data are published for all countries with data available, poverty data are published for low- and middle-income countries and countries eligible to receive loans from the World Bank (such as Chile) and recently graduated countries (such as Estonia) only. See PovcalNet (http://iresearch.worldbank.org/PovcalNet/WhatIsNew.aspx) for definitions of geographical regions and industrialized countries.

South Africa had the highest inequality in income distribution in 2024, with a Gini score of **. Its South African neighbor, Namibia, followed in second. The Gini coefficient measures the deviation of income (or consumption) distribution among individuals or households within a country from a perfectly equal distribution. A value of 0 represents absolute equality, and a value of 100 represents absolute inequality. All the 20 most unequal countries in the world were either located in Africa or Latin America & The Caribbean.

South Africa ZA: Income Share Held by Lowest 20% data was reported at 2.400 % in 2014. This records a decrease from the previous number of 2.500 % for 2010. South Africa ZA: Income Share Held by Lowest 20% data is updated yearly, averaging 2.600 % from Dec 1993 (Median) to 2014, with 7 observations. The data reached an all-time high of 3.100 % in 2000 and a record low of 2.400 % in 2014. South Africa ZA: Income Share Held by Lowest 20% 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: Poverty. Percentage share of income or consumption is the share that accrues to subgroups of population indicated by deciles or quintiles. Percentage shares by quintile may not sum to 100 because of rounding.; ; World Bank, Development Research Group. Data are based on primary household survey data obtained from government statistical agencies and World Bank country departments. Data for high-income economies are from the Luxembourg Income Study database. For more information and methodology, please see PovcalNet (http://iresearch.worldbank.org/PovcalNet/index.htm).; ; The World Bank’s internationally comparable poverty monitoring database now draws on income or detailed consumption data from more than one thousand six hundred household surveys across 164 countries in six regions and 25 other high income countries (industrialized economies). While income distribution data are published for all countries with data available, poverty data are published for low- and middle-income countries and countries eligible to receive loans from the World Bank (such as Chile) and recently graduated countries (such as Estonia) only. See PovcalNet (http://iresearch.worldbank.org/PovcalNet/WhatIsNew.aspx) for definitions of geographical regions and industrialized countries.

New Zealand’s Treasury, as illustrated by its Living Standards Framework, desires policy that not only promotes economic growth, but also sustainability and equity. This paper studies how taxation and abatement policy can work to keep an economy viable in regards to capital stocks, consumption, debt, environment and the relative factor share (a proxy for income inequality), as well as the trade-offs it faces in different levels of pollutant industry. This is done in the context of Viability Theory, a branch of mathematics suited for policy analysis. The results show that reducing an economies environmental impact is key for achieving the multi faceted growth laid out in the Living Standards Framework.

In 2023, Comoros was the most unequal country in the East African region based on the degree of inequality in income distribution measured by the Gini coefficient, which amounted to a value of 45.3. Seychelles recorded the lowest Gini coefficient, at 32.1. The Gini coefficient measures the deviation of the distribution of income (or consumption) among individuals or households within a country from a perfectly equal distribution. A value of 0 represents absolute equality, a value of 100 absolute inequalities.

About 50.4 percent of the household income of private households in the U.S. were earned by the highest quintile in 2023, which are the upper 20 percent of the workers. In contrast to that, in the same year, only 3.5 percent of the household income was earned by the lowest quintile. This relation between the quintiles is indicative of the level of income inequality in the United States. Income inequalityIncome inequality is a big topic for public discussion in the United States. About 65 percent of U.S. Americans think that the gap between the rich and the poor has gotten larger in the past ten years. This impression is backed up by U.S. census data showing that the Gini-coefficient for income distribution in the United States has been increasing constantly over the past decades for individuals and households. The Gini coefficient for individual earnings of full-time, year round workers has increased between 1990 and 2020 from 0.36 to 0.42, for example. This indicates an increase in concentration of income. In general, the Gini coefficient is calculated by looking at average income rates. A score of zero would reflect perfect income equality and a score of one indicates a society where one person would have all the money and all other people have nothing. Income distribution is also affected by region. The state of New York had the widest gap between rich and poor people in the United States, with a Gini coefficient of 0.51, as of 2019. In global comparison, South Africa led the ranking of the 20 countries with the biggest inequality in income distribution in 2018. South Africa had a score of 63 points, based on the Gini coefficient. On the other hand, the Gini coefficient stood at 16.6 in Azerbaijan, indicating that income is widely spread among the population and not concentrated on a few rich individuals or families. Slovenia led the ranking of the 20 countries with the greatest income distribution equality in 2018.

South Africa ZA: Income Share Held by Highest 20% data was reported at 68.200 % in 2014. This records a decrease from the previous number of 68.900 % for 2010. South Africa ZA: Income Share Held by Highest 20% data is updated yearly, averaging 68.200 % from Dec 1993 (Median) to 2014, with 7 observations. The data reached an all-time high of 71.000 % in 2005 and a record low of 62.700 % in 2000. South Africa ZA: Income Share Held by Highest 20% 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: Poverty. Percentage share of income or consumption is the share that accrues to subgroups of population indicated by deciles or quintiles. Percentage shares by quintile may not sum to 100 because of rounding.; ; World Bank, Development Research Group. Data are based on primary household survey data obtained from government statistical agencies and World Bank country departments. Data for high-income economies are from the Luxembourg Income Study database. For more information and methodology, please see PovcalNet (http://iresearch.worldbank.org/PovcalNet/index.htm).; ; The World Bank’s internationally comparable poverty monitoring database now draws on income or detailed consumption data from more than one thousand six hundred household surveys across 164 countries in six regions and 25 other high income countries (industrialized economies). While income distribution data are published for all countries with data available, poverty data are published for low- and middle-income countries and countries eligible to receive loans from the World Bank (such as Chile) and recently graduated countries (such as Estonia) only. See PovcalNet (http://iresearch.worldbank.org/PovcalNet/WhatIsNew.aspx) for definitions of geographical regions and industrialized countries.

South Africa ZA: Income Share Held by Lowest 10% data was reported at 0.900 % in 2014. This stayed constant from the previous number of 0.900 % for 2010. South Africa ZA: Income Share Held by Lowest 10% data is updated yearly, averaging 1.000 % from Dec 1993 (Median) to 2014, with 7 observations. The data reached an all-time high of 1.300 % in 2000 and a record low of 0.900 % in 2014. South Africa ZA: Income Share Held by Lowest 10% 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: Poverty. Percentage share of income or consumption is the share that accrues to subgroups of population indicated by deciles or quintiles.; ; World Bank, Development Research Group. Data are based on primary household survey data obtained from government statistical agencies and World Bank country departments. Data for high-income economies are from the Luxembourg Income Study database. For more information and methodology, please see PovcalNet (http://iresearch.worldbank.org/PovcalNet/index.htm).; ; The World Bank’s internationally comparable poverty monitoring database now draws on income or detailed consumption data from more than one thousand six hundred household surveys across 164 countries in six regions and 25 other high income countries (industrialized economies). While income distribution data are published for all countries with data available, poverty data are published for low- and middle-income countries and countries eligible to receive loans from the World Bank (such as Chile) and recently graduated countries (such as Estonia) only. See PovcalNet (http://iresearch.worldbank.org/PovcalNet/WhatIsNew.aspx) for definitions of geographical regions and industrialized countries.

The table presents data from 10 out of 11 CEEAC countries gathered from the WDI database. It features the following series of variables: - the income share of the lowest 20%, - the total natural resources rents (% GDP), - agriculture, value added (annual % growth), - GDP growth (annual %), - Industry, value added (annual % growth), - the total population growth (annual % growth), - Rural population growth (annual %), - Urban population growth (annual %), - Services, value added (annual % growth) , - Domestic credit to private sector by banks (% of GDP), - Employment in agriculture (% of total employment) (modeled ILO estimate), - Employment in industry (% of total employment) (modeled ILO estimate), - Employment in service (% of total employment) (modeled ILO estimate), - Inflation, GDP deflator (annual %), - GINI index (World Bank estimate), - Gini (%), - Vast majority income (annual % growth). Data are then treated in E-views to regress pro poor growth (measured by the income share held by the lowest 20% and by the vast majority income growth) with the value added of the primary (agriculture), the secondary (industry) and the tertiary (service) sectors; employment the both sectors, the natural resources rents and other control variables like GDP growth, inflation and inequality.

The compilation of a Soil and Terrain digital database for the South-African region forms a part of the ongoing activities of the Food and Agriculture Organisation of the United Nations (FAO) and the International Soil Reference and Information Centre (ISRIC) to update the world's baseline information on natural resources. The updating of world soil resources, using the Soil and Terrain (SOTER) digital database methodology, is part of a global SOTER programme and intended to replace the FAO/Unesco 1:5 million scale Soil Map of the World (1971-1981). The African sheet of this map was published in 1973 and has been compiled on basis of information and data available at that time. It is understandable that a substantial part does not reflect the present state of knowledge of the soils in that region. The national institutes, responsible for the natural resources inventories, have been collecting a wealth of new information on the distribution and occurrence of soils in their region, which has resulted in updating their national soil maps mostly at scale 1:1 million, often applying the Revised Legend (FAO, UNEP, ISRIC, 1988) for the description of the mapping units. The International Union of Soil Science (IUSS) adopted an important change in the classification used for the map by introducing lower levels of subunits of the World Reference Base for Soil Resources (IUSS, FAO, ISRIC, 1998). This, together with the new soil data available at national level, justified such an update of the soil resources for the Southern African region. The compilation of a Soil and Terrain digital database for the South-African region forms a part of the ongoing activities of the Food and Agriculture Organisation of the United Nations (FAO) and the International Soil Reference and Information Centre (ISRIC) to update the world?s baseline information on natural resources. The updating of world soil resources, using the Soil and Terrain (SOTER) digital database methodology, is part of a global SOTER programme and intended to replace the FAO/Unesco 1:5 million scale Soil Map of the World (1971-1981). The African sheet of this map was published in 1973 and has been compiled on basis of information and data available at that time. It is understandable that a substantial part does not reflect the present state of knowledge of the soils in that region. The national institutes, responsible for the natural resources inventories, have been collecting a wealth of new information on the distribution and occurrence of soils in their region, which has resulted in updating their national soil maps mostly at scale 1:1 million, often applying the Revised Legend (FAO, UNEP, ISRIC, 1988) for the description of the mapping units. The International Union of Soil Science (IUSS) adopted an important change in the classification used for the map by introducing lower levels of subunits of the World Reference Base for Soil Resources (IUSS, FAO, ISRIC, 1998). This, together with the new soil data available at national level, justified such an update of the soil resources for the Southern African region.

This data study includes social accounting matrix (SAM) for Ghana for the year 2013. The SAM is an extension of the Standard Nexus Structure. It consists of 55 activity sectors, 56 commodity sectors, three types of factors of production: labor (rural and urban disaggregated by level of education), land, and capital (disaggregated by crops, livestock, mining and other sectors). The household sector is divided spatially into urban and rural households. Rural households are further disaggregated into households that earn crop and/or livestock incomes (i.e., farm households) and those that do no earn incomes from either source (i.e., nonfarm households). Households are further disaggregated into per capita expenditure quintiles. This SAM allows analyzing issues at the detailed level and to better understand the potential impacts of policy changes for both better off and more vulnerable households.

The Tunisia Social Accounting Matrix (SAM), 2012 was built with a focus on analyzing the structure and importance of the agriculture and trade in the Tunisian economy. This SAM helps to understand the linkages between agricultural production, factor income distribution, and households' incomes and expenditures. The 2012 Input-Output (I-O) and the supply-use table were the two main data sources used in building the disaggregated activity sector and commodity accounts. One of the most important added values of this SAM is the estimation of agricultural technologies.