In 2025, nearly 11.7 percent of the world population in extreme poverty, with the poverty threshold at 2.15 U.S. dollars a day, lived in Nigeria. Moreover, the Democratic Republic of the Congo accounted for around 11.7 percent of the global population in extreme poverty. Other African nations with a large poor population were Tanzania, Mozambique, and Madagascar. Poverty levels remain high despite the forecast decline Poverty is a widespread issue across Africa. Around 429 million people on the continent were living below the extreme poverty line of 2.15 U.S. dollars a day in 2024. Since the continent had approximately 1.4 billion inhabitants, roughly a third of Africa’s population was in extreme poverty that year. Mozambique, Malawi, Central African Republic, and Niger had Africa’s highest extreme poverty rates based on the 2.15 U.S. dollars per day extreme poverty indicator (updated from 1.90 U.S. dollars in September 2022). Although the levels of poverty on the continent are forecast to decrease in the coming years, Africa will remain the poorest region compared to the rest of the world. Prevalence of poverty and malnutrition across Africa Multiple factors are linked to increased poverty. Regions with critical situations of employment, education, health, nutrition, war, and conflict usually have larger poor populations. Consequently, poverty tends to be more prevalent in least-developed and developing countries worldwide. For similar reasons, rural households also face higher poverty levels. In 2024, the extreme poverty rate in Africa stood at around 45 percent among the rural population, compared to seven percent in urban areas. Together with poverty, malnutrition is also widespread in Africa. Limited access to food leads to low health conditions, increasing the poverty risk. At the same time, poverty can determine inadequate nutrition. Almost 38.3 percent of the global undernourished population lived in Africa in 2022.

This data collection consists of aggregate population data by age and sex (for post-stratification population weighting) derived from a UN Population Division file (referred to in the report attached, under the section on post-stratification population weighting) as well as a detailed report which sets out how indicators of deprivation of basic human needs for water, sanitation, shelter, information, education, health and food were developed and used to form summary indicators of severe deprivation and absolute poverty. The report also provides information on how post-stratification population weights were derived to modify the sample weights to make samples more representative of the population as a whole. The data used for this project are from the Demographic and Health Surveys and UNICEF's Multiple Indicator Cluster Surveys (see Related Resources).This project will use high quality, nationally representative, individual level data from over 140 household surveys conducted between 1990 and 2015 in 40 sub-Saharan African countries, to produce national, sub-regional and regional estimates of absolute poverty for the years 1995, 2000, 2005, 2010 and 2015. Age appropriate and gender relevant indicators of severe deprivation of basic human needs will be operationalised, and an internationally recognised peer reviewed methodology (the ‘Bristol Approach’) used to show how poverty is patterned across Africa, and how it has changed over 20 years. It will show if rural populations have been left behind as urban areas develop, or if with increased rural to urban migration, poverty in Africa has evolved into a primarily urban problem. It will address important issues about gender and geographic disparities in poverty, which until recently have only been assessed in monetary terms. The application of the Bristol Approach, to reflect non-monetary dimensions of poverty, will reveal a more meaningful picture of poverty in Africa and how it has changed over time. Links will be developed with researchers across Africa, including academics at the Universities of Cape Town and the Western Cape.

Ghana’s Multidimensional Poverty Index (MPI), which complements the monetary poverty by providing an assessment of deprivation of basic survival needs.

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.

Poverty prevalence (percent) and density (number per square km) at $1.25/day and $2.00/day for select countries in Africa south of the Sahara (SSA). The value $1.25/day represents extreme poverty and $2.00/day represents moderate poverty but when mapped also includes the extremely poor. All $ values are expressed in terms of average 2005 purchasing power parity rates.

Abstract

The Project for Statistics on Living standards and Development was a countrywide World Bank sponsored Living Standards Measurement Survey. It covered approximately 9000 households, drawn from a representative sample of South African households. The fieldwork was undertaken during the nine months leading up to the country's first democratic elections at the end of April 1994. The purpose of the survey was to collect data on the conditions under which South Africans live in order to provide policymakers with the data necessary for development planning. This data would aid the implementation of goals such as those outlined in the Government of National Unity's Reconstruction and Development Programme.

Geographic coverage

The survey had national coverage

Analysis unit

Households and individuals

Universe

The survey covered all household members. Individuals in hospitals, old age homes, hotels and hostels of educational institutions were not included in the sample. Migrant labour hostels were included. In addition to those that turned up in the selected ESDs, a sample of three hostels was chosen from a national list provided by the Human Sciences Research Council and within each of these hostels a representative sample was drawn for the households in ESDs.

Kind of data

Sample survey data

Mode of data collection

Face-to-face [f2f]

Research instrument

The main instrument used in the survey was a comprehensive household questionnaire. This questionnaire covered a wide range of topics but was not intended to provide exhaustive coverage of any single subject. In other words, it was an integrated questionnaire aimed at capturing different aspects of living standards. The topics covered included demographics, household services, household expenditure, educational status and expenditure, remittances and marital maintenance, land access and use, employment and income, health status and expenditure and anthropometry (children under the age of six were weighed and their heights measured). This questionnaire was available to households in two languages, namely English and Afrikaans. In addition, interviewers had in their possession a translation in the dominant African language/s of the region.

In addition to the detailed household questionnaire, a community questionnaire was administered in each cluster of the sample. The purpose of this questionnaire was to elicit information on the facilities available to the community in each cluster. Questions related primarily to the provision of education, health and recreational facilities. Furthermore there was a detailed section for the prices of a range of commodities from two retail sources in or near the cluster: a formal source such as a supermarket and a less formal one such as the "corner cafe" or a "spaza". The purpose of this latter section was to obtain a measure of regional price variation both by region and by retail source. These prices were obtained by the interviewer. For the questions relating to the provision of facilities, respondents were "prominent" members of the community such as school principals, priests and chiefs.

A literacy assessment module (LAM) was administered to two respondents in each household, (a household member 13-18 years old and a one between 18 and 50) to assess literacy levels.

Data appraisal

The data collected in clusters 217 and 218 are highly unreliable and have therefore been removed from the dataset currently available on the portal. Researchers who have downloaded the data in the past should download version 2.0 of the dataset to ensure they have the corrected data. Version 2.0 of the dataset excludes two clusters from both the 1993 and 1998 samples. During follow-up field research for the KwaZulu-Natal Income Dynamics Study (KIDS) in May 2001 it was discovered that all 39 household interviews in clusters 217 and 218 had been fabricated in both 1993 and 1998. These households have been dropped in the updated release of the data. In addition, cluster 206 is now coded as urban as this was incorrectly coded as rural in the first release of the data. Note: Weights calculated by the World Bank and provided with the original data are NOT updated to reflect these changes.

Chart shows the percent of population and number of poor below the Kenya poverty line of Ksh 1,562 per month in rural areas; and Ksh 2,913 in urban areas per per person per month; based on estimated expenditures on minimum provisions of food and non-food items.

Retirement Notice: This item is in mature support as of August 2025 and will be retired in December 2026. Please use this source dataset and follow the steps in the From Vector to Raster blog as a replacement for this service. Esri recommends updating your maps and apps. The annual infant mortality rate in Africa ranges from 99 to 2031 deaths of children less than one-year-old per 10,000 live births. This layer provides access to an approximately 5 km cell sized raster of the Global Subnational Infant Mortality Rates dataset that provides the number of deaths of children less than one-year-old per 10,000 live births in the year 2000. The data cover Africa, Madagascar, and other islands near Africa and were produced by the NASA Socioeconomic Data and Applications Center in 2005. Link to source metadata Dataset SummaryAnalysis: Restricted single source analysis. Maximum size of analysis is 24,000 x 24,000 pixels. What can you do with this layer?This layer has query, identify, and export image services available. The layer is restricted to a 24,000 x 24,000 pixel limit for these services. The source data for this layer are available here. Restricted single source analysis means this layer has size constraints for analysis and it is not recommended for use with other layers in multisource analysis.

The annual infant mortality rate in Africa ranges from 99 to 2031 deaths of children less than one-year-old per 10,000 live births. This layer provides access to an approximately 5 km cell sized raster of the Global Subnational Infant Mortality Rates dataset that provides the number of deaths of children less than one-year-old per 10,000 live births in the year 2000. The data cover Africa, Madagascar, and other islands near Africa and were produced by the NASA Socioeconomic Data and Applications Center in 2005. Link to source metadata Dataset SummaryAnalysis: Restricted single source analysis. Maximum size of analysis is 24,000 x 24,000 pixels. What can you do with this layer?This layer has query, identify, and export image services available. The layer is restricted to a 24,000 x 24,000 pixel limit for these services. The source data for this layer are available here. Restricted single source analysis means this layer has size constraints for analysis and it is not recommended for use with other layers in multisource analysis. This layer is part of a larger collection of landscape layers that you can use to perform a wide variety of mapping and analysis tasks.

This dataset is about book series. It has 1 row and is filtered where the books is Reducing poverty and investing in people : the new role of safety nets in Africa. It features 10 columns including number of authors, number of books, earliest publication date, and latest publication date.

Poverty prevalence (percent) and density (number per square km) at $1.25/day and $2.00/day for select countries in Africa south of the Sahara (SSA). The value $1.25/day represents extreme poverty and $2.00/day represents moderate poverty but when mapped also includes the extremely poor. All $ values are expressed in terms of average 2005 purchasing power parity rates.

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)

The principal data collection units were sites where policy was discussed and acted on. These comprised 2 national Departments of Education (in Kenya and South Africa), 2 provincial departments, 2 schools, 2 NGOs located in large cities, and 2 located in rural areas. Data collected included interviews, focus groups, observations, analysis of school records and records of report back meetings. In addition 12 interviews with staff in global organisations dealing with this policy area were interviewed. Comparative case study was used in Kenya and South Africa to investigate similar kinds of relationship – negotiations with global policy agendas on gender, education and poverty reduction – in somewhat different sites. A selected range of units of analysis were examined for hierarchies in which policy and practice are related from global levels, ranked ‘above’ the national and local level (vertically) and forms of connection, exclusion or boundary setting between different kinds of organisation (horizontally). Both countries have in place policies on poverty, education and gender equality, and are active global policy players. However, they differ in their engagements with global policy transfer, histories of attention to gender. There was thus potential to look at how the cases did and did not vary, and the explanatory weight that could be accorded to local conditions. Five case studies were conducted in each country: the National Department of Education, South Africa, Ministry of Education in Kenya, a provincial department in each country, a matched school attended by children from a peri-urban community with high levels of poverty, a rural NGO working on education and poverty, and a global NGO engaged with the global policy agenda and local implementation. The project aims to examine initiatives which engage with global aspirations to advance gender equality in and through schooling in contexts of poverty. It looks at how these are understood, who participates in implementation, what meanings of gender, schooling and global relations are negotiated, what constraints are experienced, in what ways these are overcome, and what concerns about global obligations emerge. A key focus is what conditions how global policy goals are interpreted and acted on in different sites. Case study research will be conducted in Kenya and South Africa, two countries where reforming governments have sought to address questions of poverty and gender in the expansion of education provision. In each country data will be collected in five sites: the national Department of Education, a provincial education department, a rural primary school, the offices of a Non Governmental Organisation (NGO) engaging with global education and poverty policy, and an education NGO operating at a local level. The main methods of data collection will be documentary analysis, individual and group interviews, focus group discussions, and observations. Advisory committees in Kenya and South Africa will guide the process of data collection, comment critically on emerging analysis, and give support with dissemination. Research methods comprised documentary analysis, interviews, observations, field notes, and focus group discussions. Documents written over the last ten years including websites, policies, and publications of all the organisations were analysed. One hundred and thirty three hours of interviews and group discussions were recorded and transcribed. Observation and analysis of site dynamics were made using ethnographic methods. Report back meetings on preliminary findings in all the ten case study sites took place after the first round of data collection and were recorded and transcribed. In a second round of data collection up to a year later participants were interviewed regarding changes that had taken place. A small number of interviews were conducted with children at the peri-urban schools and rural NGO projects.

Central African Republic CF: Gini Coefficient (GINI Index): World Bank Estimate data was reported at 43.000 % in 2021. This records a decrease from the previous number of 56.200 % for 2008. Central African Republic CF: Gini Coefficient (GINI Index): World Bank Estimate data is updated yearly, averaging 56.200 % from Dec 1992 (Median) to 2021, with 3 observations. The data reached an all-time high of 61.300 % in 1992 and a record low of 43.000 % in 2021. Central African Republic CF: Gini Coefficient (GINI Index): World Bank Estimate data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Central African Republic – Table CF.World Bank.WDI: Social: Poverty and Inequality. Gini index measures the extent to which the distribution of income (or, in some cases, consumption expenditure) among individuals or households within an economy deviates from a perfectly equal distribution. A Lorenz curve plots the cumulative percentages of total income received against the cumulative number of recipients, starting with the poorest individual or household. The Gini index measures the area between the Lorenz curve and a hypothetical line of absolute equality, expressed as a percentage of the maximum area under the line. Thus a Gini index of 0 represents perfect equality, while an index of 100 implies perfect inequality.;World Bank, Poverty and Inequality Platform. Data are based on primary household survey data obtained from government statistical agencies and World Bank country departments. Data for high-income economies are mostly from the Luxembourg Income Study database. For more information and methodology, please see http://pip.worldbank.org.;;The World Bank’s internationally comparable poverty monitoring database now draws on income or detailed consumption data from more than 2000 household surveys across 169 countries. See the Poverty and Inequality Platform (PIP) for details (www.pip.worldbank.org).

Overlaying the number of marginality dimensions with percentage of people living below 1.25$/day. This map is included in a global study on mapping marginality focusing on Sub-Saharan Africa and South Asia. The Dimensions of Marginality are based on different data sources representing different spheres of life. The poverty dataset used in this study is based on calculations by Harvest Choice. The underlying Marginality map is based on the approach on Marginality Mapping (http://www.zef.de/fileadmin/webfiles/downloads/zef_wp/wp88.pdf). The respective map can be found here: https://daten.zef.de/#/metadata/ae4ae68c-cea3-44e7-8199-1c2ae04abb88 Quality/Lineage: Poverty Data was provided and generated by Harvest Choice GIS lab. Marginality hotspots are based on the approach by Graw, V. using five dimensions of marginality. In ArcGIS thresholds were defined based on percentages and overlapping dimensions. Using raster data this data was reclassified and overlayed to build a new classification with regard to the here presented purpose. This approach is similar to the overlap over marginality and poverty mass except this map shows percentage of poverty instead of number of poor people. Purpose: This map was created in the MARGIP project to identify the marginalized and poor by highlighting those areas where the "spheres of life" have a low performance. Those areas where multiple "low performance indicators" did overlap got the highest attention for further research.

This dataset contains Zanzibar Census, Survey and Statistics data.

Central African Republic CF: Income Share Held by Highest 10% data was reported at 33.100 % in 2021. This records a decrease from the previous number of 46.200 % for 2008. Central African Republic CF: Income Share Held by Highest 10% data is updated yearly, averaging 46.200 % from Dec 1992 (Median) to 2021, with 3 observations. The data reached an all-time high of 47.700 % in 1992 and a record low of 33.100 % in 2021. Central African Republic CF: Income Share Held by Highest 10% data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Central African Republic – Table CF.World Bank.WDI: Social: Poverty and Inequality. Percentage share of income or consumption is the share that accrues to subgroups of population indicated by deciles or quintiles.;World Bank, Poverty and Inequality Platform. Data are based on primary household survey data obtained from government statistical agencies and World Bank country departments. Data for high-income economies are mostly from the Luxembourg Income Study database. For more information and methodology, please see http://pip.worldbank.org.;;The World Bank’s internationally comparable poverty monitoring database now draws on income or detailed consumption data from more than 2000 household surveys across 169 countries. See the Poverty and Inequality Platform (PIP) for details (www.pip.worldbank.org).

Central African Republic CF: Income Share Held by Lowest 10% data was reported at 2.100 % in 2021. This records an increase from the previous number of 1.200 % for 2008. Central African Republic CF: Income Share Held by Lowest 10% data is updated yearly, averaging 1.200 % from Dec 1992 (Median) to 2021, with 3 observations. The data reached an all-time high of 2.100 % in 2021 and a record low of 0.700 % in 1992. Central African Republic CF: 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 Central African Republic – Table CF.World Bank.WDI: Social: Poverty and Inequality. Percentage share of income or consumption is the share that accrues to subgroups of population indicated by deciles or quintiles.;World Bank, Poverty and Inequality Platform. Data are based on primary household survey data obtained from government statistical agencies and World Bank country departments. Data for high-income economies are mostly from the Luxembourg Income Study database. For more information and methodology, please see http://pip.worldbank.org.;;The World Bank’s internationally comparable poverty monitoring database now draws on income or detailed consumption data from more than 2000 household surveys across 169 countries. See the Poverty and Inequality Platform (PIP) for details (www.pip.worldbank.org).

As of 2024, an individual living in South Africa with less than 1,109 South African rand (roughly 62.14 U.S. dollars) per month was considered poor. Furthermore, individuals having 796 South African rand (approximately 44.60 U.S. dollars) a month available for food were living below the poverty line according to South African national standards. Absolute poverty National poverty lines are affected by changes in the patterns of household consumers and fluctuations in prices of services and goods. They are calculated based on the consumer price indices (CPI) of both food and non-food items separately. The national poverty line is not the only applicable threshold. For instance,13.2 million people in South Africa were living under 2.15 U.S. dollars, which is the international absolute poverty threshold defined by the World Bank. Most unequal in the globe A prominent aspect of South Africa’s poverty is related to extreme income inequality. The country has the highest income Gini index globally at 63 percent as of 2023. One of the crucial obstacles to combating poverty and inequality in the country is linked to job availability. In fact, youth unemployment was as high as 49.14 percent in 2023.

South Africa ZA: Proportion of People Living Below 50 Percent Of Median Income: % data was reported at 23.500 % in 2014. This stayed constant from the previous number of 23.500 % for 2010. South Africa ZA: Proportion of People Living Below 50 Percent Of Median Income: % data is updated yearly, averaging 23.500 % from Dec 1993 (Median) to 2014, with 6 observations. The data reached an all-time high of 25.500 % in 2000 and a record low of 20.300 % in 2005. South Africa ZA: Proportion of People Living Below 50 Percent Of Median Income: % 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 percentage of people in the population who live in households whose per capita income or consumption is below half of the median income or consumption per capita. The median is measured at 2017 Purchasing Power Parity (PPP) using the Poverty and Inequality Platform (http://www.pip.worldbank.org). For some countries, medians are not reported due to grouped and/or confidential data. The reference year is the year in which the underlying household survey data was collected. In cases for which the data collection period bridged two calendar years, the first year in which data were collected is reported.;World Bank, Poverty and Inequality Platform. Data are based on primary household survey data obtained from government statistical agencies and World Bank country departments. Data for high-income economies are mostly from the Luxembourg Income Study database. For more information and methodology, please see http://pip.worldbank.org.;;The World Bank’s internationally comparable poverty monitoring database now draws on income or detailed consumption data from more than 2000 household surveys across 169 countries. See the Poverty and Inequality Platform (PIP) for details (www.pip.worldbank.org).