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.

The average for 2021 based on 11 countries was 37.01 index points. The highest value was in the Central African Republic: 43 index points and the lowest value was in Niger: 32.9 index points. The indicator is available from 1963 to 2023. Below is a chart for all countries where data are available.

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).

This statistic shows a ranking of the estimated Gini index in 2020 in Africa, differentiated by country. The Gini coefficient here measures the degree of income inequality on a scale from 0 (=total equality of incomes) to 1 (=total inequality). The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in more than 150 countries and regions worldwide. All input data are sourced from international institutions, national statistical offices, and trade associations. All data has been are processed to generate comparable datasets (see supplementary notes under details for more information).

The gini index in Zambia was forecast to continuously increase between 2024 and 2029 by in total 0.01 points (+1.75 percent). The gini is estimated to amount to 0.58 points in 2029. The Gini coefficient here measures the degree of income inequality on a scale from 0 (=total equality of incomes) to one (=total inequality).The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in more than 150 countries and regions worldwide. All input data are sourced from international institutions, national statistical offices, and trade associations. All data has been are processed to generate comparable datasets (see supplementary notes under details for more information).Find more key insights for the gini index in countries like Ethiopia and Uganda.

World Bank has a Poverty and Inequality Platform where country data can be downloaded for Poverty, Inequality and Multi-dimensional Poverty. The link https://pip.worldbank.org/country-profiles will take you to the Country Poverty Profile and from this page you can select any country and choose between one of three the Poverty Lines: $1.9, $3.2 or $5.5 (at 2011 international prices) and that Poverty Profile will be called up. Then you can select the Poverty, Inequality and Multi-dimensional Poverty data that you want to download. The Reporting Years are: 2000, 2008 and 2018.

Poverty in a Rising Africa, the first of two upcoming reports on poverty in Africa, documents the data challenges facing the region and reviews the status of Africa’s poverty and inequality, both monetary and nonmonetary, taking these data challenges into account.

Poverty and Inequality Indicators for all 54 African Countries

This dataset contains Poverty and Inequality indicators for all 54 African countries, spanning from 1960 to 2024. The data is sourced from the World Bank and has been cleaned and organized for easy use.

  Dataset Structure

The dataset is organized into folders, one for each of the 54 African countries. Within each country's folder, you will find:

A CSV file containing the indicator data for that country. A… See the full description on the dataset page: https://huggingface.co/datasets/electricsheepafrica/Poverty-and-Inequality-Indicators-For-African-Countries.

The dataset is used to examine the nonlinear and threshold effects of FinTech on growth, inequality and poverty in the panel of African Countries.

This statistic shows a ranking of the estimated Gini index in 2020 in the Middle East and North Africa (MENA), differentiated by country. The Gini coefficient here measures the degree of income inequality on a scale from 0 (=total equality of incomes) to 1 (=total inequality). The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in more than 150 countries and regions worldwide. All input data are sourced from international institutions, national statistical offices, and trade associations. All data has been are processed to generate comparable datasets (see supplementary notes under details for more information).

This statistic shows a ranking of the estimated population share having access to safely managed drinking water in 2020 in Africa, differentiated by country.The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in more than 150 countries and regions worldwide. All input data are sourced from international institutions, national statistical offices, and trade associations. All data has been are processed to generate comparable datasets (see supplementary notes under details for more information).

The datasets are collated from reputable international organization to analyse the links between inequality (in consumption and income) and terrorism in Africa. The data are for 46 African countries. The details of the datasets consist domter - Domestic terrorism; transter - Transnational terrorism; unter - Unclear terrorism; totter - Total terrorism; gini - Gini coefficient; theil - Theil coefficient; atkin - Atkinson coefficient; palma - Palma ratio; totnat - total natural resources; surface - Surface area(log); polreg - political regime; conflicts - Dummy (1 conflicts, 0 no conflict); trade - Trade openness; and gdppc - GDP per capita.

ZA: Multidimensional Poverty Index: scale 0-1 data was reported at 0.030 NA in 2016. This stayed constant from the previous number of 0.030 NA for 2011. ZA: Multidimensional Poverty Index: scale 0-1 data is updated yearly, averaging 0.030 NA from Dec 2011 (Median) to 2016, with 2 observations. The data reached an all-time high of 0.030 NA in 2016 and a record low of 0.030 NA in 2016. ZA: Multidimensional Poverty Index: scale 0-1 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. ;Government statistical agencies. Data for EU countires are from the EUROSTAT;;

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Income share held by second 20% of South Africa improved by 2.13% from 4.70 % in 2010 to 4.80 % in 2014. Since the 16.07% slump in 2005, income share held by second 20% grew by 2.13% in 2014. 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.

This dataset is about books. It has 1 row and is filtered where the book is Central Africa : the economics of inequality. It features 7 columns including author, publication date, language, and book publisher.

Central African Republic CF: Income Share Held by Fourth 20% data was reported at 21.400 % in 2021. This records an increase from the previous number of 17.700 % for 2008. Central African Republic CF: Income Share Held by Fourth 20% data is updated yearly, averaging 18.500 % from Dec 1992 (Median) to 2021, with 3 observations. The data reached an all-time high of 21.400 % in 2021 and a record low of 17.700 % in 2008. Central African Republic CF: Income Share Held by Fourth 20% 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. Percentage shares by quintile may not sum to 100 because of rounding.;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).

According to this forecast, the total housing-related spending will stay nearly the same over the forecast period. Consumer spending, in this case housing-related per capita spending, refers to the domestic demand of private households and non-profit institutions serving households (NPISHs). Spending by corporations and the state is not included. The forecast has been adjusted for the expected impact of COVID-19.Consumer spending is the biggest component of the gross domestic product as computed on an expenditure basis in the context of national accounts. The other components in this approach are consumption expenditure of the state, gross domestic investment as well as the net exports of goods and services. Consumer spending is broken down according to the United Nations' Classification of Individual Consumption By Purpose (COICOP). The shown data adheres broadly to group 04. As not all countries and regions report data in a harmonized way, all data shown here has been processed by Statista to allow the greatest level of comparability possible. The underlying input data are usually household budget surveys conducted by government agencies that track spending of selected households over a given period.The data is shown in nominal terms which means that monetary data is valued at prices of the respective year and has not been adjusted for inflation. For future years the price level has been projected as well. The data has been converted from local currencies to US$ using the average exchange rate of the respective year. For forecast years, the exchange rate has been projected as well. The timelines therefore incorporate currency effects.Find more key insights for the total consumer spending on housing, water and electricity in countries like Australia & Oceania and Asia.

This paper examines the importance of network-based intergenerational correlations in South Africa. I use longitudinal data on young South Africans to examine the covariance of children's employment with the usefulness of parents in their job search. I find that fathers serve as useful network connections to their sons (not daughters), and that mothers do not seem to be useful network connections. The father-son effect is robust to alternate explanations of specific human capital and correlated networks. The size of this effect is large. Present fathers' utility as network connections may be responsible for a one-third increase in their sons' employment rates. (JEL D31, J12, J13, J24, J62, O15, Z13)

Democracy is often linked to improved welfare. However, less is known about how these improvements are distributed between groups in society. Does democracy reduce the gap in welfare outcomes between disadvantaged and advantaged groups? If so, how? In answering these questions, we draw on the microeconomics literature on within-family variation to address issues of reverse causality and the omitted variable bias. We examine infant mortality in 3.8 million infants born between 1960 and 2016 in African countries, using twins and singletons as proxies for disadvantaged and advantaged groups, given that twins have a higher risk of mortality relative to singletons. We find strong and robust evidence that democracy reduces health inequality. Our evidence suggests that democracy expands the provision of basic goods in Africa. As disadvantaged groups start from a worse point than advantaged groups, they realize greater benefits. This, in turn, reduces the health disparity.