Over half of the organizations worldwide expect the widespread implementation of generative AI (GenAI) to increase economic inequality across the globe. Only about 22 percent of the respondents expect the opposite scenario. Approximately 27 percent of the respondents expect a widespread use of GenAI not to have any impact on this social aspect.

The Gini index measures economic inequality in a country. Specifically, it is the extent to which the distribution of income (or, in some cases, consumption expenditure) deviates from a perfectly equal distribution among individuals or households within an economy.

This statistic shows the inequality of income distribution in China from 2005 to 2023 based on the Gini Index. In 2023, China reached a score of ************ points. The Gini Index is a statistical measure that is used to represent unequal distributions, e.g. income distribution. It can take any value between 1 and 100 points (or 0 and 1). The closer the value is to 100 the greater is the inequality. 40 or 0.4 is the warning level set by the United Nations. The Gini Index for South Korea had ranged at about **** in 2022. Income distribution in China The Gini coefficient is used to measure the income inequality of a country. The United States, the World Bank, the US Central Intelligence Agency, and the Organization for Economic Co-operation and Development all provide their own measurement of the Gini coefficient, varying in data collection and survey methods. According to the United Nations Development Programme, countries with the largest income inequality based on the Gini index are mainly located in Africa and Latin America, with South Africa displaying the world's highest value in 2022. The world's most equal countries, on the contrary, are situated mostly in Europe. The United States' Gini for household income has increased by around ten percent since 1990, to **** in 2023. Development of inequality in China Growing inequality counts as one of the biggest social, economic, and political challenges to many countries, especially emerging markets. Over the last 20 years, China has become one of the world's largest economies. As parts of the society have become more and more affluent, the country's Gini coefficient has also grown sharply over the last decades. As shown by the graph at hand, China's Gini coefficient ranged at a level higher than the warning line for increasing risk of social unrest over the last decade. However, the situation has slightly improved since 2008, when the Gini coefficient had reached the highest value of recent times.

this graphs is ourdataworld :

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How are incomes and wealth distributed between people? Both within countries and across the world as a whole?

On this page, you can find all our data, visualizations, and writing relating to economic inequality.

This evidence demonstrates that inequality in many countries is substantial and, in numerous instances, has been escalating. Global economic inequality is extensive and exacerbated by intersecting disparities in health, education, and various other dimensions.

However, economic inequality is not uniformly increasing. In many countries, it has declined or remained steady. Furthermore, global inequality – following two centuries of ascent – is presently decreasing as well.

The significant variations observed across countries and over time are pivotal. They indicate that high and rising inequality is not inevitable and that the current extent of inequality is subject to change.

About this data This data explorer offers various inequality indicators measured according to two distinct definitions of income sourced from different outlets.

Data from the World Inequality Database pertains to inequality prior to taxes and benefits. Data from the World Bank pertains to either income post taxes and benefits or consumption, contingent on the country and year. For additional details regarding the definitions and methodologies underlying this data, refer to the accompanying article below, where you can also delve into and juxtapose a broader spectrum of indicators from various sources.

This table contains data on income inequality. The primary measure is the Gini index – a measure of the extent to which the distribution of income among families/households within a community deviates from a perfectly equal distribution. The index ranges from 0.0, when all families (households) have equal shares of income (implies perfect equality), to 1.0 when one family (household) has all the income and the rest have none (implies perfect inequality). Index data is provided for California and its counties, regions, and large cities/towns. The data is from the U.S. Census Bureau, American Community Survey. The table is part of a series of indicators in the Healthy Communities Data and Indicators Project of the Office of Health Equity. Income is linked to acquiring resources for healthy living. Both household income and the distribution of income across a society independently contribute to the overall health status of a community. On average Western industrialized nations with large disparities in income distribution tend to have poorer health status than similarly advanced nations with a more equitable distribution of income. Approximately 119,200 (5%) of the 2.4 million U.S. deaths in 2000 are attributable to income inequality. The pathways by which income inequality act to increase adverse health outcomes are not known with certainty, but policies that provide for a strong safety net of health and social services have been identified as potential buffers. More information about the data table and a data dictionary can be found in the About/Attachments section.

Replication Data for: "Income inequality and the erosion of democracy in the twenty-first century," published in PNAS.

France - Inequality of income distribution was 4.66 in December of 2024, according to the EUROSTAT. Trading Economics provides the current actual value, an historical data chart and related indicators for France - Inequality of income distribution - last updated from the EUROSTAT on June of 2025. Historically, France - Inequality of income distribution reached a record high of 4.66 in December of 2024 and a record low of 4.23 in December of 2018.

Graph and download economic data for Income Inequality in New York County, NY (2020RATIO036061) from 2010 to 2023 about New York County, NY; inequality; New York; NY; income; and USA.

In the first quarter of 2024, almost two-thirds percent of the total wealth in the United States was owned by the top 10 percent of earners. In comparison, the lowest 50 percent of earners only owned 2.5 percent of the total wealth. Income inequality in the U.S. Despite the idea that the United States is a country where hard work and pulling yourself up by your bootstraps will inevitably lead to success, this is often not the case. In 2023, 7.4 percent of U.S. households had an annual income under 15,000 U.S. dollars. With such a small percentage of people in the United States owning such a vast majority of the country’s wealth, the gap between the rich and poor in America remains stark. The top one percent The United States follows closely behind China as the country with the most billionaires in the world. Elon Musk alone held around 219 billion U.S. dollars in 2022. Over the past 50 years, the CEO-to-worker compensation ratio has exploded, causing the gap between rich and poor to grow, with some economists theorizing that this gap is the largest it has been since right before the Great Depression.

In 2023, according to the Gini coefficient, household income distribution in the United States was 0.47. This figure was at 0.43 in 1990, which indicates an increase in income inequality in the U.S. over the past 30 years. What is the Gini coefficient? The Gini coefficient, or Gini index, is a statistical measure of economic inequality and wealth distribution among a population. A value of zero represents perfect economic equality, and a value of one represents perfect economic inequality. The Gini coefficient helps to visualize income inequality in a more digestible way. For example, according to the Gini coefficient, the District of Columbia and the state of New York have the greatest amount of income inequality in the U.S. with a score of 0.51, and Utah has the greatest income equality with a score of 0.43. The Gini coefficient around the world The Gini coefficient is also an effective measure to help picture income inequality around the world. For example, in 2018 income inequality was highest in South Africa, while income inequality was lowest in Slovenia.

This paper investigates the extent to which people link policy preferences with unequal outcomes. As the American public is both aware and supportive of reducing income inequality in the abstract, it is an open question whether this concern is translated into support for policies that might help alleviate the rise in economic inequality.

These are the data for the experiment (i.e., the second study) reported in "High economic inequality leads higher-income individuals to be less generous" co-authored with Julian House and Robb Willer. Please open the "readme" file first. The questionnaire and SAS code to conduct the analyses are also stored here.

This zip file includes the knitr file (combined LaTeX + R code) that downloads the Pew Research Center datasets employed and generates the article, including all results and figures. Other data and bibliography dependencies are also included. These reproducibility materials along with their intermediate products and the complete revision history of the article are available at https://github.com/fsolt/class_consciousness

This file includes the knitr files (combined LaTeX + R code) that generate the article and the appendix, including all results and figures. An R script that downloads the Pew Research Center datasets employed is included, along with other data and bibliography dependencies. These reproducibility materials along with their intermediate products and the complete revision history of the article are available at https://github.com/fsolt/meritocracy

This paper discusses how the issue of income distribution has resumed its importance in recent decades, showing the evolution of income inequality from the post-Second World War to the present, based on data from studies elaborated by researchers who work in internationally renowned academic centers. Income inequality starts to increase with the rise of neoliberalism in the 1980s, becoming greater in the 2000s, concentrated in the richest 1% of capitalist countries. This reality has demanded new interpretations by scholars, bringing to the debate contributions from Sociology, Political Science and Political Economy. Authors have shown how changes in the international order and its consequences on the pattern of capitalist accumulation in favor of rentier capitalism – a predominantly financial accumulation of capital – have altered the functioning of representative Democracies and affected economic inequality in capitalist countries, contrasting what happened in the “Golden Age” of capitalism (1945-1980) with what has been happening since the 1980s and especially in the 2000s.

Estonia - Inequality of income distribution was 5.03 in December of 2024, according to the EUROSTAT. Trading Economics provides the current actual value, an historical data chart and related indicators for Estonia - Inequality of income distribution - last updated from the EUROSTAT on July of 2025. Historically, Estonia - Inequality of income distribution reached a record high of 6.48 in December of 2014 and a record low of 5.01 in December of 2010.

Replication package for "The regional dispersion of income inequality in nineteenth-century Norway", to be published in Explorations in Economic History (accepted September 2017). The files contain micro data foundations for estimates of Norwegian income inequality in 1868.The file "data_compact.dta" (Stata format) contains "pseudo-invididual" observations of all men age 25 or more in Norway in 1868, estimated as described in the paper. Note that any one individual data point cannot stand by itself; analysis must be conducted at the municipality and/or occupation level. This is further explained in the paper.The file "municipalityfile.dta" (Stata format) contains municipality-level Gini coefficients and covariates.The file "replicate.zip" contains the necessary files (Stata and Matlab) to replicate the analysis. See "DataReadMe.pdf" for instructions.Abstract for the paper: This paper documents, for the first time, municipality- and occupation-level estimates of income inequality between individuals in a European country in the nineteenth century, using a combination of several detailed data sets for Norway in the late 1860s. Urban incomes were on average 4.5 times as high as rural incomes, and the average city Gini coefficient was twice the average rural municipality Gini. All high- or medium-income occupation groups exhibited substantial within-occupation income inequality. Across municipalities, income inequality is higher in high-income municipalities, and lower in muncipalities with high levels of fisheries and pastoral agriculture. While manufacturing activity is positively correlated with income inequality, the association is not apparent when other economic factors such as the mode of food production is accounted for. The income Gini for Norway as a whole is found to have been 0.546, slightly higher than estimates for the UK and US in the same period.

This study examines whether or not political representation in the Netherlands is biased toward the rich and higher educated by comparing the political orientations of members of parliament to those of the electorate. The analyses reveal stark differences in the representation of different socio-economic groups. The political views of elected national representatives are far more similar to those of rich, higher educated citizens than to those with less income and education. Moreover, a longitudinal analysis reveals that inequalities in political representation have actually grown in recent years. We also show that the use of measures of ideological self-identification might to lead to highly misleading results regarding the nature of political representation as opposed to the use of issue items. We conclude that, despite a highly proportional electoral system, the views which are represented in the Dutch lower house of parliament contain major distortions of the views of the broader electorate.

Graph and download economic data for Share of Net Worth Held by the Top 1% (99th to 100th Wealth Percentiles) (WFRBST01134) from Q3 1989 to Q1 2025 about net worth, wealth, percentile, Net, and USA.

Brazil BR: Gini Coefficient (GINI Index): World Bank Estimate data was reported at 52.000 % in 2022. This records a decrease from the previous number of 52.900 % for 2021. Brazil BR: Gini Coefficient (GINI Index): World Bank Estimate data is updated yearly, averaging 56.400 % from Dec 1981 (Median) to 2022, with 38 observations. The data reached an all-time high of 63.300 % in 1989 and a record low of 48.900 % in 2020. Brazil BR: 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 Brazil – Table BR.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).