In the first quarter of 2025, 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 was the country with the most billionaires in the world in 2025. Elon Musk, with a net worth of 342 billion U.S. dollars, was among the richest people in the United States in 2025. 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, roughly 1.49 billion adults worldwide had a net worth of less than 10,000 U.S. dollars. By comparison, 58 million adults had a net worth of more than one million U.S. dollars in the same year. Wealth distribution The distribution of wealth is an indicator of economic inequality. The United Nations says that wealth includes the sum of natural, human, and physical assets. Wealth is not synonymous with income, however, because having a large income can be depleted if one has significant expenses. In 2023, nearly 1,700 billionaires had a total wealth between one to two billion U.S. dollars. Wealth worldwide China had the highest number of billionaires in 2023, with the United States following behind. That same year, New York had the most billionaires worldwide.

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.

Source: https://www.wider.unu.edu/database/wiid User Guide: https://www.wider.unu.edu/sites/default/files/WIID/PDF/WIID-User_Guide_06MAY2020.pdf

The World Income Inequality Database (WIID) contains information on income inequality in various countries and is maintained by the United Nations University-World Institute for Development Economics Research (UNU-WIDER). The database was originally compiled during 1997-99 for the research project Rising Income Inequality and Poverty Reduction, directed by Giovanni Andrea Corina. A revised and updated version of the database was published in June 2005 as part of the project Global Trends in Inequality and Poverty, directed by Tony Shorrocks and Guang Hua Wan. The database was revised in 2007 and a new version was launched in May 2008.

The database contains data on inequality in the distribution of income in various countries. The central variable in the dataset is the Gini index, a measure of income distribution in a society. In addition, the dataset contains information on income shares by quintile or decile. The database contains data for 159 countries, including some historical entities. The temporal coverage varies substantially across countries. For some countries there is only one data entry; in other cases there are over 100 data points. The earliest entry is from 1867 (United Kingdom), the latest from 2003. The majority of the data (65%) cover the years from 1980 onwards. The 2008 update (version WIID2c) includes some major updates and quality improvements, in fact leading to a reduced number of variables in the new version. The new version has 334 new observations and several revisions/ corrections made in 2007 and 2008.

An October 2021 study estimated the regional distribution of ultra high net worth individuals' (UHNWI) art and collectible wealth worldwide. Based on the report, North America had the largest market share of UHNWI art and collectible wealth, accounting for ** percent of the global market in 2020. In contrast, the art and collectible wealth in the Pacific amounted to only *** percent of the market in the same year.

Graph and download economic data for Income Inequality in Blue Earth County, MN (2020RATIO027013) from 2010 to 2023 about Blue Earth County, MN; Mankato; inequality; MN; income; and USA.

Bolivia BO: Income Share Held by Lowest 20% data was reported at 5.300 % in 2021. This records an increase from the previous number of 4.700 % for 2020. Bolivia BO: Income Share Held by Lowest 20% data is updated yearly, averaging 3.500 % from Dec 1990 (Median) to 2021, with 24 observations. The data reached an all-time high of 5.600 % in 1990 and a record low of 1.100 % in 2000. Bolivia BO: 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 Bolivia – Table BO.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).

BR: Income Share Held by Second 20% data was reported at 7.700 % in 2022. This records an increase from the previous number of 7.500 % for 2021. BR: Income Share Held by Second 20% data is updated yearly, averaging 6.500 % from Dec 1981 (Median) to 2022, with 38 observations. The data reached an all-time high of 8.700 % in 2020 and a record low of 5.000 % in 1989. BR: Income Share Held by Second 20% 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. 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).

Germany DE: Income Share Held by Second 20% data was reported at 12.800 % in 2020. This records a decrease from the previous number of 13.100 % for 2019. Germany DE: Income Share Held by Second 20% data is updated yearly, averaging 13.100 % from Dec 1991 (Median) to 2020, with 30 observations. The data reached an all-time high of 13.700 % in 1996 and a record low of 12.800 % in 2020. Germany DE: Income Share Held by Second 20% data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Germany – Table DE.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).

Replication data and code for Ellora Derenoncourt, Chi Hyun Kim, Moritz Kuhn, Moritz Schularick, Wealth of Two Nations: The U.S. Racial Wealth Gap, 1860–2020, The Quarterly Journal of Economics, 2023;, qjad044, https://doi.org/10.1093/qje/qjad044

Graph and download economic data for GINI Index for the United States (SIPOVGINIUSA) from 1963 to 2023 about gini, indexes, and USA.

Belarus BY: Gini Coefficient (GINI Index): World Bank Estimate data was reported at 24.400 % in 2020. This records a decrease from the previous number of 25.300 % for 2019. Belarus BY: Gini Coefficient (GINI Index): World Bank Estimate data is updated yearly, averaging 27.600 % from Dec 1998 (Median) to 2020, with 23 observations. The data reached an all-time high of 32.000 % in 1998 and a record low of 24.400 % in 2020. Belarus BY: 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 Belarus – Table BY.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).

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).

This data is part of a research project on the impact of consumption taxes on inequality by Julien Blasco, Elvire Guillaud and Michaël Zemmour.

Our project is currently published in the LIS Working Paper Series. You may cite it as:

Blasco J., Guillaud E., Zemmour M. (2020) “Consumption Taxes and Income Inequality: An International Perspective with Microsimulation”, LIS Working Paper Series, No. 785.

You are free to use the datasets we provide here, but please cite them as:

Blasco J., Guillaud E., Zemmour M., Data on the Impact of Consumption Taxes on Income Inequality, https://doi.org/10.5281/zenodo.4291984, October 2020.

For detailed information on the method used, please refer to Blasco, Guillaud and Zemmour (2020). In particular, the appendices describe the imputation models used for consumption. All the coefficients are given, which allows for a replication of this imputation method in other datasets.

The code used is available at https://github.com/JulienBlasco/consumption-taxes.

Our data is base on surveys on income and consumption, harmonized by the Luxembourg Income Study. We used OECD Statistics for National Accounts data on income, consumption and consumption tax revenue.

Description of the data

The data is constituted of five tables.

Two datasets are aggregated indicators at the country-year level, such as Gini coefficients for different concepts of income, global consumption tax-to-income ratios, anti-redistributive effect of consumption taxes:

For the core model (82 country-years): ConsumptionTaxes_indicators_coremodel.dta

For the lighter model (126 country-years): ConsumptionTaxes_indicators_xtnddmodel

Two datasets are variables broken down by percentiles of disposable income, within each country-year. Please note that these data are mainly for graphing purposes, not detailed analysis at the percentile level:

Core model (82 country-years): ConsumptionTaxes_percentiles_coremodel

Lighter model (126 country-years): ConsumptionTaxes_percentiles_xtnddmodel

One dataset that contains the implicit effective tax rates on consumption, computed with National Accounts data: 18-07-27 OECD_itrcs.dta

This data file includes the Gini coefficient calculated for different wealth welfare aggregates constructed for all Luxembourg Wealth Study (LWS) datasets in all waves (as of March 2022). It includes Gini coefficients calculated on: • Disposable Net Worth • Value of Principal residence • Financial AssetsThis project sought to renew the ESRC's invaluable financial support to LIS (formerly the Luxembourg Income Study) for a period of five more years. LIS is an independent, non-profit cross-national data archive and research institute located in Luxembourg. LIS relies on financial contributions from national science foundations, other research institutions and consortia, data-providing agencies, and supranational organisations to support data harmonisation and enable free and unlimited data access to researchers in the participating countries and to students world-wide. LIS' primary activity is to make harmonised household microdata available to researchers, thus enabling cross-national, interdisciplinary primary research into socio-economic outcomes and their determinants. Users of the Luxembourg Income Study Database and Luxembourg Wealth Study Database come from countries around the globe, including the UK. LIS has four goals: 1) to harmonise microdatasets from high- and middle-income countries that include data on income, wealth, employment, and demography; 2) to provide a secure method for researchers to query data that would otherwise be unavailable due to country-specific privacy restrictions; 3) to create and maintain a remote-execution system that sends research query results quickly back to users at off-site locations; and 4) to enable, facilitate, promote and conduct crossnational comparative research on the social and economic wellbeing of populations across countries. LIS contains the Luxembourg Income Study (LIS) Database, which includes income data, and the Luxembourg Wealth Study (LWS) Database, which focuses on wealth data. LIS currently includes microdata from 46 countries in Europe, the Americas, Africa, Asia and Australasia. LIS contains over 250 datasets, organised into eight time "waves," spanning the years 1968 to 2011. Since 2007, seventeen more countries have been added to LIS, including the BRICS countries (Brazil, Russia, India, China, South Africa), Japan, South Korea and a number of other Latin American countries. LWS contains 20 wealth datasets from 12 countries, including the UK, and covers the period 1994 to 2007. All told, LIS and LWS datasets together cover 86% of world GDP and 64% of world population. Users submit statistical queries to the microdatabases using a Java-based job submission interface or standard email. The databases are especially valuable for primary research in that they offer access to cross-national data at the micro-level - at the level of households and persons. Users are economists, sociologists, political scientists, and policy analysts, among others, and they employ a range of statistical approaches and methods. LIS also provides extensive documentation - metadata - for both LIS and LWS, concerning technical aspects of the survey data, the harmonisation process, and the social institutions of income and wealth provision in participating countries. In the next five years, for which support is sought, LIS will: - expand LIS, adding Waves IX (2013) and X (2016), and add new middle-income countries; - develop LWS, adding another wave of datasets to existing countries; acquire new wealth datasets for 14 more countries in cooperation with the European Central Bank (based on the Household Finance and Consumption Survey); - create a state-of-the-art metadata search and storage system; - maintain international standards in data security and data infrastructure systems; - provide high-quality harmonised household microdata to researchers around the world; - enable interdisciplinary cross-national social science research covering 45+ countries, including the UK; - aim to broaden its reach and impact in academic and non-academic circles through focused communications strategies and collaborations. All surveyed households and their members are included in our estimates of Gini and Atkinson coefficients, percentile ratios, and poverty lines. Poverty lines are calculated based on the total population. Those lines are then used to calculate poverty rates among subgroups (children and the elderly). Thus, when calculating poverty rates, the subgroups vary, but the poverty lines remain constant within any given dataset. The data file includes the Gini coefficient calculated for different wealth welfare aggregates constructed for all LWS datasets in all waves (as of March 2022).

About this dataset

Context

The dataset tabulates the median household income in White Earth. It can be utilized to understand the trend in median household income and to analyze the income distribution in White Earth by household type, size, and across various income brackets.

Content

The dataset will have the following datasets when applicable

Please note: The 2020 1-Year ACS estimates data was not reported by the Census Bureau due to the impact on survey collection and analysis caused by COVID-19. Consequently, median household income data for 2020 is unavailable for large cities (population 65,000 and above).

• White Earth, ND Median Household Income Trends (2010-2021, in 2022 inflation-adjusted dollars)
• Median Household Income Variation by Family Size in White Earth, ND: Comparative analysis across 7 household sizes
• Income Distribution by Quintile: Mean Household Income in White Earth, ND
• White Earth, ND households by income brackets: family, non-family, and total, in 2022 inflation-adjusted dollars

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Interested in deeper insights and visual analysis?

Explore our comprehensive data analysis and visual representations for a deeper understanding of White Earth median household income. You can refer the same here

Brazil BR: Income Share Held by Highest 10% data was reported at 41.000 % in 2022. This records a decrease from the previous number of 41.600 % for 2021. Brazil BR: Income Share Held by Highest 10% data is updated yearly, averaging 44.550 % from Dec 1981 (Median) to 2022, with 38 observations. The data reached an all-time high of 51.100 % in 1989 and a record low of 39.500 % in 2020. Brazil BR: 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 Brazil – Table BR.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).

About this dataset

Context

The dataset tabulates the Black Earth median household income by race. The dataset can be utilized to understand the racial distribution of Black Earth income.

Content

The dataset will have the following datasets when applicable

Please note: The 2020 1-Year ACS estimates data was not reported by the Census Bureau due to the impact on survey collection and analysis caused by COVID-19. Consequently, median household income data for 2020 is unavailable for large cities (population 65,000 and above).

• Black Earth, WI median household income breakdown by race betwen 2011 and 2021
• Median Household Income by Racial Categories in Black Earth, WI (2021, in 2022 inflation-adjusted dollars)

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Interested in deeper insights and visual analysis?

Explore our comprehensive data analysis and visual representations for a deeper understanding of Black Earth median household income by race. You can refer the same here

Chile CL: Income Share Held by Lowest 10% data was reported at 2.300 % in 2022. This records an increase from the previous number of 1.700 % for 2020. Chile CL: Income Share Held by Lowest 10% data is updated yearly, averaging 1.550 % from Dec 1987 (Median) to 2022, with 16 observations. The data reached an all-time high of 2.300 % in 2022 and a record low of 1.200 % in 1994. Chile CL: 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 Chile – Table CL.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).

Chile CL: Income Share Held by Lowest 20% data was reported at 5.900 % in 2022. This records an increase from the previous number of 4.900 % for 2020. Chile CL: Income Share Held by Lowest 20% data is updated yearly, averaging 4.450 % from Dec 1987 (Median) to 2022, with 16 observations. The data reached an all-time high of 5.900 % in 2022 and a record low of 3.300 % in 1987. Chile CL: 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 Chile – Table CL.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).