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.

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.

The massive wealth inequality in the world is underpinned by this chart: while just above *** percent of the world's population had fortunes of more than one million U.S. dollars in 2022, more than **** of the global population had a total wealth of less than 10,000 U.S. dollars.

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.

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.

Abstract

Is global inequality (inequality among world citizens) stable, decreasing or increasing? How high it is? Is it mostly due to inequalities within nations or between nations? Is there a global middle class? See the working papers above: "True world income distribution 1988 and 1993: first calculations based on household surveys alone" no. 2244, and "Decomposing global income distribution: Does the world have a middle class?" no. 2562

Household survey data (1988-2002) used in these papers, and subsequent book "Worlds Apart: Measuring International and Global Inequality", Princeton University Press, 2005. The data are for three benchmark years: 1988, 1993 and 1998

Kind of data

Aggregate data [agg]

Mode of data collection

Other [oth]

The World Income Inequality database is part of the United Nations University World Institute for Development Economics Research (UNU-WIDER) and contains information on income inequality for 189 developed, developing and transition countries.

Data and insights on Wealth Distribution in India - share of wealth, average wealth, HNIs, wealth inequality GINI, and comparison with global peers.

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.

Abstract

Dataset used in World Bank Policy Research Working Paper #2876, published in World Bank Economic Review, No. 1, 2005, pp. 21-44.

The effects of globalization on income distribution in rich and poor countries are a matter of controversy. While international trade theory in its most abstract formulation implies that increased trade and foreign investment should make income distribution more equal in poor countries and less equal in rich countries, finding these effects has proved elusive. The author presents another attempt to discern the effects of globalization by using data from household budget surveys and looking at the impact of openness and foreign direct investment on relative income shares of low and high deciles. The author finds some evidence that at very low average income levels, it is the rich who benefit from openness. As income levels rise to those of countries such as Chile, Colombia, or Czech Republic, for example, the situation changes, and it is the relative income of the poor and the middle class that rises compared with the rich. It seems that openness makes income distribution worse before making it better-or differently in that the effect of openness on a country's income distribution depends on the country's initial income level.

Kind of data

Aggregate data [agg]

This data package includes the underlying data and files to replicate the calculations, charts, and tables presented in The Future of Worldwide Income Distribution, PIIE Working Paper 15-7. If you use the data, please cite as: Hellebrandt, Tomas, and Paolo Mauro. (2015). The Future of Worldwide Income Distribution. PIIE Working Paper 15-7. Peterson Institute for International Economics.

About this dataset

Context

The dataset presents the mean household income for each of the five quintiles in Black Earth, WI, as reported by the U.S. Census Bureau. The dataset highlights the variation in mean household income across quintiles, offering valuable insights into income distribution and inequality.

Key observations

• Income disparities: The mean income of the lowest quintile (20% of households with the lowest income) is 16,027, while the mean income for the highest quintile (20% of households with the highest income) is 183,651. This indicates that the top earners earn 11 times compared to the lowest earners.
• *Top 5%: * The mean household income for the wealthiest population (top 5%) is 250,158, which is 136.21% higher compared to the highest quintile, and 1560.85% higher compared to the lowest quintile.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Income Levels:

• Lowest Quintile
• Second Quintile
• Third Quintile
• Fourth Quintile
• Highest Quintile
• Top 5 Percent

Variables / Data Columns

• Income Level: This column showcases the income levels (As mentioned above).
• Mean Household Income: Mean household income, in 2023 inflation-adjusted dollars for the specific income level.

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/.

Recommended for further research

This dataset is a part of the main dataset for Black Earth median household income. You can refer the same here

About this dataset

Context

The dataset presents the mean household income for each of the five quintiles in International Falls, MN, as reported by the U.S. Census Bureau. The dataset highlights the variation in mean household income across quintiles, offering valuable insights into income distribution and inequality.

Key observations

• Income disparities: The mean income of the lowest quintile (20% of households with the lowest income) is 16,712, while the mean income for the highest quintile (20% of households with the highest income) is 180,382. This indicates that the top earners earn 11 times compared to the lowest earners.
• *Top 5%: * The mean household income for the wealthiest population (top 5%) is 280,962, which is 155.76% higher compared to the highest quintile, and 1681.20% higher compared to the lowest quintile.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Income Levels:

• Lowest Quintile
• Second Quintile
• Third Quintile
• Fourth Quintile
• Highest Quintile
• Top 5 Percent

Variables / Data Columns

• Income Level: This column showcases the income levels (As mentioned above).
• Mean Household Income: Mean household income, in 2023 inflation-adjusted dollars for the specific income level.

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/.

Recommended for further research

This dataset is a part of the main dataset for International Falls median household income. You can refer the same here

The difference in the average wealth of Black and white Americans narrowed in the first century after the Civil War, but remained large and even widened again after 1980. Given high levels of wealth concentration both historically and today, dynamics at the average may not capture important heterogeneity in racial wealth gaps across the distribution. This paper looks into the historical evolution of the Black and white wealth distributions since Emancipation. The picture that emerges is an even starker one than racial wealth inequality at the mean. Tracing, for the first time, the evolution of wealth of the median Black household and the gap between the typical Black and white household over time, we estimate that the majority of Black households only began to dispose of measurable wealth around World War II. While the civil rights era brought substantial wealth gains for the median Black household, the gap between Black and white wealth at the median has not changed much since the 1970s. The top and the bottom of the wealth distribution show even greater persistence, with Black households consistently over-represented in the bottom half of the wealth distribution and under-represented in the top-10% over the past seven decades.

This folder contains the Python and MATLAB code used to reproduce all numerical results in the paper: “Capital Flows and Wealth Distribution in a Global Economy with Asymmetric Countries” By Been-Lon Chen, Yunfang Hu, and Kazuo Mino

******* of the ** million U.S. dollar millionaires worldwide in 2022 had a wealth of more than ** million U.S. dollars. The vast majority of the global millionaires had a wealth between *** and **** million U.S. dollars.

The OECD Income Distribution database (IDD) has been developed to benchmark and monitor countries' performance in the field of income inequality and poverty. It contains a number of standardised indicators based on the central concept of "equivalised household disposable income", i.e. the total income received by the households less the current taxes and transfers they pay, adjusted for household size with an equivalence scale. While household income is only one of the factors shaping people's economic well-being, it is also the one for which comparable data for all OECD countries are most common. Income distribution has a long-standing tradition among household-level statistics, with regular data collections going back to the 1980s (and sometimes earlier) in many OECD countries. Achieving comparability in this field is a challenge, as national practices differ widely in terms of concepts, measures, and statistical sources. In order to maximise international comparability as well as inter-temporal consistency of data, the IDD data collection and compilation process is based on a common set of statistical conventions (e.g. on income concepts and components). The information obtained by the OECD through a network of national data providers, via a standardized questionnaire, is based on national sources that are deemed to be most representative for each country. Small changes in estimates between years should be treated with caution as they may not be statistically significant. Fore more details, please refer to: https://www.oecd.org/els/soc/IDD-Metadata.pdf and https://www.oecd.org/social/income-distribution-database.htm

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.

1) Data Introduction • The Billionaires Statistics Dataset (2023) is a comprehensive set of personal and business information, including rankings of billionaires worldwide, net assets, industries, businesses, nationalities, birth and residence information, and asset sources.

2) Data Utilization (1) Billionaires Statistics Dataset (2023) has characteristics that: • The dataset consists of more than 35 columns, including the billionaire's rank, final Worth, industry, country, age, country of residence, source of assets, related industries, citizenship, organization, selfMade, birth information, data collection date, economic and social indicators (GDP, CPI, education enrollment, life expectancy, tax revenue, population, etc.). • In addition to individual asset information, economic indicators and demographic data by country are combined, allowing a three-dimensional analysis of billionaires and each country's economic and social environment. (2) Billionaires Statistics Dataset (2023) can be used to: • Wealth Distribution and Industry Analysis: Using billionaires' net worth, industry, and national data, we can analyze global wealth concentration and wealth distribution by industry and region. • A study linking demographics and economic indicators: Billionaire data can be combined with various economic and social indicators such as GDP, CPI, tax revenue, education, and life expectancy to be used for in-depth research on wealth formation, social background, ratio of self-made and inherited wealth, and regional characteristics.

Uruguay UY: Gini Coefficient (GINI Index): World Bank Estimate data was reported at 39.700 % in 2016. This records a decrease from the previous number of 40.200 % for 2015. Uruguay UY: Gini Coefficient (GINI Index): World Bank Estimate data is updated yearly, averaging 42.400 % from Dec 1981 (Median) to 2016, with 13 observations. The data reached an all-time high of 46.400 % in 2007 and a record low of 39.700 % in 2016. Uruguay UY: 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 Uruguay – Table UY.World Bank.WDI: Poverty. 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, Development Research Group. Data are based on primary household survey data obtained from government statistical agencies and World Bank country departments. 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.