Over ** million individuals residing in the United States belonged to the global top one percent of ultra-high net worth individuals worldwide in 2022. China ranked second, with over **** million top one percent wealth holders globally. France followed in third.

Graph and download economic data for Net Worth Held by the Top 0.1% (99.9th to 100th Wealth Percentiles) (WFRBLTP1246) from Q3 1989 to Q1 2025 about net worth, wealth, percentile, Net, and USA.

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, there were nearly 22 million people with a net worth of over one million U.S. dollars in the United States, which put the country on the top of the ranking. China was ranked second in that year, with more than six million individuals with wealth exceeding one million U.S. dollars. The United Kingdom followed in third with around three million millionaires.

With a net worth of 342 billion U.S. dollars, Elon Musk, the cofounder of seven companies, such as electric car maker Tesla and the rocket producer SpaceX, was the wealthiest man in the world in March 2025. The wealthiest people in the world Marc Zuckerberg, the cofounder of Meta Platforms, came second with a wealth of 235.6 billion U.S. dollars. Amazon-founder Jeff Bezos followed in third. All the 10 richest people in the world were men. Wealth distribution worldwide As of 2022, one percent of people held nearly half of the world's combined wealth. Moreover, 2.8 billion of the world's population hold a combined wealth of less than 10,000 U.S. dollars, compared to 59 million people having a combined wealth of 1 billion dollars or more, underlining the vast inequalities around the world. Where do the most affluent people live? Most millionaires live in the United States, while Hong Konk was the city hosting the largest number of high net worth individuals worldwide. The country with the highest number of billionaires is China.

According to the Hurun Global Rich List 2025, the United States housed the highest number of billionaires worldwide in 2025. In detail, there were *** billionaires living in the United States as of January that year. By comparison, *** billionaires resided in China. India, the United Kingdom, and Germany were also the homes of a significant number of billionaires that year. United States has regained its first place As the founder and exporter of consumer capitalism, it is no surprise that the United States is home to a large number of billionaires. Although China had briefly overtaken the U.S. in recent years, the United States has reclaimed its position as the country with the most billionaires in the world. Moreover, North America leads the way in terms of the highest number of ultra high net worth individuals – those with a net worth of more than ***** million U.S. dollars. The prominence of Europe and North America is a reflection of the higher degree of economic development in those states. However, this may also change as China and other emerging economies continue developing. Female billionaires Moreover, the small proportion of female billionaires does little to counter critics claiming the global economy is dominated by an elite comprised mainly of men. On the list of the 20 richest people in the world, only one was a woman. Moreover, recent political discourse has put a great amount of attention on the wealth held by the super-rich with the wealth distribution of the global population being heavily unequal.

Graph and download economic data for Minimum Wealth Cutoff for the Top 0.1% (99.9th to 100th Wealth Percentiles) (WFRBLTP1311) from Q3 1989 to Q3 2022 about wealth, percentile, 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.

Graph and download economic data for Net Worth Held by the Bottom 50% (1st to 50th Wealth Percentiles) (WFRBLB50107) from Q3 1989 to Q1 2025 about net worth, wealth, percentile, Net, and USA.

In 2023, Switzerland led the ranking of countries with the highest average wealth per adult, with approximately ******* U.S. dollars per person. Luxembourg was ranked second with an average wealth of around ******* U.S. dollars per adult, followed by Hong Kong SAR. However, the figures do not show the actual distribution of wealth. The Gini index shows wealth disparities in countries worldwide. Does wealth guarantee a longer life? As the old adage goes, “money can’t buy you happiness”, yet wealth and income are continuously correlated to the quality of life of individuals in different countries around the world. While greater levels of wealth may not guarantee a higher quality of life, it certainly increases an individual’s chances of having a longer one. Although they do not show the whole picture, life expectancy at birth is higher in the wealthier world regions. Does money bring happiness? A number of the world’s happiest nations also feature in the list of those countries for which average income was highest. Finland, however, which was the happiest country worldwide in 2022, is missing from the list of the top twenty countries with the highest wealth per adult. As such, the explanation for this may be the fact that the larger proportion of the population has access to a high income relative to global levels. Measures of quality of life Criticism of the use of income or wealth as a proxy for quality of life led to the creation of the United Nations’ Human Development Index. Although income is included within the index, it also has other factors taken into account, such as health and education. As such, the countries with the highest human development index can be correlated to those with the highest income levels. That said, none of the above measures seek to assess the physical and mental environmental impact of a high quality of life sourced through high incomes. The happy planet index demonstrates that the inclusion of experienced well-being and ecological footprint in place of income and other proxies for quality of life results in many of the world’s materially poorer nations being included in the happiest.

Korea Median: AH: 1 Net Worth Quintile: Net Worth data was reported at 10,700,000.000 KRW in 2017. This records an increase from the previous number of 10,400,000.000 KRW for 2016. Korea Median: AH: 1 Net Worth Quintile: Net Worth data is updated yearly, averaging 9,810,000.000 KRW from Mar 2010 (Median) to 2017, with 8 observations. The data reached an all-time high of 10,700,000.000 KRW in 2017 and a record low of 6,430,000.000 KRW in 2010. Korea Median: AH: 1 Net Worth Quintile: Net Worth data remains active status in CEIC and is reported by Statistics Korea. The data is categorized under Global Database’s Korea – Table KR.H083: SHFLC: Household Assets, Liabilities & Income By Net Worth Quintiles.

Korea Average: HPL: 1 Net Worth Quintile: Other Than Non-Financial Assets data was reported at 5,840,000.000 KRW in 2017. This records an increase from the previous number of 5,510,000.000 KRW for 2016. Korea Average: HPL: 1 Net Worth Quintile: Other Than Non-Financial Assets data is updated yearly, averaging 4,755,000.000 KRW from Mar 2010 to 2017, with 8 observations. The data reached an all-time high of 5,840,000.000 KRW in 2017 and a record low of 2,570,000.000 KRW in 2010. Korea Average: HPL: 1 Net Worth Quintile: Other Than Non-Financial Assets data remains active status in CEIC and is reported by Statistics Korea. The data is categorized under Global Database’s Korea – Table KR.H083: SHFLC: Household Assets, Liabilities & Income By Net Worth Quintiles.

The World Top Incomes Database provides statistical information on the shares of top income groups for 30 countries. The construction of this database was possible thanks to the research of over thirty contributing authors. There has been a marked revival of interest in the study of the distribution of top incomes using tax data. Beginning with the research by Thomas Piketty of the long-run distribution of top incomes in France, a succession of studies has constructed top income share time series over the long-run for more than twenty countries to date. These projects have generated a large volume of data, which are intended as a research resource for further analysis. In using data from income tax records, these studies use similar sources and methods as the pioneering work by Kuznets for the United States.The findings of recent research are of added interest, since the new data provide estimates covering nearly all of the twentieth century -a length of time series unusual in economics. In contrast to existing international databases, generally restricted to the post-1970 or post-1980 period, the top income data cover a much longer period, which is important because structural changes in income and wealth distributions often span several decades. The data series is fairly homogenous across countries, annual, long-run, and broken down by income source for several cases. Users should be aware also about their limitations. Firstly, the series measure only top income shares and hence are silent on how inequality evolves elsewhere in the distribution. Secondly, the series are largely concerned with gross incomes before tax. Thirdly, the definition of income and the unit of observation (the individual vs. the family) vary across countries making comparability of levels across countries more difficult. Even within a country, there are breaks in comparability that arise because of changes in tax legislation affecting the definition of income, although most studies try to correct for such changes to create homogenous series. Finally and perhaps most important, the series might be biased because of tax avoidance and tax evasion. The first theme of the research programme is the assembly and analysis of historical evidence from fiscal records on the long-run development of economic inequality. “Long run” is a relative term, and here it means evidence dating back before the Second World War, and extending where possible back into the nineteenth century. The time span is determined by the sources used, which are based on taxes on incomes, earnings, wealth and estates. Perspective on current concerns is provided by the past, but also by comparison with other countries. The second theme of the research programme is that of cross-country comparisons. The research is not limited to OECD countries and will draw on evidence globally. In order to understand the drivers of inequality, it is necessary to consider the sources of economic advantage. The third theme is the analysis of the sources of income, considering separately the roles of earned incomes and property income, and examining the historical and comparative evolution of earned and property income, and their joint distribution. The fourth theme is the long-run trend in the distribution of wealth and its transmission through inheritance. Here again there are rich fiscal data on the passing of estates at death. The top income share series are constructed, in most of the cases presented in this database, using tax statistics (China is an exception; for the time being the estimates come from households surveys). The use of tax data is often regarded by economists with considerable disbelief. These doubts are well justified for at least two reasons. The first is that tax data are collected as part of an administrative process, which is not tailored to the scientists' needs, so that the definition of income, income unit, etc., are not necessarily those that we would have chosen. This causes particular difficulties for comparisons across countries, but also for time-series analysis where there have been substantial changes in the tax system, such as the moves to and from the joint taxation of couples. Secondly, it is obvious that those paying tax have a financial incentive to present their affairs in a way that reduces tax liabilities. There is tax avoidance and tax evasion. The rich, in particular, have a strong incentive to understate their taxable incomes. Those with wealth take steps to ensure that the return comes in the form of asset appreciation, typically taxed at lower rates or not at all. Those with high salaries seek to ensure that part of their remuneration comes in forms, such as fringe benefits or stock-options which receive favorable tax treatment. Both groups may make use of tax havens that allow income to be moved beyond the reach of the national tax net. These shortcomings limit what can be said from tax data, but this does not mean that the data are worthless. Like all economic data, they measure with error the 'true' variable in which we are interested. References Atkinson, Anthony B. and Thomas Piketty (2007). Top Incomes over the Twentieth Century: A Contrast between Continental European and English-Speaking Countries (Volume 1). Oxford: Oxford University Press, 585 pp. Atkinson, Anthony B. and Thomas Piketty (2010). Top Incomes over the Twentieth Century: A Global Perspective (Volume 2). Oxford: Oxford University Press, 776 pp. Atkinson, Anthony B., Thomas Piketty and Emmanuel Saez (2011). Top Incomes in the Long Run of History, Journal of Economic Literature, 49(1), pp. 3-71. Kuznets, Simon (1953). Shares of Upper Income Groups in Income and Savings. New York: National Bureau of Economic Research, 707 pp. Piketty, Thomas (2001). Les Hauts Revenus en France au 20ème siècle. Paris: Grasset, 807 pp. Piketty, Thomas (2003). Income Inequality in France, 1901-1998, Journal of Political Economy, 111(5), pp. 1004-42.

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

Korea Average: HPL: 1 Net Worth Quintile: Savings data was reported at 11,240,000.000 KRW in 2017. This records an increase from the previous number of 10,410,000.000 KRW for 2016. Korea Average: HPL: 1 Net Worth Quintile: Savings data is updated yearly, averaging 10,450,000.000 KRW from Mar 2010 (Median) to 2017, with 8 observations. The data reached an all-time high of 11,240,000.000 KRW in 2017 and a record low of 7,720,000.000 KRW in 2010. Korea Average: HPL: 1 Net Worth Quintile: Savings data remains active status in CEIC and is reported by Statistics Korea. The data is categorized under Global Database’s Korea – Table KR.H083: SHFLC: Household Assets, Liabilities & Income By Net Worth Quintiles.

Korea Median: AH: 1 Net Worth Quintile: Savings data was reported at 3,110,000.000 KRW in 2017. This records an increase from the previous number of 2,700,000.000 KRW for 2016. Korea Median: AH: 1 Net Worth Quintile: Savings data is updated yearly, averaging 2,850,000.000 KRW from Mar 2010 (Median) to 2017, with 8 observations. The data reached an all-time high of 3,110,000.000 KRW in 2017 and a record low of 1,750,000.000 KRW in 2010. Korea Median: AH: 1 Net Worth Quintile: Savings data remains active status in CEIC and is reported by Statistics Korea. The data is categorized under Global Database’s Korea – Table KR.H083: SHFLC: Household Assets, Liabilities & Income By Net Worth Quintiles.

Korea Average: AH: 1 Net Worth Quintile: Total Assets data was reported at 31,470,000.000 KRW in 2017. This records an increase from the previous number of 29,880,000.000 KRW for 2016. Korea Average: AH: 1 Net Worth Quintile: Total Assets data is updated yearly, averaging 28,070,000.000 KRW from Mar 2010 (Median) to 2017, with 8 observations. The data reached an all-time high of 31,470,000.000 KRW in 2017 and a record low of 22,820,000.000 KRW in 2010. Korea Average: AH: 1 Net Worth Quintile: Total Assets data remains active status in CEIC and is reported by Statistics Korea. The data is categorized under Global Database’s Korea – Table KR.H083: SHFLC: Household Assets, Liabilities & Income By Net Worth Quintiles.

Goal 10Reduce inequality within and among countriesTarget 10.1: By 2030, progressively achieve and sustain income growth of the bottom 40 per cent of the population at a rate higher than the national averageIndicator 10.1.1: Growth rates of household expenditure or income per capita among the bottom 40 per cent of the population and the total populationSI_HEI_TOTL: Growth rates of household expenditure or income per capita (%)Target 10.2: By 2030, empower and promote the social, economic and political inclusion of all, irrespective of age, sex, disability, race, ethnicity, origin, religion or economic or other statusIndicator 10.2.1: Proportion of people living below 50 per cent of median income, by sex, age and persons with disabilitiesSI_POV_50MI: Proportion of people living below 50 percent of median income (%)Target 10.3: Ensure equal opportunity and reduce inequalities of outcome, including by eliminating discriminatory laws, policies and practices and promoting appropriate legislation, policies and action in this regardIndicator 10.3.1: Proportion of population reporting having personally felt discriminated against or harassed in the previous 12 months on the basis of a ground of discrimination prohibited under international human rights lawVC_VOV_GDSD: Proportion of population reporting having felt discriminated against, by grounds of discrimination, sex and disability (%)Target 10.4: Adopt policies, especially fiscal, wage and social protection policies, and progressively achieve greater equalityIndicator 10.4.1: Labour share of GDPSL_EMP_GTOTL: Labour share of GDP (%)Indicator 10.4.2: Redistributive impact of fiscal policySI_DST_FISP: Redistributive impact of fiscal policy, Gini index (%)Target 10.5: Improve the regulation and monitoring of global financial markets and institutions and strengthen the implementation of such regulationsIndicator 10.5.1: Financial Soundness IndicatorsFI_FSI_FSANL: Non-performing loans to total gross loans (%)FI_FSI_FSERA: Return on assets (%)FI_FSI_FSKA: Regulatory capital to assets (%)FI_FSI_FSKNL: Non-performing loans net of provisions to capital (%)FI_FSI_FSKRTC: Regulatory Tier 1 capital to risk-weighted assets (%)FI_FSI_FSLS: Liquid assets to short term liabilities (%)FI_FSI_FSSNO: Net open position in foreign exchange to capital (%)Target 10.6: Ensure enhanced representation and voice for developing countries in decision-making in global international economic and financial institutions in order to deliver more effective, credible, accountable and legitimate institutionsIndicator 10.6.1: Proportion of members and voting rights of developing countries in international organizationsSG_INT_MBRDEV: Proportion of members of developing countries in international organizations, by organization (%)SG_INT_VRTDEV: Proportion of voting rights of developing countries in international organizations, by organization (%)Target 10.7: Facilitate orderly, safe, regular and responsible migration and mobility of people, including through the implementation of planned and well-managed migration policiesIndicator 10.7.1: Recruitment cost borne by employee as a proportion of monthly income earned in country of destinationIndicator 10.7.2: Number of countries with migration policies that facilitate orderly, safe, regular and responsible migration and mobility of peopleSG_CPA_MIGRP: Proportion of countries with migration policies to facilitate orderly, safe, regular and responsible migration and mobility of people, by policy domain (%)SG_CPA_MIGRS: Countries with migration policies to facilitate orderly, safe, regular and responsible migration and mobility of people, by policy domain (1 = Requires further progress; 2 = Partially meets; 3 = Meets; 4 = Fully meets)Indicator 10.7.3: Number of people who died or disappeared in the process of migration towards an international destinationiSM_DTH_MIGR: Total deaths and disappearances recorded during migration (number)Indicator 10.7.4: Proportion of the population who are refugees, by country of originSM_POP_REFG_OR: Number of refugees per 100,000 population, by country of origin (per 100,000 population)Target 10.a: Implement the principle of special and differential treatment for developing countries, in particular least developed countries, in accordance with World Trade Organization agreementsIndicator 10.a.1: Proportion of tariff lines applied to imports from least developed countries and developing countries with zero-tariffTM_TRF_ZERO: Proportion of tariff lines applied to imports with zero-tariff (%)Target 10.b: Encourage official development assistance and financial flows, including foreign direct investment, to States where the need is greatest, in particular least developed countries, African countries, small island developing States and landlocked developing countries, in accordance with their national plans and programmesIndicator 10.b.1: Total resource flows for development, by recipient and donor countries and type of flow (e.g. official development assistance, foreign direct investment and other flows)DC_TRF_TOTDL: Total assistance for development, by donor countries (millions of current United States dollars)DC_TRF_TOTL: Total assistance for development, by recipient countries (millions of current United States dollars)DC_TRF_TFDV: Total resource flows for development, by recipient and donor countries (millions of current United States dollars)Target 10.c: By 2030, reduce to less than 3 per cent the transaction costs of migrant remittances and eliminate remittance corridors with costs higher than 5 per centIndicator 10.c.1: Remittance costs as a proportion of the amount remittedSI_RMT_COST: Remittance costs as a proportion of the amount remitted (%)SI_RMT_COST_BC: Corridor remittance costs as a proportion of the amount remitted (%)SI_RMT_COST_SC: SmaRT corridor remittance costs as a proportion of the amount remitted (%)

Korea Average: HPL: 1 Net Worth Quintile: Regular Income data was reported at 30,480,000.000 KRW in 2017. This records an increase from the previous number of 29,130,000.000 KRW for 2016. Korea Average: HPL: 1 Net Worth Quintile: Regular Income data is updated yearly, averaging 26,885,000.000 KRW from Mar 2010 (Median) to 2017, with 8 observations. The data reached an all-time high of 30,480,000.000 KRW in 2017 and a record low of 24,460,000.000 KRW in 2011. Korea Average: HPL: 1 Net Worth Quintile: Regular Income data remains active status in CEIC and is reported by Statistics Korea. The data is categorized under Global Database’s Korea – Table KR.H083: SHFLC: Household Assets, Liabilities & Income By Net Worth Quintiles.

The average for 2021 based on 5 countries was 28.86 percent. The highest value was in the Philippines: 32.5 percent and the lowest value was in India: 25.5 percent. The indicator is available from 1963 to 2023. Below is a chart for all countries where data are available.