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 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.

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.

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.

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.

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.

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.

Worldwide, income and wealth inequality have increased in many countries. Such inequalities yield various social ills, including the decline of social cohesion, a pattern visible amidst recent crises and global political upheavals. Nevertheless, scholars of social and political disaffection have focused primarily on income inequality in OECD countries. This article’s contribution is to expand that focus by: (1) examining the impact of wealth inequality, which no study has done before; (2) extending the link between inequality and social trust to political trust; and (3) taking a global approach. The authors produce an original dataset, combining a novel wealth-inequality measure with 60 waves of Barometers and Values surveys across 309 country-year units and other relevant indicators. Results show that income inequality undermines social trust, but reveal no significant effect for wealth inequality. The effects of income and wealth inequality on political trust are moderated by democracy levels and country wealth, respectively.

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.

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 (%)

A: Results of differences in mean z-scores of nutritional indicators of Bengali children between Bangladesh and India. B: Results of differences in the proportions of undernutrition between under-five Bengali children in India and Bangladesh.

Thailand Household Current Income: % Share: Greater Bangkok (GB): Quintile 1 data was reported at 9.400 % in 2015. This records an increase from the previous number of 7.600 % for 2013. Thailand Household Current Income: % Share: Greater Bangkok (GB): Quintile 1 data is updated yearly, averaging 7.600 % from Dec 2011 (Median) to 2015, with 3 observations. The data reached an all-time high of 9.400 % in 2015 and a record low of 6.900 % in 2011. Thailand Household Current Income: % Share: Greater Bangkok (GB): Quintile 1 data remains active status in CEIC and is reported by National Statistical Office. The data is categorized under Global Database’s Thailand – Table TH.G042: Household Income & Assets Statistics.

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.

Around ** percent of the world's collected net personal wealth belongs to the richest one percent. The share of global wealth owned by the richest percent fell during the global financial crisis in 2008/2009, and has been fluctuating since. One-third of the world's billionaires reside in North America.

This dataset provides a gridded subnational datasets for

• Income inequality (Gini coefficient) at admin 1 level
• Gross national income (GNI) per capita PPP at admin 1 level

The datasets are based on reported subnational admin data and spans three decades from 1990 to 2021.

The dataset is presented in details in the following publication. Please cite this paper when using data.

Chrisendo D, Niva V, Hoffman R, Sayyar SM, Rocha J, Sandström V, Solt F, Kummu M. 2024. Income inequality has increased for over two-thirds of the global population. Preprint. doi: https://doi.org/10.21203/rs.3.rs-5548291/v1

Code is available at following repositories:

• Gini coefficient data creation: https://github.com/mattikummu/subnatGini
• GNI per capita data creation: https://github.com/mattikummu/subnatGNI
• analyses for the article: https://github.com/mattikummu/gini_gni_analyses

The following data is given (formats in brackets)

• Income inequality (Gini coefficient) at admin 0 level (national) (GeoTIFF, gpkg, csv)
• Income inequality (Gini coefficient) at admin 1 level (subnational) (GeoTIFF, gpkg, csv)
• Gross national income (GNI) per capita PPP at admin 0 level (national) (GeoTIFF, gpkg, csv)
• Gross national income (GNI) per capita PPP at admin 1 level (subnational) (GeoTIFF, gpkg, csv)
• Slope for Gini coefficient at admin 1 level (GeoTIFF; slope is given also in gpk and csv files)
• Slope for GNI per capita at admin 1 level (GeoTIFF; slope is given also in gpk and csv files)
• Input data for the script that was used to generate the Gini coefficient (input_data_gini.zip)
• Input data for the script that was used to generate the GNI per capita PPP (input_data_GNI.zip)

Files are named as follows
Format: raster data (GeoTIFF) starts with rast_*, polygon data (gpkg) with polyg_*, and tabulated with tabulated_*.
Admin levels: adm0 for admin 0 level, adm1 for admin 1 level
Product type:

• _gini_disp_ for gini coefficient based on disposable income
• _gni_perCapita_ for GNI per capita PPP

Metadata

Grids

Resolution: 5 arc-min (0.083333333 degrees)

Spatial extent: Lon: -180, 180; -90, 90 (xmin, xmax, ymin, ymax)

Coordinate ref system: EPSG:4326 - WGS 84

Format: Multiband geotiff; one band for each year over 1990-2021

Unit: no unit for Gini coefficient and PPP USD in 2017 international dollars for GNI per capita

Geospatial polygon (gpkg) files:

Spatial extent: -180, 180; -90, 83.67 (xmin, xmax, ymin, ymax)

Temporal extent: annual over 1990-2021

Coordinate ref system: EPSG:4326 - WGS 84

Format: gkpk

Unit: no unit for Gini coefficient and PPP USD in 2017 international dollars for GNI per capita

This dataset provides values for HOUSEHOLDS DEBT TO INCOME reported in several countries. The data includes current values, previous releases, historical highs and record lows, release frequency, reported unit and currency.

Household Current Income: % Share: South Region (SR): Quintile 1 data was reported at 7.180 % in 2015. This records an increase from the previous number of 5.038 % for 2013. Household Current Income: % Share: South Region (SR): Quintile 1 data is updated yearly, averaging 6.800 % from Dec 2011 (Median) to 2015, with 3 observations. The data reached an all-time high of 7.180 % in 2015 and a record low of 5.038 % in 2013. Household Current Income: % Share: South Region (SR): Quintile 1 data remains active status in CEIC and is reported by National Statistical Office. The data is categorized under Global Database’s Thailand – Table TH.G042: Household Income & Assets Statistics.

Russia Household Income: Year to Date: Group 1: 20% of Households with Lowest Income data was reported at 5.400 % in Dec 2018. This records a decrease from the previous number of 5.500 % for Sep 2018. Russia Household Income: Year to Date: Group 1: 20% of Households with Lowest Income data is updated quarterly, averaging 5.500 % from Mar 1995 (Median) to Dec 2018, with 96 observations. The data reached an all-time high of 6.500 % in Jun 1997 and a record low of 5.100 % in Dec 2009. Russia Household Income: Year to Date: Group 1: 20% of Households with Lowest Income data remains active status in CEIC and is reported by Federal State Statistics Service. The data is categorized under Russia Premium Database’s Household Survey – Table RU.HA012: Household Income Structure.

Classification of explanatory factors and its distribution by country among under-five Bengali children.

The result of binary logistic regression of child’s nutrition indicators on the explanatory factors among the overall Bengalis.