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.

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.

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.

In 2023, the average net personal wealth among the most affluent one percent of Spanish population was 5.15 million euros. On the other side, the wealth of bottom 50 percent of the wealth holders in the country was estimated at 14,700 euros. When looking at the evolution of wealth distribution in Spain during the last decades, it can be observed, that the share of wealth held by the least affluent half of the population is now lower than it was at the beginning of the 21th century.

In 2023, by far the highest number of individuals with net assets of at least ** million U.S. dollars worldwide were residing in North America, reaching over ******* people. Asia recorded the second highest number of UHNWIs in the world with over ******* individuals.A small share owns vast sums of wealthThe vast majority of global wealth is concentrated in the hands of a few people. Only *** percent of the global population owns assets worth more than *** million U.S. dollars. The richest people in the world are Elon Mask, Jeff Bezos, and Bernard Arnault. When it comes to women, Francoise Bettencourt Meyers led the ranking of the most affluent women worldwide. The wealth of over ** percent of UHNWIs was self-made. Where UHNWIs live and where they leave Unsurprisingly, as North America is the world region with the highest number of UHNWIs, the United States is the country with the highest UHNWI count. However, Hong Kong, special administrative (SAR) region in China, is the city with the highest number of UHNWIs. Nevertheless, China was the country that recorded the highest outflux of UHNWIs in 2022.

In Europe, the variation in average amounts of financial wealth per adult varied considerably as of 2022, from approximately ******* U.S. dollars in Switzerland to roughly ***** U.S. dollars in Azerbaijan. In Europe, the overall average financial wealth per adult as of 2022 was ****** U.S. dollars. In terms of private wealth, Europe held the second highest value in the world, after North America. What is financial wealth? Financial wealth, also known as financial assets or liquid assets can include wealth that an individual has in the forms of cash, stocks, bonds, mutual funds, and bank deposits. In addition to financial wealth, wealth can also be measured in other assets, called non-financial wealth. This includes physical assets, such as real estate, land, vehicles, jewelry, and art, just to name a few. Where do most wealthy individuals live? Individuals with a net worth over *********** U.S. dollars are called high-net worth individuals (HNWI). The United States was the home country to the highest number of HNWIs in 2021. China followed, although their number of HNWIs did not even reach ********* of the number in the United States. In Europe, Switzerland is the country with the highest average financial wealth per adult, but with its small population size, the number of HNWIs does not come near the numbers in the United Kingdom, Germany, France, and Italy – the European countries with the highest number of HNWIs. Considering Switzerland’s small population size, however, it is the country in the world with the highest proportion of millionaires.

This dataset contains estimates of the socioeconomic status (SES) position of each of 149 countries covering the period 1880-2010. Measures of SES, which are in decades, allow for a 130 year time-series analysis of the changing position of countries in the global status hierarchy. SES scores are the average of each country’s income and education ranking and are reported as percentile rankings ranging from 1-99. As such, they can be interpreted similarly to other percentile rankings, such has high school standardized test scores. If country A has an SES score of 55, for example, it indicates that 55 percent of the countries in this dataset have a lower average income and education ranking than country A. ISO alpha and numeric country codes are included to allow users to merge these data with other variables, such as those found in the World Bank’s World Development Indicators Database and the United Nations Common Database.

See here for a working example of how the data might be used to better understand how the world came to look the way it does, at least in terms of status position of countries.

VARIABLE DESCRIPTIONS:

unid: ISO numeric country code (used by the United Nations)

wbid: ISO alpha country code (used by the World Bank)

SES: Country socioeconomic status score (percentile) based on GDP per capita and educational attainment (n=174)

country: Short country name

year: Survey year

gdppc: GDP per capita: Single time-series (imputed)

yrseduc: Completed years of education in the adult (15+) population

region5: Five category regional coding schema

regionUN: United Nations regional coding schema

DATA SOURCES:

The dataset was compiled by Shawn Dorius (sdorius@iastate.edu) from a large number of data sources, listed below. GDP per Capita:

1. Maddison, Angus. 2004. 'The World Economy: Historical Statistics'. Organization for Economic Co-operation and Development: Paris. GDP & GDP per capita data in (1990 Geary-Khamis dollars, PPPs of currencies and average prices of commodities). Maddison data collected from: http://www.ggdc.net/MADDISON/Historical_Statistics/horizontal-file_02-2010.xls.

2. World Development Indicators Database Years of Education 1. Morrisson and Murtin.2009. 'The Century of Education'. Journal of Human Capital(3)1:1-42. Data downloaded from http://www.fabricemurtin.com/ 2. Cohen, Daniel & Marcelo Cohen. 2007. 'Growth and human capital: Good data, good results' Journal of economic growth 12(1):51-76. Data downloaded from http://soto.iae-csic.org/Data.htm

3. Barro, Robert and Jong-Wha Lee, 2013, "A New Data Set of Educational Attainment in the World, 1950-2010." Journal of Development Economics, vol 104, pp.184-198. Data downloaded from http://www.barrolee.com/

4. Maddison, Angus. 2004. 'The World Economy: Historical Statistics'. Organization for Economic Co-operation and Development: Paris. 13.

5. United Nations Population Division. 2009.

This layer uses data from the 2023 Pacific Northwest Quantitative Wildfire Risk Assessment (PNW QWRA 2023). The purpose of the PNW QWRA 2023 is to provide foundational information about wildfire risk across the Pacific Northwest Region (which encompasses the states of Oregon and Washington). Analytics from the QWRA are used to guide vegetation management, fire response, and community planning at multiple scales.A QWRA considers several different components, each resolved spatially across the region, including:likelihood of a fire burning,the intensity of a fire if one should occur,the exposure of assets and resources based on their locations, andthe susceptibility of those assets and resourcesData users are encouraged to refer to the PNW QWRA 2023 Methods Report for full details: https://oe.oregonexplorer.info/externalcontent/wildfire/PNW_QWRA_2023Methods.pdfPyrologix LLC modeled wildfire intensity and likelihood for the PNW QWRA 2023. Wildfire intensity was modeled using the WildEST model. These WildEST results were completed on the 2022 current-condition fuelscape (derived from LANDFIRE v2.2.0), which reflects fuelscape conditions for the year 2022 and includes all historical fuel disturbances through 2021. WildEST results were modified for risk calculations in the PNW QWRA 2023 using an irrigated agriculture mask to assign FLPs to pixels that are likely to be irrigated during fire season. An irrigated agriculture mask was created from LANDFIRE 2.2.0 Fire Behavior Fuel Models (where the model = “NB3”) and data was collected from IrrMapper (Ketchum et al., 2020). All NB3 pixels and pixels that were classified as irrigated in three of the most recent five years in IrrMapper were included in the irrigated agriculture mask. Pixels in the irrigated agriculture mask were assigned an FLP of 0.75 for flame lengths between 0 – 2 feet, 0.25 for flame lengths 2 – 4 feet, and an FLP of 0 for all intensity values greater than 4 feet. Fire-effects flame-length probability rasters generated in WildEST were used for effects analysis in a landscape wildfire risk assessment, as described in USFS GTR-315.The PNW QWRA 2023 evaluated risk to eight highly-valued resources and assets (HVRAs): People and Property, Infrastructure, Drinking Water, Timber, Ecological Integrity, Wildlife Habitat, Agriculture, and Recreation. This data layer represents the cNVC integrated across the people and property, infrastructure, and drinking water HVRAs. Risk is estimated within the QWRA framework by integrating wildfire hazard with HVRA susceptibility (Scott et al., 2013). Risk is calculated for each pixel separately based on the fire hazard data for that pixel and based on which HVRAs are present. Fire impacts to each HVRA are characterized by the estimated change in value, a unitless approximation of whether the HVRA is beneficially or adversely affected by fire and to what magnitude. Accordingly, risk is expressed as net value change (NVC). Net value change is first calculated for all pixels across a sub-HVRA. The NVC for each HVRA is then calculated by summing the NVC of all its constituent sub-HVRAs. Finally, HVRA-level NVC risk can be summed across several or all HVRAs to calculate integrated NVC, representing a risk to multiple HVRAs. Positive values indicate that wildfire is likely to have beneficial impacts on the HVRA while negative values indicate that the net outcomes are likely to be adverse. Risk is calculated based on a very wide range of plausible weather conditions, much wider than the range under which we have typically experienced large fires in the past. The specific conditions under which a wildfire occurs will determine the outcomes. When interpreting QWRA risk results bear in mind that fire will not always be beneficial in areas with positive NVC values and, likewise, it may be possible to experience a beneficial fire in areas with negative NVC values.Citations:Ketchum, D., Jencso, K., Maneta, M.P., Melton, F., Jones, M.O., Huntington, J., 2020. IrrMapper: A Machine Learning Approach for High Resolution Mapping of Irrigated Agriculture Across the Western U.S. Remote Sensing 12, 2328. https://doi.org/10.3390/rs12142328Scott, J.H., Thompson, M.P., Calkin, D.E., 2013. A wildfire risk assessment framework for land and resource management (No. RMRS-GTR-315). U.S. Department of Agriculture, Forest Service, Rocky Mountain Research Station, Ft. Collins, CO. https://doi.org/10.2737/RMRS-GTR-315Finney, M.A., McHugh, C.W., Grenfell, I.C., Riley, K.L., Short, K.C., 2011. A simulation of probabilistic wildfire risk components for the continental United States. Stoch Environ Res Risk Assess 25, 973–1000. https://doi.org/10.1007/s00477-011-0462-zPrimary Data Contact: Ian Rickert, Regional Fire Planner, Forest Service R6/R10, ian.rickert@usda.gov

This table presents income shares, thresholds, tax shares, and total counts of individual Canadian tax filers, with a focus on high income individuals (95% income threshold, 99% threshold, etc.). Income thresholds are geography-specific; for example, the number of Nova Scotians in the top 1% will be calculated as the number of taxfiling Nova Scotians whose total income exceeded the 99% income threshold of Nova Scotian tax filers. Different definitions of income are available in the table namely market, total, and after-tax income, both with and without capital gains.

Information on farm household income and farm household composition. Source agency: Environment, Food and Rural Affairs Designation: National Statistics Language: English Alternative title: Farm Household Income and Household Composition, England

If you require the datasets in a more accessible format, please contact fbs.queries@defra.gsi.gov.uk

Background and guidance on the statistics

Information on farm household income and farm household composition was collected in the Farm Business Survey (FBS) for England for the first time in 2004/05. Collection of household income data is restricted to the household of the principal farmer from each farm business. For practical reasons, data is not collected for the households of any other farmers and partners. Two-thirds of farm businesses have an input only from the principal farmer’s household (see table 5). However, details of household composition are collected for the households of all farmers and partners in the business, but not employed farm workers.

Data on the income of farm households is used in conjunction with other economic information for the agricultural sector (e.g. farm business income) to help inform policy decisions and to help monitor and evaluate current policies relating to agriculture in the United Kingdom by Government. It also informs wider research into the economic performance of the agricultural industry.

This release gives the main results from the income and composition of farm households and the off-farm activities of the farmer and their spouse (Including common law partners) sections of the FBS. These sections include information on the household income of the principal farmer’s household, off-farm income sources for the farmer and spouse and incomes of other members of their household and the number of working age and pensionable adults and children in each of the households on the farm (the information on household composition can be found in Appendix B).

This release provides the main results from the 2013/14 FBS. The results are presented together with confidence intervals.

Survey content and methodology

The Farm Business Survey (FBS) is an annual survey providing information on the financial position and physical and economic performance of farm businesses in England. The sample of around 1,900 farm businesses covers all regions of England and all types of farming with the data being collected by face to face interview with the farmer. Results are weighted to represent the whole population of farm businesses that have at least 25 thousand Euros of standard output as recorded in the annual June Survey of Agriculture and Horticulture. In 2013 there were just over 58 thousand farm businesses meeting this criteria.

Since 2009/10 a sub-sample of around 1,000 farms in the FBS has taken part in both the additional surveys on the income and composition of farm households and the off-farm activities of the farmer and their spouse. In previous years, the sub-sample had included over 1,600 farms. As such, caution should be taken when comparing to earlier years.

The farms that responded to the additional survey on household incomes and off-farm activities of the farmer and spouse had similar characteristics to those farms in the main FBS in terms of farm type and geographical location. However, there is a smaller proportion of very large farms in the additional survey than in the main FBS. Full details of the characteristic of responding farms can be found at Appendix A of the notice.

For further information about the Farm Business Survey please see: https://www.gov.uk/government/organisations/department-for-environment-food-rural-affairs/series/farm-business-survey

Data analysis

The results from the FBS relate to farms which have a standard output of at least 25,000 Euros. Initial weights are applied to the FBS records based on the inverse sampling fraction for each design stratum (farm type by farm size). These weights are then adjusted (calibration weighting) so that they can produce unbiased estimators of a number of different target variables. Completion of the additional survey on household incomes and off-farm activities of the farmer and spouse was voluntary and a sample of around 1,000 farms was achieved. In order to take account of non-response, the results have been reweighted using a method that preserves marginal totals for populations according to farm type and farm size groups. As such, farm population totals for other classifications (e.g. regions) will not be in-line with results using the main FBS weights, nor will any results produced for variables derived from the rest of the FBS (e.g. farm business income).

Accuracy and reliability of the results

We show 95% confidence intervals against the results. These show the range of values that may apply to the figures. They mean that we are 95% confident that this range contains the true value. They are calculated as the standard errors (se) multiplied by 1.96 to give the 95% confidence interval. The standard errors only give an indication of the sampling error. They do not reflect any other sources of survey errors, such as non-response bias. For the Farm Business Survey, the confidence limits shown are appropriate for comparing groups within the same year only; they should not be used for comparing with previous years since they do not allow for the fact that many of the same farms will have contributed to the Farm Business Survey in both years.

Availability of results

This release contains headline results for each section. The full set of results can be found at: https://www.gov.uk/government/organisations/department-for-environment-food-rural-affairs/series/farm-business-survey#publications

Defra statistical notices can be viewed on the on the statistics pages of the Defra website at https://www.gov.uk/government/organisations/department-for-environment-food-rural-affairs/about/statistics. This site also shows details of future publications, with pre-announced dates.

Data Uses

Data from the Farm Business Survey (FBS) are provided to the EU as part of the Farm Accountancy Data Network (FADN). The data have been used to help inform policy decisions (e.g. Reform of Pillar 1 and Pillar 2 of Common Agricultural Policy) and to help monitor and evaluate current policies relating to agriculture in England (and the EU). It is also widely used by the industry for benchmarking and informs wider research into the economic performance of the agricultural industry.

User engagement

As part of our ongoing commitment to compliance with the Code of Practice for Official Statistics http://www.statisticsauthority.gov.uk/assessment/code-of-practice/index.html, we wish to strengthen our engagement with users of these statistics and better understand the use made of them and the types of decisions that they inform. Consequently, we invite users to make themselves known, to advise us of the use they do, or might, make of these statistics, and what their wishes are in terms of engagement. Feedback on this notice and enquiries about these statistics are also welcome.

Definitions

Household income of the principal farmer Principal farmer’s household income has the following components: (1) The share of farm business income (FBI) (including income from farm diversification) attributable to the principal farmer and their spouse. (2) Principal farmer’s and spouse’s off farm income from employment and self-employment, investment income, pensions and social payments. (3) Income of other household members. The share of farm business income and all employment and self-employment incomes, investment income and pension income are recorded as gross of income tax payments and National Insurance contributions, but after pension contributions. In addition, no deduction is made for council tax.

Household A household is defined as a single person or group of people living at the same address as their only or main residence, who either share one meal a day together or share the living accommodation. A household must contain at least one person who received drawings from the farm business or who took a share of the profit from the business.

Drawings Drawings represent the monies which the farmer takes from the business for their own personal use. The percentage of total drawings going to each household is collected and is used to calculate the total share of farm business income for the principal farmer’s household.

Mean Mean household income of individuals is the ”average”, found by adding up the weighted household incomes for each individual farm in the population for analysis and dividing the result by the corresponding weighted number of farms. In this report average is usually taken to refer to the mean.

Percentiles These are the values which divide the population for analysis, when ranked by an output variable (e.g. household income or net worth), into 100 equal-sized groups. E.g. twenty five per cent of the population would have incomes below the 25th percentile.

Median Median household income divides the population, when ranked by an output variable, into two equal sized groups. The median of the whole population is the same as the 50th percentile. The term is also used for the midpoint of the subsets of the income distribution

Quartiles Quartiles are values which divide the population, when ranked by an output variable, into four equal-sized groups. The lowest quartile is the same as the 25th percentile. The divisions of a population split by quartiles are referred to as quarters in this publication.

Quintiles Quintiles are values which divide the population, when ranked by an output variable, into five equal-sized groups. The divisions of a population split by quintiles are referred to as fifths in this publication.

Assets Assets include

In the 3 years to March 2021, black households were most likely out of all ethnic groups to have a weekly income of under £600.

In 2023, just over 50 percent of Americans had an annual household income that was less than 75,000 U.S. dollars. The median household income was 80,610 U.S. dollars in 2023. Income and wealth in the United States After the economic recession in 2009, income inequality in the U.S. is more prominent across many metropolitan areas. The Northeast region is regarded as one of the wealthiest in the country. Maryland, New Jersey, and Massachusetts were among the states with the highest median household income in 2020. In terms of income by race and ethnicity, the average income of Asian households was 94,903 U.S. dollars in 2020, while the median income for Black households was around half of that figure. What is the U.S. poverty threshold? The U.S. Census Bureau annually updates its list of poverty levels. Preliminary estimates show that the average poverty threshold for a family of four people was 26,500 U.S. dollars in 2021, which is around 100 U.S. dollars less than the previous year. There were an estimated 37.9 million people in poverty across the United States in 2021, which was around 11.6 percent of the population. Approximately 19.5 percent of those in poverty were Black, while 8.2 percent were white.

In 2020, Italy counted approximately *** million individuals boasting a wealth worth over one million U.S. dollars. Individuals in this wealth bracket are forecasted to reach nearly two million in 2025, an increase of approximately ** percent compared to 2020. When compared to other Western European countries, the number of individuals worth over five million U.S. dollars is less impressive. France, for example, counted about *** million individuals in that wealth bracket in 2019.

Wealthiest individuals in Italy

In 2019, Giovanni Ferrero, the CEO of the Italian confectionary company Ferrero S.p.A., lead the ranking of the wealthiest individuals in Italy published yearly by Forbes. With a total net worth estimated at **** billion U.S. dollars, Ferrero overshadowed Leonardo Del Vecchio, founder of Luxottica, whose fortune amounted to **** U.S. dollars.

Wealthiest individuals in the world

According to the Forbes’ ranking, in 2019, Giovanni Ferrero was the **** wealthiest person in the world and Leonardo Del Vecchio ranked **** in the list. Despite their massive fortune, no one can compete with Jeff Bezos. For the second year in a row, the founder of Amazon dominated the Forbes’ ranking: his wealth was estimated at over *** billion dollars in 2019.

A dataset listing the richest zip codes in Missouri per the most current US Census data, including information on rank and average income.

A dataset listing the richest zip codes in Puerto Rico per the most current US Census data, including information on rank and average income.

** percent of Australians were in the wealth range between 100,000 and *********** U.S. dollars in 2020. Just *** percent of Australian adults had wealth of over *********** U.S. dollars, which was slightly less than the share of people who had under 10,000 U.S. dollars in wealth.

Wealth distribution in the Asia-Pacific

In 2020, China had the highest number of millionaires, followed by Japan and Australia. The number of millionaires in Australia was forecasted to increase from *** million to ************* by 2025. According to a source, among the Asia-Pacific countries, Australia ranked second in the share of wealth per adult. The source had revealed the wealth per adult in Australia was more than *** thousand U.S. dollars in 2020.

LGBTQ community of Australia

In 2020, a survey of working adults in Australia revealed that LGBTQ adults were employed in public services and the law enforcement across the country. On the one hand, more than ** percent of LGBTQ individuals had a role as as a team member, above ** percent of respondents answered that they were either team leader or supervisor.