This statistic shows the share of overall income held by the richest 1 percent of each country in 2005. The richest top percent of U.S. citizens had an income share of 17.4 percent of the country's total income. Since 1949, the U.S. has experienced a leap in inequality while an equally marked drop has occurred in the Netherlands.

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.

In 2022 the top one percent of earners in the United Kingdom accounted for around 10.2 percent of the overall national income of the UK. The share of national income earned by the top one percent increased from 6.8 percent in 1980 to a peak of 14.8 percent in 2007.

Graph and download economic data for Net Worth Held by the Top 1% (99th to 100th Wealth Percentiles) (WFRBLT01026) from Q3 1989 to Q1 2025 about net worth, wealth, percentile, Net, and USA.

Many studies have used tax data to measure the U.S. income distribution, but their results vary widely. For example, in 2014 the top 1 percent share of income is 21.5 percent in Piketty and Saez (2003 and updates), 16.7 percent in the Congressional Budget Office (2018), and 13.1 percent in our analysis. What accounts for such large differences? We provide a step-by-step analysis of how methodological differences affect the results and address issues raised in Piketty, Saez, and Zucman (2018, 2019). Important differences include accounting for declining marriage rates, including social insurance and employer benefits, accounting for tax reforms, and including income missing from tax returns.

The rising share of national income taken by the top one percent of earners is a common thread amongst almost all European countries over the past half century. As economic globalization took hold throughout the 1980s and 1990s, European countries experienced de-industrialization due to the emergence of international competitors, mostly in East Asia. At the same time, information technology and finance became much more important for most European economies, while growth in these sectors tends to favor high earners. This rise in inequality is also often also attributed to the ascendence of 'neoliberal' economic and political ideas which prioritized free markets and the privatization of government-owned businesses. Russia: the explosion of inequality after the fall of communismAmong the largest European economies, the Russian Federation stands out as the country which experienced the sharpest increase in inequality, as a small number of 'oligarchs' took control of the major industries after the collapse of the Soviet Union and the end of communist rule in 1991. The top one percent in Russia increased their share of national income five-fold over the 20 years from 1987 to 2007, when inequality in the country reached its peak as the oligarchs took home over a quarter of the country's income. Turkey: falling share of national income taken by top earners****** has bucked the trend of the rising income share for the richest over this period, as its extremely concentrated income distribution has in fact become somewhat more equitable. The highest earners in Turkey saw their share of national income drop from almost ** percent in the early *****, to a low of ** percent in 2007, after which it has stabilized between ** and ** percent. Western Europe: gradually rising share of national income for the richThe five western European democracies, Germany, France, Italy, Spain, and the United Kingdom, have all seen increases in their top earners' shares of national income over this period. The United Kingdom, Italy, and Germany have in particular seen their shares increase sharply, while Spain and France have experienced a more gradual increase.

This statistic represents the tax burden of the leading one percent in the U.S. in 2018, by state. The tax rate is the total average state and local taxes as a percentage of income. In 2018, the leading one percent in California paid around **** percent of their family income as tax.

Income from capital was the main source of annual household income for the top percentile of earners in Israel during 2021. That year, earnings from capital reached *** million Israeli shekels on average, about ******* U.S. dollars, which represented about ** percent of annual income. Over the period observed, capital income grew significantly, peaking in 2017 at *** million Israeli shekels, about *** million U.S. dollars. The 2017 spike was due to a government decision to implement a one-time tax incentive to release "trapped" capital gains taxes. On the other hand, employment income accounted for almost ** percent of household earnings among the wealthiest in the country.

Income of individuals by age group, sex and income source, Canada, provinces and selected census metropolitan areas, annual.

Graph and download economic data for Checkable Deposits and Currency Held by the Top 1% (99th to 100th Wealth Percentiles) (DISCONTINUED) (WFRBLT01005) from Q3 1989 to Q1 2022 about checkable, wealth, percentile, deposits, currency, and USA.

In March 2025, the top one percent of earners in the United Kingdom received an average pay of over 16,000 British pounds per month, compared with the bottom ten percent of earners who earned around 800 pounds a month.

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.

Recent studies suggest that public policy in established democracies mostly caters to the interests of the rich and ignores the average citizen when their preferences diverge. I argue that high-income taxation has become a clear illustration of this pattern, and I test the proposition on a least likely case: Norway. I asked Norwegians to design their preferred tax rate structure, and subsequently matched their answers with registry data on what people at different incomes actually pay in tax. I find that within the top 1 percent, tax rates are far below (as much as 23 percentage points) from where citizens want them to be. A follow-up survey showed that this divergence is entirely driven by capital incomes being taxed too low. My results suggest that even in a fairly egalitarian society like Norway, the rich get away with paying considerably less in tax than what people deem fair.

These Public Use Microdata Sample (PUMS) files contain records representing 1-percent samples of the occupied and vacant housing units in the United States and the people in the occupied units in 2000. Group quarters people also are included. The files contain individual weights for each person and housing unit, which when applied to the individual records, expand the sample to the relevant total. Some of the items included on the housing record are: acreage, agricultural sales, bedrooms, condominium fee, contract rent, cost of utilities, family income in 1999, farm residence, fire, hazard, and flood insurance, fuels used, gross rent, heating fuel, household income in 1999, household type, kitchen facilities, linguistic isolation, meals included in rent, mobile home costs, mortgage payment, mortgage status, plumbing facilities, presence and age of own children, presence of subfamilies in household, real estate taxes, rooms, selected monthly owner costs, size of building (units in structure), telephone service, tenure, vacancy status, value (of housing unit), vehicles available, year householder moved into unit, and year structure was built. Some of the items included on the person record are: ability to speak English, age, ancestry, citizenship, class of worker, disability status, earnings in 1999, educational attainment, grandparents as caregivers, Hispanic origin, hours worked, income in 1999 by type, industry, language spoken at home, marital status, means of transportation to work, migration Public Use Microdata Area (PUMA), migration state, mobility status, veteran period of service, years of military service, occupation, personal care limitation, place of birth, place of work PUMA, place of work state, poverty status in 1999, race, relationship, school enrollment and type of school, time of departure for work, travel time to work, vehicle occupancy, weeks worked in 1999, work limitation status, work status in 1999, and year of entry. The Public Use Microdata Sample (PUMS) files contain geographic units known as super-Public Use Microdata Areas (super-PUMAs) and Public Use Microdata Areas (PUMAs). To maintain the confidentiality of the PUMS data, minimum population thresholds are set for PUMAs and super-PUMAs. For the 1-percent state-level files, the super-PUMAs contain a minimum population of 400,000 and are composed of a PUMA or a group of contiguous PUMAs delineated on the 5-percent state-level PUMS files. Super-PUMAs are a new geographic entity for Census 2000. Super-PUMAs and PUMAs also are defined for place of residence on April 1, 1995, and place of work. (Source: ICPSR, retrieved 06/15/2011)

Our aim is to provide an overview of associations between income inequality and intergenerational mobility in the United States, Canada, and eight European countries (Denmark, Finland, France, Germany, Italy, Norway, Sweden, and the United Kingdom). We analyze whether this correlation is observed across and within countries over time. Developed countries have been experiencing increases in inequality in recent decades, mostly due to a steep concentration of income at the top of the distribution. We investigate Great Gatsby curves and perform meta-regression analyses based upon several papers on this topic. Results suggest that countries with high levels of inequality tend to have lower levels of mobility. Intergenerational income elasticities have stronger associations with the Gini coefficient, compared to associations with the top one percent income share. Once models are controlled for methodological variables, country indicators, and paper indicators, correlations of mobility with the Gini coefficient lose significance, but not with the top one percent income share. This result is an indication that recent increases in inequality at the top of the distribution (captured by the top one percent income share) might be negatively affecting mobility on a greater magnitude, compared to variations across the income distribution (captured by the Gini coefficient).

This statistic shows the ten colleges in the United States that had the highest ratio of students in, or came from families in, the top 1 percent of income to students from the bottom ** percent of incomes. The statistic is based on the 1991 cohort and therefore is approximate to the class of 2013. In the 1991 cohort, Washington University in St. Louis had **** students from the * percent for every student from the bottom ** percent.

The bottom 50 percent in Russia earned an average of 7.7 thousand euros at purchasing power parity (PPP) before income tax in 2021. To compare, the mean income of the top 10 percent stood at 104.6 thousand euros in the same year. Looking at the percentage distribution of national wealth in the country, the poorest half held only three percent of the total in 2021.

This dataset, prepared by the Inter-university Consortium for Political and Social Research, comprises 1 percent of the cases in the second release of CENSUS OF POPULATION AND HOUSING, 1990 [UNITED STATES]: PUBLIC USE MICRODATA SAMPLE: 1-PERCENT SAMPLE (ICPSR 9951). As 1 percent of the 1-Percent Public Use Microdata Sample (PUMS), the file constitutes a 1-in-10,000 sample, and contains all housing and population variables in the original 1-Percent PUMS. Housing variables include area type, state and area of residence, farm/nonfarm status, type of structure, year structure was built, vacancy and boarded-up status, number of rooms and bedrooms, presence or absence of a telephone, presence or absence of complete kitchen and plumbing facilities, type of sewage, water source and heating fuel used, property value, tenure, year moved into house/apartment, type of household/family, type of group quarters, language spoken in household, number of persons, related children, own/adopted children, and stepchildren in the household, number of persons and workers in the family, status of mortgage, second mortgage, and home equity loan, number of vehicles available, household income, sales of agricultural products, payments for rent, mortgage, and property tax, condominium fees, mobile home costs, and costs for electricity, water, heating fuel, and flood/fire/hazard insurance. Person variables cover age, sex, and relationship to householder, educational attainment, school enrollment, race, Hispanic origin, ancestry, language spoken at home, citizenship, place of birth, year of immigration, place of residence in 1985, marital status, number of children ever born, presence and age of own children, military service, mobility and personal care limitations, work limitation status, employment status, employment status of parents, occupation, industry, and class of worker, hours worked last week, weeks worked in 1989, usual hours worked per week, temporary absences from work, place of work, time of departure for work, travel time to work, means of transportation to work, number of occupants in vehicle during ride to work, total earnings, total income, wages, and salary income, farm and nonfarm self-employment income, Social Security income, public assistance income, retirement income, and rent, dividend, and net rental income.

The Public Use Microdata Samples (PUMS) contain person- and household-level information from the "long-form" questionnaires distributed to a sample of the population enumerated in the 1980 Census. The C Sample, containing 1 percent data, identifies census regions, divisions, 27 individual states, and the District of Columbia. Four types of areas are shown: inside central cities, urban fringe, other urban, and rural. The C Sample separately identifies every urbanized area with a total population over 800,000, and roughly half of the urbanized areas between 200,000 and 800,000. Household-level variables include housing tenure, year structure was built, number and types of rooms in dwelling, plumbing facilities, heating equipment, taxes and mortgage costs, number of children, and household and family income. Person-level variables include sex, age, marital status, race, Spanish origin, income, occupation, transportation to work, and education.