This indicator represents the tracts ranked by their percentile level of median household incomes per census tract, per capita income. The data source is 2017-2021 American Community Survey, 5-year estimates. The percentile and the rank were calculated. A percentile is a score indicating the value below which a given percentage of observations in a group of observations fall. It indicates the relative position of a particular value within a dataset. For example, the 20th percentile is the value below which 20% of the observations may be found. The rank refers to a process of arranging percentiles in descending order, starting from the highest percentile and ending with the lowest percentile. Once the percentiles are ranked, a normalization step is performed to rescale the rank values between 0 and 10. A rank value of 10 represents the highest percentile, while a rank value of 0 corresponds to the lowest percentile in the dataset. The normalized rank provides a relative assessment of the position of each percentile within the distribution, making it simpler to understand the relative magnitude of differences between percentiles. Normalization between 0 and 10 ensures that the rank values are standardized and uniformly distributed within the specified range. This normalization allows for easier interpretation and comparison of the rank values, as they are now on a consistent scale. For detailed methods, go to connecticut-environmental-justice.circa.uconn.edu.

Composition of Overall Weekly & Annual Earnings Percentiles

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

Abstract

The Health Inequality Project uses big data to measure differences in life expectancy by income across areas and identify strategies to improve health outcomes for low-income Americans.

Section 7

This table reports life expectancy point estimates and standard errors for men and women at age 40 for each percentile of the national income distribution. Both race-adjusted and unadjusted estimates are reported.

Source

Section 13

This table reports life expectancy point estimates and standard errors for men and women at age 40 for each percentile of the national income distribution separately by year. Both race-adjusted and unadjusted estimates are reported.

Source

Section 6

This dataset was created on 2020-01-10 18:53:00.508 by merging multiple datasets together. The source datasets for this version were:

Commuting Zone Life Expectancy Estimates by year: CZ-level by-year life expectancy estimates for men and women, by income quartile

Commuting Zone Life Expectancy: Commuting zone (CZ)-level life expectancy estimates for men and women, by income quartile

Commuting Zone Life Expectancy Trends: CZ-level estimates of trends in life expectancy for men and women, by income quartile

Commuting Zone Characteristics: CZ-level characteristics

Commuting Zone Life Expectancy for larger populations: CZ-level life expectancy estimates for men and women, by income ventile

Section 15

This table reports life expectancy point estimates and standard errors for men and women at age 40 for each quartile of the national income distribution by state of residence and year. Both race-adjusted and unadjusted estimates are reported.

Source

Section 11

This table reports US mortality rates by gender, age, year and household income percentile. Household incomes are measured two years prior to the mortality rate for mortality rates at ages 40-63, and at age 61 for mortality rates at ages 64-76. The “lag” variable indicates the number of years between measurement of income and mortality.

Observations with 1 or 2 deaths have been masked: all mortality rates that reflect only 1 or 2 deaths have been recoded to reflect 3 deaths

Source

Section 3

This table reports coefficients and standard errors from regressions of life expectancy estimates for men and women at age 40 for each quartile of the national income distribution on calendar year by commuting zone of residence. Only the slope coefficient, representing the average increase or decrease in life expectancy per year, is reported. Trend estimates for both race-adjusted and unadjusted life expectancies are reported. Estimates are reported for the 100 largest CZs (populations greater than 590,000) only.

Source

Section 9

This table reports life expectancy estimates at age 40 for Males and Females for all countries. Source: World Health Organization, accessed at: http://apps.who.int/gho/athena/

Source

Section 10

This table reports life expectancy point estimates and standard errors for men and women at age 40 for each quartile of the national income distribution by county of residence. Both race-adjusted and unadjusted estimates are reported. Estimates are reported for counties with populations larger than 25,000 only

Source

Section 2

This table reports life expectancy point estimates and standard errors for men and women at age 40 for each quartile of the national income distribution by commuting zone of residence and year. Both race-adjusted and unadjusted estimates are reported. Estimates are reported for the 100 largest CZs (populations greater than 590,000) only.

Source

Section 8

This table reports US population and death counts by age, year, and sex from various sources. Counts labelled “dm1” are derived from the Social Security Administration Data Master 1 file. Counts labelled “irs” are derived from tax data. Counts labelled “cdc” are derived from NCHS life tables.

Source

Section 12

This table reports numerous county characteristics, compiled from various sources. These characteristics are described in the county life expectancy table.

Two variables constructed by the Cen

Dataset from Singapore Department of Statistics. For more information, visit https://data.gov.sg/datasets/d_1c2bdf63e25a4d2f01dca47dd228fb2e/view

About this dataset

Context

The dataset presents the mean household income for each of the five quintiles in Greece, New York, 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 14,828, while the mean income for the highest quintile (20% of households with the highest income) is 204,681. This indicates that the top earners earn 14 times compared to the lowest earners.
• *Top 5%: * The mean household income for the wealthiest population (top 5%) is 331,791, which is 162.10% higher compared to the highest quintile, and 2237.60% higher compared to the lowest quintile.

https://i.neilsberg.com/ch/greece-ny-mean-household-income-by-quintiles.jpeg" alt="Mean household income by quintiles in Greece, New York (in 2022 inflation-adjusted dollars))">

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2017-2021 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 2022 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 Greece town median household income. You can refer the same here

Survey years

The Poverty and Inequality Platform: Percentiles database reports 100 points ranked according to the consumption or income distributions for country-year survey data available in the World Bank’s Poverty and Inequality Platform (PIP). There are, as of September 19, 2024, a total of 2,456 country-survey-year data points, which include 2,274 distributions based on microdata, binned data, or imputed/synthetic data, and 182 based on grouped data. For the grouped data, the percentiles are derived by fitting a parametric Lorenz distribution following Datt (1998). For ease of communication, all distributions are referred to as survey data henceforth, and the welfare variable is referred to as income.

Details

Each distribution reports 100 points per country per survey year ranked from the smallest (percentile 1) to the largest (percentile 100) income or consumption. For each income percentile, the database reports the following variables: the average daily per person income or consumption (avg_welfare); the income or consumption value for the upper threshold of the percentile (quantile); the share of the population in the percentile (which might deviate slightly from 1% due to coarseness in the raw data) (pop_share); and the share of income or consumption held by each percentile (welfare_share). In addition, the database reports the welfare measure (welfare_type) used in the survey data—income or consumption—and the region covered (reporting_level)—urban, rural, or national. The distributions are available in 2011 or 2017 PPP$.

Stata code example

Below is an example of how to use the database to generate an anonymous growth incidence curve for Bangladesh between 2005 and 2010

keep if country_code"BGD" & reporting_level1 & ///

inlist(year,2005,2010)

bys country_code percentile (year): ///

gen growth05_10 = (avg_welfare/avg_welfare[_n-1] - 1) * 100

twoway connected growth05_10 percentile, ytitle("%") ///

title("Cumulative growth in Bangladesh, 2005-2010")

Metadata

Some metadata of the data set, such as the version of the data, can be found by typing char dir in the Stata console. Alternatively, please refer to this portal, which contains all the information available.

PIP version date: 20250401 (updated June 05, 2025)

Lineup years

Not currently available

Comprehensive salary benchmarking dataset covering compensation data across major technology companies, job families, locations, and experience levels. Includes base salary, total compensation, equity, and bonus information.

Percentiles Liabilities, Assets, Net worth, Total annual income and Disposable income 1997-2016

Upper income limit, income share and average of market, total and after-tax income by economic family type and income decile, annual.

Families of tax filers; Distribution of total income by census family type and age of older partner, parent or individual (final T1 Family File; T1FF).

This map is visualizing the changes in average household income (in 2023 dollars) for individuals at the county level, based on their parents" income level (see table below). Changes are defined by the mean household income earned by individuals born in 1978 and individuals born in 1992 (measured at age 27). Income is an important measure of economic mobility, which is the ability to improve economic status over time. The data is sourced from the Opportunity Atlas, a comprehensive dataset developed through a collaboration between researchers at the U.S. Census Bureau and Opportunity Insights at Harvard University. It includes data from the 2000 and 2010 decennial Census, Federal Income Tax returns, and the 2005-2015 American Community Surveys (ACS).Parent income percentileAverage household income (2023 dollars)Lowest (1st percentile)$1,150Low (25th percentile)$33,320Middle (50th percentile)$69,520High (75th percentile)$122,040Highest (100th percentile)$1,840,000 The table outlines the approximate dollar values for each parent percentile group that are referenced in the datasets. See more information on the Opportunity Insights FAQ page.

The Distributional Financial Accounts (DFAs) provide a quarterly measure of the distribution of U.S. household wealth since 1989, based on a comprehensive integration of disaggregated household-level wealth data with official aggregate wealth measures. The data set contains the level and share of each balance sheet item on the Financial Accounts' household wealth table (Table B.101.h), for various sub-populations in the United States. In our core data set, aggregate household wealth is allocated to each of four percentile groups of wealth: the top 1 percent, the next 9 percent (i.e., 90th to 99th percentile), the next 40 percent (50th to 90th percentile), and the bottom half (below the 50th percentile). Additionally, the data set contains the level and share of aggregate household wealth by income, age, generation, education, and race. The quarterly frequency makes the data useful for studying the business cycle dynamics of wealth concentration--which are typically difficult to observe in lower-frequency data because peaks and troughs often fall between times of measurement. These data will be updated about 10 or 11 weeks after the end of each quarter, making them a timely measure of the distribution of wealth.

Annual estimates of paid hours worked and earnings for UK employees by sex, and full-time and part-time, by region, and public and private sector, and non-profit bodies and mutual associations. Hourly and weekly estimates are provided for the pay period that included a specified date in April. They relate to employees on adult rates of pay, whose earnings for the survey pay period were not affected by absence. Estimates for 2020 and 2021 include employees who have been furloughed under the Coronavirus Job Retention Scheme (CJRS). Annual estimates are provided for the tax year that ended on 5th April in the reference year. They relate to employees on adult rates of pay who have been in the same job for more than a year.

New Earnings Survey (NES) and Annual Survey of Hours and Earnings (ASHE) percentile and median time series by full-time employees, full-time males and full-time females.

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 based on national threshold values, regardless of selected geography; 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% national income threshold. Different definitions of income are available in the table namely market, total, and after-tax income, both with and without capital gains.

Household income statistics by household type (couple family, one-parent family, non-census family households) and household size for Canada, provinces and territories, census divisions and census subdivisions.

This dataset contains replication files for "Is the United States Still a Land of Opportunity? Recent Trends in Intergenerational Mobility" by Raj Chetty, Nathaniel Hendren, Patrick Kline, Emmanuel Saez, and Nicholas Turner. For more information, see https://opportunityinsights.org/paper/recentintergenerationalmobility/. A summary of the related publication follows. We present new evidence on trends in intergenerational mobility in the U.S. using administrative earnings records. We find that percentile rank-based measures of intergenerational mobility have remained extremely stable for the 1971-1993 birth cohorts. For children born between 1971 and 1986, we measure intergenerational mobility based on the correlation between parent and child income percentile ranks. For more recent cohorts, we measure mobility as the correlation between a child’s probability of attending college and her parents’ income rank. We also calculate transition probabilities, such as a child’s chances of reaching the top quintile of the income distribution starting from the bottom quintile. Based on all of these measures, we find that children entering the labor market today have the same chances of moving up in the income distribution (relative to their parents) as children born in the 1970s. However, because inequality has risen, the consequences of the “birth lottery” – the parents to whom a child is born – are larger today than in the past. The views expressed in this paper are those of the authors and do not necessarily represent the views or policies of the US Treasury Department or the Internal Revenue Service or the National Bureau of Economic Research.

Dataset consisting of inequality measures for 46 nation states and a global bibliography of all known household expenditure surveys covering the period roughly 1880-1960. Each entry notes when and where the survey was carried out and salient characteristics of the survey such as number of households, whether income and/or expenditure data are collected etc. These bibliographies are organised by six world regions and then by 118 nation states. For a sub-set of the most useful surveys we have estimated various inequality measures from the published data for 46 nation states, organised by world region.This project will calculate new estimates of world inequality in the period from the end of the nineteenth century until the 1960s, based on the results of household expenditure surveys. Our investigations have located a vast cache of household expenditure surveys for the period. Thus far, we have identified around 800 household surveys from around the world, carried out between the 1880s and 1960s, of which around half are of sufficient scope as to be potentially useful for the investigation of inequality. We will extract the reported demographic and expenditure data by income group from these reports and use them to estimate parameters of the income distribution. Using these estimates, we will investigate the changing nature of inequality within a number of key nation states, and also investigate the time path and geography of global inequality 1880-1960. In addition, we would use these data to estimate other indicators of living conditions, such as nutritional attainment, which may provide further insights into the impact of industrialisation on inequality. This project utilised the published reports of household expenditure surveys. These published reports are held at copyright libraries or national statistical offices and were typically part of the output of government departments (for example, the UK Board of Trade). We compiled our bibliographies through library searches and requests to various national statistical offices. Many of these reports are published in English, but a substantial number are only published in the language of the relevant nation state. The published household expenditure survey reports typically include summary tables of grouped data of income, expenditures, and household structure. All of these reports, and the data therein, are already in the public domain, and our bibliography provides details of when and where they were published. From these data we estimated a suite of inequality measures, using three different techniques. The inequality measures are: Gini coefficient, 90/10 percentile ratio, 90/50 percentile ratio, and the 50/10 percentile ratio. These inequality measures were estimated three ways: linear interpolation, the Beta-Lorenz method and a log normal density estimation. Not all published household expenditure survey reports contain sufficient data to estimate inequality measures. Our selection was based simply on whether the reports published the appropriate data. All that we required to estimate inequality were total household income or expenditure grouped by class (and the group average incomes/expenditures) and the total number of households and average household size.

Distribution of total income in constant 2020 dollars by age and gender.