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.
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.
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.
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
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.
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
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.
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/
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
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.
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.
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
Income of individuals by age group, sex and income source, Canada, provinces and selected census metropolitan areas, annual.
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DDA10 - Earnings Percentiles. Published by Central Statistics Office. Available under the license Creative Commons Attribution 4.0 (CC-BY-4.0).Earnings Percentiles...
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.
Upper income limit, income share and average of market, total and after-tax income by economic family type and income decile, annual.
https://data.gov.sg/open-data-licencehttps://data.gov.sg/open-data-licence
Dataset from Singapore Department of Statistics. For more information, visit https://data.gov.sg/datasets/d_ff1988785f3e36cfc4757cf604ae0acd/view
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License information was derived automatically
DDA12 - Earnings Percentiles. Published by Central Statistics Office. Available under the license Creative Commons Attribution 4.0 (CC-BY-4.0).Earnings Percentiles...
https://data.gov.sg/open-data-licencehttps://data.gov.sg/open-data-licence
Dataset from Singapore Department of Statistics. For more information, visit https://data.gov.sg/datasets/d_1f57cbc6cc41d72fb1b96c7c266c2eaf/view
This dataset was derived from federal data collected by the Census Bureau and Environmental Protection Agency and originally made available to the public on July 31, 2024. These data provide both summary and detailed information at the Census block group level for both demographic and environmental indicators.These data were selected from the Harvard Environment and Law Data (HELD) Collection to inform environmental justice in New York State. The data was uploaded to the HELD Collection on December 3rd, 2024 and downloaded by NYSDOS-OPDCI for service to the Geographic Information Gateway via this item on March 18th, 2025.This dataset is intended to act as the basis for various View Layers including:Low Income PopulationPeople of ColorPopulation with less than a High School EducationLinguistically Isolated PopulationPopulation Under 5 Years of AgePopulation Over 64 Years of AgeLead Paint Hazard RiskAir Toxics and Respiratory Hazard RiskAir Quality by Particulate MatterAir Quality by Diesel Particulate MatterOzone ConcentrationTraffic Proximity and VolumeSuperfund (CERCLA) Site ProximityRisk Management Plan Facility ProximityHazardous Waste ProximityWater Pollution Hazard RiskWastewater Discharge
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Analysis of ‘SID09 - Current percentile of equivalised disposable income by the perceived financial situation of the teenage household’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from http://data.europa.eu/88u/dataset/5981d36f-ab08-4a4c-822b-cd5bd473794f on 19 January 2022.
--- Dataset description provided by original source is as follows ---
Current percentile of equivalised disposable income by the perceived financial situation of the teenage household
--- Original source retains full ownership of the source dataset ---
These geospatial data resources and the linked mapping tool below reflect currently available data on three categories of potentially qualifying Low-Income communities: Census tracts that meet the CDFI's New Market Tax Credit Program's threshold for Low Income, thereby are able to apply to Category 1. Census tracts that meet the White House's Climate and Economic Justice Screening Tool's threshold for disadvantage in the 'Energy' category, thereby are able to apply for Additional Selection Criteria Geography. Counties that meet the USDA's threshold for Persistent Poverty, thereby are able to apply for Additional Selection Criteria Geography. Note that Category 2 - Indian Lands are not shown on this map. Note that Persistent Poverty is not calculated for US Territories. Note that CEJST Energy disadvantage is not calculated for US Territories besides Puerto Rico. The excel tool provides the land area percentage of each 2023 census tract meeting each of the above categories. To examine geographic eligibility for a specific address or latitude and longitude, visit the program's mapping tool. Additional information on this tax credit program can be found on the DOE Landing Page for the 48e program at https://www.energy.gov/diversity/low-income-communities-bonus-credit-program or the IRS Landing Page at https://www.irs.gov/credits-deductions/low-income-communities-bonus-credit. Maps last updated: September 1st, 2024 Next map update expected: December 7th, 2024 Disclaimer: The spatial data and mapping tool is intended for geolocation purposes. It should not be relied upon by taxpayers to determine eligibility for the Low-Income Communities Bonus Credit Program. Source Acknowledgements: The New Market Tax Credit (NMTC) Tract layer using data from the 2016-2020 ACS is from the CDFI Information Mapping System (CIMS) and is created by the U.S. Department of Treasury Community Development Financial Institutions Fund. To learn more, visit CDFI Information Mapping System (CIMS) | Community Development Financial Institutions Fund (cdfifund.gov). https://www.cdfifund.gov/mapping-system. Tracts are displayed that meet the threshold for the New Market Tax Credit Program. The 'Energy' Category Tract layer from the Climate and Economic Justice Screening Tool (CEJST) is created by the Council on Environmental Quality (CEQ) within the Executive Office of the President. To learn more, visit https://screeningtool.geoplatform.gov/en/. Tracts are displayed that meet the threshold for the 'Energy' Category of burden. I.e., census tracts that are at or above the 90th percentile for (energy burden OR PM2.5 in the air) AND are at or above the 65th percentile for low income. The Persistent Poverty County layer is created by joining the U.S. Department of Agriculture, Economic Research Service's Poverty Area Official Measures dataset, with relevant county TIGER/Line Shapefiles from the US Census Bureau. To learn more, visit https://www.ers.usda.gov/data-products/poverty-area-measures/. Counties are displayed that meet the thresholds for Persistent Poverty according to 'Official' USDA updates. i.e. areas with a poverty rate of 20.0 percent or more for 4 consecutive time periods, about 10 years apart, spanning approximately 30 years (baseline time period plus 3 evaluation time periods). Until Dec 7th, 2024 both the USDA estimates using 2007-2011 and 2017-2021 ACS 5-year data. On Dec 8th, 2024, only the USDA estimates using 2017-2021 data will be accepted for program eligibility.
Percentiles Liabilities, Assets, Net worth, Total annual income and Disposable income 1997-2016
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SID09 - Current percentile of equivalised disposable income by the perceived financial situation of the teenage household. Published by Central Statistics Office. Available under the license Creative Commons Attribution 4.0 (CC-BY-4.0).Current percentile of equivalised disposable income by the perceived financial situation of the teenage household...
This map uses an archive of Version 1.0 of the CEJST data as a fully functional GIS layer. See an archive of the latest version of the CEJST tool using Version 2.0 of the data released in December 2024 here.This map assesses and identifies communities that are Housing Disadvantaged according to Justice40 Initiative criteria. "Communities are identified as disadvantaged if they are in census tracts that:Experienced historic underinvestment OR are at or above the 90th percentile for the housing cost OR lack of green space OR lack of indoor plumbing OR lead paintAND are at or above the 65th percentile for low income"Census tracts in the U.S. and its territories that meet the criteria are shaded in blue colors. Suitable for dashboards, apps, stories, and grant applications.Details of the assessment are provided in the popup for every census tract in the United States and its territories American Samoa, Guam, the Northern Mariana Islands, Puerto Rico, and the U.S. Virgin Islands. This map uses 2010 census tracts from Version 1.0 of the source data downloaded November 22, 2022.Use this map to help plan for grant applications, to perform spatial analysis, and to create informative dashboards and web applications.From the source:This data "highlights disadvantaged census tracts across all 50 states, the District of Columbia, and the U.S. territories. Communities are considered disadvantaged:If they are in census tracts that meet the thresholds for at least one of the tool’s categories of burden, orIf they are on land within the boundaries of Federally Recognized TribesCategories of BurdensThe tool uses datasets as indicators of burdens. The burdens are organized into categories. A community is highlighted as disadvantaged on the CEJST map if it is in a census tract that is (1) at or above the threshold for one or more environmental, climate, or other burdens, and (2) at or above the threshold for an associated socioeconomic burden.In addition, a census tract that is completely surrounded by disadvantaged communities and is at or above the 50% percentile for low income is also considered disadvantaged.Census tracts are small units of geography. Census tract boundaries for statistical areas are determined by the U.S. Census Bureau once every ten years. The tool utilizes the census tract boundaries from 2010. This was chosen because many of the data sources in the tool currently use the 2010 census boundaries."PurposeThe goal of the Justice40 Initiative is to provide 40 percent of the overall benefits of certain Federal investments in [eight] key areas to disadvantaged communities. These [eight] key areas are: climate change, clean energy and energy efficiency, clean transit, affordable and sustainable housing, training and workforce development, the remediation and reduction of legacy pollution, [health burdens] and the development of critical clean water infrastructure." Source: Climate and Economic Justice Screening tool"Sec. 219. Policy. To secure an equitable economic future, the United States must ensure that environmental and economic justice are key considerations in how we govern. That means investing and building a clean energy economy that creates well‑paying union jobs, turning disadvantaged communities — historically marginalized and overburdened — into healthy, thriving communities, and undertaking robust actions to mitigate climate change while preparing for the impacts of climate change across rural, urban, and Tribal areas. Agencies shall make achieving environmental justice part of their missions by developing programs, policies, and activities to address the disproportionately high and adverse human health, environmental, climate-related and other cumulative impacts on disadvantaged communities, as well as the accompanying economic challenges of such impacts. It is therefore the policy of my Administration to secure environmental justice and spur economic opportunity for disadvantaged communities that have been historically marginalized and overburdened by pollution and underinvestment in housing, transportation, water and wastewater infrastructure, and health care." Source: Executive Order on Tackling the Climate Crisis at Home and AbroadUse of this Data"The pilot identifies 21 priority programs to immediately begin enhancing benefits for disadvantaged communities. These priority programs will provide a blueprint for other agencies to help inform their work to implement the Justice40 Initiative across government." Source: The Path to Achieving Justice 40
Rent estimates at the 50th percentile (or median) are calculated for all Fair Market Rent areas. Fair Market Rents (FMRs) are primarily used to determine payment standard amounts for the Housing Choice Voucher program, to determine initial renewal rents for some expiring project-based Section 8 contracts, to determine initial rents for housing assistance payment (HAP) contracts in the Moderate Rehabilitation Single Room Occupancy program (Mod Rehab), and to serve as a rent ceiling in the HOME rental assistance program. FMRs are gross rent estimates. They include the shelter rent plus the cost of all tenant-paid utilities, except telephones, cable or satellite television service, and internet service. The U.S. Department of Housing and Urban Development (HUD) annually estimates FMRs for 530 metropolitan areas and 2,045 nonmetropolitan county FMR areas. Under certain conditions, as set forth in the Interim Rule (Federal Register Vol. 65, No. 191, Monday October 2, 2000, pages 58870-58875), these 50th percentile rents can be used to set success rate payment standards.
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.
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License information was derived automatically
DDA08 - Composition of Overall Weekly & Annual Earnings Percentiles. Published by Central Statistics Office. Available under the license Creative Commons Attribution 4.0 (CC-BY-4.0).Composition of Overall Weekly & Annual Earnings Percentiles...
The 90th percentile means 90% of the population with an income falls below this threshold, the 50th percentile is the median where 50% of the population is above and 50% is below. The 25th percentile means 75% of the population is above this threshold and 25% of the population is below.
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
DDA05 - Composition of Overall Weekly & Annual Earnings Percentiles. Published by Central Statistics Office. Available under the license Creative Commons Attribution 4.0 (CC-BY-4.0).Composition of Overall Weekly & Annual Earnings Percentiles...
https://dataverse.harvard.edu/api/datasets/:persistentId/versions/1.0/customlicense?persistentId=doi:10.7910/DVN/HM91JNhttps://dataverse.harvard.edu/api/datasets/:persistentId/versions/1.0/customlicense?persistentId=doi:10.7910/DVN/HM91JN
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.
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.
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.
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.
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
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.
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
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.
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/
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
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.
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.
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