In 2023, **** percent of Black people living in the United States were living below the poverty line, compared to *** percent of white people. That year, the total poverty rate in the U.S. across all races and ethnicities was **** percent. Poverty in the United States Single people in the United States making less than ****** U.S. dollars a year and families of four making less than ****** U.S. dollars a year are considered to be below the poverty line. Women and children are more likely to suffer from poverty, due to women staying home more often than men to take care of children, and women suffering from the gender wage gap. Not only are women and children more likely to be affected, racial minorities are as well due to the discrimination they face. Poverty data Despite being one of the wealthiest nations in the world, the United States had the third highest poverty rate out of all OECD countries in 2019. However, the United States' poverty rate has been fluctuating since 1990, but has been decreasing since 2014. The average median household income in the U.S. has remained somewhat consistent since 1990, but has recently increased since 2014 until a slight decrease in 2020, potentially due to the pandemic. The state that had the highest number of people living below the poverty line in 2020 was California.

Between 2019 and 2023, people living in households in the Asian and ‘Other’ ethnic groups were most likely to be in persistent low income before and after housing costs

In 2023, about 26.9 percent of Asian private households in the U.S. had an annual income of 200,000 U.S. dollars and more. Comparatively, around 13.9 percent of Black households had an annual income under 15,000 U.S. dollars.

In 2023, the gross median household income for Asian households in the United States stood at 112,800 U.S. dollars. Median household income in the United States, of all racial and ethnic groups, came out to 80,610 U.S. dollars in 2023. Asian and Caucasian (white not Hispanic) households had relatively high median incomes, while the median income of Hispanic, Black, American Indian, and Alaskan Native households all came in lower than the national median. A number of related statistics illustrate further the current state of racial inequality in the United States. Unemployment is highest among Black or African American individuals in the U.S. with 8.6 percent unemployed, according to the Bureau of Labor Statistics in 2021. Hispanic individuals (of any race) were most likely to go without health insurance as of 2021, with 22.8 percent uninsured.

Users can customize data applying to low-income families and children. Tables and statistics generated from interactive tools can be downloaded. BackgroundThe National Center for Children in Poverty (NCCP), a division of the Columbia Mailman School of Public Health, is a non-partisan public interest research center. NCCP generates research regarding economic security and the health and development of low-income American families, children and adolescents to inform public health policy and practice. The NCCP site contains state profiles and analyses of the impact of state-level policies on families. Topics include: polici es designed to assist low-income families, the impact of work supports on family resources and statistics about low-income families and children. User FunctionalityUsers can build and compare customized tables of state low-income demographics and policies; compare the impact of work support policies on family resources by state; calculate a minimum family budget according to household size by state; and convert income to annual income, percent of federal poverty level (%FPL) and percent of state median income (%SMI) by state. Users can download tables regarding state demographics and policies into SAS statistical software. Users have great flexibility in terms of which demographics they use to view data. Users can view demographic information by: family structure, age group, race/ethnicity, income level, parental education, parental employment, marital status, parental nativity, homeownership, and family size. Data NotesData included in the Data Tools are derived from multiple sources. Data sources include: Current Population Survey; National Conference of State Legislators; National Partnership for Women and Families; US Department of Agriculture, Food and Nutrition Service; US Department of Labor, Office of Workforce Security; U.S. Department of Labor, Employment Standards Administration Wage and Hour Division; U.S. Department of Health and Human Services, Centers for Medicare and Med icaid Services; U.S. Census Bureau; State EITC Online Resource Center; Community Resources Information, Inc.; National Employment Law Project; Department of Treasury Internal Revenue Service, Center on Budget and Policy Priorities; The Urban Institute; The Center for Law and Social Policy; Department of Health and Human Services, Administration for Children and Families; National Immigration Law Center; Kaiser Commission on Medicaid and the Uninsured; and National Women’s Law Center. Years to which the data apply is noted under “Data Notes and Sources.” Depending on the tool, users can view information on national, regional, state, county, or city levels.

Poverty and low-income statistics by visible minority group, Indigenous group and immigration status, Canada and provinces.

In 2023, 15.4 percent of Black families were living below the poverty line in the United States. Poverty is the state of one who lacks a certain amount of material possessions or money. Absolute poverty or destitution is inability to afford basic human needs, which commonly includes clean and fresh water, nutrition, health care, education, clothing, and shelter.

How does your organization use this dataset? What other NYSERDA or energy-related datasets would you like to see on Open NY? Let us know by emailing OpenNY@nyserda.ny.gov. The Low- to Moderate-Income (LMI) New York State (NYS) Census Population Analysis dataset is resultant from the LMI market database designed by APPRISE as part of the NYSERDA LMI Market Characterization Study (https://www.nyserda.ny.gov/lmi-tool). All data are derived from the U.S. Census Bureau’s American Community Survey (ACS) 1-year Public Use Microdata Sample (PUMS) files for 2013, 2014, and 2015. Each row in the LMI dataset is an individual record for a household that responded to the survey and each column is a variable of interest for analyzing the low- to moderate-income population. The LMI dataset includes: county/county group, households with elderly, households with children, economic development region, income groups, percent of poverty level, low- to moderate-income groups, household type, non-elderly disabled indicator, race/ethnicity, linguistic isolation, housing unit type, owner-renter status, main heating fuel type, home energy payment method, housing vintage, LMI study region, LMI population segment, mortgage indicator, time in home, head of household education level, head of household age, and household weight. The LMI NYS Census Population Analysis dataset is intended for users who want to explore the underlying data that supports the LMI Analysis Tool. The majority of those interested in LMI statistics and generating custom charts should use the interactive LMI Analysis Tool at https://www.nyserda.ny.gov/lmi-tool. This underlying LMI dataset is intended for users with experience working with survey data files and producing weighted survey estimates using statistical software packages (such as SAS, SPSS, or Stata).

A statistical method called a dissimilarity index was used to quantify how segregated the region is among its census tracts, first by race and ethnicity, and then by income. The indices use decennial census tract data for years 1990 and 2000. The U.S. Census Bureau’s American Communities Survey (ACS) five-year period estimates. The Racial/Ethnic Segregation Index is an indicator of how segregated the region's census tracts are, relative to the regional distribution of white, non-Latinx population and people of color and Latinx populations. In a given year, values show dissimilarity in the racial and ethnic makeup of tracts across the region, and demonstrates the percentage of the population that would, theoretically, need to relocate in order to match the region's racial and ethnic makeup. The higher the index value, the higher the segregation is among white, non-Latinx and other races/ethnicities. The Income Segregation Index is an indicator of how segregated the region's census tracts are by income, relative to the regional distribution of low-income households. Low-income is defined as households below 200% of the federal poverty rate. This index shows dissimilarity in income for census tracts across the region for each year displayed, and demonstrates what percentage of the population would need to relocate to another census tract in order to match the income distribution for the entire region. The higher the index value, the greater the geographic concentration of wealth or poverty.

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.

This dataset contains replication files for "The Opportunity Atlas: Mapping the Childhood Roots of Social Mobility" by Raj Chetty, John Friedman, Nathaniel Hendren, Maggie R. Jones, and Sonya R. Porter. For more information, see https://opportunityinsights.org/paper/the-opportunity-atlas/. A summary of the related publication follows. We construct a publicly available atlas of children’s outcomes in adulthood by Census tract using anonymized longitudinal data covering nearly the entire U.S. population. For each tract, we estimate children’s earnings distributions, incarceration rates, and other outcomes in adulthood by parental income, race, and gender. These estimates allow us to trace the roots of outcomes such as poverty and incarceration back to the neighborhoods in which children grew up. We find that children’s outcomes vary sharply across nearby tracts: for children of parents at the 25th percentile of the income distribution, the standard deviation of mean household income at age 35 is $5,000 across tracts within counties. We illustrate how these tract-level data can provide insight into how neighborhoods shape the development of human capital and support local economic policy using two applications. First, we show that the estimates permit precise targeting of policies to improve economic opportunity by uncovering specific neighborhoods where certain subgroups of children grow up to have poor outcomes. Neighborhoods matter at a very granular level: conditional on characteristics such as poverty rates in a child’s own Census tract, characteristics of tracts that are one mile away have little predictive power for a child’s outcomes. Our historical estimates are informative predictors of outcomes even for children growing up today because neighborhood conditions are relatively stable over time. Second, we show that the observational estimates are highly predictive of neighborhoods’ causal effects, based on a comparison to data from the Moving to Opportunity experiment and a quasi-experimental research design analyzing movers’ outcomes. We then identify high-opportunity neighborhoods that are affordable to low-income families, providing an input into the design of affordable housing policies. Our measures of children’s long-term outcomes are only weakly correlated with traditional proxies for local economic success such as rates of job growth, showing that the conditions that create greater upward mobility are not necessarily the same as those that lead to productive labor markets. Click here to view the Opportunity Atlas Any opinions and conclusions expressed herein are those of the authors and do not necessarily reflect the views of the U.S. Census Bureau. All results have been reviewed to ensure that no confidential information is disclosed. The statistical summaries reported in this paper have been cleared by the Census Bureau’s Disclosure Review Board release authorization number CBDRB-FY18-319.

Between 2018 and 2022, Americans who identified as Black, American Indian or Alaska Native, and Hispanic or Latino were most likely to be living in low-income households across all generations in the United States. Within the provided time period, 60 percent of Generation Alpha who were Black lived in families with incomes below the federal poverty line in the United States, followed by 59 percent who were American Indian or Alaska Native, and 52 percent who were Hispanic or Latino.

Low income (LI) equity focus areas are Census tracts that represent communities where the rate of people with low income, i.e., incomes equal to or less than 200% of the Federal Poverty Level, is greater than the regional average and the density of low income persons (per acre) is double the regional average. The original development of the equity focus areas occurred in conjunction with the 2018 Regional Transportation Plan and were informed through discussions of the transportation equity work group, regional advisory committees (TPAC, MTAC, JPACT, and MPAC), four Regional Leadership Forums, and direction from Metro Council.The equity focus areas here are based on data from the American Community Survey 2017 5-year estimates. We include census tracts outside the Metro boundary. However, only census tracts inside the Metro jurisdictional boundary were used when determining criteria to qualify a census tract as an equity focus area.Tract-level compilation and aggregation of population estimates, including sets of attributes related to sex, age, race/ethnicity, language, income, and educational attainment. Estimates are accompanied by margins of error. Aggregate estimates are accompanied by recalculated margins of error. Geometry source: 2010 Census. Attribute source: 2013-2017 ACS 5-year estimates, tables B01001, B03002, B06001, B06007, B06009, B16004, C16001, and C17002.

Census Tract Data - Census 2000 This data layer represents Census 2000 demographic data derived from the PL94-171 redistricting files and SF3. Census geographic entities include blocks, blockgroups and tracts. Tiger line files are the source of the geometry representing the Census blocks. Attributes include total population counts, racial/ethnic, and poverty/income information. Racial/ethnic classifications are represented in units of blocks, blockgroups and tracts. Poverty and income data are represented in units of blockgroups and tracts. Percentages of each racial/ethnic group have been calculated from the population counts. Total Minority counts and percentages were compiled from each racial/ethnic non-white category. Categories compiled to create the Total Minority count includes the following: African American, Asian, American Indian, Pacific Islander, White Hispanic, Other and all mixed race categories. The percentage poverty attribute represents the percent of the population living at or below poverty level. The per capita income attribute represents the sum of all income within the geographic entity, divided by the total population of that entity. Special fields designed to be used for EJ analysis have been derived from the PL data and include the following: Percentage difference of block, blockgroup and total minority from the state and county averages, percentile rank for each percent total minority within state and county entities. Food Desert Locator Documenation The Healthy Food Financing Initiative (HFFI) Working Group defines a food desert as a low-income census tract where a substantial number or share of residents has low access to a supermarket or large grocery store. To qualify as low-income, census tracts must meet the Treasury Department's New Markets Tax Credit (NMTC) program eligibility criteria. Furthermore, to qualify as a food desert tract at least 33% of the tract's population (or a minimum of 500 people) must have low access to a supermarket or large grocery store. Low access to a healty food retail outlet is defined as more than 1 mile from a supermarket or large grocery store in urban ares and as more than 10 miles in rural areas. The Food Desert Locator includes characteristics only for census tracts that qualify as food deserts. All store data come from the 2006 directory of stores, and all population and household data come from the 2000 Census of Population and Housing. For the 140 urban census tracts for which grid-level data are not available, all people in the tract are assumed to have low-access to a supermarket or large grocery store.

This data is pulled from the U.S. Census website. This data is for years Calendar Years 2009-2014.

Product: SAHIE File Layout Overview

Small Area Health Insurance Estimates Program - SAHIE

Filenames: SAHIE Text and SAHIE CSV files 2009 – 2014

Source: Small Area Health Insurance Estimates Program, U.S. Census Bureau.

Internet Release Date: May 2016

Description: Model‐based Small Area Health Insurance Estimates (SAHIE) for Counties and States File Layout and Definitions

The Small Area Health Insurance Estimates (SAHIE) program was created to develop model-based estimates of health insurance coverage for counties and states. This program builds on the work of the Small Area Income and Poverty Estimates (SAIPE) program. SAHIE is only source of single-year health insurance coverage estimates for all U.S. counties.

For 2008-2014, SAHIE publishes STATE and COUNTY estimates of population with and without health insurance coverage, along with measures of uncertainty, for the full cross-classification of:

•5 age categories: 0-64, 18-64, 21-64, 40-64, and 50-64

•3 sex categories: both sexes, male, and female

•6 income categories: all incomes, as well as income-to-poverty ratio (IPR) categories 0-138%, 0-200%, 0-250%, 0-400%, and 138-400% of the poverty threshold

•4 races/ethnicities (for states only): all races/ethnicities, White not Hispanic, Black not Hispanic, and Hispanic (any race).

In addition, estimates for age category 0-18 by the income categories listed above are published.

Each year’s estimates are adjusted so that, before rounding, the county estimates sum to their respective state totals and for key demographics the state estimates sum to the national ACS numbers insured and uninsured.

This program is partially funded by the Centers for Disease Control and Prevention's (CDC), National Breast and Cervical Cancer Early Detection ProgramLink to a non-federal Web site (NBCCEDP). The CDC have a congressional mandate to provide screening services for breast and cervical cancer to low-income, uninsured, and underserved women through the NBCCEDP. Most state NBCCEDP programs define low-income as 200 or 250 percent of the poverty threshold. Also included are IPR categories relevant to the Affordable Care Act (ACA). In 2014, the ACA will help families gain access to health care by allowing Medicaid to cover families with incomes less than or equal to 138 percent of the poverty line. Families with incomes above the level needed to qualify for Medicaid, but less than or equal to 400 percent of the poverty line can receive tax credits that will help them pay for health coverage in the new health insurance exchanges.

We welcome your feedback as we continue to research and improve our estimation methods. The SAHIE program's age model methodology and estimates have undergone internal U.S. Census Bureau review as well as external review. See the SAHIE Methodological Review page for more details and a summary of the comments and our response.

The SAHIE program models health insurance coverage by combining survey data from several sources, including:

•The American Community Survey (ACS)

•Demographic population estimates

•Aggregated federal tax returns

•Participation records for the Supplemental Nutrition Assistance Program (SNAP), formerly known as the Food Stamp program

•County Business Patterns

•Medicaid

•Children's Health Insurance Program (CHIP) participation records

•Census 2010

Margin of error (MOE). Some ACS products provide

an MOE instead of confidence intervals. An MOE is the

difference between an estimate and its upper or lower

confidence bounds. Confidence bounds can be created

by adding the margin of error to the estimate (for the

upper bound) and subtracting the margin of error from

the estimate (for the lower bound). All published ACS

margins of error are based on a 90-percent confidence

level.

Splitgraph serves as an HTTP API that lets you run SQL queries directly on this data to power Web applications. For example:

See the Splitgraph documentation for more information.

This administrative dataset provides descriptive information about the families and children served through the federal Child Care and Development Fund (CCDF). CCDF dollars are provided to states, territories, and tribes to provide assistance to low-income families receiving or in transition from temporary public assistance, to obtain quality child care so they can work, or depending on their state's policy, to attend training or receive education. The Personal Responsibility and Work Opportunity Act of 1996 requires states and territories to collect information on all family units receiving assistance through the CCDF and to submit monthly case-level data to the Office of Child Care. States are permitted to report case-level data for the entire population, or a sample of the population, under approved sampling guidelines.

The Summary Records file contains monthly state-level summary information including the number of families served. The Family Records file contains family-level data including single parent status of the head of household, monthly co-payment amount, date on which child care assistance began, reasons for care (e.g., employment, training/education, protective services, etc.), income used to determine eligibility, source of income, and the family size on which eligibility is based. The Child Records file contains child-level data including ethnicity, race, and date of birth. The Setting Records file contains information about the type of child care setting, the total amount paid to the provider, and the total number of hours of care received by the child. The Pooling Factor file provides state-level data on the percentage of child care funds that is provided through the CCDF, the federal Head Start region the grantee (state) is in and is monitored by, and the state FIPS code for the grantee.

Units of Response: United States and Territories, CCDF Family Recipients, CCDF Children Recipients

Type of Data: Administrative

Tribal Data: No

Periodicity: Annual

Demographic Indicators: Ethnicity;Household Income;Household Size;Race

SORN: Not Applicable

Data Use Agreement: Not Applicable

Data Use Agreement Location: https://www.icpsr.umich.edu/rpxlogin

Granularity: Family;Individual

Spatial: United States

Geocoding: Tribe

From landing page:FHFA establishes annual single-family and multifamily housing goals for mortgages purchased by Fannie Mae and Freddie Mac. The Enterprise Housing Goals include separate categories for single-family mortgages on housing that is affordable to low-income and very low-income families, as well as refinanced mortgages for low-income borrowers. FHFA also establishes separate annual goals for multifamily housing. Loans that are eligible for housing goals credit are mortgages on owner-occupied housing with one to four units. The mortgages must be conventional, conforming mortgages, defined as mortgages that are not insured or guaranteed by the Federal Housing Administration or another government agency and with principal balances that do not exceed the conforming loan limits for Enterprise mortgages. This page provides data on Enterprise performance and activity related to the single-family housi​​ng goals. A full glossary of terms is provided below. Single-Family Enterprise Mortgage Acquisitions: Race and Ethnicity Data The new housing goals data tables provide insight on the racial and ethnic composition of loans acquired by the Enterprises that are eligible for housing goals credit. FHFA has provided the racial and ethnic distribution of the Enterprises' acquisitions across each of the current single-family housing goals categories. ​ Single-Family Housing Goal Loan Segments: State-Level Data FHFA is publishing state-level data for each single-family goal loan purchase and refinance segment. It is important to note that FHFA does not set state-level targets but only at the national level. These tables provide the Enterprises' share in each state along with the market share, as calculated by FHFA using the 'static' HMDA data for each year to determine Enterprise housing goals performance each year. It is important to note that HMDA state-level data are impacted by the number of HMDA-exempt reporters in each state. For more information on HMDA reporting requirements, visit the CFPB HMDA Reporting Requirements page.Low-Income Census Tracts, Minority Census Tracts and Designated Disaster Areas Data The Federal Housing Enterprises Financial Safety and Soundness Act of 1992 (Safety and Soundness Act) provides for the establishment of single-family and multifamily goals each year, including a single-family purchase money mortgage goal for families residing in low-income areas. The Safety and Soundness Act defines "low-income area" for the single-family low-income areas home purchase goal as: Census tracts or block numbering areas in which the median income does not exceed 80 percent of area median income (AMI). In addition, for the purposes of this goal, "families residing in low-income areas" also include: Families with income not greater than 100 percent of AMI who reside in minority census tracts. Families with income not greater than 100 percent of AMI who reside in designated disaster areas. ​A "minority census tract" is a census tract that has a minority population of at least 30 percent and a median income of less than 100 percent of the AMI. A "low-income census tract" is census tract in which the median income does not exceed 80 percent of the AMI. Designated disaster areas are identified by FHFA based on the three most recent years' declarations by the Federal Emergency Management Agency (FEMA), where individual assistance payments were authorized by FEMA. A map of census tracts identified as minority census tracts in 2024 can be ​found here. A map of census tracts identified as low-income census tracts in 2024 can be found here. ​Learn more about low-income census tracts, minority census tracts, and designated disaster areas.

In 1990, 48.1 percent of all Black families with a single mother in the United States lived below the poverty level. In 2023, that figure had decreased to 25.9 percent. This is significantly higher than white households with a single mother. Poverty is the state of one who lacks a certain amount of material possessions or money. Absolute poverty or destitution is inability to afford basic human needs, which commonly includes clean and fresh water, nutrition, health care, education, clothing and shelter.

The poly shapefile shows income levels needed to support a family of four living in low-cost areas withing the states. It also shows percent income above the average poverty levels (Fed poverty level $20,650)for the U.S.

We use the race and Hispanic origin information imputed to the Treasury’s tax model to examine group differences in the marriage penalty and bonus. The microsimulation results show that, for married couples in higher income categories, the marriage penalty rate is higher, and the marriage bonus rate is lower, for Black and Hispanic couples than for White couples. In contrast, White couples in several lower income categories face a higher penalty rate and a lower bonus rate. These Black-White differences in marriage penalty rates are consistent with the patterns of spousal income splits in the underlying data. Unlike survey data, the tax model does not suggest a higher prevalence of two equal-earning spouses among Black families throughout the entire range of the income distribution. Because of the different conclusions that would be drawn about group differences in the marriage penalty and bonus outcomes, further investigation regarding the data differences should be pursued.