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 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.

This poverty rate data shows what percentage of the measured population* falls below the poverty line. Poverty is closely related to income: different “poverty thresholds” are in place for different sizes and types of household. A family or individual is considered to be below the poverty line if that family or individual’s income falls below their relevant poverty threshold. For more information on how poverty is measured by the U.S. Census Bureau (the source for this indicator’s data), visit the U.S. Census Bureau’s poverty webpage.

The poverty rate is an important piece of information when evaluating an area’s economic health and well-being. The poverty rate can also be illustrative when considered in the contexts of other indicators and categories. As a piece of data, it is too important and too useful to omit from any indicator set.

The poverty rate for all individuals in the measured population in Champaign County has hovered around roughly 20% since 2005. However, it reached its lowest rate in 2021 at 14.9%, and its second lowest rate in 2023 at 16.3%. Although the American Community Survey (ACS) data shows fluctuations between years, given their margins of error, none of the differences between consecutive years’ estimates are statistically significant, making it impossible to identify a trend.

Poverty rate data was sourced from the U.S. Census Bureau’s American Community Survey 1-Year Estimates, which are released annually.

As with any datasets that are estimates rather than exact counts, it is important to take into account the margins of error (listed in the column beside each figure) when drawing conclusions from the data.

Due to the impact of the COVID-19 pandemic, instead of providing the standard 1-year data products, the Census Bureau released experimental estimates from the 1-year data in 2020. This includes a limited number of data tables for the nation, states, and the District of Columbia. The Census Bureau states that the 2020 ACS 1-year experimental tables use an experimental estimation methodology and should not be compared with other ACS data. For these reasons, and because data is not available for Champaign County, no data for 2020 is included in this Indicator.

For interested data users, the 2020 ACS 1-Year Experimental data release includes a dataset on Poverty Status in the Past 12 Months by Age.

*According to the U.S. Census Bureau document “How Poverty is Calculated in the ACS," poverty status is calculated for everyone but those in the following groups: “people living in institutional group quarters (such as prisons or nursing homes), people in military barracks, people in college dormitories, living situations without conventional housing, and unrelated individuals under 15 years old."

Sources: U.S. Census Bureau; American Community Survey, 2023 American Community Survey 1-Year Estimates, Table S1701; generated by CCRPC staff; using data.census.gov; (17 October 2024).; U.S. Census Bureau; American Community Survey, 2022 American Community Survey 1-Year Estimates, Table S1701; generated by CCRPC staff; using data.census.gov; (25 September 2023).; U.S. Census Bureau; American Community Survey, 2021 American Community Survey 1-Year Estimates, Table S1701; generated by CCRPC staff; using data.census.gov; (16 September 2022).; U.S. Census Bureau; American Community Survey, 2019 American Community Survey 1-Year Estimates, Table S1701; generated by CCRPC staff; using data.census.gov; (8 June 2021).; U.S. Census Bureau; American Community Survey, 2018 American Community Survey 1-Year Estimates, Table S1701; generated by CCRPC staff; using data.census.gov; (8 June 2021).; U.S. Census Bureau; American Community Survey, 2017 American Community Survey 1-Year Estimates, Table S1701; generated by CCRPC staff; using American FactFinder; (13 September 2018).; U.S. Census Bureau; American Community Survey, 2016 American Community Survey 1-Year Estimates, Table S1701; generated by CCRPC staff; using American FactFinder; (14 September 2017).; U.S. Census Bureau; American Community Survey, 2015 American Community Survey 1-Year Estimates, Table S1701; generated by CCRPC staff; using American FactFinder; (19 September 2016).; U.S. Census Bureau; American Community Survey, 2014 American Community Survey 1-Year Estimates, Table S1701; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2013 American Community Survey 1-Year Estimates, Table S1701; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2012 American Community Survey 1-Year Estimates, Table S1701; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2011 American Community Survey 1-Year Estimates, Table S1701; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2010 American Community Survey 1-Year Estimates, Table S1701; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2009 American Community Survey 1-Year Estimates, Table S1701; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2008 American Community Survey 1-Year Estimates, Table S1701; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2007 American Community Survey 1-Year Estimates, Table S1701; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2006 American Community Survey 1-Year Estimates, Table S1701; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2005 American Community Survey 1-Year Estimates, Table S1701; generated by CCRPC staff; using American FactFinder; (16 March 2016).

AM: Income Share Held by Highest 10% data was reported at 23.000 % in 2022. This records a decrease from the previous number of 23.600 % for 2021. AM: Income Share Held by Highest 10% data is updated yearly, averaging 25.200 % from Dec 1999 (Median) to 2022, with 23 observations. The data reached an all-time high of 31.700 % in 2004 and a record low of 21.500 % in 2020. AM: Income Share Held by Highest 10% data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Armenia – Table AM.World Bank.WDI: Social: Poverty and Inequality. Percentage share of income or consumption is the share that accrues to subgroups of population indicated by deciles or quintiles.;World Bank, Poverty and Inequality Platform. Data are based on primary household survey data obtained from government statistical agencies and World Bank country departments. Data for high-income economies are mostly from the Luxembourg Income Study database. For more information and methodology, please see http://pip.worldbank.org.;;The World Bank’s internationally comparable poverty monitoring database now draws on income or detailed consumption data from more than 2000 household surveys across 169 countries. See the Poverty and Inequality Platform (PIP) for details (www.pip.worldbank.org).

Armenia AM: Poverty Gap at National Poverty Lines: Urban: % data was reported at 4.500 % in 2014. This records a decrease from the previous number of 6.300 % for 2013. Armenia AM: Poverty Gap at National Poverty Lines: Urban: % data is updated yearly, averaging 6.300 % from Dec 2008 (Median) to 2014, with 7 observations. The data reached an all-time high of 8.400 % in 2011 and a record low of 4.500 % in 2014. Armenia AM: Poverty Gap at National Poverty Lines: Urban: % data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Armenia – Table AM.World Bank.WDI: Social: Poverty and Inequality. Urban poverty gap at national poverty lines is the urban population's mean shortfall from the poverty lines (counting the nonpoor as having zero shortfall) as a percentage of the poverty lines. This measure reflects the depth of poverty as well as its incidence.; ; World Bank, Global Poverty Working Group. Data are compiled from official government sources or are computed by World Bank staff using national (i.e. country–specific) poverty lines.; ; This series only includes estimates that to the best of our knowledge are reasonably comparable over time for a country. Due to differences in estimation methodologies and poverty lines, estimates should not be compared across countries.

Graph and download economic data for Estimated Percent of People of All Ages in Poverty for Will County, IL (PPAAIL17197A156NCEN) from 1989 to 2023 about Will County, IL; Chicago; IL; child; poverty; percent; and USA.

Will all children be able to read by 2030? The ability to read with comprehension is a foundational skill that every education system around the world strives to impart by late in primary school—generally by age 10. Moreover, attaining the ambitious Sustainable Development Goals (SDGs) in education requires first achieving this basic building block, and so does improving countries’ Human Capital Index scores. Yet past evidence from many low- and middle-income countries has shown that many children are not learning to read with comprehension in primary school. To understand the global picture better, we have worked with the UNESCO Institute for Statistics (UIS) to assemble a new dataset with the most comprehensive measures of this foundational skill yet developed, by linking together data from credible cross-national and national assessments of reading. This dataset covers 115 countries, accounting for 81% of children worldwide and 79% of children in low- and middle-income countries. The new data allow us to estimate the reading proficiency of late-primary-age children, and we also provide what are among the first estimates (and the most comprehensive, for low- and middle-income countries) of the historical rate of progress in improving reading proficiency globally (for the 2000-17 period). The results show that 53% of all children in low- and middle-income countries cannot read age-appropriate material by age 10, and that at current rates of improvement, this “learning poverty” rate will have fallen only to 43% by 2030. Indeed, we find that the goal of all children reading by 2030 will be attainable only with historically unprecedented progress. The high rate of “learning poverty” and slow progress in low- and middle-income countries is an early warning that all the ambitious SDG targets in education (and likely of social progress) are at risk. Based on this evidence, we suggest a new medium-term target to guide the World Bank’s work in low- and middle- income countries: cut learning poverty by at least half by 2030. This target, together with improved measurement of learning, can be as an evidence-based tool to accelerate progress to get all children reading by age 10.

For further details, please refer to https://thedocs.worldbank.org/en/doc/e52f55322528903b27f1b7e61238e416-0200022022/original/Learning-poverty-report-2022-06-21-final-V7-0-conferenceEdition.pdf

Graph and download economic data for Percent of Population Below the Poverty Level (5-year estimate) in Will County, IL (S1701ACS017197) from 2012 to 2023 about Will County, IL; Chicago; IL; poverty; percent; 5-year; population; and USA.

In 2023, about four percent of the people with a Bachelor's degree or higher were living below the poverty line in the United States. This is far below the poverty rate of those without a high school diploma, which was 25.1 percent in 2023.

This map compares the relationship between annual average particulate matter 2.5 (PM 2.5) air quality data for the US between 1998 and 2016 to the percent of households that are below the poverty level. Poverty data is from the American Community Survey estimates and air quality data is from NASA SEDAC gridded data aggregated to states, counties, congressional districts, and 50km hex bins. Click on the map to view more information such as the trend over time.
Click here to view more information on how this layer was created.
Citations:

The PEN network was launched in September 2004 by the Center for International Forestry Research (CIFOR) with the aim of collecting uniform socio-economic and environmental data at household and village levels in rural areas of developing countries. The data presented here were collected by 33 PEN partners (mainly PhD students) and comprise 8,301 households in 334 villages located in 24 countries in Asia, Africa and Latin America. Three types of quantitative surveys were conducted: 1. Village surveys (V1, V2) 2. Annual household surveys (A1, A2) 3. Quarterly household surveys (Q1, Q2, Q3, Q4) The village surveys (V1-V2) collected data that were common to all or showed little variation among households. The first village survey, V1, was conducted at the beginning of the fieldwork to get background information on the villages while the second survey, V2 was conducted the end of the fieldwork period to get information for the 12 months period covered by the surveys. The household surveys were grouped into two categories: quarterly surveys (Q1, Q2, Q3, Q4) to collect income information, and, household surveys (A1, A2) to collect all other household information. A critical feature of the PEN research project was to collect detailed, high-quality data on forest use. This was done through quarterly income household surveys, for two reasons: first, short recall periods increase accuracy and reliability and, second, quarterly data would allow us to document seasonal variation in (forest) income and thus, inter alia, help us understand to what extent forests act as seasonal “gap fillers”. There are, however, three partners (10101, 10203, and 10301 ) who, because of various particular circumstances, only conducted three of the four income surveys. In addition, 598 of the households missed out on one of the quarterly surveys, e.g., due to temporal absence or sickness, or insecurity in the area. These are still included in the database, while households missing more than one quarter were excluded. Two other household surveys were conducted. The first annual household survey (A1) collected basic household information (demographics, assets, forest-related information) and was done at the beginning of the survey period while the second (A2) collected information for the 12-month period covered by the surveys (e.g., on risk management) and was done at the end of the survey period. Note, however, that we did not collect any systematic data on the time allocation of households: while highly relevant for many analyses, we believed that it would be too time-consuming a component to add to our standard survey questions. The project is further described and discussed in two edited volumes by Angelsen et al. (2011) (describes particular the methods used) and Wunder et al. (2014) (includes six articles based on the PEN project).

Armenia AM: Multidimensional Poverty Intensity (average share of deprivations experienced by the poor) data was reported at 4.750 % in 2018. Armenia AM: Multidimensional Poverty Intensity (average share of deprivations experienced by the poor) data is updated yearly, averaging 4.750 % from Dec 2018 (Median) to 2018, with 1 observations. The data reached an all-time high of 4.750 % in 2018 and a record low of 4.750 % in 2018. Armenia AM: Multidimensional Poverty Intensity (average share of deprivations experienced by the poor) data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Armenia – Table AM.World Bank.WDI: Social: Poverty and Inequality. ;Government statistical agencies. Data for EU countires are from the EUROSTAT;;

Graph and download economic data for Poverty Universe, All Ages for Will County, IL (PUAAIL17197A647NCEN) from 1998 to 2023 about Will County, IL; Chicago; IL; poverty; and USA.

Armenia AM: Poverty Gap at $1.90 a Day: 2011 PPP: % data was reported at 0.100 % in 2020. This stayed constant from the previous number of 0.100 % for 2019. Armenia AM: Poverty Gap at $1.90 a Day: 2011 PPP: % data is updated yearly, averaging 0.200 % from Dec 1999 (Median) to 2020, with 21 observations. The data reached an all-time high of 3.100 % in 2001 and a record low of 0.100 % in 2020. Armenia AM: Poverty Gap at $1.90 a Day: 2011 PPP: % data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Armenia – Table AM.World Bank.WDI: Social: Poverty and Inequality. Poverty gap at $1.90 a day (2011 PPP) is the mean shortfall in income or consumption from the poverty line $1.90 a day (counting the nonpoor as having zero shortfall), expressed as a percentage of the poverty line. This measure reflects the depth of poverty as well as its incidence. As a result of revisions in PPP exchange rates, poverty rates for individual countries cannot be compared with poverty rates reported in earlier editions.; ; World Bank, Poverty and Inequality Platform. Data are based on primary household survey data obtained from government statistical agencies and World Bank country departments. Data for high-income economies are mostly from the Luxembourg Income Study database. For more information and methodology, please see http://pip.worldbank.org.; ; The World Bank’s internationally comparable poverty monitoring database now draws on income or detailed consumption data from around 2000 household surveys across 169 countries. See the Poverty and Inequality Platform (PIP) for details (www.pip.worldbank.org).

Armenia AM: Proportion of People Living Below 50 Percent Of Median Income: % data was reported at 6.900 % in 2022. This records an increase from the previous number of 6.300 % for 2021. Armenia AM: Proportion of People Living Below 50 Percent Of Median Income: % data is updated yearly, averaging 7.300 % from Dec 1999 (Median) to 2022, with 23 observations. The data reached an all-time high of 11.800 % in 2016 and a record low of 4.600 % in 2020. Armenia AM: Proportion of People Living Below 50 Percent Of Median Income: % data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Armenia – Table AM.World Bank.WDI: Social: Poverty and Inequality. The percentage of people in the population who live in households whose per capita income or consumption is below half of the median income or consumption per capita. The median is measured at 2017 Purchasing Power Parity (PPP) using the Poverty and Inequality Platform (http://www.pip.worldbank.org). For some countries, medians are not reported due to grouped and/or confidential data. The reference year is the year in which the underlying household survey data was collected. In cases for which the data collection period bridged two calendar years, the first year in which data were collected is reported.;World Bank, Poverty and Inequality Platform. Data are based on primary household survey data obtained from government statistical agencies and World Bank country departments. Data for high-income economies are mostly from the Luxembourg Income Study database. For more information and methodology, please see http://pip.worldbank.org.;;The World Bank’s internationally comparable poverty monitoring database now draws on income or detailed consumption data from more than 2000 household surveys across 169 countries. See the Poverty and Inequality Platform (PIP) for details (www.pip.worldbank.org).

Graph and download economic data for Estimate of People Age 0-17 in Poverty in Will County, IL (PEU18IL17197A647NCEN) from 1989 to 2023 about Will County, IL; under 18 years; Chicago; IL; child; poverty; persons; and USA.

Polygons in this layer represent low food access areas: areas of the District of Columbia which are estimated to be more than a 10-minute walk from the nearest full-service grocery store. These have been merged with Census poverty data to estimate how much of the population within these areas is food insecure (below 185% of the federal poverty line in addition to living in a low food access area).Office of Planning GIS followed several steps to create this layer, including: transit analysis, to eliminate areas of the District within a 10-minute walk of a grocery store; non-residential analysis, to eliminate areas of the District which do not contain residents and cannot classify as low food access areas (such as parks and the National Mall); and Census tract division, to estimate population and poverty rates within the newly created polygon boundaries.Fields contained in this layer include:Intermediary calculation fields for the aforementioned analysis, and:PartPop2: The total population estimated to live within the low food access area polygon (derived from Census tract population, assuming even distribution across the polygon after removing non-residential areas, followed by the removal of population living within a grocery store radius.)PrtOver185: The portion of PartPop2 which is estimated to have household income above 185% of the federal poverty line (the food secure population)PrtUnd185: The portion of PartPop2 which is estimated to have household income below 185% of the federal poverty line (the food insecure population)PercentUnd185: A calculated field showing PrtUnd185 as a percent of PartPop2. This is the percent of the population in the polygon which is food insecure (both living in a low food access area and below 185% of the federal poverty line).Note that the polygon representing Joint Base Anacostia-Bolling was removed from this analysis. While technically classifying as a low food access area based on the OP Grocery Stores layer (since the JBAB Commissary, which only serves military members, is not included in that layer), it is recognized that those who do live on the base have access to the commissary for grocery needs.Last updated November 2017.

Estimate of People Age 0-17 in Poverty in Will County, IL was 14931.00000 Persons in January of 2023, according to the United States Federal Reserve. Historically, Estimate of People Age 0-17 in Poverty in Will County, IL reached a record high of 23823.00000 in January of 2010 and a record low of 10750.00000 in January of 2000. Trading Economics provides the current actual value, an historical data chart and related indicators for Estimate of People Age 0-17 in Poverty in Will County, IL - last updated from the United States Federal Reserve on September of 2025.

Estimated Percent of Related Children Age 5-17 in Families in Poverty for Will County, IL was 8.90% in January of 2023, according to the United States Federal Reserve. Historically, Estimated Percent of Related Children Age 5-17 in Families in Poverty for Will County, IL reached a record high of 10.90 in January of 2013 and a record low of 6.10 in January of 2000. Trading Economics provides the current actual value, an historical data chart and related indicators for Estimated Percent of Related Children Age 5-17 in Families in Poverty for Will County, IL - last updated from the United States Federal Reserve on August of 2025.

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.