In 2025, just over 45 percent of American households had an annual income that was less than 75,000 U.S. dollars. On the other hand, some 16 percent had an annual income of 200,000 U.S. dollars or more. The median household income in the country reached almost 84,000 U.S. dollars in 2024. 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. Massachusetts, New Hampshire, and Maryland were among the states with the highest median household income in 2024. In terms of income by race and ethnicity, the average income of Asian households was highest, at over 120,000 U.S. dollars, while the median income among Black households was around half of that figure. What is the U.S. poverty threshold? The U.S. Census Bureau annually updates the poverty threshold based on the income of various household types. As of 2023, the threshold for a single-person household was 15,480 U.S. dollars. For a family of four, the poverty line increased to 31,200 U.S. dollars. There were an estimated 38.9 million people living in poverty across the United States in 2024, which reflects a poverty rate of 10.6 percent.

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

In 2024, households with incomes between 100,000 and 149,999 U.S. dollars accounted for 16.7 percent of household in the United States. The highest earning group, who bring in over 200,000 U.S. dollars annually, accounted for 16 percent of households.

In 2024, the median household income in the United States was 83,730 U.S. dollars. This reflected an increase from the previous year. Household income The median household income depicts the income of households, including the income of the householder and all other individuals aged 15 years or over living in the household. Income includes wages and salaries, unemployment insurance, disability payments, child support payments received, regular rental receipts, as well as any personal business, investment, or other kinds of income received routinely. The median household income in the United States varied from state to state. In 2024, Massachusetts recorded the highest median household income in the country, at 113,900 U.S. dollars. On the other hand, Mississippi, recorded the lowest, at 55,980 U.S. dollars.Household income is also used to determine the poverty rate in the United States. In 2024, 10.6 percent of the U.S. population was living below the national poverty line. This was the lowest level since 2019. Similarly, the child poverty rate, which represents people under the age of 18 living in poverty, reached a three-decade low of 14.3 percent of the children. The state with the widest gap between the rich and the poor was New York, with a Gini coefficient score of 0.52 in 2024. The Gini coefficient is calculated by looking at average income rates. A score of zero would reflect perfect income equality, while a score of one indicates complete inequality.

In 2021, the average income of households among Israel's highest one percent of earners, reached *** million Israeli shekels, about *** million U.S. dollars. Moreover, incomes peaked in 2017, due to a one-time tax incentive introduced by the government to release "trapped" capital gains tax. Overall, the average income of wealthy families in the country increased by ** percent between 2013 and 2021.

Context

College students are back on campus in the US, so we're exploring economic diversity and student outcomes! The dataset this week comes from Opportunity Insights via an article and associated interactive visualization from the Upshot at the New York Times. Thank you to Havisha Khurana for suggesting this dataset!

A new study, based on millions of anonymous tax records, shows that some colleges are even more economically segregated than previously understood, while others are associated with income mobility.

Content

Geography: USA

Time period: 2024

Unit of analysis: Economic Diversity and Student Outcomes Data

Variables

Variable	Description
super_opeid	Institution OPEID / Cluster ID when combining multiple OPEIDs.
name	Name of college (or college group).
par_income_bin	Parent household income group based on percentile in the income distribution.
par_income_lab	Parent household income label.
attend	Test-score-reweighted absolute attendance rate: Calculated as the fraction of students attending that college among all test-takers within a parent income bin in the Pipeline Analysis Sample.
stderr_attend	Standard error on the attend variable.
attend_level	The school average estimates reweighting on test score. Divide the test-score-reweighted absolute variables by this average to calculate the test-score-reweighted relative variables.
attend_sat	Absolute attendance rate for specific test score band based on school tier/category.
stderr_attend_sat	Standard error on the attend_sat variable.
attend_level_sat	The school average estimates reweighting on test score. Divide the test-score-reweighted absolute variables by this average to calculate the test-score-reweighted relative variables.
rel_apply	Test-score-reweighted relative application rate: Calculated using adjusted score-sending rates, the relative fraction of all standardized test takers who send test scores to a given college.
stderr_rel_apply	Standard error on the rel_apply variable.
rel_attend	Test-score-reweighted relative attendance rate: Calculated as the fraction of students attending that college among all test-takers within a parent income bin in the Pipeline Analysis Sample. Relative attendance rates are reported as a proportion of the mean attendance rate across all parent income bins for each college.
stderr_rel_attend	Standard error on the rel_attend variable.
rel_att_cond_app	Calculated as the ratio of rel_attend to rel_apply.
rel_apply_sat	Relative application rate for specific test score band based on school tier/category. Selected test score band is the 50-point band that had the most attendees in each school tier/category. The selected range: Ivy Plus: SAT 1460-1510; Elite Public: SAT 1180-1230; Top Private: SAT 1410-1460; NESCAC: SAT 1370-1420; Tier 2 Private: SAT 1290-1340; Top 100 Private: SAT 1170-1220; Top 100 Public: SAT 1110-1160; Other Flagship: SAT 1070-1120.
stderr_rel_apply_sat	Standard error on the rel_apply_sat variable.
r...

Graph and download economic data for Real Disposable Personal Income (DSPIC96) from Jan 1959 to Aug 2025 about disposable, personal income, personal, income, real, and USA.

The layer "FCC ACP EligibleHH" shows the tracts containing households that meet the Affordable Connectivity household income requirements based on their household size per the data table on the ACP website https://www.affordableconnectivity.gov/do-i-qualify/. This layer is symbolized to show the Median Household Income for all households within a tract. The data was downloaded Feb. 2023 using 5-year ACS data for 2017-2021.Data Processing Note from author:The eligible tracts were determined by 1) Selecting census tracts with centroids that fell within Dallas Proper 2) Applying a filter with a series of "OR" clauses that selected for households that were less than or equal to the ACP threshold median income for the respective household size (Ex. 1-person households whose median income was less than or equal to $27,180).The American Community Survey (ACS) Household dataset (https://dallasgis.maps.arcgis.com/home/item.html?id=388cebd5976e49faa77af91a5d73dfee&view=list&sortOrder=desc&sortField=defaultFSOrder#overview) is limited to household sizes with the largest household size being "7 or more." Where the ACP had a median income threshold for household sizes 8 and 9, the threshold for household size 7 was used. Data Update Note:In order to retain historical data, this layer will require an update as the Census Bureau releases new data. The source layer is updated automatically when the most current vintage of ACS data is released each year, usually in December. The layer always contains the latest available ACS 5-year estimates. It is updated annually within days of the Census Bureau's release schedule. Click here to learn more about ACS data releases.Data Source Note from the Census: This layer shows household size by tenure (owner or renter). This is shown by tract, county, and state boundaries. This service is updated annually to contain the most currently released American Community Survey (ACS) 5-year data, and contains estimates and margins of error. There are also additional calculated attributes related to this topic, which can be mapped or used within analysis. To see the full list of attributes available in this service, go to the "Data" tab, and choose "Fields" at the top right. Current Vintage: 2017-2021ACS Table(s): B25009, B25010, B19019Data downloaded from: Census Bureau's API for American Community Survey Date of API call: December 8, 2022National Figures: data.census.govData Note from the Census:Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see Accuracy of the Data). The effect of nonsampling error is not represented in these tables.

The Survey of Consumer Finances (SCF) is normally a triennial cross-sectional survey of U.S. families. The survey data include information on families' balance sheets, pensions, income, and demographic characteristics.

Data SourcesAmerican Community Survey (ACS):Conducted by: U.S. Census BureauDescription: The ACS is an ongoing survey that provides detailed demographic and socio-economic data on the population and housing characteristics of the United States.Content: The survey collects information on various topics such as income, education, employment, health insurance coverage, and housing costs and conditions.Frequency: The ACS offers more frequent and up-to-date information compared to the decennial census, with annual estimates produced based on a rolling sample of households.Purpose: ACS data is essential for policymakers, researchers, and communities to make informed decisions and address the evolving needs of the population.CDC/ATSDR Social Vulnerability Index (SVI):Created by: ATSDR’s Geospatial Research, Analysis & Services Program (GRASP)Utilized by: CDCDescription: The SVI is designed to identify and map communities that are most likely to need support before, during, and after hazardous events.Content: SVI ranks U.S. Census tracts based on 15 social factors, including unemployment, minority status, and disability, and groups them into four related themes. Each tract receives rankings for each Census variable and for each theme, as well as an overall ranking, indicating its relative vulnerability.Purpose: SVI data provides insights into the social vulnerability of communities at both the tract and county levels, helping public health officials and emergency response planners allocate resources effectively.Utilization and IntegrationBy integrating data from both the ACS and the SVI, this dataset enables an in-depth analysis and understanding of various socio-economic and demographic indicators at the census tract level. This integrated data is valuable for research, policymaking, and community planning purposes, allowing for a comprehensive understanding of social and economic dynamics across different geographical areas in the United States.ApplicationsPolicy Development: Helps policymakers develop targeted interventions to address the needs of vulnerable populations.Resource Allocation: Assists emergency response planners in allocating resources more effectively based on community vulnerability.Research: Provides a robust foundation for academic and applied research in socio-economic and demographic studies.Community Planning: Aids in the planning and development of community programs and initiatives aimed at improving living conditions and reducing vulnerabilities.Note: Due to limitations in the ArcGIS Pro environment, the data variable names may be truncated. Refer to the provided table for a clear understanding of the variables.CSV Variable NameShapefile Variable NameDescriptionStateNameStateNameName of the stateStateFipsStateFipsState-level FIPS codeState nameStateNameName of the stateCountyNameCountyNameName of the countyCensusFipsCensusFipsCounty-level FIPS codeState abbreviationStateFipsState abbreviationCountyFipsCountyFipsCounty-level FIPS codeCensusFipsCensusFipsCounty-level FIPS codeCounty nameCountyNameName of the countyAREA_SQMIAREA_SQMITract area in square milesE_TOTPOPE_TOTPOPPopulation estimates, 2013-2017 ACSEP_POVEP_POVPercentage of persons below poverty estimateEP_UNEMPEP_UNEMPUnemployment Rate estimateEP_HBURDEP_HBURDHousing cost burdened occupied housing units with annual income less than $75,000EP_UNINSUREP_UNINSURUninsured in the total civilian noninstitutionalized population estimate, 2013-2017 ACSEP_PCIEP_PCIPer capita income estimate, 2013-2017 ACSEP_DISABLEP_DISABLPercentage of civilian noninstitutionalized population with a disability estimate, 2013-2017 ACSEP_SNGPNTEP_SNGPNTPercentage of single parent households with children under 18 estimate, 2013-2017 ACSEP_MINRTYEP_MINRTYPercentage minority (all persons except white, non-Hispanic) estimate, 2013-2017 ACSEP_LIMENGEP_LIMENGPercentage of persons (age 5+) who speak English "less than well" estimate, 2013-2017 ACSEP_MUNITEP_MUNITPercentage of housing in structures with 10 or more units estimateEP_MOBILEEP_MOBILEPercentage of mobile homes estimateEP_CROWDEP_CROWDPercentage of occupied housing units with more people than rooms estimateEP_NOVEHEP_NOVEHPercentage of households with no vehicle available estimateEP_GROUPQEP_GROUPQPercentage of persons in group quarters estimate, 2013-2017 ACSBelow_5_yrBelow_5_yrUnder 5 years: Percentage of Total populationBelow_18_yrBelow_18_yrUnder 18 years: Percentage of Total population18-39_yr18_39_yr18-39 years: Percentage of Total population40-64_yr40_64_yr40-64 years: Percentage of Total populationAbove_65_yrAbove_65_yrAbove 65 years: Percentage of Total populationPop_malePop_malePercentage of total population malePop_femalePop_femalePercentage of total population femaleWhitewhitePercentage population of white aloneBlackblackPercentage population of black or African American aloneAmerican_indianamerican_iPercentage population of American Indian and Alaska native aloneAsianasianPercentage population of Asian aloneHawaiian_pacific_islanderhawaiian_pPercentage population of Native Hawaiian and Other Pacific Islander aloneSome_othersome_otherPercentage population of some other race aloneMedian_tot_householdsmedian_totMedian household income in the past 12 months (in 2019 inflation-adjusted dollars) by household size – total householdsLess_than_high_schoolLess_than_Percentage of Educational attainment for the population less than 9th grades and 9th to 12th grade, no diploma estimateHigh_schoolHigh_schooPercentage of Educational attainment for the population of High school graduate (includes equivalency)Some_collegeSome_collePercentage of Educational attainment for the population of Some college, no degreeAssociates_degreeAssociatesPercentage of Educational attainment for the population of associate degreeBachelor’s_degreeBachelor_sPercentage of Educational attainment for the population of Bachelor’s degreeMaster’s_degreeMaster_s_dPercentage of Educational attainment for the population of Graduate or professional degreecomp_devicescomp_devicPercentage of Household having one or more types of computing devicesInternetInternetPercentage of Household with an Internet subscriptionBroadbandBroadbandPercentage of Household having Broadband of any typeSatelite_internetSatelite_iPercentage of Household having Satellite Internet serviceNo_internetNo_internePercentage of Household having No Internet accessNo_computerNo_computePercentage of Household having No computer

About this dataset

Context

The dataset presents median income data over a decade or more for males and females categorized by Total, Full-Time Year-Round (FT), and Part-Time (PT) employment in Vance. It showcases annual income, providing insights into gender-specific income distributions and the disparities between full-time and part-time work. The dataset can be utilized to gain insights into gender-based pay disparity trends and explore the variations in income for male and female individuals.

Key observations: Insights from 2021

Based on our analysis ACS 2017-2021 5-Year Estimates, we present the following observations: - All workers, aged 15 years and older: In Vance, the median income for all workers aged 15 years and older, regardless of work hours, was $20,267 for males and $20,144 for females.

Based on these incomes, we observe a gender gap percentage of approximately 1%, indicating a significant disparity between the median incomes of males and females in Vance. Women, regardless of work hours, still earn 99 cents to each dollar earned by men, highlighting an ongoing gender-based wage gap.

- Full-time workers, aged 15 years and older: In Vance, for full-time, year-round workers aged 15 years and older, while the Census reported a median income of $29,146 for males, while data for females was unavailable due to an insufficient number of sample observations.

As there was no available median income data for females, conducting a comprehensive assessment of gender-based pay disparity in Vance was not feasible.

https://i.neilsberg.com/ch/vance-sc-income-by-gender.jpeg" alt="Vance, SC gender based income disparity">

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates. All incomes have been adjusting for inflation and are presented in 2022-inflation-adjusted dollars.

Gender classifications include:

• Male
• Female

Employment type classifications include:

• Full-time, year-round: A full-time, year-round worker is a person who worked full time (35 or more hours per week) and 50 or more weeks during the previous calendar year.
• Part-time: A part-time worker is a person who worked less than 35 hours per week during the previous calendar year.

Variables / Data Columns

• Year: This column presents the data year. Expected values are 2010 to 2022
• Male Total Income: Annual median income, for males regardless of work hours
• Male FT Income: Annual median income, for males working full time, year-round
• Male PT Income: Annual median income, for males working part time
• Female Total Income: Annual median income, for females regardless of work hours
• Female FT Income: Annual median income, for females working full time, year-round
• Female PT Income: Annual median income, for females working part time

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 Vance median household income by gender. You can refer the same here

About this dataset

Context

The dataset presents median income data over a decade or more for males and females categorized by Total, Full-Time Year-Round (FT), and Part-Time (PT) employment in Weston town. It showcases annual income, providing insights into gender-specific income distributions and the disparities between full-time and part-time work. The dataset can be utilized to gain insights into gender-based pay disparity trends and explore the variations in income for male and female individuals.

Key observations: Insights from 2021

Based on our analysis ACS 2017-2021 5-Year Estimates, we present the following observations: - All workers, aged 15 years and older: In Weston town, the median income for all workers aged 15 years and older, regardless of work hours, was $26,409 for males and $26,122 for females.

Based on these incomes, we observe a gender gap percentage of approximately 1%, indicating a significant disparity between the median incomes of males and females in Weston town. Women, regardless of work hours, still earn 99 cents to each dollar earned by men, highlighting an ongoing gender-based wage gap.

- Full-time workers, aged 15 years and older: In Weston town, for full-time, year-round workers aged 15 years and older, the Census reported a median income of $39,453 for females, while data for males was unavailable due to an insufficient number of sample observations.

As there was no available median income data for males, conducting a comprehensive assessment of gender-based pay disparity in Weston town was not feasible.

https://i.neilsberg.com/ch/weston-me-income-by-gender.jpeg" alt="Weston, Maine gender based income disparity">

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates. All incomes have been adjusting for inflation and are presented in 2022-inflation-adjusted dollars.

Gender classifications include:

• Male
• Female

Employment type classifications include:

• Full-time, year-round: A full-time, year-round worker is a person who worked full time (35 or more hours per week) and 50 or more weeks during the previous calendar year.
• Part-time: A part-time worker is a person who worked less than 35 hours per week during the previous calendar year.

Variables / Data Columns

• Year: This column presents the data year. Expected values are 2010 to 2022
• Male Total Income: Annual median income, for males regardless of work hours
• Male FT Income: Annual median income, for males working full time, year-round
• Male PT Income: Annual median income, for males working part time
• Female Total Income: Annual median income, for females regardless of work hours
• Female FT Income: Annual median income, for females working full time, year-round
• Female PT Income: Annual median income, for females working part time

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 Weston town median household income by gender. You can refer the same here

This layer shows census tracts that meet the following definitions: Census tracts with median household incomes at or below 80 percent of the statewide median income or with median household incomes at or below the threshold designated as low income by the Department of Housing and Community Development’s list of state income limits adopted under Healthy and Safety Code section 50093 and/or Census tracts receiving the highest 25 percent of overall scores in CalEnviroScreen 4.0 or Census tracts lacking overall scores in CalEnviroScreen 4.0 due to data gaps, but receiving the highest 5 percent of CalEnviroScreen 4.0 cumulative population burden scores or Census tracts identified in the 2017 DAC designation as disadvantaged, regardless of their scores in CalEnviroScreen 4.0 or Lands under the control of federally recognized Tribes.

In 2021, the aggregated total household income of the top percentile of earners in Israel reached approximately *** billion Israeli shekels, around ** billion U.S. dollar. This reflected an increase of about ** percent compared to the previous year. There was a steady rise in aggregated incomes during the observed period, with a peak in 2017 at ***** billion Israeli shekels, around **** billion U.S. dollars. This was due to a one-time tax incentive introduced by the government to release "trapped" capital gains tax.

This layer shows computer ownership and internet access by income group. This is shown by County boundaries. [Source Metadata]This service is updated annually to contain the most currently released American Community Survey (ACS) 5-year data, and contains estimates and margins of error. There are also additional calculated attributes related to this topic, which can be mapped or used within analysis. This layer is symbolized to show the percent of households without a broadband internet subscription. To see the full list of attributes available in this service, go to the "Data" tab, and choose "Fields" at the top right. Current Vintage: 2017-2021ACS Table(s): B28004Data downloaded from: Census Bureau's API for American Community Survey Date of API call: December 8, 2022National Figures: data.census.govThe United States Census Bureau's American Community Survey (ACS):About the SurveyGeography & ACSTechnical DocumentationNews & UpdatesThis ready-to-use layer can be used within ArcGIS Pro, ArcGIS Online, its configurable apps, dashboards, Story Maps, custom apps, and mobile apps. Data can also be exported for offline workflows. For more information about ACS layers, visit the FAQ. Please cite the Census and ACS when using this data.Data Note from the Census:Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see Accuracy of the Data). The effect of nonsampling error is not represented in these tables.Data Processing Notes:This layer is updated automatically when the most current vintage of ACS data is released each year, usually in December. The layer always contains the latest available ACS 5-year estimates. It is updated annually within days of the Census Bureau's release schedule. Click here to learn more about ACS data releases.Boundaries come from the US Census TIGER geodatabases, specifically, the National Sub-State Geography Database (named tlgdb_(year)_a_us_substategeo.gdb). Boundaries are updated at the same time as the data updates (annually), and the boundary vintage appropriately matches the data vintage as specified by the Census. These are Census boundaries with water and/or coastlines erased for cartographic and mapping purposes. For census tracts, the water cutouts are derived from a subset of the 2020 Areal Hydrography boundaries offered by TIGER. Water bodies and rivers which are 50 million square meters or larger (mid to large sized water bodies) are erased from the tract level boundaries, as well as additional important features. For state and county boundaries, the water and coastlines are derived from the coastlines of the 2021 500k TIGER Cartographic Boundary Shapefiles. These are erased to more accurately portray the coastlines and Great Lakes. The original AWATER and ALAND fields are still available as attributes within the data table (units are square meters).The States layer contains 52 records - all US states, Washington D.C., and Puerto RicoCensus tracts with no population that occur in areas of water, such as oceans, are removed from this data service (Census Tracts beginning with 99).Percentages and derived counts, and associated margins of error, are calculated values (that can be identified by the "_calc_" stub in the field name), and abide by the specifications defined by the American Community Survey.Field alias names were created based on the Table Shells file available from the American Community Survey Summary File Documentation page.Negative values (e.g., -4444...) have been set to null, with the exception of -5555... which has been set to zero. These negative values exist in the raw API data to indicate the following situations:The margin of error column indicates that either no sample observations or too few sample observations were available to compute a standard error and thus the margin of error. A statistical test is not appropriate.Either no sample observations or too few sample observations were available to compute an estimate, or a ratio of medians cannot be calculated because one or both of the median estimates falls in the lowest interval or upper interval of an open-ended distribution.The median falls in the lowest interval of an open-ended distribution, or in the upper interval of an open-ended distribution. A statistical test is not appropriate.The estimate is controlled. A statistical test for sampling variability is not appropriate.The data for this geographic area cannot be displayed because the number of sample cases is too small.

Wages in the United States increased 4.86 percent in August of 2025 over the same month in the previous year. This dataset provides the latest reported value for - United States Wages and Salaries Growth - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

The housing affordability measure illustrates the relationship between income and housing costs. A household that spends 30% or more of its collective monthly income to cover housing costs is considered to be “housing cost-burden[ed].”[1] Those spending between 30% and 49.9% of their monthly income are categorized as “moderately housing cost-burden[ed],” while those spending more than 50% are categorized as “severely housing cost-burden[ed].”[2]

How much a household spends on housing costs affects the household’s overall financial situation. More money spent on housing leaves less in the household budget for other needs, such as food, clothing, transportation, and medical care, as well as for incidental purchases and saving for the future.

The estimated housing costs as a percentage of household income are categorized by tenure: all households, those that own their housing unit, and those that rent their housing unit.

Throughout the period of analysis, the percentage of housing cost-burdened renter households in Champaign County was higher than the percentage of housing cost-burdened homeowner households in Champaign County. All three categories saw year-to-year fluctuations between 2005 and 2023, and none of the three show a consistent trend. However, all three categories were estimated to have a lower percentage of housing cost-burdened households in 2023 than in 2005.

Data on estimated housing costs as a percentage of monthly income was sourced from the U.S. Census Bureau’s American Community Survey (ACS) 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 Housing Tenure.

[1] Schwarz, M. and E. Watson. (2008). Who can afford to live in a home?: A look at data from the 2006 American Community Survey. U.S. Census Bureau.

[2] Ibid.

Sources: U.S. Census Bureau; American Community Survey, 2023 American Community Survey 1-Year Estimates, Table B25106; 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 B25106; generated by CCRPC staff; using data.census.gov; (22 September 2023).; U.S. Census Bureau; American Community Survey, 2021 American Community Survey 1-Year Estimates, Table B25106; generated by CCRPC staff; using data.census.gov; (30 September 2022).; U.S. Census Bureau; American Community Survey, 2019 American Community Survey 1-Year Estimates, Table B25106; generated by CCRPC staff; using data.census.gov; (10 June 2021).; U.S. Census Bureau; American Community Survey, 2018 American Community Survey 1-Year Estimates, Table B25106; generated by CCRPC staff; using data.census.gov; (10 June 2021).;U.S. Census Bureau; American Community Survey, 2017 American Community Survey 1-Year Estimates, Table B25106; 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 B25106; 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 B25106; 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 B25106; 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 B25106; 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 B25106; 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 B25106; 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 B25106; 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 B25106; 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 B25106; 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 B25106; 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 B25106; 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 B25106; generated by CCRPC staff; using American FactFinder; (16 March 2016).

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).

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Abstract

Statistical information on Family Planning expenditures within a country is critical for evidence-based policy and decision making in resource allocation in the Health sector. The information is also critical for monitoring progress in achievement of Government commitments to FP service provision in Uganda. The Uganda Bureau of Statistics (UBOS) has been conducting the Resource Flows Survey (RFS) in Uganda since 2011. The 2018 RFS covered all institutions undertaking Family Planning (FP) activities namely; Government Ministries, Department and Agencies (MDA), Non-Government Organisations (NGO), Importers of Contraceptives and Private Health facilities in 30 of the 128 districts in Uganda. Field data collection was spread over a 2-month period from November 2018 to December 2018. A total of 471 institutions were covered including 371 private health facilities scientifically selected countrywide. The Survey focused on income received and domestic expenditures on Family Planning activities in the country in Calendar year 2016-2017 and Financial year 2016/17-2017/18.

Income received and spent on Family Planning activities in Uganda The 2018 Resource Flows Survey on Family Planning in Uganda revealed that about UGX 106Billion was received for Family Planning (FP) activities in 2017; reflecting a 34 percent increment from about UGX 79Billion that was received in 2016. Financial Year (FY) findings showed that there was an increase in income received for FP from UGX 71Billion in 2016/17 to UGX 80Billion in 2017/18. International organisations remained the main source of income for Family Planning activities in Uganda in 2017 accounting for 73 percent (UGX 76 Billion) of the total income received in 2017. FP funds absorption was reportedly high at about 92 percent in 2017/18 and 96 percent in 2017. Internal service staff cost took the greatest share of the total FP expenditures in 2017 at about 28 percent. Purchase of Contraceptives, medicine & other consumables constituted the largest proportion (42%) of Family Planning expenditure in FY 2017/18. Condoms were the most purchased contraceptive accounting for 30 percent of the expenditure towards Contraceptives medicine & other consumables in 2017 and 29 percent in 2017/18.

Income received and spent on Family Planning activities among Government Ministries, Departments and Agencies (MDAs) A total of UGX 16Billion was received for Family planning in 2017/18; an increment from UGX 10Billion in 2016/17. Purchase and distribution of Contraceptives, medicine, and other consumables took the greatest share of the FP funds at about 93 percent in 2017/18. Of the FP income spent on Contraceptives, medicine & other consumables in 2016/17 and 2017/18, more than a third was spent on purchase of IUDs followed by condoms.

Findings from the national budget showed that UGX 1.70 Billion was spent by Government of Uganda on FP activities in 2017/18. This was a decrease from UGX 1.74 Billion in FY 2016/17. About UGX 1Billion was spent on Human Resource in both years.

Income received and spent on Family Planning activities in the Private Sector

There was an increase in income received from FP services in the private sector from UGX 78Billion in 2016 to UGX 105Billion in 2017. Of the income received, almost all was spent (99% in 2016 and 96% in 2017).

Non-Government Organisations (NGOs) reported receipt of UGX 101Billion in 2017; an increment from UGX 73Billion in 2016. Most of the FP income in 2017 (28%) was spent on internal service staff cost for direct FP service provision. A reduction in the percentage spent on long term FP methods namely injectables (15% to 12%), implants (17% to 12%) and IUDs (22% to 14%) was realized between 2016 and 2017.

Private Health Facilities on the other hand reported a decrease in income received for FP activities from UGX 4.9Billion in 2016 to UGX 3.9Billion in 2017. Most of the expenditures towards FP services (38%) in public health facilities were on purchase and provision of Contraceptives, medicine & other FP consumables in 2017. The most purchased contraceptives were injectables at 19 percent in 2017 and 27 percent in FY 2017/18.

Importers of FP commodities. There was an increase in expenditure towards importation of FP contraceptives from UGX 46Billion in 2016 to UGX 58Billion in 2017.

Geographic coverage

National Coverage

Kind of data

Sample survey data [ssd]

Sampling procedure

Sampling Design: From a list of FP impllementing partners in the counrty a total of 12 Government MDAs, 80 NGOs and 12 importers of FP contraceptives were identified. All 104 FP implementing partners in Uganda, were visited with the exception of private health facilities where sampling was done.

The frame for Private Health Care Facilities (HFs) that provided Family Planning services as of December 2017 in 125 districts was obtained from the District Health Management Information System (DHMIS) at Ministry of Health, comprising of 1,444 Private Health Facilities. All facilities with less than 10 FP users (0.04% of total FP users) in 2017 were dropped from the frame (8% of the private HFs were excluded).

Given that the data from the sampling frame (DHMIS) was incomplete without data on expenditures towards FP among the private health facilities, the available variable number of FP users was used as a proxy measure for FP financial expenditure. Hence the sample selection at all stages was done using Probability Proportional to Size (PPS), the size being the number of Family Planning users.

Sample Size determination: A number of factors were taken into consideration during the determination of a sample size that is nationally representative and these included; 1. Contraceptive Prevalence Rate among married women (39%) and sexually active unmarried women (51%) in the reproductive age group (15-49 years) from the 2016 UDHS 2. Non-response among health facilities (1.3%) based on other health facility based studies and 3. The overall cost of the survey among others. A sample size of 450 private Health Facilities were then selected. Based on findings from the pre-test, clinics were excluded in the sampling due to inadequate data and duplication in FP methods provision.

Sample Selection: The sample selection followed a two-stage stratified sampling design. In the first stage, all districts were grouped into 15 sub regions of similar socio-economic characteristics prior to sampling. These included; North Buganda, South Buganda, Kampala, Ankole, Bukedi, Busoga, Acholi, Lango, West Nile, Bunyoro, Kigezi, Tooro, Teso, Elgon and Karamoja. A total of 30 districts with regional representation were then selected using Probability Proportional to Size (PPS), the size being the number of Family Planning users in each district.

In the second stage, the number of Private Health facilities to be included in the sample from each district was also determined using PPS including (41 Hospitals, 10 Health Centre IVs, and 156 HC IIIs) in the selected districts, all of which were included in the sample. The 243 HC IIs were then selected using PPS in each district.

Finally, a total of 554 organisations including 12 Government MDAs, 80 NGOs, 12 Pharmacies and agencies that import contraceptives, and 450 private health facilities were identified and visited.

Mode of data collection

Face-to-face [f2f]

Research instrument

The 2018 RFS on FP used a set of four (4) questionnaires each with an accompanying manual of instructions. These were developed by NIDI and localised by UBOS in consultation with stakeholders. The questionnaires collected information on income received by source, funds absorption by type (recurrent expenses and capital investment), specific FP programme expenditure details, expenditure on contraceptives by commodity, and future expected expenditures.

The survey questionnaires included: 1) National Consultant questionnaire which was filled in by the survey coordinator from UBOS-lead implementing agency. This provided information on average price of contraceptives from administrative sources. 2) Government questionnaire for the public sector which was filled in by accountants in consultation with the technical officers from Government MDAs providing information on the income received and spent on FP activities. 3) Private Sector questionnaires that included; i. Non-Government Organisations (Non-Profit Institutions) questionnaire which was administered to NGOs, Universities, Foundations among others and filled in accountants in consultation with the technical officers. ii. Corporations' questionnaire providing information on the income received and spent on FP activities by Private for Profit agencies like private hospitals and Pharmacies. Corporations in this survey refer to the private providers of Family Planning services and methods.

The Government and NGO questionnaires were sent to respondents (accountants who worked with the technical officers) via email or hand delivered by the data collector. Where possible, face to face interviews were conducted with the respondent. Computer Assisted Personal Interviews (CAPI) were conducted at the selected Private Health Facilities.

Cleaning operations

Two data editors were identified among the data collectors whose main tasks included making the relevant edits to ensure that: a) The dates most especially for the financial year dates and the project period time are correct. b) The totals on income in section B equals to the summation of breakdowns on income received from domestic, international and own incomes in section c c) The expenditure distribution of all individual projects in section D is equaled to the given / indicated total expenditure in section