The Community Development Block Grant (CDBG) program requires that each CDBG funded activity must either principally benefit low- and moderate-income persons, aid in the prevention or elimination of slums or blight, or meet a community development need having a particular urgency because existing conditions pose a serious and immediate threat to the health or welfare of the community and other financial resources are not available to meet that need. With respect to activities that principally benefit low- and moderate-income persons, at least 51 percent of the activity's beneficiaries must be low and moderate income. For CDBG, a person is considered to be of low income only if he or she is a member of a household whose income would qualify as "very low income" under the Section 8 Housing Assistance Payments program. Generally, these Section 8 limits are based on 50% of area median. Similarly, CDBG moderate income relies on Section 8 "lower income" limits, which are generally tied to 80% of area median. These data are from the 2011-2015 American Community Survey (ACS). To learn more about the Low to Moderate Income Populations visit: https://www.hudexchange.info/programs/acs-low-mod-summary-data/, for questions about the spatial attribution of this dataset, please reach out to us at GISHelpdesk@hud.gov. Data Dictionary: DD_Low to Moderate Income Populations by Block GroupDate of Coverage: ACS 2020-2016

California State Income Limits reflect updated median income and household income levels for acutely low-, extremely low-, very low-, low- and moderate-income households for California’s 58 counties (required by Health and Safety Code Section 50093). These income limits apply to State and local affordable housing programs statutorily linked to HUD income limits and differ from income limits applicable to other specific federal, State, or local programs.

This dataset provides access to Qualified Census Tracts (QCTs) in Connecticut to assist in administration of American Rescue Plan (ARP) funds. The Secretary of HUD must designate QCTs, which are areas where either 50 percent or more of the households have an income less than 60 percent of the AMGI for such year or have a poverty rate of at least 25 percent. HUD designates QCTs based on new income and poverty data released in the American Community Survey (ACS). Specifically, HUD relies on the most recent three sets of ACS data to ensure that anomalous estimates, due to sampling, do not affect the QCT status of tracts. QCTs are identified for the purpose of Low-Income Housing Credits under IRC Section 42, with the purpose of increasing the availability of low-income rental housing by providing an income tax credit to certain owners of newly constructed or substantially rehabilitated low-income rental housing projects. Also included are the number of households from the 2010 census (the “p0150001” variable), the average poverty rate using the 2014-2018 ACS data (the “pov_rate_18” variable), and the ratio of Tract Average Household Size Adjusted Income Limit to Tract Median Household Income using the 2014-2018 ACS data (the “inc_factor_18” variable). For the last variable mentioned in the previous paragraph, the income limit is the limit for being considered a very low income household (size-adjusted and based on Area Mean Gross Income). This value is divided by the median household income for the given tract, to get a sense of how the limit and median incomes compare. For example, if ratio>1, it implies that the tract is very low income because the limit income is greater than the median income. This ratio is a compact way to include the separate variables for the household income limit and median household income for each tract.

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.Data downloaded in May 2022 from https://webmaps.arb.ca.gov/PriorityPopulations/.

Graph and download economic data for Real Median Family Income in the United States (MEFAINUSA672N) from 1953 to 2023 about family, median, income, real, and USA.

Number of persons in low income, low income rate and average gap ratio by age, sex and economic family type, annual.

FY2024 full and partial census tracts that qualify as Low-Moderate Income Areas (LMA) where 51% or more of the population are considered as having Low-Moderate Income. The low- and moderate-income summary data (LMISD) is based on the 2016-2020 American Community Survey (ACS). As of August 1, 2024, to qualify any new low- and moderate-income area (LMA) activities, Community Development Block Grant (CDBG) grantees should use this map and data.

For more information about LMA/LMI click the following link to open in new browser tab https://www.hudexchange.info/programs/cdbg/cdbg-low-moderate-income-data/

Low income measure (LIM) thresholds by household size for market income, total income and after-tax income, in current and constant dollars, annual.

The Department of Housing Preservation and Development (HPD) receives a sub-allocation of 9% Low Income Housing Tax Credits and allocated its credits through one competitive round each calendar year. It is also charged with allocating 4% Low Income Housing Tax Credits to projects receiving tax exempt bonds through New York City Housing Development Corporation. Each entry represents an allocation to a low income housing development project with households at or below 60% of Area Median Income. For the Low Income Housing Tax Credits Awarded by HPD: Project-Level (4% Awards) dataset, please follow this link

Relative concentration of the estimated number of people in the Sierra Nevada region that live in a household defined as "low income." There are multiple ways to define low income. These data apply the most common standard: low income population consists of all members of households that collectively have income less than twice the federal poverty threshold that applies to their household type. Household type refers to the household's resident composition: the number of independent adults plus dependents that can be of any age, from children to elderly. For example, a household with four people ' one working adult parent and three dependent children ' has a different poverty threshold than a household comprised of four unrelated independent adults.

Due to high estimate uncertainty for many block group estimates of the number of people living in low income households, some records cannot be reliably assigned a class and class code comparable to those assigned to race/ethnicity data from the decennial Census.

"Relative concentration" is a measure that compares the proportion of population within each Census block group data unit to the proportion of all people that live within the 775 block groups in the Sierra Nevada RRK region. See the "Data Units" description below for how these relative concentrations are broken into categories in this "low income" metric.

About this dataset

Context

The dataset presents the mean household income for each of the five quintiles in Orange, CA, as reported by the U.S. Census Bureau. The dataset highlights the variation in mean household income across quintiles, offering valuable insights into income distribution and inequality.

Key observations

• Income disparities: The mean income of the lowest quintile (20% of households with the lowest income) is 27,530, while the mean income for the highest quintile (20% of households with the highest income) is 367,764. This indicates that the top earners earn 13 times compared to the lowest earners.
• *Top 5%: * The mean household income for the wealthiest population (top 5%) is 654,355, which is 177.93% higher compared to the highest quintile, and 2376.88% higher compared to the lowest quintile.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Income Levels:

• Lowest Quintile
• Second Quintile
• Third Quintile
• Fourth Quintile
• Highest Quintile
• Top 5 Percent

Variables / Data Columns

• Income Level: This column showcases the income levels (As mentioned above).
• Mean Household Income: Mean household income, in 2023 inflation-adjusted dollars for the specific income level.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Orange median household income. You can refer the same here

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

Graph and download economic data for Income Before Taxes: Wages and Salaries by Quintiles of Income Before Taxes: Lowest 20 Percent (1st to 20th Percentile) (CXU900000LB0102M) from 1984 to 2023 about percentile, salaries, tax, wages, income, and USA.

Urban Displacement Project’s (UDP) Estimated Displacement Risk (EDR) model for California identifies varying levels of displacement risk for low-income renter households in all census tracts in the state from 2015 to 2019(1). The model uses machine learning to determine which variables are most strongly related to displacement at the household level and to predict tract-level displacement risk statewide while controlling for region. UDP defines displacement risk as a census tract with characteristics which, according to the model, are strongly correlated with more low-income population loss than gain. In other words, the model estimates that more low-income households are leaving these neighborhoods than moving in.This map is a conservative estimate of low-income loss and should be considered a tool to help identify housing vulnerability. Displacement may occur because of either investment, disinvestment, or disaster-driven forces. Because this risk assessment does not identify the causes of displacement, UDP does not recommend that the tool be used to assess vulnerability to investment such as new housing construction or infrastructure improvements. HCD recommends combining this map with on-the-ground accounts of displacement, as well as other related data such as overcrowding, cost burden, and income diversity to achieve a full understanding of displacement risk.If you see a tract or area that does not seem right, please fill out this form to help UDP ground-truth the method and improve their model.How should I read the displacement map layers?The AFFH Data Viewer includes three separate displacement layers that were generated by the EDR model. The “50-80% AMI” layer shows the level of displacement risk for low-income (LI) households specifically. Since UDP has reason to believe that the data may not accurately capture extremely low-income (ELI) households due to the difficulty in counting this population, UDP combined ELI and very low-income (VLI) household predictions into one group—the “0-50% AMI” layer—by opting for the more “extreme” displacement scenario (e.g., if a tract was categorized as “Elevated” for VLI households but “Extreme” for ELI households, UDP assigned the tract to the “Extreme” category for the 0-50% layer). For these two layers, tracts are assigned to one of the following categories, with darker red colors representing higher displacement risk and lighter orange colors representing less risk:• Low Data Quality: the tract has less than 500 total households and/or the census margins of error were greater than 15% of the estimate (shaded gray).• Lower Displacement Risk: the model estimates that the loss of low-income households is less than the gain in low-income households. However, some of these areas may have small pockets of displacement within their boundaries. • At Risk of Displacement: the model estimates there is potential displacement or risk of displacement of the given population in these tracts.• Elevated Displacement: the model estimates there is a small amount of displacement (e.g., 10%) of the given population.• High Displacement: the model estimates there is a relatively high amount of displacement (e.g., 20%) of the given population.• Extreme Displacement: the model estimates there is an extreme level of displacement (e.g., greater than 20%) of the given population. The “Overall Displacement” layer shows the number of income groups experiencing any displacement risk. For example, in the dark red tracts (“2 income groups”), the model estimates displacement (Elevated, High, or Extreme) for both of the two income groups. In the light orange tracts categorized as “At Risk of Displacement”, one or all three income groups had to have been categorized as “At Risk of Displacement”. Light yellow tracts in the “Overall Displacement” layer are not experiencing UDP’s definition of displacement according to the model. Some of these yellow tracts may be majority low-income experiencing small to significant growth in this population while in other cases they may be high-income and exclusive (and therefore have few low-income residents to begin with). One major limitation to the model is that the migration data UDP uses likely does not capture some vulnerable populations, such as undocumented households. This means that some yellow tracts may be experiencing high rates of displacement among these types of households. MethodologyThe EDR is a first-of-its-kind model that uses machine learning and household level data to predict displacement. To create the EDR, UDP first joined household-level data from Data Axle (formerly Infogroup) with tract-level data from the 2014 and 2019 5-year American Community Survey; Affirmatively Furthering Fair Housing (AFFH) data from various sources compiled by California Housing and Community Development; Longitudinal Employer-Household Dynamics (LEHD) Origin-Destination Employment Statistics (LODES) data; and the Environmental Protection Agency’s Smart Location Database.UDP then used a machine learning model to determine which variables are most strongly related to displacement at the household level and to predict tract-level displacement risk statewide while controlling for region. UDP modeled displacement risk as the net migration rate of three separate renter households income categories: extremely low-income (ELI), very low-income (VLI), and low-income (LI). These households have incomes between 0-30% of the Area Median Income (AMI), 30-50% AMI, and 50-80% AMI, respectively. Tracts that have a predicted net loss within these groups are considered to experience displacement in three degrees: elevated, high, and extreme. UDP also includes a “At Risk of Displacement” category in tracts that might be experiencing displacement.What are the main limitations of this map?1. Because the map uses 2019 data, it does not reflect more recent trends. The pandemic, which started in 2020, has exacerbated income inequality and increased housing costs, meaning that UDP’s map likely underestimates current displacement risk throughout the state.2. The model examines displacement risk for renters only, and does not account for the fact that many homeowners are also facing housing and gentrification pressures. As a result, the map generally only highlights areas with relatively high renter populations, and neighborhoods with higher homeownership rates that are known to be experiencing gentrification and displacement are not as prominent as one might expect.3. The model does not incorporate data on new housing construction or infrastructure projects. The map therefore does not capture the potential impacts of these developments on displacement risk; it only accounts for other characteristics such as demographics and some features of the built environment. Two of UDP’s other studies—on new housing construction and green infrastructure—explore the relationships between these factors and displacement.Variable ImportanceFigures 1, 2, and 3 show the most important variables for each of the three models—ELI, VLI, and LI. The horizontal bars show the importance of each variable in predicting displacement for the respective group. All three models share a similar order of variable importance with median rent, percent non-white, rent gap (i.e., rental market pressure calculated using the difference between nearby and local rents), percent renters, percent high-income households, and percent of low-income households driving much of the displacement estimation. Other important variables include building types as well as economic and socio-demographic characteristics. For a full list of the variables included in the final models, ranked by descending order of importance, and their definitions see all three tabs of this spreadsheet. “Importance” is defined in two ways: 1. % Inclusion: The average proportion of times this variable was included in the model’s decision tree as the most important or driving factor.2. MeanRank: The average rank of importance for each variable across the numerous model runs where higher numbers mean higher ranking. Figures 1 through 3 below show each of the model variable rankings ordered by importance. The red lines represent Jenks Breaks, which are designed to sort values into their most “natural” clusters. Variable importance for each model shows a substantial drop-off after about 10 variables, meaning a relatively small number of variables account for a large amount of the predictive power in UDP’s displacement model.Figure 1. Variable Importance for Low Income HouseholdsFor a description of each variable and its source, see this spreadsheet.Figure 2. Variable Importance for Very Low Income HouseholdsFor a description of each variable and its source, see this spreadsheet. Figure 3. Variable Importance for Extremely Low Income HouseholdsFor a description of each variable and its source, see this spreadsheet.Source: Chapple, K., & Thomas, T., and Zuk, M. (2022). Urban Displacement Project website. Berkeley, CA: Urban Displacement Project.(1) UDP used this time-frame because (a) the 2020 census had a large non-response rate and it implemented a new statistical modification that obscures and misrepresents racial and economic characteristics at the census tract level and (b) pandemic mobility trends are still in flux and UDP believes 2019 is more representative of “normal” or non-pandemic displacement trends.

The Low-Income Energy Affordability Data (LEAD) Tool was created by the Better Building's Clean Energy for Low Income Communities Accelerator (CELICA) to help state and local partners understand housing and energy characteristics for the low- and moderate-income (LMI) communities they serve. The LEAD Tool provides estimated LMI household energy data based on income, energy expenditures, fuel type, housing type, and geography, which stakeholders can use to make data-driven decisions when planning for their energy goals. From the LEAD Tool website, users can also create and download customized heat-maps and charts for various geographies, housing, and energy characteristics. Datasets are available for 50 states plus Puerto Rico and Washington D.C., along with their cities, counties, and census tracts. The file below, "1. Description of Files," provides a list of all files included in this dataset. A description of the abbreviations and units used in the LEAD Tool data can be found in the file below titled "2. Data Dictionary 2018". The Low-Income Energy Affordability Data comes primarily from the 2018 U.S. Census American Community Survey 5-Year Public Use Microdata Samples and is calibrated to 2018 U.S. Energy Information Administration electric utility (Survey Form-861) and natural gas utility (Survey Form-176) data. The methodology for the LEAD Tool can viewed below (3. Methodology Document). For more information, and to access the interactive LEAD Tool platform, please visit the "4. LEAD Tool Platform" resource link below. For more information on the Better Building's Clean Energy for Low Income Communities Accelerator (CELICA), please visit the "5. CELICA Website" resource below.

Low-income cut-offs, after tax (LICO-AT) - The Low-income cut-offs, after tax refers to an income threshold, defined using 1992 expenditure data, below which economic families or persons not in economic families would likely have devoted a larger share of their after-tax income than average to the necessities of food, shelter and clothing. More specifically, the thresholds represented income levels at which these families or persons were expected to spend 20 percentage points or more of their after-tax income than average on food, shelter and clothing. These thresholds have been adjusted to current dollars using the all-items Consumer Price Index (CPI).The LICO-AT has 35 cut-offs varying by seven family sizes and five different sizes of area of residence to account for economies of scale and potential differences in cost of living in communities of different sizes. These thresholds are presented in Table 4.3 Low-income cut-offs, after tax (LICO-AT - 1992 base) for economic families and persons not in economic families, 2015, Dictionary, Census of Population, 2016.When the after-tax income of an economic family member or a person not in an economic family falls below the threshold applicable to the person, the person is considered to be in low income according to LICO-AT. Since the LICO-AT threshold and family income are unique within each economic family, low-income status based on LICO-AT can also be reported for economic families.Return to footnote1referrerFootnote 2Low-income status - The income situation of the statistical unit in relation to a specific low-income line in a reference year. Statistical units with income that is below the low-income line are considered to be in low income.For the 2016 Census, the reference period is the calendar year 2015 for all income variables.Return to footnote2referrerFootnote 3The low-income concepts are not applied in the territories and in certain areas based on census subdivision type (such as Indian reserves). The existence of substantial in-kind transfers (such as subsidized housing and First Nations band housing) and sizeable barter economies or consumption from own production (such as product from hunting, farming or fishing) could make the interpretation of low-income statistics more difficult in these situations.Return to footnote3referrerFootnote 4Prevalence of low income - The proportion or percentage of units whose income falls below a specified low-income line.

About this dataset

Context

The dataset presents the mean household income for each of the five quintiles in True, Wisconsin, as reported by the U.S. Census Bureau. The dataset highlights the variation in mean household income across quintiles, offering valuable insights into income distribution and inequality.

Key observations

• Income disparities: The mean income of the lowest quintile (20% of households with the lowest income) is 27,030, while the mean income for the highest quintile (20% of households with the highest income) is 134,333. This indicates that the top earners earn 5 times compared to the lowest earners.
• *Top 5%: * The mean household income for the wealthiest population (top 5%) is 165,048, which is 122.86% higher compared to the highest quintile, and 610.61% higher compared to the lowest quintile.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates.

Income Levels:

• Lowest Quintile
• Second Quintile
• Third Quintile
• Fourth Quintile
• Highest Quintile
• Top 5 Percent

Variables / Data Columns

• Income Level: This column showcases the income levels (As mentioned above).
• Mean Household Income: Mean household income, in 2022 inflation-adjusted dollars for the specific income level.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for True town median household income. You can refer the same here

About this dataset

Context

The dataset presents the mean household income for each of the five quintiles in Price, UT, as reported by the U.S. Census Bureau. The dataset highlights the variation in mean household income across quintiles, offering valuable insights into income distribution and inequality.

Key observations

• Income disparities: The mean income of the lowest quintile (20% of households with the lowest income) is 11,467, while the mean income for the highest quintile (20% of households with the highest income) is 148,423. This indicates that the top earners earn 13 times compared to the lowest earners.
• *Top 5%: * The mean household income for the wealthiest population (top 5%) is 200,861, which is 135.33% higher compared to the highest quintile, and 1751.64% higher compared to the lowest quintile.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Income Levels:

• Lowest Quintile
• Second Quintile
• Third Quintile
• Fourth Quintile
• Highest Quintile
• Top 5 Percent

Variables / Data Columns

• Income Level: This column showcases the income levels (As mentioned above).
• Mean Household Income: Mean household income, in 2023 inflation-adjusted dollars for the specific income level.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Price median household income. You can refer the same here

About this dataset

Context

The dataset presents the mean household income for each of the five quintiles in Media, PA, as reported by the U.S. Census Bureau. The dataset highlights the variation in mean household income across quintiles, offering valuable insights into income distribution and inequality.

Key observations

• Income disparities: The mean income of the lowest quintile (20% of households with the lowest income) is 23,507, while the mean income for the highest quintile (20% of households with the highest income) is 270,183. This indicates that the top earners earn 11 times compared to the lowest earners.
• *Top 5%: * The mean household income for the wealthiest population (top 5%) is 518,814, which is 192.02% higher compared to the highest quintile, and 2207.06% higher compared to the lowest quintile.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Income Levels:

• Lowest Quintile
• Second Quintile
• Third Quintile
• Fourth Quintile
• Highest Quintile
• Top 5 Percent

Variables / Data Columns

• Income Level: This column showcases the income levels (As mentioned above).
• Mean Household Income: Mean household income, in 2023 inflation-adjusted dollars for the specific income level.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Media median household income. 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 Lower township. 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 2023

Based on our analysis ACS 2019-2023 5-Year Estimates, we present the following observations: - All workers, aged 15 years and older: In Lower township, the median income for all workers aged 15 years and older, regardless of work hours, was $55,671 for males and $36,017 for females.

These income figures highlight a substantial gender-based income gap in Lower township. Women, regardless of work hours, earn 65 cents for each dollar earned by men. This significant gender pay gap, approximately 35%, underscores concerning gender-based income inequality in the township of Lower township.

- Full-time workers, aged 15 years and older: In Lower township, among full-time, year-round workers aged 15 years and older, males earned a median income of $68,361, while females earned $54,396, leading to a 20% gender pay gap among full-time workers. This illustrates that women earn 80 cents for each dollar earned by men in full-time roles. This analysis indicates a widening gender pay gap, showing a substantial income disparity where women, despite working full-time, face a more significant wage discrepancy compared to men in the same roles.

Surprisingly, the gender pay gap percentage was higher across all roles, including non-full-time employment, for women compared to men. This suggests that full-time employment offers a more equitable income scenario for women compared to other employment patterns in Lower township.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates. All incomes have been adjusting for inflation and are presented in 2023-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 2023
• 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 Lower township median household income by race. You can refer the same here