This table presents income shares, thresholds, tax shares, and total counts of individual Canadian tax filers, with a focus on high income individuals (95% income threshold, 99% threshold, etc.). Income thresholds are geography-specific; for example, the number of Nova Scotians in the top 1% will be calculated as the number of taxfiling Nova Scotians whose total income exceeded the 99% income threshold of Nova Scotian tax filers. Different definitions of income are available in the table namely market, total, and after-tax income, both with and without capital gains.

This dataset and map service provides information on the U.S. Housing and Urban Development's (HUD) low to moderate income areas. The term Low to Moderate Income, often referred to as low-mod, has a specific programmatic context within the Community Development Block Grant (CDBG) program. Over a 1, 2, or 3-year period, as selected by the grantee, not less than 70 percent of CDBG funds must be used for activities that benefit low- and moderate-income persons. HUD uses special tabulations of Census data to determine areas where at least 51% of households have incomes at or below 80% of the area median income (AMI). This dataset and map service contains the following layer.

Upper income limits and shares of total income quintiles.

Upper income limits and income shares of after-tax income quintiles for all family units.

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.

Upper income limit, income share and average of market, total and after-tax income by economic family type and income decile, annual.

The Low-Income Housing Tax Credit (LIHTC) is the most important resource for creating affordable housing in the United States today. The LIHTC database, created by HUD and available to the public since 1997, contains information on 48,672 projects and 3.23 million housing units placed in service since 1987. Low-Income Housing Tax Credit Qualified Census Tracts must have 50 percent of households with incomes below 60 percent of the Area Median Gross Income (AMGI) or have a poverty rate of 25 percent or more. Difficult Development Areas (DDA) are areas with high land, construction and utility costs relative to the area median income and are based on Fair Market Rents, income limits, the 2010 census counts, and 5-year American Community Survey (ACS) data.

Upper income limits and income shares of adjusted after-tax income quintiles for all economic family units.

This dataset contains counts for Medicaid recipients served by month in Iowa, starting with month ending 1/31/2011.

Eligibility groups are a category of people who meet certain common eligibility requirements. Some Medicaid eligibility groups cover additional services, such as nursing facility care and care received in the home. Others have higher income and resource limits, charge a premium, only pay the Medicare premium or cover only expenses also paid by Medicare, or require the recipient to pay a specific dollar amount of their medical expenses. Eligible Medicaid recipients may be considered medically needy if their medical costs are so high that they use up most of their income. Those considered medically needy are responsible for paying some of their medical expenses. This is called meeting a spend down. Then Medicaid would start to pay for the rest. Think of the spend down like a deductible that people pay as part of a private insurance plan.

This table presents income shares, thresholds, tax shares, and total counts of individual Canadian tax filers, with a focus on high income individuals (95% income threshold, 99% threshold, etc.). Income thresholds are geography-specific; for example, the number of Nova Scotians in the top 1% will be calculated as the number of taxfiling Nova Scotians whose total income exceeded the 99% income threshold of Nova Scotian tax filers. Different definitions of income are available in the table namely market, total, and after-tax income, both with and without capital gains.

A Qualified Census Tract (QCT) is any census tract (or equivalent geographic area defined by the Census Bureau) in which at least 50% of households have an income less than 60% of the Area Median Gross Income (AMGI). HUD has defined 60% of AMGI as 120% of HUD's Very Low Income Limits (VLILs), which are based on 50% of area median family income, adjusted for high cost and low income areas.

Upper income limits and income shares of adjusted after-tax income quintiles for all economic family units.

Definitions:Urban: Contiguous urban census tracts with a population of 50,000 or greater. Urban census tracts are tracts where at least 10 percent of the tract's land areas is designated as urban by the Census Bureau using the 2020 urbanized area criteria.Rural Center: Contiguous urban census tracts with a population of less than 50,000. Urban census tracts are tracts where at least 10 percent of the tract's land area is designated as urban by the Census Bureau using the 2020 urbanized area criteria.Rural: Census tracts where less than 10 percent of the tract's land area is designated as urban by the Census Bureau using the 2020 urbanized area criteria.Disadvantaged Community (DAC): Census tracts that score within the top 25th percentile of the Office of Environmental Health Hazards Assessment’s California Communities Environmental Health Screening Tool (CalEnviroScreen) 4.0 scores, as well as areas of high pollution and low population, such as ports.Low-income Community (LIC): 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 pursuant to Section 50093 of the California Health and Safety Code.Middle-income Community (MIC): Census tracts with median household incomes between 80 to 120 percent of the statewide median income, or with median household incomes between the threshold designated as low- and moderate-income by the Department of Housing and Community Development’s list of state income limits adopted pursuant to section 50093 of the California Health and Safety Code. High-income Community (HIC): Census tracts with median household income at or above 120 percent of the statewide median income or with median household incomes at or above the threshold designated as moderate-income by the Department of Housing and Community Development’s list of state income limits adopted pursuant to section 50093 of the California Health and Safety Code.Data Dictionary:ObjectID1_: Unique IDShape: Geometric form of the featureSTATEFP: State FIPS CodeCOUNTYFP: County FIPS CodeCOUNTY: County NameTract: Census Tract IDPopulation_2019_5YR: Population from the American Community Survey 2019 5-Year EstimatesPop_dens: Census tract designation as Urban, Rural Center, or RuralDAC: Census tract designation as Disadvantaged or not (DAC or Not DAC)Income_Group: Census tract designation as Low-, Middle-, or High-income Community (LIC, MIC, or HIC)Priority_pop: Census tract designation as Low-income and/or Disadvantaged or not (LIC and/or DAC, or Not LIC and/or DAC)Shape_Length: Census tract shape area (square meters)Shape_Area: Census tract shape length (square meters)Data sources:Urban, rural center, and rural designations are from the 2025 Senate Bill (SB) 1000 AssessmentDisadvantaged community designations are from the California Environmental Protection Agency (CalEPA) under Senate Bill (SB) 535Low-income community designations are from the California Air Resources Board under Assembly Bill (AB) 1550. Middle- and high-income designations are from the SB 1000 Assessments.

Although the American Community Survey (ACS) produces population, demographic and housing unit estimates, the decennial census is the official source of population totals for April 1st of each decennial year. In between censuses, the Census Bureau's Population Estimates Program produces and disseminates the official estimates of the population for the nation, states, counties, cities, and towns and estimates of housing units for states and counties..Information about the American Community Survey (ACS) can be found on the ACS website. Supporting documentation including code lists, subject definitions, data accuracy, and statistical testing, and a full list of ACS tables and table shells (without estimates) can be found on the Technical Documentation section of the ACS website.Sample size and data quality measures (including coverage rates, allocation rates, and response rates) can be found on the American Community Survey website in the Methodology section..Source: U.S. Census Bureau, 2018-2022 American Community Survey 5-Year Estimates.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 roughly 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 ACS Technical Documentation). The effect of nonsampling error is not represented in these tables..Between 2018 and 2019 the American Community Survey retirement income question changed. These changes resulted in an increase in both the number of households reporting retirement income and higher aggregate retirement income at the national level. For more information see Changes to the Retirement Income Question ..The 2018-2022 American Community Survey (ACS) data generally reflect the March 2020 Office of Management and Budget (OMB) delineations of metropolitan and micropolitan statistical areas. In certain instances, the names, codes, and boundaries of the principal cities shown in ACS tables may differ from the OMB delineation lists due to differences in the effective dates of the geographic entities..Estimates of urban and rural populations, housing units, and characteristics reflect boundaries of urban areas defined based on 2020 Census data. As a result, data for urban and rural areas from the ACS do not necessarily reflect the results of ongoing urbanization..Explanation of Symbols:- The estimate could not be computed because there were an insufficient number of sample observations. For a ratio of medians estimate, one or both of the median estimates falls in the lowest interval or highest interval of an open-ended distribution. For a 5-year median estimate, the margin of error associated with a median was larger than the median itself.N The estimate or margin of error cannot be displayed because there were an insufficient number of sample cases in the selected geographic area. (X) The estimate or margin of error is not applicable or not available.median- The median falls in the lowest interval of an open-ended distribution (for example "2,500-")median+ The median falls in the highest interval of an open-ended distribution (for example "250,000+").** The margin of error could not be computed because there were an insufficient number of sample observations.*** The margin of error could not be computed because the median falls in the lowest interval or highest interval of an open-ended distribution.***** A margin of error is not appropriate because the corresponding estimate is controlled to an independent population or housing estimate. Effectively, the corresponding estimate has no sampling error and the margin of error may be treated as zero.

This layer shows median household income by race and by age of householder. 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. Median income and income source is based on income in past 12 months of survey. This layer is symbolized to show median household income. 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: 2019-2023ACS Table(s): B19013B, B19013C, B19013D, B19013E, B19013F, B19013G, B19013H, B19013I, B19049, B19053Data downloaded from: Census Bureau's API for American Community Survey Date of API call: December 12, 2024National 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 2023 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.

Upper income limits and shares of total income quintiles.

Although the American Community Survey (ACS) produces population, demographic and housing unit estimates, it is the Census Bureau's Population Estimates Program that produces and disseminates the official estimates of the population for the nation, states, counties, cities, and towns and estimates of housing units for states and counties..Supporting documentation on code lists, subject definitions, data accuracy, and statistical testing can be found on the American Community Survey website in the Technical Documentation section.Sample size and data quality measures (including coverage rates, allocation rates, and response rates) can be found on the American Community Survey website in the Methodology section..Source: U.S. Census Bureau, 2017-2021 American Community Survey 5-Year Estimates.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 roughly 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 ACS Technical Documentation). The effect of nonsampling error is not represented in these tables..Between 2018 and 2019 the American Community Survey retirement income question changed. These changes resulted in an increase in both the number of households reporting retirement income and higher aggregate retirement income at the national level. For more information see Changes to the Retirement Income Question ..The 2017-2021 American Community Survey (ACS) data generally reflect the March 2020 Office of Management and Budget (OMB) delineations of metropolitan and micropolitan statistical areas. In certain instances, the names, codes, and boundaries of the principal cities shown in ACS tables may differ from the OMB delineation lists due to differences in the effective dates of the geographic entities..Estimates of urban and rural populations, housing units, and characteristics reflect boundaries of urban areas defined based on Census 2010 data. As a result, data for urban and rural areas from the ACS do not necessarily reflect the results of ongoing urbanization..Explanation of Symbols:- The estimate could not be computed because there were an insufficient number of sample observations. For a ratio of medians estimate, one or both of the median estimates falls in the lowest interval or highest interval of an open-ended distribution. For a 5-year median estimate, the margin of error associated with a median was larger than the median itself.N The estimate or margin of error cannot be displayed because there were an insufficient number of sample cases in the selected geographic area. (X) The estimate or margin of error is not applicable or not available.median- The median falls in the lowest interval of an open-ended distribution (for example "2,500-")median+ The median falls in the highest interval of an open-ended distribution (for example "250,000+").** The margin of error could not be computed because there were an insufficient number of sample observations.*** The margin of error could not be computed because the median falls in the lowest interval or highest interval of an open-ended distribution.***** A margin of error is not appropriate because the corresponding estimate is controlled to an independent population or housing estimate. Effectively, the corresponding estimate has no sampling error and the margin of error may be treated as zero.

This dataset was introduced in a competition on Zindi to challenge data professionals to predict whether members of the test population would be earning below or above $50,000 based on the variables taken into account in the analysis.

Analysis Objective.

The objective of this challenge is to create a machine learning model to predict whether an individual earns above or below a certain amount.

This solution can potentially reduce the cost and improve the accuracy of monitoring key population indicators such as income level in between census years. This information will help policymakers to better manage and avoid income inequality globally.

Data background.

This data has been collected from a random population.

There are ~200 000 individuals in train and ~100 000 individuals in the test file.

The train & test data will be used to create a machine learning model to predict if an individual earns above 50 000 of a specific currency.

The key variables are as follows: * Age. * Gender. * Education. * Class. * Education institute. * Marital status. * Race. * Is hispanic. * Employment commitment. * Unemployment reason. * Employment state. * Wage per hour. * Is part of labor union. * Working week per year. * Industry code. * Main Industry code. * Occupation code. * Main Occupation code. * Total employed. * Household stat. * Household summary. * Under 18 family. * Veterans adminquestionnaire. * Veteran benefit. * Tax status. * Gains. * Losses. * Stocks status. * Citizenship. * Migration year. * Country of birth own. * Country of birth father. * Country of birth mother. * Migration code change in msa. * Migration previous sunbelt. * Migration code move within registration. * Migration code change in registration. * Residence 1 year ago. * Old residence registration. * Old residence state. * Importance of record. * Income above limit.

Based on the variables set up and the data target requirements, the analysis can be assumed to be based on 20th century American population data where the median income was about $ 50,000.

Why Predict Income?

Income prediction extracts insights from individual and population-level data as it offers the ability to forecast income levels, assess financial risks, target marketing campaigns, and inform crucial decision-making in diverse spheres. However, ethical considerations, potential biases, and data privacy concerns demand careful attention alongside its undeniable benefits.

Applications in Specific Industries:

Finance:

• Credit scoring and loan approval: Predicting income to assess risk and determine loan eligibility.
• Fraud detection: Identifying suspicious financial activity based on income patterns.
• Targeted marketing: Tailoring financial products and services to individuals based on predicted income.
• Wealth management: Personalized investment strategies based on income potential and risk tolerance.

Healthcare:

• Predicting healthcare costs and resource allocation based on patient income and demographics.
• Identifying individuals at risk of financial hardship due to medical bills.
• Developing targeted health insurance plans based on income segments.
• Assessing eligibility for government healthcare programs.

Marketing and Retail:

• Customer segmentation and targeted advertising based on predicted income groups.
• Product pricing strategies tailored to different income segments.
• Predicting customer lifetime value and optimizing marketing spend.
• Identifying potential high-value customers based on income potential.

Human Resources:

• Salary benchmarking and compensation planning based on industry standards and predicted income trends.
• Predicting employee turnover risk based on job satisfaction and income-related factors.
• Identifying and attracting top talent by offering competitive compensation packages.
• Optimizing talent management strategies based on income potential and skillsets.

Public Policy:

• Predicting tax revenue and allocating government resources based on income distribution.
• Identifying individuals in need of social assistance programs based on income poverty.
• Evaluating the effectiveness of government policies aimed at income inequality.
• Designing progressive taxation systems based on predicted income levels.

Challenges and Considerations:

• Data privacy and security concerns related to income information.
• Potential for biases and discrimination based on income prediction models.
• Explainability and transparency of income prediction algorithms.
• The impact of income prediction on individual behavior and choices.

This data set presents annual enrollment counts of Medicaid and CHIP beneficiaries by major eligibility group (children, adult expansion group, adult, aged, persons with disabilities, or COVID newly-eligible). There are three metrics presented: (1) the number of beneficiaries ever enrolled in each major eligibility group over the year (duplicated count); (2) the number of beneficiaries enrolled in each major eligibility group as of an individual’s last month of enrollment (unduplicated count); and (3) average monthly enrollment in each major eligibility group. These metrics are based on data in the T-MSIS Analytic Files (TAF). Some states have serious data quality issues, making the data unusable for calculating these measures. To assess data quality, analysts used measures featured in the DQ Atlas. Data for a state and year are considered unusable or of high concern based on DQ Atlas thresholds for the topic Eligibility Group Code. Please refer to the DQ Atlas at http://medicaid.gov/dq-atlas for more information about data quality assessment methods. Some cells have a value of “DS”. This indicates that data were suppressed for confidentiality reasons because the group included fewer than 11 beneficiaries.

Low-Income Program Eligibility Analytics Market Outlook

According to our latest research, the global Low-Income Program Eligibility Analytics market size reached USD 2.41 billion in 2024, reflecting the growing need for data-driven decision-making in public assistance programs. The market is expected to expand at a CAGR of 13.2% from 2025 to 2033, reaching a forecasted value of USD 7.32 billion by 2033. This robust growth is primarily driven by increasing governmental and non-profit initiatives to ensure targeted and efficient distribution of benefits, as well as the rising adoption of advanced analytics and automation technologies to streamline eligibility determination processes.

The rapid digital transformation across the public and private sectors is a significant growth factor for the Low-Income Program Eligibility Analytics market. As governments and organizations strive to maximize the impact of their limited resources, there is a pressing need for accurate, real-time data analysis to identify eligible beneficiaries and minimize errors or fraud. The integration of artificial intelligence (AI) and machine learning (ML) into eligibility analytics platforms enhances the precision and speed of eligibility assessments, reducing administrative burdens and improving the overall efficiency of program delivery. Additionally, the proliferation of big data and cloud computing has enabled organizations to aggregate and analyze vast datasets from disparate sources, further optimizing the eligibility determination process.

Another key driver fueling market expansion is the increasing complexity and diversity of low-income assistance programs globally. As social safety nets evolve to address multifaceted challenges such as healthcare access, affordable housing, education, and utility support, the demand for sophisticated analytics solutions continues to rise. These solutions not only ensure compliance with regulatory requirements but also help organizations adapt to changing policy landscapes and beneficiary needs. Moreover, the growing emphasis on transparency and accountability in public spending is prompting agencies to invest in robust analytics tools that provide auditable and data-backed eligibility decisions, thereby fostering public trust and program integrity.

The surge in collaborations between government agencies, non-profit organizations, and technology providers is also contributing to the market’s momentum. Joint efforts to develop interoperable, user-friendly analytics platforms are enabling stakeholders to share insights, standardize eligibility criteria, and reduce redundancies across programs. Furthermore, the COVID-19 pandemic underscored the importance of agile, data-driven eligibility systems as governments worldwide rushed to deploy emergency aid. This experience has accelerated digital adoption and highlighted the need for scalable analytics solutions capable of handling sudden surges in application volumes while maintaining accuracy and fairness.

From a regional perspective, the North American market currently leads in terms of adoption and innovation, driven by significant investments from the United States and Canada in modernizing public assistance infrastructure. However, Asia Pacific is emerging as a high-growth region, propelled by expanding government digitization initiatives and increasing focus on inclusive development. Europe remains a key market, with the European Union’s social welfare directives spurring demand for advanced eligibility analytics. Latin America and the Middle East & Africa are gradually catching up, leveraging analytics to address persistent poverty and improve access to essential services. Overall, the global Low-Income Program Eligibility Analytics market is poised for substantial growth as stakeholders recognize the transformative potential of data-driven eligibility determination.

Component Analysis

The Low-Income Program Eligibility Analytics market is segmented by component into Software and Services, each playing a pivotal role in the market’s overall development. The software segment encompasses a wide array of analytics platforms, data integration tools, and eligibility engines designed to automate and streamline the eligibility determination process. These solutions leverage advanced data analytics, AI, and ML algorithms to assess applicant data against program-specific criteria, ensuring accurate and timely decisions. The software segment is