IRS US Income Data by Zip Code

Number of Returns, Adjusted Gross Income, Total Income, and Taxable Income

By Jon Loyens [source]

About this dataset

This dataset provides a unique insight into the US income patterns in 2013, by zip code. With this data, you can explore how taxes and adjusted gross income (AGI) vary according to geographic area. The data includes total and average incomes reported, number of returns filed in each ZIP code and taxable incomes reported. This dataset is ideal for studying how economic trends have shifted geographically over time or examining regional economic disparities within the US. In addition, this dataset has been cleansed from data removed from items such as ZIP codes with fewer than 100 returns or those identified as a single building or nonresidential ZIP codes that were categorized as “other” (99999) by the IRS. Finally, dollar amounts for all variables are in thousands for better accuracy

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Research Ideas

• Using this dataset to identify potential locations for commercial developments by maping taxable incomes, total income amounts, and average incomes in different zip codes.
• Comparing the number of returns with total income, taxes payable, and income variance between different zip codes to gain insights into areas with higher financial prosperity or disparities between zip codes due to wider economic trends.
• Analyzing average adjusted gross incomes on a state-by-state basis to identify states where high net worth citizens or individuals earning high wages live in order to target marketing campaigns or develop high-end service offerings

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

License: Dataset copyright by authors - You are free to: - Share - copy and redistribute the material in any medium or format for any purpose, even commercially. - Adapt - remix, transform, and build upon the material for any purpose, even commercially. - You must: - Give appropriate credit - Provide a link to the license, and indicate if changes were made. - ShareAlike - You must distribute your contributions under the same license as the original. - Keep intact - all notices that refer to this license, including copyright notices.

Columns

File: IRSIncomeByZipCode.csv | Column name | Description | |:------------------------------------------|:-------------------------------------------------------------------------------------| | STATE | The two-letter abbreviation for the state in which the zip code is located. (String) | | ZIPCODE | The five-digit US zip code. (Integer) | | Number of returns | The total number of tax returns filed in the zip code. (Integer) | | Adjusted gross income (AGI) | The total amount of adjusted gross income reported in the zip code. (Integer) | | Avg AGI | The average amount of adjusted gross income reported in the zip code. (Integer) | | Number of returns with total income | The total number of returns with total income reported in the zip code. (Integer) | | Total income amount | The total amount of income reported in the zip code. (Integer) | | Avg total income | The average amount of total income reported in the zip code. (Integer) | | Number of returns with taxable income | The total number of returns with taxable income reported in the zip code. (Integer) | | Taxable income amount | The total amount of taxable income reported in the zip code. (Integer) | | Avg taxable income | The average amount of taxable income reported in the zip code. (Integer) |

File: IRSIncomeByZipCode_NoStateTotalsNoSmallZips.csv | Column name | Description | |:------------------------------------------|:-------------------------------------------------------------------------------------| | STATE | The two-letter abb...

This annual study provides selected income and tax items classified by State, ZIP Code, and the size of adjusted gross income. These data include the number of returns, which approximates the number of households; the number of personal exemptions, which approximates the population; adjusted gross income; wages and salaries; dividends before exclusion; and interest received. Data are based who reported on U.S. Individual Income Tax Returns (Forms 1040) filed with the IRS. SOI collects these data as part of its Individual Income Tax Return (Form 1040) Statistics program, Data by Geographic Areas, ZIP Code Data.

ZIP Code data show selected income and tax items classified by State, ZIP Code, and size of adjusted gross income. Data are based on individual income tax returns filed with the IRS. The data include items, such as:

• Number of returns, which approximates the number of households
• Number of personal exemptions, which approximates the population
• Adjusted gross income
• Wages and salaries
• Dividends before exclusion
• Interest received

Content

For details of the exact fields available, please see the field_definitions.csv. Please note that the exact fields available can change from year to year, this definitions file was generated by retaining only the most recent year's entry from the years which had pdf manuals. The associated IRS form numbers are the most likely to change over time.

Acknowledgements

This data was generated by the Internal Revenue Service.

ZIP Code data show selected income and tax items classified by State, ZIP Code, and size of adjusted gross income. Data are based on individual income tax returns filed with the IRS and are available for Tax Years 1998, 2001, and 2004 through 2015. The data include items, such as:

• Number of returns, which approximates the number of households
• Number of personal exemptions, which approximates the population
• Adjusted gross income
• Wages and salaries
• Dividends before exclusion
• Interest received

Context

Learning how to create a data pipeline.

Content

https://www.irs.gov/pub/irs-soi/16zpdoc.doc

Individual Income Tax ZIP Code Data

ZIP Code data show selected income and tax items classified by State, ZIP Code, and size of adjusted gross income. Data are based on individual income tax returns filed with the IRS and are available for Tax Years 2016. The data include items, such as:

-Number of returns, which approximates the number of households -Number of personal exemptions, which approximates the population -Adjusted gross income -Wages and salaries -Dividends before exclusion -Interest received

Acknowledgements

https://www.irs.gov/statistics

This dataset combines annual files from 2005 to 2017 published by the IRS. ZIP Code data show selected income and tax items classified by State, ZIP Code, and size of adjusted gross income. Data are based on individual income tax returns filed with the IRS.

The data include items, such as:

• Number of returns, which approximates the number of households
• Number of personal exemptions, which approximates the population
• Adjusted gross income (AGI)
• Wages and salaries
• Dividends before exclusion
• Interest received

Enrichment and notes: - the original data sheets (a column per variable, a line per year, zipcode and AGI group) have been transposed to get a record per year, zipcode, AGI group and variable - the data for Wyoming in 2006 was removed because AGI classes were not correctly defined, making the resulting data unfit for analysis. - the AGI groups have seen their definitions change: the variable "AGI Class" was used until 2008, with various intervals of AGI; "AGI Stub" replaced it in 2009. We provided the literal intervals (eg. "$50,000 under $75,000") as "AGI Group" in each case to help the analysis. - the codes for each tax item have been joined with a dataset of variables to provide full names. - some tax items are available since 2005, others since more recent years, depending on their introduction date (available in the dataset of variables); as a consequence, the time range of the plots or graphs may vary. - the unit for amounts and AGIs is a thousand dollars.

License: https://www.usa.gov/copyrighted-government-works

This layer shows an approximation of the percent of households in the US might see a full refund check from the stimulus bill being passed to help deal with the COVID-19 economic impact. The criteria used for this map was:The amount of the credit allowed by subsection (a) (determined without regard to this subsection and subsection (e)) shall be reduced (but not below zero) by 5 percent of so much of the taxpayer’s adjusted gross income as exceeds—‘‘(1) $150,000 in the case of a joint return,‘‘(2) $112,500 in the case of a head of household, and‘‘(3) $75,000 in the case of a taxpayer not described in paragraph (1) or (2).‘‘(d) ELIGIBLE INDIVIDUAL.—For purposes of thissection, the term ‘eligible individual’ means any individualother than—‘‘(1) any nonresident alien individual,‘‘(2) any individual with respect to whom a deduction under section 151 is allowable to another taxpayer for a taxable year beginning in the calendar year in which the individual’s taxable year beginsThe data comes from the IRS SOI Tax Stats by ZIP Code from 2017 tax returns. The calculation for this map was calculated using the following aggregated income categories:$1 to under $25,000$25,000 to under $50,000$50,000 to under $75,000$75,000 to under $100,000$100,000 to under $200,000$200,000 or moreThese were used to estimate how many individuals who filed tax returns would warrant a full $1,200 from the stimulus bill. This was based on if a filer was single, joint, or head of household. Because some of the cutoffs for the bill fall within these categories, they were split to estimate refunds that fall within that range.

Graph and download economic data for Median Household Income in the United States (MEHOINUSA646N) from 1984 to 2024 about households, median, income, and USA.

This dataset contain information from IRS 990 series forms for 2020. Tax-exempt organizations, nonexempt charitable trusts, and section 527 political organizations file Form 990 to provide the IRS with the information required by section 6033.

Column description

• ein : Employer Identification Number
• business_name_control : Business name
• business_name_ln1 : Business name line 1
• business_name_ln2 : Business name line 2
• zip_code: ZIP code
• address : Address
• city : City
• state : State abbreviation code
• principal_officer : Name of Principal Officer
• gross_receipts : Gross receipts
• 501c3_org : Indicates a 501(c)(3) organization
• website : Website address
• website_ez : Website address in 990 EZ Form
• org_type_corporation : Type of organization - corporation
• org_type_corporation_ez : Type of organization - corporation in 990 EZ Form
• org_type_trust : Type of organization - trust
• org_type_trust_ez : Type of organization - trust in 990 EZ Form
• org_type_association : Type of organization - association
• org_type_association_ez : Type of organization - association in 990 EZ Form
• org_type_other : Type of organization - other
• org_type_other_ez : Type of organization - other in 990 EZ Form
• org_type_other_description : Type of organization - other, description
• formation_year : Formation year
• legal_domicile_state : Legal domicile state
• activity_or_mission_description : Activity or mission description
• num_voting_members_governing_body : Number of voting members of the governing body
• num_independent_voting_members : Number of independent voting members of the governing body
• total_num_employees : Total number of individuals employed in calendar year 2020
• total_num_volunteers : Total number of volunteers
• total_unrelated_business_income : Total unrelated business income
• net_unrelated_business_taxable_income : Net unrelated business taxable income
• contribution_grants_py : Contributions and grants (revenue) - prior year
• contribution_grants_cy : Contributions and grants (revenue) - current year
• program_service_revenue_py : Program service revenue - prior year
• program_service_revenue_cy : Program service revenue - current year
• investment_income_py : Investment Income Amount - prior year
• investment_income_cy : Investment Income Amount - current year
• other_revenue_py : Other Revenue Amount - prior year
• other_revenue_cy : Other Revenue Amount - current year
• total_revenue_py : Total Revenue Amount - prior year
• total_revenue_cy : Total Revenue Amount - current year
• grants_and_similar_amounts_paid_py : Grants and similar amounts - prior year
• grants_and_similar_amounts_paid_cy : Grants and similar amounts - current year
• benefits_paid_to_members_py : Benefits paid to members - prior year
• benefits_paid_to_members_cy : Benefits paid to members - current year
• salaries_compensations_emp_benefits_paid_py : Salaries, compensations, employee benefits paid prior year
• salaries_compensations_emp_benefits_paid_cy : Salaries, compensations, employee benefits paid current year
• total_professional_fundraising_expense_py : Total professional fundraising expense - prior year
• total_professional_fundraising_expense_cy : Total professional fundraising expense - current year
• total_fundrasing_expense_cy : Description: Total fundraising expense - current year
• other_expenses_py : Other expenses - prior year
• other_expenses_cy : Other expenses - current year
• total_expenses_py : Total Expenses - prior year
• total_expenses_cy : Total Expenses - current year
• revenue_less_expenses_py : Revenues less expenses - prior year
• revenue_less_expenses_cy : Revenues less expenses - current year
• total_assests_boy : Total Assets, Beginning of the year amount
• total_assests_eoy : Total Assets, End of the year amount
• total_liabilities_boy : Total Liabilities, Beginning of the year amount
• total_liabilities_eoy : Total Liabilities, End of the year amount
• net_assets_fund_balances_boy : Net Assets or Fund Balances, Beginning of the year amount
• net_assets_fund_balances_eoy : Net Assets or Fund Balances, End of the year amount
• mission_description : Mission Description
• significant_new_program_services : Did the organization undertake any significant program services during the year which were not listed on the prior Form 990 or 990-EZ?
• significant_change_program_services : Did the organization cease conducting, or make significant changes in how it conducts, any program services?
• program_expense_amount : Program Expenses Amount
• program_grant_amount : Program Grants Amount
• program_revenue_amount : Program Revenue Amount
• other_program_services_description : Other program services description
• total_program_service_expenses : ...

Exempt organization information is extracted monthly from the Internal Revenue Service’s Business Master File. This is a cumulative file, and the data are the most recent information the IRS has for these organizations. If you have any questions about the tax-exempt organizations or the content of the files, please contact TE/GE Customer Account Services toll-free line at 1-877-829-5500 State is determined from the filing address and generally represent the location of an organization’s headquarters, which may or may not represent the state(s) in which an organization has operations. Records are sorted by Employer Identification Number (EIN). This dataset is for Connecticut only. The IRS exempt organization data have been accumulated since the inception of the tax-exempt statutes. A determination letter is issued to an organization upon the granting of an exemption and is considered valid throughout the life of the organization, as long as the organization complies with the provisions of its exemption. If an organization's exemption is revoked, an announcement to inform potential donors of the revocation is published in the Internal Revenue Bulletin. In addition, the organization’s name is removed from publicly accessible venues, including this file. Updated nightly. NOTE: Split-interest trusts are not included in this database. FIELDS AVAILABLE All exempt organization records on this file will contain the following data fields: Column Name Contents EIN Employer Identification Number (EIN) NAME Primary Name of Organization ICO In Care of Name STREET Street Address CITY City STATE State ZIP Zip Code GROUP Group Exemption Number SUBSECTION Subsection Code AFFILIATION Affiliation Code CLASSIFICATION Classification Code(s) RULING Ruling Date DEDUCTIBILITY Deductibility Code FOUNDATION Foundation Code ACTIVITY Activity Codes ORGANIZATION Organization Code STATUS Exempt Organization Status Code TAX_PERIOD Tax Period ASSET_CD Asset Code INCOME_CD Income Code FILING_REQ_CD Filing Requirement Code PF_FILING_REQ_CD PF Filing Requirement Code ACCT_PD Accounting Period ASSET_AMT Asset Amount INCOME_AMT Income Amount (includes negative sign if amount is negative) REVENUE_AMT Form 990 Revenue Amount (includes negative sign if amount is negative) NTEE_CD National Taxonomy of Exempt Entities (NTEE) Code SORT_NAME Sort Name (Secondary Name Line)

The Los Angeles Index of Neighborhood Change is a tool that allows users to explore the extent to which Los Angeles Zip Codes have undergone demographic change from 2000 to 2014. Created in 2015/2016, the data comes from 2000, 2005, 2013, and 2014. Please read details about each measure for exact years.Index scores are an aggregate of six demographic measures indicative of gentrification. The measures are standardized and combined using weights that reflect the proportion of each measure that is statistically significant.Measure 1: Percent change in low/high IRS filer ratio. For the purposes of this measure, High Income = >$75K Adjust Gross Income tax filer and Low Income = <$25k filers who also received an earned income tax credit. Years Compared for Measure 1: 2005 and 2013 | Source: IRS Income Tax Return DataMeasure 2: Change in percent of residents 25 years or older with Bachelor's Degrees or HigherMeasure 3: Change in percent of White, non-Hispanic/Latino residentsMeasure 4: Percent change in median household income (2000 income is adjusted to 2014 dollars)Measure 5: % Change in median gross rent (2000 rent is adjusted to 2013/2014 dollars)Measure 6: Percent change in average household size Year Compared for Measures 2-5: 2000 and 2014, Measure 6: 2013Sources: Decennial Census, 2000 | American Community Survey (5-Year Estimate, 2009-2013; 2010; 2014)Date Updated: December 13, 2016Refresh Rate: Never - Historical data

Context

This dataset comes from "The Exempt Organization Business Master File Extract" (EO BMF) which includes cumulative information on tax-exempt organizations.

Content

Data is current up to: 8/14/2017

• EIN: Employer Identification Number (EIN)
• NAME: Primary Name of Organization
• ICO: In Care of Name
• STREET: Street Address
• CITY: City
• STATE: State
• ZIP: Zip Code
• GROUP: Group Exemption Number
• SUBSECTION: Subsection Code
• AFFILIATION: Affiliation Code
• CLASSIFICATION: Classification Code(s)
• RULING: Ruling Date
• DEDUCTIBILITY: Deductibility Code
• FOUNDATION: Foundation Code
• ACTIVITY: Activity Codes
• ORGANIZATION: Organization Code
• STATUS: Exempt Organization Status Code
• TAX_PERIOD: Tax Period
• ASSET_CD: Asset Code
• INCOME_CD: Income Code
• FILING_REQ_CD: Filing Requirement Code
• PF_FILING_REQ_CD: PF Filing Requirement Code
• ACCT_PD: Accounting Period
• ASSET_AMT: Asset Amount
• INCOME_AMT: Income Amount (includes negative sign if amount is negative)
• REVENUE_AMT: Form 990 Revenue Amount (includes negative sign if amount is negative)
• NTEE_CD: National Taxonomy of Exempt Entities (NTEE) Code
• SORT_NAME: Sort Name (Secondary Name Line)

There are six data files separated by regions

eo1:

• CT
• MA
• ME
• NH
• NJ
• NY
• RI
• VT

eo2:

• DC
• DE
• IA
• IL
• IN
• KY
• MD
• MI
• MN
• NC
• ND
• NE
• OH
• PA
• SC
• SD
• VA
• WI
• WV

eo3:

• AK
• AL
• AR
• AZ
• CA
• CO
• FL
• GA
• HI
• ID
• KS
• LA
• MO
• MS
• MT
• NM
• NV
• OK
• OR
• TN
• TX
• UT
• WA
• WY

eo4:

• AA
• AE
• AP
• AS
• FM
• GU
• MH
• MP
• PR
• PW
• VI

eo_pr:

• Puerto Rico

eo_xx:

Various international non-profits (too many countries to list). See columns 5 and 6.

Acknowledgements

More information and updated data an be found here

Created by the Tax Reform Act of 1986, the Low-Income Housing Tax Credit program (LIHTC) gives State and local LIHTC-allocating agencies the equivalent of nearly $8 billion in annual budget authority to issue tax credits for the acquisition, rehabilitation, or new construction of rental housing targeted to lower-income households. Although some data about the program have been made available by various sources, HUD's database is the only complete national source of information on the size, unit mix, and location of individual projects. With the continued support of the national LIHTC database, HUD hopes to enable researchers to learn more about the effects of the tax credit program.HUD has no administrative authority over the LIHTC program. IRS has authority at the federal level and it is structured so that the states truly administer the program. The LIHTC property locations depicted in this map service represent the general location of the property. The locations of individual buildings associated with each property are not depicted here. The location of the property is derived from the address of the building with the most units. Location data for HUD-related properties and facilities are derived from HUD's enterprise geocoding service. While not all addresses are able to be geocoded and mapped to 100% accuracy, we are continuously working to improve address data quality and enhance coverage. Please consider this issue when using any datasets provided by HUD. When using this data, take note of the field titled “LVL2KX” which indicates the overall accuracy of the geocoded address using the following return codes:‘R’ - Interpolated rooftop (high degree of accuracy, symbolized as green)‘4’ - ZIP+4 centroid (high degree of accuracy, symbolized as green)‘B’ - Block group centroid (medium degree of accuracy, symbolized as yellow)‘T’ - Census tract centroid (low degree of accuracy, symbolized as red)‘2’ - ZIP+2 centroid (low degree of accuracy, symbolized as red)‘Z’ - ZIP5 centroid (low degree of accuracy, symbolized as red)‘5’ - ZIP5 centroid (same as above, low degree of accuracy, symbolized as red)Null - Could not be geocoded (does not appear on the map)For the purposes of displaying the location of an address on a map only use addresses and their associated lat/long coordinates where the LVL2KX field is coded ‘R’ or ‘4’. These codes ensure that the address is displayed on the correct street segment and in the correct census block.The remaining LVL2KX codes provide a cascading indication of the most granular level geography for which an address can be confirmed. For example, if an address cannot be accurately interpolated to a rooftop (‘R’), or ZIP+4 centroid (‘4’), then the address will be mapped to the centroid of the next nearest confirmed geography: block group, tract, and so on. When performing any point-in polygon analysis it is important to note that points mapped to the centroids of larger geographies will be less likely to map accurately to the smaller geographies of the same area. For instance, a point coded as ‘5’ in the correct ZIP Code will be less likely to map to the correct block group or census tract for that address. To learn more about the Low-Income Housing Tax Credit Program visit: https://www.hud.gov/program_offices/public_indian_housing/programs/ph/, for questions about the spatial attribution of this dataset, please reach out to us at GISHelpdesk@hud.gov. Data Dictionary: DD_Low Income Tax Credit Program

Finclusion_Programs.zip is a replication package that creates and analyzes pseudo-microdata with the same variables as the actual tax microdata used in "Financial Inclusion Across the United States" by Yogo, Whitten, and Cox (Journal of Financial Economics, 2025).

The actual tax microdata are confidential and may not be shared. To gain access, researchers can apply to the Joint Statistical Research Program of the Internal Revenue Service. https://www.irs.gov/statistics/soi-tax-stats-joint-statistical-research-program

Yogo et al. ran their code using Stata-MP 18.0.

_master.do runs all of the code in the proper sequence. Simply unzip the archive and type "do _master" in Stata to run all of the code. _master.do runs the following do files: data1.do creates the pseudo-microdata. summary2.do creates Table A.1. summary3.do creates Tables 1-4 and A.2. regression1.do creates Tables 5-6. regression4.do creates Tables 7, 9, and C.1-C.3.

In addition, Yogo et al. provide aggregate datasets, based on the actual tax microdata, to facilitate future research. These datasets are in Finclusion_Excel.zip (Excel format) and Finclusion_Stata.zip (Stata format). Each zip file contains Data_incm is aggregated by year and income quintile. Data_ZCTA is aggregated by year and ZCTA. Data_ZCTA_incm is aggregated by year, ZCTA, and income quintile. Used to create Figures 1-2. See Appendix A.3 for a detailed description of these data.

There is ongoing debate about whether the relationship between income and pro-social behaviour depends on economic inequality. Studies investigating this question differ in their conclusions but are consistent in measuring inequality at aggregated geographic levels (i.e. at the state, region, or country-level). I hypothesise that local, more immediate manifestations of inequality are important for driving pro-social behaviour, and test the interaction between income and inequality at a much finer geographical resolution than previous studies. I first analyse the charitable giving of US households using ZIP-code level measures of inequality and data on tax deductible charitable donations reported to the IRS. I then examine whether the results generalise using a large-scale UK household survey and neighbourhood-level inequality measures. In both samples I find robust evidence of a significant interaction effect, albeit in the opposite direction as that which has been previously postulated–higher income individuals behave more pro-socially rather than less when local inequality is high.

Created by the Tax Reform Act of 1986, the Low-Income Housing Tax Credit program (LIHTC) gives State and local LIHTC-allocating agencies the equivalent of nearly $8 billion in annual budget authority to issue tax credits for the acquisition, rehabilitation, or new construction of rental housing targeted to lower-income households. Although some data about the program have been made available by various sources, HUD's database is the only complete national source of information on the size, unit mix, and location of individual projects. With the continued support of the national LIHTC database, HUD hopes to enable researchers to learn more about the effects of the tax credit program.HUD has no administrative authority over the LIHTC program. IRS has authority at the federal level and it is structured so that the states truly administer the program. The LIHTC property locations depicted in this map service represent the general location of the property. The locations of individual buildings associated with each property are not depicted here. The location of the property is derived from the address of the building with the most units. Location data for HUD-related properties and facilities are derived from HUD's enterprise geocoding service. While not all addresses are able to be geocoded and mapped to 100% accuracy, we are continuously working to improve address data quality and enhance coverage. Please consider this issue when using any datasets provided by HUD. When using this data, take note of the field titled “LVL2KX” which indicates the overall accuracy of the geocoded address using the following return codes:‘R’ - Interpolated rooftop (high degree of accuracy, symbolized as green)‘4’ - ZIP+4 centroid (high degree of accuracy, symbolized as green)‘B’ - Block group centroid (medium degree of accuracy, symbolized as yellow)‘T’ - Census tract centroid (low degree of accuracy, symbolized as red)‘2’ - ZIP+2 centroid (low degree of accuracy, symbolized as red)‘Z’ - ZIP5 centroid (low degree of accuracy, symbolized as red)‘5’ - ZIP5 centroid (same as above, low degree of accuracy, symbolized as red)Null - Could not be geocoded (does not appear on the map)For the purposes of displaying the location of an address on a map only use addresses and their associated lat/long coordinates where the LVL2KX field is coded ‘R’ or ‘4’. These codes ensure that the address is displayed on the correct street segment and in the correct census block.The remaining LVL2KX codes provide a cascading indication of the most granular level geography for which an address can be confirmed. For example, if an address cannot be accurately interpolated to a rooftop (‘R’), or ZIP+4 centroid (‘4’), then the address will be mapped to the centroid of the next nearest confirmed geography: block group, tract, and so on. When performing any point-in polygon analysis it is important to note that points mapped to the centroids of larger geographies will be less likely to map accurately to the smaller geographies of the same area. For instance, a point coded as ‘5’ in the correct ZIP Code will be less likely to map to the correct block group or census tract for that address.

Voting precincts are the most granular spatial units for reporting election outcomes, whereas census geographies, such as block groups, census tracts, and ZIP Code Tabulation Areas (ZCTAs), are commonly used for publishing demographic, economic, health, and environmental data. This dataset bridges the two by reallocating precinct-level votes to standard census geographies through a systematic and replicable framework. The reallocation assumes that votes within each precinct are distributed proportionally to the household population. Household population counts from census block groups—the smallest census unit with regularly updated population estimates—are used to allocate votes to fractions created by the intersection of precinct and census boundaries. This process is implemented using three allocation strategies: areal weighting, impervious surface weighting, and Regionalized Land Cover Regression (RLCR). Results from all three methods are provided. Among these, the RLCR method demonstrates the highest accuracy based on validation against voter-level ground truth data and is recommended as the primary version for analysis. The alternative methods may serve as robustness checks or sensitivity tests. The dataset currently includes the 2016 and 2020 U.S. general elections and is designed for seamless integration with other datasets, such as the American Community Survey (ACS), CDC PLACES, or IRS Statistics of Income (SOI), via the GEOID field.

Release Date: 2018-06-21.Table Name All Sectors: Nonemployer Statistics by Legal Form of Organization and Receipts Size Class for the U.S., States, and Selected Geographies: 2016 Release Schedule The data in this file were released on June 21, 2018. Key Table Information Beginning with reference year 2005, Nonemployer data are released using the Noise Infusion methodology to protect confidentiality. See Survey Methodology for complete information on the coverage and methodology of the Nonemployer Statistics data series. Universe The universe of this file is all firms with no paid employees or payroll with receipts of $1,000 or more (or $1 for the construction sector) and are subject to federal income tax. The universe is limited to industries in approximately 450 of the nearly 1,200 recognized North American Industry Classification System industries. The universe contains only those codes that are available through administrative records sources and are common to all three legal forms of organization applicable to nonemployer businesses. This is generally a broader level of detail than would typically be provided for employer data. For specific exclusions and inclusions, see Survey Methodology. Geographic Coverage The data are shown at the U.S. and State level for LFO and the U.S. level for Receipt Size Class. All other data is shown at the U.S., State, County, Combined Statistical Area, and Metropolitan/Micropolitan Statistical Area levels. Industry Coverage The data are shown at the 2- through 6-digit NAICS code levels for all sectors with published data. Data Items and Other Identifying Records This file contains data on the total number of firms and receipts. Sort Order Data are presented in ascending geography by NAICS code sequence then by Legal Form of Organization. FTP Download Download the entire table at http://www2.census.gov/programs-surveys/nonemployer-statistics/data/2016/NS1600NONEMP.zip. Contact Information U.S. Census Bureau Economy-Wide Statistics Division Business Statistics Branch Tel: (301)763-2580 Email: ewd.nonemployer.statistics@census.gov .NOTE: Nonemployer Statistics originate from tax return information of the Internal Revenue Service. The data are subject to nonsampling error such as errors of self-classification by industry on tax forms, as well as errors of response, nonreporting and coverage. Values provided by each firm are slightly modified to protect the respondent's confidentiality. For further information about methodology and data limitations, see Survey Methodology..Symbols:D - Withheld to avoid disclosing data for individual companies; data are included in higher level totalsN - Not available or not comparableG - Low Noise; cell value was changed by less than 2 percent by the application of noiseH - Moderate Noise; cell value was changed by 2 percent of more but less than 5 percent by the application of noiseJ - High Noise; cell value was changed by 5 percent or more by the application of noiseFor a complete list, see the Nonemployer Glossary.Source: U.S. Census Bureau, 2016 Nonemployer Statistics.

Release Date: 2017-05-25.Table Name All Sectors: Nonemployer Statistics by Legal Form of Organization and Receipts Size Class for the U.S., States, and Selected Geographies: 2015 Release Schedule The data in this file were released on May 25, 2017. Key Table Information Beginning with reference year 2005, Nonemployer data are released using the Noise Infusion methodology to protect confidentiality. See Survey Methodology for complete information on the coverage and methodology of the Nonemployer Statistics data series. Universe The universe of this file is all firms with no paid employees or payroll with receipts of $1,000 or more (or $1 for the construction sector) and are subject to federal income tax. The universe is limited to industries in approximately 300 of the nearly 1,200 recognized North American Industry Classification System industries. The universe contains only those codes that are available through administrative records sources and are common to all three legal forms of organization applicable to nonemployer businesses. This is generally a broader level of detail than would typically be provided for employer data. For specific exclusions and inclusions, see Survey Methodology. Geographic Coverage The data are shown at the U.S. and State level for LFO and the U.S. level for Receipt Size Class. All other data is shown at the U.S., State, and County levels. Industry Coverage The data are shown at the 2- through 6-digit NAICS code levels for all sectors with published data. Data Items and Other Identifying Records This file contains data on the total number of firms and receipts. Sort Order Data are presented in ascending geography by NAICS code sequence then by Legal Form of Organization. FTP Download Download the entire table at https://www2.census.gov/programs-surveys/nonemployer-statistics/data/2015/NS1500NONEMP.zip. Contact Information U.S. Census Bureau Economy-Wide Statistics Division Tel: (301)763-2580 Email: ewd.nonemployer.statistics@census.gov .NOTE: Nonemployer Statistics originate from tax return information of the Internal Revenue Service. The data are subject to nonsampling error such as errors of self-classification by industry on tax forms, as well as errors of response, nonreporting and coverage. Values provided by each firm are slightly modified to protect the respondent's confidentiality. For further information about methodology and data limitations, see Survey Methodology..Symbols:D - Withheld to avoid disclosing data for individual companies; data are included in higher level totalsN - Not available or not comparableG - Low Noise; cell value was changed by less than 2 percent by the application of noiseH - Moderate Noise; cell value was changed by 2 percent of more but less than 5 percent by the application of noiseJ - High Noise; cell value was changed by 5 percent or more by the application of noiseFor a complete list, see the Nonemployer Glossary.Source: U.S. Census Bureau, 2015 Nonemployer Statistics.

Release Date: 2016-05-24.Table Name All Sectors: Nonemployer Statistics by Legal Form of Organization and Receipts Size Class for the U.S., States, and Selected Geographies: 2014 Release Schedule The data in this file were released on May 24, 2016. Key Table Information Beginning with reference year 2005, Nonemployer data are released using the Noise Infusion methodology to protect confidentiality. See Survey Methodology for complete information on the coverage and methodology of the Nonemployer Statistics data series. Universe The universe of this file is all firms with no paid employees or payroll with receipts of $1,000 or more (or $1 for the construction sector) and are subject to federal income tax. The universe is limited to industries in approximately 450 of the 1,065 recognized North American Industry Classification System industries. The universe contains only those codes that are available through administrative records sources and are common to all four legal forms of organization applicable to nonemployer businesses. This is generally a broader level of detail than would typically be provided for employer data. For specific exclusions and inclusions, see Survey Methodology. Geographic Coverage The data are shown at the U.S. and State level for LFO and the U.S. level for Receipt Size Class. All other data is shown at the U.S., State, and County levels. Industry Coverage The data are shown at the 2- through 6-digit NAICS code levels for all sectors with published data. Data Items and Other Identifying Records This file contains data on the total number of firms and receipts. Sort Order Data are presented in ascending geography by NAICS code sequence then by Legal Form of Organization. FTP Download Download the entire table at https://www2.census.gov/programs-surveys/nonemployer-statistics/data/2014/NS1400NONEMP.zip. Contact Information U.S. Census Bureau Economy-Wide Statistics Division Tel: (301)763-2580 Email: ewd.nonemployer.statistics@census.gov .NOTE: Nonemployer Statistics originate from tax return information of the Internal Revenue Service. The data are subject to nonsampling error such as errors of self-classification by industry on tax forms, as well as errors of response, nonreporting and coverage. Values provided by each firm are slightly modified to protect the respondent's confidentiality. For further information about methodology and data limitations, see Survey Methodology..Symbols:D - Withheld to avoid disclosing data for individual companies; data are included in higher level totalsN - Not available or not comparableG - Low Noise; cell value was changed by less than 2 percent by the application of noiseH - Moderate Noise; cell value was changed by 2 percent of more but less than 5 percent by the application of noiseFor a complete list, see the Nonemployer Glossary.Source: U.S. Census Bureau, 2014 Nonemployer Statistics.