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.

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...

A dataset listing the richest zip codes in New York per the most current US Census data, including information on rank and average income.

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This dataset contains data from California resident tax returns filed with California adjusted gross income and self-assessed tax listed by zip code. This dataset contains data for taxable years 1992 to the most recent tax year available.

A dataset listing the richest zip codes in Missouri per the most current US Census data, including information on rank and average income.

Dataset

This dataset was created by Hamish Gunasekara

Released under CC0: Public Domain

Contents

Taken from data.census.gov, this dataset includes median household income for each zip code in Washington state, broken down by race, age, and family size. You can find the table on the US Census website here: https://data.census.gov/cedsci/table?t=Income%20%28Households,%20Families,%20Individuals%29&g=0400000US53%248600000&tid=ACSST5Y2020.S1903

For overall median household income (not broken down further into subcategories) use the column "S1903_C03_001E".

A dataset listing the richest zip codes in South Carolina per the most current US Census data, including information on rank and average income.

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 map shows the average household income in the U.S. in 2022 in a multiscale map by country, state, county, ZIP Code, tract, and block group. Information for the average household income is an estimate of income for calendar year 2022. Income amounts are expressed in current dollars, including an adjustment for inflation or cost-of-living increases.The pop-up is configured to include the following information for each geography level:Average household incomeMedian household incomeCount of households by income groupAverage household income by householder age groupThe data shown is from Esri's 2022 Updated Demographic estimates using Census 2020 geographies. The map adds increasing level of detail as you zoom in, from state, to county, to ZIP Code, to tract, to block group data.Esri's U.S. Updated Demographic (2022/2027) Data: Population, age, income, sex, race, home value, and marital status are among the variables included in the database. Each year, Esri's Data Development team employs its proven methodologies to update more than 2,000 demographic variables for a variety of U.S. geographies.Additional Esri Resources:Esri DemographicsU.S. 2022/2027 Esri Updated DemographicsEssential demographic vocabularyThis item is for visualization purposes only and cannot be exported or used in analysis.Permitted use of this data is covered in the DATA section of the Esri Master Agreement (E204CW) and these supplemental terms.

Households within Davidson County (the greater metropolitan Nashville area) were grouped geographically according to ZIP code. The percentage of the metropolitan population residing within each ZIP code, and the median annual household income for each ZIP code was calculated based on data compiled by the U.S. Census Bureau (2006–2008 American Community Survey) for all ZIP codes within Davidson County.

Splitgraph serves as an HTTP API that lets you run SQL queries directly on this data to power Web applications. For example:

See the Splitgraph documentation for more information.

This feature layer provides Esri 2018 demographic estimates for popular variables including: 2018 Total Population, 2018 Household Population, 2018 Median Age, 2018 Median Household Income, 2018 Per Capita Income, 2018 Diversity Index and many more. Data is available from country, state, county, ZIP Code, tract, and block group level with adjustable scale visibility. It is intended as a sample feature service to demonstrate smart mapping capabilities with Esri's Demographic data. Example feature views and web maps built from this layer include:Predominant Generations in the United StatesUnemployment in the United StatesMedian Home Value and IncomePopulation Growth or Decline?For more information, visit the Updated Demographics documentation. For a full list of variables, click the Data tab. This data is featured on the Mapping page of www.esri.com

TIGER, TIGER/Line, and Census TIGER are registered trademarks of the Bureau of the Census. The Redistricting Census 2000 TIGER/Line files are an extract of selected geographic and cartographic information from the Census TIGER data base. The geographic coverage for a single TIGER/Line file is a county or statistical equivalent entity, with the coverage area based on January 1, 2000 legal boundaries. A complete set of Redistricting Census 2000 TIGER/Line files includes all counties and statistically equivalent entities in the United States and Puerto Rico. The Redistricting Census 2000 TIGER/Line files will not include files for the Island Areas. The Census TIGER data base represents a seamless national file with no overlaps or gaps between parts. However, each county-based TIGER/Line file is designed to stand alone as an independent data set or the files can be combined to cover the whole Nation. The Redistricting Census 2000 TIGER/Line files consist of line segments representing physical features and governmental and statistical boundaries. The Redistricting Census 2000 TIGER/Line files do NOT contain the ZIP Code Tabulation Areas (ZCTAs) and the address ranges are of approximately the same vintage as those appearing in the 1999 TIGER/Line files. That is, the Census Bureau is producing the Redistricting Census 2000 TIGER/Line files in advance of the computer processing that will ensure that the address ranges in the TIGER/Line files agree with the final Master Address File (MAF) used for tabulating Census 2000. The files contain information distributed over a series of record types for the spatial objects of a county. There are 17 record types, including the basic data record, the shape coordinate points, and geographic codes that can be used with appropriate software to prepare maps. Other geographic information contained in the files includes attributes such as feature identifiers/census feature class codes (CFCC) used to differentiate feature types, address ranges and ZIP Codes, codes for legal and statistical entities, latitude/longitude coordinates of linear and point features, landmark point features, area landmarks, key geographic features, and area boundaries. The Redistricting Census 2000 TIGER/Line data dictionary contains a complete list of all the fields in the 17 record types.

Median Household Income Report based on US Census and American Community Survey Data.

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.

IntroductionOur study explores how New York City (NYC) communities of various socioeconomic strata were uniquely impacted by the COVID-19 pandemic.MethodsNew York City ZIP codes were stratified into three bins by median income: high-income, middle-income, and low-income. Case, hospitalization, and death rates obtained from NYCHealth were compared for the period between March 2020 and April 2022.ResultsCOVID-19 transmission rates among high-income populations during off-peak waves were higher than transmission rates among low-income populations. Hospitalization rates among low-income populations were higher during off-peak waves despite a lower transmission rate. Death rates during both off-peak and peak waves were higher for low-income ZIP codes.DiscussionThis study presents evidence that while high-income areas had higher transmission rates during off-peak periods, low-income areas suffered greater adverse outcomes in terms of hospitalization and death rates. The importance of this study is that it focuses on the social inequalities that were amplified by the pandemic.

Graph and download economic data for Median Household Income in California (MEHOINUSCAA646N) from 1984 to 2024 about CA, households, median, income, and USA.

This dataset was developed by the Research & Analytics Group at the Atlanta Regional Commission using data from the U.S. Census Bureau.For a deep dive into the data model including every specific metric, see the Infrastructure Manifest. The manifest details ARC-defined naming conventions, field names/descriptions and topics, summary levels; source tables; notes and so forth for all metrics.Naming conventions:Prefixes: None Countp Percentr Ratem Mediana Mean (average)t Aggregate (total)ch Change in absolute terms (value in t2 - value in t1)pch Percent change ((value in t2 - value in t1) / value in t1)chp Change in percent (percent in t2 - percent in t1)s Significance flag for change: 1 = statistically significant with a 90% CI, 0 = not statistically significant, blank = cannot be computed Suffixes: _e19 Estimate from 2014-19 ACS_m19 Margin of Error from 2014-19 ACS_00_v19 Decennial 2000, re-estimated to 2019 geography_00_19 Change, 2000-19_e10_v19 2006-10 ACS, re-estimated to 2019 geography_m10_v19 Margin of Error from 2006-10 ACS, re-estimated to 2019 geography_e10_19 Change, 2010-19The user should note that American Community Survey data represent estimates derived from a surveyed sample of the population, which creates some level of uncertainty, as opposed to an exact measure of the entire population (the full census count is only conducted once every 10 years and does not cover as many detailed characteristics of the population). Therefore, any measure reported by ACS should not be taken as an exact number – this is why a corresponding margin of error (MOE) is also given for ACS measures. The size of the MOE relative to its corresponding estimate value provides an indication of confidence in the accuracy of each estimate. Each MOE is expressed in the same units as its corresponding measure; for example, if the estimate value is expressed as a number, then its MOE will also be a number; if the estimate value is expressed as a percent, then its MOE will also be a percent. The user should also note that for relatively small geographic areas, such as census tracts shown here, ACS only releases combined 5-year estimates, meaning these estimates represent rolling averages of survey results that were collected over a 5-year span (in this case 2015-2019). Therefore, these data do not represent any one specific point in time or even one specific year. For geographic areas with larger populations, 3-year and 1-year estimates are also available. For further explanation of ACS estimates and margin of error, visit Census ACS website.Source: U.S. Census Bureau, Atlanta Regional CommissionDate: 2015-2019Data License: Creative Commons Attribution 4.0 International (CC by 4.0)Link to the manifest: https://www.arcgis.com/sharing/rest/content/items/3d489c725bb24f52a987b302147c46ee/data