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

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

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

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...

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.

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 the United States (MEHOINUSA646N) from 1984 to 2024 about households, median, income, and USA.

Demographics, population, housing, income, education, schools, and geography for ZIP Code 27260 (High Point, NC). Interactive charts load automatically as you scroll for improved performance.

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

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

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F1842206%2F4408fd0c0561e4a48a03776b784ed650%2Fzip2.jpeg?generation=1728526740859651&alt=media" alt="">

US Zip Codes Database We're proud to offer a simple, accurate and up-to-date database of US Zip Codes. It's been built from the ground up using authoritative sources including the U.S. Postal Service™, U.S. Census Bureau, National Weather Service, American Community Survey, and the IRS. - Up-to-date: Data updated as of October 8, 2024. Includes data from the most recent American Community Survey (2022)! - Comprehensive: 41,618 unique zip codes including ZCTA, unique, military, and PO box zips. - Useful fields: From latitude and longitude to household income. - Accurate: Aggregated from official sources and precisely geocoded to latitude and longitude. - Simple: A single CSV file, concise field names, only one entry per zip code.

From https://simplemaps.com/data/us-zips

Image

Generated with Bing Image Generator

Note

I just downloaded and uploaded it here. All credits to https://simplemaps.com/data/us-zips

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.

Significant high-rate spatial clusters of diabetes-related hospitalizations at the ZIP code tabulation area level in Florida, 2016–2019.

This is the subset of parcels that meet the FIRST TWO of the “specified criteria” in the King County Code 26.12.003J definition of “Opportunity Areas.” Areas within King County that: (a) “are located in a census tract in which the median household income is in the lowest one-third for median household income for census tracts in King County;” (b) “are located in a ZIP code in which hospitalization rates for asthma, diabetes, and heart disease are in the highest one-third for ZIP Codes in King County.”

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.

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.

Zip Code; Median household income; Unemployed (ages GE 16); Families below 185% FPL; Children (ages 0-17) below 185% FPL; Children (ages 3-4) enrolled in preschool or nursery school; Less than high school; High school graduate; Some college or associates degree; College graduate or higher; High school graduate or less. Percentages unless otherwise noted. Source information provided at: https://www.sccgov.org/sites/phd/hi/hd/Documents/City%20Profiles/Methodology/Neighborhood%20profile%20methodology_082914%20final%20for%20web.pdf

Small business transactions and revenue data aggregated from several credit card processors, collected by Womply and compiled by Opportunity Insights. Transactions and revenue are reported based on the ZIP code where the business is located.

Data provided for CT (FIPS code 9), MA (25), NJ (34), NY (36), and RI (44).

Data notes from Opportunity Insights: Seasonally adjusted change since January 2020. Data is indexed in 2019 and 2020 as the change relative to the January index period. We then seasonally adjust by dividing year-over-year, which represents the difference between the change since January observed in 2020 compared to the change since January observed since 2019. We account for differences in the dates of federal holidays between 2019 and 2020 by shifting the 2019 reference data to align the holidays before performing the year-over-year division.

Small businesses are defined as those with annual revenue below the Small Business Administration’s thresholds. Thresholds vary by 6 digit NAICS code ranging from a maximum number of employees between 100 to 1500 to be considered a small business depending on the industry.

County-level and metro-level data and breakdowns by High/Middle/Low income ZIP codes have been temporarily removed since the August 21st 2020 update due to revisions in the structure of the raw data we receive. We hope to add them back to the OI Economic Tracker soon.

More detailed documentation on Opportunity Insights data can be found here: https://github.com/OpportunityInsights/EconomicTracker/blob/main/docs/oi_tracker_data_documentation.pdf

This repository contains data for a data science class exercise.Students: This exercise is about income mobility over three generations: grandparents (g1), parents (g2), and children (g3). Your task is to predict log income in generation 3 using data on log incomes in generations 1 and 2. Additional predictors available include education in each generation, race as reported by the grandparent (g1), and sex of the respondent in g3.The data you will use are in for_students.zip.learning.csv contains 1,365 observations for which the outcome g3_log_income is recordedholdout_public.csv contains 1,365 observations for which the outcome g3_log_income is NAYour task is to build a predictive model using learning.csv. Then, make predictions for the cases in holdout_public.csv.Here are some details about the variables in the data. All cases are from the cross-sectional Survey Research Sample of the PSID. In each generation, we took each respondent's annual income over several surveys from age 30 to 45, adjusted to 2022 dollars, and took the average. We truncated the data to the range from $5,000 to $448,501.10, where the bottom code is arbitrary and the top code is what we believe to be the lowest PSID top code over the series (in 1978), converted to 2022 dollars. Education is the first report at ages 30-45, coded as less than high school, high school, some college, or 4+ years of college. We merged the data together across generations using the PSID Family Identification Mapping System 3-generation prospective linkage file. See for_replication.zip for code to produce these data as well as a log file noting sample restrictions.We are trusting the students to not open the instructor data, which contains the outcomes you are trying to predict. You could peek of course, but that would be no fun! We are trusting you not to peek.Instructors: The file for_instructors.zip contains the true holdout outcomes in holdout_private.csv. You can use these to evaluate students' predictive performance (as long as you trust that they have not peeked).For those replicating: The file for_replication.zip contains the directory structure and code that produced this exercise from raw files downloaded from the PSID.