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

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 dataset is part of the Geographical repository maintained by Opendatasoft. 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.Contains most USPS zip codes (lat/long).

• Context I am greatly inspired with this dataset containing geo spatial details for each zip code and contains the total wages for each area.This gave me opportunity to create a data visualisation in Tableau using HexBin chart which is added as a Kernel to this dataset.

• Content

  • About the data: 81,831 rows of data. All 41,891 active zipcodes + 634 decommisioned zipcodes from the recent past. All 80673 active Primary(41885), Acceptable(13988), and Not Acceptable(24800) placenames. Some additonal placenames for decommisioned codes. 29,971 Standard, 9465 PO BOX, 2437 Unique, and 649 Military codes.

• 50 States + 361 AA Military

• Americas 38 AE Military

• Europe 164 AP Military

• Pacific 1 AS American Samoa 290 DC Washinton DC 4 FM Federated States Micronesia 13 GU Guam 2 MH Marshall Islands 3 MP Northern Mariana Islands 176 PR Puerto Rico 2 PW Palau 16 VI Virgin Islands

• Name Type Description

• Zipcode Text 5 digit Zipcode or military postal code(FPO/APO)

• ZipCodeType Text Standard, PO BOX Only, Unique, Military(implies APO or FPO)

• City Text USPS offical city name(s)

• State Text USPS offical state, territory, or quasi-state (AA, AE, AP) abbreviation code

• LocationType Text Primary, Acceptable,Not Acceptable

• Lat Double Decimal Latitude, if available

• Long Double Decimal Longitude, if available

• Location Text Standard Display (eg Phoenix, AZ ; Pago Pago, AS ; Melbourne, AU )

• Decommissioned Text If Primary location, Yes implies historical Zipcode, No Implies current Zipcode; If not Primary, Yes implies Historical Placename

• TaxReturnsFiled Long Integer Number of Individual Tax Returns Filed in 2008

• EstimatedPopulation Long Integer Tax returns filed + Married filing jointly + Dependents

• TotalWages Long Integer Total of Wages Salaries and Tips

  • Source: http://federalgovernmentzipcodes.us/

  • Current zipcodes, placenames, zipcode type(Standard, PO, Unique, Military), placename type (Primary, Acceptable, Not Acceptable)

• : USPS Military place names (base or ship name)

• : MPSA 2008 Election Ballot information Tax returns filed, estimated population, total wages: IRS 2008 Latitude and Longitude; National Weather Service supplemented by Google Earth and Maps and occasionally other sources Decommissioned zip codes, Our old database--usually quality sources, but not verifiable.

• Acknowledgements

  Other Sources of zipcode information:

• Placenames (Cities, towns, geographic features) can be found at US Geological Survey GNIS Dataset The IRS has additional data fields for 2008 and is reviewing their publication procedures for later years.

  see http://www.irs.gov/taxstats/indtaxstats/article/0,,id=96947,00.html

• The Census publishes data, but they use Zipcode Tabulation Areas (ZCTAs) which

• 1) have changed areas between the 2000 census and the 2010 census

• 2) do not map well to USPS zipcodes well. If needed http://www.census.gov/geo/ZCTA/zcta.html Social Security recipients by zipcode http://www.ssa.gov/policy/docs/statcomps/oasdi_zip/ For economic researchers and those who want tons of background on data sources by zipcode, University of Missouri OSEDA project

  • Free Zipcode Database (8.7 MB) Updated 1/22/2012 All Locations (Multiple locations for some zipcodes)

  Inspiration I am hoping that people in the right government agencies/authorities would be able to utilize this data to focus on

  community developments where it needs immediate attention.

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)

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.

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 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 : ...

Comprehensive Federal Tax Lien Data by CompCurve Unlock unparalleled insights into tax lien records with CompCurve Federal Tax Lien Data, a robust dataset sourced directly from IRS records. This dataset is meticulously curated to provide detailed information on federal tax liens, unsecured liens, and tax-delinquent properties across the United States. Whether you're a real estate investor, financial analyst, legal professional, or data scientist, this dataset offers a treasure trove of actionable data to fuel your research, decision-making, and business strategies. Available in flexible formats like .json, .csv, and .xls, it’s designed for seamless integration via bulk downloads or API access, ensuring you can harness its power in the way that suits you best.

IRS Tax Lien Data: Unsecured Liens in Focus At the heart of this offering is the IRS Tax Lien Data, capturing critical details about unsecured federal tax liens. Each record includes key fields such as taxpayer full name, taxpayer address (broken down into street number, street name, city, state, and ZIP), tax type (e.g., payroll taxes under Form 941), unpaid balance, date of assessment, and last day for refiling. Additional fields like serial number, document ID, and lien unit phone provide further granularity, making this dataset a goldmine for tracking tax liabilities. With a history spanning 5 years, this data offers a longitudinal view of tax lien trends, enabling users to identify patterns, assess risk, and uncover opportunities in the tax lien market.

Detailed Field Breakdown for Precision Analysis The Federal Tax Lien Data is structured with precision in mind. Every record includes a document_id (e.g., 2025200700126004) as a unique identifier, alongside the IRS-assigned serial_number (e.g., 510034325). Taxpayer details are comprehensive, featuring full name (e.g., CASTLE HILL DRUGS INC), and, where applicable, parsed components like first name, middle name, last name, and suffix. Address fields are equally detailed, with street number, street name, unit, city, state, ZIP, and ZIP+4 providing pinpoint location accuracy. Financial fields such as unpaid balance (e.g., $15,704.43) and tax period ending (e.g., 09/30/2024) offer a clear picture of tax debt, while place of filing and prepared_at_location tie the data to specific jurisdictions and IRS offices.

National Coverage and Historical Depth Spanning the entire United States, this dataset ensures national coverage, making it an essential resource for anyone needing a coast-to-coast perspective on federal tax liens. With 5 years of historical data, users can delve into past tax lien activity, track refiling deadlines (e.g., 01/08/2035), and analyze how tax debts evolve over time. This historical depth is ideal for longitudinal studies, predictive modeling, or identifying chronic tax delinquents—key use cases for real estate professionals, lien investors, and compliance experts.

Expanded Offerings: Secured Real Property Tax Liens Beyond unsecured IRS liens, CompCurve enhances its portfolio with the Real Property Tax Lien File, focusing on secured liens tied to real estate. This dataset includes detailed records of property tax liens, featuring fields like tax year, lien year, lien number, sale date, interest rate, and total due. Property-specific data such as property address, APN (Assessor’s Parcel Number), FIPS code, and property type ties liens directly to physical assets. Ownership details—including owner first name, last name, mailing address, and owner-occupied status—add further context, while financial metrics like face value, tax amount, and estimated equity empower users to assess investment potential.

Tax Delinquent Properties: A Wealth of Insights The Real Property Tax Delinquency File rounds out this offering, delivering a deep dive into tax-delinquent properties. With fields like tax delinquent flag, total due, years delinquent, and delinquent years, this dataset identifies properties at risk of lien escalation or foreclosure. Additional indicators such as bankruptcy flag, foreclosure flag, tax deed status, and payment plan flag provide a multi-dimensional view of delinquency status. Property details—property class, building sqft, bedrooms, bathrooms, and estimated value—combined with ownership and loan data (e.g., total open loans, estimated LTV) make this a powerhouse for real estate analysis, foreclosure tracking, and tax lien investment.

Versatile Formats and Delivery Options CompCurve ensures accessibility with data delivered in .json, .csv, and .xls formats, catering to a wide range of technical needs. Whether you prefer bulk downloads for offline analysis or real-time API access for dynamic applications, this dataset adapts to your workflow. The structured fields and consistent data types—such as varchar, decimal, date, and boolean—ensure compatibility with databases, spreadsheets, and programming environments, making it easy to integrate into your existing systems.

...

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

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

Demographics, population, housing, income, education, schools, and geography for ZIP Code 84201 (Ogden, UT). Interactive charts load automatically as you scroll for improved performance.

Blockchain data query: Solana Token Sales by IRS Address Since April 1, 2024

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

The Intelligent Reflecting Surface (IRS) is a ground-breaking technology that can boost the efficiencyof wireless data transmission systems. Specifically, the wireless signal transmitting environment isreconfigured by adjusting a large number of small reflecting units simultaneously. Therefore, intelligentreflecting surface (IRS) has been suggested as a possible solution for improving several aspects of futurewireless communication. However, individual nodes are empowered in IRS, but decisions and learning ofdata are still made by the centralized node in the IRS mechanism. Whereas, in previous works, theproblem of energy-efficient and delayed awareness learning IRS-assisted communications has beenlargely overlooked. The federated learning aware Intelligent Reconfigurable Surface Task Schedulingschemes (FL-IRSTS) algorithm is proposed in this paper to achieve high-speed communication withenergy and delay efficient offloading and scheduling. The training of models is divided into differentnodes. Therefore, the trained model will decide the IRSTS configuration that best meets the goals interms of communication rate. Multiple local models trained with the local healthcare fog-cloud networkfor each workload using federated learning (FL) to generate a global model. Then, each trained modelshared its initial configuration with the global model for the next training round. Each application’shealthcare data is handled and processed locally during the training process. Simulation results showthat the proposed algorithm’s achievable rate output can effectively approach centralized machinelearning (ML) while meeting the study’s energy and delay objectives.

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.

Multilevel analysis of predictors of IRS acceptability.