Graph and download economic data for State Tax Collections: Total Taxes for Maine (QTAXTOTALQTAXCAT3MENO) from Q1 1994 to Q1 2025 about collection, ME, tax, and USA.

This annual study provides migration pattern data for the United States by State or by county and are available for inflows (the number of new residents who moved to a State or county and where they migrated from) and outflows (the number of residents who left a State or county and where they moved to). The data include the number of returns filed, number of personal exemptions claimed, total adjusted gross income, and aggregate migration flows at the State level, by the size of adjusted gross income (AGI) and by age of the primary taxpayer. Data are collected and based on year-to-year address changes reported on U.S. Individual Income Tax Returns (Form 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, U.S. Population Migration Data.

This data collection contains aggregate information from income tax returns for 5-digit ZIP-code areas for the entire United States. Data are provided for three income classes with adjusted gross income returns of under $3,000, $3,000 to $10,000, and over $10,000. Information is provided on gross income, taxes paid, personal exemptions, total number of joint returns filed by married couples, and aggregate number of returns filed by all taxpayers. These data, originally prepared by the Internal Revenue Service, were supplied to ICPSR in computer-readable form by Philip Lankford of the University of California at Los Angeles.

description: The Department of Taxation and Finance annually produces a mandated dataset of credit activity under the General Business Corporation Franchise Tax (Article 9 A) to help analyze the effects of the claims. The data used to generate this report come from an annual study file based on the latest available data drawn from New York State corporation tax returns. The totals in the summary datasets may not match the detail datasets due to rounding and disclosure requirements. The totals in the summary datasets may not match the detail data due to rounding and disclosure requirements. Total values for numbers of taxpayers and amount of credit, in addition to mean and median credit, were computed using all taxpayers in the study file. A series of datasets presents profiles of the credits distributed by different subgroupings. These include: Summarization of tax credit activity by credit and component Summarization of tax credit activity by credit, component and basis of taxation. Summarization of tax credit activity by credit, component and NAICS industry description. Summarization of tax credit activity by credit, component and the size of the credit used. Summarization of tax credit activity by credit, component and the size of the entire net income of the taxpayer. Secrecy provisions preclude providing all subgroupings for all credits and also generally require the omission of credit refund data. These datasets only contains data for corporate franchise taxpayers filing under Article 9-A. It does not include statistics for taxpayers filing as banks under Article 32 (however, starting in 2015 banks and general business corporations will file under the same tax article, Article 9A), insurance companies filing under Article 33, or taxpayers filing under any of the various sections of Article 9. Nor does it provide data for taxpayers claiming credits under Article 22, the Personal Income Tax. These taxpayers claim credit by virtue of being sole proprietors or as recipients of credit that originated with flow-through entities (i.e., S corporations, limited liability companies, or partnerships).; abstract: The Department of Taxation and Finance annually produces a mandated dataset of credit activity under the General Business Corporation Franchise Tax (Article 9 A) to help analyze the effects of the claims. The data used to generate this report come from an annual study file based on the latest available data drawn from New York State corporation tax returns. The totals in the summary datasets may not match the detail datasets due to rounding and disclosure requirements. The totals in the summary datasets may not match the detail data due to rounding and disclosure requirements. Total values for numbers of taxpayers and amount of credit, in addition to mean and median credit, were computed using all taxpayers in the study file. A series of datasets presents profiles of the credits distributed by different subgroupings. These include: Summarization of tax credit activity by credit and component Summarization of tax credit activity by credit, component and basis of taxation. Summarization of tax credit activity by credit, component and NAICS industry description. Summarization of tax credit activity by credit, component and the size of the credit used. Summarization of tax credit activity by credit, component and the size of the entire net income of the taxpayer. Secrecy provisions preclude providing all subgroupings for all credits and also generally require the omission of credit refund data. These datasets only contains data for corporate franchise taxpayers filing under Article 9-A. It does not include statistics for taxpayers filing as banks under Article 32 (however, starting in 2015 banks and general business corporations will file under the same tax article, Article 9A), insurance companies filing under Article 33, or taxpayers filing under any of the various sections of Article 9. Nor does it provide data for taxpayers claiming credits under Article 22, the Personal Income Tax. These taxpayers claim credit by virtue of being sole proprietors or as recipients of credit that originated with flow-through entities (i.e., S corporations, limited liability companies, or partnerships).

In total, about 59.9 percent of U.S. households paid income tax in 2022. The remaining 40.1 percent of households paid no individual income tax. In that same year, about 47.1 percent of U.S. households with an income between 40,000 and 50,000 U.S. dollars paid no individual income taxes.

Graph and download economic data for Personal Taxes: Federal Income Taxes by Race: White and All Other Races, Not Including Black or African American (CXUFEDTAXESLB0903M) from 2003 to 2023 about tax, white, federal, personal, income, and USA.

In 2020, the average tax rate of the top 10 percent of earners in the United States stood at **** percent. For the top one percent of earners, the average tax rate stood at ***** percent, and for all taxpayers, the average tax rate was ***** percent.

Information about data sets to be published in the form of open data (name of data sets, their number), the manager of which is the Office of Large Taxpayers of the State Tax Service

Analysis of ‘New York State Corporate Tax Credits by Basis of Taxation: Beginning Tax Year 2001’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/5b0f2ca0-da89-4ab7-a2e5-185590d0cd5a on 12 February 2022.

--- Dataset description provided by original source is as follows ---

The Department of Taxation and Finance annually produces a mandated dataset of credit activity under the General Business Corporation Franchise Tax (Article 9‐A) to help analyze the effects of the claims. The data used to generate this report come from an annual study file based on the latest available data drawn from New York State corporation tax returns. The totals in the summary datasets may not match the detail datasets due to rounding and disclosure requirements. The totals in the summary datasets may not match the detail data due to rounding and disclosure requirements. Total values for numbers of taxpayers and amount of credit, in addition to mean and median credit, were computed using all taxpayers in the study file.

A series of datasets presents profiles of the credits distributed by different subgroupings. These include: • Summarization of tax credit activity by credit and component • Summarization of tax credit activity by credit, component and basis of taxation. • Summarization of tax credit activity by credit, component and NAICS industry description.
• Summarization of tax credit activity by credit, component and the size of the credit used.
• Summarization of tax credit activity by credit, component and the size of the entire net income of the taxpayer.

Secrecy provisions preclude providing all subgroupings for all credits and also generally require the omission of credit refund data. These datasets only contains data for corporate franchise taxpayers filing under Article 9-A. It does not include statistics for taxpayers filing as banks under Article 32 (however, starting in 2015 banks and general business corporations will file under the same tax article, Article 9A), insurance companies filing under Article 33, or taxpayers filing under any of the various sections of Article 9. Nor does it provide data for taxpayers claiming credits under Article 22, the Personal Income Tax. These taxpayers claim credit by virtue of being sole proprietors or as recipients of credit that originated with flow-through entities (i.e., S corporations, limited liability companies, or partnerships).

--- Original source retains full ownership of the source dataset ---

Ten-year comparison (2004-2013) of taxpayer income showing an analysis of the number of filers and the Adjusted Gross Income.

Analysis of ‘New York State Corporate Tax Credit Utilization: Beginning Tax Year 2001’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/650992ab-6712-4a21-883b-aad7fb80090d on 27 January 2022.

--- Dataset description provided by original source is as follows ---

The Department of Taxation and Finance annually produces a mandated dataset of credit activity under the General Business Corporation Franchise Tax (Article 9‐A) to help analyze the effects of the claims. The data used to generate this report come from an annual study file based on the latest available data drawn from New York State corporation tax returns. The totals in the summary datasets may not match the detail datasets due to rounding and disclosure requirements. The totals in the summary datasets may not match the detail data due to rounding and disclosure requirements. Total values for numbers of taxpayers and amount of credit, in addition to mean and median credit, were computed using all taxpayers in the study file.

A series of datasets presents profiles of the credits distributed by different subgroupings. These include: • Summarization of tax credit activity by credit and component • Summarization of tax credit activity by credit, component and basis of taxation. • Summarization of tax credit activity by credit, component and NAICS industry description.
• Summarization of tax credit activity by credit, component and the size of the credit used.
• Summarization of tax credit activity by credit, component and the size of the entire net income of the taxpayer.

Secrecy provisions preclude providing all subgroupings for all credits and also generally require the omission of credit refund data. These datasets only contains data for corporate franchise taxpayers filing under Article 9-A. It does not include statistics for taxpayers filing as banks under Article 32 (however, starting in 2015 banks and general business corporations will file under the same tax article, Article 9A), insurance companies filing under Article 33, or taxpayers filing under any of the various sections of Article 9. Nor does it provide data for taxpayers claiming credits under Article 22, the Personal Income Tax. These taxpayers claim credit by virtue of being sole proprietors or as recipients of credit that originated with flow-through entities (i.e., S corporations, limited liability companies, or partnerships).

--- Original source retains full ownership of the source dataset ---

The Department of Taxation and Finance annually produces a mandated dataset of credit activity under the General Business Corporation Franchise Tax (Article 9‐A) to help analyze the effects of the claims. The data used to generate this report come from an annual study file based on the latest available data drawn from New York State corporation tax returns. The totals in the summary datasets may not match the detail datasets due to rounding and disclosure requirements. The totals in the summary datasets may not match the detail data due to rounding and disclosure requirements. Total values for numbers of taxpayers and amount of credit, in addition to mean and median credit, were computed using all taxpayers in the study file.

A series of datasets presents profiles of the credits distributed by different subgroupings. These include: • Summarization of tax credit activity by credit and component • Summarization of tax credit activity by credit, component and basis of taxation. • Summarization of tax credit activity by credit, component and NAICS industry description.
• Summarization of tax credit activity by credit, component and the size of the credit used.
• Summarization of tax credit activity by credit, component and the size of the entire net income of the taxpayer.

Secrecy provisions preclude providing all subgroupings for all credits and also generally require the omission of credit refund data. These datasets only contains data for corporate franchise taxpayers filing under Article 9-A. It does not include statistics for taxpayers filing as banks under Article 32 (however, starting in 2015 banks and general business corporations will file under the same tax article, Article 9A), insurance companies filing under Article 33, or taxpayers filing under any of the various sections of Article 9. Nor does it provide data for taxpayers claiming credits under Article 22, the Personal Income Tax. These taxpayers claim credit by virtue of being sole proprietors or as recipients of credit that originated with flow-through entities (i.e., S corporations, limited liability companies, or partnerships).

The Department of Taxation and Finance (the Department) annually publishes statistical information on the New York State earned income tax credit (EITC). This includes data on the separate New York City EITC and the New York State noncustodial parent EITC. Summary data are presented for all taxpayers which includes full-year New York state residents, part-year residents and nonresidents (where applicable). Data are shown for the total number of claimants and credit claimed by county and/or region for all filing statuses.

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 ...

Issuer's Allocation Percentage for all corporations subject to taxes.

These reports are used by general corporations and unincorporated taxpayers to compute their investment allocation percentages, and by general corporation taxpayers to compute their allocated subsidiary capital. For 2006 and prior, the list include only corporations whose issuer's allocation percentages are known to be less than 100%. For 2007 and later, the lists include corporations whose issuer's allocation percentages are 100% or less.

The Department of Taxation and Finance annually publishes statistical information on the New York State real property tax credit (RPTC). Summary data are presented for taxpayers who were full-year New York state residents. Taxpayers may claim the credit even if they had no New York State personal income tax liability and, therefore, were not required to file an income tax return. Data are shown for the total number of claimants and credit claimed by county, age under and over 65, type of residence, filing category, and household gross income.

Qualified Opportunity ZonesThis feature layer, utilizing data from the U.S. Department of the Treasury, depicts all Qualified Opportunity Zones in the United States. Per IRS, "Opportunity Zones are an economic development tool that allows people to invest in distressed areas in the United States. Their purpose is to spur economic growth and job creation in low-income communities while providing tax benefits to investors.Opportunity Zones were created under the Tax Cuts and Jobs Act of 2017 (Public Law No. 115-97). Thousands of low-income communities in all 50 states, the District of Columbia and five U.S. territories are designated as Qualified Opportunity Zones. Taxpayers can invest in these zones through Qualified Opportunity Funds." Chicago, Illinois Opportunity ZonesData currency: December 14, 2018Data source: Opportunity Zones ResourcesData modification: NoneFor more information: Opportunity NowFor feedback, please contact: ArcGIScomNationalMaps@esri.comCommunity Development Financial InstitutionsPer CDFI, "The CDFI Fund was created for the purpose of promoting economic revitalization and community development through investment in and assistance to Community Development Financial Institutions (CDFIs)."

This map provides an interactive tool for exploring planned clean energy projects across the United States. Users can sort projects by sector and state, gaining insights into each project’s location, name, status, size, and the companies involved. Additionally, the map estimates the number of construction jobs associated with each project. The data is drawn from publicly available sources and is designed to provide a detailed overview of ongoing clean energy initiatives. Please note that the information does not reflect eligibility for federal tax incentives and does not include federal taxpayer information. (Data source: U.S. Department of Labor)

Data, geospatial data resources, and the linked mapping tool and web services reflect data for two types of potentially qualifying energy communities: 1) Census tracts and directly adjoining tracts that have had coal mine closures since 1999 or coal-fired electric generating unit retirements since 2009. These census tracts qualify as energy communities. 2) Metropolitan statistical areas (MSAs) and non-metropolitan statistical areas (non-MSAs) that are energy communities for 2023 and 2024, along with their fossil fuel employment (FFE) status. Additional information on energy communities and related tax credits can be accessed on the Interagency Working Group on Coal & Power Plant Communities & Economic Revitalization Energy Communities website (https://energycommunities.gov/energy-community-tax-credit-bonus/). Use limitations: these spatial data and mapping tool may not be relied upon by taxpayers to substantiate a tax return position or for determining whether certain penalties apply and will not be used by the IRS for examination purposes. The mapping tool does not reflect the application of the law to a specific taxpayer’s situation, and the applicable Internal Revenue Code provisions ultimately control.

The Department of Taxation and Finance annually publishes statistical information on the New York State child and dependent care credit (NYS CDCC). Summary data are presented for all taxpayers, including full-year New York state residents, part-year residents and nonresidents (where applicable). Data are presented on a statewide and county-level basis for numbers and amounts of credit claims based on filing status and number of qualifying dependents. Taxpayers filing “married separate” generally are not allowed to take the child and dependent care credit.

The tables also include summary information for the New York City child and dependent care credit (NYC CDCC). The data are presented on a county-level basis for numbers and amounts of credit claims at the aggregate level only.