46 datasets found
  1. T

    United States Federal Corporate Tax Rate

    • tradingeconomics.com
    • fr.tradingeconomics.com
    • +13more
    csv, excel, json, xml
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    TRADING ECONOMICS, United States Federal Corporate Tax Rate [Dataset]. https://tradingeconomics.com/united-states/corporate-tax-rate
    Explore at:
    xml, csv, json, excelAvailable download formats
    Dataset authored and provided by
    TRADING ECONOMICS
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Time period covered
    Dec 31, 1909 - Dec 31, 2025
    Area covered
    United States
    Description

    The Corporate Tax Rate in the United States stands at 21 percent. This dataset provides - United States Corporate Tax Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

  2. s

    USA IRS Zipcode data

    • data.smartidf.services
    • smartregionidf.outscale-euw2.opendatasoft.com
    • +1more
    csv, excel, json
    Updated Mar 12, 2020
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    (2020). USA IRS Zipcode data [Dataset]. https://data.smartidf.services/explore/dataset/usa-irs-zipcode-data/
    Explore at:
    json, excel, csvAvailable download formats
    Dataset updated
    Mar 12, 2020
    License

    https://www.usa.gov/government-workshttps://www.usa.gov/government-works

    Area covered
    United States
    Description

    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 householdsNumber of personal exemptions, which approximates the populationAdjusted gross income (AGI)Wages and salariesDividends before exclusionInterest 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.

  3. T

    United States Personal Income Tax Rate

    • tradingeconomics.com
    • id.tradingeconomics.com
    • +13more
    csv, excel, json, xml
    Updated Dec 15, 2024
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    TRADING ECONOMICS (2024). United States Personal Income Tax Rate [Dataset]. https://tradingeconomics.com/united-states/personal-income-tax-rate
    Explore at:
    xml, excel, csv, jsonAvailable download formats
    Dataset updated
    Dec 15, 2024
    Dataset authored and provided by
    TRADING ECONOMICS
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Time period covered
    Dec 31, 2004 - Dec 31, 2025
    Area covered
    United States
    Description

    The Personal Income Tax Rate in the United States stands at 37 percent. This dataset provides - United States Personal Income Tax Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

  4. d

    Individuals, ZIP Code Data

    • catalog.data.gov
    • gimi9.com
    Updated Aug 22, 2024
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    Statistics of Income (SOI) (2024). Individuals, ZIP Code Data [Dataset]. https://catalog.data.gov/dataset/zip-code-data
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    Dataset updated
    Aug 22, 2024
    Dataset provided by
    Statistics of Income (SOI)
    Description

    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.

  5. 🇺🇸 Fiscally US Cities

    • kaggle.com
    Updated Jul 31, 2024
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    mexwell (2024). 🇺🇸 Fiscally US Cities [Dataset]. https://www.kaggle.com/datasets/mexwell/fiscally-us-cities
    Explore at:
    CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
    Dataset updated
    Jul 31, 2024
    Dataset provided by
    Kaggle
    Authors
    mexwell
    Area covered
    United States
    Description

    Motivation

    In the United States, city governments provide many services: they run public school districts, administer certain welfare and health programs, build roads and manage airports, provide police and fire protection, inspect buildings, and often run water and utility systems. Cities also get revenues through certain local taxes, various fees and permit costs, sale of property, and through the fees they charge for the utilities they run.

    It would be interesting to compare all these expenses and revenues across cities and over time, but also quite difficult. Cities share many of these service responsibilities with other government agencies: in one particular city, some roads may be maintained by the state government, some law enforcement provided by the county sheriff, some schools run by independent school districts with their own tax revenue, and some utilities run by special independent utility districts. These governmental structures vary greatly by state and by individual city. It would be hard to make a fair comparison without taking into account all these differences.

    This dataset takes into account all those differences. The Lincoln Institute of Land Policy produces what they call “Fiscally Standardized Cities” (FiSCs), aggregating all services provided to city residents regardless of how they may be divided up by different government agencies and jurisdictions. Using this, we can study city expenses and revenues, and how the proportions of different costs vary over time.

    Data

    The dataset tracks over 200 American cities between 1977 and 2020. Each row represents one city for one year. Revenue and expenditures are broken down into more than 120 categories.

    Values are available for FiSCs and also for the entities that make it up: the city, the county, independent school districts, and any special districts, such as utility districts. There are hence five versions of each variable, with suffixes indicating the entity. For example, taxes gives the FiSC’s tax revenue, while taxes_city, taxes_cnty, taxes_schl, and taxes_spec break it down for the city, county, school districts, and special districts.

    The values are organized hierarchically. For example, taxes is the sum of tax_property (property taxes), tax_sales_general (sales taxes), tax_income (income tax), and tax_other (other taxes). And tax_income is itself the sum of tax_income_indiv (individual income tax) and tax_income_corp (corporate income tax) subcategories.

    Variable Description

    • year Year for these values
    • city_name Name of the city, such as “AK: Anchorage”, where “AK” is the standard two-letter abbreviation for Alaska
    • city_population Estimated city population, based on Census data
    • county_name Name of the county the city is in
    • county_population Estimated county population, based on Census data
    • cpi Consumer Price Index for this year, scaled so that 2020 is 1.
    • relationship_city_school Type of school district. 1: City-wide independent school district that serves the entire city. 2: County-wide independent school district that serves the entire county. 3: One or more independent school districts whose boundaries extend beyond the city. 4: School district run by or dependent on the city. 5: School district run by or dependent on the county.
    • enrollment Estimated number of public school students living in the city.
    • districts_in_city Estimated number of school districts in the city.
    • consolidated_govt Whether the city has a consolidated city-county government (1 = yes, 0 = no). For example, Philadelphia’s city and county government are the same entity; they are not separate governments.
    • id2_city 12-digit city identifier, from the Annual Survey of State and Local Government Finances
    • id2_county 12-digit county identifier
    • city_types Two types: core and legacy. There are 150 core cities, “including the two largest cities in each state, plus all cities with populations of 150,000+ in 1980 and 200,000+ in 2010”. Legacy cities include “95 cities with population declines of at least 20 percent from their peak, poverty rates exceeding the national average, and a peak population of at least 50,000”. Some cities are both (denoted “core

    The revenue and expenses variables are described in this detailed table. Further documentation is available on the FiSC Database website, linked in References below.

    All monetary data is already adjusted for inflation, and is given in terms of 2020 US dollars per capita. The Consumer Price Index is provided for each year if you prefer to use numbers not adjusted for inflation, scaled so that 2020 is 1; simply divide each value by the CPI to get the value in that year’s nominal dollars. The total population is also provided if you want total values instead of per-capita values.

    Questions

    • Do some exploratory data analysis. Are there any outlying cities? Any interesting trends and rela...
  6. d

    Annual Tax Effort in NYC since 1929

    • catalog.data.gov
    • data.cityofnewyork.us
    Updated Nov 10, 2020
    + more versions
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    data.cityofnewyork.us (2020). Annual Tax Effort in NYC since 1929 [Dataset]. https://catalog.data.gov/dataset/annual-tax-effort-in-nyc-since-1929
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    Dataset updated
    Nov 10, 2020
    Dataset provided by
    data.cityofnewyork.us
    Area covered
    New York
    Description

    Taxes are grouped into six major categories: property, general sales, personal income, business income, real estate-related, and other. We also separate non-exported and exported taxes, that is, taxes levied on New York City resident households and businesses and taxes levied on nonresidents. Taxes in the former category enter into the calculation of New York City tax effort. The latter category includes sales and other taxes on hotel occupancy, city income taxes paid by commuters into the city, and portions of state and MTA auto rental taxes remitted in the city. We could not, however, estimate and net out non-hotel sales and other taxes paid by visitors to the city. Nor could we account, as we did in our previous report, for any New York City tax imports, that is, taxes of other, non-overlapping jurisdictions paid by city residents.1 For brief descriptions of the tables and figures along with methodological notes please see the Tax Effort Background and Methodology document.

  7. g

    Archival Version

    • datasearch.gesis.org
    Updated Jun 18, 2018
    + more versions
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    Asdrubali, Pierfederico; Sorensen 1958-, Bent; Yosha, Oved (2018). Archival Version [Dataset]. http://doi.org/10.3886/ICPSR25541
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    Dataset updated
    Jun 18, 2018
    Dataset provided by
    da|ra (Registration agency for social science and economic data)
    Authors
    Asdrubali, Pierfederico; Sorensen 1958-, Bent; Yosha, Oved
    Area covered
    United States
    Description

    This study developed a framework for quantifying the amount of risk sharing among states in the United States, and constructed data that allowed researchers to decompose the cross-sectional variance in gross state product into levels of smoothing capital markets, federal government, and credit market smoothing. The collection contains 67 Excel data files, that were grouped into 17 datasets based on the organizational ordering schematic provided by the principal investigator, including:

    Dataset 1 - State Personal Income: n=1,938, 51 variables; Dataset 2 - Federal Taxes and Contributions: n=17,948, 424 variables; Dataset 3 - State Population: n=1,887, 51 variables; Dataset 4 - State and Local Personal Taxes: n=11,526, 306 variables; Dataset 5 - Interests on State and Local Funds: n=7,609, 205 variables; Dataset 6 - Transfers: n=5,814, 153 variables; Dataset 7 - Non Federal State Income: n=1,887, 51 variables; Dataset 8 - Federal Grants: n=1,938, 51 variables; Dataset 9 - Federal Transfers to Individuals: n=27,415, 766 variables; Dataset 10 - Federal Personal Taxes: n=1,938, 51 variables; Dataset 11 - State Government Expenditure: n=1,887, 51 variables; Dataset 12 - Disposable State Income: n=1,836, 51 variables; Dataset 13 - State Consumption: n=5,508, 153 variables; Dataset 14 - State and Local Transfers: n=1,836, 51 variables; Dataset 15 - Gross State Product: n=1,910, 52 variables; Dataset 16 - Retail Sales: n=3,774, 102 variables; Dataset 17 - Personal Consumption Expenditures: n=38, 2 variables;

  8. IRS Form 990 Data

    • kaggle.com
    zip
    Updated Mar 20, 2019
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    Internal Revenue Service (2019). IRS Form 990 Data [Dataset]. https://www.kaggle.com/irs/irs-990
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    zip(0 bytes)Available download formats
    Dataset updated
    Mar 20, 2019
    Dataset authored and provided by
    Internal Revenue Servicehttp://www.irs.gov/
    License

    https://creativecommons.org/publicdomain/zero/1.0/https://creativecommons.org/publicdomain/zero/1.0/

    Description

    Context

    Form 990 (officially, the "Return of Organization Exempt From Income Tax"1) is a United States Internal Revenue Service form that provides the public with financial information about a nonprofit organization. It is often the only source of such information. It is also used by government agencies to prevent organizations from abusing their tax-exempt status. Source: https://en.wikipedia.org/wiki/Form_990

    Content

    Form 990 is used by the United States Internal Revenue Service to gather financial information about nonprofit/exempt organizations. This BigQuery dataset can be used to perform research and analysis of organizations that have electronically filed Forms 990, 990-EZ and 990-PF. For a complete description of data variables available in this dataset, see the IRS’s extract documentation: https://www.irs.gov/uac/soi-tax-stats-annual-extract-of-tax-exempt-organization-financial-data.

    Update Frequency: Annual

    Fork this kernel to get started with this dataset.

    Acknowledgements

    https://bigquery.cloud.google.com/dataset/bigquery-public-data:irs_990

    https://cloud.google.com/bigquery/public-data/irs-990

    Dataset Source: U.S. Internal Revenue Service. This dataset is publicly available for anyone to use under the following terms provided by the Dataset Source - http://www.data.gov/privacy-policy#data_policy - and is provided "AS IS" without any warranty, express or implied, from Google. Google disclaims all liability for any damages, direct or indirect, resulting from the use of the dataset.

    Banner Photo by @rawpixel from Unplash.

    Inspiration

    What organizations filed tax exempt status in 2015?

    What was the revenue of the American Red Cross in 2017?

  9. r

    Data from: Financing the State: Government Tax Revenue from 1800 to 2012

    • researchdata.se
    • demo.researchdata.se
    Updated Feb 20, 2020
    + more versions
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    Per F. Andersson; Thomas Brambor (2020). Financing the State: Government Tax Revenue from 1800 to 2012 [Dataset]. http://doi.org/10.5878/nsbw-2102
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    (1146002)Available download formats
    Dataset updated
    Feb 20, 2020
    Dataset provided by
    Lund University
    Authors
    Per F. Andersson; Thomas Brambor
    Time period covered
    1800 - 2012
    Area covered
    South America, Japan, North America, Europe, Oceania
    Description

    This dataset presents information on historical central government revenues for 31 countries in Europe and the Americas for the period from 1800 (or independence) to 2012. The countries included are: Argentina, Australia, Austria, Belgium, Bolivia, Brazil, Canada, Chile, Colombia, Denmark, Ecuador, Finland, France, Germany (West Germany between 1949 and 1990), Ireland, Italy, Japan, Mexico, New Zealand, Norway, Paraguay, Peru, Portugal, Spain, Sweden, Switzerland, the Netherlands, the United Kingdom, the United States, Uruguay, and Venezuela. In other words, the dataset includes all South American, North American, and Western European countries with a population of more than one million, plus Australia, New Zealand, Japan, and Mexico. The dataset contains information on the public finances of central governments. To make such information comparable cross-nationally we have chosen to normalize nominal revenue figures in two ways: (i) as a share of the total budget, and (ii) as a share of total gross domestic product. The total tax revenue of the central state is disaggregated guided by the Government Finance Statistics Manual 2001 of the International Monetary Fund (IMF) which provides a classification of types of revenue, and describes in detail the contents of each classification category. Given the paucity of detailed historical data and the needs of our project, we combined some subcategories. First, we are interested in total tax revenue (centaxtot), as well as the shares of total revenue coming from direct (centaxdirectsh) and indirect (centaxindirectsh) taxes. Further, we measure two sub-categories of direct taxation, namely taxes on property (centaxpropertysh) and income (centaxincomesh). For indirect taxes, we separate excises (centaxexcisesh), consumption (centaxconssh), and customs(centaxcustomssh).

    For a more detailed description of the dataset and the coding process, see the codebook available in the .zip-file.

    Purpose:

    This dataset presents information on historical central government revenues for 31 countries in Europe and the Americas for the period from 1800 (or independence) to 2012. The countries included are: Argentina, Australia, Austria, Belgium, Bolivia, Brazil, Canada, Chile, Colombia, Denmark, Ecuador, Finland, France, Germany (West Germany between 1949 and 1990), Ireland, Italy, Japan, Mexico, New Zealand, Norway, Paraguay, Peru, Portugal, Spain, Sweden, Switzerland, the Netherlands, the United Kingdom, the United States, Uruguay, and Venezuela. In other words, the dataset includes all South American, North American, and Western European countries with a population of more than one million, plus Australia, New Zealand, Japan, and Mexico. The dataset contains information on the public finances of central governments. To make such information comparable cross-nationally we have chosen to normalize nominal revenue figures in two ways: (i) as a share of the total budget, and (ii) as a share of total gross domestic product. The total tax revenue of the central state is disaggregated guided by the Government Finance Statistics Manual 2001 of the International Monetary Fund (IMF) which provides a classification of types of revenue, and describes in detail the contents of each classification category. Given the paucity of detailed historical data and the needs of our project, we combined some subcategories. First, we are interested in total tax revenue (centaxtot), as well as the shares of total revenue coming from direct (centaxdirectsh) and indirect (centaxindirectsh) taxes. Further, we measure two sub-categories of direct taxation, namely taxes on property (centaxpropertysh) and income (centaxincomesh). For indirect taxes, we separate excises (centaxexcisesh), consumption (centaxconssh), and customs(centaxcustomssh).

  10. i

    Family Income Levels - Dataset - The Indiana Data Hub

    • hub.mph.in.gov
    Updated Jun 29, 2018
    + more versions
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    (2018). Family Income Levels - Dataset - The Indiana Data Hub [Dataset]. https://hub.mph.in.gov/dataset/family-income-levels
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    Dataset updated
    Jun 29, 2018
    Description

    This U.S. Census Bureau American Community Survey (ACS) five-year estimates data set contains family income estimates during the past 12 months and in inflation-adjusted dollars. The data is available from a number of geographies ranging from statewide to census tract level. The data is available for year 2009-2016. This includes the income of the householder and all related individuals 15 years old and over in the household. Income is based on “money” income–income received on a regular basis before payments for personal income taxes, social security, union dues, etc. Money income does not include noncash benefits that may be received.

  11. T

    United States Withholding Tax Rate

    • tradingeconomics.com
    • id.tradingeconomics.com
    • +13more
    csv, excel, json, xml
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    TRADING ECONOMICS, United States Withholding Tax Rate [Dataset]. https://tradingeconomics.com/united-states/withholding-tax-rate
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    excel, json, xml, csvAvailable download formats
    Dataset authored and provided by
    TRADING ECONOMICS
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Time period covered
    Dec 31, 2022 - Dec 31, 2024
    Area covered
    United States
    Description

    The Withholding Tax Rate in the United States stands at 30 percent. This dataset includes a chart with historical data for the United States Withholding Tax Rate.

  12. c

    Augmented Individual Income Tax Model Exact Match File, 1972

    • archive.ciser.cornell.edu
    • icpsr.umich.edu
    Updated Jan 1, 2020
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    Social Security Administration (2020). Augmented Individual Income Tax Model Exact Match File, 1972 [Dataset]. http://doi.org/10.6077/b3sr-m502
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    Dataset updated
    Jan 1, 2020
    Dataset authored and provided by
    Social Security Administration
    Variables measured
    Family.HouseholdFamily
    Description

    This data collection was developed for general use as part of CURRENT POPULATION SURVEY, 1973, AND SOCIAL SECURITY RECORDS: EXACT MATCH DATA (ICPSR 7616). This file merges information from two administrative sources: the Internal Revenue Service (IRS) and the Social Security Administration (SSA). The starting point of the merged dataset was the IRS Tax Model File of Individual Income Tax Returns, a public-use IRS file designed to simulate the administrative and revenue impact of tax law changes. It contains over 100,000 federal income tax returns subsampled from the STATISTICS OF INCOME sample of the following 1972 tax forms: (1) 1040, Individual Income Tax Return (and its associated schedules), (2) 1040A, Individual Income Tax Return, Short Form, (3) 4625, Computation of Minimum Tax, (4) Maximum Tax on Earned Income, (5) Application for Automatic Extension of Time to File United States Individual Income Tax Return, (6) 4874, Credit for Wages Paid or Incurred in Work Incentive (WIN) Programs, and (7) 4875, Presidential Election Campaign Fund Statement. The nearly 170 items extracted from these tax forms include exemptions, earned and unearned income, income loss, foreign tax credit, medical and dental expenses over 3 percent of AGI, state and local income taxes, and capital gains and losses. To this individual income tax data, the Social Security Administration matched (using the unique identifier of Social Security number) selected demographic information (including such variables as the race, sex, and age of the primary taxpayer) from the SSA's longitudinal summary earnings files for income year 1972. The data are weighted. (Source: downloaded from ICPSR 7/13/10)

    Please Note: This dataset is part of the historical CISER Data Archive Collection and is also available at ICPSR at https://doi.org/10.3886/ICPSR07667.v1. We highly recommend using the ICPSR version as they may make this dataset available in multiple data formats in the future.

  13. United States US: Revenue and Grants: Revenue: Taxes on Income: Profits and...

    • ceicdata.com
    Updated Mar 15, 2009
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    CEICdata.com (2021). United States US: Revenue and Grants: Revenue: Taxes on Income: Profits and Capital Gains: % of Revenue [Dataset]. https://www.ceicdata.com/en/united-states/government-revenue-expenditure-and-finance/us-revenue-and-grants-revenue-taxes-on-income-profits-and-capital-gains--of-revenue
    Explore at:
    Dataset updated
    Mar 15, 2009
    Dataset provided by
    CEIC Data
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Time period covered
    Sep 1, 2005 - Sep 1, 2016
    Area covered
    United States
    Variables measured
    Operating Statement
    Description

    United States US: Revenue and Grants: Revenue: Taxes on Income: Profits and Capital Gains: % of Revenue data was reported at 53.743 % in 2016. This records a decrease from the previous number of 54.048 % for 2015. United States US: Revenue and Grants: Revenue: Taxes on Income: Profits and Capital Gains: % of Revenue data is updated yearly, averaging 53.312 % from Sep 1972 (Median) to 2016, with 45 observations. The data reached an all-time high of 59.358 % in 1972 and a record low of 45.501 % in 2009. United States US: Revenue and Grants: Revenue: Taxes on Income: Profits and Capital Gains: % of Revenue data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s United States – Table US.World Bank.WDI: Government Revenue, Expenditure and Finance. Taxes on income, profits, and capital gains are levied on the actual or presumptive net income of individuals, on the profits of corporations and enterprises, and on capital gains, whether realized or not, on land, securities, and other assets. Intragovernmental payments are eliminated in consolidation.; ; International Monetary Fund, Government Finance Statistics Yearbook and data files.; Median;

  14. t

    Income Tax Refunds Issued

    • fiscaldata.treasury.gov
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    Income Tax Refunds Issued [Dataset]. https://fiscaldata.treasury.gov/datasets/daily-treasury-statement/
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    Description

    This table represents the breakdown of tax refunds by recipient (individual vs business) and type (check vs electronic funds transfer). Tax refunds are also represented as withdrawals in the Deposits and Withdrawals of Operating Cash table. All figures are rounded to the nearest million. As of February 14, 2023, Table VI Income Tax Refunds Issued was renamed to Table V Income Tax Refunds Issued within the published report.

  15. d

    Replication Data for: Information, Equal Treatment, and Support for...

    • dataone.org
    • dataverse.harvard.edu
    Updated Nov 8, 2023
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    Wang, Hsu Yumin (2023). Replication Data for: Information, Equal Treatment, and Support for Regressive Taxation: Experimental Evidence from the United States [Dataset]. http://doi.org/10.7910/DVN/QL0KJN
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    Dataset updated
    Nov 8, 2023
    Dataset provided by
    Harvard Dataverse
    Authors
    Wang, Hsu Yumin
    Description

    Regressive taxation has increasingly played an important role in financing public programs, but current scholarship remains largely silent on the conditions under which people would support such financing strategies. This paper fills this gap by focusing on the United States, where sales taxes account for nearly one-third of state government revenue, and where sales tax ballot measures have received majority support. This paper utilizes an online survey experiment to examine two potential sources of public support for a sales tax increase: equal treatment beliefs (i.e., that all should pay the same tax rate) and a lack of public awareness of the distributive consequences of sales taxes. I find that exposure to information about sales taxes' distributive consequences significantly reduced respondents' support for a sales tax increase, but that equal treatment beliefs had no significant effect on such support. Additional analyses suggest that other-regarding motivations are a plausible mechanism underlying the effects of information provision. These findings shed light on how misperceptions of tax burdens shape support for regressive taxation and have broad implications for the role of fairness beliefs in the formation of tax policy preferences.

  16. t

    Summary of Receipts and Outlays of the U.S. Government

    • fiscaldata.treasury.gov
    Updated Jul 13, 2020
    + more versions
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    (2020). Summary of Receipts and Outlays of the U.S. Government [Dataset]. https://fiscaldata.treasury.gov/datasets/monthly-treasury-statement/
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    Dataset updated
    Jul 13, 2020
    Area covered
    United States
    Description

    This summary table shows, for Budget Receipts, the total amount of activity for the current month, the current fiscal year-to-date, the comparable prior period year-to-date and the budgeted amount estimated for the current fiscal year for various types of receipts (i.e. individual income tax, corporate income tax, etc.). The Budget Outlays section of the table shows the total amount of activity for the current month, the current fiscal year-to-date, the comparable prior period year-to-date and the budgeted amount estimated for the current fiscal year for agencies of the federal government. The table also shows the amounts for the budget/surplus deficit categorized as listed above. This table includes total and subtotal rows that should be excluded when aggregating data. Some rows represent elements of the dataset's hierarchy, but are not assigned values. The classification_id for each of these elements can be used as the parent_id for underlying data elements to calculate their implied values. Subtotal rows are available to access this same information.

  17. United States US: Revenue and Grants: Revenue: Taxes on Income: Profits and...

    • ceicdata.com
    Updated Mar 15, 2023
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    CEICdata.com (2023). United States US: Revenue and Grants: Revenue: Taxes on Income: Profits and Capital Gains [Dataset]. https://www.ceicdata.com/en/united-states/government-revenue-expenditure-and-finance/us-revenue-and-grants-revenue-taxes-on-income-profits-and-capital-gains
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    Dataset updated
    Mar 15, 2023
    Dataset provided by
    CEIC Data
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Time period covered
    Sep 1, 2005 - Sep 1, 2016
    Area covered
    United States
    Variables measured
    Operating Statement
    Description

    United States US: Revenue and Grants: Revenue: Taxes on Income: Profits and Capital Gains data was reported at 1,871.737 USD bn in 2016. This records a decrease from the previous number of 1,876.251 USD bn for 2015. United States US: Revenue and Grants: Revenue: Taxes on Income: Profits and Capital Gains data is updated yearly, averaging 683.680 USD bn from Sep 1972 (Median) to 2016, with 45 observations. The data reached an all-time high of 1,876.251 USD bn in 2015 and a record low of 126.960 USD bn in 1972. United States US: Revenue and Grants: Revenue: Taxes on Income: Profits and Capital Gains data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s United States – Table US.World Bank.WDI: Government Revenue, Expenditure and Finance. Taxes on income, profits, and capital gains are levied on the actual or presumptive net income of individuals, on the profits of corporations and enterprises, and on capital gains, whether realized or not, on land, securities, and other assets. Intragovernmental payments are eliminated in consolidation.; ; International Monetary Fund, Government Finance Statistics Yearbook and data files.; ;

  18. c

    California Overlapping Cities and Counties and Identifiers with Coastal...

    • gis.data.ca.gov
    • data.ca.gov
    • +1more
    Updated Oct 25, 2024
    + more versions
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    California Department of Technology (2024). California Overlapping Cities and Counties and Identifiers with Coastal Buffers [Dataset]. https://gis.data.ca.gov/datasets/California::california-overlapping-cities-and-counties-and-identifiers-with-coastal-buffers
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    Dataset updated
    Oct 25, 2024
    Dataset authored and provided by
    California Department of Technology
    License

    MIT Licensehttps://opensource.org/licenses/MIT
    License information was derived automatically

    Area covered
    Description

    WARNING: This is a pre-release dataset and its fields names and data structures are subject to change. It should be considered pre-release until the end of 2024. Expected changes:Metadata is missing or incomplete for some layers at this time and will be continuously improved.We expect to update this layer roughly in line with CDTFA at some point, but will increase the update cadence over time as we are able to automate the final pieces of the process.This dataset is continuously updated as the source data from CDTFA is updated, as often as many times a month. If you require unchanging point-in-time data, export a copy for your own use rather than using the service directly in your applications.PurposeCounty and incorporated place (city) boundaries along with third party identifiers used to join in external data. Boundaries are from the authoritative source the California Department of Tax and Fee Administration (CDTFA), altered to show the counties as one polygon. This layer displays the city polygons on top of the County polygons so the area isn"t interrupted. The GEOID attribute information is added from the US Census. GEOID is based on merged State and County FIPS codes for the Counties. Abbreviations for Counties and Cities were added from Caltrans Division of Local Assistance (DLA) data. Place Type was populated with information extracted from the Census. Names and IDs from the US Board on Geographic Names (BGN), the authoritative source of place names as published in the Geographic Name Information System (GNIS), are attached as well. Finally, the coastline is used to separate coastal buffers from the land-based portions of jurisdictions. This feature layer is for public use.Related LayersThis dataset is part of a grouping of many datasets:Cities: Only the city boundaries and attributes, without any unincorporated areasWith Coastal BuffersWithout Coastal BuffersCounties: Full county boundaries and attributes, including all cities within as a single polygonWith Coastal BuffersWithout Coastal BuffersCities and Full Counties: A merge of the other two layers, so polygons overlap within city boundaries. Some customers require this behavior, so we provide it as a separate service.With Coastal Buffers (this dataset)Without Coastal BuffersPlace AbbreviationsUnincorporated Areas (Coming Soon)Census Designated Places (Coming Soon)Cartographic CoastlinePolygonLine source (Coming Soon)Working with Coastal BuffersThe dataset you are currently viewing includes the coastal buffers for cities and counties that have them in the authoritative source data from CDTFA. In the versions where they are included, they remain as a second polygon on cities or counties that have them, with all the same identifiers, and a value in the COASTAL field indicating if it"s an ocean or a bay buffer. If you wish to have a single polygon per jurisdiction that includes the coastal buffers, you can run a Dissolve on the version that has the coastal buffers on all the fields except COASTAL, Area_SqMi, Shape_Area, and Shape_Length to get a version with the correct identifiers.Point of ContactCalifornia Department of Technology, Office of Digital Services, odsdataservices@state.ca.govField and Abbreviation DefinitionsCOPRI: county number followed by the 3-digit city primary number used in the Board of Equalization"s 6-digit tax rate area numbering systemPlace Name: CDTFA incorporated (city) or county nameCounty: CDTFA county name. For counties, this will be the name of the polygon itself. For cities, it is the name of the county the city polygon is within.Legal Place Name: Board on Geographic Names authorized nomenclature for area names published in the Geographic Name Information SystemGNIS_ID: The numeric identifier from the Board on Geographic Names that can be used to join these boundaries to other datasets utilizing this identifier.GEOID: numeric geographic identifiers from the US Census Bureau Place Type: Board on Geographic Names authorized nomenclature for boundary type published in the Geographic Name Information SystemPlace Abbr: CalTrans Division of Local Assistance abbreviations of incorporated area namesCNTY Abbr: CalTrans Division of Local Assistance abbreviations of county namesArea_SqMi: The area of the administrative unit (city or county) in square miles, calculated in EPSG 3310 California Teale Albers.COASTAL: Indicates if the polygon is a coastal buffer. Null for land polygons. Additional values include "ocean" and "bay".GlobalID: While all of the layers we provide in this dataset include a GlobalID field with unique values, we do not recommend you make any use of it. The GlobalID field exists to support offline sync, but is not persistent, so data keyed to it will be orphaned at our next update. Use one of the other persistent identifiers, such as GNIS_ID or GEOID instead.AccuracyCDTFA"s source data notes the following about accuracy:City boundary changes and county boundary line adjustments filed with the Board of Equalization per Government Code 54900. This GIS layer contains the boundaries of the unincorporated county and incorporated cities within the state of California. The initial dataset was created in March of 2015 and was based on the State Board of Equalization tax rate area boundaries. As of April 1, 2024, the maintenance of this dataset is provided by the California Department of Tax and Fee Administration for the purpose of determining sales and use tax rates. The boundaries are continuously being revised to align with aerial imagery when areas of conflict are discovered between the original boundary provided by the California State Board of Equalization and the boundary made publicly available by local, state, and federal government. Some differences may occur between actual recorded boundaries and the boundaries used for sales and use tax purposes. The boundaries in this map are representations of taxing jurisdictions for the purpose of determining sales and use tax rates and should not be used to determine precise city or county boundary line locations. COUNTY = county name; CITY = city name or unincorporated territory; COPRI = county number followed by the 3-digit city primary number used in the California State Board of Equalization"s 6-digit tax rate area numbering system (for the purpose of this map, unincorporated areas are assigned 000 to indicate that the area is not within a city).Boundary ProcessingThese data make a structural change from the source data. While the full boundaries provided by CDTFA include coastal buffers of varying sizes, many users need boundaries to end at the shoreline of the ocean or a bay. As a result, after examining existing city and county boundary layers, these datasets provide a coastline cut generally along the ocean facing coastline. For county boundaries in northern California, the cut runs near the Golden Gate Bridge, while for cities, we cut along the bay shoreline and into the edge of the Delta at the boundaries of Solano, Contra Costa, and Sacramento counties.In the services linked above, the versions that include the coastal buffers contain them as a second (or third) polygon for the city or county, with the value in the COASTAL field set to whether it"s a bay or ocean polygon. These can be processed back into a single polygon by dissolving on all the fields you wish to keep, since the attributes, other than the COASTAL field and geometry attributes (like areas) remain the same between the polygons for this purpose.SliversIn cases where a city or county"s boundary ends near a coastline, our coastline data may cross back and forth many times while roughly paralleling the jurisdiction"s boundary, resulting in many polygon slivers. We post-process the data to remove these slivers using a city/county boundary priority algorithm. That is, when the data run parallel to each other, we discard the coastline cut and keep the CDTFA-provided boundary, even if it extends into the ocean a small amount. This processing supports consistent boundaries for Fort Bragg, Point Arena, San Francisco, Pacifica, Half Moon Bay, and Capitola, in addition to others. More information on this algorithm will be provided soon.Coastline CaveatsSome cities have buffers extending into water bodies that we do not cut at the shoreline. These include South Lake Tahoe and Folsom, which extend into neighboring lakes, and San Diego and surrounding cities that extend into San Diego Bay, which our shoreline encloses. If you have feedback on the exclusion of these items, or others, from the shoreline cuts, please reach out using the contact information above.Offline UseThis service is fully enabled for sync and export using Esri Field Maps or other similar tools. Importantly, the GlobalID field exists only to support that use case and should not be used for any other purpose (see note in field descriptions).Updates and Date of ProcessingConcurrent with CDTFA updates, approximately every two weeks, Last Processed: 12/17/2024 by Nick Santos using code path at https://github.com/CDT-ODS-DevSecOps/cdt-ods-gis-city-county/ at commit 0bf269d24464c14c9cf4f7dea876aa562984db63. It incorporates updates from CDTFA as of 12/12/2024. Future updates will include improvements to metadata and update frequency.

  19. Low-Income Housing Tax Credit Properties

    • hub.arcgis.com
    • data.lojic.org
    • +1more
    Updated Nov 12, 2024
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    Department of Housing and Urban Development (2024). Low-Income Housing Tax Credit Properties [Dataset]. https://hub.arcgis.com/maps/HUD::low-income-housing-tax-credit-properties-1
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    Dataset updated
    Nov 12, 2024
    Dataset provided by
    United States Department of Housing and Urban Developmenthttp://www.hud.gov/
    Authors
    Department of Housing and Urban Development
    Area covered
    Description

    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

  20. Qualified Opportunity Zones

    • giscommons-countyplanning.opendata.arcgis.com
    • regionaldatahub-brag.hub.arcgis.com
    • +1more
    Updated Jul 29, 2020
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    Esri U.S. Federal Datasets (2020). Qualified Opportunity Zones [Dataset]. https://giscommons-countyplanning.opendata.arcgis.com/datasets/fedmaps::qualified-opportunity-zones
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    Dataset updated
    Jul 29, 2020
    Dataset provided by
    Esrihttp://esri.com/
    Authors
    Esri U.S. Federal Datasets
    Area covered
    Description

    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)."

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TRADING ECONOMICS, United States Federal Corporate Tax Rate [Dataset]. https://tradingeconomics.com/united-states/corporate-tax-rate

United States Federal Corporate Tax Rate

United States Federal Corporate Tax Rate - Historical Dataset (1909-12-31/2025-12-31)

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14 scholarly articles cite this dataset (View in Google Scholar)
xml, csv, json, excelAvailable download formats
Dataset authored and provided by
TRADING ECONOMICS
License

Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically

Time period covered
Dec 31, 1909 - Dec 31, 2025
Area covered
United States
Description

The Corporate Tax Rate in the United States stands at 21 percent. This dataset provides - United States Corporate Tax Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

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