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.

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.

Source: Publication 15-A Cat. No. 21453T Employer's Supplemental Tax Guide (Supplement to Pub. 15, Employer's Tax Guide) For use in 2018

Full Name: Wage Bracket Percentage Method Tables for Computing Income Tax Withholding From Gross Wages (For Wages Paid in 2018, America)

This data set represents the American 2018 tables for withholding gross wages (not from wages exceeding allowance amount). Wages withheld from employee checks by the employer are computed by looking employee's W-4 form, finding the number of allowances, the pay-period of the business(Weekly, Semi-Monthly, etc..) and filing status (Married/Single), and then finding the wage range the employees change fall into. Once this is done, the employer will subtract a pre determined base amount (Base Amount Subtracted from Gross Wages), multiply that by a per-determined percentage (Percentage to Multiply), and the result will be the amount withheld from the employee's check for federal taxes. This amount does not include state, social security, and other taxes.
For example, an employee that is paid on a Bi-Weekly basis, that has filed for 2 allowances and Single, who has earned $2000 in the pay-period will have the following amount withheld from their check for federal taxes:

$2000 - $1171.38(base amount) = $828.62

$828.62 * .22 (per-determined percentage ) = $182.30 (amount withheld from their check for federal taxes)

This data set was developed from a burning hatred for not being able to find this information in an excel or csv format. So I have made one that can hopefully save someone else a little time and energy. For more information on tax laws and practices in America, please refer to the IRS.

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).

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.

The Equivalent Fiscal Pressure (EFP) for Latin America and the Caribbean for the period 1990-2018, calculated using the IDB-CIAT methodology, measures the total resources collected by the countries of the region. This includes mandatory contributions to private (actuarial) social security systems and non-tax revenues from natural resource exploitation activities. In 2018, the EFP reached 25.2% of GDP, an increase of 0.4% compared to 2017. The sustained increase is based on three fiscal pillars: the Value-Added Tax (VAT), the Income Tax System (ISR), and mandatory Social Security Contributions (SSC), both public and private. From 1990 to 2018, these pillars collectively grew as follows: VAT by 3.4 percentage points of GDP (87.0%), ISR by 2.7 points (77.5%), mandatory SSC by 1.6 points (59.5%), and non-tax revenue from natural resources by 0.7 points (317.5%). Over the most recent five-year period (2013-2018), EFP growth was limited to 1 percentage point of GDP, equivalent to a 4.1% increase. VAT and ISR grew by only 4.8% (to 7.3% of GDP in 2018) and 11.8% (to 6.3% of GDP), respectively, while revenues from natural resources declined by 51.9% (to 1.0% of GDP).

This table contains data on the living wage and the percent of families with incomes below the living wage for California, its counties, regions and cities/towns. Living wage is the wage needed to cover basic family expenses (basic needs budget) plus all relevant taxes; it does not include publicly provided income or housing assistance. The percent of families below the living wage was calculated using data from the Living Wage Calculator and the U.S. Census Bureau, American Community Survey. The table is part of a series of indicators in the Healthy Communities Data and Indicators Project of the Office of Health Equity. The living wage is the wage or annual income that covers the cost of the bare necessities of life for a worker and his/her family. These necessities include housing, transportation, food, childcare, health care, and payment of taxes. Low income populations and non-white race/ethnic have disproportionately lower wages, poorer housing, and higher levels of food insecurity. More information about the data table and a data dictionary can be found in the About/Attachments section.

Selected annual aggregate balance sheet and income statement items representing incorporated enterprises operating in Canada, by the North American Industry Classification System (NAICS), presented in millions of dollars or percentages unless otherwise specified.

Detta dataset presenterar information över statens skatteintäkter för 31 länder i Europa, Nordamerika och Sydamerika från 1800 (eller självständighet) till 2012. Länderna i datasetet är: Argentina, Australien, Österrike, Belgien, Bolivia, Brasilien, Kanada, Chile, Colombia, Danmark, Ecuador, Finland, Frankrike, Tyskland (Västtyskland mellan 1949 och 1990), Irland, Italien, Japan, Mexiko, Nya Zeeland, Norge, Paraguay, Peru, Portugal, Spanien, Sverige, Schweiz, Nederländerna, USA Storbritannien, USA, Uruguay och Venezuela. Med andra ord innehåller datasetet alla sydamerikanska, nordamerikanska och västeuropeiska länder med en befolkning på mer än en miljon plus Australien, nya Zeeland, Japan och Mexiko. Datasetet innehåller information om den centrala statens offentliga finanser. För att göra denna information jämförbar mellan länder har vi valt att normalisera de nominella intäktssiffrorna på två sätt: (i) som en andel av den totala budgeten och (ii) som en andel av den totala bruttonationalprodukten. Den centrala statens totala skatteintäkter är uppdelade baserat på Internationella valutafondens (IMF) handbok över statsfinanser från 2001. Denna ger en klassificering av intäktstyper och beskriver innehållet i varje klassificeringskategori. Med tanke på den bristfälliga historiska datan och våra projektbehov kombinerade vi några underkategorier. Till att börja med är vi intresserade av totala skatteintäkter (centaxtot), liksom andelarna av totala intäkter som kommer från direkta (centaxdirectsh) och indirekta (centaxindirectsh) skatter. Vidare mäter vi två underkategorier av direkt beskattning, nämligen skatter på egendom (centaxpropertysh) och inkomst (centaxincomesh). För indirekta skatter skiljer vi på punktskatter (centaxexcisesh), konsumtion (centaxconssh) och tullar (centaxcustomssh).

För en mer detaljerad beskrivning av datan och insamlingsprocessen, se kodboken som finns tillgänlig i .zip-filen.

Syfte:

Detta dataset presenterar information över statens skatteintäkter för 31 länder i Europa, Nordamerika och Sydamerika från 1800 (eller självständighet) till 2012. Länderna i datasetet är: Argentina, Australien, Österrike, Belgien, Bolivia, Brasilien, Kanada, Chile, Colombia, Danmark, Ecuador, Finland, Frankrike, Tyskland (Västtyskland mellan 1949 och 1990), Irland, Italien, Japan, Mexiko, Nya Zeeland, Norge, Paraguay, Peru, Portugal, Spanien, Sverige, Schweiz, Nederländerna, USA Storbritannien, USA, Uruguay och Venezuela. Med andra ord innehåller datasetet alla sydamerikanska, nordamerikanska och västeuropeiska länder med en befolkning på mer än en miljon plus Australien, nya Zeeland, Japan och Mexiko. Datasetet innehåller information om den centrala statens offentliga finanser. För att göra denna information jämförbar mellan länder har vi valt att normalisera de nominella intäktssiffrorna på två sätt: (i) som en andel av den totala budgeten och (ii) som en andel av den totala bruttonationalprodukten. Den centrala statens totala skatteintäkter är uppdelade baserat på Internationella valutafondens (IMF) handbok över statsfinanser från 2001. Denna ger en klassificering av intäktstyper och beskriver innehållet i varje klassificeringskategori. Med tanke på den bristfälliga historiska datan och våra projektbehov kombinerade vi några underkategorier. Till att börja med är vi intresserade av totala skatteintäkter (centaxtot), liksom andelarna av totala intäkter som kommer från direkta (centaxdirectsh) och indirekta (centaxindirectsh) skatter. Vidare mäter vi två underkategorier av direkt beskattning, nämligen skatter på egendom (centaxpropertysh) och inkomst (centaxincomesh). För indirekta skatter skiljer vi på punktskatter (centaxexcisesh), konsumtion (centaxconssh) och tullar (centaxcustomssh).

US Industry Data by State, by Industry

Number of Establishments, Sales, Payroll, and Employees

By Gary Hoover [source]

About this dataset

This data set provides a detailed look into the US economy. It includes information on establishments and nonemployer businesses, as well as sales revenue, payrolls, and the number of employees. Gleaned from the Economic Census done every five years, this data is a valuable resource to anyone curious about where the nation was economically at the time. With columns including geographic area name, North American Industry Classification System (NAICS) codes for industries, descriptions of those codes meaning of operation or tax status, and annual payroll, this information-rich dataset contains all you need to track economic trends over time. Whether you’re a researcher studying industry patterns or an entrepreneur looking for market insight — this dataset has what you’re looking for!

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How to use the dataset

This dataset provides detailed US industry data by state, including the number of establishments, value of sales, payroll, and number of employees. All the data is based on the North American Industry Classification System (NAICS) code for each specific industry. This will allow you to easily analyze and compare industries across different states or regions.

Research Ideas

• Analyzing the economic impact of a new business or industry trends in different states: Comparing the change in the number of establishments, payroll, and employees over time can give insight into how a state is affected by a new industry trend or introduction of a new service or product.
• Estimating customer sales potential for businesses: This dataset can be used to estimate the potential customer base for businesses in different geographic areas. By analyzing total business done by non-employers in an area along with its estimated population can help estimate how much overall sales potential exists for a given region.
• Tracking competitor performance: By looking at shipments, receipts, and value of business done across industries in different regions or even cities, companies can track their competitors’ performance and compare it to their own to better assess their strategies going forward

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: 2012 Industry Data by Industry and State.csv | Column name | Description | |:----------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------| | Geographic area name | The name of the geographic area the data is for. (String) | | NAICS code | The North American Industry Classification System (NAICS) code for the industry. (String) | | Meaning of NAICS code | The description of the NAICS code. (String) | | Meaning of Type of operation or tax status code | The description of the type of operation or tax status code. (String) ...

This table contains data on the living wage and the percent of families with incomes below the living wage for California, its counties, regions and cities/towns. Living wage is the wage needed to cover basic family expenses (basic needs budget) plus all relevant taxes; it does not include publicly provided income or housing assistance. The percent of families below the living wage was calculated using data from the Living Wage Calculator and the U.S. Census Bureau, American Community Survey. The table is part of a series of indicators in the Healthy Communities Data and Indicators Project of the Office of Health Equity. The living wage is the wage or annual income that covers the cost of the bare necessities of life for a worker and his/her family. These necessities include housing, transportation, food, childcare, health care, and payment of taxes. Low income populations and non-white race/ethnic have disproportionately lower wages, poorer housing, and higher levels of food insecurity. More information about the data table and a data dictionary can be found in the About/Attachments section.

The Alberta Tax Advantage is a set of estimates of the additional tax burdens Albertans and Alberta businesses would be subject to if Alberta had the tax systems of other provinces. The estimates are published each year in the provincial budget. Tax advantage estimates are calculated for personal and corporate income taxes, sales taxes, health care premiums, payroll taxes, fuel taxes, and other taxes, which includes tourism levy, capital taxes, land transfer taxes, tobacco taxes, and revenue from alcohol sales (Alberta does not tax alcohol sales, but does levy a liquor mark-up. Other provinces tax alcohol sales, so all provincial revenues collected from alcohol sales are included for comparative purposes). Each estimate is then used to determine the tax advantage or disadvantage that Albertans or Alberta businesses have as a result of the structure of Alberta’s tax system over the tax system in the other province. The estimates are aggregated to determine the total advantage provided by Alberta’s tax system. The estimates are calculated by applying the known tax rates of other provinces to Alberta tax bases. For example, the fuel tax rates for other provinces are applied to Alberta fuel volumes, while provincial personal income tax systems are applied to Alberta tax filer data. If a province levies a tax that is not levied in Alberta, the tax burden and Alberta tax base will be estimated using alternative methods.

Key Table Information.Table Title.Mortgage Status by Aggregate Real Estate Taxes Paid (Dollars).Table ID.ACSDT1Y2024.B25090.Survey/Program.American Community Survey.Year.2024.Dataset.ACS 1-Year Estimates Detailed Tables.Source.U.S. Census Bureau, 2024 American Community Survey, 1-Year Estimates.Dataset Universe.The dataset universe of the American Community Survey (ACS) is the U.S. resident population and housing. For more information about ACS residence rules, see the ACS Design and Methodology Report. Note that each table describes the specific universe of interest for that set of estimates..Methodology.Unit(s) of Observation.American Community Survey (ACS) data are collected from individuals living in housing units and group quarters, and about housing units whether occupied or vacant. For more information about ACS sampling and data collection, see the ACS Design and Methodology Report..Geography Coverage.ACS data generally reflect the geographic boundaries of legal and statistical areas as of January 1 of the estimate year. For more information, see Geography Boundaries by Year.Estimates of urban and rural populations, housing units, and characteristics reflect boundaries of urban areas defined based on 2020 Census data. As a result, data for urban and rural areas from the ACS do not necessarily reflect the results of ongoing urbanization..Sampling.The ACS consists of two separate samples: housing unit addresses and group quarters facilities. Independent housing unit address samples are selected for each county or county-equivalent in the U.S. and Puerto Rico, with sampling rates depending on a measure of size for the area. For more information on sampling in the ACS, see the Accuracy of the Data document..Confidentiality.The Census Bureau has modified or suppressed some estimates in ACS data products to protect respondents' confidentiality. Title 13 United States Code, Section 9, prohibits the Census Bureau from publishing results in which an individual's data can be identified. For more information on confidentiality protection in the ACS, see the Accuracy of the Data document..Technical Documentation/Methodology.Information about the American Community Survey (ACS) can be found on the ACS website. Supporting documentation including code lists, subject definitions, data accuracy, and statistical testing, and a full list of ACS tables and table shells (without estimates) can be found on the Technical Documentation section of the ACS website.Sample size and data quality measures (including coverage rates, allocation rates, and response rates) can be found on the American Community Survey website in the Methodology section.Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted roughly as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see ACS Technical Documentation). The effect of nonsampling error is not represented in these tables.Users must consider potential differences in geographic boundaries, questionnaire content or coding, or other methodological issues when comparing ACS data from different years. Statistically significant differences shown in ACS Comparison Profiles, or in data users' own analysis, may be the result of these differences and thus might not necessarily reflect changes to the social, economic, housing, or demographic characteristics being compared. For more information, see Comparing ACS Data..Weights.ACS estimates are obtained from a raking ratio estimation procedure that results in the assignment of two sets of weights: a weight to each sample person record and a weight to each sample housing unit record. Estimates of person characteristics are based on the person weight. Estimates of family, household, and housing unit characteristics are based on the housing unit weight. For any given geographic area, a characteristic total is estimated by summing the weights assigned to the persons, households, families or housing units possessing the characteristic in the geographic area. For more information on weighting and estimation in the ACS, see the Accuracy of the Data document.Although the American Community Survey (ACS) produces population, demographic and housing unit estimates, the decennial census is the official source of population totals for April 1st of each decennial year. In between censuses, the Census Bureau's Population Estimates Program produces and disseminates the official estimates of the population for the nation, states, counties, ci...

This dataset provides values for TAXES ON EXPORTS PERCENT OF TAX REVENUE WB DATA.HTML reported in several countries. The data includes current values, previous releases, historical highs and record lows, release frequency, reported unit and currency.

Like other Assessor and Recorder data sets from First American, BlackKnight, ATTOM or HouseCanary, we provide both residential real estate and commercial restate data on homes, properties and pracels nationally.

Over 250M parcels, updated daily.

Access detailed property and tax assessment records with our extensive nationwide database. This robust dataset provides comprehensive information about residential and commercial properties, including detailed ownership, valuation, and transaction history. Core Data Elements:

Complete property identification (APNs, Tax IDs) Full property addresses with geocoding Precise latitude/longitude coordinates FIPS codes and Census tract information School district assignments

Property Characteristics:

Detailed lot dimensions and size Building square footage breakdowns Living area measurements Basement and attic specifications Garage and parking information Year built and effective year Number of bedrooms and bathrooms Room counts and configurations Building class and condition codes Construction details and materials Property amenities and features

Valuation Information:

Current AVM (Automated Valuation Model) values Confidence scores and value ranges Market valuations with dates Assessed values (land and improvements) Tax amounts and years Tax rate codes and districts Various tax exemption statuses

Transaction History:

Current and previous sale details Recording dates and document numbers Sale prices and price codes Buyer and seller information Multiple mortgage records including:

Loan amounts and terms Lender information Recording dates Interest rates Due dates Loan types and positions

Ownership Details:

Current owner information Corporate ownership indicators Owner-occupied status Mailing addresses Care of names Foreign address indicators

Legal Information:

Complete legal descriptions Subdivision details Lot and block numbers Zoning information Land use codes HOA information and fees

Property Status Indicators:

Vacancy flags Pre-foreclosure status Current listing status Price ranges Market position

Perfect For:

Real Estate Professionals

Property researchers Title companies Real estate attorneys Appraisers Market analysts

Financial Services

Mortgage lenders Insurance companies Investment firms Risk assessment teams Portfolio managers

Government & Planning

Urban planners Tax assessors Economic developers Policy researchers Municipal agencies

Data Analytics

Market researchers Data scientists Economic analysts GIS specialists Demographics experts

Data Delivery Features:

Multiple format options Regular updates Bulk download capability Custom field selection Geographic filtering API access available Standardized formatting Quality assured data

Quality Assurance:

Verified against public records Regular updates Standardized formatting Address verification Geocoding validation Duplicate removal Data normalization Quality control processes

This comprehensive property database provides unprecedented access to detailed property information, perfect for industry professionals requiring in-depth property data for analysis, research, or business development. Our data undergoes rigorous quality control processes to ensure accuracy and completeness, making it an invaluable resource for real estate professionals, financial institutions, and government agencies. Updated continuously from authoritative sources, this dataset offers the most current and accurate property information available in the market. Custom data extracts and specific geographic coverage options are available to meet your exact needs.

Weekly/Quarterly/Annual and One-time options are available for sale.

See our sample

This dataset provides values for TAX REVENUE PERCENT OF GDP WB DATA.HTML reported in several countries. The data includes current values, previous releases, historical highs and record lows, release frequency, reported unit and currency.

This dataset provides values for TOTAL TAX RATE PERCENT OF PROFIT WB DATA.HTML reported in several countries. The data includes current values, previous releases, historical highs and record lows, release frequency, reported unit and currency.

This dataset provides values for BANK RETURN ON EQUITY PERCENT AFTER TAX WB DATA.HTML reported in several countries. The data includes current values, previous releases, historical highs and record lows, release frequency, reported unit and currency.