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 Sweden stands at 52 percent. This dataset provides - Sweden Personal Income Tax Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

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.

This table provides census family taxation statistics, including effective tax and transfer rates, the total amount of taxes paid and government transfers received, and the proportion of Canadian census families that pay tax or receive government transfers.

This table provides individual taxation statistics, including effective tax and transfer rates, the total amount of taxes paid and government transfers received, and the proportion of Canadian taxfilers that pay tax or receive government transfers.

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.

This annual study provides county income and tax data (and State totals) that include the number of returns, which approximates the number of households; number of personal exemptions, which approximates the population; adjusted gross income; wages and salaries; dividends before exclusion; and interest received. Data are based on the addresses reported on U.S. Individual Income Tax Returns (Forms 1040) filed with the IRS. SOI collects these data as part of its annual study on Individual Tax Return Statistics by Geographic Areas, County Data.

Personal income tax is collected annually from Ontario residents and those who earned income in the province.

The tax is calculated separately from federal income tax. There are 5 Ontario income tax brackets and 5 corresponding tax rates.

For an explanation of these rates and credits, refer to the federal and provincial personal income tax return for the applicable year. To get a copy of the return (also known as a T1) contact the Canada Revenue Agency at 1-800-959-8281 or visit canada.ca/cra-forms.

Read on: about personal income tax

This data is related to:

• Filing your tax return
• Taxes and benefits
• Business income tax

Related data:

• Personal income tax
• Provincial tax database

The Personal Income Tax Rate in Finland stands at 57.65 percent. This dataset provides - Finland Personal Income Tax Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

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?

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.

The Personal Income Tax Rate in Denmark stands at 55.90 percent. This dataset provides - Denmark Personal Income Tax Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

Very Important

• Check out the new must-see kernel for this dataset Click Here
• Make Sure to upvote for more datasets and kernel :D

Overview:

Explore the dataset and potentially gain valuable insight into your data science project through interesting features. The dataset was developed for a portfolio optimization graduate project I was working on. The goal was to the monetize risk of company deleveraging by associated with changes in economic data. Applications of the dataset may include. To see the data in action visit my analytics page. Analytics Page & Dashboard and to access all 295,000+ records click here.

• Mortgage-Backed Securities
• Geographic Business Investment
• Real Estate Analysis

For any questions, you may reach us at research_development@goldenoakresearch.com. For immediate assistance, you may reach me on at 585-626-2965. Please Note: the number is my personal number and email is preferred

Statistical Themes:

Note: in total there are 75 fields the following are just themes the fields fall under Home Owner Costs: Sum of utilities, property taxes.

• Second Mortgage: Households with a second mortgage statistics.
• Home Equity Loan: Households with a Home equity Loan statistics.
• Debt: Households with any type of debt statistics.
• Mortgage Costs: Statistics regarding mortgage payments, home equity loans, utilities and property taxes
• Home Owner Costs: Sum of utilities, property taxes statistics
• Gross Rent: Contract rent plus the estimated average monthly cost of utility features
• Gross Rent as Percent of Income Gross rent as the percent of income very interesting
• High school Graduation: High school graduation statistics.
• Population Demographics: Population demographic statistics.
• Age Demographics: Age demographic statistics.
• Household Income: Total income of people residing in the household.
• Family Income: Total income of people related to the householder.

Sources, if you wish to get the data your self :)

2012-2016 ACS 5-Year Documentation was provided by the U.S. Census Reports. Retrieved May 2, 2018, from

• Census estimate, error, and location data
• Census location information

Access All 325,258 Location of Our Most Complete Database Ever:

Providing you the potential to monetize risk and optimize your investment portfolio through quality economic features at unbeatable price. Access all 295,000+ records on an incredibly small scale, see links below for more details:

• Full Dataset: View Full Dataset
• Real Estate Research: View Research

The Social Policy Simulation Database and Model (SPSD/M) is a tool designed to assist those interested in analyzing the financial interactions of governments and individuals in Canada. It can help one to assess the cost implications or income redistributive effects of changes in the personal taxation and cash transfer system. As the name implies, SPSD/M consists of two integrated parts: a database (SPSD), and a model (SPSM). The SPSD is a non-confidential, statistically representative database of individuals in their family context, with enough information on each individual to compute taxes paid to and cash transfers received from government. The SPSM is a static accounting model which processes each individual and family on the SPSD, calculates taxes and transfers using legislated or proposed programs and algorithms, and reports on the results. A sophisticated software environment gives the user a high degree of control over the inputs and outputs to the model and can allow the user to modify existing programs or test proposals for entirely new programs. The model comes with full documentation including an on-line help facility. Users and Applications The SPSD/M has been used in hundreds of sites across Canada. These sites have diverse research interests in the area of income tax-transfer and commodity tax systems in Canada as well as varied experience in micro-simulation. Our growing client base includes federal departments, provincial governments, universities, interest groups, corporate divisions, and private consultants. The diverse applications of the SPSD/M can be seen in the following examples of studies and published research reports: Costing out proposals for amendments to the Income Tax Act affecting the tax treatment of seniors and the disabled Estimating the fiscal viability of major personal tax reform options, including three flat tax scenarios The comparison low income (poverty) measures and their effect on the estimates of the number of poor An Analysis of the Distributional Impact of the Goods and Services Tax Married and Unmarried Couples: The Tax Question Taxes and Transfers in Rural Canada Equivalencies in Canadian Public Policy When the Baby Boom Grows Old: Impact on Canada's Public Sector Some potential uses of the model are illustrated by the following list of questions which may be answered using the SPSM: How large an increase in the federal Child Tax Benefit could be financed by allocating an additional $500 million to the program? Which province would have the most advantageous tax structure for an individual with $45,000 earned income, 2 children and $15,000 of investment income? What is the after-tax value of the major federal child support programs on a per child basis, and how are these benefits distributed across family types and income groups? How many individuals otherwise paying no tax would have to pay tax under various minimum tax systems, and what would additional government revenues be? How much money would be needed to raise all low income families and persons to Statistics Canada's low income cut-offs in 2014? How much would average household "consumable" income rise if a province eliminated its gasoline taxes? How much would federal government revenue rise by if there was an increase in the GST rate?

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.

The Personal Income Tax Rate in Ivory Coast stands at 60 percent. This dataset provides - Ivory Coast Personal Income Tax Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

The Personal Income Tax Rate in India stands at 39 percent. This dataset provides - India Personal Income Tax Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

Nonemployer Statistics is an annual series that provides subnational economic data for businesses that have no paid employees and are subject to federal income tax. The data consist of the number of businesses and total receipts by industry. Most nonemployers are self-employed individuals operating unincorporated businesses (known as sole proprietorships), which may or may not be the owner's principal source of income. The majority of all business establishments in the United States are nonemployers, yet these firms average less than 4 percent of all sales and receipts nationally. Due to their small economic impact, these firms are excluded from most other Census Bureau business statistics (the primary exception being the Survey of Business Owners). The Nonemployers Statistics series is the primary resource available to study the scope and activities of nonemployers at a detailed geographic level. For complementary statistics on the firms that do have paid employees, refer to the County Business Patterns. Additional sources of data on small businesses include the Economic Census, and the Statistics of U.S. Businesses.

US E-Cig Tax Regulations

1995-2016 State Taxation Policies

By Health [source]

About this dataset

This dataset, provided by the Centers for Disease Control and Prevention (CDC) through the State Tobacco Activities Tracking and Evaluation (STATE) System, contains information on state-level legislative data on tobacco use prevention and control policies related to e-cigarette taxes. It captures various measures of state excise taxes for e-cigarettes implemented over a span of almost two decades. The STATE System stores comprehensive historical data which can be used to track changes in these policies at the state level over time. This dataset includes fields such as location abbreviations, topic descriptions, measure descriptions, provision value, provision description, citations and more that provide valuable insight into understanding how these measures have evolved overtime across states in the US

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How to use the dataset

This dataset contains information on state-level legislative data on tobacco use prevention and control policies related to e-cigarette taxes from 1995-2016. It includes the following columns: location abbreviations, location descriptions, topic descriptions, measure descriptions, data sources, provision group descriptions, provision descriptions, provision values, citations for the provisions cited in the dataset as well as alternative values for those provisions if they are used. Additionally it contains dates when certain provisions become effective or enacted and also geographic locations of the data which can be used as a helpful reference point.

In order to best use this dataset you should familiarize yourself with its columns and their definitions. This will help you better understand how each element relates to others within the set and give you an idea of what type of analyses can be conducted using it. You should also take note of any relevant comments that may shed light on specific elements or provide additional information not captured in other columns. After understanding the contents of this dataset it is suggested that individuals analyze it according to their individual needs and interests but some general uses may include exploring trends in e-cigarette taxation over time by examining yearly changes in tax rates or seeing how tax regulation varies among states depending on location abbreviations provided in each row entry etc.. With these tools one could potentially make meaningful connections between different variables within this set and gain valuable insights into how US states legislate taxes related to tobacco use prevention methods

Research Ideas

• Analyzing the impact of e-cigarette taxes on usage rates in different states, in order to inform tax policy decisions.
• Examining the differences between enacted and effective dates for legislations by state and across the country, in order to gain a better understanding of how long it takes for new laws to become implemented.
• Tracking changes of e-cigarette regulation over time and studying how they correlate with measures such as number of youth users or youth perception on risk associated with e-cigarettes by state

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

License: Open Database License (ODbL) v1.0 - You are free to: - Share - copy and redistribute the material in any medium or format. - 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. - No Derivatives - If you remix, transform, or build upon the material, you may not distribute the modified material. - No additional restrictions - You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.

Columns

File: CDC_STATE_System_E-Cigarette_Legislation_-_Tax.csv | Column name | Description | |:-----------------------|:------------------------------------------------------------------| | YEAR | Year of the policy (Integer) ...

Abstract

The Opportunity Atlas has collected contextual data by county and tract. Rather than providing contextual socioeconomic data of where people currently live, the data represents average socioeconomic indicators (e.g., earnings) of where people grew up.

Documentation

A core element of Population Health Science is that health outcomes can only be fully understood when they are studied within their context. Therefore, we have a copy of The Opportunity Atlas, a dataset that provides socioeconomic data by county and tract.

Several studies have shown that especially childhood neighborhoods drive adult outcomes and that residential areas lived in through adulthood have much smaller effects. The focus of the Opportunity Atlas is therefore on contextual data of where people grew up:

%3E Traditional measures of poverty and neighborhood conditions provide snapshots of income and other variables for residents in an area at a given point in time. But to study how economic opportunity varies across neighborhoods, we really need to follow people over many years and see how one’s outcomes depend upon family circumstances and where on grew up. The Opportunity Atlas is the first dataset that provides such longitudinal information at a detailed neighborhood level. Using the Atlas, you can see not just where the rich and poor currently live – which was possible in previously available data from the Census Bureau – but whether children in a given area tend to grow up to become rich of poor. This focus on mobility out of poverty across generations allows us to trace the roots of outcomes such as poverty and incarceration back to where kids grew up, potentially permitting much more effective interventions.

As such, The Opportunity Atlas data provides a rich source of data for researchers who wish to overlay health data with contextual data.

Methodology

Three sources of Census Bureau are linked to compute the data

1. The 2000 and 2010 Decennial Census short form
2. Federal income tax returns for 1989, 1994, 1995, 1998-2015
3. The 2000 Decennial Census long form and the 2005-2015 American Community Surveys (ACS).

%3C!-- --%3E

20.5 million Americans born between 1987-1983 are sampled from these data and mapped back to the Census tracts they lived in through age 23. After that step, a range of outcomes are then estimated for each of the 70,000 tracts. In order to comply with federal data disclosure standards and protect the privacy of individuals no estimates in tracts with 20 or fewer children are published and noise (small random numbers) is added to all the estimates.

For more information on the data collection and methodology, please visit:

Website

Documentation

Data availability

Some variables are available for counties only. The table below gives you an overview. Open the table in a new tab for a larger view.

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