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.

IRS US Income Data by Zip Code

Number of Returns, Adjusted Gross Income, Total Income, and Taxable Income

By Jon Loyens [source]

About this dataset

This dataset provides a unique insight into the US income patterns in 2013, by zip code. With this data, you can explore how taxes and adjusted gross income (AGI) vary according to geographic area. The data includes total and average incomes reported, number of returns filed in each ZIP code and taxable incomes reported. This dataset is ideal for studying how economic trends have shifted geographically over time or examining regional economic disparities within the US. In addition, this dataset has been cleansed from data removed from items such as ZIP codes with fewer than 100 returns or those identified as a single building or nonresidential ZIP codes that were categorized as “other” (99999) by the IRS. Finally, dollar amounts for all variables are in thousands for better accuracy

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Research Ideas

• Using this dataset to identify potential locations for commercial developments by maping taxable incomes, total income amounts, and average incomes in different zip codes.
• Comparing the number of returns with total income, taxes payable, and income variance between different zip codes to gain insights into areas with higher financial prosperity or disparities between zip codes due to wider economic trends.
• Analyzing average adjusted gross incomes on a state-by-state basis to identify states where high net worth citizens or individuals earning high wages live in order to target marketing campaigns or develop high-end service offerings

Acknowledgements

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

License

License: Dataset copyright by authors - You are free to: - Share - copy and redistribute the material in any medium or format for any purpose, even commercially. - Adapt - remix, transform, and build upon the material for any purpose, even commercially. - You must: - Give appropriate credit - Provide a link to the license, and indicate if changes were made. - ShareAlike - You must distribute your contributions under the same license as the original. - Keep intact - all notices that refer to this license, including copyright notices.

Columns

File: IRSIncomeByZipCode.csv | Column name | Description | |:------------------------------------------|:-------------------------------------------------------------------------------------| | STATE | The two-letter abbreviation for the state in which the zip code is located. (String) | | ZIPCODE | The five-digit US zip code. (Integer) | | Number of returns | The total number of tax returns filed in the zip code. (Integer) | | Adjusted gross income (AGI) | The total amount of adjusted gross income reported in the zip code. (Integer) | | Avg AGI | The average amount of adjusted gross income reported in the zip code. (Integer) | | Number of returns with total income | The total number of returns with total income reported in the zip code. (Integer) | | Total income amount | The total amount of income reported in the zip code. (Integer) | | Avg total income | The average amount of total income reported in the zip code. (Integer) | | Number of returns with taxable income | The total number of returns with taxable income reported in the zip code. (Integer) | | Taxable income amount | The total amount of taxable income reported in the zip code. (Integer) | | Avg taxable income | The average amount of taxable income reported in the zip code. (Integer) |

File: IRSIncomeByZipCode_NoStateTotalsNoSmallZips.csv | Column name | Description | |:------------------------------------------|:-------------------------------------------------------------------------------------| | STATE | The two-letter abb...

The Tax Foundation’s publication Corporate Tax Rates around the World shows how statutory corporate income tax rates have developed since 1980, with data for over 200 jurisdictions for the year 2023. The dataset we compiled for the years 1980 to 2023 is made available as a resource for research.

Scope

The dataset compiled for this publication includes the 2023 statutory corporate income tax rates of 225 sovereign states and dependent territories around the world. Tax rates were researched only for jurisdictions that are among the around 250 sovereign states and dependent territories that have been assigned a country code by the International Organization for Standardization (ISO). (The jurisdictions Netherland Antilles (which was split into different jurisdictions in 2010) and Kosovo (which has not yet officially been assigned a country code) were added to the dataset.) As a result, zones or territories that are independent taxing jurisdictions but do not have their own country code are generally not included in the dataset.

In addition, the dataset includes historic statutory corporate income tax rates for the time period 1980 to 2022. However, these years cover tax rates of fewer than 225 jurisdictions due to missing data points. Please let Tax Foundation know if you are aware of any sources for historic corporate tax rates that are not mentioned in this report, as we constantly strive to improve our datasets.

To be able to calculate average statutory corporate income tax rates weighted by GDP, the dataset includes GDP data for 181 jurisdictions. When used to calculate average statutory corporate income tax rates, either weighted by GDP or unweighted, only these 181 jurisdictions are included (to ensure the comparability of the unweighted and weighted averages).

Definition of Selected Corporate Income Tax Rate

The dataset captures standard top statutory corporate income tax rates levied on domestic businesses. This means:

The dataset does not reflect special tax regimes, including but not limited to patent boxes, offshore regimes, or special rates for specific industries. A number of countries levy lower rates for businesses below a certain revenue threshold. The dataset does not capture these lower rates. A few countries levy gross revenue taxes on businesses instead of corporate income taxes. Since the tax rates of a corporate income tax and a gross revenue tax are not comparable, these countries are excluded from the dataset. Some countries have a separate tax rate for nonresident companies. This dataset does not consider nonresident tax rates that differ from the general corporate rate.

Explanation of Files

source-data

• country_codes.csv Dataset that includes all 250 sovereign states and dependent territories that have been assigned a country code by the International Organization for Standardization (ISO). Includes official country names in various languages, ISO country codes, continents, and further geographical information.

• data_rates_1980_2022.csv Tax Foundation's dataset of statutory corporate income tax rates for the years 1980 to 2022. This dataset has been built in stages since 2015.

• RealGDPValues.xlsx U.S. Department of Agriculture's dataset of historical and projected real Gross Domestic Product (GDP) and growth rates of GDP for 181 countries and various regions (in billions of 2015 dollars) for the years 1970 to 2032.

intermediate-ouptuts

• gdp_iso.csv GDP data paired with ISO country codes for the years 1980 to 2023.

• rates_final.csv Statutory corporate income tax rates for the years 1980 to 2023. Includes rates of all countries for which data was available in 2023 (data from OECD, KPMG, and researched individually).

• rates_preliminary.csv Statutory corporate income tax rates for the years 1980 to 2023. Includes rates of countries for - which OECD data was available for the year 2023. Does not include countries for which the rate was researched and added individually.

final-data

• final_data_2023.csv Statutory corporate income tax rates and GDP levels of countries paired with ISO country codes, continents, and country groups for the year 2023. Only includes countries for which both the corporate income tax rates and GDP data were available.

• final_data_2023_gdp_incomplete.csv Statutory corporate income tax rates and GDP levels of countries paired with ISO country codes, continents, and country groups for the year 2023. Includes all countries for which we have data for the corporate income tax rate, including countries for which we do not have GDP data.

• final_data_long.csv Statutory corporate income tax rates and GDP levels of all countries paired with ISO country codes, continents, and country groups for the years 1980 to 2023. Includes all countries that have an ISO countr...

This dataset provides values for PERSONAL INCOME TAX RATE reported in several countries. The data includes current values, previous releases, historical highs and record lows, release frequency, reported unit and currency.

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).

Many people report disliking taxes despite the fact that tax funds are used to provide essential services for the taxpayer and fellow citizens. In light of past research demonstrating that people are more likely to engage in prosocial action when they recognize how their assistance positively impacts the recipient, we examine whether recognition of how one’s tax contributions help other citizens–perceived prosocial taxation–predicts more supportive views of taxation and greater engagement. We conducted three correlational studies using North American samples (N = 902, including a nationally representative sample of over 500 US residents) in which we find that perceived prosocial taxation is associated with greater enjoyment paying taxes, willingness to continue paying taxes, and larger financial contributions in a tax-like payment. Findings hold when controlling for several demographic variables, participants’ general prosocial orientation, and the perception that tax dollars are being put to good use. In addition, we examined data from six waves of the World Values Survey (N > 474,000 across 107 countries). We find that people expressing trust in their government and civil service–thereby indicating some confidence that their taxes will be used in prosocial ways–are significantly more likely to state that it is never justifiable to cheat on taxes. Together, these studies offer a new and optimistic perspective on taxation; people may hold more positive views and be more willing to contribute if they believe their contribution benefits others.

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.

Content

The Department of Taxation and Finance annually produces a data (study) file and provides a report of statistical information on New York State personal income tax returns that were timely filed. Timely filing means that the tax return was delivered to the Department on or before the due date of the tax return. The data are from full-year resident, full-year nonresident, and part-year resident returns. This dataset defines individuals filing a resident tax return as full-year residents and individuals filing a nonresident tax return are defined as either a full- year nonresident or a part-year resident.Data presented in this dataset provide the major income tax structure components by size of income. The components include income, deductions, dependent exemptions, and tax liability. The data also provides this information by size of income and by the filer’s permanent place of residence (county, state or country). For a more detailed explanation on the determination of residency and components of income see the attachment: NYSTF_PlaceOfResidence_Introduction.Researchers agree to: Use the data for statistical reporting an analysis only. The author will include a disclaimer that states any analyses, interpretations or conclusions were reached by the author and not the New York State Department of Taxation and Finance.

Context

This is a dataset hosted by the State of New York. The state has an open data platform found here and they update their information according the amount of data that is brought in. Explore New York State using Kaggle and all of the data sources available through the State of New York organization page!

• Update Frequency: This dataset is updated annually.

Acknowledgements

This dataset is maintained using Socrata's API and Kaggle's API. Socrata has assisted countless organizations with hosting their open data and has been an integral part of the process of bringing more data to the public.

Cover photo by rawpixel on Unsplash
Unsplash Images are distributed under a unique Unsplash License.

Beginning with tax year 2015, the Department of Taxation and Finance (hereafter “the Department”) began producing a new annual population data study file to provide more comprehensive statistical information on New York State personal income tax returns. The data are from full‐year resident, nonresident, and part‐year resident returns filed between January 1 and December 31 of the year after the start of the liability period (hereafter referred to as the “processing year”). The four datasets display major income tax components by tax year. This includes the distribution of New York adjusted gross income and tax liability by county or place of residence, as well as the value of deductions, exemptions, taxable income and tax before credits by size of income. In addition, three of the four datasets include all the components of income, the components of deductions, and the addition/subtraction modifications. Caution: The current datasets are based on population data. For tax years prior to 2015, data were based on sample data. Data customers are advised to use caution when drawing conclusions comparing data for tax years prior to 2015 and subsequent tax years. Further details are included in the Overview.

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.

Beginning with tax year 2015, the Department of Taxation and Finance (hereafter “the Department”) began producing a new annual population data study file to provide more comprehensive statistical information on New York State personal income tax returns. The data are from full‐year resident, nonresident, and part‐year resident returns filed between January 1 and December 31 of the year after the start of the liability period (hereafter referred to as the “processing year”). The four datasets display major income tax components by tax year. This includes the distribution of New York adjusted gross income and tax liability by county or place of residence, as well as the value of deductions, exemptions, taxable income and tax before credits by size of income. In addition, three of the four datasets include all the components of income, the components of deductions, and the addition/subtraction modifications. Caution: The current datasets are based on population data. For tax years prior to 2015, data were based on sample data. Data customers are advised to use caution when drawing conclusions comparing data for tax years prior to 2015 and subsequent tax years. Further details are included in the Overview.

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).

Government Revenues in the United States decreased to 404371 USD Million in October from 543663 USD Million in September of 2025. This dataset provides - United States Government Revenues- actual values, historical data, forecast, chart, statistics, economic calendar and news.

The IRS publishes migration data for the US population based upon the individual tax returns filed with the IRS, where they track on a year-by-year basis

• where people were coming from - the prior state of residency
• where people moving to - the new state of residency
• number of returns filed - approximate number of households that migrated
• number of exemptions - approximate number of individuals
• the adjusted gross income (AGI) - recorded in thousands of dollars

The raw data published on the IRS website clearly shows patterns of evolution - changing patterns of what is recorded, how it is record, and naming conventions used - making it a challenge to track changes in the underlying data over time. The current dataset attempts to address these shortcomings by normalizing the record layout, standardizing the conventions, and collecting the annual into a single, coherent dataset.

An individual record is laid out with 9 fields

Y1 Y1_STATE_FIPS Y1_STATE_ABBR Y1_STATE_NAME Y2 Y2_STATE_FIPS Y2_STATE_ABBR Y2_STATE_NAME NUM_RETURNS NUM_EXEMPTIONS AGI Here, Y1 refers to the first year (from where the people are migrating) while Y2 refers to the second year (to where the people are migrating). As this is annual data, Y2 should always be the next year after Y1. Associated with each year are three different ways of identifying a state - the name of the state, it's two-letter abbreviaion, and it's FIPS code. Granted, carrying around three IDs per state is redundant; however, the various IDs are useful in different contexts. One thing to note - the IRS data represents migration into and out of the country via the introduction of a fake state, identified by STATE_NAME=FOREIGN, STATE_ABBR=FR, and STATE_FIPS=57.

From any given state, the dataset records migration to 52 destinations

• either not moving, or staying in the same state
• migrating to one of the other 49 states
• migrating to Washington DC
• migrating overseas (i.e., to the FOREIGN state)

Similarly, the dataset represents the migation into any given state as being from one of 52 destinations. Typically, the numbers associated with "staying put" constitute, by far, the largest contingent of tax payers for the given state. The one exception to this description is the FOREIGN state. The dataset does not record "staying put" outside of the country; there is no record for FOREIGN-to-FOREIGN migration. As such, there are 51, not 52, destinations paired with migration to-and-from the FOREIGN state.

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.

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.

The data in this file include for each county the number of Federal income tax returns filed and the number of exemptions claimed. Within each category, data are provided on the number of tax filers that migrated into the county, the number that migrated out of the county, and the number for which migration status was unknown. The total number of returns filed is also provided. (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/ICPSR08477.v1. We highly recommend using the ICPSR version as they may make this dataset available in multiple data formats in the future.

Income-Before-Tax Time Series for Dave Inc. Dave Inc. provides various financial products and services through its financial services platform in the United States. The company offers Budget, personal financial management tool that helps members with budgeting, and managing income and expenses; ExtraCash, a short-term liquidity alternative, which allows members to advance funds to their account through automated clearing house network and avoid a fee; Side Hustle, a job application portal to find supplemental or temporary work; and Surveys, which allows member to take paid surveys within the Dave mobile application. It also provides Dave Banking, a digital checking and demand deposit account. Dave Inc. was founded in 2015 and is headquartered in Los Angeles, California.

Tax-Provision Time Series for Invitation Homes Inc. Invitation Homes, an S&P 500 company, is the nation's premier single-family home leasing and management company, meeting changing lifestyle demands by providing access to high-quality homes with valued features such as close proximity to jobs and access to good schools. Our purpose, Unlock the power of home, reflects our commitment to providing living solutions and Genuine CARE to the growing share of people who count on the flexibility and savings of leasing a home.

Income-Before-Tax Time Series for Daily Journal Corp. Daily Journal Corporation publishes newspapers and websites covering in California, Arizona, Utah, and Australia. It operates in two segments, Traditional Business and Journal Technologies. The company publishes 10 newspapers of general circulation, including Los Angeles Daily Journal, San Francisco Daily Journal, Daily Commerce, The Daily Recorder, The Inter-City Express, San Jose Post-Record, Orange County Reporter, Business Journal, The Daily Transcript, and The Record Reporter. It also provides specialized information services; and serves as an advertising and newspaper representative for commercial and public notice advertising. In addition, the company offers case management software systems and related products, including eCourt, eProsecutor, eDefender, and eProbation, which are browser-based case processing systems; eFile, a browser-based interface that allows attorneys and the public to electronically file documents with the court; and ePayIt, a service primarily for the online payment of traffic citations. It provides its software systems and related products to courts; prosecutor and public defender offices; probation departments; and other justice agencies, including administrative law organizations, city and county governments, and bar associations to manage cases and information electronically, to interface with other justice partners, and to extend electronic services to bar members and the public in 32 states and internationally. Daily Journal Corporation was incorporated in 1987 and is based in Los Angeles, California.