The Quarterly Summary of State and Local Government Tax Revenue provides quarterly estimates of state and local government tax revenue at a national level, as well as detailed tax revenue data for individual states. This quarterly survey has been conducted continuously since 1962. The information contained in this survey is the most current information available on a nationwide basis for government tax collections.

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...

Per capita values are calculated by dividing the estimated population into total revenues per city, per fiscal year.

Graph and download economic data for National Totals of State and Local Tax Revenue: Total Taxes for the United States (QTAXTOTALQTAXCAT1USNO) from Q1 1992 to Q1 2025 about state & local, revenue, tax, government, and USA.

About this dataset

Context

The dataset presents median household incomes for various household sizes in State Line City, IN, as reported by the U.S. Census Bureau. The dataset highlights the variation in median household income with the size of the family unit, offering valuable insights into economic trends and disparities within different household sizes, aiding in data analysis and decision-making.

Key observations

• Of the 7 household sizes (1 person to 7-or-more person households) reported by the census bureau, State Line City did not include 4, 5, 6, or 7-person households. Across the different household sizes in State Line City the mean income is $64,968, and the standard deviation is $33,244. The coefficient of variation (CV) is 51.17%. This high CV indicates high relative variability, suggesting that the incomes vary significantly across different sizes of households.
• In the most recent year, 2021, The smallest household size for which the bureau reported a median household income was 1-person households, with an income of $36,144. It then further increased to $101,336 for 3-person households, the largest household size for which the bureau reported a median household income.

https://i.neilsberg.com/ch/state-line-city-in-median-household-income-by-household-size.jpeg" alt="State Line City, IN median household income, by household size (in 2022 inflation-adjusted dollars)">

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates.

Household Sizes:

• 1-person households
• 2-person households
• 3-person households
• 4-person households
• 5-person households
• 6-person households
• 7-or-more-person households

Variables / Data Columns

• Household Size: This column showcases 7 household sizes ranging from 1-person households to 7-or-more-person households (As mentioned above).
• Median Household Income: Median household income, in 2022 inflation-adjusted dollars for the specific household size.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for State Line City median household income. You can refer the same here

Government Transportation Financial Statistics is no longer being updated by the Bureau of Transportation Statistics as of June 2024! It is being replaced by our new product, Transportation Public Financial Statistics (TPFS) which provides more granularity by expanding the categories of revenues and expenditures. The new dataset can be found: https://data.bts.gov/Research-and-Statistics/Transportation-Public-Financial-Statistics-TPFS-/6aiz-ybqx/about_data Further information about the TPFS can be found at: https://www.bts.gov/tpfs The government plays an important role in the U.S. transportation system, as a provider of transportation infrastructure and as an administrator and regulator of the system. The government spends a large amount of funds on building, rehabilitating, maintaining, operating, and administering the infrastructure system. Government revenue generated from several sources including user fees, taxes from transportation and non-transportation-related activities, borrowing, and grants from federal, state, and local governments primarily supports these activities. Government Transportation Financial Statistics (GTFS) provides a set of maps, charts, and tables with information on transportation-related revenue and expenditures for all levels of government, including federal, state, and local, and for all modes of transportation. Related tables can be found in National Transportation Statistics, Section 3.D - Government Finance (https://www.bts.gov/topics/national-transportation-statistics). For further information, data definitions, and methodology, see https://www.bts.gov/gtfs

Data from Fortune 500's 2023 ranking.
Includes data on top 1000 companies w/ additional info (Stock symbol/*ticker*, CEO name).

Update (New dataset): 2024 Fortune 1000 Companies

What Is the Fortune 1000?

From Investopedia:

The Fortune 1000 is an annual list of the 1000 largest American companies maintained by the popular magazine Fortune Fortune ranks the eligible companies by revenue generated from core operations, discounted operations, and consolidated subsidiaries Since revenue is the basis for inclusion, every company is authorized to operate in the United States and files a 10-K or comparable financial statement with a government agency -- .

Project Background

Fortune magazine publishes this list every year and some lists can be found from different sources. From looking at this year's available datasets, some features were missing or could not be found. This was built from scraping the standard features as well as what's included on Company Info (such as CEO, Ticker and website) from the Fortune magazine website. Details on how the data was generated can be found on this notebook where a few of the features were also visualized.

The source code from the 2023 fortune 500 Ranking includes 1000 companies. A reference page (slug) to additional info is included for each companies which were also scrapped to complete the dataset.

The Dataset

Available formats: csv, parquet

Features are follows:

[Note: References to datatypes are relevant when using the parquet file; Labels refer to the original website names]

• Rank
    dtype: int64; Label: Rank
• Company
    dtype: object; Label: Company
• Ticker
    dtype: object; Label: Ticker
• Sector
    dtype: category; Label: Sector
• Industry
    dtype: category; Label: Industry
• Profitable
    dtype: category; Label: Profitable
• Founder_is_CEO
    dtype: category; Label: Founder is CEO
• FemaleCEO
    dtype: category; Label: Female CEO
• Growth_in_Jobs
    dtype: category; Label: Growth in Jobs
• Change_in_Rank
    dtype: float64; Label: Change in Rank (Full 1000)
• Gained_in_Rank
    dtype: category; Label: Gained in Rank
• Dropped_in_Rank
    dtype: category; Label: Dropped in Rank
• Newcomer_to_the_Fortune500
    dtype: category; Label: Newcomer to the Fortune 500
• Global500
    dtype: category; Label: Global 500
• Best_Companies
    dtype: category; Label: Best Companies
• Number_of_employees
    dtype: int64; Label: Employees
• MarketCap_March31_M
    dtype: float64; Label: Market Value — as of March 31, 2023 ($M)
• Revenues_M
    dtype: int64; Label: Revenues ($M)
• RevenuePercentChange
    dtype: float64; Label: Revenue Percent Change
• Profits_M
    dtype: int64; Label: Profits ($M)
• ProfitsPercentChange
    dtype: float64; Label: Profits Percent Change
• Assets_M
    dtype: int64; Label: Assets ($M)
• CEO
    dtype: object; Label: CEO
• Country
    dtype: category; Label: Country
• HeadquartersCity
    dtype: object; Label: Headquarters City
• HeadquartersState
    dtype: category; Label: Headquarters State
• Website
    dtype: object; Label: Website
• CompanyType
    dtype: category; Label: Company type
• Footnote
    dtype: object; Label: Footnote
• MarketCap_Updated_M
    dtype: float64; Label: Market value ($M)
• Updated
    dtype: datetime64[ns]; Label: Updated Click to add a cell.

Corporate Profits in the United States decreased to 3203.60 USD Billion in the first quarter of 2025 from 3312 USD Billion in the fourth quarter of 2024. This dataset provides the latest reported value for - United States Corporate Profits - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

This dataset contains tables and figures for the publication "Revenue Collection Is Not Enough: Taxes as an Instrument of Development" (Related publication only available in Spanish). Taxation in Latin America is largely viewed as a means of generating income to keep the government in business. In recent years, progress has been made towards increasing total revenue, but most countries in the region still lag well behind other countries with similar levels of development. More importantly, Latin America policymakers still largely ignore the potential of taxation to contribute to other important development goals. Governments have repeatedly missed the chance to influence consumption and production patterns by using taxes to effect relative price changes. More than Revenue aims to provide an up-to-date overview of the current state of taxation in the Latin American and Caribbean (LAC) region, its main reform needs, and possible reform strategies that take into account the likely economic, institutional, and political constraints on the reform process.

About Dataset The dataset contains information about sales transactions, including details such as the customer's age, gender, location, and the products sold. The dataset includes data on both the cost of the product and the revenue generated from its sale, allowing for calculations of profit and profit margins. The dataset includes information on customer age and gender, which could be used to analyze purchasing behavior across different demographic groups. The dataset likely includes both numeric and categorical data, which would require different types of analysis and visualization techniques. Overall, the dataset appears to provide a comprehensive view of sales transactions, with the potential for analysis at multiple levels, including by product, customer, and location. But it does not contain any useful information or insights for decision makers. - After understanding the dataset. - I cleaned it and add some columns & calculations like (Net profit, Age Status). - Making a model in Power Pivot, calculate some measures like (Total profit, COGS, Total revenues) and Making KPIS Model. - Then asked some questions: About Distribution What are the total revenues and profits? What is the best-selling country in terms of revenue? What are the five best-selling states in terms of revenue? What are the five lowest-selling states in terms of revenues? What is the position of age in relation to revenues? About profitability What are the total revenues and profits? Monthly position in terms of revenues and profits? Months position in terms of COGS? What are the top category-selling in terms of revenues & Profit? What are the three best-selling sub-category in terms of profit? About KPIS Explain to me each salesperson's position in terms of Target

• Then Answering that questions, analysis the data and Visualize with Dashboards.

Exempt organization information is extracted monthly from the Internal Revenue Service’s Business Master File. This is a cumulative file, and the data are the most recent information the IRS has for these organizations.

If you have any questions about the tax-exempt organizations or the content of the files, please contact TE/GE Customer Account Services toll-free line at 1-877-829-5500

State is determined from the filing address and generally represent the location of an organization’s headquarters, which may or may not represent the state(s) in which an organization has operations.

Records are sorted by Employer Identification Number (EIN).

This dataset is for Connecticut only.

The IRS exempt organization data have been accumulated since the inception of the tax-exempt statutes. A determination letter is issued to an organization upon the granting of an exemption and is considered valid throughout the life of the organization, as long as the organization complies with the provisions of its exemption. If an organization's exemption is revoked, an announcement to inform potential donors of the revocation is published in the Internal Revenue Bulletin. In addition, the organization’s name is removed from publicly accessible venues, including this file.

Updated nightly.

NOTE: Split-interest trusts are not included in this database.

FIELDS AVAILABLE All exempt organization records on this file will contain the following data fields: Column Name Contents EIN Employer Identification Number (EIN) NAME Primary Name of Organization ICO In Care of Name STREET Street Address CITY City STATE State ZIP Zip Code GROUP Group Exemption Number SUBSECTION Subsection Code AFFILIATION Affiliation Code CLASSIFICATION Classification Code(s) RULING Ruling Date DEDUCTIBILITY Deductibility Code FOUNDATION Foundation Code ACTIVITY Activity Codes ORGANIZATION Organization Code STATUS Exempt Organization Status Code TAX_PERIOD Tax Period ASSET_CD Asset Code INCOME_CD Income Code FILING_REQ_CD Filing Requirement Code PF_FILING_REQ_CD PF Filing Requirement Code ACCT_PD Accounting Period ASSET_AMT Asset Amount INCOME_AMT Income Amount (includes negative sign if amount is negative) REVENUE_AMT Form 990 Revenue Amount (includes negative sign if amount is negative) NTEE_CD National Taxonomy of Exempt Entities (NTEE) Code SORT_NAME Sort Name (Secondary Name Line)

This table contains data on income inequality. The primary measure is the Gini index – a measure of the extent to which the distribution of income among families/households within a community deviates from a perfectly equal distribution. The index ranges from 0.0, when all families (households) have equal shares of income (implies perfect equality), to 1.0 when one family (household) has all the income and the rest have none (implies perfect inequality). Index data is provided for California and its counties, regions, and large cities/towns. The data is from 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. Income is linked to acquiring resources for healthy living. Both household income and the distribution of income across a society independently contribute to the overall health status of a community. On average Western industrialized nations with large disparities in income distribution tend to have poorer health status than similarly advanced nations with a more equitable distribution of income. Approximately 119,200 (5%) of the 2.4 million U.S. deaths in 2000 are attributable to income inequality. The pathways by which income inequality act to increase adverse health outcomes are not known with certainty, but policies that provide for a strong safety net of health and social services have been identified as potential buffers. More information about the data table and a data dictionary can be found in the About/Attachments section.

The dataset contains NSS 78th round compiled data on Percentage Distribution of People who are living in their last usual place of residence i.e. their last usual place of residence in the last six months or more. Together with this, the dataset also contains percentage change in the income i.e. whether the income has increased, decreased, remained static, etc. of those who have migrated from their Previous Usual Place of Residence and have Migrated from it.

In 2023, the real median household income in the state of Alabama was 60,660 U.S. dollars. The state with the highest median household income was Massachusetts, which was 106,500 U.S. dollars in 2023. The average median household income in the United States was at 80,610 U.S. dollars.

A. SUMMARY This dataset contains data from financial statements of state committees that (1) contribute to or (2) receive funds from a San Francisco committee which was Primarily Formed for a local election, or (3) filed a Late Reporting Period statement with the SFEC during the 90 days before an election. The search period for financial statements begins two years before an election and runs through the next semi-annual filing deadline. The dataset currently filters by the elections of 2024-03-05 and 2024-11-05. B. HOW THE DATASET IS CREATED During an election period, an automated script runs nightly to examine filings by Primarily Formed San Francisco committees. If a primarily formed committee reports accepting money from or giving money to a second committee, that second committee's ID number is added to a filter list. If a committee electronically files a late reporting period form with the San Francisco Ethics Commission, the committee's ID number is also included in the filter list. The filter list is used in a second step that looks for filings by committees that file with the San Francisco Ethics Commission or the California Secretary of State. This dataset shows the committees that file with the California Secretary of State. The data comes from a nightly export of the Secretary of State's database. A second dataset includes Non-Primarily Formed committees that file with the San Francisco Ethics Commission. C. UPDATE PROCESS This dataset is rewritten nightly based on data derived from campaign filings. The update script runs automatically on a timer during the 90 days before an election. Refer to the "Data Last Updated" date in the section "About This Dataset" on the landing page to see when the script last ran successfully. D. HOW TO USE THIS DATASET Transactions from all FPPC Form 460 schedules are presented together, refer to the Form Type to differentiate. Transactions with a Form Type of D, E, F, G, H, F496, or F497P2 represent expenditures or money spent by the committee. Transactions with Form Type A, B1, C, I, F496P3, and F497P1 represent receipts or money taken in by the committee. Refer to the instructions for Forms 460, 496, and 497 for more details. Transactions on Form 460 Schedules D, F, G, and H are also reported on Schedule E. When doing summary statistics use care not to double count expenditures. Transactions from FPPC Form 496 and Form 497 filings are also in this dataset. Transactions that were reported on these forms are also reported on the Form 460 at the next filing deadline. If a 460 filing deadline has passed and the committee has filed a campaign statement, transactions on filings from the late reporting period should be disregarded. This dataset only shows transactions from the most recent filing version. Committee's amendments overwrite filings which come before in sequence. Campaign Committees are required to file statements according to a schedule set out by the California Fair Political Practices Commission. Depending on timing, transactions which have occurred may not be listed as they might not have been reported yet. E. RELATED DATASETS <a href=

Disposable Personal Income in the United States decreased to 22454.56 USD Billion in May from 22579.58 USD Billion in April of 2025. This dataset provides - United States Disposable Personal Income - 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.

From 2004 to 2024, the net revenue of Amazon e-commerce and service sales has increased tremendously. In the fiscal year ending December 31, the multinational e-commerce company's net revenue was almost *** billion U.S. dollars, up from *** billion U.S. dollars in 2023.Amazon.com, a U.S. e-commerce company originally founded in 1994, is the world’s largest online retailer of books, clothing, electronics, music, and many more goods. As of 2024, the company generates the majority of it's net revenues through online retail product sales, followed by third-party retail seller services, cloud computing services, and retail subscription services including Amazon Prime. From seller to digital environment Through Amazon, consumers are able to purchase goods at a rather discounted price from both small and large companies as well as from other users. Both new and used goods are sold on the website. Due to the wide variety of goods available at prices which often undercut local brick-and-mortar retail offerings, Amazon has dominated the retailer market. As of 2024, Amazon’s brand worth amounts to over *** billion U.S. dollars, topping the likes of companies such as Walmart, Ikea, as well as digital competitors Alibaba and eBay. One of Amazon's first forays into the world of hardware was its e-reader Kindle, one of the most popular e-book readers worldwide. More recently, Amazon has also released several series of own-branded products and a voice-controlled virtual assistant, Alexa. Headquartered in North America Due to its location, Amazon offers more services in North America than worldwide. As a result, the majority of the company’s net revenue in 2023 was actually earned in the United States, Canada, and Mexico. In 2023, approximately *** billion U.S. dollars was earned in North America compared to only roughly *** billion U.S. dollars internationally.

The Local Food Marketing Practices Survey (LFMPS) is a dataset created by the U.S. Department of Agriculture's National Agricultural Statistics Service (NASS) to track marketing practices of farms selling locally or regionally produced agricultural food products. First conducted in 2015 and updated in 2020 as part of the Census of Agriculture, it provides benchmark data on direct-to-consumer and direct-to-intermediate-market sales, including revenue, channels (e.g., farmers' markets, CSAs, on-farm sales), and operational characteristics. Key features include exclusion of farms not engaged in local sales, detailed breakdowns of sales by state (e.g., California accounted for the largest share of direct sales in 2020), and insights into trends like the dominance of direct-to-consumer marketing (77% of operations in 2020). The dataset supports policy development, academic research, and industry analysis by quantifying the economic impact of local food systems. For example, in 2020, 147,307 operations generated $9.0 billion through direct marketing. Unique aspects include its focus on branded regional products and granular data on sales distribution (e.g., direct-to-consumer sales constituted 33% of total direct sales despite being the most common channel). Data is accessible via the NASS Quick Stats database.

Part of the Utah Governor's Office of Economic Opportunity (GOEO) is the Grants and Incentives department. The main program run by the Grants and Incentives department, is known as the Economic Development Tax Increment Financing incentive (EDTIF). In exchange for companies making investments in Utah, ie, creating jobs, paying taxes, capital expenditure, etc, the state grants a certain percentage of taxes, payed by qualified companies, back in the form of a post performance tax credit. To qualify, companies must detail out a plan for their investment in Utah (AKA the "Project"). This project must then be approved by the GOEO Board.

By Utah law, select details about the project are made available to the public. The website below is where these details are published in order to stay compliant, and is also the source of the data presented here.

https://business.utah.gov/incented-companies/

Below is a more detailed description of each column's name and significance within the data set.

Company: The name of the company that qualified for the EDTIF program.

Year: The year in which the company qualified for the EDTIF.

Jobs: The estimated number of Jobs to be created by the company's project over the lifetime of the project. (See Terms.)

State Wages: The estimated new state wages generated by the company, AKA, the estimated total new taxable wages (in the form of payroll) created by the new jobs.

New State Revenue Projected: The projected total amount of new revenue for the state, produced by the company and its activities, over the life of the project.

Capital Investment Projected: The amount of capital expenditure the company plans on investing in the project within the state of Utah.

Max Cap Incentive: The most that the company can receive back in the form of the post performance tax credit over the lifetime of the project.

Rebate %: The agreed upon % of new state revenue that the company can qualify to receive back. As a rule, Rebate% = (Max Cap Incentive)/(New State Revenue Projected) +- rounding.

Terms: The number of years associated with completing the project in years. Also can be interpreted as the number of annual audits the compliance team will perform to determine the actual yearly EDTIF rebate.

Contract Status*: Though approved, not all companies choose to submit materials for audit by the compliance team, which determines the actual amount of tax incentives the company receives. Companies can fall into 4 "Contract Status*" categories;

a. "Active": The company is participating in the program and submitting materials to the compliance team for audit.

b. "Unissued, Available": The company has qualified for the EDTIF program, but they are not (or haven't yet) submitting materials for the yearly audits. They still can submit materials for audit as long as they are not past their terms.

c. "Unissued, Unavailable": The company has not participated in the yearly audits, and the terms of the EDTIF have passed. No tax rebates are awarded.

d. "Complete": The company has participated in the audits and the terms of the EDTIF have passed.

"% of New State Revenue Assessed*": Amount of the new state revenue generated by the company that has been assessed by the compliance team, measured in steps of 25%

"% of tax Credit Issued": The amount of the total possible EDTIF granted, measured in steps of 25%