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...

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 the researchers chose 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, researchers combined some subcategories. First, they were interested in total tax revenue, as well as the shares of total revenue coming from direct and indirect taxes. Further, they measured two sub-categories of direct taxation, namely taxes on property and income. For indirect taxes, they separated excises, consumption, and customs.

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 Q4 2024 about state & local, revenue, tax, government, and USA.

Corporate Profits in the United States decreased to 3191.90 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.

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

Retail Sales in the United States increased 0.10 percent in April of 2025 over the previous month. This dataset provides - U.S. December Retail Sales Increased More Than Forecast - actual values, historical data, forecast, chart, statistics, economic calendar and news.

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

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 638 billion U.S. dollars, up from 575 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 185 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 353 billion U.S. dollars was earned in North America compared to only roughly 131 billion U.S. dollars internationally.

Money Supply M2 in the United States increased to 21862.50 USD Billion in April from 21706.80 USD Billion in March of 2025. This dataset provides - United States Money Supply M2 - actual values, historical data, forecast, chart, statistics, economic calendar and news.

Retail Sales in the United States increased 5.20 percent in April of 2025 over the same month in the previous year. This dataset provides - United States Retail Sales YoY - actual values, historical data, forecast, chart, statistics, economic calendar and news.

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.

Dataset available only to University of Arizona affiliates. To obtain access, you must log in to ReDATA with your NetID. Data is for research use by each individual downloader only. Sharing and/or redistribution of any portion of this dataset is prohibited.This ReferenceUSA dataset from Data Axle (formerly Infogroup) contains household data about US consumers in annual snapshots from 2006-2021. It includes details such as family demographics, income, home ownership status, lifestyle, location and more, which can help users to create marketing plans and conduct competitive analyses.Consumer profiles are described with 58-66 indicators. Data for all states are combined into single files for each year between 2006 and 2012 while there is a file for each state in 2013-2021. The Layout - Consumer DB Historical 2006-2012.xlsx in Documentation.zip applies to 2006-2012. Codebooks for 2013, 2014, 2015, 2017, 2018, 2019 and 2021 are not included but files in 2013-2021 have similar layouts therefore 2016 Historical Residential File Layout.xlsx and 2020 Historical Residential File Layout.xlsx in Documentation.zip apply to 2013-2021.The University of Arizona University Libraries also subscribe to Data Axle Reference Solutions which provides this data in a searchable, online database with historical data available going back to 2003.NOTE: The uncompressed datasets are very large.Detailed file descriptions and MD5 hash values for each file can be found in the README.txt file.For inquiries regarding the contents of this dataset, please contact the Corresponding Author listed in the README.txt file. Administrative inquiries (e.g., removal requests, trouble downloading, etc.) can be directed to data-management@arizona.edu

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

Personal Income in the United States increased 0.80 percent in April of 2025 over the previous month. This dataset provides the latest reported value for - United States Personal Income - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

Existing Home Sales in the United States decreased to 4000 Thousand in April from 4020 Thousand in March of 2025. This dataset provides the latest reported value for - United States Existing Home Sales - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

This dataset comes from state tax expenditure reports. Nearly every state prepares an annual or biennial report estimating the revenues that are foregone as a result of tax incentives. We extract data from the most recent report, typically prepared for fiscal year 2022 or 2023, and we collect data from the most recently completed fiscal year. For each tax expenditure, we collect the following information: the name of the incentive, the type of subsidy (eg. deduction vs credit), the source of taxation (eg. income tax vs sales tax), and the estimated amount of revenue foregone. Where available, we also extract information about the date when the tax incentive was enacted and any other information about the purpose and targeting of the incentive. In a small number of cases, the reports did not clearly specify a fiscal year, and we were forced to make an educated guess. There were also a small number of instances when states did not provide an estimate for a particular incentive due to confidentiality reasons, often because of the small number of recipients. Having identified a list of subsidies, we classify the data into mitigation and adaptation. For the mitigation subsidies, we adopt a further classification scheme according to the economic sectoral categories utilised by the IPCC: energy, industry, transport, buildings, and agriculture, forestry & land use. We also identify adaptation efforts. Separately, we collected fossil fuel related tax expenditures and include them here.

Key Table Information.Table Title.All Sectors: Summary Statistics for the U.S., States, and Selected Geographies: 2022.Table ID.ECNBASIC2022.EC2200BASIC.Survey/Program.Economic Census.Year.2022.Dataset.ECN Core Statistics Summary Statistics for the U.S., States, and Selected Geographies: 2022.Source.U.S. Census Bureau, 2022 Economic Census, Core Statistics.Release Date.2024-12-05.Release Schedule.The Economic Census occurs every five years, in years ending in 2 and 7.The data in this file come from the 2022 Economic Census data files released on a flow basis starting in January 2024 with First Look Statistics. Preliminary U.S. totals released in January 2024 are superseded with final data shown in the releases of later economic census statistics through March 2026.For more information about economic census planned data product releases, see 2022 Economic Census Release Schedule..Dataset Universe.The dataset universe consists of all establishments that are in operation for at least some part of 2022, are located in one of the 50 U.S. states, associated offshore areas, or the District of Columbia, have paid employees, and are classified in one of nineteen in-scope sectors defined by the 2022 North American Industry Classification System (NAICS)..Methodology.Data Items and Other Identifying Records.Number of firmsNumber of establishmentsSales, value of shipments, or revenue ($1,000)Annual payroll ($1,000)First-quarter payroll ($1,000)Number of employeesRange indicating imputed percentage of total sales, value of shipments, or revenueRange indicating imputed percentage of total annual payrollRange indicating imputed percentage of total employeesDefinitions can be found by clicking on the column header in the table or by accessing the Economic Census Glossary..Unit(s) of Observation.The reporting units for the economic census are employer establishments. An establishment is generally a single physical location where business is conducted or where services or industrial operations are performed. A company or firm is comprised of one or more in-scope establishments that operate under the ownership or control of a single organization. For some industries, the reporting units are instead groups of all establishments in the same industry belonging to the same firm..Geography Coverage.The data are shown for the U.S., State, Combined Statistical Area, Metropolitan and Micropolitan Statistical Area, Metropolitan Division, Consolidated City, County (and equivalent), and Economic Place (and equivalent; incorporated and unincorporated) levels that vary by industry. For information about economic census geographies, including changes for 2022, see Geographies..Industry Coverage.The data are shown at the 2- through 6-digit 2022 NAICS code levels for all sectors except Agriculture, which is releasing 3-through 6-digit NAICS code levels for 115 only. Data are also shown for selected 7- and 8-digit 2022 NAICS-based code levels for various sectors. For information about NAICS, see Economic Census Code Lists..Business Characteristics.For Wholesale Trade (42), data are presented by Type of Operation (All establishments; Merchant Wholesalers, except Manufacturers’ Sales Branches and Offices; and Manufacturers’ Sales Branches and Offices).For selected Services sectors, data are presented by Tax Status (All establishments, Establishments subject to federal income tax, and Establishments exempt from federal income tax)..Sampling.The 2022 Economic Census sample includes all active operating establishments of multi-establishment firms and approximately 1.7 million single-establishment firms, stratified by industry and state. Establishments selected to the sample receive a questionnaire. For all data on this table, establishments not selected into the sample are represented with administrative data. For more information about the sample design, see 2022 Economic Census Methodology..Confidentiality.The Census Bureau has reviewed this data product to ensure appropriate access, use, and disclosure avoidance protection of the confidential source data (Project No. 7504609, Disclosure Review Board (DRB) approval number: CBDRB-FY23-099).To protect confidentiality, the U.S. Census Bureau suppresses cell values to minimize the risk of identifying a particular business’ data or identity.To comply with disclosure avoidance guidelines, data rows with fewer than three contributing firms or three contributing establishments are not presented. Additionally, establishment counts are suppressed when other select statistics in the same row are suppressed. More information on disclosure avoidance is available in the 2022 Economic Census Methodology..Technical Documentation/Methodology.For detailed information about the methods used to collect data and produce statistics, survey questionnaires, Primary Business Activity/NAICS codes, NAPCS codes, and more, see Economic Census Technical Documentation..Weights.No weighting applied as establishments not samp...

About this dataset

Context

The dataset presents the detailed breakdown of the count of individuals within distinct income brackets, categorizing them by gender (men and women) and employment type - full-time (FT) and part-time (PT), offering valuable insights into the diverse income landscapes within United States. The dataset can be utilized to gain insights into gender-based income distribution within the United States population, aiding in data analysis and decision-making..

Key observations

• Employment patterns: Within United States, among individuals aged 15 years and older with income, there were 120.93 million men and 118.44 million women in the workforce. Among them, 67.70 million men were engaged in full-time, year-round employment, while 51.47 million women were in full-time, year-round roles.
• Annual income under $24,999: Of the male population working full-time, 7.76% fell within the income range of under $24,999, while 11.43% of the female population working full-time was represented in the same income bracket.
• Annual income above $100,000: 27.43% of men in full-time roles earned incomes exceeding $100,000, while 17.09% of women in full-time positions earned within this income bracket.
• Refer to the research insights for more key observations on more income brackets ( Annual income under $24,999, Annual income between $25,000 and $49,999, Annual income between $50,000 and $74,999, Annual income between $75,000 and $99,999 and Annual income above $100,000) and employment types (full-time year-round and part-time)

https://i.neilsberg.com/ch/united-states-income-distribution-by-gender-and-employment-type.jpeg" alt="United States gender and employment-based income distribution analysis (Ages 15+)">

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2022 1-Year Estimates.

Income brackets:

• $1 to $2,499 or loss
• $2,500 to $4,999
• $5,000 to $7,499
• $7,500 to $9,999
• $10,000 to $12,499
• $12,500 to $14,999
• $15,000 to $17,499
• $17,500 to $19,999
• $20,000 to $22,499
• $22,500 to $24,999
• $25,000 to $29,999
• $30,000 to $34,999
• $35,000 to $39,999
• $40,000 to $44,999
• $45,000 to $49,999
• $50,000 to $54,999
• $55,000 to $64,999
• $65,000 to $74,999
• $75,000 to $99,999
• $100,000 or more

Variables / Data Columns

• Income Bracket: This column showcases 20 income brackets ranging from $1 to $100,000+..
• Full-Time Males: The count of males employed full-time year-round and earning within a specified income bracket
• Part-Time Males: The count of males employed part-time and earning within a specified income bracket
• Full-Time Females: The count of females employed full-time year-round and earning within a specified income bracket
• Part-Time Females: The count of females employed part-time and earning within a specified income bracket

Employment type classifications include:

• Full-time, year-round: A full-time, year-round worker is a person who worked full time (35 or more hours per week) and 50 or more weeks during the previous calendar year.
• Part-time: A part-time worker is a person who worked less than 35 hours per week during the previous calendar year.

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 United States median household income by gender. You can refer the same here

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%

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.