Government Revenues in the United States increased to 850169 USD Million in April from 367645 USD Million in March of 2025. This dataset provides - United States Government Revenues- actual values, historical data, forecast, chart, statistics, economic calendar and news.

Government Revenues in Brazil increased to 261265 BRL Million in December from 209218 BRL Million in November of 2024. This dataset provides - Brazil Government Revenues- actual values, historical data, forecast, chart, statistics, economic calendar and news.

Monthly state sales tax collections is an experimental dataset published by the U.S. Census Bureau. It provides data for collections from sales taxes including motor fuel taxes. Data reported for a specific month generally represent sales taxes collected on sales made during the prior month. Tax collections primarily rely on unaudited data collected from existing state reports or state data sources available from and posted on the Internet. Secondarily, states report the data via the Quarterly Survey of State and Local Tax Revenue. Data are updated monthly, but due to differing reporting cycles data for some states may lag.

This summary table shows, for Budget Receipts, the total amount of activity for the current month, the current fiscal year-to-date, the comparable prior period year-to-date and the budgeted amount estimated for the current fiscal year for various types of receipts (i.e. individual income tax, corporate income tax, etc.). The Budget Outlays section of the table shows the total amount of activity for the current month, the current fiscal year-to-date, the comparable prior period year-to-date and the budgeted amount estimated for the current fiscal year for functions of the federal government. The table also shows the amounts for the budget/surplus deficit categorized as listed above. This table includes total and subtotal rows that should be excluded when aggregating data. Some rows represent elements of the dataset's hierarchy, but are not assigned values. The classification_id for each of these elements can be used as the parent_id for underlying data elements to calculate their implied values. Subtotal rows are available to access this same information.

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...

Government Revenues in Canada decreased to 44977 CAD Million in March from 51247 CAD Million in February of 2025. This dataset provides - Canada Government Revenues- actual values, historical data, forecast, chart, statistics, economic calendar and news.

This dataset contains revenue source level data for revenue actuals. Dataset is intended to match charts and tables in the "Tax Revenue" section of the Mayor`s Message publication. The amount is in millions of dollars. Data are from FY2001 and updated once a year.

Russia Federal Government Revenue: Non Tax Revenue data was reported at 217.119 RUB bn in Dec 2021. This records an increase from the previous number of 127.041 RUB bn for Nov 2021. Russia Federal Government Revenue: Non Tax Revenue data is updated monthly, averaging 257.735 RUB bn from Oct 1999 (Median) to Dec 2021, with 267 observations. The data reached an all-time high of 1,164.268 RUB bn in Dec 2016 and a record low of -4.735 RUB bn in Feb 2021. Russia Federal Government Revenue: Non Tax Revenue data remains active status in CEIC and is reported by Ministry of Finance of the Russian Federation. The data is categorized under Global Database’s Russian Federation – Table RU.FB006: Federal Government Revenue and Expenditure: General.

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.

This table gives a detailed breakdown of the different tax revenue data reported. NEC stands for not elsewhere classified. The indentation of the variables in the first column indicates how subcategories of data collapse into larger categories. More detailed descriptions of each category can be found in the Census’ 2006 classification manual (http://www.census.gov/govs/classification/) included with the database download.Tax Revenue Categories.

Government Revenues in Mexico increased to 2297571.60 MXN Million in April from 1714120.90 MXN Million in March of 2025. This dataset provides - Mexico Government Revenues- actual values, historical data, forecast, chart, statistics, economic calendar and news.

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.

This table shows the gross receipts, refunds and net receipts for the current month, the current fiscal year-to-date and the prior fiscal year-to-date for the various receipts of the federal government. This table includes total and subtotal rows that should be excluded when aggregating data. Some rows represent elements of the dataset's hierarchy, but are not assigned values. The classification_id for each of these elements can be used as the parent_id for underlying data elements to calculate their implied values. Subtotal rows are available to access this same information.

This table shows the gross outlays, applicable receipts and net outlays for the current month, current fiscal year-to-date and prior fiscal year-to-date by various agency programs accounted for in the budget of the federal government. This table includes total and subtotal rows that should be excluded when aggregating data. Some rows represent elements of the dataset's hierarchy, but are not assigned values. The classification_id for each of these elements can be used as the parent_id for underlying data elements to calculate their implied values. Subtotal rows are available to access this same information.

This table shows the receipts and outlays of the United States Government by month for the current fiscal year, up to and including the current accounting month. The table also shows the total receipts and outlays for the current fiscal year-to-date and the comparable prior fiscal year-to-date. This table includes total and subtotal rows that should be excluded when aggregating data. Some rows represent elements of the dataset's hierarchy, but are not assigned values. The classification_id for each of these elements can be used as the parent_id for underlying data elements to calculate their implied values. Subtotal rows are available to access this same information.

A historical tabulation of selected tax return statistics by counties and other geographic areas reported every year by the Research Section at the Department of Revenue. Source data comes from Tables A through D in the "Returns by county, other states, and city" spreadsheet that accompanies every annual Personal Income Tax publication. Data are reported for all 36 Oregon counties as well as five areas outside Oregon, based on the mailing address on the return when it was filed. Clark County, Washington, is reported separately from the remainder of Washington because so many Clark County residents work in Portland. Idaho and California also have individual tables. Returns from all other states (and outside of the US) are grouped together as "Other". For full-year resident returns, Oregon AGI is the same as federal AGI. For part-year resident and nonresident returns, Oregon AGI is determined from Oregon sourced income and adjustments. Note that some rows have blank cells indicating that data has been omitted, often for disclosure reasons. See "data limitations" below.

This table represents the breakdown of taxes that are received by the federal government. Federal taxes received are represented as deposits in the Deposits and Withdrawals of Operating Cash table. All figures are rounded to the nearest million.

Personal income tax is collected annually from Ontario residents and those who earned income in the province. The tax is calculated separately from federal income tax. There are 5 Ontario income tax brackets and 5 corresponding tax rates. For an explanation of these rates and credits, refer to the federal and provincial personal income tax return for the applicable year. To get a copy of the return (also known as a T1) contact the Canada Revenue Agency at 1-800-959-8281 or visit canada.ca/cra-forms. Read on: about personal income tax This data is related to: * Filing your tax return * Taxes and benefits * Business income tax Related data: * Personal income tax * Provincial tax database

Taxes are grouped into six major categories: property, general sales, personal income, business income, real estate-related, and other. We also separate non-exported and exported taxes, that is, taxes levied on New York City resident households and businesses and taxes levied on nonresidents. Taxes in the former category enter into the calculation of New York City tax effort. The latter category includes sales and other taxes on hotel occupancy, city income taxes paid by commuters into the city, and portions of state and MTA auto rental taxes remitted in the city. We could not, however, estimate and net out non-hotel sales and other taxes paid by visitors to the city. Nor could we account, as we did in our previous report, for any New York City tax imports, that is, taxes of other, non-overlapping jurisdictions paid by city residents.1 For brief descriptions of the tables and figures along with methodological notes please see the Tax Effort Background and Methodology document.

This table is a subsidiary table for Means of Financing the Deficit or Disposition of Surplus by the U.S. Government providing a detailed view of federal funds and trust funds that are invested in Government Account Series (GAS) securities. Federal funds include general funds, special funds, and revolving funds (public enterprise revolving funds, intragovernmental revolving funds, and credit financing accounts). A trust fund is a type of account, designated by law, for receipts or offsetting receipts dedicated to specific purposes and the expenditure of these receipts. This table includes total and subtotal rows that should be excluded when aggregating data. Some rows represent elements of the dataset's hierarchy, but are not assigned values. The classification_id for each of these elements can be used as the parent_id for underlying data elements to calculate their implied values. Subtotal rows are available to access this same information.