Government Payrolls in the United States increased by 22 thousand in September of 2025. This dataset provides the latest reported value for - United States Government Payrolls - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

Jobless Claims - Federal Workers in the United States increased to 635 People in September 13 from 572 People in the previous week. This dataset includes a chart with historical data for the United States Initial Jobless Claims - Federal Workers.

This dataset contains annual average CES data for California statewide and areas from 1990 to 2024. The Current Employment Statistics (CES) program is a Federal-State cooperative effort in which monthly surveys are conducted to provide estimates of employment, hours, and earnings based on payroll records of business establishments. The CES survey is based on approximately 119,000 businesses and government agencies representing approximately 629,000 individual worksites throughout the United States. CES data reflect the number of nonfarm, payroll jobs. It includes the total number of persons on establishment payrolls, employed full- or part-time, who received pay (whether they worked or not) for any part of the pay period that includes the 12th day of the month. Temporary and intermittent employees are included, as are any employees who are on paid sick leave or on paid holiday. Persons on the payroll of more than one establishment are counted in each establishment. CES data excludes proprietors, self-employed, unpaid family or volunteer workers, farm workers, and household workers. Government employment covers only civilian employees; it excludes uniformed members of the armed services. The Bureau of Labor Statistics (BLS) of the U.S. Department of Labor is responsible for the concepts, definitions, technical procedures, validation, and publication of the estimates that State workforce agencies prepare under agreement with BLS.

The Current Employment Statistics (CES) program is a Federal-State cooperative effort in which monthly surveys are conducted to provide estimates of employment, hours, and earnings based on payroll records of business establishments. The CES survey is based on approximately 119,000 businesses and government agencies representing approximately 629,000 individual worksites throughout the United States.

CES data reflect the number of nonfarm, payroll jobs. It includes the total number of persons on establishment payrolls, employed full- or part-time, who received pay (whether they worked or not) for any part of the pay period that includes the 12th day of the month. Temporary and intermittent employees are included, as are any employees who are on paid sick leave or on paid holiday. Persons on the payroll of more than one establishment are counted in each establishment. CES data excludes proprietors, self-employed, unpaid family or volunteer workers, farm workers, and household workers. Government employment covers only civilian employees; it excludes uniformed members of the armed services.

The Bureau of Labor Statistics (BLS) of the U.S. Department of Labor is responsible for the concepts, definitions, technical procedures, validation, and publication of the estimates that State workforce agencies prepare under agreement with BLS.

Continued Jobless Claims - Federal Workers in the United States increased to 8168 People in September 6 from 7863 People in the previous week. This dataset includes a chart with historical data for the United States Continued Jobless Claims - Federal Workers.

Data is collected because of public interest in how the City’s budget is being spent on salary and overtime pay for all municipal employees. Data is input into the City's Personnel Management System (“PMS”) by the respective user Agencies. Each record represents the following statistics for every city employee: Agency, Last Name, First Name, Middle Initial, Agency Start Date, Work Location Borough, Job Title Description, Leave Status as of the close of the FY (June 30th), Base Salary, Pay Basis, Regular Hours Paid, Regular Gross Paid, Overtime Hours worked, Total Overtime Paid, and Total Other Compensation (i.e. lump sum and/or retro payments). This data can be used to analyze how the City's financial resources are allocated and how much of the City's budget is being devoted to overtime. The reader of this data should be aware that increments of salary increases received over the course of any one fiscal year will not be reflected. All that is captured, is the employee's final base and gross salary at the end of the fiscal year. In very limited cases, a check replacement and subsequent refund may reflect both the original check as well as the re-issued check in employee pay totals.

NOTE 1: To further improve the visibility into the number of employee OT hours worked, beginning with the FY 2023 report, an updated methodology will be used which will eliminate redundant reporting of OT hours in some specific instances. In the previous calculation, hours associated with both overtime pay as well as an accompanying overtime “companion code” pay were included in the employee total even though they represented pay for the same period of time. With the updated methodology, the dollars shown on the Open Data site will continue to be inclusive of both types of overtime, but the OT hours will now reflect a singular block of time, which will result in a more representative total of employee OT hours worked. The updated methodology will primarily impact the OT hours associated with City employees in uniformed civil service titles. The updated methodology will be applied to the Open Data posting for Fiscal Year 2023 and cannot be applied to prior postings and, as a result, the reader of this data should not compare OT hours prior to the 2023 report against OT hours published starting Fiscal Year 2023. The reader of this data may continue to compare OT dollars across all published Fiscal Years on Open Data.
NOTE 2: As a part of FISA-OPA’s routine process for reviewing and releasing Citywide Payroll Data, data for some agencies (specifically NYC Police Department (NYPD) and the District Attorneys’ Offices (Manhattan, Kings, Queens, Richmond, Bronx, and Special Narcotics)) have been redacted since they are exempt from disclosure pursuant to the Freedom of Information Law, POL § 87(2)(f), on the ground that disclosure of the information could endanger the life and safety of the public servants listed thereon. They are further exempt from disclosure pursuant to POL § 87(2)(e)(iii), on the ground that any release of the information would identify confidential sources or disclose confidential information relating to a criminal investigation, and POL § 87(2)(e)(iv), on the ground that disclosure would reveal non-routine criminal investigative techniques or procedures. Some of these redactions will appear as XXX in the name columns.

US Federal Funding Gaps History

Historical US Federal Funding Gaps & Political Party Control

By Throwback Thursday [source]

About this dataset

This dataset is a comprehensive historical record of federal funding gaps in the United States, spanning from 1976 to 2018. It provides detailed information on each funding gap, including the start and end dates, total duration in days, and whether or not employees were furloughed.

The dataset also includes data on the political party control during each funding gap, specifically for both the Senate and the House of Representatives. For each chamber, it indicates which party had control - either Democrats or Republicans - as well as any representation by Independent members.

Additionally, this dataset contains valuable insights into the impact of federal funding gaps on government employees. It records the number of employees who were furloughed during each gap, allowing for analysis of workforce disruption and potential economic consequences.

By leveraging this dataset's wealth of information on federal funding gaps in the United States over more than four decades, researchers can gain a deeper understanding of these significant events in governmental operations and their broader implications for various stakeholders

How to use the dataset

Introduction:

• Understanding the Columns: a) Start Date: The date when a federal funding gap began. b) End Date: The date when a federal funding gap ended. c) Total days: The duration of the federal funding gap in days. d) Employees furloughed: A boolean value indicating whether or not employees were furloughed during that specific funding gap. (True = Employees were furloughed, False = No employee was furloughed.) e) Number of Employees Furloughed: The actual count of employees who were furloughed during that specific funding gap. f) Senate Control: The political party that had control over the Senate during each particular period specified. (Categorical - Democratic, Republican) g) Senate Democrats: The number of Democratic senators serving during that specific funding gap. h) Senate Republicans: The number of Republican senators serving during that particular period specified. i) Senate Independents: The number of Independent senators serving at that time frame. j ) House Control :He political party that had control over House Representatives throughoted specific dataried by each perticularnce k ) House Democrats -

• Analyzing Duration and Furloughs: You can compute various statistics about federal funding gaps using relevant columns such as 'Start Date,' 'End Date,' 'Total days,' 'Employees furloughed,' 'Number of Employees Furloughed. For example:

  • Calculate the average duration of funding gaps during a specific time period.
  • Determine the total number of funding gaps that resulted in employee furloughs.
  • Analyze the average number of employees furloughed during various periods.

• Understanding Party Control: The dataset includes information about political party control over Senate and House Representatives during funding gaps. • Analyzing Senate Control:

  • Determine which party controlled the Senate during each funding gap period.
  • Compare the prevalence of Democratic, Republican, or Independent control over time.
  1. Exploring

Research Ideas

• Analyzing the impact of federal funding gaps on government employees: This dataset can be used to study the number of employees who were furloughed during each funding gap and analyze the duration of their furlough. It can provide insights into the economic effects and hardships faced by government workers during such periods.
• Examining the political dynamics during funding gaps: By analyzing the control of both the House of Representatives and Senate during each funding gap, this dataset can shed light on how political party control affected negotiations and resolutions. It can help identify patterns or trends in bipartisan cooperation or conflict during these periods.
• Comparing different funding gaps over time: With information on start dates, end dates, and total days for each gap, this dataset allows for comparisons across different periods in history. Researchers can assess whether funding gaps have become more frequent or longer-lasting over time and identify any patterns that may exist in relation to economic factors or political developments

Acknowledgements

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

License

See the dataset d...

The Local Employment Dynamics (LED) Partnership is a voluntary federal-state enterprise created for the purpose of merging employee, and employer data to provide a set of enhanced labor market statistics known collectively as Quarterly Workforce Indicators (QWI). The QWI are a set of economic indicators including employment, job creation, earnings, and other measures of employment flows. For the purposes of this dataset, LED data for 2018 is aggregated to Census Summary Level 070 (State + County + County Subdivision + Place/Remainder), and joined with the Emergency Solutions Grantee (ESG) areas spatial dataset for FY2018. The Emergency Solutions Grants (ESG), formally the Emergency Shelter Grants, program is designed to identify sheltered and unsheltered homeless persons, as well as those at risk of homelessness, and provide the services necessary to help those persons quickly regain stability in permanent housing after experiencing a housing crisis and/or homelessness. The ESG is a non-competitive formula grant awarded to recipients which are state governments, large cities, urban counties, and U.S. territories. Recipients make these funds available to eligible sub-recipients, which can be either local government agencies or private nonprofit organizations. The recipient agencies and organizations, which actually run the homeless assistance projects, apply for ESG funds to the governmental grantee, and not directly to HUD. Please note that this version of the data does not include Community Planning and Development (CPD) entitlement grantees. LED data for CPD entitlement areas can be obtained from the LED for CDBG Grantee Areas feature service. To learn more about the Local Employment Dynamics (LED) Partnership visit: https://lehd.ces.census.gov/, for questions about the spatial attribution of this dataset, please reach out to us at GISHelpdesk@hud.gov. Data Dictionary: DD_LED for ESG Grantee Areas

Date of Coverage: ESG-2021/LED-2018

Federal Funding Gaps in the US

Overview of US Federal Funding Gaps (1976-2018)

By Throwback Thursday [source]

About this dataset

The dataset includes columns such as Start Date, End Date, Total days, Employees furloughed, Number of Employees Furloughed, Senate Control, Senate Democrats, Senate Republicans ,Senate Independents ,House Control ,House Democrats ,House Republicans and House Independents.

Start Date indicates the date when each federal funding gap began. On the other hand End date shows when these funding gaps came to an end. By examining this information for each gap individually along with calculations from other columns like Total days one can gain insights into how long each funding gap lasted.

Numerical values such as number of employees affected by furloughs are provided within columns like Employees furloughed and Number of Employees Furloughed. The latter column represents a total count for all affected employees throughout a particular funding gap period.

This dataset delves even deeper into political dynamics by revealing which political party was in control during each federal funding gap period through columns like Senate Control and House Control. Specifically defining whether Democrats or Republicans were leading is very crucial to understand any potential ramifications associated with those particular party affiliations being at power during a given time period.

Moreover,the numerical data found under columns named Senate Democrats,Senate Republicans,Senate Independents indicate how many members from respective parties were active participants within United States Senate for each individual government fund shutdown event.As we continue through other sections more details about representation will be present .

Similarly,namesake parties committed to House representatives then find representation factors being unearthed and can be seen through President's Government House Control metric here . Columns like House Democrats, House Republicans and House Independents serve as additional measures to provide a census of who controlled the power dynamic during these respective campaign titanic struggles.Thus , for each federal funding gap period one can ascertain which political party held sway over the decisions made within America's lower parliamentary body.

In total,this comprehensive dataset offers profound insights into how the United States government experienced financial funding gaps throughout several decades of its history. The information provided in this dataset is crucial for anyone looking to study, analyze, or understand the dynamics, duration, impacts, and control factors associated

How to use the dataset

• Understand the Columns:

  • Start Date: The date when a federal funding gap started.
  • End Date: The date when a federal funding gap ended.
  • Total days: The duration of each federal funding gap in days.
  • Employees furloughed: A brief description of the employees affected by each funding gap, providing an insight into different government sectors impacted.
  • Number of Employees Furloughed: The total number of employees who were furloughed during each funding gap.
  • Senate Control and House Control: Political party in control of both chambers during each funding gap (Democrats or Republicans).

• Gain Insight into Duration and Employee Impact: Explore which federal funding gaps had longer durations and higher numbers of furloughed employees. Sort or filter based on Total days or Number of Employees Furloughed columns, respectively, to identify significant instances.

• Analyze Political Party Control: Observe which political party was in control during different periods. Analyze if there is any correlation between party control and decision-making leading to a governmental shutdown.

• Compare Senate and House Representation: Compare Republican, Democrat, Independent representation within both chambers during each period using respective columns like Senate Republicans, House Democrats, etc., providing insights into potential political dynamics affecting these gaps.

• Highlight Interesting Findings: Communicate your data-driven discoveries by visualizing interesting trends with graphs or summarizing them through storytelling techniques.

• Respect Data Privacy Please note that while analyzing the dataset, it is essential to respect any data privacy guidelines and not draw conclusions about individual employees or reveal any sensitive information.

Best of luck with your analysis!

Research Ideas

• Analyzing the impact of federal funding gaps: This datas...

Context

The Bureau of Labor Statistics (BLS) is a unit of the United States Department of Labor. It is the principal fact-finding agency for the U.S. government in the broad field of labor economics and statistics and serves as a principal agency of the U.S. Federal Statistical System. The BLS is a governmental statistical agency that collects, processes, analyzes, and disseminates essential statistical data to the American public, the U.S. Congress, other Federal agencies, State and local governments, business, and labor representatives. Source: https://en.wikipedia.org/wiki/Bureau_of_Labor_Statistics

Content

Bureau of Labor Statistics including CPI (inflation), employment, unemployment, and wage data.

Update Frequency: Monthly

Querying BigQuery Tables

Fork this kernel to get started.

Acknowledgements

https://bigquery.cloud.google.com/dataset/bigquery-public-data:bls

https://cloud.google.com/bigquery/public-data/bureau-of-labor-statistics

Dataset Source: http://www.bls.gov/data/

This dataset is publicly available for anyone to use under the following terms provided by the Dataset Source - http://www.data.gov/privacy-policy#data_policy - and is provided "AS IS" without any warranty, express or implied, from Google. Google disclaims all liability for any damages, direct or indirect, resulting from the use of the dataset.

Banner Photo by Clark Young from Unsplash.

Inspiration

What is the average annual inflation across all US Cities? What was the monthly unemployment rate (U3) in 2016? What are the top 10 hourly-waged types of work in Pittsburgh, PA for 2016?

Dataset

This dataset was created by Manuel Juarez

Contents

U.S. Pandemic Mental Health Care

Impact on Households in Previous 4 Weeks

By US Open Data Portal, data.gov [source]

About this dataset

This U.S. Household Pandemic Impacts dataset assesses the mental health care that households in America have been receiving over the past four weeks during the Covid-19 pandemic. Produced by a collaboration between the U.S. Census Bureau, and five other federal agencies, this survey was designed to measure both social and economic impacts of Covid-19 on American households, such as employment status, consumer spending trends, food security levels and housing disruptions among other important factors. The data collected was based on an internet questionnaire which was conducted through emails and text messages sent to randomly selected housing units from across America linked with email addresses or cell phone numbers from the Census Bureau Master Address File Data; all estimates comply with NCHS Data Presentation Standards for Proportions. Be sure to check out more about how U.S Government Works for further details!

More Datasets

For more datasets, click here.

Featured Notebooks

• 🚨 Your notebook can be here! 🚨!

How to use the dataset

This dataset can be useful to examine the impact of the Covid-19 pandemic on access to and utilization of mental health care by U.S. households in the last 4 weeks.

By studying this dataset, you can gain insight into how people’s mental health has been affected by the pandemic and identify trends based on population subgroups, states, phases of the survey and more.

Instructions for Use: - To get started, open up ‘csv-1’ found in this dataset. This file contains information on access to and utilization of mental health care by U.S households in the last 4 weeks, broken down into 14 different columns (e.g., Indicator, Group, State).
- Familiarize yourself with each column label (e.g., Time Period Start Date), data type (e

Research Ideas

• Analyzing the impact of pandemic-induced stress on different demographic groups, such as age and race/ethnicity.
• Comparing the mental health care services received in different states over time.
• Investigating the correlation between socio-economic status and access to mental health care services during Covid-19 pandemic

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: csv-1.csv | Column name | Description | |:---------------------------|:-------------------------------------------------------------------| | Indicator | The type of indicator being measured. (String) | | Group | The group (by age, gender or race) being measured. (String) | | State | The state where the data was collected. (String) | | Subgroup | A narrower level categorization within Group. (String) | | Phase | Phase number reflective of survey iteration. (Integer) | | Time Period | A label indicating duration captured by survey period. (String) | | Time Period Label | A label indicating duration captured by survey period. (String) | | Time Period Start Date | Beginning date for surveyed period. (DateFormat ‘YYYY-MM-DD’) | | Time Period End Date | End date for surveyed period. (DateFormat ‘YYYY-MM-DD’) | | Value | The value of the indicator being measured. (Float) | | LowCI | The lower confidence interval of the value. (Float) | | HighCI | The higher confidence interval of the value. (Float) | | Quartile Range | The quartile range of the value. (String) | | Suppression Flag | A f...

U.S. Virgin Islands - Government Employees - Historical chart and current data through 2025.

Government spending in the United States was last recorded at 39.7 percent of GDP in 2024 . This dataset provides - United States Government Spending To Gdp- actual values, historical data, forecast, chart, statistics, economic calendar and news.

Key Table Information.Table Title.Local Government Employment and Payroll Data: U.S. and States: 2017 - 2024.Table ID.GOVSTIMESERIES.GS00EP03.Survey/Program.Public Sector.Year.2024.Dataset.PUB Public Sector Annual Surveys and Census of Governments.Source.U.S. Census Bureau, Public Sector.Release Date.2025-03-27.Release Schedule.The Annual Survey of Public Employment & Payroll occurs every year, except in Census years. Data are typically released yearly in the first quarter. There is approximately one year between the reference period and data release. Revisions to published data occur annually for the next two years. Census of Governments years, those ending in '2' and '7' may have slightly later releases due to extended processing time..Dataset Universe.Census of Governments - Organization (CG):The universe of this file is all federal, state, and local government units in the United States. In addition to the federal government and the 50 state governments, the Census Bureau recognizes five basic types of local governments. The government types are: County, Municipal, Township, Special District, and School District. Of these five types, three are categorized as General Purpose governments: County, municipal, and township governments are readily recognized and generally present no serious problem of classification. However, legislative provisions for school district and special district governments are diverse. These two types are categorized as Special Purpose governments. Numerous single-function and multiple-function districts, authorities, commissions, boards, and other entities, which have varying degrees of autonomy, exist in the United States. The basic pattern of these entities varies widely from state to state. Moreover, various classes of local governments within a particular state also differ in their characteristics. Refer to the Individual State Descriptions report for an overview of all government entities authorized by state.The Public Use File provides a listing of all independent government units, and dependent school districts active as of fiscal year ending June 30, 2024. The Annual Surveys of Public Employment & Payroll (EP) and State and Local Government Finances (LF):The target population consists of all 50 state governments, the District of Columbia, and a sample of local governmental units (counties, cities, townships, special districts, school districts). In years ending in '2' and '7' the entire universe is canvassed. In intervening years, a sample of the target population is surveyed. Additional details on sampling are available in the survey methodology descriptions for those years.The Annual Survey of Public Pensions (PP):The target population consists of state- and locally-administered defined benefit funds and systems of all 50 state governments, the District of Columbia, and a sample of local governmental units (counties, cities, townships, special districts, school districts). In years ending in '2' and '7' the entire universe is canvassed. In intervening years, a sample of the target population is surveyed. Additional details on sampling are available in the survey methodology descriptions for those years.The Annual Surveys of State Government Finance (SG) and State Government Tax Collections (TC):The target population consists of all 50 state governments. No local governments are included. For the purpose of Census Bureau statistics, the term "state government" refers not only to the executive, legislative, and judicial branches of a given state, but it also includes agencies, institutions, commissions, and public authorities that operate separately or somewhat autonomously from the central state government but where the state government maintains administrative or fiscal control over their activities as defined by the Census Bureau. Additional details are available in the survey methodology description.The Annual Survey of School System Finances (SS):The Annual Survey of School System Finances targets all public school systems providing elementary and/or secondary education in all 50 states and the District of Columbia..Methodology.Data Items and Other Identifying Records.Full-time and part-time employmentFull-time and part-time payrollPart-time hours worked (prior to 2019)Full-time equivalent employmentTotal full-time and part-time employmentTotal full-time and part-time payrollDefinitions can be found by clicking on the column header in the table or by accessing the Glossary.For detailed information, see Government Finance and Employment Classification Manual..Unit(s) of Observation.The basic reporting unit is the governmental unit, defined as an organized entity which in addition to having governmental character, has sufficient discretion in the management of its own affairs to distinguish it as separate from the administrative structure of any other governmental unit.The reporting units for the Annual Survey of School System Finances are public school systems that...

This data set contains information about any US government agency participating in the transit benefits program, funding agreements, individual participating Federal employees and details about commutes, supervisors and supervisory approvals, fare media in use, and transaction histories.

Context

demographics and employment in the united states In the wake of the Great Recession of 2009, there has been a good deal of focus on employment statistics, one of the most important metrics policymakers use to gauge the overall strength of the economy. In the United States, the government measures unemployment using the Current Population Survey (CPS), which collects demographic and employment information from a wide range of Americans each month.

Content

The observations in the dataset represent people surveyed in the September 2013 CPS who actually completed a survey. While the full dataset has 385 variables, in this exercise we will use a more compact version of the dataset, CPSData.csv, which has the following variables:

PeopleInHousehold: The number of people in the interviewee's household.

Region: The census region where the interviewee lives.

State: The state where the interviewee lives.

MetroAreaCode: A code that identifies the metropolitan area in which the interviewee lives (missing if the interviewee does not live in a metropolitan area). The mapping from codes to names of metropolitan areas is provided in the file MetroAreaCodes.csv.

Age: The age, in years, of the interviewee. 80 represents people aged 80-84, and 85 represents people aged 85 and higher.

Married: The marriage status of the interviewee.

Sex: The sex of the interviewee.

Education: The maximum level of education obtained by the interviewee.

Race: The race of the interviewee.

Hispanic: Whether the interviewee is of Hispanic ethnicity.

CountryOfBirthCode: A code identifying the country of birth of the interviewee. The mapping from codes to names of countries is provided in the file CountryCodes.csv.

Citizenship: The United States citizenship status of the interviewee.

EmploymentStatus: The status of employment of the interviewee.

Industry: The industry of employment of the interviewee (only available if they are employed).

Acknowledgements

MITx ANALYTIX

Percentage of workforce laid off because of COVID-19, by North American Industry Classification System (NAICS) code, business employment size, type of business and majority ownership.

Unemployment Rate in the United States increased to 4.40 percent in September from 4.30 percent in August of 2025. This dataset provides the latest reported value for - United States Unemployment Rate - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

The files in this deposit were scraped from https://www.opm.gov/data/datasets/ on Feb 12, 2025.The U.S. Office of Personnel Management (OPM) serves as the chief human resources agency and personnel policy manager for the Federal Government.Archive created by Lars Vilhuber, Cornell University. Original data are "a work prepared by an officer or employee of the United States Government as part of that person's official duties". Under section 105 of the Copyright Act of 1976,such works are not entitled to domestic copyright protection under U.S. law and are therefore in the public domain.