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.

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.

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?

Context

USASpending.gov is the government's official tool for tracking spending, it shows where money goes and who benefits from federal funds.

The Federal Funding Accountability and Transparency Act of 2006 required that federal contract, grant, loan awards over $25k be searchable online to give the American public access to government spending. The data that is collected in USAspending.gov is derived from data gathered at more than a hundred agencies, as well as other government systems. Federal agencies submit contracts, grants, loans and other awards information to be uploaded on USAspending.gov at least twice a month.

Content

The United States spends a lot of money on contracts every year but where does it all go? This data set has information about how much different agencies have spent on awards for the fiscal year 2021. More data can be downloaded, for other years, on USAspending.gov.

Contracts are published to the GSA's Federal Procurement Data System within five days of being awarded, with contract reporting automatically getting posted on USAspending.gov by 9 AM the next day and going live at 8:00 am EST two mornings later

Learn more about the contents here: https://www.usaspending.gov/data-dictionary

The Bureau of the Fiscal Service, United States Department of the Treasury, is dedicated to making government spending data available to everyone.

Data Description

This data starts off separated into smaller files that need to be joined.

Data Overview

The federal government buys a lot of things, like office furniture and aircraft. It also buys services, like telephone and Internet access. The Federal Government and its sub-agencies use contracts to buy these things. They use Product and Service Codes (PSC) to classify the items and services they purchase.

An obligation is a promise to spend money. An outlay is when the government spends money. When the government enters into a contract or grant, it promises to spend all of the money. This is so it can pay people who do what they agreed to do. When the government actually pays someone, then it counts as an outlay.

Data Items that Help get Started

There are many different variables in this database, which are spread across multiple files. The most important ones to start learning are:

1. The contractor who won the award - recipient_name
2. The agency issuing the award - awarding_agency_name
3. The product or service code (PSC) - product_or_service_code
4. The industry classification code (NAICS) of the vendor - naics_code
5. How much was obligated - total_dollars_obligated or total_obligated_amount
6. The contract modification number - modification_number
7. The description of the award - award_description
8. The date of award - action_date or award_base_action_date

Data Dictionary and Analyst Guide

To learn more about the data, you can reference the data dictionary. The data dictionary includes information on outlays, which are not included in the data provided here. https://www.usaspending.gov/data-dictionary

Please see the analysts guide for more information: https://datalab.usaspending.gov/analyst-guide/

License

The U.S. Department of the Treasury, Bureau of the Fiscal Service is committed to providing open data to enable effective tracking of federal spending. The data is available to copy, adapt, redistribute, or otherwise use for non-commercial or for commercial purposes, subject to the Limitation on Permissible Use of Dun & Bradstreet, Inc. Data noted on the homepage. https://www.usaspending.gov/db_info

Acknowledgements

USAspending.gov collects data from all over the government to provide information to the public. Special thanks for the Data Transparency Team within the Office of the Chief Data Officer at the Bureau of Fiscal Services.

Inspiration

Can we find any patterns to help the public? How about predicting future spending needs or opportunities? Test out your ideas here!

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.

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...

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...

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.

As a Human Resources professional in People Analytics for the past 5 years, I have found that there is a huge gap in free datasets to use for practicing data analytics related to employee metrics outside of turnover or attrition.

This dataset is completely created by myself, utilizing randomized names, ethnicities, genders, ages, positions, departments, and more data that you may encounter in a People Analytics role. Any resemblance to actual persons, living or dead, or actual business distribution or representation is purely coincidental. The data contained in this file is not intended to discriminate on basis of race, color, religion, sex (including pregnancy and gender identity), national origin, political affiliation, sexual orientation, marital status, disability, genetic information, age, membership in an employee organization, retaliation, parental status, military service, or other non-merit factor. All data was randomized based on free data available from US Government websites, and when not available from a .gov website, the data was randomized based on Microsoft Excel's =rand and =randbetween functions.

This dataset can be used to practice people analytics related to diversity, position, department, performance, and more. You may freely use the data for any purpose, including personal usage, school or university projects, videos or courses, or whatever else you would like to use it for.

This intermediate level data set was extracted from the census bureau database. There are 48842 instances of data set, mix of continuous and discrete (train=32561, test=16281).

The data set has 15 attribute which include age, sex, education level and other relevant details of a person. The data set will help to improve your skills in Exploratory Data Analysis, Data Wrangling, Data Visualization and Classification Models.

Feel free to explore the data set with multiple supervised and unsupervised learning techniques. The Following description gives more details on this data set:

• age: the age of an individual.
• workclass: The type of work or employment of an individual. It can have the following categories:
  • Private: Working in the private sector.
  • Self-emp-not-inc: Self-employed individuals who are not incorporated.
  • Self-emp-inc: Self-employed individuals who are incorporated.
  • Federal-gov: Working for the federal government.
  • Local-gov: Working for the local government.
  • State-gov: Working for the state government.
  • Without-pay: Not working and without pay.
  • Never-worked: Never worked before.
• Final Weight: The weights on the CPS files are controlled to independent estimates of the civilian noninstitutional population of the US. These are prepared monthly for us by Population Division here at the Census Bureau. We use 3 sets of controls.

These are: 1. A single cell estimate of the population 16+ for each state. 2. Controls for Hispanic Origin by age and sex. 3. Controls by Race, age and sex.

We use all three sets of controls in our weighting program and "rake" through them 6 times so that by the end we come back to all the controls we used.

People with similar demographic characteristics should have similar weights. There is one important caveat to remember about this statement. That is that since the CPS sample is actually a collection of 51 state samples, each with its own probability of selection, the statement only applies within state.

• education: The highest level of education completed.
• education-num: The number of years of education completed.
• marital-status: The marital status.
• occupation: Type of work performed by an individual.
• relationship: The relationship status.
• race: The race of an individual.
• sex: The gender of an individual.
• capital-gain: The amount of capital gain (financial profit).
• capital-loss: The amount of capital loss an individual has incurred.
• hours-per-week: The number of hours works per week.
• native-country: The country of origin or the native country.
• income: The income level of an individual and serves as the target variable. It indicates whether the income is greater than $50,000 or less than or equal to $50,000, denoted as (>50K, <=50K).

Context

NYC Open Data is an opportunity to engage New Yorkers in the information that is produced and used by City government. We believe that every New Yorker can benefit from Open Data, and Open Data can benefit from every New Yorker. Source: https://opendata.cityofnewyork.us/overview/

Content

Thanks to NYC Open Data, which makes public data generated by city agencies available for public use, and Citi Bike, we've incorporated over 150 GB of data in 5 open datasets into Google BigQuery Public Datasets, including:

• Over 8 million 311 service requests from 2012-2016

• More than 1 million motor vehicle collisions 2012-present

• Citi Bike stations and 30 million Citi Bike trips 2013-present

• Over 1 billion Yellow and Green Taxi rides from 2009-present

• Over 500,000 sidewalk trees surveyed decennially in 1995, 2005, and 2015

This dataset is deprecated and not being updated.

Fork this kernel to get started with this dataset.

Acknowledgements

https://opendata.cityofnewyork.us/

https://cloud.google.com/blog/big-data/2017/01/new-york-city-public-datasets-now-available-on-google-bigquery

This dataset is publicly available for anyone to use under the following terms provided by the Dataset Source - https://data.cityofnewyork.us/ - 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.

By accessing datasets and feeds available through NYC Open Data, the user agrees to all of the Terms of Use of NYC.gov as well as the Privacy Policy for NYC.gov. The user also agrees to any additional terms of use defined by the agencies, bureaus, and offices providing data. Public data sets made available on NYC Open Data are provided for informational purposes. The City does not warranty the completeness, accuracy, content, or fitness for any particular purpose or use of any public data set made available on NYC Open Data, nor are any such warranties to be implied or inferred with respect to the public data sets furnished therein.

The City is not liable for any deficiencies in the completeness, accuracy, content, or fitness for any particular purpose or use of any public data set, or application utilizing such data set, provided by any third party.

Banner Photo by @bicadmedia from Unplash.

Inspiration

On which New York City streets are you most likely to find a loud party?

Can you find the Virginia Pines in New York City?

Where was the only collision caused by an animal that injured a cyclist?

What’s the Citi Bike record for the Longest Distance in the Shortest Time (on a route with at least 100 rides)?

https://cloud.google.com/blog/big-data/2017/01/images/148467900588042/nyc-dataset-6.png" alt="enter image description here"> https://cloud.google.com/blog/big-data/2017/01/images/148467900588042/nyc-dataset-6.png

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

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.

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.

Facebook received 73,390 user data requests from federal agencies and courts in the United States during the second half of 2023. The social network produced some user data in 88.84 percent of requests from U.S. federal authorities. The United States accounts for the largest share of Facebook user data requests worldwide.

Dataset

This dataset was created by Kaeli Hall

Released under U.S. Government Works

Contents

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.

Non Farm Payrolls in the United States increased by 119 thousand in September of 2025. This dataset provides the latest reported value for - United States Non Farm Payrolls - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.