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?

Employment Rate in the United States remained unchanged at 59.70 percent in June. This dataset provides - United States Employment Rate- actual values, historical data, forecast, chart, statistics, economic calendar and news.

This EnviroAtlas dataset portrays the commute time of workers to their workplace for each Census Block Group (CBG) during 2008-2012. Data were compiled from the Census ACS (American Community Survey) 5-year Summary Data. The commute time is the amount of travel time in minutes for workers to get from home to work. This value includes private vehicle use, carpooling, public transit, bicycling, or walking. This dataset was produced by the US EPA to support research and online mapping activities related to EnviroAtlas. EnviroAtlas (https://www.epa.gov/enviroatlas) allows the user to interact with a web-based, easy-to-use, mapping application to view and analyze multiple ecosystem services for the contiguous United States. The dataset is available as downloadable data (https://edg.epa.gov/data/Public/ORD/EnviroAtlas) or as an EnviroAtlas map service. Additional descriptive information about each attribute in this dataset can be found in its associated EnviroAtlas Fact Sheet (https://www.epa.gov/enviroatlas/enviroatlas-fact-sheets).

About this Dataset

This dataset combines automation probability data with a breakdown of the number of jobs and salary in each occupation by state within the USA. Automation probability was acquired from the work of Carl Benedikt Freyand Michael A. Osborne; State employment data is from the Bureau of Labor Statistics. Note that for simplicity of analysis, all jobs where data was not available or there were less than 10 employees were marked as zero.

How to Cite this Dataset

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

Salary Data

@misc{u.s. bureau of labor statistics, title={Occupational Employment Statistics}, url={https://www.bls.gov/oes/current/oes_nat.htm}, journal={U.S. BUREAU OF LABOR STATISTICS}}

Automation Data

@article{frey_osborne_2017, title={The future of employment: How susceptible are jobs to computerisation?}, volume={114}, DOI={10.1016/j.techfore.2016.08.019}, journal={Technological Forecasting and Social Change}, author={Frey, Carl Benedikt and Osborne, Michael A.}, year={2017}, pages={254–280}}

License

License was not specified at the source.

Splash Banner

Photo by Alex Knight on Unsplash

Labor Force Participation Rate in the United States decreased to 62.30 percent in June from 62.40 percent in May of 2025. This dataset provides the latest reported value for - United States Labor Force Participation Rate - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

The labor force participation rate is the percentage of the population that is either employed or unemployed (that is, either working or actively seeking work). People with jobs are employed. People who are jobless, looking for a job, and available for work are unemployed. The labor force is made up of the employed and the unemployed. People who are neither employed nor unemployed are not in the labor force.

The number of employed persons in The United States increased to 163366 Thousand in June of 2025 from 163273 Thousand in May of 2025. This dataset provides - United States Employed Persons - actual values, historical data, forecast, chart, statistics, economic calendar and news.

Unemployment Rate in the United States decreased to 4.10 percent in June from 4.20 percent in May 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 O*NET Database contains hundreds of standardized and occupation-specific descriptors on almost 1,000 occupations covering the entire U.S. economy. The database, which is available to the public at no cost, is continually updated by a multi-method data collection program. Sources of data include: job incumbents, occupational experts, occupational analysts, employer job postings, and customer/professional association input.

Data content areas include:

• Worker Characteristics (e.g., Abilities, Interests, Work Styles)
• Worker Requirements (e.g., Education, Knowledge, Skills)
• Experience Requirements (e.g., On-the-Job Training, Work Experience)
• Occupational Requirements (e.g., Detailed Work Activities, Work Context)
• Occupation-Specific Information (e.g., Job Titles, Tasks, Technology Skills)

The Means of Transportation to Work dataset was compiled using information from December 31, 2023 and updated December 12, 2024 from the Bureau of Transportation Statistics (BTS) and is part of the U.S. Department of Transportation (USDOT)/Bureau of Transportation Statistics (BTS) National Transportation Atlas Database (NTAD). The Means of Transportation to Work table from the 2023 American Community Survey (ACS) 5-year estimates was joined to 2023 tract-level geographies for all 50 States, District of Columbia and Puerto Rico provided by the Census Bureau. A new file was created that combines the demographic variables from the former with the cartographic boundaries of the latter. The national level census tract layer contains data on the number and percentage of commuters (workers 16 years and over) that used various transportation modes to get to work.

Full Time Employment in the United States increased to 135277 Thousand in June from 134840 Thousand in May of 2025. This dataset provides - United States Full Time Employment- actual values, historical data, forecast, chart, statistics, economic calendar and news.

This dataset was obtained from the Google Jobs API through serpAPI and contains information about job offers for data scientists in companies based in the United States of America (USA). The data may include details such as job title, company name, location, job description, salary range, and other relevant information. The dataset is likely to be valuable for individuals seeking to understand the job market for data scientists in the USA and for companies looking to recruit data scientists. It may also be useful for researchers who are interested in exploring trends and patterns in the job market for data scientists. The data should be used with caution, as the API source may not cover all job offers in the USA and the information provided by the companies may not always be accurate or up-to-date.

Average Weekly Hours in the United States decreased to 34.20 Hours in June from 34.30 Hours in May of 2025. This dataset provides - United States Average Weekly Hours - actual values, historical data, forecast, chart, statistics, economic calendar and news.

Part Time Employment in the United States decreased to 28190 Thousand in June from 28557 Thousand in May of 2025. This dataset provides - United States Part Time Employment- actual values, historical data, forecast, chart, statistics, economic calendar and news.

Context

This dataset was created by our in-house Web Scraping and Data Mining teams at PromptCloud and DataStock. You can download the full dataset here. This sample contains 30K records. You can download the full dataset here

Content

Total Records Count : 3288410  Domain Name : careerbuilder.usa.com  Date Range : 01st Oct 2021 - 31st Dec 2021   File Extension : ldjson

Available Fields : url, job_title, category, company_name, logo_url, city, state, country, post_date, test_months_of_experience, occupation_category, job_description, job_type, valid_through, html_job_description, extra_fields, test_onetsoc_code, test_onetsoc_name, uniq_id, crawl_timestamp, job_board, geo, job_post_lang, inferred_iso2_lang_code, is_remote, test1_cities, test1_states, test1_countries, site_name, domain, postdate_yyyymmdd, predicted_language, inferred_iso3_lang_code, test1_inferred_city, test1_inferred_state, test1_inferred_country, inferred_city, inferred_state, inferred_country, has_expired, last_expiry_check_date, latest_expiry_check_date, dataset, postdate_in_indexname_format, segment_name, duplicate_status, job_desc_char_count, ijp_reprocessed_flag_1, ijp_reprocessed_flag_2, ijp_reprocessed_flag_3, fitness_score 

Acknowledgements

We wouldn't be here without the help of our in house web scraping and data mining teams at PromptCloud, DataStock and live job data from JobsPikr.

Inspiration

This dataset was created keeping in mind our data scientists and researchers across the world.

The Quarterly Census of Employment and Wages (QCEW) Program is a Federal-State cooperative program between the U.S. Department of Labor’s Bureau of Labor Statistics (BLS) and the California EDD’s Labor Market Information Division (LMID). The QCEW program produces a comprehensive tabulation of employment and wage information for workers covered by California Unemployment Insurance (UI) laws and Federal workers covered by the Unemployment Compensation for Federal Employees (UCFE) program. The QCEW program serves as a near census of monthly employment and quarterly wage information by 6-digit industry codes from the North American Industry Classification System (NAICS) at the national, state, and county levels. At the national level, the QCEW program publishes employment and wage data for nearly every NAICS industry. At the state and local area level, the QCEW program publishes employment and wage data down to the 6-digit NAICS industry level, if disclosure restrictions are met. In accordance with the BLS policy, data provided to the Bureau in confidence are used only for specified statistical purposes and are not published. The BLS withholds publication of Unemployment Insurance law-covered employment and wage data for any industry level when necessary to protect the identity of cooperating employers. Data from the QCEW program serve as an important input to many BLS programs. The Current Employment Statistics and the Occupational Employment Statistics programs use the QCEW data as the benchmark source for employment. The UI administrative records collected under the QCEW program serve as a sampling frame for the BLS establishment surveys. In addition, the data serve as an input to other federal and state programs. The Bureau of Economic Analysis (BEA) of the Department of Commerce uses the QCEW data as the base for developing the wage and salary component of personal income. The U.S. Department of Labor’s Employment and Training Administration (ETA) and California's EDD use the QCEW data to administer the Unemployment Insurance program. The QCEW data accurately reflect the extent of coverage of California’s UI laws and are used to measure UI revenues; national, state and local area employment; and total and UI taxable wage trends. The U.S. Department of Labor’s Bureau of Labor Statistics publishes new QCEW data in its County Employment and Wages news release on a quarterly basis. The BLS also publishes a subset of its quarterly data through the Create Customized Tables system, and full quarterly industry detail data at all geographic levels.

About this dataset

Context

The dataset presents median income data over a decade or more for males and females categorized by Total, Full-Time Year-Round (FT), and Part-Time (PT) employment in Lake View. It showcases annual income, providing insights into gender-specific income distributions and the disparities between full-time and part-time work. The dataset can be utilized to gain insights into gender-based pay disparity trends and explore the variations in income for male and female individuals.

Key observations: Insights from 2023

Based on our analysis ACS 2019-2023 5-Year Estimates, we present the following observations: - All workers, aged 15 years and older: In Lake View, the median income for all workers aged 15 years and older, regardless of work hours, was $13,167 for males and $19,338 for females.

Contrary to expectations, women in Lake View, women, regardless of work hours, earn a higher income than men, earning 1.47 dollars for every dollar earned by men. This analysis indicates a significant shift in income dynamics favoring females.

As there was no available median income data for males, conducting a comprehensive assessment of gender-based pay disparity in Lake View was not feasible.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates. All incomes have been adjusting for inflation and are presented in 2023-inflation-adjusted dollars.

Gender classifications include:

• Male
• Female

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.

Variables / Data Columns

• Year: This column presents the data year. Expected values are 2010 to 2023
• Male Total Income: Annual median income, for males regardless of work hours
• Male FT Income: Annual median income, for males working full time, year-round
• Male PT Income: Annual median income, for males working part time
• Female Total Income: Annual median income, for females regardless of work hours
• Female FT Income: Annual median income, for females working full time, year-round
• Female PT Income: Annual median income, for females working part time

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 Lake View median household income by race. You can refer the same here

Context

This dataset was created by our in-house Web Scraping and Data Mining teams at PromptCloud and DataStock. You can download the full dataset here. This sample contains 30K records. You can download the full dataset here

Content

Total Records Count : 3933743  Domain Name : indeed.usa  Date Range : 01st Apr 2021 - 30th Jun 2021   File Extension : ldjson

Available Fields : uniq_id, crawl_timestamp, url, job_title, category, company_name, city, state, country, post_date, job_description, job_type, apply_url, job_board, geo, job_post_lang, inferred_iso2_lang_code, is_remote, test1_cities, test1_states, test1_countries, site_name, html_job_description, domain, postdate_yyyymmdd, predicted_language, inferred_iso3_lang_code, test1_inferred_city, test1_inferred_state, test1_inferred_country, inferred_city, inferred_state, inferred_country, has_expired, last_expiry_check_date, latest_expiry_check_date, dataset, postdate_in_indexname_format, segment_name, duplicate_status, fitness_score  

Acknowledgements

We wouldn't be here without the help of our in house web scraping and data mining teams at PromptCloud, DataStock and live job data from JobsPikr.

Inspiration

This dataset was created keeping in mind our data scientists and researchers across the world.

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.

analyze the current population survey (cps) annual social and economic supplement (asec) with r the annual march cps-asec has been supplying the statistics for the census bureau's report on income, poverty, and health insurance coverage since 1948. wow. the us census bureau and the bureau of labor statistics ( bls) tag-team on this one. until the american community survey (acs) hit the scene in the early aughts (2000s), the current population survey had the largest sample size of all the annual general demographic data sets outside of the decennial census - about two hundred thousand respondents. this provides enough sample to conduct state- and a few large metro area-level analyses. your sample size will vanish if you start investigating subgroups b y state - consider pooling multiple years. county-level is a no-no. despite the american community survey's larger size, the cps-asec contains many more variables related to employment, sources of income, and insurance - and can be trended back to harry truman's presidency. aside from questions specifically asked about an annual experience (like income), many of the questions in this march data set should be t reated as point-in-time statistics. cps-asec generalizes to the united states non-institutional, non-active duty military population. the national bureau of economic research (nber) provides sas, spss, and stata importation scripts to create a rectangular file (rectangular data means only person-level records; household- and family-level information gets attached to each person). to import these files into r, the parse.SAScii function uses nber's sas code to determine how to import the fixed-width file, then RSQLite to put everything into a schnazzy database. you can try reading through the nber march 2012 sas importation code yourself, but it's a bit of a proc freak show. this new github repository contains three scripts: 2005-2012 asec - download all microdata.R down load the fixed-width file containing household, family, and person records import by separating this file into three tables, then merge 'em together at the person-level download the fixed-width file containing the person-level replicate weights merge the rectangular person-level file with the replicate weights, then store it in a sql database create a new variable - one - in the data table 2012 asec - analysis examples.R connect to the sql database created by the 'download all microdata' progr am create the complex sample survey object, using the replicate weights perform a boatload of analysis examples replicate census estimates - 2011.R connect to the sql database created by the 'download all microdata' program create the complex sample survey object, using the replicate weights match the sas output shown in the png file below 2011 asec replicate weight sas output.png statistic and standard error generated from the replicate-weighted example sas script contained in this census-provided person replicate weights usage instructions document. click here to view these three scripts for more detail about the current population survey - annual social and economic supplement (cps-asec), visit: the census bureau's current population survey page the bureau of labor statistics' current population survey page the current population survey's wikipedia article notes: interviews are conducted in march about experiences during the previous year. the file labeled 2012 includes information (income, work experience, health insurance) pertaining to 2011. when you use the current populat ion survey to talk about america, subract a year from the data file name. as of the 2010 file (the interview focusing on america during 2009), the cps-asec contains exciting new medical out-of-pocket spending variables most useful for supplemental (medical spending-adjusted) poverty research. confidential to sas, spss, stata, sudaan users: why are you still rubbing two sticks together after we've invented the butane lighter? time to transition to r. :D