Unemployment Rate in the United States increased to 4.20 percent in July from 4.10 percent in June 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.

This dataset contains unemployment rates for the U.S. (1948 - Present) and California (1976 - Present). The unemployment rate represents the number of unemployed as a percentage of the labor force. Labor force data are restricted to people 16 years of age and older, who currently reside in 1 of the 50 states or the District of Columbia, who do not reside in institutions (e.g., penal and mental facilities, homes for the aged), and who are not on active duty in the Armed Forces. This rate is also defined as the U-3 measure of labor underutilization.

Employment Rate in the United States decreased to 59.60 percent in July from 59.70 percent in June of 2025. This dataset provides - United States Employment Rate- actual values, historical data, forecast, chart, statistics, economic calendar and news.

The employment and unemployment indicator shows several data points. The first figure is the number of people in the labor force, which includes the number of people who are either working or looking for work. The second two figures, the number of people who are employed and the number of people who are unemployed, are the two subcategories of the labor force. The unemployment rate is a calculation of the number of people who are in the labor force and unemployed as a percentage of the total number of people in the labor force.

The unemployment rate does not include people who are not employed and not in the labor force. This includes adults who are neither working nor looking for work. For example, full-time students may choose not to seek any employment during their college career, and are thus not considered in the unemployment rate. Stay-at-home parents and other caregivers are also considered outside of the labor force, and therefore outside the scope of the unemployment rate.

The unemployment rate is a key economic indicator, and is illustrative of economic conditions in the county at the individual scale.

There are additional considerations to the unemployment rate. Because it does not count those who are outside the labor force, it can exclude individuals who were looking for a job previously, but have since given up. The impact of this on the overall unemployment rate is difficult to quantify, but it is important to note because it shows that no statistic is perfect.

The unemployment rates for Champaign County, the City of Champaign, and the City of Urbana are extremely similar between 2000 and 2023.

All three areas saw a dramatic increase in the unemployment rate between 2006 and 2009. The unemployment rates for all three areas decreased overall between 2010 and 2019. However, the unemployment rate in all three areas rose sharply in 2020 due to the effects of the COVID-19 pandemic. The unemployment rate in all three areas dropped again in 2021 as pandemic restrictions were removed, and were almost back to 2019 rates in 2022. However, the unemployment rate in all three areas rose slightly from 2022 to 2023.

This data is sourced from the Illinois Department of Employment Security’s Local Area Unemployment Statistics (LAUS), and from the U.S. Bureau of Labor Statistics.

Sources: Illinois Department of Employment Security, Local Area Unemployment Statistics (LAUS); U.S. Bureau of Labor Statistics.

U6 Unemployment Rate in the United States increased to 7.90 percent in July from 7.70 percent in June of 2025. This dataset includes a chart with historical data for the United States U-6 Unemployment Rate.

This dataset contains the Local Area Unemployment Statistics (LAUS), annual averages from 1990 to 2024.

The Local Area Unemployment Statistics (LAUS) program is a Federal-State cooperative effort in which monthly estimates of total employment and unemployment are prepared for approximately 7,600 areas, including counties, cities and metropolitan statistical areas. These estimates are key indicators of local economic conditions.

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.

Estimates for counties are produced through a building-block approach known as the "Handbook method." This procedure also uses data from several sources, including the CPS, the CES program, state UI systems, and the Census Bureau's American Community Survey (ACS), to create estimates that are adjusted to the statewide measures of employment and unemployment. Estimates for cities are prepared using disaggregation techniques based on inputs from the ACS, annual population estimates, and current UI data.

This dataset contains non-seasonally adjusted California Unemployment Rate by age groups, from the Current Population Survey (CPS). The age group ranges are as follows; 16-19 ; 20 - 24; 25 - 34; 35 - 44; 45 - 54; 55 -64; 65+. This data is based on a 12-month moving average.

This dataset is invaluable for data science applications due to its granularity and the historical depth it offers. With detailed monthly data on unemployment rates by age groups, data scientists can perform a myriad of analyses:

• Time Series Analysis: Analyzing trends over time to forecast future unemployment rates or identify cyclic patterns.
• Demographic Analysis: Understanding which age groups are most affected by unemployment and how this changes through economic conditions.
• Geographic Analysis: Although the dataset currently focuses on California, if similar data is available for other regions, comparisons can be made to understand regional economic differences.

The dataset can also be merged with other socioeconomic indicators like GDP, education levels, and industry growth metrics to examine broader economic narratives or policy impacts.

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?

Regional unemployment rates used by the Employment Insurance program, by effective date, current month.

Initial Jobless Claims in the United States decreased to 229 thousand in the week ending August 23 of 2025 from 234 thousand in the previous week. This dataset provides the latest reported value for - United States Initial Jobless Claims - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

This dataset uses seasonally adjusted data from the US Bureau of Labor Statistics to present information on Maryland's labor force participation rate, employment rate, and unemployment rate.

Context

To contribute towards the research and analysis on COVID-19 and it's impact on the human life, I have made this data available in usable format for analysis.

Content

1. This dataset contains time series data of unemployment rate in US from January 2010 to Present. It contains unemployment rate records based on education qualification, race, and gender of adults.
2. This dataset also contains state-wise unemployment rate data for year 2020. The data set is clean and doesn't require much pre-processing. Dataset can be used with BI tools like Tableau as well.

Acknowledgements

I would like to thank "U.S. BUREAU OF LABOR STATISTICS" for making the data available. URL: https://data.bls.gov/cgi-bin/surveymost?ln

Inspiration

1. Combine with COVID-19 dataset to understand the impact on Unemployment rate in the United States.
2. Perform Individual study on Unemployment in United States.

Unemployment rate, participation rate, and employment rate by educational attainment, gender and age group, annual.

Unemployment Rate in India decreased to 5.20 percent in July from 5.60 percent in June of 2025. This dataset provides - India Unemployment Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

Context

This is a dataset that I built by scraping the United States Department of Labor's Bureau of Labor Statistics. I was looking for county-level unemployment data and realized that there was a data source for this, but the data set itself hadn't existed yet, so I decided to write a scraper and build it out myself.

Content

This data represents the Local Area Unemployment Statistics from 1990-2016, broken down by state and month. The data itself is pulled from this mapping site:

https://data.bls.gov/map/MapToolServlet?survey=la&map=county&seasonal=u

Further, the ever-evolving and ever-improving codebase that pulled this data is available here:

https://github.com/jayrav13/bls_local_area_unemployment

Acknowledgements

Of course, a huge shoutout to bls.gov and their open and transparent data. I've certainly been inspired to dive into US-related data recently and having this data open further enables my curiosities.

Inspiration

I was excited about building this data set out because I was pretty sure something similar didn't exist - curious to see what folks can do with it once they run with it! A curious question I had was surrounding Unemployment vs 2016 Presidential Election outcome down to the county level. A comparison can probably lead to interesting questions and discoveries such as trends in local elections that led to their most recent election outcome, etc.

Next Steps

Version 1 of this is as a massive JSON blob, normalized by year / month / state. I intend to transform this into a CSV in the future as well.

This dataset includes economic statistics on inflation, prices, unemployment, and pay & benefits provided by the Bureau of Labor Statistics (BLS). This public dataset is hosted in Google BigQuery and is included in BigQuery's 1TB/mo of free tier processing. This means that each user receives 1TB of free BigQuery processing every month, which can be used to run queries on this public dataset. Watch this short video to learn how to get started quickly using BigQuery to access public datasets. What is BigQuery .

The Local Area Unemployment Statistics (LAUS) program is a Federal-State cooperative effort in which monthly estimates of total employment and unemployment are prepared for approximately 7,600 areas, including counties, cities and metropolitan statistical areas. These estimates are key indicators of local economic conditions. 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. Estimates for counties are produced through a building-block approach known as the "Handbook method." This procedure also uses data from several sources, including the CPS, the CES program, state UI systems, and the Census Bureau's American Community Survey (ACS), to create estimates that are adjusted to the statewide measures of employment and unemployment. Estimates for cities are prepared using disaggregation techniques based on inputs from the ACS, annual population estimates, and current UI data.

Youth Unemployment Rate in the United States remained unchanged at 10 percent in July. This dataset provides - United States Youth Unemployment Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

Shows the "probability" of being without a job for those who would like to have one, broken-down by educational attainment level. Measures the difficulties that people with different levels of education have to face on the labour market. Gives an initial idea of the impact of education on reducing the chances of being unemployed. Educational attainment level is coded according to the International Standard Classification of Education (ISCED). Data until 2013 are classified according to ISCED 1997 and data as from 2014 according to ISCED 2011.

Unemployment Rate in Japan decreased to 2.30 percent in July from 2.50 percent in June of 2025. This dataset provides the latest reported value for - Japan Unemployment Rate - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.