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.

In 2023, Nevada had the highest annual unemployment rate in the United States at 5.1 percent. North Dakota had the lowest unemployment rate that year, at 1.9 percent. The monthly unemployment rate by state can be found here.

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 table contains data on the percent of the population in the labor force who are unemployed (unemployment rate), for California, its regions, counties, county divisions, cities/towns, and census tracts. Data is from the Local Area Unemployment Statistics (LAUS), Bureau of Labor Statistics (BLS), and the U.S. Census Bureau, American Community Survey (ACS). The table is part of a series of indicators in the Healthy Communities Data and Indicators Project of the Office of Health Equity. Unemployment is associated with higher rates of self-reported poor health, long-term illnesses, higher incidence of risky health behaviors (alcoholism, smoking), and increased mortality. Various explanations have been proposed for the link between poor health and unemployment; for example, economic deprivation that results in reduced access to essential goods and services. Another explanation is that unemployment causes the loss of latent functions (social contact, social status, time structure and personal identity) which can result in stigma, isolation and loss of self-worth. More information about the data table and a data dictionary can be found in the About/Attachments section.

United States Unemployment Rate: Age 25 to 54 data was reported at 3.000 % in Oct 2018. This records an increase from the previous number of 2.900 % for Sep 2018. United States Unemployment Rate: Age 25 to 54 data is updated monthly, averaging 4.400 % from Jan 1948 (Median) to Oct 2018, with 850 observations. The data reached an all-time high of 9.700 % in Jan 1983 and a record low of 1.800 % in Aug 1953. United States Unemployment Rate: Age 25 to 54 data remains active status in CEIC and is reported by Bureau of Labor Statistics. The data is categorized under Global Database’s United States – Table US.G018: Current Population Survey: Unemployment Rate.

Youth Unemployment Rate in the United States increased to 10 percent in June from 9.70 percent in May of 2025. This dataset provides - United States Youth Unemployment Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

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?

Initial Jobless Claims in the United States decreased to 227 thousand in the week ending July 5 of 2025 from 232 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 layer contains the latest 14 months of unemployment statistics from the U.S. Bureau of Labor Statistics (BLS). The data is offered at the nationwide, state, and county geography levels. Puerto Rico is included. These are not seasonally adjusted values. The layer is updated monthly with the newest unemployment statistics available from BLS. There are attributes in the layer that specify which month is associated to each statistic. Most current month: May 2025 (preliminary values at the state and county level) The attributes included for each month are:Unemployment rate (%)Count of unemployed populationCount of employed population in the labor forceCount of people in the labor forceData obtained from the U.S. Bureau of Labor Statistics. Data downloaded: July 2nd, 2025Local Area Unemployment Statistics table download: https://www.bls.gov/lau/#tablesLocal Area Unemployment FTP downloads:State and County NationData Notes:This layer is updated automatically when the BLS releases their most current monthly statistics. The layer always contains the most recent estimates. It is updated within days of the BLS"s county release schedule. BLS releases their county statistics roughly 2 months after-the-fact. The data is joined to 2023 TIGER boundaries from the U.S. Census Bureau.Monthly values are subject to revision over time.For national values, employed plus unemployed may not sum to total labor force due to rounding.As of the January 2022 estimates released on March 18th, 2022, BLS is reporting new data for the two new census areas in Alaska - Copper River and Chugach - and historical data for the previous census area - Valdez Cordova. As of the March 17th, 2025 release, BLS now reports data for 9 planning regions in Connecticut rather than the 8 previous counties. To better understand the different labor force statistics included in this map, see the diagram below from BLS:

United States Unemployment Insurance: Unemployment Rate: Colorado data was reported at 7.410 % in 01 Aug 2020. This records a decrease from the previous number of 8.360 % for 25 Jul 2020. United States Unemployment Insurance: Unemployment Rate: Colorado data is updated weekly, averaging 1.400 % from Dec 1986 (Median) to 01 Aug 2020, with 1754 observations. The data reached an all-time high of 9.960 % in 16 May 2020 and a record low of 0.530 % in 30 Sep 2000. United States Unemployment Insurance: Unemployment Rate: Colorado data remains active status in CEIC and is reported by US Department of Labor. The data is categorized under Global Database’s United States – Table US.G072: Unemployment Insurance: Jobless Claims: by State.

This dataset provides the Unemployment Rate (UR) in percentage according to usual status (ps+ss) for each State and Union Territory in India, categorized by age groups: 15-29 years, 15-59 years, 15 years and above, and all ages. It is sourced from the PLFS by the Ministry of Statistics and Programme Implementation and offers insights into regional and age-group-specific unemployment rates. For 2023-24, Chandigarh's entire area has been considered urban for this survey, with data available only for the age groups 15-59 years, 15 years and above, and all ages. Before 2019-20, Ladakh was part of Jammu and Kashmir, and since 2020-21, Daman and Diu has been merged with Dadra and Nagar Haveli to form the union territory of Dadra and Nagar Haveli and Daman and Diu.

This dataset provides the unemployment rate (UR) by usual status (ps+ss) for individuals aged 15 years and above, categorized by their highest level of education completed, for each state and union territory. Sourced from the PLFS, it enables the examination of unemployment trends relative to education levels across regions. For 2023-24, Chandigarh's entire area has been considered urban for this survey. Before 2019-20, Ladakh was part of Jammu and Kashmir, and since 2020-21, Daman and Diu have been merged with Dadra and Nagar Haveli to form the union territory of Dadra and Nagar Haveli and Daman and Diu.

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.

U6 Unemployment Rate in the United States decreased to 7.70 percent in June from 7.80 percent in May of 2025. This dataset includes a chart with historical data for the United States U-6 Unemployment Rate.

Long Term Unemployment Rate in the United States increased to 0.97 percent in June from 0.85 percent in May of 2025. This dataset provides the latest reported value for - United States Long Term Unemployment Rate - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

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.

Context & Content

This dataset consists of the unemployment rate and education level of adults in the USA by county. That is, for each county in the USA, this dataset provides the count and percentage of unemployed adults as well as the count and percentage of adults of various educational backgrounds. Each county was been assigned one of four locale categories (City, Suburb, Town, Rural) according to its 2013 Urban Influence Code and their descriptions provided in UIC_codes.csv. From the descriptions of each of the codes and the descriptions of the locales "City", "Suburb", "Town", and "Rural" provided on page 2 of the locale user manual (locale_user_manual.pdf), each county was assigned one of four locales.

The unemployment rate data includes the count and percentage of unemployed adults for each county in the USA for each year from 2000-2020. The median household income for 2019 is also included. The education level data includes the count and percentage of adults with less than a high school diploma, a high school diploma only, some college, and a bachelor's degree/four years of college or more for the years 1970, 1980, 1990, 2000, and 2019. The Urban Influence Code data includes the UIC and locale description of each county in the USA and the locale user manual has been included as a PDF as strictly a reference file, to understand how each county was assigned a locale within the unemployment.csv and education.csv files.

Data Sources

Source for the unemployment rate and education level data by county: "County-level Data Sets." USDA Economic Research Service, US Department of Agriculture. Access date: Sept 8, 2021. URL: https://www.ers.usda.gov/data-products/county-level-data-sets/

Source for Urban Influence Codes by county: "Urban Influence Codes." USDA Economic Research Service, US Department of Agriculture. Access date: Sept 8, 2021. URL: https://www.ers.usda.gov/data-products/urban-influence-codes/#:~:text=The%202013%20Urban%20Influence%20Codes,to%20metro%20and%20micropolitan%20areas.&text=An%20update%20of%20the%20Urban,is%20planned%20for%20mid%2D2023.

Inspiration

This dataset was created to be used as an additional data source for the LearnPlatform COVID-19 Impact on Digital Learning Kaggle competition, but is suitable for other analyses related to unemployment rate and education level in the USA.

United States Unemployment Rate: North Carolina data was reported at 4.200 % in Jun 2018. This records an increase from the previous number of 3.700 % for May 2018. United States Unemployment Rate: North Carolina data is updated monthly, averaging 5.400 % from Jan 1976 (Median) to Jun 2018, with 510 observations. The data reached an all-time high of 12.000 % in Feb 2010 and a record low of 2.700 % in Dec 1998. United States Unemployment Rate: North Carolina data remains active status in CEIC and is reported by Bureau of Labor Statistics. The data is categorized under Global Database’s USA – Table US.G057: Unemployment Rate: By State.

Continued Claims for UI released by the CT Department of Labor. Continued Claims are total number of individuals being paid benefits in any particular week. Claims data can be access directly from CT DOL here: https://www1.ctdol.state.ct.us/lmi/claimsdata.asp

Claims are disaggregated by age, education, industry, race/national origin, sex, and wages.

The claim counts in this dataset may not match claim counts from other sources.

Unemployment claims tabulated in this dataset represent only one component of the unemployed. Claims do not account for those not covered under the Unemployment system (e.g. federal workers, railroad workers or religious workers) or the unemployed self-employed.

Claims filed for a particular week will change as time goes on and the backlog is addressed.

For data on continued claims at the town level, see the dataset "Continued Claims for Unemployment Benefits by Town" here: https://data.ct.gov/Government/Continued-Claims-for-Unemployment-Benefits-by-Town/r83t-9bjm

For data on initial claims see the following two datasets:

"Initial Claims for Unemployment Benefits in Connecticut," https://data.ct.gov/Government/Initial-Claims-for-Unemployment-Benefits/j3yj-ek9y

"Initial Claims for Unemployment Benefits by Town," https://data.ct.gov/Government/Initial-Claims-for-Unemployment-Benefits-by-Town/twvc-s7wy

The US Family Budget Dataset provides insights into the cost of living in different US counties based on the Family Budget Calculator by the Economic Policy Institute (EPI).

This dataset offers community-specific estimates for ten family types, including one or two adults with zero to four children, in all 1877 counties and metro areas across the United States.

Interesting Task Ideas:

1. See how family budgets compare to the federal poverty line and the Supplemental Poverty Measure in different counties.
2. Look into the money challenges faced by different types of families using the budgets provided.
3. Find out which counties have the most affordable places to live, food, transportation, healthcare, childcare, and other things people need.
4. Explore how the average income of families relates to the overall cost of living in different counties.
5. Investigate how family size affects the estimated budget and find counties where bigger families have higher costs.
6. Create visuals showing how the cost of living varies across different states and big cities.
7. Check whether specific counties are affordable for families of different sizes and types.
8. Use the dataset to compare living standards and economic security in different US counties.

If you find this dataset valuable, don't forget to hit the upvote button! 😊💝

Checkout my other datasets

Employment-to-Population Ratio for USA

Productivity and Hourly Compensation

130K Kindle Books

900K TMDb Movies

USA Unemployment Rates by Demographics & Race

Photo by Alev Takil on Unsplash