Graph and download economic data for Unemployment Rate in Bledsoe County, TN (TNBLED7URN) from Jan 1990 to Apr 2025 about Bledsoe County, TN; TN; unemployment; rate; and USA.

Graph and download economic data for Multiple Jobholders, Primary Job Full Time, Secondary Job Part Time (LNU02026625) from Jan 1994 to May 2025 about multiple jobholders, part-time, full-time, 16 years +, household survey, employment, and USA.

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: April 2025 (preliminary values at the 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: June 24th, 2025Local Area Unemployment Statistics table download: https://www.bls.gov/lau/#tablesLocal Area Unemployment FTP downloads:State and CountyNationData 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:

The Occupational Employment and Wage Statistics (OES) program conducts a semi-annual survey to produce estimates of employment and wages for specific occupations. The OES program collects data on wage and salary workers in nonfarm establishments in order to produce employment and wage estimates for about 800 occupations. Data from self-employed persons are not collected and are not included in the estimates. The OES program produces these occupational estimates by geographic area and by industry. Estimates based on geographic areas are available at the National, State, Metropolitan, and Nonmetropolitan Area levels. The Bureau of Labor Statistics produces occupational employment and wage estimates for over 450 industry classifications at the national level. The industry classifications correspond to the sector, 3-, 4-, and 5-digit North American Industry Classification System (NAICS) industrial groups. More information and details about the data provided can be found at http://www.bls.gov/oes

Historical Employment Statistics 1990 - current. The Current Employment Statistics (CES) more information program provides the most current estimates of nonfarm employment, hours, and earnings data by industry (place of work) for the nation as a whole, all states, and most major metropolitan areas. The CES survey is a federal-state cooperative endeavor in which states develop state and sub-state data using concepts, definitions, and technical procedures prescribed by the Bureau of Labor Statistics (BLS). Estimates produced by the CES program include both full- and part-time jobs. Excluded are self-employment, as well as agricultural and domestic positions. In Connecticut, more than 4,000 employers are surveyed each month to determine the number of the jobs in the State. For more information please visit us at http://www1.ctdol.state.ct.us/lmi/ces/default.asp.

The Occupational Employment and Wage Statistics (OEWS) Survey is a federal-state cooperative program between the Bureau of Labor Statistics (BLS) and State Workforce Agencies (SWAs). The BLS provides the procedures and technical support, draws the sample, and produces the survey materials, while the SWAs collect the data. SWAs from all fifty states, plus the District of Columbia, Puerto Rico, Guam, and the Virgin Islands participate in the survey. Occupational employment and wage rate estimates at the national level are produced by BLS using data from the fifty states and the District of Columbia. Employers who respond to states' requests to participate in the OEWS survey make these estimates possible.

The OEWS survey collects data from a sample of establishments and calculates employment and wage estimates by occupation, industry, and geographic area. The semiannual survey covers all non-farm industries. Data are collected by the Employment Development Department in cooperation with the Bureau of Labor Statistics, US Department of Labor. The OEWS Program estimates employment and wages for approximately 830 occupations. It also produces employment and wage estimates for statewide, Metropolitan Statistical Areas (MSAs), and Balance of State areas. Estimates are a snapshot in time and should not be used as a time series.

The OEWS estimates are published annually.

SOURCE: https://www.bls.gov/oes/oes_emp.htm

This dataset contains annual average CES data for California statewide and areas from 1990 - 2023. 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.

Youth unemployment stood at 9.7 percent in February 2025. Seasonal adjustment is a statistical method for removing the seasonal component of a time series that is used when analyzing non-seasonal trends. The unemployment rate by state can be found here, and the annual national unemployment rate can be found here. Youth unemployment in the United States The United States Bureau of Labor Statistics track unemployment of persons between the ages of 16 and 24 years each month. In analyzing the data, the Bureau of Labor Statistics performed a seasonal adjustment—removing seasonal influences from the time series, such that one month’s rate of unemployment could be analyzed in comparison with another month’s rate of unemployment. During the period in question, youth unemployment ranged from a high of 9.9 percent in April 2021, to a low of 6.5 percent in April 2023. The national youth unemployment rate can be compared to the monthly national unemployment rate in the United States, although youth unemployment tends to be much higher due to higher rates of participation in education. In May 2023, U.S. unemployment was at 3.7 percent, compared with 7.4 percent amongst those 16 to 24 years old. Additionally, as of May 2023, Nevada had the highest state unemployment rate of all U.S. states, at 5.4 percent.

AHE: sa: PW: FA: Nondepository Credit Intermediation data was reported at 37.010 USD in Mar 2025. This records an increase from the previous number of 36.930 USD for Feb 2025. AHE: sa: PW: FA: Nondepository Credit Intermediation data is updated monthly, averaging 19.610 USD from Jan 1990 (Median) to Mar 2025, with 423 observations. The data reached an all-time high of 37.010 USD in Mar 2025 and a record low of 9.830 USD in Feb 1990. AHE: sa: PW: FA: Nondepository Credit Intermediation data remains active status in CEIC and is reported by U.S. Bureau of Labor Statistics. The data is categorized under Global Database’s United States – Table US.G: Current Employment Statistics: Average Hourly Earnings: Production Workers: Seasonally Adjusted.

Graph and download economic data for All Employees: Leisure and Hospitality: Food Services and Drinking Places in New York City, NY (SMU36935617072200001SA) from Jan 1990 to Apr 2025 about beverages, New York, NY, food, services, employment, and USA.

AHE: sa: PW: EH: Child & Youth Services data was reported at 24.850 USD in Mar 2025. This records an increase from the previous number of 24.750 USD for Feb 2025. AHE: sa: PW: EH: Child & Youth Services data is updated monthly, averaging 15.010 USD from Jan 1990 (Median) to Mar 2025, with 423 observations. The data reached an all-time high of 24.850 USD in Mar 2025 and a record low of 8.520 USD in Jan 1990. AHE: sa: PW: EH: Child & Youth Services data remains active status in CEIC and is reported by U.S. Bureau of Labor Statistics. The data is categorized under Global Database’s United States – Table US.G: Current Employment Statistics: Average Hourly Earnings: Production Workers: Seasonally Adjusted.

This dataset contains the Local Area Unemployment Statistics (LAUS), annual averages from 1990 to 2023. 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.

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.

AHE: sa: PW: EH: Freestanding Emergency Medical Centers data was reported at 39.890 USD in Mar 2025. This records an increase from the previous number of 39.660 USD for Feb 2025. AHE: sa: PW: EH: Freestanding Emergency Medical Centers data is updated monthly, averaging 25.040 USD from Jan 1990 (Median) to Mar 2025, with 423 observations. The data reached an all-time high of 39.890 USD in Mar 2025 and a record low of 10.640 USD in Jan 1990. AHE: sa: PW: EH: Freestanding Emergency Medical Centers data remains active status in CEIC and is reported by U.S. Bureau of Labor Statistics. The data is categorized under Global Database’s United States – Table US.G: Current Employment Statistics: Average Hourly Earnings: Production Workers: Seasonally Adjusted.

Abstract

This dataset includes economic statistics on inflation, prices, unemployment, and pay & benefits provided by the Bureau of Labor Statistics (BLS)

Documentation

Update frequency: Monthly Dataset source: U.S. Bureau of Labor Statistics Terms of use: 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. See the GCP Marketplace listing for more details and sample queries: https://console.cloud.google.com/marketplace/details/bls-public-data/bureau-of-labor-statistics

Job Openings and Labor Turnover Survey data from the U.S. Bureau of Labor Statistics

The Texas Workforce Commission provides Texas Labor Market Information with counts for the civilian labor force, employment, unemployment, and unemployment rate estimates by place of residence. According to the U.S. Bureau of Labor Statistics, the definition of unemployed is to be "jobless, actively seeking work, and available to take a job." The unemployment rate is an important indicator of economic and workforce health in Austin over time. Unemployment Rate for the City of Austin = Number of Unemployed / Civilian Labor Force

AHE: sa: PW: EH: Health Care & Social Assistance data was reported at 32.960 USD in Mar 2025. This records an increase from the previous number of 32.840 USD for Feb 2025. AHE: sa: PW: EH: Health Care & Social Assistance data is updated monthly, averaging 18.430 USD from Jan 1990 (Median) to Mar 2025, with 423 observations. The data reached an all-time high of 32.960 USD in Mar 2025 and a record low of 9.840 USD in Jan 1990. AHE: sa: PW: EH: Health Care & Social Assistance data remains active status in CEIC and is reported by U.S. Bureau of Labor Statistics. The data is categorized under Global Database’s United States – Table US.G076: Current Employment Statistics: Average Hourly Earnings: Production Workers: Seasonally Adjusted.

Context:

The Occupational Employment Statistics (OES) and National Compensation Survey (NCS) programs have produced estimates by borrowing from the strength and breadth of each survey to provide more details on occupational wages than either program provides individually. Modeled wage estimates provide annual estimates of average hourly wages for occupations by selected job characteristics and within geographical location. The job characteristics include bargaining status (union and nonunion), part- and full-time work status, incentive- and time-based pay, and work levels by occupation.

Direct estimates are based on survey responses only from the particular geographic area to which the estimate refers. In contrast, modeled wage estimates use survey responses from larger areas to fill in information for smaller areas where the sample size is not sufficient to produce direct estimates. Modeled wage estimates require the assumption that the patterns to responses in the larger area hold in the smaller area.

The sample size for the NCS is not large enough to produce direct estimates by area, occupation, and job characteristic for all of the areas for which the OES publishes estimates by area and occupation. The NCS sample consists of 6 private industry panels with approximately 3,300 establishments sampled per panel, and 1,600 sampled state and local government units. The OES full six-panel sample consists of nearly 1.2 million establishments.

The sample establishments are classified in industry categories based on the North American Industry Classification System (NAICS). Within an establishment, specific job categories are selected to represent broader occupational definitions. Jobs are classified according to the Standard Occupational Classification (SOC) system.

Content:

Summary: Average hourly wage estimates for civilian workers in occupations by job characteristic and work levels. These data are available at the national, state, metropolitan, and nonmetropolitan area levels.

Frequency of Observations: Data are available on an annual basis, typically in May.

Data Characteristics: All hourly wages are published to the nearest cent.

Acknowledgements:

This dataset was taken directly from the Bureau of Labor Statistics and converted to CSV format.

Inspiration:

This dataset contains the estimated wages of civilian workers in the United States. Wage changes in certain industries may be indicators for growth or decline. Which industries have had the greatest increases in wages? Combine this dataset with the Bureau of Labor Statistics Consumer Price Index dataset and find out what kinds of jobs you would need to afford your snacks and instant coffee!

AHE: sa: PW: EH: Vocational Rehabilitation Services data was reported at 20.290 USD in Mar 2025. This records an increase from the previous number of 20.120 USD for Feb 2025. AHE: sa: PW: EH: Vocational Rehabilitation Services data is updated monthly, averaging 8.950 USD from Jan 1972 (Median) to Mar 2025, with 639 observations. The data reached an all-time high of 20.290 USD in Mar 2025 and a record low of 1.480 USD in Jan 1972. AHE: sa: PW: EH: Vocational Rehabilitation Services data remains active status in CEIC and is reported by U.S. Bureau of Labor Statistics. The data is categorized under Global Database’s United States – Table US.G: Current Employment Statistics: Average Hourly Earnings: Production Workers: Seasonally Adjusted.