Graph and download economic data for Total Unemployed, Plus All Persons Marginally Attached to the Labor Force, Plus Total Employed Part Time for Economic Reasons, as a Percent of the Civilian Labor Force Plus All Persons Marginally Attached to the Labor Force (U-6) (U6RATE) from Jan 1994 to Jul 2025 about marginally attached, part-time, labor underutilization, workers, 16 years +, labor, household survey, unemployment, and USA.

This dataset includes the historical series of sample Unemployment Insurance (UI) data collected through the benefit accuracy measurement (BAM) program. BAM is a statistical survey used to identify and support resolutions of deficiencies in the state’s (UI) system as well as to estimate state UI improper payments to be reported to DOL as required by the Improper Payments Information Act (IPIA) and the Elimination and Recovery Act (IPERA). BAM is also used to identify the root causes of improper payments and supports other analyses conducted by DOL to highlight improper payment prevention strategies and measure progress in meeting improper payment reduction targets.

This web map provides estimates for the percentage of unemployment among people 16 years and older in the labor force from the American Community Survey 5-year data for the United States—50 states and the District of Columbia at county, place, census tract, and ZCTA-levels. Data were downloaded from data.census.gov using Census API and processed by the Centers for Disease Control and Prevention (CDC), Division of Population Health, Epidemiology and Surveillance Branch. The project was funded by the Robert Wood Johnson Foundation in conjunction with the CDC Foundation. Year: 2017–2021 ACS table(s): DP03 Data downloaded from: Census Bureau's API for American Community Survey Date of API call: September 12, 2023 For questions or feedback send an email to places@cdc.gov.

The seasonally-adjusted national unemployment rate is measured on a monthly basis in the United States. In February 2025, the national unemployment rate was at 4.1 percent. Seasonal adjustment is a statistical method of removing the seasonal component of a time series that is used when analyzing non-seasonal trends. U.S. monthly unemployment rate According to the Bureau of Labor Statistics - the principle fact-finding agency for the U.S. Federal Government in labor economics and statistics - unemployment decreased dramatically between 2010 and 2019. This trend of decreasing unemployment followed after a high in 2010 resulting from the 2008 financial crisis. However, after a smaller financial crisis due to the COVID-19 pandemic, unemployment reached 8.1 percent in 2020. As the economy recovered, the unemployment rate fell to 5.3 in 2021, and fell even further in 2022. Additional statistics from the BLS paint an interesting picture of unemployment in the United States. In November 2023, the states with the highest (seasonally adjusted) unemployment rate were the Nevada and the District of Columbia. Unemployment was the lowest in Maryland, at 1.8 percent. Workers in the agricultural and related industries suffered the highest unemployment rate of any industry at seven percent in December 2023.

The long-term unemployment rate expresses the number of long-term unemployed aged 15-74 as a percentage of the active population of the same age. Long-term unemployed (12 months and more) comprise persons aged at least 15, who are not living in collective households, who will be without work during the next two weeks, who would be available to start work within the next two weeks and who are seeking work (have actively sought employment at some time during the previous four weeks or are not seeking a job because they have already found a job to start later). The total active population (labour force) is the total number of the employed and unemployed population. The duration of unemployment is defined as the duration of a search for a job or as the period of time since the last job was held (if this period is shorter than the duration of the search for a job). The indicator is based on the EU Labour Force Survey. Copyright notice and free re-use of data on: https://ec.europa.eu/eurostat/about-us/policies/copyright

VITAL SIGNS INDICATOR Unemployment (EC3)

FULL MEASURE NAME Unemployment rate by residential location

LAST UPDATED July 2019

DESCRIPTION Unemployment refers to the share of the labor force – by place of residence – that is not currently employed full-time or part-time. The unemployment rate reflects the strength of the overall employment market.

DATA SOURCE California Employment Development Department: Historical Unemployment Rates 1990-2018 https://data.edd.ca.gov/Labor-Force-and-Unemployment-Rates/Local-Area-Unemployment-Statistics-LAUS-Annual-Ave/7jbb-3rb8

CONTACT INFORMATION vitalsigns.info@bayareametro.gov

METHODOLOGY NOTES (across all datasets for this indicator) Unemployment rates produced by EDD for the region and county levels are not adjusted for seasonality (as they reflect annual data) and are final data (i.e., not preliminary). Unemployment rates produced by BLS for the metro regions are adjusted for seasonality; they reflect the primary MSA for the named region, except for the San Francisco Bay Area which uses the nine-county region. The unemployment rate is calculated based on the number of unemployed persons divided by the total labor force. Note that the unemployment rate can decline or increase as a result of changes in either variable.

This dataset contains information about the unemployment rate in Austin (SD23 measure EOA.A.1). Texas Workforce Comission provides Texas Labor Market Information for Austin, the Austin Round-Rock MSA, Texas, and the United States. This dataset includes the average number of people in the civilian labor force, the employment count, the unemployment count, and the unemployment rate for Austin, the Austin Round-Rock MSA, Texas, and the United States. The unemployment rate can be useful in understanding economic and workforce trends in Austin over time. View more details and insights related to this dataset on the story page: https://data.austintexas.gov/stories/s/Percentage-Unemployment-Rate/ehhu-nafn/

Abstract

THE CLEANED AND HARMONIZED VERSION OF THE SURVEY DATA PRODUCED AND PUBLISHED BY THE ECONOMIC RESEARCH FORUM REPRESENTS 100% OF THE ORIGINAL SURVEY DATA COLLECTED BY THE DEPARTMENT OF STATISTICS OF THE HASHEMITE KINGDOM OF JORDAN

The Department of Statistics (DOS) carried out four rounds of the 2016 Employment and Unemployment Survey (EUS). The survey rounds covered a sample of about fourty nine thousand households Nation-wide. The sampled households were selected using a stratified multi-stage cluster sampling design.

It is worthy to mention that the DOS employed new technology in data collection and data processing. Data was collected using electronic questionnaire instead of a hard copy, namely a hand held device (PDA).

The survey main objectives are: - To identify the demographic, social and economic characteristics of the population and manpower. - To identify the occupational structure and economic activity of the employed persons, as well as their employment status. - To identify the reasons behind the desire of the employed persons to search for a new or additional job. - To measure the economic activity participation rates (the number of economically active population divided by the population of 15+ years old). - To identify the different characteristics of the unemployed persons. - To measure unemployment rates (the number of unemployed persons divided by the number of economically active population of 15+ years old) according to the various characteristics of the unemployed, and the changes that might take place in this regard. - To identify the most important ways and means used by the unemployed persons to get a job, in addition to measuring durations of unemployment for such persons. - To identify the changes overtime that might take place regarding the above-mentioned variables.

The raw survey data provided by the Statistical Agency were cleaned and harmonized by the Economic Research Forum, in the context of a major project that started in 2009. During which extensive efforts have been exerted to acquire, clean, harmonize, preserve and disseminate micro data of existing labor force surveys in several Arab countries.

Geographic coverage

Covering a sample representative on the national level (Kingdom), governorates, and the three Regions (Central, North and South).

Analysis unit

1- Household/family. 2- Individual/person.

Universe

The survey covered a national sample of households and all individuals permanently residing in surveyed households.

Kind of data

Sample survey data [ssd]

Sampling procedure

THE CLEANED AND HARMONIZED VERSION OF THE SURVEY DATA PRODUCED AND PUBLISHED BY THE ECONOMIC RESEARCH FORUM REPRESENTS 100% OF THE ORIGINAL SURVEY DATA COLLECTED BY THE DEPARTMENT OF STATISTICS OF THE HASHEMITE KINGDOM OF JORDAN

Mode of data collection

Computer Assisted Personal Interview [capi]

Cleaning operations

----> Raw Data

A tabulation results plan has been set based on the previous Employment and Unemployment Surveys while the required programs were prepared and tested. When all prior data processing steps were completed, the actual survey results were tabulated using an ORACLE package. The tabulations were then thoroughly checked for consistency of data. The final report was then prepared, containing detailed tabulations as well as the methodology of the survey.

----> Harmonized Data

• The SPSS package is used to clean and harmonize the datasets.
• The harmonization process starts with a cleaning process for all raw data files received from the Statistical Agency.
• All cleaned data files are then merged to produce one data file on the individual level containing all variables subject to harmonization.
• A country-specific program is generated for each dataset to generate/ compute/ recode/ rename/ format/ label harmonized variables.
• A post-harmonization cleaning process is then conducted on the data.
• Harmonized data is saved on the household as well as the individual level, in SPSS and then converted to STATA, to be disseminated.

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.

Unemployment insurance policies are multidimensional objects, with variable waiting periods, eligibility duration, benefit levels and asset tests, making intertemporal or international comparisons very difficult. Furthermore, labor market conditions, such as the likelihood and duration of unemployment matter when assessing the generosity of different policies. In this paper, we develop a new methodology to measure the generosity of unemployment insurance programs with a single metric. We build a first model with all characteristics of the complex unemployment insurance policy. Our model features heterogeneous agents that are liquidity constrained but can self-insure. We then build a second model, similar in all aspects but one: the unemployment insurance policy is one-dimensional (no waiting periods, eligibility limits, or asset tests, but constant benefits). We then determine which level of benefits in this second model makes society indifferent between both policies. We apply this measurement strategy to the unemployment insurance program of the United Kingdom.

This table contains details about unemployment in in King County. It has been developed for the Determinant of Equity - Jobs and Jobs Training. It includes information about Unemployment equity indicator. Fields describe the total adults (16+ years) in the civilian labor force in King County (Denominator), number of adults 16+ in the civilian labor force who were unemployed (Numerator), the type of equity indicator being measured (Indicator), and the value that describes this measurement (Indicator Value).The data was compiled from the American Community Survey (ACS).American Community SurveyPublic Use Microdata Sample (PUMS)For more information about King County's equity efforts, please see:Equity, Racial & Social Justice VisionOrdinance 16948 describing the determinates of equityDeterminants of Equity and Data Tool

Unemployed jobseekers 2007-2018 by year, month, region, unemployment measurement, age group and sex

VITAL SIGNS INDICATOR
Unemployment (EC3)

FULL MEASURE NAME
Unemployment rate by residential location

LAST UPDATED
December 2022

DESCRIPTION
Unemployment refers to the share of the labor force – by place of residence – that is not currently employed full-time or part-time. The unemployment rate reflects the strength of the overall employment market.

DATA SOURCE
California Employment Development Department: Historical Unemployment Rates
1990-2010
Spreadsheet provided by CAEDD

California Employment Development Department: Labor Force and Unemployment Rate for California Sub-County Areas - https://data.edd.ca.gov/Labor-Force-and-Unemployment-Rates/Labor-Force-and-Unemployment-Rate-for-California-S/8z4h-2ak6
2010-2022

California Employment Development Department: Local Area Unemployment Statistics (LAUS) - https://data.edd.ca.gov/Labor-Force-and-Unemployment-Rates/Local-Area-Unemployment-Statistics-LAUS-/e6gw-gvii
1990-2022

U.S. Bureau of Labor Statistics: Local Area Unemployment Statistics (LAUS) - https://download.bls.gov/pub/time.series/la
1990-2021

CONTACT INFORMATION
vitalsigns.info@bayareametro.gov

METHODOLOGY NOTES (across all datasets for this indicator)
Unemployment rates produced by the CA Employment Development Department (EDD) for the region and county levels are not adjusted for seasonality (as they reflect annual data) and are final data (i.e., not preliminary). Unemployment rates produced by U.S. Bureau of Labor Statistics (BLS) for the metro regions are annual and not adjusted for seasonality; they reflect the primary metropolitan statistical area (MSA) for the named region, except for the San Francisco Bay Area which uses the nine-county region. The unemployment rate is calculated based on the number of unemployed persons divided by the total labor force. Note that the unemployment rate can decline or increase as a result of changes in either variable.

The long-term unemployment rate is the number of persons unemployed for 12 months or longer, expressed as a percentage of the labour force (the total number of people employed and unemployed). Unemployed persons are those aged 15 to 74 who meet all three of the following conditions: were not employed during the reference week; were available to start working within two weeks after the reference week; and have actively sought work in the four weeks prior to the reference week or have already found a job to begin within the next three months.

The MIP auxiliary indicator is expressed as a percentage of the active population aged 15 to 74 years. In the table, the values are also presented as changes over a three-year period (in percentage points). The data source is the quarterly EU Labour Force Survey (EU-LFS), which covers the resident population living in private households.

The unemployment rate measures the proportion of Americans aged 16 and older who are currently unemployed and looking for work. This measure does not account for individuals who have given up on searching due to a lack of opportunities or otherwise, such as discouraged workers. The data presented in this report are annual averages based on unadjusted monthly data sourced from the Bureau of Labor Statistics (BLS).

In 2024, the rate of surveyed unemployment in urban areas of China amounted to approximately 5.1 percent. The unemployment rate is expected to remain at 5.1 percent in 2025 and the following years. Monthly unemployment ranged at a level of around 5.3 percent in the first quarter of 2025. Unemployment rate in China In 2017, the National Statistics Bureau of China introduced surveyed unemployment as a new indicator of unemployment in the country. It is based on monthly surveys among the labor force in urban areas of China. Surveyed unemployment replaced registered unemployment figures, which were often criticized for missing out large parts of the urban labor force and thereby not presenting a true picture of urban unemployment levels. However, current unemployment figures still do not include rural areas.A main concern in China’s current state of employment lies within the large regional differences. As of 2021, the unemployment rate in northeastern regions of China was notably higher than in China’s southern parts. In Beijing, China’s political and cultural center, registered unemployment ranged at around 3.2 percent for 2021. Indicators of economic activities Apart from the unemployment rate, most commonly used indicators to measure economic activities of a country are GDP growth and inflation rate. According to an IMF forecast, GDP growth in China will decrease to about four percent in 2025, after five percent in 2023, depicting a decrease of more than six percentage points from 10.6 percent in 2010. Quarterly growth data published by the National Bureau of Statistics indicated 5.4 percent GDP growth for the first quarter of 2025.

1990 to present (approximate 2 month lag) Virginia Labor Force and Unemployment estimates by Month by County.

Special data considerations: Period values of "M01-M12" represent Months of Year; "M13" is the Annual Average.

U.S. Bureau of Labor Statistics; Local Area Unemployment Statistics, table la.data.54.Virginia Data accessed from the Bureau of Labor Statistics public database LABSTAT (https://download.bls.gov/pub/time.series/la/)

Supporting documentation can be found on the U.S. Bureau of Labor Statistics website under Local Area Unemployment Statistics, Handbook of Methods (https://www.bls.gov/opub/hom/lau/home.htm)

Survey Description: Labor force and unemployment estimates for States and local areas are developed by State workforce agencies to measure local labor market conditions under a Federal-State cooperative program. The Department of Labor develops the concepts, definitions, and technical procedures which are used by State agencies for preparation of labor force and unemployment estimates.

These estimates are derived from a variety of sources, including the Current Population Survey, the Current Employment Statistics survey, the Quarterly Census of Employment and Wages, various programs at the Census Bureau, and unemployment insurance claims data from the State workforce agencies.

To establish uniform labor force concepts and definitions in all States and areas consistent with those used for the U.S. as a whole, monthly national estimates of employment and unemployment from the Current Population Survey are used as controls (benchmarks) for the State labor force statistics.

Summary Data Available: Monthly labor force and unemployment series are available for approximately 7,500 geographic areas, including cities over 25,000 population, counties, metropolitan areas, States, and other areas.

For each area, the following measures are presented by place of residence:

• Total civilian labor force,
• Total number of people employed,
• Total number of people unemployed, and
• Unemployment rate

Data Characteristics: Rates are expressed as percents with one decimal place. Levels are measured as individual persons (not thousands) and are stored with no decimal places.

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 over 7,500 areas: Census regions and divisionsStatesMetropolitan Statistical AreasMetropolitan DivisionsMicropolitan Statistical AreasCombined Metropolitan Statistical AreasSmall Labor Market AreasCounties and county equivalentsCities of 25,000 population or moreCities and towns in New England regardless of population 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. A wide variety of customers use these estimates: Federal programs use the data for allocations to states and areas, as well as eligibility determinations for assistance.State and local governments use the estimates for planning and budgetary purposes and to determine the need for local employment and training services.Private industry, researchers, the media, and other individuals use the data to assess localized labor market developments and make comparisons across areas. The concepts and definitions underlying LAUS data come from the Current Population Survey (CPS), the household survey that is the source of the national unemployment rate. State monthly model-based estimates are controlled in "real time" to sum to national monthly employment and unemployment estimates from the CPS. These models combine current and historical data from the CPS, the Current Employment Statistics (CES) survey, and state unemployment insurance (UI) systems. Estimates for seven large areas and their respective balances of state also are model-based. 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.

VITAL SIGNS INDICATOR Unemployment (EC3)

FULL MEASURE NAME Unemployment rate by residential location

LAST UPDATED July 2019

DESCRIPTION Unemployment refers to the share of the labor force – by place of residence – that is not currently employed full-time or part-time. The unemployment rate reflects the strength of the overall employment market.

DATA SOURCE California Employment Development Department: Historical Unemployment Rates 1990-2018 https://data.edd.ca.gov/Labor-Force-and-Unemployment-Rates/Local-Area-Unemployment-Statistics-LAUS-Annual-Ave/7jbb-3rb8

CONTACT INFORMATION vitalsigns.info@bayareametro.gov

METHODOLOGY NOTES (across all datasets for this indicator) Unemployment rates produced by EDD for the region and county levels are not adjusted for seasonality (as they reflect annual data) and are final data (i.e., not preliminary). Unemployment rates produced by BLS for the metro regions are adjusted for seasonality; they reflect the primary MSA for the named region, except for the San Francisco Bay Area which uses the nine-county region. The unemployment rate is calculated based on the number of unemployed persons divided by the total labor force. Note that the unemployment rate can decline or increase as a result of changes in either variable.