As of October 2024, about 27.93 million people were employed on a part-time basis in the United States. In line with the definition of the BLS, part-time workers are persons who usually work less than 35 hours per week. Seasonal adjustment is a statistical method for removing the seasonal component of a time series used when analyzing non-seasonal trends, whereas non-seasonally-adjusted reflects the actual current data.

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.

Employment in motion picture, video and television programme production, sound recording and music publishing activities (ISIC-Rev.4, 2 digit level: 59). Value expressed in thousands. For more details: https://ilostat.ilo.org/methods/concepts-and-definitions/description-labour-force-statistics/

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F12064410%2Fe1c439d356d34481d8343cfcc5f32bb2%2Funemployment%20flag.png?generation=1677706474308274&alt=media" alt="">

DAY ~17,000 (January 1976 to December 2022)

This is a dataset that tracks relevant population statistics and employment rates per US state since 1976.

All data are official figures from the Bureau of Labor Statistics that have been compiled and structured by myself. Besides the 50 US states, the unemployment data of three other areas are also being tracked in order to increase the analytical potential of the dataset: the District of Columbia, the Los Angeles-Long Beach-Glendale metropolitan division, and New York City.

Why did I create this dataset? Employment continues to be a significant issue in America today and contributes to other predicaments such as the homelessness crisis. By uploading time-series data regarding American unemployment over the past four decades, I hope that the community is able to determine the various statistical trends offered. In my personal opinion, achieving a quantitative yet objective viewpoint of a subject such as unemployment is crucial to understanding the issues at hand.

Data Sources

The primary data source used was the Bureau of Labor Statistics's official website, which publishes employment data pertaining to America. Considering the meticulous documentation of federal statistics by such a accredited government agency, no other authority is more equipped to provide insight on US unemployment.

1. The Bureau of Labor Statistics's Economic News Release on (Monthly) State Employment and Unemployment - The Bureau of Labor Statistics has published monthly updates on unemployment rates since January 1976.
2. The Bureau of Labor Statistics's State Employment and Unemployment Technical Note - The Bureau of Labor Statistics released a detailed overview of their unemployment data, the methodology behind their data, and the proper definitions and terminologies for the variables tracked. The guide mainly provided essential contextual knowledge needed to create a meaningful dataset.

Statistics Being Tracked

• FIPS Code of State/Area
• Year/Month of Statistic
• Total Civilian Non-Institutional Population in State/Area
• Total Civilian Labor Force in State/Area
• Percent (%) of State/Area's Population
• Total Employment in State/Area
• Percent (%) of Labor Force Employed in State/Area
• Total Unemployment in State/Area
• Percent (%) of Labor Force Unemployed in State/Area

Dataset History

2023-03-01 - Dataset is created (17,227 days after temporal coverage start date).

GitHub Repository - The same data but on GitHub.

Code Starter

Link to Notebook

Labour Force Survey 1999 - 3rd quarter.

As of the 1st quarter of 1972, SSB has conducted official quarterly labour force surveys (AKU). These surveys aim to give the labour force authorities (and other people interested) knowledge of the occupational structure of the population and how it develops over time. The surveys are meant to give a foundation and statistical material for occupational prognoses and labour research. The samples in AKU are from 1992 representative at county level. In the period 1972-1991 they were representative on county pair level.

Originally, AKU respondents were interviewed in two consecutive quarters of a year, followed by a pause of two quarters, and then another two quarters of interviews. The sample was approximately 10-11.000 respondents in each quarter up until 1988. Originally, AKU was intended to be an analytical supplement to the monthly occupational statistics that was based on the social security membership index file. However, the social security-based statistics disappeared when the sickness benefit was included in the National Insurance as of 1st of January 1971, and AKU has after gradually developed into the most significant source of knowledge of the state of the labour market and its development.

In 1975, Statistics Norway changed the sampling frame of survey research, see article 37: “Om bruk av stikkprøver ved kontoret for intervjuundersøkelser”, SSB (About the Use of Random Samples at the Office for Survey Research, Statistics Norway) by Steinar Tamsfoss, and SØS 33: “Prinsipper og metoder for Statistisk sentralbyrås utvalgsundersøkelse (Principles and Methods for Statistics Norway's sample research) by Ib Thomsen. Simultaneously, the method for estimation of inflation to national numbers was changed, so that reasonable numbers for regions do exist from 1975 and onwards. The change in 1975 led to a different way of interviewing in groups. This caused amongst other things a break with the AKU panel systematics.

In the AKU survey of 1976, a slightly changed questionnaire was introduced. Also, there was a return to the original 6-quarter rotation scheme. The new questionnaire implied a better identification of family workers and persons that are temporarily without paid work. Thus, 30-35 000 more people were defined as employed. The group of "job-seekers without income" were also extended to include persons that were on an involuntary leave of absence. The questions concerning underemployment and “over employment” in the original questionnaire were abandoned.

From the 1st quarter of 1987, the estimation method (inflation to national numbers) was slightly changed. There was also a minor adjustment in the definition of employment. In order to ensure that the numbers were to be comparable to earlier surveys, new versions of the 1980-1986 AKU-files were drawn up. Consequently two versions of the 1980-1987 files - respectively with the old and new methods of estimation - exist. The “old” means that the data are comparable to the original numbers published in the period of 1972 - 1987, whilst the “new” implies that the data are comparable to numbers published after 1987.

Between the 1st and 2nd quarter of 1988, the AKU file description was changed. The variable “Labour-market status” was given a different coding. In addition, adjustments in the data collections were made - from interviewing a specific week every quarter to carry out continuous weekly interviews. SSB also started up an escalation scheme to increase the sample size. This affected the weights, and from the 2nd quarter of 1988, these were recalculated monthly. To balance out the quarterly or yearly files to total national numbers, the monthly weights therefore had to be divided in three or twelve to give the correct total number.

In 1996, AKU was significantly revised: The questionnaire, the file description and the standard for coding of industry and occupation. The data collection also changed to CATI - Computer Assisted Telephone Interviewing. A new classification of industry was put into use (NOS C 182, based on the EU standard NACE, Rev.1). This standard was updated in 2002 and 2007. Also, the new occupational classification (STYRK) based on ISCO 88 was used from 1996 and onwards. The variable indicating socio-economic status was omitted, as a similar variable was not developed in the new occupational classification.

As from January 2006 some major changes were introduced to AKU in order to enhance its comparability to similar surveys in other countries. The changes consist of minor definitional adjustments of unemployment, some adjustments and enlargement of the questionnaire and a change in age definition (age at reference point instead of at the end of the year). Simultaneously the lower age limit to be included in AKU was lowered from 16 to 15 years. This led to...

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.

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.

Labour force survey (LFS) Purpose and short description The Labour Force Survey (LFS) is a household sample survey, conducted throughout the year. It is based on the responses of approximately 110,000 persons aged 15-89. Its main objective is to classify the population of 15-89 years into three groups (employed, unemployed and inactive persons on the labous market) and to provide descriptive and explanatory data on every category. This survey is also carried out in the other EU Member States and is coordinated by Eurostat, the statistical office of the European Union. In Belgium, the LFS is organised by Statbel. The objective is to obtain comparable information at European level, in particular as regards employment and unemployment rates as defined by the International Labour Office (ILO), but also to collect and disseminate data that are otherwise not available, for example about the mobility of workers, the reasons for working part-time, the various forms of part-time employment, the occupation, the educational level of the working age population, ... . Survey population Members of private households aged 15-89. Sample frame Demographic data from the National Register. Data collection method and sample size Data are collected through face-to-face interviews for the first wave of the survey. Since 2017, there have been three (shorter) follow-up waves to which households respond online or by telephone. Households with only inactive persons older than 64 can also be interviewed by telephone. Every year, around 34,000 households take part in this survey. Response rate On average, the response rate in the first wave of the survey is around 68% and in the follow-up waves between 90% and 95%. Periodicity Quarterly Release calendar Results availability: around 3 months after the end of the reference period. Forms Labour Force Survey 2025 (PDF, 1 Mb) Definitions regarding employment and unemployment The survey is harmonised at European level. The definitions regarding employment and unemployment that are mentioned are those of the International Labour Office (ILO) to allow international comparison. People with a job (employed people) comprise all people who during the reference week performed some work ‘for wage or salary’ or ‘for profit’ regardless of the duration (even if this was only one hour), or who had a job but were temporarily absent. For example, one can be temporarily absent for holidays, illness, technical or economic reasons (temporary unemployment),.... Family workers are also included in the category ‘employed’. Since 2021, people who have been temporarily unemployed for an uninterrupted period of more than three months are counted as unemployed or inactive, and no longer as employed. The unemployed comprise all people who: (a) during the reference week were without work, i.e. were not in paid employment or self-employment; (b) were available for work, i.e. were available for paid employment or self-employment within two weeks after the reference week; (c) were actively seeking work, i.e. had taken specific steps during the last four weeks including the reference week to seek paid employment or self-employment, or who had found a job to start within a maximum period of three months. Please note: The ILO unemployment figures are unrelated to any possible registration with the VDAB, Actiris, FOREM or the ADG, or to the receipt of unemployment benefits from ONEM (National Employment Office). As a result, they cannot be compared with administrative unemployment figures. The labour force is made up of the employed and the unemployed. The economically inactive population comprises all people who were not considered as employed or unemployed. The employment rate represents employed persons as a percentage of the same age population. The employment rate as part of the Europe 2020 Strategy represents the share of persons employed in the population aged 20 to 64. The unemployment rate represents the share of unemployed people in the labour force (employed + unemployed) within a given age group. The economic activity rate represents the share of the labour force (employed + unemployed) in the total population within a given age group. The above indicators (employment rate, unemployment rate and economic activity rate) are the most important indicators for international comparisons of the labour market evolution. Low-skilled people are people who have at best a lower secondary education diploma. Medium-skilled people have obtained an upper secondary education diploma, but no higher education diploma. High-skilled people have a higher education diploma. Metadata Employment, unemployment, labour market (NL-FR) Labour force survey (LFS) (NL-FR) Survey methodology Modifications to the Labour Force Survey (LFS) in 2021 LFS: Methodological improvements to the Labour Force Survey 2017 (PDF, 99 Kb) LFS: Presentation of the survey until 2016 (NL-FR) LFS: Presentation of the survey from 2017 (NL-FR) Note on the occasion

Background
The Labour Force Survey (LFS) is a unique source of information using international definitions of employment and unemployment and economic inactivity, together with a wide range of related topics such as occupation, training, hours of work and personal characteristics of household members aged 16 years and over. It is used to inform social, economic and employment policy. The LFS was first conducted biennially from 1973-1983. Between 1984 and 1991 the survey was carried out annually and consisted of a quarterly survey conducted throughout the year and a 'boost' survey in the spring quarter (data were then collected seasonally). From 1992 quarterly data were made available, with a quarterly sample size approximately equivalent to that of the previous annual data. The survey then became known as the Quarterly Labour Force Survey (QLFS). From December 1994, data gathering for Northern Ireland moved to a full quarterly cycle to match the rest of the country, so the QLFS then covered the whole of the UK (though some additional annual Northern Ireland LFS datasets are also held at the UK Data Archive). Further information on the background to the QLFS may be found in the documentation.

Household datasets
Up to 2015, the LFS household datasets were produced twice a year (April-June and October-December) from the corresponding quarter's individual-level data. From January 2015 onwards, they are now produced each quarter alongside the main QLFS. The household datasets include all the usual variables found in the individual-level datasets, with the exception of those relating to income, and are intended to facilitate the analysis of the economic activity patterns of whole households. It is recommended that the existing individual-level LFS datasets continue to be used for any analysis at individual level, and that the LFS household datasets be used for analysis involving household or family-level data. From January 2011, a pseudonymised household identifier variable (HSERIALP) is also included in the main quarterly LFS dataset instead.

Change to coding of missing values for household series
From 1996-2013, all missing values in the household datasets were set to one '-10' category instead of the separate '-8' and '-9' categories. For that period, the ONS introduced a new imputation process for the LFS household datasets and it was necessary to code the missing values into one new combined category ('-10'), to avoid over-complication. This was also in line with the Annual Population Survey household series of the time. The change was applied to the back series during 2010 to ensure continuity for analytical purposes. From 2013 onwards, the -8 and -9 categories have been reinstated.

LFS Documentation
The documentation available from the Archive to accompany LFS datasets largely consists of the latest version of each volume alongside the appropriate questionnaire for the year concerned. However, LFS volumes are updated periodically by ONS, so users are advised to check the ONS LFS User Guidance page before commencing analysis.

Additional data derived from the QLFS
The Archive also holds further QLFS series: End User Licence (EUL) quarterly datasets; Secure Access datasets (see below); two-quarter and five-quarter longitudinal datasets; quarterly, annual and ad hoc module datasets compiled for Eurostat; and some additional annual Northern Ireland datasets.

End User Licence and Secure Access QLFS Household datasets
Users should note that there are two discrete versions of the QLFS household datasets. One is available under the standard End User Licence (EUL) agreement, and the other is a Secure Access version. Secure Access household datasets for the QLFS are available from 2009 onwards, and include additional, detailed variables not included in the standard EUL versions. Extra variables that typically can be found in the Secure Access versions but not in the EUL versions relate to: geography; date of birth, including day; education and training; household and family characteristics; employment; unemployment and job hunting; accidents at work and work-related health problems; nationality, national identity and country of birth; occurrence of learning difficulty or disability; and benefits. For full details of variables included, see data dictionary documentation. The Secure Access version (see SN 7674) has more restrictive access conditions than those made available under the standard EUL. Prospective users will need to gain ONS Accredited Researcher status, complete an extra application form and demonstrate to the data owners exactly why they need access to the additional variables. Users are strongly advised to first obtain the standard EUL version of the data to see if they are sufficient for their research requirements.

Changes to variables in QLFS Household EUL datasets
In order to further protect respondent confidentiality, ONS have made some changes to variables available in the EUL datasets. From July-September 2015 onwards, 4-digit industry class is available for main job only, meaning that 3-digit industry group is the most detailed level available for second and last job.

Review of imputation methods for LFS Household data - changes to missing values
A review of the imputation methods used in LFS Household and Family analysis resulted in a change from the January-March 2015 quarter onwards. It was no longer considered appropriate to impute any personal characteristic variables (e.g. religion, ethnicity, country of birth, nationality, national identity, etc.) using the LFS donor imputation method. This method is primarily focused to ensure the 'economic status' of all individuals within a household is known, allowing analysis of the combined economic status of households. This means that from 2015 larger amounts of missing values ('-8'/-9') will be present in the data for these personal characteristic variables than before. Therefore if users need to carry out any time series analysis of households/families which also includes personal characteristic variables covering this time period, then it is advised to filter off 'ioutcome=3' cases from all periods to remove this inconsistent treatment of non-responders.

Occupation data for 2021 and 2022 data files

The ONS has identified an issue with the collection of some occupational data in 2021 and 2022 data files in a number of their surveys. While they estimate any impacts will be small overall, this will affect the accuracy of the breakdowns of some detailed (four-digit Standard Occupational Classification (SOC)) occupations, and data derived from them. Further information can be found in the ONS article published on 11 July 2023: https://www.ons.gov.uk/employmentandlabourmarket/peopleinwork/employmentandemployeetypes/articles/revisionofmiscodedoccupationaldataintheonslabourforcesurveyuk/january2021toseptember2022" style="background-color: rgb(255, 255, 255);">Revision of miscoded occupational data in the ONS Labour Force Survey, UK: January 2021 to September 2022.

Monthly update of data from BLS LAUS data.

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.

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.

Please visit https://www.censtatd.gov.hk/en/EIndexbySubject.html?scode=200&pcode=D5250030 for the historical issues, related publications, concept, methods, definitions of terms, and notes of this dataset. User can download, distribute and reproduce free of charge for both commercial and non-commercial purposes subject to the Terms and Conditions of Use as stipulated under DATA.GOV.HK.

Key Table Information.Table Title.Mining: Location of Mining Establishments by Employment Size for the U.S., States, and Offshore Areas: 2022.Table ID.ECNLOCMINE2022.EC2221LOCMINE.Survey/Program.Economic Census.Year.2022.Dataset.ECN Sector Statistics Economic Census: Mining: Location of Mines by Employment Size for Subsectors and Industries for the U.S., States, and Offshore Areas.Source.U.S. Census Bureau, 2022 Economic Census, Sector Statistics.Release Date.2025-05-15.Release Schedule.The Economic Census occurs every five years, in years ending in 2 and 7.The data in this file come from the 2022 Economic Census data files released on a flow basis starting in January 2024 with First Look Statistics. Preliminary U.S. totals released in January 2024 are superseded with final data shown in the releases of later economic census statistics through March 2026.For more information about economic census planned data product releases, see 2022 Economic Census Release Schedule..Dataset Universe.The dataset universe consists of all establishments that are in operation for at least some part of 2022, are located in one of the 50 U.S. states, associated offshore areas, or the District of Columbia, have paid employees, and are classified in one of nineteen in-scope sectors defined by the 2022 North American Industry Classification System (NAICS)..Methodology.Data Items and Other Identifying Records.Employment size of establishmentsNumber of establishmentsDefinitions can be found by clicking on the column header in the table or by accessing the Economic Census Glossary..Unit(s) of Observation.The reporting units for the economic census are employer establishments. An establishment is generally a single physical location where business is conducted or where services or industrial operations are performed. A company or firm is comprised of one or more in-scope establishments that operate under the ownership or control of a single organization. For some industries, the reporting units are instead groups of all establishments in the same industry belonging to the same firm..Geography Coverage.The data are shown for the U.S., State, and Offshore Area levels that vary by industry. For information about economic census geographies, including changes for 2022, see Geographies..Industry Coverage.The data are shown at the 2- through 6-digit 2022 NAICS code levels for U.S., States, and Offshore Area. For information about NAICS, see Economic Census Code Lists..Sampling.The 2022 Economic Census sample includes all active operating establishments of multi-establishment firms and approximately 1.7 million single-establishment firms, stratified by industry and state. Establishments selected to the sample receive a questionnaire. For all data on this table, establishments not selected into the sample are represented with administrative data. For more information about the sample design, see 2022 Economic Census Methodology..Confidentiality.The Census Bureau has reviewed this data product to ensure appropriate access, use, and disclosure avoidance protection of the confidential source data (Project No. 7504609, Disclosure Review Board (DRB) approval number: CBDRB-FY23-099).To protect confidentiality, the U.S. Census Bureau suppresses cell values to minimize the risk of identifying a particular business’ data or identity.To comply with disclosure avoidance guidelines, data rows with fewer than three contributing firms or three contributing establishments are not presented. Additionally, establishment counts are suppressed when other select statistics in the same row are suppressed. More information on disclosure avoidance is available in the 2022 Economic Census Methodology..Technical Documentation/Methodology.For detailed information about the methods used to collect data and produce statistics, survey questionnaires, Primary Business Activity/NAICS codes, NAPCS codes, and more, see Economic Census Technical Documentation..Weights.No weighting applied as establishments not sampled are represented with administrative data..Table Information.FTP Download.https://www2.census.gov/programs-surveys/economic-census/data/2022/sector21/.API Information.Economic census data are housed in the Census Bureau Application Programming Interface (API)..Symbols.D - Withheld to avoid disclosing data for individual companies; data are included in higher level totalsN - Not available or not comparableS - Estimate does not meet publication standards because of high sampling variability, poor response quality, or other concerns about the estimate quality. Unpublished estimates derived from this table by subtraction are subject to these same limitations and should not be attributed to the U.S. Census Bureau. For a description of publication standards and the total quantity response rate, see link to program methodology page.X - Not applicableA - Relative standard error of 100% or morer - Reviseds - Relative standard error exceeds 40%For a complete list of sy...

Background

The Labour Force Survey (LFS) is a unique source of information using international definitions of employment and unemployment and economic inactivity, together with a wide range of related topics such as occupation, training, hours of work and personal characteristics of household members aged 16 years and over. It is used to inform social, economic and employment policy. The LFS was first conducted biennially from 1973-1983. Between 1984 and 1991 the survey was carried out annually and consisted of a quarterly survey conducted throughout the year and a 'boost' survey in the spring quarter (data were then collected seasonally). From 1992 quarterly data were made available, with a quarterly sample size approximately equivalent to that of the previous annual data. The survey then became known as the Quarterly Labour Force Survey (QLFS). From December 1994, data gathering for Northern Ireland moved to a full quarterly cycle to match the rest of the country, so the QLFS then covered the whole of the UK (though some additional annual Northern Ireland LFS datasets are also held at the UK Data Archive). Further information on the background to the QLFS may be found in the documentation.

New reweighting policy
Following the new reweighting policy ONS has reviewed the latest population estimates made available during 2019 and have decided not to carry out a 2019 LFS and APS reweighting exercise. Therefore, the next reweighting exercise will take place in 2020. These will incorporate the 2019 Sub-National Population Projection data (published in May 2020) and 2019 Mid-Year Estimates (published in June 2020). It is expected that reweighted Labour Market aggregates and microdata will be published towards the end of 2020/early 2021.

Secure Access QLFS household data
Up to 2015, the LFS household datasets were produced twice a year (April-June and October-December) from the corresponding quarter's individual-level data. From January 2015 onwards, they are now produced each quarter alongside the main QLFS. The household datasets include all the usual variables found in the individual-level datasets, with the exception of those relating to income, and are intended to facilitate the analysis of the economic activity patterns of whole households. It is recommended that the existing individual-level LFS datasets continue to be used for any analysis at individual level, and that the LFS household datasets be used for analysis involving household or family-level data. For some quarters, users should note that all missing values in the data are set to one '-10' category instead of the separate '-8' and '-9' categories. For that period, the ONS introduced a new imputation process for the LFS household datasets and it was necessary to code the missing values into one new combined category ('-10'), to avoid over-complication. From the 2013 household datasets, the standard -8 and -9 missing categories have been reinstated.

Secure Access household datasets for the QLFS are available from 2002 onwards, and include additional, detailed variables not included in the standard 'End User Licence' (EUL) versions. Extra variables that typically can be found in the Secure Access versions but not in the EUL versions relate to: geography; date of birth, including day; education and training; household and family characteristics; employment; unemployment and job hunting; accidents at work and work-related health problems; nationality, national identity and country of birth; occurence of learning difficulty or disability; and benefits.

Prospective users of a Secure Access version of the QLFS will need to fulfil additional requirements, commencing with the completion of an extra application form to demonstrate to the data owners exactly why they need access to the extra, more detailed variables, in order to obtain permission to use that version. Secure Access users must also complete face-to-face training and agree to Secure Access' User Agreement (see 'Access' section below). Therefore, users are encouraged to download and inspect the EUL version of the data prior to ordering the Secure Access version.

LFS Documentation
The documentation available from the Archive to accompany LFS datasets largely consists of each volume of the User Guide including the appropriate questionnaires for the years concerned. However, LFS volumes are updated periodically by ONS, so users are advised to check the ONS LFS User Guidance pages before commencing analysis.

The study documentation presented in the Documentation section includes the most recent documentation for the LFS only, due to available space. Documentation for previous years is provided alongside the data for access and is also available upon request.

Review of imputation methods for LFS Household data - changes to missing values
A review of the imputation methods used in LFS Household and Family analysis resulted in a change from the January-March 2015 quarter onwards. It was no longer considered appropriate to impute any personal characteristic variables (e.g. religion, ethnicity, country of birth, nationality, national identity, etc.) using the LFS donor imputation method. This method is primarily focused to ensure the 'economic status' of all individuals within a household is known, allowing analysis of the combined economic status of households. This means that from 2015 larger amounts of missing values ('-8'/-9') will be present in the data for these personal characteristic variables than before. Therefore if users need to carry out any time series analysis of households/families which also includes personal characteristic variables covering this time period, then it is advised to filter off 'ioutcome=3' cases from all periods to remove this inconsistent treatment of non-responders.

Variables DISEA and LNGLST
Dataset A08 (Labour market status of disabled people) which ONS suspended due to an apparent discontinuity between April to June 2017 and July to September 2017 is now available. As a result of this apparent discontinuity and the inconclusive investigations at this stage, comparisons should be made with caution between April to June 2017 and subsequent time periods. However users should note that the estimates are not seasonally adjusted, so some of the change between quarters could be due to seasonality. Further recommendations on historical comparisons of the estimates will be given in November 2018 when ONS are due to publish estimates for July to September 2018.

An article explaining the quality assurance investigations that have been conducted so far is available on the ONS Methodology webpage. For any queries about Dataset A08 please email Labour.Market@ons.gov.uk.

Latest Edition Information
For the seventeenth edition (August 2025), one quarterly data file covering the time period July-September, 2024 has been added to the study.

Abstract

The Labour Force Survey (LFS) is a statistical study of the households selected by the appropriate method. The objective of the survey is providing information on not only well-known employment and unemployment as well as providing comprehensive information on other standard indicators characterizing the country labour market.

Geographic coverage

National

Analysis unit

• Households
• Individuals

Kind of data

Sample survey data [ssd]

Mode of data collection

Face-to-face [f2f]

Research instrument

The design of the questionnaire, used concepts, set of indicators and calculation methodology, sampling method basically comply with the definitions and concepts recommended by the ILO and Eurostat, while taking into account the peculiarities of their application in Armenia to the extent possible, at the same time, by providing the comparability with the international similar indicators.

The 2011 Population and Housing Census marks a milestone in census exercises in Europe. For the first time, European legislation defined in detail a set of harmonised high-quality data from the population and housing censuses conducted in the EU Member States. As a result, the data from the 2011 round of censuses offer exceptional flexibility to cross-tabulate different variables and to provide geographically detailed data.

EU Member States have developed different methods to produce these census data. The national differences reflect the specific national situations in terms of data source availability, as well as the administrative practices and traditions of that country.

The EU census legislation respects this diversity. The Regulation of the European Parliament and of the Council on population and housing censuses (Regulation (EC) No 763/2008) is focussed on output harmonisation rather than input harmonisation. Member States are free to assess for themselves how to conduct their 2011 censuses and which data sources, methods and technology should be applied given the national context. This gives the Member States flexibility, in line with the principles of subsidiarity and efficiency, and with the competences of the statistical institutes in the Member States.

However, certain important conditions must be met in order to achieve the objective of comparability of census data from different Member States and to assess the data quality:

Regulation (EC) No 1201/20092 contains definitions and technical specifications for the census topics (variables) and their breakdowns that are required to achieve Europe-wide comparability.

The specifications are based closely on international recommendations and have been designed to provide the best possible information value. The census topics include geographic, demographic, economic and educational characteristics of persons, international and internal migration characteristics as well as household, family and housing characteristics.

Regulation (EU) No 519/2010 requires the data outputs that Member States transmit to the Eurostat to comply with a defined programme of statistical data (tabulation) and with set rules concerning the replacement of statistical data. The content of the EU census programme serves major policy needs of the European Union. Regionally, there is a strong focus on the NUTS 2 level. The data requirements are adapted to the level of regional detail. The Regulation does not require transmission of any data that the Member States consider to be confidential.

The statistical data must be completed by metadata that will facilitate interpretation of the numerical data, including country-specific definitions plus information on the data sources and on methodological issues. This is necessary in order to achieve the transparency that is a condition for valid interpretation of the data.

Users of output-harmonised census data from the EU Member States need to have detailed information on the quality of the censuses and their results.

Regulation (EU) No 1151/2010) therefore requires transmission of a quality report containing a systematic description of the data sources used for census purposes in the Member States and of the quality of the census results produced from these sources. A comparably structured quality report for all EU Member States will support the exchange of experience from the 2011 round and become a reference for future development of census methodology (EU legislation on the 2011 Population and Housing Censuses - Explanatory Notes ).

In order to ensure proper transmission of the data and metadata and provide user-friendly access to this information, a common technical format is set for transmission for all Member States and for the Commission (Eurostat). The Regulation therefore requires the data to be transmitted in a harmonised structure and in the internationally established SDMX format from every Member State. In order to achieve this harmonised transmission, a new system has been developed – the CENSUS HUB.

The Census Hub is a conceptually new system used for the dissemination of the 2011 Census. It is based on the concept of data sharing, where a group of partners (Eurostat on one hand and National Statistical Institutes on the other) agree to provide access to their data according to standard processes, formats and technologies.

The Census Hub is a readily-accessible system that provided the following functions:

• Data providers (the NSIs) can make data available directly from their systems through a querying system. In parallel,
• Data users browse the hub to define a dataset of interest via the above structural metadata and retrieve the dataset from the NSIs.

From the data management point of view, the hub is based on agreed hypercubes (data-sets in the form of multi-dimensional aggregations). The hypercubes are not sent to the central system. Instead the following process operates:

1. a user defines a dataset through the web interface of the central hub and requests it;
2. the central hub translates the user request in one or more queries and sends them to the related NSIs’ systems;
3. NSIs’ systems process the query and send the result to the central hub in a standard format;
4. the central hub puts together all the results sent by the NSI systems and presents them in a user-specified format.

Key Table Information.Table Title.Annual Business Survey: Statistics for Employer Firms by Race for the U.S.: 2023.Table ID.ABSCS2023.AB00MYCSA01C.Survey/Program.Economic Surveys.Year.2023.Dataset.ECNSVY Annual Business Survey Company Summary.Source.U.S. Census Bureau, 2023 Economic Surveys, Annual Business Survey.Release Date.2025-11-20.Release Schedule.The Annual Business Survey (ABS) occurs every year, beginning in reference year 2017.For more information about ABS planned data product releases, see Tentative ABS Schedule..Dataset Universe.The dataset universe consists of employer firms that are in operation for at least some part of the reference year, are located in one of the 50 U.S. states, associated offshore areas, or the District of Columbia, have paid employees and annual receipts of $1,000 or more, and are classified in one of nineteen in-scope sectors defined by the 2022 North American Industry Classification System (NAICS), except for NAICS 111, 112, 482, 491, 521, 525, 813, 814, and 92 which are not covered..Sponsor.National Center for Science and Engineering Statistics, U.S. National Science Foundation.Methodology.Data Items and Other Identifying Records.Number of employer firms (firms with paid employees)Sales and receipts of employer firms (reported in $1,000s of dollars)Number of employees (during the March 12 pay period)Annual payroll (reported in $1,000s of dollars)These data are aggregated by the following demographic classifications of firm for:All firms Classifiable (firms classifiable by sex, ethnicity, race, and veteran status) Race White Black or African American American Indian and Alaska Native Asian Native Hawaiian and Other Pacific Islander Minority (Firms classified as any race and ethnicity combination other than non-Hispanic and White) Equally minority/nonminority Nonminority (Firms classified as non-Hispanic and White) Unclassifiable (firms not classifiable by sex, ethnicity, race, and veteran status) Definitions can be found by clicking on the column header in the table or by accessing the Economic Census Glossary..Unit(s) of Observation.The reporting units for the ABS are employer companies or firms rather than establishments. A company or firm is comprised of one or more in-scope establishments that operate under the ownership or control of a single organization..Geography Coverage.The data are shown for the U.S. only.For information about geographies, see Geographies..Industry Coverage.The data are shown for the total of all sectors ("00") NAICS code. Sector "00" is not an official NAICS sector but is rather a way to indicate a total for multiple sectors. Note: Other programs outside of ABS may use sector 00 to indicate when multiple NAICS sectors are being displayed within the same table and/or dataset.The following are excluded from the total of all sectors:Crop and Animal Production (NAICS 111 and 112)Rail Transportation (NAICS 482)Postal Service (NAICS 491)Monetary Authorities-Central Bank (NAICS 521)Funds, Trusts, and Other Financial Vehicles (NAICS 525)Office of Notaries (NAICS 541120)Religious, Grantmaking, Civic, Professional, and Similar Organizations (NAICS 813)Private Households (NAICS 814)Public Administration (NAICS 92)For information about NAICS, see North American Industry Classification System..Sampling.The ABS sample includes firms that are selected with certainty if they have known research and development activities, were included in the 2023 BERD sample, or have high receipts, payroll, or employment. Total sample size is 330,000 firms. The universe is stratified by state, industry group, and expected demographic group. Firms selected to the sample receive a questionnaire. For all data on this table, firms not selected into the sample are represented with administrative, 2022 Economic Census, or other economic surveys records.For more information about the sample design, see Annual Business Survey Methodology..Confidentiality.The Census Bureau has reviewed this data product to ensure appropriate access, use, and disclosure avoidance protection of the confidential source data (Project No. P-7504866, Disclosure Review Board (DRB) approval numbers: CBDRB-FY25-0115 and CBDRB-FY25-0410).To protect confidentiality, the U.S. Census Bureau suppresses cell values to minimize the risk of identifying a particular business' data or identity.To comply with data quality standards, data rows with high relative standard errors (RSE) are not presented. Additionally, firm counts are suppressed when other select statistics in the same row are suppressed. More information on disclosure avoidance is available in the Annual Business Survey Methodology..Technical Documentation/Methodology.For detailed information about the methods used to collect data and produce statistics, survey questionnaires, Primary Business Activity/NAICS codes, and more, see Technical Documentation..Weights.For more information about weighting, see Annual Business Survey Methodology..Table Inf...

Labour Force Survey 2003 - merged year file.

As of the 1st quarter of 1972, SSB has conducted official quarterly labour force surveys (AKU). These surveys aim to give the labour force authorities (and other people interested) knowledge of the occupational structure of the population and how it develops over time. The surveys are meant to give a foundation and statistical material for occupational prognoses and labour research. The samples in AKU are from 1992 representative at county level. In the period 1972-1991 they were representative on county pair level.

Originally, AKU respondents were interviewed in two consecutive quarters of a year, followed by a pause of two quarters, and then another two quarters of interviews. The sample was approximately 10-11.000 respondents in each quarter up until 1988. Originally, AKU was intended to be an analytical supplement to the monthly occupational statistics that was based on the social security membership index file. However, the social security-based statistics disappeared when the sickness benefit was included in the National Insurance as of 1st of January 1971, and AKU has after gradually developed into the most significant source of knowledge of the state of the labour market and its development.

In 1975, Statistics Norway changed the sampling frame of survey research, see article 37: “Om bruk av stikkprøver ved kontoret for intervjuundersøkelser”, SSB (About the Use of Random Samples at the Office for Survey Research, Statistics Norway) by Steinar Tamsfoss, and SØS 33: “Prinsipper og metoder for Statistisk sentralbyrås utvalgsundersøkelse (Principles and Methods for Statistics Norway's sample research) by Ib Thomsen. Simultaneously, the method for estimation of inflation to national numbers was changed, so that reasonable numbers for regions do exist from 1975 and onwards. The change in 1975 led to a different way of interviewing in groups. This caused amongst other things a break with the AKU panel systematics.

In the AKU survey of 1976, a slightly changed questionnaire was introduced. Also, there was a return to the original 6-quarter rotation scheme. The new questionnaire implied a better identification of family workers and persons that are temporarily without paid work. Thus, 30-35 000 more people were defined as employed. The group of "job-seekers without income" were also extended to include persons that were on an involuntary leave of absence. The questions concerning underemployment and “over employment” in the original questionnaire were abandoned.

From the 1st quarter of 1987, the estimation method (inflation to national numbers) was slightly changed. There was also a minor adjustment in the definition of employment. In order to ensure that the numbers were to be comparable to earlier surveys, new versions of the 1980-1986 AKU-files were drawn up. Consequently two versions of the 1980-1987 files - respectively with the old and new methods of estimation - exist. The “old” means that the data are comparable to the original numbers published in the period of 1972 - 1987, whilst the “new” implies that the data are comparable to numbers published after 1987.

Between the 1st and 2nd quarter of 1988, the AKU file description was changed. The variable “Labour-market status” was given a different coding. In addition, adjustments in the data collections were made - from interviewing a specific week every quarter to carry out continuous weekly interviews. SSB also started up an escalation scheme to increase the sample size. This affected the weights, and from the 2nd quarter of 1988, these were recalculated monthly. To balance out the quarterly or yearly files to total national numbers, the monthly weights therefore had to be divided in three or twelve to give the correct total number.

In 1996, AKU was significantly revised: The questionnaire, the file description and the standard for coding of industry and occupation. The data collection also changed to CATI - Computer Assisted Telephone Interviewing. A new classification of industry was put into use (NOS C 182, based on the EU standard NACE, Rev.1). This standard was updated in 2002 and 2007. Also, the new occupational classification (STYRK) based on ISCO 88 was used from 1996 and onwards. The variable indicating socio-economic status was omitted, as a similar variable was not developed in the new occupational classification.

As from January 2006 some major changes were introduced to AKU in order to enhance its comparability to similar surveys in other countries. The changes consist of minor definitional adjustments of unemployment, some adjustments and enlargement of the questionnaire and a change in age definition (age at reference point instead of at the end of the year). Simultaneously the lower age limit to be included in AKU was lowered from 16 to 15 years. This led...

Release Date: 2015-12-15.[NOTE: Includes firms with payroll at any time during 2012. Employment reflects the number of paid employees during the March 12 pay period. Data are based on the 2012 Economic Census, and the estimates of business ownership by gender, ethnicity, race, and veteran status are from the 2012 Survey of Business Owners. Detail may not add to total due to rounding or because a Hispanic firm may be of any race. Moreover, each owner had the option of selecting more than one race and therefore is included in each race selected. Firms with more than one domestic establishment are counted in each geographic area and industry in which they operate, but only once in the U.S. and state totals for all sectors. For information on confidentiality protection, sampling error, nonsampling error, and definitions, see Survey Methodology.]..Table Name. . Statistics for All U.S. Firms With Paid Employees by Industry, Race, and Employment Size of Firm for the U.S. and States: 2012. ..Release Schedule. . This file was released in December 2015. Included are statistics for:. . Black-Owned Firms (BLK). American Indian- and Alaska Native-Owned Firms (AIAN). Asian-Owned Firms (ASIAN). Native Hawaiian- and Other Pacific Islander-Owned Firms (NHPI). Company Summary (CS)-- Includes estimates for minority- and nonminority-owned firms. . ..Key Table Information. . This data supersedes all preliminary results released on August 18, 2015, and is related to all other 2012 SBO files.. Refer to the Methodology section of the Survey of Business Owners website for additional information.. ..Universe. . The universe for the 2012 Survey of Business Owners (SBO) includes all U.S. firms operating during 2012 with receipts of $1,000 or more which are classified in the North American Industry Classification System (NAICS) sectors 11 through 99, except for NAICS 111, 112, 482, 491, 521, 525, 813, 814, and 92 which are not covered. Firms with more than one domestic establishment are counted in each geographic area and industry in which they operate, but only once in the U.S. total.. ..Geographic Coverage. . The data are shown for the United States at the national and state levels.. ..Industry Coverage. . The data are shown for the total of all sectors (00) and the 2-digit NAICS code levels.. ..Data Items and Other Identifying Records. . Statistics for All U.S. Firms With Paid Employees by Industry, Race, and Employment Size of Firm for the U.S. and States: 2012 contains data on:. . Numbers of firms with paid employees. Sales and receipts for firms with paid employees. Number of employees for firms with paid employees. Annual payroll for firms with paid employees. . The data are shown for:. . All firms classifiable by gender, ethnicity, race, and veteran status. . Race. . White. Black or African American. American Indian and Alaska Native. Asian. . Asian Indian. Chinese. Filipino. Japanese. Korean. Vietnamese. Other Asian. . . Native Hawaiian and Other Pacific Islander. . Native Hawaiian. Samoan. Guamanian or Chamorro. Other Pacific Islander. . . Some other race. Minority. Equally minority/nonminority. Nonminority. . . Employment size of firm during the March 12 pay period for firms with paid employees at any time during 2012. . All firms. Firms with no employees. Firms with 1 to 4 employees. Firms with 5 to 9 employees. Firms with 10 to 19 employees. Firms with 20 to 49 employees. Firms with 50 to 99 employees. Firms with 100 to 499 employees. Firms with 500 employees or more. . . . . Publicly held and other firms not classifiable by gender, ethnicity, race, and veteran status. . ..Sort Order. . Data are presented in ascending levels by:. . Geography (GEO_ID). NAICS code (NAICS2012). Race (RACE_GROUP). Employment size of firm (EMPSZFI). . The data are sorted on underlying control field values, so control fields may not appear in alphabetical order.. ..FTP Download. . Download the entire SB1200CSA11 table at: https://www2.census.gov/programs-surveys/sbo/data/2012/SB1200CSA11.zip. ..Contact Information. . To contact the Survey of Business Owner...