This set of raw data identifies race, sex, and age of all Bloomington Police Department employees, as well as lists the education of all sworn personnel.

Between 2020 and 2023, the total professional workforce of PwC in the United States had a majority of white employees. Those of an Asian background were the next most populous workforce, totaling approximately one-third of the white employment figures.

Seventy percent of the global workforce will be shared equally by Generation X and Generation Y by 2020, with forecasts suggesting that Generation Z will make up nearly a ******* of the workforce as they start to enter adulthood. Employment rates in industrialized countries In member countries of the Organisation for Economic Co-operation and Development (OECD), employment rates range between ** and ** percent of the working age population. Northern European countries such as Iceland, Sweden, and Denmark have some of the highest employment rates, along with New Zealand and Japan. Italy, Greece, and Turkey had the lowest employment rates in OECD countries. The staffing industry Recruitment firms are now well-established in many industrialized countries. The global staffing industry was estimated to have revenues of *** billion U.S. dollars in 2017, with firms from the United States generating a ** percent share of that figure. Firms from Japan also held a significant market share and this highlights the growth in the staffing industry across Asia, with the Chinese market expecting revenues to increase by around ** percent in 2019.

Publication changes: Please read the section on 'Notes on changes to publications' within the PDF report as this highlights changes to data currently published and potentially future reports. This report shows monthly numbers of NHS Hospital and Community Health Service (HCHS) staff groups working in Trusts and CCGs in England (excluding primary care staff). Data is available as headcount and full-time equivalents. This data is an accurate summary of the validated data extracted from the NHS's HR and Payroll system. In addition to the regular monthly reports there are a series of quarterly reports which include statistics on staff in Trusts and CCGs and information for NHS Support Organisations and Central Bodies. The quarterly analysis is published each September (June data), December (September data), March (December data) and June (March data). Additional healthcare workforce data relating to GPs and the Independent Healthcare Provider workforce are also available via the Related Links below. This publication of April 2020 data features a supplementary file which shows trends in HCHS workforce data observed during the NHS response to the Covid-19 pandemic. We welcome feedback on the methodology and tables within this publication. Please email us with your comments and suggestions, clearly stating Monthly HCHS Workforce as the subject heading, via enquiries@nhsdigital.nhs.uk or 0300 303 5678

The percent of persons between the ages of 16 and 64 not working out of all persons, not just those in the labor force (persons who may be looking for work). These persons are seeking work that pays a formal income. Source: American Community Survey Years Available: 2006-2010, 2007-2011, 2008-2012, 2009-2013, 2010-2014, 2011-2015, 2012-2016, 2013-2017, 2014-2018, 2015-2019, 2016-2020, 2017-2021, 2018-2022, 2019-2023Please note: We do not recommend comparing overlapping years of data due to the nature of this dataset. For more information, please visit: https://www.census.gov/programs-surveys/acs/guidance/comparing-acs-data.html

Population (15 Years & Above) by Sex, Labour Force Participation and Marital Status

Graph and download economic data for Labor Force Participation Rate - Women (LNS11300002) from Jan 1948 to Jun 2025 about females, participation, 16 years +, labor force, labor, household survey, rate, and USA.

Although the American Community Survey (ACS) produces population, demographic and housing unit estimates, for 2020, the 2020 Census provides the official counts of the population and housing units for the nation, states, counties, cities, and towns. For 2016 to 2019, the Population Estimates Program provides estimates of the population for the nation, states, counties, cities, and towns and intercensal housing unit estimates for the nation, states, and counties..Supporting documentation on code lists, subject definitions, data accuracy, and statistical testing can be found on the American Community Survey website in the Technical Documentation section.Sample size and data quality measures (including coverage rates, allocation rates, and response rates) can be found on the American Community Survey website in the Methodology section..Source: U.S. Census Bureau, 2016-2020 American Community Survey 5-Year Estimates.Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted roughly as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see ACS Technical Documentation). The effect of nonsampling error is not represented in these tables..Employment and unemployment estimates may vary from the official labor force data released by the Bureau of Labor Statistics because of differences in survey design and data collection. For guidance on differences in employment and unemployment estimates from different sources go to Labor Force Guidance..Starting with 2013 data products, same-sex married couples are shown along with all married couples. For more information, see: User Notes..Selected labor force, employment, and work-status estimates for same- and opposite-sex married people using 2020 American Community Survey (ACS) 1-year data are available for the nation, states and the District of Columbia, and for selected metropolitan areas. At the national level, estimates are available by sex, race, and Hispanic origin. For more information, see the “Employment and Labor Force Characteristics for Same-Sex and Opposite-Sex Married Householders and their Spouses: 2020” table package on the Labor Force Statistics webpage..The 2016-2020 American Community Survey (ACS) data generally reflect the September 2018 Office of Management and Budget (OMB) delineations of metropolitan and micropolitan statistical areas. In certain instances, the names, codes, and boundaries of the principal cities shown in ACS tables may differ from the OMB delineation lists due to differences in the effective dates of the geographic entities..Estimates of urban and rural populations, housing units, and characteristics reflect boundaries of urban areas defined based on Census 2010 data. As a result, data for urban and rural areas from the ACS do not necessarily reflect the results of ongoing urbanization..Explanation of Symbols:- The estimate could not be computed because there were an insufficient number of sample observations. For a ratio of medians estimate, one or both of the median estimates falls in the lowest interval or highest interval of an open-ended distribution.N The estimate or margin of error cannot be displayed because there were an insufficient number of sample cases in the selected geographic area. (X) The estimate or margin of error is not applicable or not available.median- The median falls in the lowest interval of an open-ended distribution (for example "2,500-")median+ The median falls in the highest interval of an open-ended distribution (for example "250,000+").** The margin of error could not be computed because there were an insufficient number of sample observations.*** The margin of error could not be computed because the median falls in the lowest interval or highest interval of an open-ended distribution.***** A margin of error is not appropriate because the corresponding estimate is controlled to an independent population or housing estimate. Effectively, the corresponding estimate has no sampling error and the margin of error may be treated as zero.

The percent of persons who are not in the labor force out of all persons between the ages of 16 and 64 in the area. There are several reasons why persons may not be included in the labor force. These reasons may include: they are caretakers for children or other family members; they attend school or job training; they may have a disability; and they are discouraged or frustrated and have given up seeking a job or have a history that may include criminal activity. Source: American Community Survey Years Available: 2006-2010, 2007-2011, 2008-2012, 2009-2013, 2010-2014, 2011-2015, 2012-2016, 2013-2017, 2014-2018, 2015-2019, 2016-2020, 2017-2021, 2018-2022, 2019-2023Please note: We do not recommend comparing overlapping years of data due to the nature of this dataset. For more information, please visit: https://www.census.gov/programs-surveys/acs/guidance/comparing-acs-data.html

Graph and download economic data for Civilian Labor Force Level (CLF16OV) from Jan 1948 to Jun 2025 about civilian, 16 years +, labor force, labor, household survey, and USA.

Number of persons in the labour force (employment and unemployment), unemployment rate, participation rate and employment rate by age group and gender. Data are presented for 12 months earlier, previous month and current month, as well as year-over-year and month-to-month level change and percentage change. Data are also available for the standard error of the estimate, the standard error of the month-to-month change and the standard error of the year-over-year change.

This table contains quarterly and yearly figures on labour participation in the Netherlands. The population of 15 to 74 years of age (excluding the institutionalized population) is divided into the employed labour force, the unemployed labour force and those not in the labour force. The employed labour force is subdivided on the basis of the professional status, and the average working hours. A division by sex, age and level of education is available.

Data available from: 2013

Status of the figures: The figures in this table are final.

Changes as of April 30, 2025: The figures for the 1st quarter 2025 have been added.

Changes as of November 14, 2024: The figures for 3rd quarter 2024 are added. Figures have been added on labor participation based on whether or not the state pension age has been reached.

Changes as of August 17, 2022: None, this is a new table. This table has been compiled on the basis of the Labor Force Survey (LFS). Due to changes in the research design and the questionnaire of the LFS, the figures for 2021 are not automatically comparable with the figures up to and including 2020. The key figures in this table have therefore been made consistent with the (non-seasonally adjusted) figures in the table Arbeidsdeelname, kerncijfers seizoengecorrigeerd (see section 4), in which the outcomes for the period 2013-2020 have been recalculated to align with the outcomes from 2021. When further detailing the outcomes according to job and personal characteristics, there may nevertheless be differences from 2020 to 2021 as a result of the new method.

When will new figures be released? New figures will be published in July 2025.

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.

Longitudinal data
The LFS retains each sample household for five consecutive quarters, with a fifth of the sample replaced each quarter. The main survey was designed to produce cross-sectional data, but the data on each individual have now been linked together to provide longitudinal information. The longitudinal data comprise two types of linked datasets, created using the weighting method to adjust for non-response bias. The two-quarter datasets link data from two consecutive waves, while the five-quarter datasets link across a whole year (for example January 2010 to March 2011 inclusive) and contain data from all five waves. A full series of longitudinal data has been produced, going back to winter 1992. Linking together records to create a longitudinal dimension can, for example, provide information on gross flows over time between different labour force categories (employed, unemployed and economically inactive). This will provide detail about people who have moved between the categories. Also, longitudinal information is useful in monitoring the effects of government policies and can be used to follow the subsequent activities and circumstances of people affected by specific policy initiatives, and to compare them with other groups in the population. There are however methodological problems which could distort the data resulting from this longitudinal linking. The ONS continues to research these issues and advises that the presentation of results should be carefully considered, and warnings should be included with outputs where necessary.

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.

LFS Documentation
The documentation available from the Archive to accompany LFS datasets largely consists of the latest version of each user guide volume alongside the appropriate questionnaire for the year concerned. However, volumes are updated periodically by ONS, so users are advised to check the latest documents on the ONS Labour Force Survey - User Guidance pages before commencing analysis. This is especially important for users of older QLFS studies, where information and guidance in the user guide documents may have changed over time.

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

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.

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.

2022 Weighting

The population totals used for the latest LFS estimates use projected growth rates from Real Time Information (RTI) data for UK, EU and non-EU populations based on 2021 patterns. The total population used for the LFS therefore does not take into account any changes in migration, birth rates, death rates, and so on since June 2021, and hence levels estimates may be under- or over-estimating the true values and should be used with caution. Estimates of rates will, however, be robust.

Abstract

The Quarterly Labour Force Survey (QLFS) is a household-based sample survey conducted by Statistics South Africa (Stats SA). It collects data on the labour market activities of individuals aged 15 years or older who live in South Africa.

Geographic coverage

National coverage

Analysis unit

Individuals

Universe

The QLFS sample covers the non-institutional population of South Africa with one exception. The only institutional subpopulation included in the QLFS sample are individuals in worker's hostels. Persons living in private dwelling units within institutions are also enumerated. For example, within a school compound, one would enumerate the schoolmaster's house and teachers' accommodation because these are private dwellings. Students living in a dormitory on the school compound would, however, be excluded.

Kind of data

Sample survey data [ssd]

Sampling procedure

The sampling procedure for this quarter of the QLFS had to be changed from the norm due to covid-19. In this 2020 Q4 the sample used was the same sample as from Q3, Q2 and Q1 from 2020. Because the interviews were done telephonically, any sampling units that did not have telephones dropped out of the sample. This was adjusted for in the weighting procedure.

Mode of data collection

Computer Assisted Telephone Interview [cati]

Research instrument

The survey questionnaire consists of the following sections: - Biographical information (marital status, education, etc.) - Economic activities in the last week for persons aged 15 years and older - Unemployment and economic inactivity for persons aged 15 years and above - Main work activity in the last week for persons aged 15 years and above - Earnings in the main job for employees, employers and own-account workers aged 15 years and above

From 2010 the income data collected by South Africa's Quarterly Labour Force Survey is no longer provided in the QLFS dataset (except for a brief return in QLFS 2010 Q3 which may be an error). Possibly because the data is unreliable at the level of the quarter, Statistics South Africa now provides the income data from the QLFS in an annualised dataset called Labour Market Dynamics in South Africa (LMDSA). The datasets for LMDSA are available from DataFirst's website.

Cleaning operations

In general, imputation was used for item non-response (i.e., blanks within the questionnaire) and edit failures (i.e., invalid or inconsistent responses).

Labor Force Participation Rate in the United States decreased to 62.30 percent in June from 62.40 percent in May of 2025. This dataset provides the latest reported value for - United States Labor Force Participation Rate - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

LFS is the most important source of information about conditions on the labor market in Norway. After sample surveys in the autumn of 1971, Statistics Norway has from the 1st quarter of 1972 carried out such surveys quarterly according to the sample method.

The purpose of the survey is to provide information on developments in employment and unemployment, and on the connection of different population groups to the labor market. In addition to providing the authorities and other interested parties with information on the state and development of the labor market, the LFS shall serve as a basis for forecasts and studies and provide research with statistical material. A large number of variables are included in the material. In addition to the demographics, mention can be made of education level, profession, industry, agreed and actual working hours, part-time employment and underemployment.

Important users are the ministries, the labor and welfare administration, research and study institutes, international organisations, mass media etc.

The labor force survey 2020, annual file is a collection file of the 4 quarterly files from 2020. The collection file also contains variables that deal with working time arrangements.

The weight "mmk_aarsnetto" must be divided by 4 in the annual files for the Labor Force Survey. This is because the weight was created as a quarterly weight, but is applied to the annual variables.