According to the study realized by IPSOS, most of the people who lost their jobs worked either in the sales or in the logistics industry. For 17 percent of respondents working in IT and Telecommunications nothing changed during the coronavirus crisis in Romania, while 13 percent of people working in tourism were forced to apply for technical unemployment.
For further information about the coronavirus (COVID-19) pandemic, please visit our dedicated Facts and Figures page.

Individuals working in the private sector were more concerned about the potential impact of coronavirus (COVID-19) on their jobs than those working in the public sector. As a result, 66 percent of respondents working in the private sector stated that the pandemic would affect their work. In comparison, only 50 percent of respondents working in the public sector reported the same concern. For further information about the coronavirus (COVID-19) pandemic, please visit our dedicated Facts and Figures page.

Employment Rate in the United States decreased to 59.90 percent in February from 60.10 percent in January of 2025. This dataset provides - United States Employment Rate- actual values, historical data, forecast, chart, statistics, economic calendar and news.

This dataset presents data on the numbers and status of employment by industries for metropolitan areas following the Greater Capital City Statistical Area (GCCSA) regions as of November 2020. The boundaries for this dataset follow the 2016 edition of the Australian Statistical Geography Standard (ASGS).

The Australian Department of Education, Skills and Employment publishes a range of labour market data on its Labour Market Information Portal. The data provided includes unemployment rate, employment rate, participation rate, youth unemployment rate, unemployment duration, population by age group and employment by industry and occupation.

AURIN has spatially enabled the original data. Data Source: ABS Labour Force Survey, four-quarter average, except for Australian Total Employment and Australian Employment Distribution, which are seasonally adjusted data.

Abstract

The Quarterly Labour Force Survey (QLFS) is a household-based sample survey conducted by Statistics South Africa (StatsSA). It collects data on the labour market activities of individuals aged 15 years or older who live in South Africa. Since 2008, StatsSA have produced an annual dataset based on the QLFS data, "Labour Market Dynamics in South Africa". The dataset is constructed using data from all all four QLFS datasets in the year. The dataset also includes a number of variables (including income) that are not available in any of the QLFS datasets from 2010.

Geographic coverage

National coverage

Analysis unit

Individuals

Universe

The QLFS sample covers the non-institutional population except for those in workers' hostels. However, persons living in private dwelling units within institutions are 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

Each year the LMDSA is created by combining the QLFS waves for that year and then including some additional variables. The QLFS master frame for this LMDSA was based on the 2011 population census by Stas SA. The sampling is stratified by province, district, and geographic type (urban, traditional, farm). There are 3324 PSUs drawn each year, using probability proportional to size (PPS) sampling. In the second stage Dwelling Units (DUs) are systematically selected from PSUs. The 3324 PSU are split into four groups for the year, and at each quarter the DUs from the given group are replaced by substitute DUs from the same PSU or the next PSU on the list (in the same group). It should be noted that the sampling unit is the dwelling, and the unit of observation is the household. Therefore, if a household moves out of a dwelling after being in the sample for, two quarters and a new household moves in, the new household will be enumerated for two more quarters until the DU is rotated out. If no household moves into the sampled dwelling, the dwelling will be classified as vacant (or unoccupied).

Mode of data collection

Computer Assisted Telephone Interview [cati]

Research instrument

The survey questionnaire consists of the following sections: - Particulars of each person in the household - Economic activities in the last week for persons aged 15 years - Unemployment and economic inactivity for persons aged 15 years - Main work activity in the last week for persons aged 15 years - Earnings in the main job for employees, employers and own-account workers aged 15 years - Migration for all persons aged 15 years

Data appraisal

The statistical release notes that missing values were "generally imputed" for item non-response but provides no detail on how Statistics SA did so.

This statistic reflects the perception of the Spanish population regarding the severity of the economic and labor consequences caused by the coronavirus (COVID-19). In April 2020, more than three quarters of those surveyed, specifically 80 percent, thought that the consequences of the crisis caused by this pandemic could be very serious.

LABOR MARKET ENGAGEMENT INDEXSummary

The labor market engagement index provides a summary description of the relative intensity of labor market engagement and human capital in a neighborhood. This is based upon the level of employment, labor force participation, and educational attainment in a census tract (i). Formally, the labor market index is a linear combination of three standardized vectors: unemployment rate (u), labor-force participation rate (l), and percent with a bachelor’s degree or higher (b), using the following formula:

Where means and standard errors are estimated over the national distribution. Also, the value for the standardized unemployment rate is multiplied by -1.

Interpretation

Values are percentile ranked nationally and range from 0 to 100. The higher the score, the higher the labor force participation and human capital in a neighborhood.

Data Source: American Community Survey, 2011-2015Related AFFH-T Local Government, PHA and State Tables/Maps: Table 12; Map 9.

To learn more about the Labor Market Engagement Index visit: https://www.hud.gov/program_offices/fair_housing_equal_opp/affh ; https://www.hud.gov/sites/dfiles/FHEO/documents/AFFH-T-Data-Documentation-AFFHT0006-July-2020.pdf, for questions about the spatial attribution of this dataset, please reach out to us at GISHelpdesk@hud.gov. Date of Coverage: 07/2020

In 2023, it was estimated that over 161 million Americans were in some form of employment, while 3.64 percent of the total workforce was unemployed. This was the lowest unemployment rate since the 1950s, although these figures are expected to rise in 2023 and beyond. 1980s-2010s Since the 1980s, the total United States labor force has generally risen as the population has grown, however, the annual average unemployment rate has fluctuated significantly, usually increasing in times of crisis, before falling more slowly during periods of recovery and economic stability. For example, unemployment peaked at 9.7 percent during the early 1980s recession, which was largely caused by the ripple effects of the Iranian Revolution on global oil prices and inflation. Other notable spikes came during the early 1990s; again, largely due to inflation caused by another oil shock, and during the early 2000s recession. The Great Recession then saw the U.S. unemployment rate soar to 9.6 percent, following the collapse of the U.S. housing market and its impact on the banking sector, and it was not until 2016 that unemployment returned to pre-recession levels. 2020s 2019 had marked a decade-long low in unemployment, before the economic impact of the Covid-19 pandemic saw the sharpest year-on-year increase in unemployment since the Great Depression, and the total number of workers fell by almost 10 million people. Despite the continuation of the pandemic in the years that followed, alongside the associated supply-chain issues and onset of the inflation crisis, unemployment reached just 3.67 percent in 2022 - current projections are for this figure to rise in 2023 and the years that follow, although these forecasts are subject to change if recent years are anything to go by.

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 February 14, 2025: The figures for the fourth quarter and the year 2024 have been added.

Changes as of August 23, 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 May 2025.

Brazil Employment Level: Northeast data was reported at 39.556 % in 22 Aug 2020. This records a decrease from the previous number of 40.111 % for 15 Aug 2020. Brazil Employment Level: Northeast data is updated daily, averaging 40.111 % from May 2020 (Median) to 22 Aug 2020, with 15 observations. The data reached an all-time high of 42.054 % in 16 May 2020 and a record low of 38.976 % in 25 Jul 2020. Brazil Employment Level: Northeast data remains active status in CEIC and is reported by Brazilian Institute of Geography and Statistics. The data is categorized under Brazil Premium Database’s Labour Market – Table BR.GBA001: Continuous National Household Sample Survey: Weekly.

This table contains information about the labor market situation of all young people aged 15 to 27 in the Netherlands who were registered in the Personal Records Database (BRP) on 1 October of the reference year. It is indicated whether young people work, whether they go to school, whether they receive benefits and whether they are registered as a job seeker with the UWV Werkbedrijf. For employed young people, a distinction is made between employees and the self-employed. The data can be broken down into various regional classifications (part of the country, province, COROP, labor market region and municipality). The total number of young people (aged 15 to 27) receiving benefits in this table is higher than in other StatLine tables on social security. This is because the number of benefits is counted over the entire month instead of the situation being presented on the last Friday of the month. This table also includes all social security benefits related to incapacity for work, illness, unemployment or social assistance benefits. The reporting period is the month of October of the reference year. Whether someone goes to school is determined on the basis of registration in government-funded education on 1 October of the reference year. The municipalities are classified according to the situation on 1 January 2021. To show how young people in the Netherlands are doing, the National Youth Monitor describes more than 70 topics in addition to this topic. The subjects are called indicators. Data available from 2005. Data on whether or not you are registered as a jobseeker with the UWV Werkbedrijf are available from 2018. The predecessors of this table used data from the UWV Werkbedrijf (formerly CWI). Because this source was not continued in 2019, a switch was made to Registered Job Seekers UWV (GWU). However, these data are not available for the years 2005-2017. The figures relate to the situation in October of the year in question. Status of the figures: The figures for the years 2005 to 2019 in this table are final. The figures for 2020 are provisional. Changes as of March 9, 2023: None, this table has been discontinued. When will new numbers come out? Not applicable anymore. This table is followed by the table: Labor market situation of young people (15 to 27 years); region (classification 2022). See section 3.

This dataset presents data on the population of a region by age group for the Statistical Area Level 4 (SA4) regions as as a time series for December 2015, 2019 and 2020. The boundaries for this dataset follow the 2016 edition of the Australian Statistical Geography Standard (ASGS). The Australian Department of Education, Skills and Employment publishes a range of labour market data on its Labour Market Information Portal. The data provided includes unemployment rate, employment rate, participation rate, youth unemployment rate, unemployment duration, population by age group and employment by industry and occupation. AURIN has spatially enabled the original data. Data Source: ABS Labour Force Survey, 12 month average, December 2020. The ABS advises that analysis of regional labour force estimates should typically be based on annual averages, which are important for understanding the state of the labour market and providing medium and long-term signals. The application of annual averages, however, is unlikely to accurately or quickly detect turning points in the regional data during periods of significant change (such as during the onset of the COVID-19 pandemic). Original data at the ABS Statistical Area 4 (SA4) level can be found in Table 16. The region named "Western Australia - Outback (North and South)" in the original data has been omitted as it did not match a region within the SA4 2016 ASGS.

This dataset presents data on the population of a region by labour force status for the Statistical Area Level 4 (SA4) regions as of December 2020. The boundaries for this dataset follow the 2016 edition of the Australian Statistical Geography Standard (ASGS).

The Australian Department of Education, Skills and Employment publishes a range of labour market data on its Labour Market Information Portal. The data provided includes unemployment rate, employment rate, participation rate, youth unemployment rate, unemployment duration, population by age group and employment by industry and occupation.

AURIN has spatially enabled the original data. Data Source: ABS Labour Force Survey, 12 month average, December 2020. The ABS advises that analysis of regional labour force estimates should typically be based on annual averages, which are important for understanding the state of the labour market and providing medium and long-term signals. The application of annual averages, however, is unlikely to accurately or quickly detect turning points in the regional data during periods of significant change (such as during the onset of the COVID-19 pandemic). Original data at the ABS Statistical Area 4 (SA4) level can be found in Table 16. The region named "Western Australia - Outback (North and South)" in the original data has been omitted as it did not match a region within the SA4 2016 ASGS.

United States Unemployment Rate Nowcast: sa: Contribution: Labour Market: Number of Job Postings: Active data was reported at 0.000 % in 10 Mar 2025. This stayed constant from the previous number of 0.000 % for 03 Mar 2025. United States Unemployment Rate Nowcast: sa: Contribution: Labour Market: Number of Job Postings: Active data is updated weekly, averaging 0.000 % from Jan 2020 (Median) to 10 Mar 2025, with 270 observations. The data reached an all-time high of 9.823 % in 07 Nov 2022 and a record low of 0.000 % in 10 Mar 2025. United States Unemployment Rate Nowcast: sa: Contribution: Labour Market: Number of Job Postings: Active data remains active status in CEIC and is reported by CEIC Data. The data is categorized under Global Database’s United States – Table US.CEIC.NC: CEIC Nowcast: Unemployment Rate.

Mexico Coverage: Unemployment Benefits & Active Labour Market Programs: % of Population: 3rd Quintile data was reported at 0.919 % in 2020. Mexico Coverage: Unemployment Benefits & Active Labour Market Programs: % of Population: 3rd Quintile data is updated yearly, averaging 0.919 % from Dec 2020 (Median) to 2020, with 1 observations. The data reached an all-time high of 0.919 % in 2020 and a record low of 0.919 % in 2020. Mexico Coverage: Unemployment Benefits & Active Labour Market Programs: % of Population: 3rd Quintile data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Mexico – Table MX.World Bank.WDI: Social: Social Protection and Insurance. Coverage of unemployment benefits and active labor market programs (ALMP) shows the percentage of population participating in unemployment compensation, severance pay, and early retirement due to labor market reasons, labor market services (intermediation), training (vocational, life skills, and cash for training), job rotation and job sharing, employment incentives and wage subsidies, supported employment and rehabilitation, and employment measures for the disabled. Estimates include both direct and indirect beneficiaries.;ASPIRE: The Atlas of Social Protection - Indicators of Resilience and Equity, The World Bank. Data are based on national representative household surveys. (datatopics.worldbank.org/aspire/);;

Data on labour market participation with a closer look at the gender distributions for the year 2020 in Berlin. These include employment rates, employees subject to social security contributions and the development of unemployment.

Abstract copyright UK Data Service and data collection copyright owner.

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...

The Italian Labour Force Survey is the main source of statistical information on the Italian labor market. The information gathered from the population constitutes the basis on which official estimations of employment and unemployment are calculated, as well as information on the main job’s issues – occupation, the sector of economic activity, hours worked, contracts’ type and duration, training. The survey data are used to analyze a number of individual, family and social factors too, such as the increasing labor mobility, changing professions, the growth in female participation, etc.., which determine the difference in labor participation of the population. The questionnaire is divided into several sections. In particular, in addition to the first socio-demographic information, the first section covers the employment status during the interview’s week, dealing with questions about the type of work, hours worked, motivations about the unemployment status, the type of contract. The second section – reserved for employed people – covers the main job, investigating, in particular, the position in the profession, the industry in which he works, the company he works for, working full-time or part-time and reasons for his selection, working hours, overtime hours, shift work, job transfer, salary, job satisfaction. The third section – always reserved for employed people – concerns the secondary work (if any). It’s exclusively addressed to respondents who carry out another activity compared to the main one and only detects certain information such as the type of activity, type of contract, occupation, the economic sector he works in. The fourth section – for unemployed people – collects information about previous work experiences: last work, type of contract, occupation, economic sector, the reasons why it stopped working. The fifth section deals with the job search. It investigates the reason for seeking a job, the actions put in place to look for it, the channels used to look for and the type of work sought. The sixth section deals with employment and temp agencies and investigates their use by the respondents: the number of contacts, the reason for contact, services required. The seventh section covers education and vocational education. It deals with the training courses respondents are attending. The last section focuses on the auto-perception of their employment status, compared to the previous year. 101,600 individuals, 46,413 families. Two-stage stratified random sample Computer-Assisted Telephone Interviewing (CATI) Computer-Assisted Personal Interviewing (CAPI)

Mexico Benefit Incidence: Unemployment Benefits & Active Labour Market Programs (ALMP) to Poorest Quintile: % of Total Unemployment/ALMP Benefits data was reported at 15.995 % in 2020. Mexico Benefit Incidence: Unemployment Benefits & Active Labour Market Programs (ALMP) to Poorest Quintile: % of Total Unemployment/ALMP Benefits data is updated yearly, averaging 15.995 % from Dec 2020 (Median) to 2020, with 1 observations. The data reached an all-time high of 15.995 % in 2020 and a record low of 15.995 % in 2020. Mexico Benefit Incidence: Unemployment Benefits & Active Labour Market Programs (ALMP) to Poorest Quintile: % of Total Unemployment/ALMP Benefits data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Mexico – Table MX.World Bank.WDI: Social: Social Protection and Insurance. Benefit incidence of unemployment benefits and active labor market programs (ALMP) to poorest quintile shows the percentage of total unemployment and active labor market programs benefits received by the poorest 20% of the population. Unemployment benefits and active labor market programs include unemployment compensation, severance pay, and early retirement due to labor market reasons, labor market services (intermediation), training (vocational, life skills, and cash for training), job rotation and job sharing, employment incentives and wage subsidies, supported employment and rehabilitation, and employment measures for the disabled. Estimates include both direct and indirect beneficiaries.;ASPIRE: The Atlas of Social Protection - Indicators of Resilience and Equity, The World Bank. Data are based on national representative household surveys. (datatopics.worldbank.org/aspire/);;