Worldwide, there were a total of *** cases of forced labor per 1,000 inhabitants in 2021. State-imposed forced labor was the form of forced labor with the highest prevalence rate that year, reaching *** per 1,000 population.

Arab States have the highest prevalence rate of victims of forced labor worldwide as of 2021. In this region, there are on average *** victims of forced labor per 1,000 inhabitants. By comparison, Africa is the region with the lowest prevalence rate at ***. Interestingly, while Asia and the Pacific has the highest number of victims worldwide, it has the second lowest prevalence rate together with the Americas.

This layer shows figures of quit rates and quit levels by the US, BLS regions, and states. Data is from the Bureau of Labor Statistics (BLS) and was released October and November of 2021. The layer default symbology highlights to September 2021 quit rate in comparison to the national figure of 3.0%.According to the October 2021 News Release by BLS:"The number of quits increased in August to 4.3 million (+242,000). The quits rate increased to a series high of 2.9 percent. Quits increased in accommodation and food services (+157,000); wholesale trade (+26,000); and state and local government education (+25,000). Quits decreased in real estate and rental and leasing (-23,000). The number of quits increased in the South and Midwest regions."In the following November News Release:"In September, quits rates increased in 15 states and decreased in 10 states. The largest increases in quits rates occurred in Hawaii (+3.8 percentage points), Montana (+1.5 points), as well as Nevada and New Hampshire (+1.1 points each). The largest decreases in quits rates occurred in Kentucky (-1.1 percentage points), Iowa (-1.0 point), and South Dakota (-0.7 point). Over the month, the national quits rate increased (+0.1 percentage point)."Quit rates: The quits rate is the number of quits during the entire month as a percent of total employment.Quit levels: Quits are the number of quits during the entire month.State and US figures: Table 4. Quits levels and rates by industry and region, seasonally adjustedRegion figures: Table 4. Quits levels and rates by industry and region, seasonally adjustedThis data was obtained in October and November 2021, and the months of data from BLS are as follows:August 2020September 2020April 2021 (only offered for Regions)May 2021June 2021July 2021August 2021September 2021 (preliminary values)For the full data release, click here.The states (including the District of Columbia) that comprise the regions are: Northeast: Connecticut, Maine, Massachusetts, New Hampshire, New Jersey, New York, Pennsylvania, Rhode Island, and VermontSouth: Alabama, Arkansas, Delaware, District of Columbia, Florida, Georgia, Kentucky, Louisiana, Maryland, Mississippi, North Carolina, Oklahoma, South Carolina, Tennessee, Texas, Virginia, and West VirginiaMidwest: Illinois, Indiana, Iowa, Kansas, Michigan, Minnesota, Missouri, Nebraska, North Dakota, Ohio, South Dakota, and WisconsinWest: Alaska, Arizona, California, Colorado, Hawaii, Idaho, Montana, Nevada, New Mexico, Oregon, Utah, Washington, and Wyoming.

A table showing the Jordanian labor market indicators for the year 2021

This table provides information on labour price developments, i.e. the development of wage costs per hour worked by employees, corrected for changes in the personnel structure. The labor price index divides the total increase in wage costs per hour worked into a price component and a structural effect. Data available from: 2001 Status of the figures: Data from 2001 up to and including 2022 are final. The figures concerning 2023 and 2024 are provisional. Changes as of January 31st 2025: Compared to the previous version the figures for the reference period '2024 January - 2024 December' are added and the figures are adjusted for the period '2023 October - 2024 September'. When will new figures become available? New figures are published three to four months after the end of the reference period.

Abstract

Previous surveys on labor migration from Pacific Island countries are often cross-sectional, not readily available, and focusing on one migration scheme, country, or issue and hence incompatible. Such limitation of existing data restricts analysis of a range of policy-relevant issues that present themselves over the migrants' life cycle such as those on migration pathways, long-term changes in household livelihood, and trajectory of migrants’ labor market outcomes, despite the significant impacts of labor migration on the economy of the Pacific Island countries. To address these shortfalls in the Pacific migration data landscape, the PLMS is designed to be longitudinal, spanning multiple labor sending and receiving countries and collecting omnibus information on both migrants, their households and non-migrant households. The survey allows for disaggregation and reliable comparative analysis both within and across countries and labor mobility schemes. This open-access and high-quality data will facilitate more research about the Pacific migration, help inform and improve Pacific migration policy deliberations, and engender broader positive change in the Pacific data ecosystem.

Geographic coverage

Tonga: Tongatapu, ‘Eua, Vava’u, Ha’apai, Ongo Niua. Vanuatu: Malampa, Penama, Sanma, Shefa, Tafea, Torba. Kiribati: Abaiang, Abemama, Aranuka, Arorae, Banaba, Beru, Butaritari, Kiritimati, Maiana, Makin, Marakei, Nikunau, Nonouti, North Tabiteuea, North Tarawa, Onotoa, South Tabiteuea, South Tarawa, Tabuaeran, Tamana, Teraina.

Analysis unit

• Households in Kiribati, Tonga, and Vanuatu.
• Temporary migrant workers from Kiribati, Tonga and Vanuatu who participated in the Pacific Australia Labour Mobility scheme in Australia and the Recognised Seasonal Employers scheme in New Zealand

Kind of data

Sample survey data [ssd]

Sampling procedure

Sampling frame: The PLMS sample was designed based on a Total Survey Error framework, seeking to minimize errors and bias at every stage of the process throughout preparation and implementation.

The worker sample frame is an extensive list of approximately 11,600 migrant workers from Kiribati, Tonga and Vanuatu who had participated in the RSE and PALM schemes. Due to the different modes of interviews, sampling strategies for the face-to-face segment of the household survey in Tonga was different from the rest of the surveys implemented via phone interviews. The face-to-face segment of the household survey selected households using Probability Proportional to Size sampling based on the latest population census listing and our worker sample frame, with technical inputs from the Tonga Statistics Department. The phone-based segment of the household survey used a combination of Probability Proportional to Size sampling based on the existing sample frame and random digit dialing. The design of the sample benefited from technical inputs from the Tonga Statistics Departments and the Vanuatu National Statistics Office, as well as World Bank staff from Kiribati.

As participation in the survey is voluntary, a worker might agree to participate while their household did not, and vice versa. Because of this, the survey did not achieve a complete one-to-one match between interviewed workers and sending households. Of all interviewed respondents, 418 workers in the worker survey are linked to their households in the household survey. However, after removing incomplete interviews, 341 worker-household pairs remain. They are matched by either pre-assigned serial ID numbers or contact details collected in the household and worker surveys during the post-fieldwork data cleaning process.

Sampling deviation

The survey was originally planned to be conducted face-to-face and was so for most of the collection of household data in Tonga. However, due to COVID-19, it was switched to phone-based mode and the survey instruments were adjusted accordingly to better suit the phone-based data collection while ensuring data quality. In particular, the household questionnaire was shortened, and sampling strategy changed to a combination of Probability Proportional to Size sampling based on the existing household listing and random digit dialing.

Compared to in-person data collection, the usual caveats of potential biases in phone-based survey related to disproportional phone ownership and connectivity apply here. The random digit dialing approach provides data representative of the phone-owning population. Yet due to lack of information, it is difficult to judge whether sending households in Kiribati, Tonga, and Vanuatu are more or less likely to own a phone and/or respond positively to survey request than non-sending households.

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

• The questionnaires were jointly designed in English by the World Bank and researchers at the Development Policy Centre, Australian National University. They were translated into Bislama, Gilbertese and Tongan, scripted into CAPI/CATI programs, tested and piloted before being finalized. The design of the questionnaires and the samples benefited from technical inputs from the Tonga Statistics Departments, Pacific consultants, and academic experts specialized in Pacific labor mobility and remittances.
• Enumerators are native speakers from the labor-sending countries covered in the survey and were trained to elicit information asked in the questionnaire in local languages.
• The phone-based household questionnaire is moderately shorter than the in-person version.

Cleaning operations

The published data have been cleaned and anonymized. All incomplete interview records have been removed from the final datasets. The anonymization process followed the theory of Statistical Disclosure Control for microdata, aiming to minimize re-identification risk, i.e. the risk that the identity of an individual (or a household) described by a specific record could be determined with a high level of confidence. The anonymization process employs the k-anonymity method to calculate the re-identification risk. Risk measurement, anonymization and utility measurement for the PLMS were done using sdcMicro, an add-on package for the statistical software R for Statistical Disclosure Control (SDC) of microdata.

Since the household questionnaire was shortened when the survey switched from face-to-face to phone-based data collection, there face-to-face datasets and phone-based datasets are not identical, but they are consistent and can be harmonized. The mapping guide enclosed in this publication provides a guide to data users to wish to harmonize them.

Household expenditure variables in the household dataset and individual wage variable in the household member dataset are in USD. Local currencies were converted into USD based on the following exchange rates: 1 Tongan Pa'anga= 0.42201412 USD; 1 Vanuatu Vatu= 0.0083905322 USD; 1 Kiribati dollar= 0.66942499 USD.

Response rate

Face-to-face segment of the PLMS household survey: not applicable. Phone-based segment of the PLMS household survey: 26%. The PLMS Worker survey: 31%

The Labor Force Participation Rates map shows the most recent data available for labor participation rates for all counties and independent cities in Virginia. The Labor Force Participation Rates table has the most recent data available as well as previous years for historical comparisons.

Abstract

THE CLEANED AND HARMONIZED VERSION OF THE SURVEY DATA PRODUCED AND PUBLISHED BY THE ECONOMIC RESEARCH FORUM REPRESENTS 100% OF THE ORIGINAL SURVEY DATA COLLECTED BY THE PALESTINIAN CENTRAL BUREAU OF STATISTICS

The Palestinian Central Bureau of Statistics (PCBS) carried out four rounds of the Labor Force Survey 2021 (LFS). The survey rounds covered a total sample of about 25,179 households (about 6,300 households per quarter).

The main objective of collecting data on the labour force and its components, including employment, unemployment and underemployment, is to provide basic information on the size and structure of the Palestinian labour force. Data collected at different points in time provide a basis for monitoring current trends and changes in the labour market and in the employment situation. These data, supported with information on other aspects of the economy, provide a basis for the evaluation and analysis of macro-economic policies.

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

Geographic coverage

Covering a representative sample on the region level (West Bank, Gaza Strip), the locality type (urban, rural, camp) and the governorates.

Analysis unit

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

Universe

The survey covered all Palestinian households who are a usual residence of the Palestinian Territory.

Kind of data

Sample survey data [ssd]

Sampling procedure

THE CLEANED AND HARMONIZED VERSION OF THE SURVEY DATA PRODUCED AND PUBLISHED BY THE ECONOMIC RESEARCH FORUM REPRESENTS 100% OF THE ORIGINAL SURVEY DATA COLLECTED BY THE PALESTINIAN CENTRAL BUREAU OF STATISTICS

The methodology was designed according to the context of the survey, international standards, data processing requirements and comparability of outputs with other related surveys.

---> Target Population: It consists of all individuals aged 10 years and Above and there are staying normally with their households in the state of Palestine during 2020.

---> Sampling Frame: The sampling frame consists of a comprehensive sample selected from the Population, Housing and Establishments Census 2017: This comprehensive sample consists of geographical areas with an average of 150 households, and these are considered as enumeration areas used in the census and these units were used as primary sampling units (PSUs).

---> Sampling Size: The estimated sample size is 8,040 households in each quarter of 2021.

---> Sample Design The sample is two stage stratified cluster sample with two stages : First stage: we select a systematic random sample of 536 enumeration areas for the whole round. Second stage: we select a systematic random sample of 15 households from each enumeration area selected in the first stage.

---> Sample strata: The population was divided by: 1- Governorate (17 governorates, where Jerusalem was considered as two statistical areas) 2- Type of Locality (urban, rural, refugee camps).

---> Sample Rotation: Each round of the Labor Force Survey covers all of the 536 master sample enumeration areas. Basically, the areas remain fixed over time, but households in 50% of the EAs were replaced in each round. The same households remain in the sample for two consecutive rounds, left for the next two rounds, then selected for the sample for another two consecutive rounds before being dropped from the sample. An overlap of 50% is then achieved between both consecutive rounds and between consecutive years (making the sample efficient for monitoring purposes).

Mode of data collection

Face-to-face [f2f]

Research instrument

The survey questionnaire was designed according to the International Labour Organization (ILO) recommendations. The questionnaire includes four main parts:

---> 1. Identification Data: The main objective for this part is to record the necessary information to identify the household, such as, cluster code, sector, type of locality, cell, housing number and the cell code.

---> 2. Quality Control: This part involves groups of controlling standards to monitor the field and office operation, to keep in order the sequence of questionnaire stages (data collection, field and office coding, data entry, editing after entry and store the data.

---> 3. Household Roster: This part involves demographic characteristics about the household, like number of persons in the household, date of birth, sex, educational level…etc.

---> 4. Employment Part: This part involves the major research indicators, where one questionnaire had been answered by every 15 years and over household member, to be able to explore their labour force status and recognize their major characteristics toward employment status, economic activity, occupation, place of work, and other employment indicators.

Cleaning operations

---> Raw Data PCBS started collecting data since 1st quarter 2020 using the hand held devices in Palestine excluding Jerusalem in side boarders (J1) and Gaza Strip, the program used in HHD called Sql Server and Microsoft. Net which was developed by General Directorate of Information Systems. From the beginning of March 2020, with the spread of the COVID-19 pandemic and the home quarantine imposed by the government, the personal (face to face) interview was replaced by the phone interview for households who had phone numbers from previous rounds, and for those households that did not have phone numbers, they were referred to and interviewed in person (face to face interview). Using HHD reduced the data processing stages, the fieldworkers collect data and sending data directly to server then the project manager can withdrawal the data at any time he needs. In order to work in parallel with Gaza Strip and Jerusalem in side boarders (J1), an office program was developed using the same techniques by using the same database for the HHD.

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

Response rate

The survey sample consists of about 32,160 households of which 25,179 households completed the interview; whereas 16,355 households from the West Bank and 8,824 households in Gaza Strip. Weights were modified to account for non-response rate. The response rate in the West Bank reached 79.8% while in the Gaza Strip it reached 90.5%.

Sampling error estimates

---> Sampling Errors Data of this survey may be affected by sampling errors due to use of a sample and not a complete enumeration. Therefore, certain differences can be expected in comparison with the real values obtained through censuses. Variances were calculated for the most important indicators: the variance table is attached with the final report. There is no problem in disseminating results at national or governorate level for the West Bank and Gaza Strip.

---> Non-Sampling Errors Non-statistical errors are probable in all stages of the project, during data collection or processing. This is referred to as non-response errors, response errors, interviewing errors, and data entry errors. To avoid errors and reduce their effects, great efforts were made to train the fieldworkers intensively. They were trained on how to carry out the interview, what to discuss and what to avoid, carrying out a pilot survey, as well as practical and theoretical training during the training course. Also data entry staff were trained on the data entry program that was examined before starting the data entry process. To stay in contact with progress of fieldwork activities and to limit obstacles, there was continuous contact with the fieldwork team through regular visits to the field and regular meetings with them during the different field visits. Problems faced by fieldworkers were discussed to clarify any issues. Non-sampling errors can occur at the various stages of survey implementation whether in data collection or in data processing. They are generally difficult to be evaluated statistically.

They cover a wide range of errors, including errors resulting from non-response, sampling frame coverage, coding and classification, data processing, and survey response (both respondent and interviewer-related). The use of effective training and supervision and the careful design of questions have direct bearing on limiting the magnitude of non-sampling errors, and hence enhancing the quality of the resulting data. The implementation of the survey encountered non-response where the case ( household was not present at home ) during the fieldwork visit and the case ( housing unit is vacant) become the high percentage of the non response cases. The total non-response rate reached 16.7% which is very low once compared to the

The most common line of work for victims of forced labor worldwide in 2021 was services excl. domestic work. More than ** percent of the forced labor victims worked in this industry. Manufacturing was the second most common line of work, followed by construction. However, there are significant gender differences; roughly ** percent of women worked in other industries which includes illicit activities such as prostitution, compared to ***** percent of men. Furthermore, nearly ** percent of women were deployed in domestic work, compared to only **** percent of men.

This statistic shows the annual percentage change of the unit labor costs in the Netherlands in 2021, with a forecast for 2022 to 2024. As of 2021, the annual percentage change of the unit labor costs was around **** percent.

Abstract

Focuses mainly on labour force key indicators, main characteristics of the employed, unemployed, underemployed and persons outside labour force, labour force according to level of education, distribution of the employed population by occupation, economic activity, place of work, employment status, hours and days worked and average daily wage in NIS for the employees.

Geographic coverage

The Data are representative at region level (West Bank, Gaza Strip), locality type (urban, rural, camp)

Analysis unit

Household, Individual.

Universe

The survey covered all the Palestinian persons aged 10 years and above who are a usual residence in State of Palestine

Kind of data

Sample survey data [ssd]

Sampling procedure

The sample of this survey is implemented periodically every quarter by PCBS since 1995, where this survey is implemented every quarter in the year (distributed over 13 weeks). The sample is a two-stage stratified cluster sample with two stages: First stage: selection of a stratified sample of 536 EA with (pps) method. Second stage: selection of a random area sample of 15 households from each enumeration area selected in the first stage. The estimated sample size in each quarter was 8,040 households in 2021.

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

The lfs questionnaire consists of four main sections: Identification Data: The main objective for this part is to record the necessary information to identify the household, such as, cluster code, sector, type of locality, cell, housing number and the cell code. Quality Control: This part involves groups of controlling standards to monitor the field and office operation, to keep in order the sequence of questionnaire stages (data collection, field and office coding, data entry, editing after entry and store the data. Household Roster: This part involves demographic characteristics about the household, like number of persons in the household, date of birth, sex, educational level…etc. Employment Part: This part involves the major research indicators, where one questionnaire had been answered by every 10 years and over household member, to be able to explore their labour force status and recognize their major characteristics toward employment status, economic activity, occupation, place of work, and other employment indicators.

Cleaning operations

All questionnaires were edited after data entry in order to minimize errors related data entry.

Response rate

The response rate was 85.0% in the fourth quarter 2021 The response rate was 83.3% in 2021 The response rate was 80.7% in the first quarter 2021 The response rate was 84.5% in the third quarter 2021 The response rate was 82.9% in the second quarter 2021

Sampling error estimates

Data of this survey affected by sampling errors due to use of the sample and not a complete enumeration. Therefore, certain differences are expected in comparison with the real values obtained through censuses. Variance were calculated for the most important indicators, the variance table is attached with the final report. There is no problem to disseminate results at the national level and at the level of governorates of the West Bank and Gaza Strip.

Data appraisal

The concept of data quality encompasses various aspects, started with planning of the survey to how to publish, understand and benefit from the data. The most important components of statistical quality elements are accuracy, comparability and quality control procedures

Labor Force Participation Rate in Ukraine decreased to 61.80 percent in the fourth quarter of 2021 from 62.70 percent in the third quarter of 2021. This dataset provides - Ukraine Labor Force Participation Rate- actual values, historical data, forecast, chart, statistics, economic calendar and news.

City Labor Market: Demand-Supply Ratio: Wuhan data was reported at 1.790 NA in Sep 2021. This records a decrease from the previous number of 1.930 NA for Jun 2021. City Labor Market: Demand-Supply Ratio: Wuhan data is updated quarterly, averaging 1.150 NA from Mar 2001 (Median) to Sep 2021, with 56 observations. The data reached an all-time high of 2.010 NA in Mar 2021 and a record low of 0.600 NA in Dec 2001. City Labor Market: Demand-Supply Ratio: Wuhan data remains active status in CEIC and is reported by Ministry of Human Resources and Social Security. The data is categorized under China Premium Database’s Labour Market – Table CN.GJ: City Labor Market: Demand-Supply Ratio.

The table Labor force data by county, 2021 annual averages is part of the dataset Local Area Unemployment Statistics **, available at https://redivis.com/datasets/gqcs-0rrxw8r6h. It contains 3222 rows across 9 variables.

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 QLFS uses a master sampling frame that is used by several household surveys conducted by Statistics South Africa. This wave of the QLFS is based on the 2013 master frame, which was created based on the 2011 census. There are 3324 PSUs in the master frame and roughly 33000 dwelling units.

The sample for the QLFS is based on a stratified two-stage design with probability proportional to size (PPS) sampling of PSUs in the first stage, and sampling of dwelling units (DUs) with systematic sampling in the second stage.

For each quarter of the QLFS, a quarter of the sampled dwellings are rotated out of the sample. These dwellings are replaced by new dwellings from the same PSU or the next PSU on the list. For more information see the statistical release.

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.

In 2021, *** cases were reported to the National Human Trafficking hotline regarding trafficking in forced labor in the United States being put to use on farms or elsewhere in the agricultural industry. A further ** cases were reported in the construction industry.

Graph and download economic data for Manufacturing, Durable Goods Sector: Labor Share for All Workers (PRS31006171) from 1988 to 2021 about sector, durable goods, labor, goods, manufacturing, rate, and USA.

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. Starting from the first quarter of 2021, the indications of European Regulation 1700/2019 have been transposed, which concern in particular the changes in the definitions of family and employee, and a new questionnaire has been adopted (see notes). 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, reasons for not working. 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, the type of contract, working full-time or part-time and reasons for his selection, working hours, overtime hours, shift work, night and weekend 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, hours worked. The fourth section – for unemployed people – collects information about previous work experiences: last work, type of contract, occupation, economic sector, the reasons for the interruption of work. 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 self-perceived employment conditions, and retirement. The seventh section concerns employment services and employment agencies, and investigates their use by the respondents: quantity of contacts, reason for contact, services required. The eighth section concerns education and training: degree obtained, course of study currently attended, professional training. The last section focuses on the self-perception of the employment status, compared to the previous year. 124,614 individuals, 54,668 families. Two-stage stratified random sample Computer-Assisted Telephone Interviewing (CATI) Computer-Assisted Personal Interviewing (CAPI)

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.

Latest edition information

For the second edition (February 2025), the data file was resupplied with the 2024 weighting variable included (LGWT24).