This public use microdata file (PUMF) contains non-aggregated data for a wide variety of variables collected from the Labour Force Survey (LFS). The LFS collects monthly information on the labour market activities of Canada's working age population. This product is for users who prefer to do their own analysis by focusing on specific subgroups in the population or by cross-classifying variables that are not in our catalogued products.

Poland Unemployment: by LFS data was reported at 662.000 Person th in Sep 2018. This records an increase from the previous number of 617.000 Person th for Jun 2018. Poland Unemployment: by LFS data is updated quarterly, averaging 1,735.500 Person th from Dec 1998 (Median) to Sep 2018, with 80 observations. The data reached an all-time high of 3,509.000 Person th in Mar 2004 and a record low of 617.000 Person th in Jun 2018. Poland Unemployment: by LFS data remains active status in CEIC and is reported by Central Statistical Office. The data is categorized under Global Database’s Poland – Table PL.G008: Labour Force Survey: Economic Activity.

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 copyright UK Data Service and data collection copyright owner.

The Labour Force Survey (LFS) has been carried out in the UK since 1973. From 1973 until 1983 the survey was carried out biennially, and from 1984 until 1991 it was conducted annually. In 1992 the quarterly LFS was introduced. For full background and methodological information users should refer to the main Quarterly Labour Force Survey (QLFS) series (held at the UK Data Archive (UKDA) under GN 33246).

The Local Area Data series was produced quarterly alongside the main QLFS from 1992-2006, and included aggregated data on employment, economic activity and related subjects, covering Local Authority Districts (LADs), Training and Enterprise Councils (TECs), and their Scottish and Welsh equivalents. From 1992 until August 1997, the data covered Great Britain, and from September 1997 data from Northern Ireland were added. The Local Area Data were also available as an annual database between 1994-1995 and 1999-2000, for LADs at individual level, but these are no longer produced.

LFS move from seasonal to calendar quarters
In accordance with EU regulations, the LFS moved from seasonal (spring, summer, autumn, winter) quarters to calendar quarters (January-March, April-June, July-September, October-December) in 2006. The last seasonal Local Area Data dataset issued was the Quarterly Labour Force Survey, December 2005 - February, 2006: Local Area Data (SN 5392), and the first calendar quarter dataset was the Quarterly Labour Force Survey, January - March, 2006: Local Area Data (SN 5393). Users should note that there is some overlap between these two datasets. Further information on the seasonal to calendar quarter change and its impact on LFS data may be found in the following online article:
Madouros, V. (2006) Impact of the switch from seasonal to calendar quarters in the Labour Force Survey, London: ONS.

LFS Documentation
The User Guides available with the UK Data Archive's LFS studies are those available at the time of deposit. Users can access the updated guides online via the ONS LFS User Guide pages.

Main Topics:
The Local Area Data files contain approximately 100 variables covering the following subjects: employment by age group; employees; self-employed; economic activity; employment by industrial sector; ethnic minority economic activity; persons in full-time education; qualifications; job-related training.

Canada LFS: Employment data was reported at 20,758.000 Person th in Mar 2025. This records a decrease from the previous number of 20,768.600 Person th for Feb 2025. Canada LFS: Employment data is updated monthly, averaging 14,732.800 Person th from Jan 1976 (Median) to Mar 2025, with 591 observations. The data reached an all-time high of 21,048.500 Person th in Jun 2024 and a record low of 9,271.400 Person th in Jan 1976. Canada LFS: Employment data remains active status in CEIC and is reported by Statistics Canada. The data is categorized under Global Database’s Canada – Table CA.G012: Labour Force Survey: Employment.

Statistics Canada publishes monthly labour force statistics for all Canadian Census Metropolitan Areas (CMAs) and provinces. In addition, the City of Toronto purchases a special run from Statistics Canada of Labour Force Survey (LFS) data for city of Toronto residents (i.e. separate from the rest of the Toronto CMA). LFS data are collected by place of residence, and therefore city of Toronto's "employment" represents "employed residents" and not "jobs" in the city of Toronto. There are more jobs in the city of Toronto than employed city of Toronto residents. In this LFS database, you will find 22 monthly tables and 28 annual tables. Most of the tables contain data for five geographies: city of Toronto, Toronto CMA, Toronto/Hamilton/Oshawa CMAs, Ontario and Canada ( see attachment Table of Contents below a full description ). LFS data in the IVT tables are not seasonally adjusted. Top level seasonally adjusted LFS data are available in our monthly Toronto Economic Bulletin on Open Data. LFS is based on a monthly sample of approximately 2,800 households in the Toronto CMA, about half of the sample is from the city of Toronto; therefore, estimates will vary from the results of a complete census. LFS follows a rotating panel sample design, in which households remain in the sample for six consecutive months. The total sample consists of six representative sub-samples of panels, and each month a panel is replaced after completing its six month stay in the survey. Outgoing households are replaced by households in the same or similar area. This results in a five-sixths month-to-month sample overlap, which makes the design efficient for estimating month-to-month changes. The rotation after six months prevents undue respondent burden for households that are selected for the survey ( see attachment Guide to the Labour Force Survey for more information). Upon reviewing the data, you will see that at least some cells in the IVT tables have been suppressed. For confidentiality reasons, Statistics Canada suppresses Labour Force Survey data for any cell that corresponds to less than 1,500 persons. At the beginning of 2015, Statistics Canada substantially changed the methodology used to produce LFS population estimates for the city of Toronto. These changes have resulted in large and inexplicable swings in population and related counts, which are not real. However, the unemployment and participation rates for city residents showed very little change in this revision. The red dots in the chart above represents Statistics Canada's Annual Demographics estimates for the populations of the city of Toronto, age 15 and over. These are only estimates, but they are generally accepted as the most accurate estimates for the city's population. (Source: https://www150.statcan.gc.ca/n1/pub/91-214-x/91-214-x2018000-eng.htm). The most recent Statistics Canada population estimate for the city of Toronto is for July 1, 2015; therefore, we have to use projections thereafter. There are several population projections for the city. The projection that EDC staff has chosen to use for rebasing city of Toronto LFS data is the Ontario Ministry of Finance Population Projections 2017-2041 and downloaded June, 2017 from http://www.fin.gov.on.ca/en/economy/demographics/projections/ Please see attachment Rebased Labour Force Survey for City of Toronto below for annual adjustment factors, monthly adjustment factors and an example of how to rebase the absolute numbers for the city of Toronto.

Poland Economically Inactive: by LFS data was reported at 13,246.000 Person th in Jun 2018. This records a decrease from the previous number of 13,405.000 Person th for Mar 2018. Poland Economically Inactive: by LFS data is updated quarterly, averaging 13,729.000 Person th from Dec 1998 (Median) to Jun 2018, with 79 observations. The data reached an all-time high of 14,726.000 Person th in Mar 2007 and a record low of 12,669.000 Person th in Mar 1999. Poland Economically Inactive: by LFS data remains active status in CEIC and is reported by Central Statistical Office. The data is categorized under Global Database’s Poland – Table PL.G008: Labour Force Survey: Economic Activity.

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.

This table contains 1501 series, with data for years 1987 - 2000 (not all combinations necessarily have data for all years). This table contains data described by the following dimensions (Not all combinations are available): Geography (79 items: South Coast-Burin Peninsula; Newfoundland and Labrador; Canada; Newfoundland and Labrador; Avalon Peninsula; Newfoundland and Labrador ...), North American Industry Classification System (NAICS) (19 items: Total employed; all industries; Goods-producing sector; Agriculture; Forestry; fishing; mining; quarrying; oil and gas ...).

The Labour Force Survey provides estimates of employment and unemployment which are among the timeliest and important measures of performance of the Canadian economy. With the release of the survey results only 10 days after the completion of data collection, the LFS estimates are the first of the major monthly economic data series to be released. The Canadian Labour Force Survey was developed following the Second World War to satisfy a need for reliable and timely data on the labour market. Information was urgently required on the massive labour market changes involved in the transition from a war to a peace-time economy. The main objective of the LFS is to divide the working-age population into three mutually exclusive classifications - employed, unemployed, and not in the labour force - and to provide descriptive and explanatory data on each of these. LFS data are used to produce the well-known unemployment rate as well as other standard labour market indicators such as the employment rate and the participation rate. The LFS also provides employment estimates by industry, occupation, public and private sector, hours worked and much more, all cross-classifiable by a variety of demographic characteristics. Estimates are produced for Canada, the provinces, the territories and a large number of sub-provincial regions. For employees, wage rates, union status, job permanency and workplace size are also produced. These data are used by different levels of government for evaluation and planning of employment programs in Canada. Regional unemployment rates are used by Employment and Social Development Canada to determine eligibility, level and duration of insurance benefits for persons living within a particular employment insurance region. The data are also used by labour market analysts, economists, consultants, planners, forecasters and academics in both the private and public sector. Note: Because missing values are removed from this dataset, any form of non-response (e.g. valid skip, not stated) or don't know/refusal cannot be coded as a missing. The "Sysmiss" label in the Statistics section indicates the number of non-responding records for each variable, and the "Valid" values in the Statistics section indicate the number of responding records for each variable. The total number of records for each variable is comprised of both the sysmiss and valid values. LFS revisions: LFS estimates were previously based on the 2001 Census population estimates. These data have been adjusted to reflect 2006 Census population estimates and were revised back to 1996. The census metropolitan area (CMA) variable has been expanded from the three largest CMAs in Canada to nine. Two occupation variables based on the 2016 National Occupation Classicifcation have been reintroduced: a generic 10- category variable (NOC_10) and a detailed 40-category variable (NOC_40). A new variable on immigrant status (IMMIG) has been introduced, which distingushes between recent immigrants and established immigrants. Fourteen variables related to family and spouse/partner's labour force characteristics have been removed, as well as eight out of date variables which have been removed from the record layout.

The Labour Force Survey provides estimates of employment and unemployment. With the release of the survey results only 10 days after the completion of data collection, the LFS estimates are the first of the major monthly economic data series to be released. LFS data are used to produce the well-known unemployment rate as well as other standard labour market indicators such as the employment rate and the participation rate. The LFS also provides employment estimates by industry, occupation, public and private sector, hours worked and much more, all cross-classifiable by a variety of demographic characteristics. Estimates are produced for Canada, the provinces, the territories and a large number of sub-provincial regions. For employees, data on wage rates, union status, job permanency and establishment size are also produced.

This dataset provides information regarding the labour force, employment, unemployment, and other related labour market indicators, to facilitate research, policy-making, and public understanding of labour market conditions.

Statistics Norway established the Labor Force Survey (LFS) in 1972, and it has been conducted quarterly ever since. The LFS measures the population's participation in the labor market and provides comprehensive information on unemployment, employment, people outside the labor force, temporary employees, underemployed and other subgroups that are not captured by register-based statistics. This makes the LFS one of the most important sources of information about conditions in the Norwegian labor market.

Right from the start, the aim has been to ensure that the survey is comparable with similar surveys internationally. Today, the LFS is designed in accordance with the EU's statistical regulations to ensure consistent and comparable European statistics. The LFS data contains long time series, and although there have been some breaks in the time series due to changes in the questionnaire and data collection, the most central variables have been continuously included since the start. This makes it possible to present time series data for the employed, unemployed and people outside the labor force all the way back to 1972.

The dataset consists of a single quarter, along with a quarterly weight. The dataset should be used for a quarterly average rather than an annual distribution. If an annual average is desired, separate datasets are available.

Data is no longer available for dissemination. If you require access to these files for reference purposes, please contact your library's Data Liberation Initiative liason. Series collection of LFS data files.These data represent LFS data prior to the official process of rebasing for latest population statistics (2006 Census). These data are no longer disseminated through Statistics Canada, or the Data Liberation Initiative (DLI). Rebased data is available through ODESI, see: http://odesi.ca

Canada LFS: Unemployment data was reported at 1,488.200 Person th in Feb 2025. This records a decrease from the previous number of 1,567.800 Person th for Jan 2025. Canada LFS: Unemployment data is updated monthly, averaging 1,262.750 Person th from Jan 1976 (Median) to Feb 2025, with 590 observations. The data reached an all-time high of 2,763.700 Person th in May 2020 and a record low of 668.500 Person th in Sep 1976. Canada LFS: Unemployment data remains active status in CEIC and is reported by Statistics Canada. The data is categorized under Global Database’s Canada – Table CA.G021: Labour Force Survey: Unemployment. [COVID-19-IMPACT]

in the reference week were not employed (according to the above definition of this population), were actively looking for work, i.e. had carried out activities in the four week period ending with the reference week to seek paid employment, were available to take up work within two weeks from the end of the reference week. Among unemployed persons were also include persons who did not seek work because they had already found a job and were waiting to start work during the period no longer than 3 months and they were available to take up this work.

Bangladesh LFS: Labour Force data was reported at 73,450.000 Person th in 2023. This records an increase from the previous number of 73,050.000 Person th for 2022. Bangladesh LFS: Labour Force data is updated yearly, averaging 49,500.000 Person th from Jun 1984 (Median) to 2023, with 13 observations. The data reached an all-time high of 73,450.000 Person th in 2023 and a record low of 28,500.000 Person th in 1984. Bangladesh LFS: Labour Force data remains active status in CEIC and is reported by Bangladesh Bureau of Statistics. The data is categorized under Global Database’s Bangladesh – Table BD.G002: Labour Force Survey: Labour Force.

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 CENTRAL AGENCY FOR PUBLIC MOBILIZATION AND STATISTICS (CAPMAS)

In any society, the human element represents the basis of the work force which exercises all the service and production activities. Therefore, it is a mandate to produce labor force statistics and studies, that is related to the growth and distribution of manpower and labor force distribution by different types and characteristics.

In this context, the Central Agency for Public Mobilization and Statistics conducts "Quarterly Labor Force Survey" which includes data on the size of manpower and labor force (employed and unemployed) and their geographical distribution by their characteristics.

By the end of each year, CAPMAS issues the annual aggregated labor force bulletin publication that includes the results of the quarterly survey rounds that represent the manpower and labor force characteristics during the year.

---> Historical Review of the Labor Force Survey:

1- The First Labor Force survey was undertaken in 1957. The first round was conducted in November of that year, the survey continued to be conducted in successive rounds (quarterly, bi-annually, or annually) till now.

2- Starting the October 2006 round, the fieldwork of the labor force survey was developed to focus on the following two points: a. The importance of using the panel sample that is part of the survey sample, to monitor the dynamic changes of the labor market. b. Improving the used questionnaire to include more questions, that help in better defining of relationship to labor force of each household member (employed, unemployed, out of labor force ...etc.). In addition to re-order of some of the already existing questions in much logical way.

3- Starting the January 2008 round, the used methodology was developed to collect more representative sample during the survey year. this is done through distributing the sample of each governorate into five groups, the questionnaires are collected from each of them separately every 15 days for 3 months (in the middle and the end of the month)

4- Starting the January 2012 round, in order to follow the international recommendation, to avoid asking extra questions that affect the precision and accuracy of the collected data, a shortened version of the questionnaire was designed to include the core questions that enable obtaining the basic Egyptian labor market indicators. The shortened version is collected usually in three rounds, while the long version that includes more information on housing conditions and immigration of the questionnaire is usually collected in one round .

---> The survey aims at covering the following topics:

1- Measuring the size of the Egyptian labor force among civilians (for all governorates of the republic) by their different characteristics. 2- Measuring the employment rate at national level and different geographical areas. 3- Measuring the distribution of employed people by the following characteristics: Gender, age, educational status, occupation, economic activity, and sector. 4- Measuring unemployment rate at different geographic areas. 5- Measuring the distribution of unemployed people by the following characteristics: Gender, age, educational status, unemployment type “ever employed/never employed”, occupation, economic activity, and sector for people who have ever worked.

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

Geographic coverage

Covering a sample of urban and rural areas in all the governorates.

Analysis unit

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

Universe

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

Kind of data

Sample survey data [ssd]

Sampling procedure

THE CLEANED AND HARMONIZED VERSION OF THE SURVEY DATA PRODUCED AND PUBLISHED BY THE ECONOMIC RESEARCH FORUM REPRESENTS 100% OF THE ORIGINAL SURVEY DATA COLLECTED BY THE CENTRAL AGENCY FOR PUBLIC MOBILIZATION AND STATISTICS (CAPMAS)

---> Sample Design and Selection

The sample of Labor Force Survey is a two-stage stratified cluster sample and selfweighted to the extent practical.

The main elements of the sampling design are described as follows:

• Sample Size Sample size designed for each quarter is 22500 households with a total of 90000 households per year (Designed Sample 89982, Implemented Sample 77002) , allocated 45.4% for Urban and 54.6 % for rural over all governorates ( urban / rural) in proportion to the size of each governorate , the sample divided to : a. New Households with sample size 11502 households for each quarter. b. Panel households with sample size 10998 households for each quarter selected from the same quarter in year 2020.

• Cluster size The cluster size is 18 households.

• Sampling stages:

A- First sampling stage: (1) Primary Sampling Unit: The 2017 population census data was used to obtain an electronic list of areas of aid, or in other words, enumeration areas (EAS) with an average of 600-750 households through all the republic. This list represents the framework of the first phase of the sample, where 10,000 auxiliary areas were selected as primary sampling units (PSU) for the first phase of the main sample. (2) Stratification and allocation of the initial inspection unit: The governorate is considered the primary layer, which is divided into urban and rural as sub-classes, where the allocation commensurate with the size of the sample between governorates, and between urban and rural areas in each governorate in the first phase of inspection, in order to achieve selfweighting of the basic and sub-classes at the national level .

B- SECOND Sampling phase: (1) The division of each initial survey into a number of parts approximately equal in size (200 households / part), each part is called an area plot. (2) Selecting an area plot (one part of each primary sampling unit) using the simple random sampling method.

C- THIRD stage of Sampling: 1- The basic sample areas (10,000 area plots) were divided into four groups, each group contain 2,500 area plots) using the simple random sampling method, and one of the most important advantages of random division is that each area plot has the same opportunity to appear between the four groups. 2- Field work is carried out into four phases (each phase is executed every two years). The implementation of the first phase began since March 2018. 3- The goal of this subdivision is to ensure that the sample is not outdated for as long as possible, and that household data are constantly updated. 4- The representation of urban and rural areas at the level of the governorates of the Republic has been taken into account.

D- Sample Allocation among Governorates: Governorate is considered the primary main strata that divided into substrata (Urban/ Rural), allocation proportional to size was determined among governorates as well as among urban and rural in each governorate at the first sampling stage.

Sample frame first Stage: The Census lists of EA's for each strata constitute the frame of the sample first stage. The identification information appears on the EA's list includes District code,(Shiakha/Village)code, Census Supervisor and enumerator numbers, Census Supervisor number is unique at the level of (Shiakha /Village), while the enumerator number is unique within the EA enumeration assigned to supervisor. During investigating the list of EA's with regard to the number of households and their consistency with each EA, it has been found that number of households at these EA's are below the average ( less than 200 household ). The reason for that is that some enumerators included institutional households which have been excluded later and includes only households habiting in private housing. As a result, a decision was made to adjoin any EA with less than 160 H.H. to an adjacent one. Thus, in those exceptional cases the primary Sampling Units (PSU) consists of two EA's. To select the primary sampling units (Sampling first stage), the frame was arranged to provide implicit stratification with regard to the geographic location, where the urban of each governorate was arranged in a spiral according to the geographic location (kism / district). The same sort of ordering was made on the rural frame, but according to the district location. The systematic selection of EA's sample from such a sorted frame will ensure balanced distribution of the sample over the substrata of respective governorates, After this stage, the sample was selected with probability proportional to Size (PPS), of census households number in EA (200 households) was taken as a Measure of Size (MOS). The Sample households was selected from each EA using equal probability of the systematic selection method.

D- Division of Primary Sampling Unit: Primary sampling unit (5000 EA) was divided into four rounds, each has (1250 EA). Then the household sample was then selected from each EA with equal probability, using the systematic selection method normally. The table (B) shows designed, implemented sample and Response Rate according to governorate (Urban & Rural).

A more detailed description of the different sampling stages and allocation of sample across

Poland Employment: by LFS: Urban data was reported at 10,042.000 Person th in Sep 2018. This records an increase from the previous number of 10,002.000 Person th for Jun 2018. Poland Employment: by LFS: Urban data is updated quarterly, averaging 9,513.000 Person th from Jun 2000 (Median) to Sep 2018, with 74 observations. The data reached an all-time high of 10,042.000 Person th in Sep 2018 and a record low of 8,282.000 Person th in Mar 2003. Poland Employment: by LFS: Urban data remains active status in CEIC and is reported by Central Statistical Office. The data is categorized under Global Database’s Poland – Table PL.G008: Labour Force Survey: Economic Activity.