You work hard, your colleagues work hard, and Canadians work hard. Need help proving it? Marie-Josee Major, subject matter analyst from Statistics Canada, introduces the Labour Force Survey. Topics covered include: LFS basics: Respondent classification LFS methodology, modes of access to LFS microdata, and annualizing monthly LFS data.

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.

Analysis unit

Persons

UNITS IDENTIFIED: - Dwellings: no - Vacant Units: No - Households: no - Individuals: yes - Group quarters: no

UNIT DESCRIPTIONS: - Dwellings: no - Households: The family is understood as a de facto family, that is as a group of people linked by bonds of marriage, kinship, affinity, adoption, protection or by emotional bonds, cohabiting and having habitual residence in the same municipality; if the selected family cohabits with other families, only the extracted family is interviewed. - Group quarters: no

Universe

All members of families residing in Italy, even if temporarily emigrated abroad, excluding permanent members of institutional quarters (hospices, religious institutes, barracks, etc.).

Kind of data

Sample survey data [ssd]

Sampling procedure

MICRODATA SOURCE: National Institute of Statistics (ISTAT)

SAMPLE SIZE (person records): ~100,000.

SAMPLE DESIGN: Two-stage sample with stratification of the first-stage units. The first-stage units are municipalities; second-stage units are families. Within each province municipalities are stratified according to population size. Municipalities larger than a predetermined population threshold are included in the sample; remaining municipalities are stratified by population size and extracted with probability proportional to the population size. The sample municipalities remain the same over time. In each quarterly survey, around 1,400 municipalities and 70,000 families are selected. The families included in the sample are interviewed 4 times within 15 months. Each family is interviewed for two consecutive quarters; an interruption follows for the next two quarters, after which the family is interviewed again for another two quarters. Microdata samples distributed by IPUMS are from Quarter 1 surveys.

Mode of data collection

Face-to-face [f2f]

Research instrument

A single booklet with a general record of respondents and 12 sections: 1) Classification of individuals; 2) Labor status in the reference week (for people aged 15 or more); 3) Main job for employed people; 4) Second job for employed people; 5) Previous work experience (for unemployed people); 6) Search for employment (for people aged 15 or more); 7) Employment services and agencies (for people aged 15-74); 8) Education and training (for people aged 15 or more); 9) Self-perceived condition and residency (for people aged 15 or more); 10) Information on the household (for the last family member interviewed); 11) Closing questions for the interviewer; 12) Pending coding.

The Labour Force Survey provides estimates of employment and unemployment which are among the timeliest and important measures of performance of the Canadian economy. 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. This public use microdata file contains non-aggregated data for a wide variety of variables collected from the Labour Force Survey (LFS). It contains both personal characteristics for all individuals in the household and detailed labour force characteristics for household members 15 years of age and over. The personal characteristics include age, sex, marital status, educational attainment, and family characteristics. Detailed labour force characteristics include employment information such as class of worker, usual and actual hours of work, employee hourly and weekly wages, industry and occupation of current or most recent job, public and private sector, union status, paid or unpaid overtime hours, job permanency, hours of work lost, job tenure, and unemployment information such as duration of unemployment, methods of job search and type of job sought. Labour force characteristics are also available for students during the school year and during the summer months as well as school attendance whether full or part-time and the type of institution.LFS revisions: Labour force surveys are revised on a periodic basis. The most recent revisions took place in 2025. As of January 2025, LFS microdata and estimates have been adjusted to reflect population counts from the 2021 Census, with revisions going back to 2011. Additionally, several changes were made to key variables on the PUMFs: Survey weights (FINALWT) have been updated to use 2021 Census population control totals. Sub-provincial geography (CMA) has been updated to the 2021 Standard Geographical Classification (SGC) boundaries. All industry data (NAICS_21) was revised to use the latest standard, North American Industry Classification System (NAICS) 2022. Coding enhancements were applied to improve longitudinal consistency of detailed National Occupational Classification data (NOC_10 and NOC_43). Data were revised to use the gender of person instead of sex (GENDER).

The UK Data Service also holds a Secure Access version of the QLFS (see below); household datasets; two-quarter and five-quarter longitudinal datasets; LFS datasets compiled for Eurostat; and some additional annual Northern Ireland datasets.

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 (the latest questionnaire available covers July-September 2022). Volumes are updated periodically, 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.

LFS response to COVID-19

From April 2020 to May 2022, additional non-calendar quarter LFS microdata were made available to cover the pandemic period. The first additional microdata to be released covered February to April 2020 and the final non-calendar dataset covered March-May 2022. Publication then returned to calendar quarters only. Within the additional non-calendar COVID-19 quarters, pseudonymised variables Casenop and Hserialp may contain a significant number of missing cases (set as -9). These variables may not be available in full for the additional COVID-19 datasets until the next standard calendar quarter is produced. The income weight variable, PIWT, is not available in the non-calendar quarters, although the person weight (PWT) is included. Please consult the documentation for full details.

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: Revision of miscoded occupational data in the ONS Labour Force Survey, UK: January 2021 to September 2022.

2024 Reweighting

In February 2024, reweighted person-level data from July-September 2022 onwards were released. Up to July-September 2023, only the person weight was updated (PWT23); the income weight remains at 2022 (PIWT22). The 2023 income weight (PIWT23) was included from the October-December 2023 quarter. Users are encouraged to read the ONS methodological note of 5 February, Impact of reweighting on Labour Force Survey key indicators: 2024, which includes important information on the 2024 reweighting exercise.

End User Licence and Secure Access QLFS data

Two versions of the QLFS are available from UKDS. One is available under the standard End User Licence (EUL) agreement, and the other is a Secure Access version. The EUL version includes country and Government Office Region geography, 3-digit Standard Occupational Classification (SOC) and 3-digit industry group for main, second and last job (from July-September 2015, 4-digit industry class is available for main job only).

The Secure Access version contains more detailed variables relating to:

• age: single year of age, year and month of birth, age completed full-time education and age obtained highest qualification, age of oldest dependent child and age of youngest dependent child
• family unit and household: including a number of variables concerning the number of dependent children in the family according to their ages, relationship to head of household and relationship to head of family
• nationality and country of origin
• finer detail geography: including county, unitary/local authority, place of work, Nomenclature of Territorial Units for Statistics 2 (NUTS2) and NUTS3 regions, and whether lives and works in same local authority district, and other categories;
• health: including main health problem, and current and past health problems
• education and apprenticeship: including numbers and subjects of various qualifications and variables concerning apprenticeships
• industry: including industry, industry class and industry group for main, second and last job, and industry made redundant from
• occupation: including 5-digit industry subclass and 4-digit SOC for main, second and last job and job made redundant from
• system variables: including week number when interview took place and number of households at address
• other additional detailed variables may also be included.

The Secure Access datasets (SNs 6727 and 7674) have more restrictive access conditions than those made available under the standard EUL. Prospective users will need to gain ONS Accredited Researcher status, complete an extra application form and demonstrate to the data owners exactly why they need access to the additional variables. Users are strongly advised to first obtain the standard EUL version of the data to see if they are sufficient for their research requirements.

Latest edition information

For the fourth edition (May 2025), the variables DIFFHRS20 and YLESS20 were replaced with new versions, with previously missing imputed values for 'IOUTCOME=6' cases added.

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.This public use microdata file contains non-aggregated data for a wide variety of variables collected from the Labour Force Survey (LFS). It contains both personal characteristics for all individuals in the household and detailed labour force characteristics for household members 15 years of age and over. The personal characteristics include age, sex, marital status, educational attainment, and family characteristics. Detailed labour force characteristics include employment information such as class of worker, usual and actual hours of work, employee hourly and weekly wages, industry and occupation of current or most recent job, public and private sector, union status, paid or unpaid overtime hours, job permanency, hours of work lost, job tenure, and unemployment information such as duration of unemployment, methods of job search and type of job sought. Labour force characteristics are also available for students during the school year and during the summer months as well as school attendance whether full or part-time and the type of institution.LFS revisions: Labour force surveys are revised on a periodic basis, either to adopt the most recent geography, industry and occupation classifications; to use new observations to fine-tune seasonal adjustment factors; or to introduce methodological enhancement. Prior LFS revisions were conducted in 2011, 2015 and 2021. The most recent revisions to the LFS were conducted in 2023. The first major change was a transition to the National Occupational Classification (NOC) 2021 V1.0, with all LFS series from 1987 onwards having been revised to the new classification. The second major change were methodological enhancements to LFS data processing, applied to all LFS series beginning Jan 2006. The third major change was a revision of seasonal adjustment factors, applied to LFS series Jan 2002 onward. A list of prior versions of this LFS dataset can be found under the ‘Versions’ tab.

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 Annual Population Survey, also held at the UK Data Archive, is derived from the LFS.

The LFS was first conducted biennially from 1973-1983, then annually between 1984 and 1991, comprising a quarterly survey conducted throughout the year and a 'boost' survey in the spring quarter. From 1992 it moved to a quarterly cycle with a sample size approximately equivalent to that of the previous annual data. Northern Ireland was also included in the survey from December 1994. Further information on the background to the QLFS may be found in the documentation.

The UK Data Service also holds a Secure Access version of the QLFS (see below); household datasets; two-quarter and five-quarter longitudinal datasets; LFS datasets compiled for Eurostat; and some additional annual Northern Ireland datasets.

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 (the latest questionnaire available covers July-September 2022). Volumes are updated periodically, 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.

LFS response to COVID-19

From April 2020 to May 2022, additional non-calendar quarter LFS microdata were made available to cover the pandemic period. The first additional microdata to be released covered February to April 2020 and the final non-calendar dataset covered March-May 2022. Publication then returned to calendar quarters only. Within the additional non-calendar COVID-19 quarters, pseudonymised variables Casenop and Hserialp may contain a significant number of missing cases (set as -9). These variables may not be available in full for the additional COVID-19 datasets until the next standard calendar quarter is produced. The income weight variable, PIWT, is not available in the non-calendar quarters, although the person weight (PWT) is included. Please consult the documentation for full details.

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: Revision of miscoded occupational data in the ONS Labour Force Survey, UK: January 2021 to September 2022.

2024 Reweighting

In February 2024, reweighted person-level data from July-September 2022 onwards were released. Up to July-September 2023, only the person weight was updated (PWT23); the income weight remains at 2022 (PIWT22). The 2023 income weight (PIWT23) was included from the October-December 2023 quarter. Users are encouraged to read the ONS methodological note of 5 February, Impact of reweighting on Labour Force Survey key indicators: 2024, which includes important information on the 2024 reweighting exercise.

End User Licence and Secure Access QLFS data

Two versions of the QLFS are available from UKDS. One is available under the standard End User Licence (EUL) agreement, and the other is a Secure Access version. The EUL version includes country and Government Office Region geography, 3-digit Standard Occupational Classification (SOC) and 3-digit industry group for main, second and last job...

The 'LFS main indicators' section presents a selection of the main statistics on the labour market. They encompass indicators of activity, employment and unemployment. Those indicators are based on the results of the European Labour Force Survey (EU-LFS), in few cases integrated with data sources like national accounts employment or registered unemployment. As a result of the application of adjustments, corrections and reconciliation of EU Labour Force Survey (EU-LFS) data, the 'LFS main indicators' is the most complete and reliable collection of employment and unemployment data available in the sub-domain 'Employment and unemployment'.

The EU-LFS data used for 'LFS main indicators' are, where necessary, adjusted and enriched in various ways, in accordance with the specificities of an indicator. The most common adjustments cover:

• estimation of missing values, i.e. in case of missing quarters, annual results and EU aggregates are estimated using interpolations of EU Labour Force Survey data with reference to the available quarter(s).
• for all quarterly indicators seasonally adjusted data are available.
• correction of the main breaks in the LFS series.

Those adjustments may produce some differences between data published under 'LFS main indicators' and 'LFS series – detailed quarterly/annual survey results', particularly for back data. For the most recent years, the different series converge, due to the implementation of a continuous quarterly survey and the improved quality of the data.

This page focuses on the 'LFS main indicators' in general. There are special pages for indicators that are listed below:

• Monthly Unemployment: une_rt_m.

Quarterly and annual unemployment figures are derived in line with all other LFS Main Indciators, and no longer aggregated from monthly unemployment series.

• Duration of working life - annual data: lfsi_dwl_a;
• Population in jobless households - annual data: lfsi_jhh_a;
• Labour market transitions - LFS longitudinal data: lfsi_long.

The entry of the new Framework regulation on Social Statistics (IESS) in 2021 created changes in the LFS Main Indicators. Most countries expected breaks for a number of series derived from LFS microdata, therefore Eurostat and participating countries launched a joint break correction exercise to produce comparable data before and under IESS. The 'LFS main indicators' section therefore contains two type of datasets depending on the underlying regulation. The first type of datasets are historical series under the pre-IESS regulation, and include the suffix ‘_h’ for historical series at the end of the table titles. Historical series will remain accessible and are continued until 2020Q4 LFS microdata revisions of previously released EU-LFS series. Reasons for revisions are for example weight revisions due to revised weighting routines, or census revisions. The second type of datasets are new tables that are filled with data under IESS from 2021Q1 on. These tables also include the break-corrected 2009Q1-2020Q4 data that are produced in the break correction exercise. If countries send longer complete time series than starting in 2009, that data will also be used and published. Until fully back-estimated series in line with IESS are available for all countries, EU and EA aggregates were based on the data that is available at the time and was flagged with a break flag. Fully break-free EU and EA aggregates were published for the first time in February 2022. More information can be found on the EU-LFS Breaks in Time Series (Statistics Explained) webpage.

General information on the EU-LFS can be found in the ESMS page for 'Employment and unemployment (LFS)', see link in related metadata. Detailed information on the main features, the legal basis, the methodology and the data as well as on the historical development of the EU-LFS is available on the EU-LFS (Statistics Explained) webpage.

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 The sample size was 90,000 households, distributed at a ratio of 44% to urban areas and 56% to rural areas. They were distributed by means of an average of the proportional to size (P.P.S) method and the Square root allocation method in order to raise the minimum for small governorates.

The sample was divided to be 551 in urban areas and 699 in rural areas.

• 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 (150 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 (Selection of cadastral plots): (1) A sample of cadastral plots was chosen for the Labor force survey in a systematic, random sample from the records of cadastral plots in the master sample, 1,250 cadastral plots were allocated to the research sample, which are divided into 551 in urban areas and 699 in rural areas. Where the urban/rural governorate is considered the strata of the republic, the sample is distributed proportionately to the size. (2) The representation of urban and rural areas at the level of the governorates of the Republic has been taken into account.

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.

A more detailed description of the different sampling stages and allocation of sample across governorates is provided in the Methodology document available among external resources in Arabic.

Mode of data collection

Face-to-face [f2f]

Research instrument

The questionnaire design follows the latest International Labor Organization (ILO) concepts and definitions of labor force, employment, and unemployment.

The questionnaire comprises 4 tables in addition to the identification and geographic data of household on the cover page.

---> Table 1- The housing conditions of the households

This table includes information on the housing conditions of the household: - Type of the dwelling, - Tenure of the dwelling (owned/rent) , - Availability of facilities and services connected to the house - Ownership of durables.

---> Table 2- Demographic and employment characteristics and basic data for all household individuals

Including: gender, age, educational status, marital status, residence mobility and current work status

---> Table 3- Employment characteristics table

This table is filled by employed individuals at the time of the survey or those who were engaged to work during the reference week, and provided information on: - Relationship to employer: employer, self-employed, waged worker, and unpaid family worker - Economic activity - Sector - Occupation - Effective working hours - Health and social insurance - Work place - Contract type - Average monthly wage

---> Table 4- Unemployment characteristics table

This table is filled by all unemployed individuals who satisfied the unemployment criteria, and provided information on: - Type of unemployment (unemployed, unemployed ever worked) - Economic activity and occupation in the last held job before being unemployed - Last unemployment duration in months - Main reason for unemployment

Cleaning operations

Data Processing

1- Coding Specialized staff is responsible for coding data of activity, occupation and geographical identification according to activity, occupation and geographical manual, thus the questionnaires will be valid for data entry. 2- Data entry and tabulations It included machine data entry, data validation and tabulation as well as preparing a final survey bulletin and output tables.

---> Raw Data

Office editing is one of the main stages of the survey. It started once the questionnaires were received from the field and accomplished by the selected

Abstract

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

Geographic coverage

National

Analysis unit

• Households
• Individuals

Kind of data

Sample survey data [ssd]

Mode of data collection

Face-to-face [f2f]

Research instrument

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

The 'LFS main indicators' section presents a selection of the main statistics on the labour market. They encompass indicators of activity, employment and unemployment. Those indicators are based on the results of the European Labour Force Survey (EU-LFS), in few cases integrated with data sources like national accounts employment or registered unemployment. As a result of the application of adjustments, corrections and reconciliation of EU Labour Force Survey (EU-LFS) data, the 'LFS main indicators' is the most complete and reliable collection of employment and unemployment data available in the sub-domain 'Employment and unemployment'.

The EU-LFS data used for 'LFS main indicators' are, where necessary, adjusted and enriched in various ways, in accordance with the specificities of an indicator. The most common adjustments cover:

• estimation of missing values, i.e. in case of missing quarters, annual results and EU aggregates are estimated using interpolations of EU Labour Force Survey data with reference to the available quarter(s).
• for all quarterly indicators seasonally adjusted data are available.
• correction of the main breaks in the LFS series.

Those adjustments may produce some differences between data published under 'LFS main indicators' and 'LFS series – detailed quarterly/annual survey results', particularly for back data. For the most recent years, the different series converge, due to the implementation of a continuous quarterly survey and the improved quality of the data.

This page focuses on the 'LFS main indicators' in general. There are special pages for indicators that are listed below:

• Monthly Unemployment: une_rt_m.

Quarterly and annual unemployment figures are derived in line with all other LFS Main Indciators, and no longer aggregated from monthly unemployment series.

• Duration of working life - annual data: lfsi_dwl_a;
• Population in jobless households - annual data: lfsi_jhh_a;
• Labour market transitions - LFS longitudinal data: lfsi_long.

The entry of the new Framework regulation on Social Statistics (IESS) in 2021 created changes in the LFS Main Indicators. Most countries expected breaks for a number of series derived from LFS microdata, therefore Eurostat and participating countries launched a joint break correction exercise to produce comparable data before and under IESS. The 'LFS main indicators' section therefore contains two type of datasets depending on the underlying regulation. The first type of datasets are historical series under the pre-IESS regulation, and include the suffix ‘_h’ for historical series at the end of the table titles. Historical series will remain accessible and are continued until 2020Q4 LFS microdata revisions of previously released EU-LFS series. Reasons for revisions are for example weight revisions due to revised weighting routines, or census revisions. The second type of datasets are new tables that are filled with data under IESS from 2021Q1 on. These tables also include the break-corrected 2009Q1-2020Q4 data that are produced in the break correction exercise. If countries send longer complete time series than starting in 2009, that data will also be used and published. Until fully back-estimated series in line with IESS are available for all countries, EU and EA aggregates were based on the data that is available at the time and was flagged with a break flag. Fully break-free EU and EA aggregates were published for the first time in February 2022. More information can be found on the EU-LFS Breaks in Time Series (Statistics Explained) webpage.

General information on the EU-LFS can be found in the ESMS page for 'Employment and unemployment (LFS)', see link in related metadata. Detailed information on the main features, the legal basis, the methodology and the data as well as on the historical development of the EU-LFS is available on the EU-LFS (Statistics Explained) webpage.

Joyce Thomson leads us through a hands-on session about locating and retrieving business data. We’ll think about different kinds of business questions and find answers to some of those questions using the Labour Force Survey and other StatCan/DLI resources. We’ll also explore the outer limits: what kinds of questions can’t be answered via DLI PUMF resources?

The 'LFS main indicators' section presents a selection of the main statistics on the labour market. They encompass indicators of activity, employment and unemployment. Those indicators are based on the results of the European Labour Force Survey (EU-LFS), in few cases integrated with data sources like national accounts employment or registered unemployment. As a result of the application of adjustments, corrections and reconciliation of EU Labour Force Survey (EU-LFS) data, the 'LFS main indicators' is the most complete and reliable collection of employment and unemployment data available in the sub-domain 'Employment and unemployment'.

The EU-LFS data used for 'LFS main indicators' are, where necessary, adjusted and enriched in various ways, in accordance with the specificities of an indicator. The most common adjustments cover:

• estimation of missing values, i.e. in case of missing quarters, annual results and EU aggregates are estimated using interpolations of EU Labour Force Survey data with reference to the available quarter(s).
• for all quarterly indicators seasonally adjusted data are available.
• correction of the main breaks in the LFS series.

Those adjustments may produce some differences between data published under 'LFS main indicators' and 'LFS series – detailed quarterly/annual survey results', particularly for back data. For the most recent years, the different series converge, due to the implementation of a continuous quarterly survey and the improved quality of the data.

This page focuses on the 'LFS main indicators' in general. There are special pages for indicators that are listed below:

• Monthly Unemployment: une_rt_m.

Quarterly and annual unemployment figures are derived in line with all other LFS Main Indciators, and no longer aggregated from monthly unemployment series.

• Duration of working life - annual data: lfsi_dwl_a;
• Population in jobless households - annual data: lfsi_jhh_a;
• Labour market transitions - LFS longitudinal data: lfsi_long.

The entry of the new Framework regulation on Social Statistics (IESS) in 2021 created changes in the LFS Main Indicators. Most countries expected breaks for a number of series derived from LFS microdata, therefore Eurostat and participating countries launched a joint break correction exercise to produce comparable data before and under IESS. The 'LFS main indicators' section therefore contains two type of datasets depending on the underlying regulation. The first type of datasets are historical series under the pre-IESS regulation, and include the suffix ‘_h’ for historical series at the end of the table titles. Historical series will remain accessible and are continued until 2020Q4 LFS microdata revisions of previously released EU-LFS series. Reasons for revisions are for example weight revisions due to revised weighting routines, or census revisions. The second type of datasets are new tables that are filled with data under IESS from 2021Q1 on. These tables also include the break-corrected 2009Q1-2020Q4 data that are produced in the break correction exercise. If countries send longer complete time series than starting in 2009, that data will also be used and published. Until fully back-estimated series in line with IESS are available for all countries, EU and EA aggregates were based on the data that is available at the time and was flagged with a break flag. Fully break-free EU and EA aggregates were published for the first time in February 2022. More information can be found on the EU-LFS Breaks in Time Series (Statistics Explained) webpage.

General information on the EU-LFS can be found in the ESMS page for 'Employment and unemployment (LFS)', see link in related metadata. Detailed information on the main features, the legal basis, the methodology and the data as well as on the historical development of the EU-LFS is available on the EU-LFS (Statistics Explained) webpage.

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 2008 (LFS). The survey rounds covered a total sample of about 30,180 households, and the number of completed questionaire is 23,884, which amounts to a sample of around 107,361 individuals aged 10 years and over, including 86,076 individuals in the working-age population 15 years and above.

The importance of this survey lies in that it 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.

The survey main objectives are: - To estimate the labor force and its percentage to the population. - To estimate the number of employed individuals. - To analyze labour force according to gender, employment status, educational level , occupation and economic activity. - To provide information about the main changes in the labour market structure and its socio economic characteristics. - To estimate the numbers of unemployed individuals and analyze their general characteristics. - To estimate the rate of working hours and wages for employed individuals in addition to analyze of other characteristics.

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:

All Palestinians aged 10 years or older living in the Palestinian Territory, excluding those living in institutions such as prisons or shelters.

Sampling Frame:

The sampling frame consisted of a master sample of Enumeration Areas (EAs) selected from the population housing and establishment census 1997. The master sample consists of area units of relatively equal size (number of households), these units have been used as Primary Sampling Units (PSUs).

Sample Design:

The sample is a two-stage stratified cluster random sample.

Stratification: Four levels of stratification were made:

1. Stratification by Governorates.
2. Stratification by type of locality which comprises: (a) Urban, (b) Rural, and (c) Refugee Camps
3. Stratification by classifying localities, excluding governorate centers, into three strata based on the ownership of households of durable goods within these localities.
4. Stratification by size of locality (number of households).

Sample Size

The sample size in the first round consisted of 7,552 households, which amounts to a sample of around 21,116 persons aged 15 years and over. In the second round the sample consisted of 7,552 households, which amounts to a sample of around 20,314 persons aged 15 years and over, in the third round the sample consisted of 7,546 households, which amounts to a sample of around 23,465 persons aged 15 years and over. In the fourth round the sample consisted of 7,546 households; which amounts to a sample of around 21,181 persons aged 15 years and over. The sample size allowed for non-response and related losses. In addition, the average number of households selected in each cell was 16.

Sample Rotation:

Each round of the Labor Force Survey covers all the 481 master sample areas. Basically, the areas remain fixed over time, but households in 50% of the EAs are replaced each round. The same household remains in the sample over 2 consecutive rounds, rests for the next two rounds and represented again in the sample for another and last two consecutive rounds before it is dropped from the sample. A 50 % overlap is then achieved between both consecutive rounds and between consecutive years (making the sample efficient for monitoring purposes). In earlier applications of the LFS (rounds 1 to 11); the rotation pattern used was different; requiring a household to remain in the sample for six consecutive rounds, then dropped. The objective of such a pattern was to increase the overlap between consecutive rounds. The new rotation pattern was introduced to reduce the burden on the households resulting from visiting the same household for six consecutive times.

Mode of data collection

Face-to-face [f2f]

Research instrument

The lfs questionnaire consists of four main sections: 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.

1. 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.

2. 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

Data editing took place at a number of stages through the processing including: 1. office editing and coding 2. during data entry 3. structure checking and completeness 4. structural checking of SPSS data files

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 overall response rate for the survey was 84.2

More information on the distribution of response rates by different survey rounds is available in Page 10 of the data user guide provided among the disseminated survey materials under a file named "Palestine 2008- Data User Guide (English).pdf".

Sampling error estimates

Since the data reported here are based on a sample survey and not on a complete enumeration, they are subjected to sampling errors as well as non-sampling errors. Sampling errors are random outcomes of the sample design, and are, therefore, in principle measurable by the statistical concept of standard error. Data of this survey affected by statistical errors due to use the sample, Therefore, the emergence of certain differences from the real values expect obtained through censuses. It had been calculated variation of the most important indicators exists and the facility with the report and the dissemination levels of the data were particularized at the regional level in Governorate in the West Bank and Gaza Strip.

Data appraisal

Data entry staff was trained on the entry program that was examined before starting the data entry process. To have a fair idea about the situation 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

The Labour Force Survey (LFS) is a household survey carried out monthly by Statistics Canada. Since its inception in 1945, the objectives of the LFS have been to divide the working-age population into three mutually exclusive categories in relation to the labour market – employed, unemployed, and not in the labour force – and to provide descriptive and explanatory data on each of these groups. Data from the survey provide information on major labour market trends, such as shifts in employment across industrial sectors, hours worked, labour force participation and unemployment rates. This public use microdata file (PUMF) contains non-aggregated data for a wide variety of variables collected from the LFS. 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 LFS catalogued products. The data have been modified to ensure that no individual or business is directly or indirectly identified. Variables most likely to lead to identification of an individual are removed from the microdata file or are collapsed to broader categories.

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 2014 (LFS). The survey rounds covered a total sample of about 25,736 households, and the number of completed questionaire is 16,891.

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 2014.

---> Sampling Frame: The sampling frame consists of the master sample, which was updated in 2011: each enumeration area consists of buildings and housing units with an average of about 124 households. The master sample consists of 596 enumeration areas; we used 498 enumeration areas as a framework for the labor force survey sample in 2014 and these units were used as primary sampling units (PSUs).

---> Sampling Size: The estimated sample size is 7,616 households in each quarter of 2014, but in the second quarter 2014 only 7,541 households were collected, where 75 households couldn't be collected in Gaza Strip because of the Israeli aggression.

---> Sample Design The sample is two stage stratified cluster sample with two stages : First stage: we select a systematic random sample of 494 enumeration areas for the whole round ,and we excluded the enumeration areas which its sizes less than 40 households. Second stage: we select a systematic random sample of 16 households from each enumeration area selected in the first stage, se we select a systematic random of 16 households of the enumeration areas which its size is 80 household and over and the enumeration areas which its size is less than 80 households we select systematic random of 8 households.

---> Sample strata: The population was divided by: 1- Governorate (16 governorate) 2- Type of Locality (urban, rural, refugee camps).

---> Sample Rotation: Each round of the Labor Force Survey covers all of the 494 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 2013 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. 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 30,464 households of which 25,736 households completed the interview; whereas 16,891 households from the West Bank and 8,845 households in Gaza Strip. Weights were modified to account for non-response rate. The response rate in the West Bank reached 88.8% while in the Gaza Strip it reached 93.3%.

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

Abstract

This is the fourth Labor Force Survey of Tonga. The first one was conducted in 1990. Earlier surveys were conducted in 1990, 1993/94, and 2003 and the results of those surveys were published by the Statistics Department.

The objective of the LFS survey is providing information on not only well-known employment and unemployment as well as providing comprehensive information on other standard indicators characterizing the country labour market. It covers those age 10 and over in the whole Kingdom. Information includes age, sex, activity, current and usual employment status, hours worked and wages and in addition included a seperate Food Insecurity Experiences Survey (FIES) questionniare module at the Household Level.

The conceptual framework used in this labour force survey in Tonga aligns closely with the standards and guidelines set out in Resolutions of International Conferences of Labour Statistician.

Geographic coverage

National coverage.

There are six statistical regions known as Division's in Tonga namely Tongatapu urban area, Tongatapu rural area, Vava'u, Ha'pai, Eua and the Niuas.Tongatapu Urban refers to the capital Nuku'alofa is the urban area while the other five divisions are rural areas. Each Division is subdivided into political districts, each district into villages and each village into census enumeration areas known as Census Blocks. The sample for the 2018 Labour Force Survey (LFS) was designed to cover at least 2500 employed population aged 10 years and over from all the regions. This was made mainly to have sufficient cases to provide information on the employed population.

Analysis unit

• Households (for food insecurity module questionnaire)
• Individuals.

Universe

Population living in private households in Tonga. The labour force questionnaire is directed to the population aged 10 and above. Disability short set of questions is directed to all individuals age 2 and above and the food insecurity experience scale is directed to the head of household.

Kind of data

Sample survey data [ssd]

Sampling procedure

2018 Tonga Labour force survey aimed at estimating all the main ILO indicators at the island group level (geographical stratas). The sampling strategy is based on a two stages stratified random survey.

1. Computation of the survey parameters: Total sample size per strata, number of households to interview in each Primary Sampling Unit (PSU = census block) and number of PSUs to select The stratification of the survey is the geographical breakdown by island group (6 stratas Tongatapu urban, Tongatapu rural, Vava'u, Ha'apai, 'Eua, Niuas)
2. The selection strategy is a 2 stages random survey where: Random selection of census blocks within each
3. Census blocks are randomly selected in first place, using probability proportional to size

4. 15 households per block are randomly selected using uniform probability

5. The sampling frame used to select PSUs (census blocks) and household is the 2016 Tonga population census.

The computation of sample size required the use of: - Tonga 2015 HIES dataset (labour force section) - Tonga 2016 population census (distribution of households across the stratas) The resource variable used to compute the sample size is the labour force participation rate from the 2015 HIES. The use of the 2015 labour force section of the Tonga HIES allows the computation of the design effect of the labour force participation rate within each strata. The design effect and sampling errors of the labour force participation rate estimated from the 2015 HIES in combination with the 2016 household population distribution allow to predict the minimum sample size required (per strata) to get a robust estimate from the 2018 LFS.

Total sample size: 2685 households Geographical stratification: 6 island groups Selection process: 2 stages random survey where census blocks are selected using Probability Proportional to Size (Primary Sampling Unit) in the first place and households are randomly selected within each selected blocks (15 households per block) Non response: a 10% increase of the sample happened in all stratas to account for non-response Sampling frame: the household listing from the 2016 population census was used as a sampling frame and the 2015 labour force section of the HIES was used to compute the sample size (using labour force participation rate.

Sampling deviation

No major deviation from the original sample has taken place.

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

The 2018 Tonga Labour Force Survey questionnaire included 15 sections:

IDENTIFICATION SECTION B: INDIVIDUAL CHARACTERISTICS SECTION C: EDUCATION (AGE 3+) SECTIONS B & C: EMPLOYMENT IDENTIFICATION AND TEMPORARY ABSENCE (AGE 10+) SECTION D: AGRICULTURE WORK AND MARKET DESTINATION SECTION E1: MAIN EMPLOYMENT CHARACTERISTICS SECTION E2: SECOND PAID JOB/ BUSINESS ACTIVITY CHARACTERISTICS SECTION F: INCOME FROM EMPLOYMENT SECTION G: WORKING TIME SECTION H: JOB SEARCH SECTION I: PREVIOUS WORK EXPERIENCE SECTION J: MAIN ACTIVITY SECTION K: OWN USE PRODUCTION WORK FOOD INSECURITY EXPERIENCES GPS + PHOTO

The questionniares were developed and administered in English and were translated into Tongan language. The questionnaire is provided as external resources.

The draft questionnaire was pre-tested during the supervisors training and during the enumerators training and it was finally tested during the pilot test. The pilot testing was undertaken on the 27th of May to the 1st of June 2018 in Tongatapu Urban and Rural areas. The questionnaire was revised rigorously in accordance to the feedback received from each test. At the same time, a field operations manual for supervisors and enumerators was prepared and modified accordingly for field operators to use as a reference during the field work.

Cleaning operations

The World Bank Survey Solutions software was used for Data Processing, STATA software was used for data cleaning, tabulation tabulation and analysis.

Editing and tabulation of the data will be undertaken in February/March 2019 in collaboration with SPC and ILO.

Response rate

A total, 2,685 households were selected for the sample. Of these existing households, 2,584 were successfully interviewed, giving a household response rate of 96.2%.

Response rates were higher in urban areas than in the rural area of Tongatapu.

-1 Tongatapu urban: 97.30%
-2 Tongatapu rural: 93.00%
-3 Vava'u: 100.00% -4 Ha'pai: 100.00% -5 Eua: 95.20% -6 Niuas: 80.00% -Total: 96.20%.

Sampling error estimates

Sampling errors were computed and are presented in the final report.

The sampling error were computed using the survey set package in Stata. The Finite Population Correction was included in the sample design (optional in svy set Stata command) as follow: - Fpc 1: total number of census blocks within the strata (variable toteas) - Fpc 2: Here is a list of some LF indicators presented with sampling error

-RSE: Labour force population: 2.2% Employment - population in employment: 2.2% Labour force participation rate (%): 1.7% Unemployment rate (%): 13.5% Composite rate of labour underutilization (%): 7.3% Youth unemployment rate (%): 18.2% Informal employment rate (%): 2.7% Average monthly wages - employees (TOP): 12%.

-95% Interval: Labour force population: 28,203 => 30,804 Employment - population in employment: 27,341 => 29,855 Labour force participation rate (%): 45.2% => 48.2% Unemployment rate (%): 2.2% => 3.9% Composite rate of labour underutilization (%): 16% => 21.4% Youth unemployment rate (%): 5.7% => 12.1% Informal employment rate (%): 44.3% => 49.4% Average monthly wages - employees (TOP): 1,174 => 1,904.

Contains .do and .dta files for replication. Note that the dataset contains no information on country-year immigration rates as this figure is based on microdata obtained from EU Labour Force Survey (LFS). The use of this data is subject to an agreement with Eurostat. Details can be found here: http://ec.europa.eu/eurostat/en/web/microdata/european-union-labour-force-survey (see also readme.txt).

The 'LFS main indicators' section presents a selection of the main statistics on the labour market. They encompass indicators of activity, employment and unemployment. Those indicators are based on the results of the European Labour Force Survey (EU-LFS), in few cases integrated with data sources like national accounts employment or registered unemployment. As a result of the application of adjustments, corrections and reconciliation of EU Labour Force Survey (EU-LFS) data, the 'LFS main indicators' is the most complete and reliable collection of employment and unemployment data available in the sub-domain 'Employment and unemployment'.

The EU-LFS data used for 'LFS main indicators' are, where necessary, adjusted and enriched in various ways, in accordance with the specificities of an indicator. The most common adjustments cover:

• estimation of missing values, i.e. in case of missing quarters, annual results and EU aggregates are estimated using interpolations of EU Labour Force Survey data with reference to the available quarter(s).
• for all quarterly indicators seasonally adjusted data are available.
• correction of the main breaks in the LFS series.

Those adjustments may produce some differences between data published under 'LFS main indicators' and 'LFS series – detailed quarterly/annual survey results', particularly for back data. For the most recent years, the different series converge, due to the implementation of a continuous quarterly survey and the improved quality of the data.

This page focuses on the 'LFS main indicators' in general. There are special pages for indicators that are listed below:

• Monthly Unemployment: une_rt_m.

Quarterly and annual unemployment figures are derived in line with all other LFS Main Indciators, and no longer aggregated from monthly unemployment series.

• Duration of working life - annual data: lfsi_dwl_a;
• Population in jobless households - annual data: lfsi_jhh_a;
• Labour market transitions - LFS longitudinal data: lfsi_long.

The entry of the new Framework regulation on Social Statistics (IESS) in 2021 created changes in the LFS Main Indicators. Most countries expected breaks for a number of series derived from LFS microdata, therefore Eurostat and participating countries launched a joint break correction exercise to produce comparable data before and under IESS. The 'LFS main indicators' section therefore contains two type of datasets depending on the underlying regulation. The first type of datasets are historical series under the pre-IESS regulation, and include the suffix ‘_h’ for historical series at the end of the table titles. Historical series will remain accessible and are continued until 2020Q4 LFS microdata revisions of previously released EU-LFS series. Reasons for revisions are for example weight revisions due to revised weighting routines, or census revisions. The second type of datasets are new tables that are filled with data under IESS from 2021Q1 on. These tables also include the break-corrected 2009Q1-2020Q4 data that are produced in the break correction exercise. If countries send longer complete time series than starting in 2009, that data will also be used and published. Until fully back-estimated series in line with IESS are available for all countries, EU and EA aggregates were based on the data that is available at the time and was flagged with a break flag. Fully break-free EU and EA aggregates were published for the first time in February 2022. More information can be found on the EU-LFS Breaks in Time Series (Statistics Explained) webpage.

General information on the EU-LFS can be found in the ESMS page for 'Employment and unemployment (LFS)', see link in related metadata. Detailed information on the main features, the legal basis, the methodology and the data as well as on the historical development of the EU-LFS is available on the EU-LFS (Statistics Explained) webpage.

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 33 000 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 Personal Interview [capi]

Research instrument

The survey questionnaire consists of the following sections: - Biographical information (marital status, education, etc.) - Economic activities for persons aged 15 years and older