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.

Household datasets
Up to 2015, the LFS household datasets were produced twice a year (April-June and October-December) from the corresponding quarter's individual-level data. From January 2015 onwards, they are now produced each quarter alongside the main QLFS. The household datasets include all the usual variables found in the individual-level datasets, with the exception of those relating to income, and are intended to facilitate the analysis of the economic activity patterns of whole households. It is recommended that the existing individual-level LFS datasets continue to be used for any analysis at individual level, and that the LFS household datasets be used for analysis involving household or family-level data. From January 2011, a pseudonymised household identifier variable (HSERIALP) is also included in the main quarterly LFS dataset instead.

Change to coding of missing values for household series
From 1996-2013, all missing values in the household datasets were set to one '-10' category instead of the separate '-8' and '-9' categories. For that period, the ONS introduced a new imputation process for the LFS household datasets and it was necessary to code the missing values into one new combined category ('-10'), to avoid over-complication. This was also in line with the Annual Population Survey household series of the time. The change was applied to the back series during 2010 to ensure continuity for analytical purposes. From 2013 onwards, the -8 and -9 categories have been reinstated.

LFS Documentation
The documentation available from the Archive to accompany LFS datasets largely consists of the latest version of each volume alongside the appropriate questionnaire for the year concerned. However, LFS volumes are updated periodically by ONS, so users are advised to check the ONS LFS User Guidance page before commencing analysis.

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

End User Licence and Secure Access QLFS Household datasets
Users should note that there are two discrete versions of the QLFS household datasets. One is available under the standard End User Licence (EUL) agreement, and the other is a Secure Access version. Secure Access household datasets for the QLFS are available from 2009 onwards, and include additional, detailed variables not included in the standard EUL versions. Extra variables that typically can be found in the Secure Access versions but not in the EUL versions relate to: geography; date of birth, including day; education and training; household and family characteristics; employment; unemployment and job hunting; accidents at work and work-related health problems; nationality, national identity and country of birth; occurrence of learning difficulty or disability; and benefits. For full details of variables included, see data dictionary documentation. The Secure Access version (see SN 7674) has 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.

Changes to variables in QLFS Household EUL datasets
In order to further protect respondent confidentiality, ONS have made some changes to variables available in the EUL datasets. From July-September 2015 onwards, 4-digit industry class is available for main job only, meaning that 3-digit industry group is the most detailed level available for second and last job.

Review of imputation methods for LFS Household data - changes to missing values
A review of the imputation methods used in LFS Household and Family analysis resulted in a change from the January-March 2015 quarter onwards. It was no longer considered appropriate to impute any personal characteristic variables (e.g. religion, ethnicity, country of birth, nationality, national identity, etc.) using the LFS donor imputation method. This method is primarily focused to ensure the 'economic status' of all individuals within a household is known, allowing analysis of the combined economic status of households. This means that from 2015 larger amounts of missing values ('-8'/-9') will be present in the data for these personal characteristic variables than before. Therefore if users need to carry out any time series analysis of households/families which also includes personal characteristic variables covering this time period, then it is advised to filter off 'ioutcome=3' cases from all periods to remove this inconsistent treatment of non-responders.

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.

Latest edition information

For the second edition (September 2023), the variables NSECM20, NSECMJ20, SC2010M, SC20SMJ, SC20SMN, SOC20M and SOC20O have been replaced with new versions. Further information on the SOC revisions 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.

Abstract copyright UK Data Service and data collection copyright owner.

Background
The Labour Force Survey (LFS) is a unique source of information using international definitions of employment and unemployment and economic inactivity, together with a wide range of related topics such as occupation, training, hours of work and personal characteristics of household members aged 16 years and over. It is used to inform social, economic and employment policy. The LFS was first conducted biennially from 1973-1983. Between 1984 and 1991 the survey was carried out annually and consisted of a quarterly survey conducted throughout the year and a 'boost' survey in the spring quarter (data were then collected seasonally). From 1992 quarterly data were made available, with a quarterly sample size approximately equivalent to that of the previous annual data. The survey then became known as the Quarterly Labour Force Survey (QLFS). From December 1994, data gathering for Northern Ireland moved to a full quarterly cycle to match the rest of the country, so the QLFS then covered the whole of the UK (though some additional annual Northern Ireland LFS datasets are also held at the UK Data Archive). Further information on the background to the QLFS may be found in the documentation.

Household datasets
Up to 2015, the LFS household datasets were produced twice a year (April-June and October-December) from the corresponding quarter's individual-level data. From January 2015 onwards, they are now produced each quarter alongside the main QLFS. The household datasets include all the usual variables found in the individual-level datasets, with the exception of those relating to income, and are intended to facilitate the analysis of the economic activity patterns of whole households. It is recommended that the existing individual-level LFS datasets continue to be used for any analysis at individual level, and that the LFS household datasets be used for analysis involving household or family-level data. From January 2011, a pseudonymised household identifier variable (HSERIALP) is also included in the main quarterly LFS dataset instead.

Change to coding of missing values for household series
From 1996-2013, all missing values in the household datasets were set to one '-10' category instead of the separate '-8' and '-9' categories. For that period, the ONS introduced a new imputation process for the LFS household datasets and it was necessary to code the missing values into one new combined category ('-10'), to avoid over-complication. This was also in line with the Annual Population Survey household series of the time. The change was applied to the back series during 2010 to ensure continuity for analytical purposes. From 2013 onwards, the -8 and -9 categories have been reinstated.

LFS Documentation
The documentation available from the Archive to accompany LFS datasets largely consists of the latest version of each volume alongside the appropriate questionnaire for the year concerned. However, LFS volumes are updated periodically by ONS, so users are advised to check the ONS LFS User Guidance page before commencing analysis.

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

End User Licence and Secure Access QLFS Household datasets
Users should note that there are two discrete versions of the QLFS household datasets. One is available under the standard End User Licence (EUL) agreement, and the other is a Secure Access version. Secure Access household datasets for the QLFS are available from 2009 onwards, and include additional, detailed variables not included in the standard EUL versions. Extra variables that typically can be found in the Secure Access versions but not in the EUL versions relate to: geography; date of birth, including day; education and training; household and family characteristics; employment; unemployment and job hunting; accidents at work and work-related health problems; nationality, national identity and country of birth; occurrence of learning difficulty or disability; and benefits. For full details of variables included, see data dictionary documentation. The Secure Access version (see SN 7674) has 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...

Bolivia Household Survey: Labor Force Participation Rate: Male data was reported at 72.400 % in 2017. This records a decrease from the previous number of 75.000 % for 2016. Bolivia Household Survey: Labor Force Participation Rate: Male data is updated yearly, averaging 73.250 % from Dec 1999 (Median) to 2017, with 17 observations. The data reached an all-time high of 75.900 % in 2001 and a record low of 70.360 % in 2012. Bolivia Household Survey: Labor Force Participation Rate: Male data remains active status in CEIC and is reported by National Statistics Institute. The data is categorized under Global Database’s Bolivia – Table BO.G007: Household Survey: Employment Indicators (Discontinued).

The Labour Force Survey (LFS) is the only survey conducted by Statistics Canada designed to provide the official unemployment rate every month, with a monthly sample size of approximately 56,000 households. It is the earliest and most timely indicator of the pulse of the labour market in Canada. Statistics Canada provides a Guide to the Labour Force Survey.Note: This dataset primarily focuses on employees: those who do paid work for others. Therefore, totals do not align to totals in Labour Force Characteristics dataset, which focuses on everyone in the labour force.DefinitionsEmployee - A person who does paid work for others.Work - Includes any work for pay or profit, that is, paid work in the context of an employer-employee relationship or self-employment. It also includes work performed by those working in family business without pay (unpaid family workers).Permanent - A permanent job is one that is expected to last as long as the employee wants it, business conditions permitting. That is, there is no predetermined termination date.Temporary - A temporary job has a predetermined end date, or will end as soon as a specified project is completed. Information is collected to allow the sub-classification of temporary jobs into four groups: seasonal; temporary, term or contract, including work done through a temporary help agency; casual job; and other temporary work.Employment - Employed persons are those who, during the reference week, did any work for pay or profit or had a job and were absent from work. Self-employment - Working owners of an incorporated business, farm or professional practice, or working owners of an unincorporated business, farm or professional practice. The latter group also includes self-employed workers who do not own a business (such as babysitters and newspaper carriers). Self-employed workers are further subdivided by those with or without paid help. Also included among the self-employed are unpaid family workers. They are persons who work without pay on a farm or in a business or professional practice owned and operated by another family member living in the same dwelling. They represented approximately 1% of the self-employed in 2016.Unemployment - Unemployed persons are those who, during reference week, were without work, were available for work and were either on temporary layoff, had looked for work in the past four weeks or had a job to start within the next four weeks.

Bolivia Household Survey: Labor Force Participation Rate: Female data was reported at 52.900 % in 2017. This records a decrease from the previous number of 56.800 % for 2016. Bolivia Household Survey: Labor Force Participation Rate: Female data is updated yearly, averaging 56.780 % from Dec 1999 (Median) to 2017, with 17 observations. The data reached an all-time high of 60.150 % in 2001 and a record low of 50.370 % in 2015. Bolivia Household Survey: Labor Force Participation Rate: Female data remains active status in CEIC and is reported by National Statistics Institute. The data is categorized under Global Database’s Bolivia – Table BO.G007: Household Survey: Employment Indicators (Discontinued).

Please visit https://www.censtatd.gov.hk/en/EIndexbySubject.html?scode=200&pcode=D5250029 for the historical issues, related publications, concept, methods, definitions of terms, and notes of this dataset. User can download, distribute and reproduce free of charge for both commercial and non-commercial purposes subject to the Terms and Conditions of Use as stipulated under DATA.GOV.HK.

Abstract

The GHS is an annual household survey specifically designed to measure the living circumstances of South African households. The GHS collects data on education, employment, health, housing and household access to services.

Geographic coverage

The survey is representative at national level and at provincial level.

Analysis unit

Households and individuals

Universe

The survey covered all de jure household members (usual residents) of households in the nine provinces of South Africa and residents in workers' hostels. The survey does not cover collective living quarters such as students' hostels, old age homes, hospitals, prisons and military barracks.

Kind of data

Sample survey data

Sampling procedure

A multi-stage, stratified random sample was drawn using probability-proportional-to-size principles. First level stratification was based on province and second-tier stratification on district council. The GHS 2009 represents the second year of a new master sample (the first year was GHS 2008) that will be used until 2010.

Mode of data collection

Face-to-face [f2f]

Research instrument

GHS uses questionnaires as data collection instruments

Data appraisal

The questionnaire for the General Household Survey has undergone various changes since 2002. Significant changes were made to the GHS 2009 questionnaire and this should be borne in mind when comparing across different datasets. See GHS 2009 statistical release for a detailed report on important differences between the questionnaires.

In GHS 2009-2010:

The variable on care provision (Q129acre) in the GHS 2009 and 2010 should be used with caution. The question to collect the data (question 1.29a) asks:

"Does anyone in this household personally provide care for at least two hours per day to someone in the household who - owing to frailty, old age, disability, or ill-health cannot manage without help?"

Response codes (in the questionnaire, metadata, and dataset) are:

1 = No 2 = Yes, 2-19 hours per week 3 = Yes, 20-49 hours per week 4 = Yes, 50 + hours per week 5 = Do not know

There is inconsistency between the question, which asks about hours per day, and the response options, which record hours per week. The outcome that a respondent who gives care for one hour per day (7 hours/week) would presumably not answer this question. Someone giving care for 13 hours a week would also be excluded as though they do that do serious caregiving, which is incorrect.

In GHS 2009-2015:

The variable on land size in the General Household Survey questionnaire for 2009-2015 should be used with caution. The data comes from questions on the households' agricultural activities in Section 8 of the GHS questionnaire: Household Livelihoods: Agricultural Activities. Question 8.8b asks:

“Approximately how big is the land that the household use for production? Estimate total area if more than one piece.” One of the response category is worded as:

1 = Less than 500m2 (approximately one soccer field)

However, a soccer field is 5000 m2, not 500, therefore response category 1 is incorrect. The correct category option should be 5000 sqm. This response option is correct for GHS 2002-2008 and was flagged and corrected by Statistics SA in the GHS 2016.

Bolivia Household Survey: Gross Labor Force Participation Rate: Male data was reported at 57.000 % in 2017. This records a decrease from the previous number of 58.700 % for 2016. Bolivia Household Survey: Gross Labor Force Participation Rate: Male data is updated yearly, averaging 55.870 % from Dec 1999 (Median) to 2017, with 17 observations. The data reached an all-time high of 58.700 % in 2016 and a record low of 52.100 % in 2000. Bolivia Household Survey: Gross Labor Force Participation Rate: Male data remains active status in CEIC and is reported by National Statistics Institute. The data is categorized under Global Database’s Bolivia – Table BO.G007: Household Survey: Employment Indicators (Discontinued).

Bolivia Household Survey: Employment-Population Ratio data was reported at 60.200 % in 2017. This records a decrease from the previous number of 63.400 % for 2016. Bolivia Household Survey: Employment-Population Ratio data is updated yearly, averaging 61.550 % from Dec 1999 (Median) to 2017, with 17 observations. The data reached an all-time high of 64.310 % in 2014 and a record low of 58.900 % in 2015. Bolivia Household Survey: Employment-Population Ratio data remains active status in CEIC and is reported by National Statistics Institute. The data is categorized under Global Database’s Bolivia – Table BO.G007: Household Survey: Employment Indicators (Discontinued).

Abstract

To better understand the impact of the shock induced by the COVID-19 pandemic on Tunisia and assess the policy responses in a rapidly changing context, reliable data is imperative, and the need to resort to a dynamic data collection tool at a time when countries in the region are in a state of flux cannot be overstated. The COVID-19 MENA Monitor Survey was led by the Economic Research Forum (ERF) to provide data for researchers and policy makers on the socio-economic and labor market impact of the global COVID-19 pandemic on households. The ERF COVID-19 MENA Monitor Survey is constructed using a series of short panel phone surveys that are conducted approximately every two months, covering topics such as demographic and household characteristics, education and children, labor market status, income, social safety net, employment and unemployment detection, employment characteristics, and social distancing. In addition to the survey's panel design, which will permit the study of various phenomena over time, the survey also takes into account the key demographic and socio-economic characteristics of each country in the questionnaires' design to understand the different distributional consequences of the impact of COVID-19 and responses to it. This design allows further study of the effect of the pandemic on different vulnerable groups including women, informal and irregular workers, low skilled workers, and youth. The ERF COVID-19 MENA Monitor Survey is a wide-ranging, nationally representative panel survey.Wave 3 of this dataset was collected in April 2021, harmonized by the Economic Research Forum (ERF) and is featured as data for Household/Individual. The survey is in the process of further expansion to include other waves.

The harmonization was designed to create comparable data that can facilitate cross-country and comparative research between other Arab countries (Egypt, Morocco, Jordan, and Sudan). All the COVID-19 MENA Monitor surveys incorporate similar survey designs, with data on households and individuals within those households.

Geographic coverage

National

Analysis unit

Household and Individuals

Universe

The survey covered a national random sample of mobile phone users aged 18-64.

Kind of data

Sample survey data [ssd]

Sampling procedure

The sample universe for the household survey was mobile phone users aged 18-64. Random digit dialing (RDD), within the range of valid numbers, was used, with up to three attempts if a phone number was not picked up/answered, was disconnected or busy, or picked up but could not complete the interview at that time. Samples were stratified by country-specific market shares of mobile operators. The sample is designed to cover at least 2000 unique households and individuals. A question is included in the survey for the number of phone numbers within the household to weight appropriately. Further weighting of the household and individual samples was done to reflect the demographic composition of the population as obtained by the most recent publicly available data with individual phone ownership and relevant demographic and labour market characteristics. In the individual interview, respondents who are employers or self-employed were asked to respond to either the household enterprise or farmer modules. For follow-up waves, previous wave respondents were recontacted if they consented to follow-up in the previous wave. Up to three attempts were used, including contacting second and family/friend numbers, if provided in wave one, on the third call. If the individual could not be reached or refused, a refresher individual was added to the sample in their place, randomly selected as with base wave respondents. All the respondents who consented to follow up in the prior wave were contacted in order to include them in the subsequent wave. Households are be followed up every two months up to a total of four interviews. Interviews are conducted by experienced survey research or polling firms in each country using computer-assisted telephone interviewing (CATI) techniques.

Mode of data collection

Computer Assisted Telephone Interview [cati]

Bolivia Household Survey: Employment-Population Ratio: Female data was reported at 50.800 % in 2017. This records a decrease from the previous number of 54.300 % for 2016. Bolivia Household Survey: Employment-Population Ratio: Female data is updated yearly, averaging 53.900 % from Dec 1999 (Median) to 2017, with 17 observations. The data reached an all-time high of 56.430 % in 2001 and a record low of 48.240 % in 2015. Bolivia Household Survey: Employment-Population Ratio: Female data remains active status in CEIC and is reported by National Statistics Institute. The data is categorized under Global Database’s Bolivia – Table BO.G007: Household Survey: Employment Indicators (Discontinued).

Employment Rate: Main Metropolitan Areas data was reported at 56.040 % in Dec 2021. This records an increase from the previous number of 55.890 % for Sep 2021. Employment Rate: Main Metropolitan Areas data is updated quarterly, averaging 54.350 % from Mar 1984 (Median) to Dec 2021, with 152 observations. The data reached an all-time high of 63.580 % in Dec 2014 and a record low of 44.930 % in Jun 2020. Employment Rate: Main Metropolitan Areas data remains active status in CEIC and is reported by Bank of the Republic of Colombia. The data is categorized under Global Database’s Colombia – Table CO.G022: Employment Rate: 2005 Household Survey: Quarterly.

The STEP (Skills Toward Employment and Productivity) Measurement program is the first ever initiative to generate internationally comparable data on skills available in developing countries. The program implements standardized surveys to gather information on the supply and distribution of skills and the demand for skills in labor market of low-income countries. The uniquely-designed Household Survey includes modules that measure the cognitive skills (reading, writing and numeracy), socio-emotional skills (personality, behavior and preferences) and job-specific skills (subset of transversal skills with direct job relevance) of a representative sample of adults aged 15 to 64 living in urban areas, whether they work or not. The cognitive skills module also incorporates a direct assessment of reading literacy based on the Survey of Adults Skills instruments. Modules also gather information about family, health and language.

The IZA Evaluation Dataset Survey (IZA ED) was developed in order to obtain reliable longitudinal estimates for the impact of Active Labor Market Policies (ALMP). Moreover, it is suitable for studying the processes of job search and labor market reintegration. The data allow analyzing dynamics with respect to a rich set of individual and labor market characteristics. It covers the initial period of unemployment as well as long-term outcomes, for a total period of up to 3 years after unemployment entry. A longitudinal questionnaire records monthly labor market activities and their duration in detail for the mentioned period. These activities are, for example, employment, unemployment, ALMP, other training etc. Available information covers employment status, occupation, sector, and related earnings, hours, unemployment benefits or other transfer payments. A cross-sectional questionnaire contains all basic information including the process of entering into unemployment, and demographics. The entry into unemployment describes detailed job search behavior such as search intensity, search channels and the role of the Employment Agency. Moreover, reservation wages and individual expectations about leaving unemployment or participating in ALMP programs are recorded. The available demographic information covers employment status, occupation and sector, as well as specifics about citizenship and ethnic background, educational levels, number and age of children, household structure and income, family background, health status, and workplace as well as place of residence regions. The survey provides as well detailed information about the treatment by the unemployment insurance authorities, imposed labor market policies, benefit receipt and sanctions. The survey focuses additionally on individual characteristics and behavior. Such co-variates of individuals comprise social networks, ethnic and migration background, relations and identity, personality traits, cognitive and non-cognitive skills, life and job satisfaction, risky behavior, attitudes and preferences. The main advantages of the IZA ED are the large sample size of unemployed individuals, the accuracy of employment histories, the innovative and rich set of individual co-variates and the fact that the survey measures important characteristics shortly after entry into unemployment.

analyze the current population survey (cps) annual social and economic supplement (asec) with r the annual march cps-asec has been supplying the statistics for the census bureau's report on income, poverty, and health insurance coverage since 1948. wow. the us census bureau and the bureau of labor statistics ( bls) tag-team on this one. until the american community survey (acs) hit the scene in the early aughts (2000s), the current population survey had the largest sample size of all the annual general demographic data sets outside of the decennial census - about two hundred thousand respondents. this provides enough sample to conduct state- and a few large metro area-level analyses. your sample size will vanish if you start investigating subgroups b y state - consider pooling multiple years. county-level is a no-no. despite the american community survey's larger size, the cps-asec contains many more variables related to employment, sources of income, and insurance - and can be trended back to harry truman's presidency. aside from questions specifically asked about an annual experience (like income), many of the questions in this march data set should be t reated as point-in-time statistics. cps-asec generalizes to the united states non-institutional, non-active duty military population. the national bureau of economic research (nber) provides sas, spss, and stata importation scripts to create a rectangular file (rectangular data means only person-level records; household- and family-level information gets attached to each person). to import these files into r, the parse.SAScii function uses nber's sas code to determine how to import the fixed-width file, then RSQLite to put everything into a schnazzy database. you can try reading through the nber march 2012 sas importation code yourself, but it's a bit of a proc freak show. this new github repository contains three scripts: 2005-2012 asec - download all microdata.R down load the fixed-width file containing household, family, and person records import by separating this file into three tables, then merge 'em together at the person-level download the fixed-width file containing the person-level replicate weights merge the rectangular person-level file with the replicate weights, then store it in a sql database create a new variable - one - in the data table 2012 asec - analysis examples.R connect to the sql database created by the 'download all microdata' progr am create the complex sample survey object, using the replicate weights perform a boatload of analysis examples replicate census estimates - 2011.R connect to the sql database created by the 'download all microdata' program create the complex sample survey object, using the replicate weights match the sas output shown in the png file below 2011 asec replicate weight sas output.png statistic and standard error generated from the replicate-weighted example sas script contained in this census-provided person replicate weights usage instructions document. click here to view these three scripts for more detail about the current population survey - annual social and economic supplement (cps-asec), visit: the census bureau's current population survey page the bureau of labor statistics' current population survey page the current population survey's wikipedia article notes: interviews are conducted in march about experiences during the previous year. the file labeled 2012 includes information (income, work experience, health insurance) pertaining to 2011. when you use the current populat ion survey to talk about america, subract a year from the data file name. as of the 2010 file (the interview focusing on america during 2009), the cps-asec contains exciting new medical out-of-pocket spending variables most useful for supplemental (medical spending-adjusted) poverty research. confidential to sas, spss, stata, sudaan users: why are you still rubbing two sticks together after we've invented the butane lighter? time to transition to r. :D

Bolivia Household Survey: Working Age as % of Population: Male data was reported at 78.700 % in 2017. This records an increase from the previous number of 78.200 % for 2016. Bolivia Household Survey: Working Age as % of Population: Male data is updated yearly, averaging 76.270 % from Dec 1999 (Median) to 2017, with 17 observations. The data reached an all-time high of 78.940 % in 2013 and a record low of 72.050 % in 2003. Bolivia Household Survey: Working Age as % of Population: Male data remains active status in CEIC and is reported by National Statistics Institute. The data is categorized under Global Database’s Bolivia – Table BO.G007: Household Survey: Employment Indicators (Discontinued).

Virginia Works (Department of Workforce Development and Advancement) today announced that Virginia’s seasonally adjusted unemployment rate in January remained unchanged at 3.0 percent, which is 0.1 percentage point below the rate from a year ago. According to household survey data in January, the number of employed residents increased by 8,212 to 4,448,520 and the number of unemployed residents increased by 346 to 139,731. The labor force increased by 8,558 to 4,588,251. Virginia’s seasonally adjusted unemployment rate is 0.7 percentage points below the national rate, which remained unchanged at 3.7 percent.

The Commonwealth’s labor force participation rate increased by 0.1 percentage points to 66.6 percent in January. The labor force participation rate measures the proportion of the civilian population age 16 and older that is employed or actively looking for work.

In January, Virginia’s nonagricultural employment, from the monthly establishment survey increased by 8,700 to 4,200,000. December’s preliminary estimate of employment, after revision, increased by 34,300 to 4,191,300. In January, private sector employment increased by 4,200 to 3,458,500 while government employment increased by 4,500 to 741,500. Within that sector, federal government jobs increased by 700 to 190,500, state government employment increased by 3,700 to 157,400, and local government increased by 100 to 393,600 over the month. Seasonally adjusted total nonfarm employment data is produced for eleven industry sectors. In January, six experienced over-the-month job gains, one remained unchanged, and four experienced a decline. The largest job gain occurred in Professional and Business Services (+5,100) to 807,900. The second largest job gain occurred in Government (+4,500) to 741,500. The third largest job gain occurred in Financial Activities (+1,400) to 224,000. The other gains were in Manufacturing (+1,100) to 248,600; Education and Health Services (+500) to 594,600; and Mining and Logging (+100) to 7,300.

Abstract

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

Geographic coverage

National coverage

Analysis unit

Individuals

Universe

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

Kind of data

Sample survey data [ssd]

Sampling procedure

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

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

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

Mode of data collection

Computer Assisted Telephone Interview [cati]

Research instrument

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

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

Abstract

To better understand the impact of the shock induced by the COVID-19 pandemic on Jordan and assess the policy responses in a rapidly changing context, reliable data is imperative, and the need to resort to a dynamic data collection tool at a time when countries in the region are in a state of flux cannot be overstated. The COVID-19 MENA Monitor Survey was led by the Economic Research Forum (ERF) to provide data for researchers and policy makers on the socio-economic and labor market impact of the global COVID-19 pandemic on households. The ERF COVID-19 MENA Monitor Survey is constructed using a series of short panel phone surveys that are conducted approximately every two months covering topics such as demographic and household characteristics, education and children, labor market status, income, social safety net, employment and unemployment detection, employment characteristics, and social distancing. In addition to the survey's panel design, which will permit the study of various phenomena over time, the survey also takes into account the key demographic and socio-economic characteristics of each country in the questionnaires' design to understand the different distributional consequences of the impact of COVID-19 and responses to it. This design allows further study of the effect of the pandemic on different vulnerable groups including women, informal and irregular workers, low skilled workers, and youth. The ERF COVID-19 MENA Monitor Survey is a wide-ranging, nationally representative panel survey.The baseline wave of this dataset was collected in February 2021. This dataset was collected in August 2021, harmonized by the Economic Research Forum (ERF) and is featured as the third wave for Jordan in the COVID-19 MENA Monitor Surveys.

The harmonization was designed to create comparable data that can facilitate cross-country and comparative research between other Arab countries (Egypt, Tunisia, Morocco, and Sudan). All the COVID-19 MENA Monitor surveys incorporate similar survey designs, with data on households and individuals within those households.

Geographic coverage

National

Analysis unit

Household and Individuals

Universe

The survey covered a national random sample of mobile phone users aged 18-64.

Kind of data

Sample survey data [ssd]

Sampling procedure

The sample universe for the household survey was mobile phone users aged 18-64. Random digit dialing (RDD), within the range of valid numbers, was used, with up to three attempts if a phone number was not picked up/answered, was disconnected or busy, or picked up but could not complete the interview at that time. Samples were stratified by country-specific market shares of mobile operators. The sample will be designed to cover at least 2,500 unique households and individuals (2000 Jordanians, 500 Syrian Refugees). Attrition is addressed through the addition of refresher households in later waves to maintain that target. A question is included in the survey for the number of phone numbers within the household to weight appropriately. Further weighting of the household and individual samples was done to reflect the demographic composition of the population as obtained by the most recent publicly available data with individual phone ownership and relevant demographic and labour market characteristics. In the individual interview, respondents who are employers or self-employed were asked to respond to either the household enterprise or farmer modules.

Households were be followed up every two months up to a total of three interviews. Interviews are conducted by experienced survey research or polling firms in each country using computer-assisted telephone interviewing (CATI) techniques.

Mode of data collection

Computer Assisted Telephone Interview [cati]

Bolivia Household Survey: Economic Burden Index data was reported at 60.300 % in 2017. This records an increase from the previous number of 52.400 % for 2016. Bolivia Household Survey: Economic Burden Index data is updated yearly, averaging 54.370 % from Dec 1999 (Median) to 2017, with 17 observations. The data reached an all-time high of 63.820 % in 2015 and a record low of 47.500 % in 2001. Bolivia Household Survey: Economic Burden Index data remains active status in CEIC and is reported by National Statistics Institute. The data is categorized under Global Database’s Bolivia – Table BO.G007: Household Survey: Employment Indicators (Discontinued).