{"type":"survey","doc_desc":{"title":"Labour Force Survey 2002, September","idno":"DDI_ZAF_2002_LFS-SEP_v02_M","producers":[{"name":"DataFirst","abbreviation":"","affiliation":"University of Cape Town","role":"DDI Producer"}],"prod_date":"2012-02-01","version_statement":{"version":"Version 01 (July 2012) - Adapted version of the DDI \"ddi-zaf-datafirst-lfs-2002-sep-v1.2\" received from Data First."}},"study_desc":{"title_statement":{"idno":"ZAF_2002_LFS-SEP_v02_M","title":"Labour Force Survey 2002","sub_title":"September","alt_title":"LFS 2002_2"},"authoring_entity":[{"name":"Statistics South Africa","affiliation":""}],"production_statement":{"copyright":"Copyright, Statistics South Africa"},"distribution_statement":{"contact":[{"name":"Manager, DataFirst","affiliation":"University of Cape Town","email":"info@data1st.org","uri":"http:\/\/www.datafirst.uct.ac.za"}]},"series_statement":{"series_name":"Labor Force Survey [hh\/lfs]"},"version_statement":{"version":"v2.1: Edited, anonymised dataset for licensed distribution","version_date":"2011-09-25","version_notes":"The South African September 2002 LFS dataset was originally released in March 2003 as 5 data files (household, worker, migrant, person and stratum psu). A second version was downloaded from the Statistics South Africa website subsequent to that in July 2006. This version differed slightly from the originally obtained release. Most notably, weights were recast to reflect population estimates released in February 2005. This version was also benchmarked to the 2001 South African census (whereas previously it had been benchmarked to the 1996 South African census). As a result, the weight variable(s) in versions 1.0 and 2.0 are different. \n\nA third version (version 2.1) was downloaded by DataFirst on 11 August 2011, which differed slightly from version 2.0 in the following ways:\n  \n  1. No suffix \u201d Sep2002\u201d attached to the variable names\n  2. Year and Month variables included\n  3. Variable labels were altered. Previously, all variable labels were literal questions. Now the variable labels describe the variables. \n  4. A number of the variables have been renamed slightly This could prove confusing when comparing between current and previous versions of the data file.\n  \nVersion 2.1 and 2.0 also have some substantive differences:\n\nWorker data file\nSector: The variable representing employment sector has had a number of\\ observations recoded into various other categories. There are 33 differences between versions.\nIndustry: The variables representing \u201dMain industry\u201d (one is specific, the other more general) have been recoded for a number of observations. There are 175 differences between versions for the general (14 unique values) industry variable and 23 differences between versions for the specific (191 unique values) industry label. This would suggest that the differences between the more general industry variables are not driven exclusively by changes to observations in the specific industry variable entry.\nMain occupation: The variables representing \u201dMain occupation\u201d (one is specific, the other more general) have been recoded for a number of observations. There are 147 differences between versions for the general (13 unique values) occupation variable and 23 differences between versions for the specific (377 unique values) occupation variable. This would suggest that the differences between general occupation variable are not driven exclusively by changes to observations in the specific occupation variable entry.\nEmployment Status: The variables representing employment status have several substantive differences. The two variables reflect two definitions, narrow and expanded, of employment status. Some observations that were previously defined as having one particular (within both variables!) are now defined as another. Official employment status (STATUS1) has 229 differences, whereas expanded employment status (STATUS2) has 353 differences.\n\nHousehold\/general data file \nThe household\/general file in version 2.0 has 55 extra observations, which appear to come from all around the country, but mostly from Gauteng (37 of the \u201dextra\u201d observations are from Gauteng)"},"study_info":{"topics":[{"topic":"employment [3.1]","vocab":"CESSDA","uri":"http:\/\/www.nesstar.org\/rdf\/common"},{"topic":"in-job training [3.2]","vocab":"CESSDA","uri":"http:\/\/www.nesstar.org\/rdf\/common"},{"topic":"labour relations\/conflict [3.3]","vocab":"CESSDA","uri":"http:\/\/www.nesstar.org\/rdf\/common"},{"topic":"retirement [3.4]","vocab":"CESSDA","uri":"http:\/\/www.nesstar.org\/rdf\/common"},{"topic":"unemployment [3.5]","vocab":"CESSDA","uri":"http:\/\/www.nesstar.org\/rdf\/common"},{"topic":"working conditions [3.6]","vocab":"CESSDA","uri":"http:\/\/www.nesstar.org\/rdf\/common"},{"topic":"LABOUR AND EMPLOYMENT [3]","vocab":"CESSDA","uri":"http:\/\/www.nesstar.org\/rdf\/common"},{"topic":"TRADE, INDUSTRY AND MARKETS [2]","vocab":"CESSDA","uri":"http:\/\/www.nesstar.org\/rdf\/common"},{"topic":"DEMOGRAPHY AND POPULATION [14]","vocab":"CESSDA","uri":"http:\/\/www.nesstar.org\/rdf\/common"}],"abstract":"The LFS is a twice-yearly rotating panel household survey, specifically designed to measure the dynamics of employment and unemployment in South Africa.  It measures a variety of issues related to the labour market,including unemployment rates (official and expanded), according to standard definitions of the International Labour Organisation (ILO).\n\nAll editions of the LFS have been updated (some more than once) since their release.  These version changes are detailed in a document available from DataFirst (in the \"external documents\" section titled \"LFS 2000-2008 Collated Version Notes on the South African LFS\").","coll_dates":[{"start":"2002-09","end":"2002-09","cycle":""}],"nation":[{"name":"South Africa","abbreviation":"zaf"}],"geog_coverage":"National Coverage","geog_unit":"Province (variable name: \"Prov\")","analysis_unit":"Households (dwellings) and individuals","universe":"The LFS sample covers the non-institutional population except for workers' hostels. However, 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.","data_kind":"Sample survey data [ssd]","notes":"Household characteristics, household listing, demographics, education, economic activity, work for pay, business ownership, unemployment, employers, main work activity in the past week, wages, salary, employment, migration"},"method":{"data_collection":{"sampling_procedure":"The LFS  is a  twice-yearly  rotating panel household  survey.  A rotating panel sample involves visiting the same dwelling units on a number of occasions (in this instance, five  at  most),  and  replacing  a  proportion  of  these  dwelling  units  each  round. New \ndwelling units are added to the sample to replace those that are taken out.  The  pilot  round  of  LFS  fieldwork  took  place  in  February  2000,  based  on  a  probability  sample  of  10  000  dwelling  units. This  survey  took  place  six months  later,  using  a  larger  probability  sample  of  30,000 dwelling  units.  Among  the  10,000  households  visited  in  February,  approximately  40%  were  re-visited  in September 2000. The fieldworkers had some difficulty in identifying certain dwelling units in the sample, particularly in those areas where there are no addresses.\n\nThe Master Sample is based on the 1996 Population Census of enumeration areas (EA) and the estimated number of dwelling units from the 1996 Population Census. All 3000 PSUs included in the Master Sample were used in the Labour Force Survey. A PSU is either one EA or several EAs when the number of dwelling units in the base or originally selected EA was found to have less than 100 dwelling units. Each EA had to have approximately 150 dwelling units but it was discovered that many contained less. Thus, in some cases, it has been found necessary to add EAs to the original (census) EA to ensure that the minimum requirement of 100 dwellings, in the first stage of forming the PSUs, was met. The size of the PSUs in the Master Sample varied from 100 to 2445 dwelling units. Special dwellings such as prisons, hospitals, boarding houses, hotels, guest houses (whether catering or self-catering), schools and churches were excluded from the sample.\n\nExplicit stratification of the PSUs was done by province and area type (urban\/rural). Within each explicit stratum, the PSUs were implicitly stratified by District Council, Magisterial District and, within the magisterial district, by average household income (for formal urban areas and hostels) or EA. The allocated number of EAs was systematically selected with \"probability proportional to size\" in each stratum. Once the PSUs included in the sample were known, their boundaries had to be identified on the ground. After boundary identification, the next stage was to list accurately all the dwelling units in the PSUs. \n\nThe second stage of the sample selection was to draw from the dwelling units listing whereby a systematic sample of 10 dwelling units was drawn from each PSU. As a result, approximately 30,000 households (units) were interviewed. However, if there was growth of more than 20% in a PSU, then the sample size was increased systematically according to the proportion of growth in the PSU.","coll_mode":"Face-to-face [f2f]","weight":"The initial weights (household weights), based on the sample design, were equal to the inverse of the probability of selection.  The initial weight for each member of the household was the same as the weight for the household itself.  Further adjustment factors were then calculated within PSUs to account for non-response.  To adjust for under-enumeration and to align survey estimates with independent population estimates, the weights were calibrated against Person benchmarks. A software package called CALMAR was used to perform this calibration. Using an iterative procedure, CALMAR adjusted the weights so that Person estimates conformed as closely as possible to external Person benchmarks. Gender, race and age group parameters were used for the Person cross-classification of the population.\n\nStats SA revised their population model to produce mid-year population estimates in the light of mortality data released in 2005 (see Stats SA Statistical Release P0309.3, 2005). The benchmarks for the LFS discussed in this statistical release have been adjusted accordingly.  Weights were then adjusted according to those 2005 population estimates."}},"data_access":{"dataset_use":{"contact":[{"name":"DataFirst","affiliation":"University of Cape Town","email":"info@data1st.org","uri":"http:\/\/www.datafirst.uct.ac.za"}],"cit_req":"Statistics South Africa. Labour Force Survey: September 2002. [dataset]. Version 2.1. Pretoria: Statistics South Africa [producer], 2003. Cape Town: DataFirst [distributor], 2011.","conditions":"Licensed dataset, accessible under conditions","disclaimer":"The user of the data acknowledges that the original collector of the data, the authorized distributor of the data, and the relevant funding agency bear no responsibility for use of the data or for interpretations or inferences based upon such uses."}}},"schematype":"survey","data_files":[{"file_id":"F12","file_name":"LFS 2002_2 House_v2","description":null,"case_count":26474,"var_count":100,"producer":null,"data_checks":null,"version":null,"notes":null,"metadata":null},{"file_id":"F13","file_name":"LFS 2002_2 Migrant_v2","description":null,"case_count":7084,"var_count":22,"producer":null,"data_checks":null,"version":null,"notes":null,"metadata":null},{"file_id":"F14","file_name":"LFS 2002_2 Person_v2","description":null,"case_count":102480,"var_count":32,"producer":null,"data_checks":null,"version":null,"notes":null,"metadata":null},{"file_id":"F15","file_name":"LFS 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