{"doc_desc":{"title":"Quarterly Labour Force Survey 2008 - Q3","idno":"ddi-zaf-datafirst-qlfs-2008-q3-v3","producers":[{"name":"DataFirst","abbreviation":"","affiliation":"University of Cape Town","role":"DDI Producer"}],"prod_date":"2020-03-30","version_statement":{"version":"Version 4"}},"study_desc":{"title_statement":{"idno":"zaf-statssa-qlfs-2008-q3-v2.1","title":"Quarterly Labour Force Survey 2008","sub_title":"Quarter 3","alt_title":"QLFS"},"authoring_entity":[{"name":"Statistics South Africa","affiliation":""}],"distribution_statement":{"contact":[{"name":"DataFirst Helpdesk","affiliation":"University of Cape Town ","email":"support@data1st.org ","uri":"http:\/\/support.data1st.org\/ "}]},"series_statement":{"series_name":"Labor Force Survey [hh\/lfs]","series_info":"Statistics South Africa. Quarterly Labour Force Survey 2008: Q3 [dataset]. Version 2.0. Pretoria: Statistics South Africa [producer], 2008. Cape Town: DataFirst [distributor], 2012. DOI: https:\/\/doi.org\/10.25828\/mmgm-9692"},"version_statement":{"version":"v2.1: Edited, anonymised dataset for public distribution","version_date":"2008-10-28","version_notes":"Version 2.0 of the QLFS 2008 Q3 was downloaded from the Statistics South Africa (Stats SA) website by DataFirst in January 2012. This version differs in a number of ways from the version that was obtained by DataFirst (from Stats SA) at some undeteremined time prior. The first of these differences is the way in which observations that fit into \"unspecified\", \u201cnot applicable\u201d or \"missing\" type categories are coded for certain variables. For example, in the older version of the QLFS 2008 Q3 the \"Q26ETIME\" variable is coded 88, with the associated label \"Not applicable\", for 93,694  observations. In the newest version this category of responses is assigned the code 0 and is not labelled (as it was in the previous version) for the same 93,694 observations. This recoding process has been applied to a large number of categorical variables in the datafile. A few other categorical variables have instead been recoded in a similar vein but as different (non-zero) values. For example, values of 88 for Q4213MONHRSWRK have been redefined as having the value 8. \n\nSecond, there is an apparent difference between the definitions of underemployment (\"underempl\") between versions. Note that this variable was also subjected to the abovementioned recoding procedure. 2691 observations shift status from \"underemployed\" to \"not underemployed\" between versions. Encouragingly, the \"not applicable\" coded observations are consistent.\n\nThe metadata accompanying the release of the QLFS 2008 Q3 is somewhat ambiguous with its description of the variable derivation, which is supposedly constructed based on the following criteria (taken directly from the Stats SA metadata):\n\n   Underemployment (underempl) (@233 1.)\n   Derived variable:\n   If hours usually work is less than 35 or total hours usually work is less than 35 and if additional hours that could have been worked is between 0 and 3 and if available to start work in the next four weeks should extra work become available then that is underemployment.\n\nwith the relevant variables named as follows:\n\n  Q418HRSWRK (Hours usually worked, Question 4.18)\n  Q420TOTALHRS (Total hours usually work, Question 4.19)\n  Q422MOREHRS (Like to be able to work more hours, Question 4.22)\n  Q425STARTXWRK (Able to start extra work, Question 4.25)\n\nThe above definition provided could have a number of possible interpretations. This complicated the process of checking for the source of the between version discrepancy. One plausible interpretation could be that workers were defined as unemployed if they worked fewer than 35 hours per week in one job (Q418HRSWRK < 35) or less than a total of 35 hours a week on more than one job (Q420TOTALHRS < 35).  Simultaneously. they must also have expressed some interest in working more (1 <= Q422MOREHRS <= 3) and confirmed that they were available to work more (Q425STARTXWRK == 1) to fulfil all criterion and be defined as underemployed. Note that this definition (in terms of the numerical ranges specified) would only apply to the original version, as the recoding of missing, non-applicable or unspecified variable values as 0 would alter the listed mathematical inequalities that comprise the logical tests assigning status to observations.\n\nThis definition produces results that agree with Stats SA's derived version of \"underempl\" in the later version of the datafile only. In the older version, entries instead appear to be erroneously assigned into the underemployed category largely on the basis of their answer for Q422MOREHRS and Q425STARTXWRK. More specifically, having values in the ranges defined for both of these two variables is a necessary and sufficient condition for being assigned into the underemployed category. However, the ostensible cutoffs for the hours worked variables (Q418HRSWRK and Q420TOTALHRS) are irrelevant in the underemployment calculation.\n\nThis issue was fixed in the newer version, which has a definition congruent with the one detailed above. Users looking to check this themselves are advised that the redefinition of the \"not applicable\" category to zero valued entries for the Q418HRSWRK and Q420TOTALHRS variables must be taken into account when generating their versions of the underemployment variable.\n\nThird, a number of extra variables were introduced in the later version. It is unclear why these are not present in the older version of the datafile as they are detailed in metadata that was released at the same time as the original data:\n  1) \"Geo_type\" - Geography type (e.g. urban formal, rural informal, etc.)\n  2) \"Hrswrk\" - Hourse worked. A derived variable that was probably aimed at getting around problems created by the recoding of the hours worked variables used in the derivation of the underemployment variable\n  3) \"Metro_code\" - Metropolitan area code (e.g. Cape Town, eThekwini, Johannesburg, etc.)\n  4) \"Status_Exp\" - Expanded unemployment status.\n  5) \"Stratum\" - 6 digit number representing stratum formed during master sample 2006 where digit 1 represents province, based on 2005 provincial boundaries, digits 2-3 represent the metro\/non-metro area and digit 4 confers geography type.  \n\nFinally, the two versions have different weights. To DataFirst's knowledge, the weighting changes are not clearly documented by Stats SA. The most likely explanation for the difference between the two sets of weights is that the newer version is calibrated to an updated set of mid-year population estimates. Users are advised to remain aware of these slight calibration differences when employing weights.\n\nA new version of this dataset was released in 2014. The new version (our version 2.1) was reweighted to reflect the new population benchmarks from Census 2011. \n\nThis version, version 2.1 includes the new weights for the QLFS 2008-2013 series."},"study_info":{"keywords":[{"keyword":"Employment","vocab":"","uri":""},{"keyword":"in-job training","vocab":"","uri":""},{"keyword":"labour relations\/conflict","vocab":"","uri":""},{"keyword":"retirement","vocab":"","uri":""},{"keyword":"unemployment","vocab":"","uri":""},{"keyword":"working conditions","vocab":"","uri":""},{"keyword":"labour and employment","vocab":"","uri":""},{"keyword":"trade, industry and markets","vocab":"","uri":""},{"keyword":"demography and population.","vocab":"","uri":""}],"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.","coll_dates":[{"start":"2008-07","end":"2008-09","cycle":""}],"nation":[{"name":"South Africa","abbreviation":"zaf"}],"geog_coverage":"National coverage","geog_unit":"Provincial and metropolitan level","analysis_unit":"Individuals","universe":"The QLFS sample covers the non-institutional population except for those in workers' hostels. However, persons  living in private dwelling units within institutions are 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":"INDIVIDUALS: labour market activity, labour preferences, labour market history, demographic characteristics, marital status, employment status, education, grants, tax."},"method":{"data_collection":{"sampling_procedure":"The QLFS frame has been developed as a general purpose household survey frame that can be used by all other household surveys irrespective of the sample size requirement of the survey. The sample size for the QLFS is roughly 30 000 dwellings per quarter.\n\nThe sample is based on information collected during the 2001 Population Census conducted by Stats SA. In preparation for the 2001 Census, the country was divided into 80 787 enumeration areas (EAs). Stats SA's household-based surveys use a Master Sample of Primary Sampling Units (PSUs) which comprises of EAs that are drawn from across the country.\n\nThe sample is designed to be representative at the provincial level and within provinces at the metro\/non-metro level. Within the metros, the sample is further distributed by geography type. The four geography types are: urban formal, urban informal, farms and tribal. This implies, for example, that within a metropolitan area the sample is representative at the different geography types that may exist within that metro.\n\nThe current sample size is 3 080 PSUs. It is divided equally into four sub-groups or panels called rotation groups. The rotation groups are designed in such a way that each of these groups has the same distribution pattern as that which is observed in the whole sample. They are numbered from one to four and these numbers also correspond to the quarters of the year in which the sample will be rotated for the particular group.\n\nThe sample for the QLFS is based on a stratified two-stage design with probability proportional to size (PPS) sampling of primary sampling units (PSUs) in the first stage, and sampling of dwelling units (DUs) with systematic sampling in the second stage.","coll_mode":"Face-to-face [f2f]","weight":"Weighting\nThe sampling weights for the data collected from the sampled households are constructed so that the\nresponses could be properly expanded to represent the entire civilian population of South Africa. The\nweights are the result of calculations involving several factors, including original selection probabilities,\nadjustment for non-response, and benchmarking to known population estimates from the Demographic\ndivision of Stats SA.\nThe base weight is defined as the product of the provincial Inverse Sampling Rate (ISR) and the three\nadjustment factors, namely adjustment factor for informal PSUs, adjustment factor for subsampling of\ngrowth PSUs, and an adjustment factor to account for small EAs excluded from the sampling frame (i.e.\nEAs with fewer than 25 households).\n\nNon-response adjustment\nIn general, imputation is used for item non-response (i.e. blanks within the questionnaire), and edit failure\n(i.e. invalid or inconsistent responses). The eligible households in the sampled dwellings can be divided\ninto two response categories: respondents and non-respondents, and weight adjustment is applied to\naccount for the non-respondent households (e.g. refusal, no contact, etc.). The sampled dwellings with no\neligible households, e.g. foreigners only, or no households, (i.e. vacant dwellings), do not contribute to the\nsurvey.\nThe non-response adjusted weight is the product of the base weight with the non-response adjustment\nfactor given above. If the PSU level non-response rate is too high, the non-response adjustment is applied\nat the VARUNIT level, where two VARUNITs have been created by grouping PSUs within strata. PSU\nlevel non-response adjustment is applied only if the corresponding adjustment factor is less than 1,5.\n\nFinal survey weights\nThe final survey weights are constructed using regression estimation to calibrate to the known population\ncounts at the national level population estimates (which are supplied by the Demography division) crossclassified\nby 5-year age groups, gender and race, and provincial population estimates by broad age groups\nare used for calibration weighting. The 5-year age groups are: 0\u20134, 5\u20139, 10\u201314, 55\u201359, 60\u201364, and 65 and\nover. The provincial level age groups are: 0\u201314, 15\u201334, 35-64, and 65 years and over. The final weights\nare constructed in such a manner that all persons within a household would have the same weight."}},"data_access":{"dataset_use":{"contact":[{"name":"DataFirst","affiliation":"University of Cape Town","email":"support@data1st.org","uri":"http:\/\/www.datafirst.uct.ac.za"}],"cit_req":"Statistics South Africa. Quarterly Labour Force Survey 2008: Q3 [dataset]. Version 2.0. Pretoria: Statistics South Africa [producer], 2008. Cape Town: DataFirst [distributor], 2012. DOI: https:\/\/doi.org\/10.25828\/mmgm-9692","conditions":"Public use files, accessible to all"}}},"schematype":"survey"}