Project for Statistics on Living Standards and Development 1993
Living Standards Measurement Study [hh/lsms]
The Project for Statistics on Living standards and Development was a coutrywide World Bank Living Standards Measurement Survey. It covered approximately 9000 households, drawn from a representative sample of South African households. The fieldwork was undertaken during the nine months leading up to the country's first democratic elections at the end of April 1994. The purpose of the survey was to collect statistical information about the conditions under which South Africans live in order to provide policymakers with the data necessary for planning strategies. This data would aid the implementation of goals such as those outlined in the Government of National Unity's Reconstruction and Development Programme.
Kind of Data
Sample survey data [ssd]
Unit of Analysis
v2.0 Edited anonymous dataset for public distribution
Individuals in hospitals, old age homes, hotels and hostels of educational institutions were not included in the sample. Migrant labour hostels were included. In addition to those that turned up in the selected ESDs, a sample of three hostels was chosen from a national list provided by the Human Sciences Research Council and within each of these hostels a representative sample was drawn on a similar basis as described above for the households in ESDs.
Producers and sponsors
Southern Africa Labour and Development Research Unit
University of Cape Town
The World Bank
Government of Denmark
Financing the survey
Government of the Netherlands
Financing the survey
Government of Norway
Financing the survey
Sample size is 9,000 households
The sample design adopted for the study was a two-stage self-weightingdesign in which the first stage units were Census Enumerator Subdistricts (ESDs, or their equivalent) and the second stage were households.
The advantage of using such a design is that it provides a representative sample that need not be based on accurate census population distribution.in the case of South Africa, the sample will automatically include many poor people, without the need to go beyond this and oversample the poor. Proportionate sampling as in such a self-weighting sample design offers the simplest possible data files for further analysis, as weights do not have to be added. However, in the end this advantage could not be retained and weights had to be added.
The sampling frame was drawn up on the basis of small, clearly demarcated area units, each with a population estimate. The nature of the self-weighting procedure adopted ensured that this population estimate was not important for determining the final sample, however. For most of the country, census ESDs were used. Where some ESDs comprised relatively large populations as for instance in some black townships such as Soweto, aerial photographs were used to divide the areas into blocks of approximately equal population size. In other instances, particularly in some of the former homelands, the area units were not ESDs but villages or village groups.
In the sample design chosen, the area stage units (generally ESDs) were selected with probability proportional to size, based on the census population. Systematic sampling was used throughout that is, sampling at fixed interval in a list of ESDs, starting at a randomly selected starting point. Given that sampling was self-weighting, the impact of stratification was expected to be modest. The main objective was to ensure that the racial and geographic breakdown approximated the national population distribution. This was done by listing the area stage units (ESDs) by statistical region and then within the statistical region by urban or rural. Within these sub-statistical regions, the ESDs were then listed in order of percentage African. The sampling interval for the selection of the ESDs was obtained by dividing the 1991 census population of 38,120,853 by the 300 clusters to be selected. This yielded 105,800. Starting at a randomly selected point, every 105,800th person down the cluster list was selected. This ensured both geographic and racial diversity (ESDs were ordered by statistical sub-region and proportion of the population African). In three or four instances, the ESD chosen was judged inaccessible and replaced with a similar one.
In the second sampling stage the unit of analysis was the household. In each selected ESD a listing or enumeration of households was carried out by means of a field operation. From the households listed in an ESD a sample of households was selected by systematic sampling. Even though the ultimate enumeration unit was the household, in most cases "stands" were used as enumeration units. However, when a stand was chosen as the enumeration unit all households on that stand had to be interviewed.
Census population data, however, was available only for 1991. An assumption on population growth was thus made to obtain an approximation of the population size for 1993, the year of the survey. The sampling interval at the level of the household was determined in the following way: Based on the decision to have a take of 125 individuals on average per cluster (i.e. assuming 5 members per household to give an average cluster size of 25 households), the interval of households to be selected was determined as the census population divided by 118.1, i.e. allowing for population growth since the census. It was subsequently discovered that population growth was slightly over-estimated but this had little effect on the findings of the survey.
Individuals in hospitals, old age homes, hotels and hostels of educational institutions were not included in the sample. Migrant labour hostels were included. In addition to those that turned up in the selected ESDs, a sample of three hostels was chosen from a national list provided by the Human Sciences Research Council and within each of these hostels a representative sample was drawn on a similar basis as described abovefor the households in ESDs.
A self-weighting sample design should in principle eliminate the need for weighting. A number of factors intervened, however, which made it essential to use weights after all. Amongst these was violence, which prevented survey teams from conducting interviews in two clusters on the East Rand; failure to continue interviewing in a cluster until the required take had been interviewed; and systematic under-representation of whites in the sample. This last problem resulted both from systematic non-response (whites were found to be more likely to refuse to be interviewed, or to be absent than other groups) and from sampling problems themselves.
The importance of race in determining living standards in South Africa is such that the racial distribution of the population has a major bearing on measures of living standards and inequality. It was thus regarded as essential that the problems mentioned above should be overcome by applying appropriate weights to the data. The most appropriate weights to apply would usually be the average values obtained in a cluster for the missing questionnaires from that cluster in order to capture the homogeneity usually inherent in residential contiguity. However, that presented some difficulty for the two clusters in which violence prevented surveying and for those clusters in which there were only a small number of questionnaires completed. It was felt that this method would therefore not be appropriate.
Accordingly it was decided to use weights as far as possible at the level of the old provincial/homeland boundaries and race. The listing of households in each cluster combined with the sampling interval was used to determine how many households should have been interviewed. Where this deviated from the number actually interviewed, this was taken into account. The assumption was that the households left out were racially distributed in the same proportion as the actual households interviewed. When these numbers were then calculated at the provincial level, a weight could be calculated for each race group to rectify errors made in the field work. These errors typically resulted from the fact that most field work organizations involved had little experience of using anything but a weighted sample and were used to replacements that could easily be added ex post, not necessarily in the same area. When these mistakes were discovered, it was too late to go back to the field.
The sample of 360 clusters of 25 households each based on an expected household size of 5 should have yielded a resident population of 45,000. In fact, a different household size should not affect the results. In any particular cluster, the expected take of individuals would remain the same if the census population were accurate, irrespective of household size, for a smaller household size (as in the case of whites) would only have yielded more households, of whom a given proportion would have been interviewed. If in a particular cluster the census population was 472, every fourth household should have been interviewed (based on a sampling interval calculated to produce 125 persons per cluster in 1993, the expected take based on the census data of 118.1 per duster divided into the same population size). Irrespective of household size, then, one quarter of the cluster population would have been included in the survey. An average household size of 5 would have given 94 households of whom 23 would have been interviewed, i.e. 115 resident household members would have been found. If the household size were only three, on the other hand, one-quarter of the 157 households would have been 39, representing 117 household members. Only small differences from the expected take of 118 should thus arise, due to rounding. Only if the estimate of population based on the census is wrong, however, would the actual number of households deviate substantially from the expected take. In such a case, one quarter of the actual (i.e. listed or enumerated) rather than of the census population would have been included in the survey, i.e. there would have been an automatic adjustment. This gives the sample design its self-weighting character.
The census population for the survey data was estimated by applying Sadie's population growth rates to the adjusted 1991 census figures. The resultant racial and geographic distribution of the population of 40.1 million was presuming, of course, that no migration across provincial and homeland boundaries had occurred since the census. This implies that a raising factor of 891.4154 (40.1 million divided by an expected take of 45,000) should be applied to the results weighted by enumeration to obtain the population it represents. Applying the weights according to enumeration, 38.1 million people were covered by the survey, i.e. there was a 2 million under-enumeration amounting to about 5 per cent. Broken down by race, the under-enumeration was particularly large amongst whites, for whom the best census data exists, indicating that the problem did not lie so much with the census as with the survey. However, this is to be expected - a survey of this nature is better at capturing inequality and living standards than population size. Nevertheless, the margin of error in aggregate population estimates is relatively small, considering the presence of some homeless people, uncertainties about ESD boundaries in some areas and the likelihood of incomplete listings of households for various reasons. These results are therefore encouraging regarding the accuracy of the survey and also confirm that the adjusted census does not deviate substantially from population estimates obtained in a different manner.
However, the raised enumeration results deviate more from the census results where the provincial breakdown is concerned. The reason for this is not hard to find. The sample design introduced stratification only by geographic area (statistical regions) and proportion of the ESD population that was black. South African population clusters are still predominantly racially homogeneous, inter alia, because of past controls on residential patterns. It is therefore not surprising that in particular regions too few or too many clusters of a particular group were selected. In Natal, for instance, Coloureds and Indians are over represented in the data, even when weighted by enumeration, while Whites are under-represented. At the aggregate level, this should have little effect on the validity of the conclusions drawn, but it emphasizes the fact that care should be taken when drawing implications from the survey for Small populations. In small provinces (for instance, the new Northern Cape), only a small number of clusters has been included, with the result that little can be concluded about living standards there, even though these clusters are important in determining overall distribution.
As a final comment on weights, the data provided for the user contains weights to correct for the enumeration difficulties discussed above as well as census based weights. If the user of the data wishes to use these weights they are found in the data file named "weight02". The variable name for the enumeration-based weight is "rsweight" and the name for the census-based weight is "rcweight". (Do not use the "sweight" and "cweight" variables.)
Dates of Data Collection
Data Collection Mode
Data Collection Notes
The Bureau of Market Research was responsible for the rural and the predominantly non- African urban areas of the Transvaal excluding the homelands. Mark Data conducted surveys in the Orange Free State, Qwa-Qwa, Bophuthatswana and Lebowa. Social Surveys covered the African townships in the PWW as well as Venda, Gazankulu and Kwandebele. Data Research Africa from Natal was responsible for the field work in Kwazulu and Kangwane. The rest of Natal and the Ciskei was covered by the HSRC in Durban. The HSRC in Cape Town covered the Northern, Western and Eastern Cape. Finally, a team under Sintu Mpambani from the University of the Transkei covered the difficult terrain in the Transkei. Completed questionnaires were sent to Saldru where data entry management and cleaning were centralized.
Bureau of Market Research
Data Research Africa
Human Sciences Research Council
Mr. Sintu Mpambani
The main instrument used in the survey was a comprehensive household questionnaire. This questionnaire covered a wide range of topics but was not intended to provide exhaustive coverage of any single subject. In other words, it was an integrated questionnaire aimed at capturing different aspects of living standards. The topics covered included demography, household services, household expenditure, educational status and expenditure, remittances and marital maintenance, land access and use, employment and income, health status and expenditure and anthropometry (children under the age of six were weighed and their heights measured). This questionnaire was available to households in two languages, namely English and Afrikaans. In addition, interviewers had in their possession a translation in the dominant African language/s of the region.
In addition to the detailed household questionnaire referred to above, a community questionnaire was administered in each cluster of the sample. The purpose of this questionnaire was to elicit information on the facilities available to the community in each cluster. Questions related primarily to the provision of education, health and recreational facilities. Furthermore there was a detailed section for the prices of a range of commodities from two retail sources in or near the cluster: a formal source such as a supermarket and a less formal one such as the "corner cafe" or a "spaza". The purpose of this latter section was to obtain a measure of regional price variation both by region and by retail source. These prices were obtained by the interviewer. For the questions relating to the provision of facilities, respondents were "prominent" members of the community such as school principals, priests and chiefs.
All the questionnaires were checked when received. Where information was incomplete or appeared contradictory, the questionnaire was sent back to the relevant survey organization. As soon as the data was available, it was captured using local development platform ADE. This was completed in February 1994. Following this, a series of exploratory programs were written to highlight inconsistencies and outlier. For example, all person level files were linked together to ensure that the same person code reported in different sections of the questionnaire corresponded to the same person. The error reports from these programs were compared to the questionnaires and the necessary alterations made. This was a lengthy process, as several files were checked more than once, and completed at the beginning of August 1994. In some cases questionnaires would contain missing values, or comments that the respondent did not know, or refused to answer a question.
These responses are coded in the data files with the following values: VALUE MEANING
-1 : The data was not available on the questionnaire or form
-2 : The field is not applicable
-3 : Respondent refused to answer
-4 : Respondent did not know answer to question
The data collected in clusters 217 and 218 should be viewed as highly unreliable and therefore removed from the data set. The data currently available on the web site has been revised to remove the data from these clusters. Researchers who have downloaded the data in the past should revise their data sets. For information on the data in those clusters, contact SALDRU http://www.saldru.uct.ac.za/.
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Southern Africa Labour and Development Research Unit. Integrated Household Survey (IHS) 1993 Ref. ZAF_1993_IHS_v01_M. Dataset downloaded from www.microdata.worldbank.org on [date]
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