Description: This is aggregated data of individuals or households. The data originates from the South African censuses of 1991, 1996 and 2001, as well as the community survey of 2007. The geographical units were standardised to the 2005 municipal boundaries so that spatial measuring was consistent. The data therefore covers the whole country at a municipal level for different time periods. The major variables focus on employment status.

The data set consists of 32 variables and 257 cases. It contains the same socio-economic variables for different time periods, namely 1991, 1996, 2001 and 2007.

Combined ranking - municipalities were ranked for each year, i.e. 1991, 1996, 2001 and 2007, in terms of unemployment rate and assigned a rank value. There is also a combined unemployment rank value for all years and all municipalities.

Population density - this was calculated by dividing the total population of a municipality in 1991 by the area and the answer is expressed as number of people per square kilometer.

The linking of different census geographies was done by using areal interpolation to transfer data from one set of boundaries to another. The 2005 municipality boundaries were used as the common denominator and it is part of a spatial hierarchy developed by Statistics SA for the 2001 census. Abstract: Global unemployment has risen in the past few years and spatial data is required to address the problem effectively. South African unemployment literature focused mostly on a national level of spatial analysis. Some literature refers to spatial aspects that affect unemployment trends, but does not assign a location, e.g. a suburb or municipality. The research was conducted to obtain an understanding of geographical unemployment changes in South Africa over time.

The data sets from the South African censuses of 1991, 1996 and 2001, as well as the community survey of 2007 were compared by spatial extent and associated attributes. The representation of change over time was explored and aggregation to a common boundary, such as municipalities was suggested to overcome modifiable areal unit problems. Census data is spatially more detailed than labour force survey data, and census data from pre-1991 might not reflect the post-apartheid labour trends effectively. To determine which unemployment data set is useful for a spatial understanding of unemployment in South Africa, the attributes of various datasets were compared, the completeness of the spatial data, as well as the geographic scale of presentation.

South African census data represents employment statistics at the most detailed spatial level. Census data is collected every five to ten years. Initial data capture for censuses was usually at Enumerator Area (EA) level. Prior to 1991 the spatial data (EA and census district boundaries) were represented on hard copy maps only and no digital spatial data were captured.

In the 1991 census, unemployment statistics were not directly calculated at EA level. To generate these statistics the number of employed people was subtracted from the economically active population.

In the 1996 census, the number of unemployed, employed and economically active people per small area layer (SAL) was provided by Stats SA. The data were re-aggregated by the Human Sciences Research Council (HSRC), which could then be compared with EA data from other years.

The 2001 census attribute data were not released at an EA level, and this consequently made comparisons with the previous two censuses very difficult. However, the spatial boundaries for the EAs were made available, and statistical modelling techniques were used by the HSRC to compute unemployment statistics for these boundaries. CS 2007 released statistics only at a municipality level.

The linking of different census geographies was done by using areal interpolation to transfer data from one set of boundaries to another. The 2005 municipality boundaries were used as the common denominator and it is part of a spatial hierarchy developed by Statistics SA for the 2001 census.

Municipalities were ranked for each year in terms of unemployment rate and assigned a rank value. There is also a combined unemployment rank value for all years and all municipalities.

This resulted in a new data set of aggregated data of individuals or households. The geographical units were standardised to the 2005 municipal boundaries so that spatial measuring was consistent. The data therefore covers the whole country at a municipal level for different time periods. The major variables focus on employment status.

All people in South Africa on the date of the census in 1991, 1996, and 2001 as well as the households at the time when the 2007 Community Survey was conducted.

The South African Census 1996 covered every person present in South Africa on Census Night, 9-10 October 1996 (except foreign diplomats and their families). The South African Census 2001 covered every person present in South Africa on Census Night, 9-10 October 2001 including all de jure household members and residents of institutions. The South African Census 1991 was enumerated on a de facto basis, that is, according to the place where persons were located during the census. All persons who were present on Republic of South African territory during census night (i.e. at midnight between 7 and 8 March 1991) were therefore enumerated and included in the data. Visitors from abroad who were present in the RSA on holiday or business on the night of the census, as well as foreigners (and their families) who were studying or economically active were enumerated and included in the figures. The Diplomatic and Consular Corps of& foreign countries were not included. Crews and passengers of ships were also not enumerated, except those who were present at the harbours of the RSA on census night. Similarly, residents of the RSA who were absent from the night were not enumerated. Personnel of the South African Government stationed abroad and their families were, however enumerated. Such persons were included in the Transvaal (Pretoria).

The South African Community Survey 2007 covered all de jure household members (usual residents) in South Africa. The survey excluded collective living quarters (institutions) and some households in EAs classified as recreational areas or institutions. However, an approximation of the out-of-scope population was made from the 2001 Census and added to the final estimates of the CS 2007 results.

Sampling is not applicable since the data used here refers to aggregated data of the universe.

Census 1996 and 2001 = universe

Census 1991 = representative survey of South Africa and data extrapolated to all municipalities.

Community Survey 2007 = household.

For more information see www.statssa.gov.za.