The Gauteng City-Region Observatory (GCRO) (based at the University of Johannesburg (UJ)) in partnership with the Gauteng Provincial Government, contracted Development Research Africa (DRA) to conduct an integrated Quality of Life/Customer Satisfaction Survey in the Gauteng City-Region (GCR). The objective of the GCRO is to assist the Gauteng Government to build Gauteng as an integrated and globally competitive region, where the economic activities of different parts of the province complement each other in consolidating Gauteng as an economic hub of Africa, and an internationally recognised global city-region. The this end, the main aim of the survey, conducted from July to October 2009, was to inform the GCRO and the Provincial Government, as well as other role-players about the perceived state of the municipalities within the GCR footprint about the quality of life of their inhabitants.
Kind of Data
Qualitative and quantitative data
Unit of Analysis
Households and individuals
v1: Edited, anonymised dataset shared under a CC-BY 4.0 Attribution-only license
The Gauteng City-Region Observatory's Quality of Life Survey collects data on demographic details of the enumerated population (population group, gender, age, language) and on housing (dwelling type, tenure, satisfaction with dwelling, perceived quality of housing and housing allocation) as well as household services (water, sanitation, refuse, energy sources). Data was also collected on migration, health (including disability), education and employment (including employment sector). Data on community services and amenities was also collected, and on transport, leisure activities and safety and crime. Financial data was collected (including on debts, income, and social grants) and data on household assets. Data on public participation and governance was also collected during the survey. Finally, the survey collected data on the perceived personal wellbeing and quality of life of respondents.
quality of life
The Quality of Life Survey covers the whole of Gauteng and also areas with GCR 'footprints' in the four neighbouring provinces of Free State, North West, Limpopo and Mpumalanga.
The lowest level of geographic aggregation in the data is municipality
The Gauteng City-Region Observatory Quality of Life Survey 2009 covered all household residents of Gauteng and selected areas of the four neighbouring provinces of Free State, North West, Limpopo and Mpumalanga.
Producers and sponsors
Gauteng City-Region Observatory
University of Johannesburg
Unversity of the Witwatersrand
Gauteng Provincial Government
South African Local Government Association (SALGA)
Gauteng Provincial Government
For the purpose of this study, multi-stage cluster sampling was used as no sampling frame containing all members in the universe or population exists. The sample was drawn in stages, with wards being selected at the first stage, dwelling
units within the wards being selected in the second stage and respondents selected at the third stage.
The wards formed the primary sampling units (PSUs). A random starting point(s) was used as a method to select the dwelling units to be surveyed. A total number of 602 wards in 4 provinces (Gauteng 448 wards), (Mpumalanga 72 wards), (North-West 70 wards) and (Free State 12 wards) were completed. A total of 6639 interviews were completed in these wards.
During the second phase, the field teams were required to complete a certain number of interviews, depending on the population size of that particular ward. The teams had to complete for an example in ward X 3 interviews and in ward Y they had to complete 33 interviews. This meant that the field teams had different target number of interviews that they needed to complete in all the pre-selected wards. Ward maps were obtained before fieldwork commenced, and random starting points were identified, marked and numbered on the map. This allowed for the random selection of one (if more than one existed) starting point. The field managers concerned will firstly identify where the starting point(s) is/are on the ground. Oncethat has been established he/she will from the starting point count 20 households from the starting point moving to his/her left. The 20th household that he/she has selected was the household were the interviews was supposed to take place Thereafter, the next 20th household was selected and approached until the target number of interviews was obtained.
The following process of household selection was adhered to:
From the starting point 20 houses were counted in a ward. However, if there were:
• 1-5 target number of interviews to be completed in a ward; 01 starting point was used;
• 6-10 target number of interviews to be completed in a ward; 02 starting points were used;
• 11-15 target number of interviews to be completed in the ward; 03 starting points were used;
• 16-20 target number of interviews to be completed in the ward; 04 starting points were used;
• 21-25 target number of interviews to be completed in the ward; 05 starting points were used; and
• 25 and above target number of interviews to be completed in a ward; 06 starting points were used
In the case of a household refusal or if a selected respondent was mentally disabled, the household was immediately substituted with the household on the left. If still there was no interview completed then another substitution, going to the right of the originally selected household, was done. In case of non-contact whereby there was no-one home after two visits at two different times (afternoon and evenings) on the same day, the same substitution method was followed. Therefore, at least two-revisits at different times were done in cases where selected dwelling units, households or individuals were not at home i.e. non-contact. However, in some cases households visited after 19:00 on the day were substituted as agreed to in order to ensure that all the target number of households would be completed in the allocated time per ward.
For the purpose of this study, one randomly selected household respondent was selected per household. All household members qualified if they met the following criteria:
• Resident(s) of the household irrespective of nationality but excluding nonresidents and visitors; and
• 18 years of age or older
• In the event of a child headed household (all household members are under 18 years old), the oldest child was assumed to be the head of household, and should be interviewed
If more than one eligible person was found per dwelling unit, the ideal and most practical and accurate method of random selection of an individual was the use of a KISH grid. One individual per household was selected using the KISH grid after a comprehensive listing exercise was completed of all eligible individuals at the dwelling unit. Once the respondent had been selected the fieldworker will follow up only that person per household. If selected, substitutions could not be made where there were refusals or non-contact over a period of a day after two or more re-visits on the same day.
The 2009 ward boundaries supplied by the Municipal Demarcation Board were used as the sampling frame for the GCRO 2009 Quality of Life survey 2009. Statistics South Africa recomputed the Census 2001 population data and supplied a population database that was matched to the 2009 ward boundaries to obtain a Census 2001 population per ward and per municipality. Due to a field sampling error, there were a number of wards that were over sampled, and a few that were not sampled. It was thus decided to apply the weights at a municipal level within Gauteng, and per total ward population for the sampled areas outside Gauteng.
In these outlying areas, the sizes of the sampled wards in municipalities such as Metsimaholo, Madibeng etc, do not reflect the whole municipalities, but only those wards that were included in the study area or should have been included were used for weighting purposes. In Gauteng for example, in Kungwini, the total sample of 160 was based on a population 109067 (a minimum of 160 per municipality was set to ensure that enough interviews were done in the smaller municipalities). The population of Kungwini is 0.0102 (1.02%) of the total population of the study area 109067/10696850).The number of interviews in Kungwini was 188. This was 0.0283 (2.83%) of the total number of questionnaires (188/6636). This means that Kungwini has proportionally too many interviews (which is possible due to the minimum numbers per municipality). To adjust for this, a weight of 0.35990 (0.0102/0.0283) is given to all the interviews conducted in Kungwini, which causes every interview in that municipality to have less weight in the total sample. A weight of 1 would mean no adjustment, and a weight of above 1 would mean that interviews in areas with proportionally too few interviews are given a “bigger” voice in the overall sample. This would mean that if a frequency of the municipalities is run after applying the weights, the proportion of respondents would be the same as the proportion of the population/total population, instead of an unweighted frequency.
Dates of Data Collection
Data Collection Mode
Data Collection Notes
Fieldwork for the survey commenced on the 31st of July 2009 and was completed on the 8th October 2009. There were 9 teams altogether from the Johannesburg office of the survey company, Data Research Africa. The teams were composed of 1 field manager and 4 fieldworkers (mostly 2 males and 2 females).
The Gauteng City-Region Observatory and Data Research Africa (DRA) developed the quantitative evaluation tool for the survey. DRA reformatted the pre-pilot questionnaire and provided input into the layout and flow as well as question structure to ensure accurate data capturing. DRA field managers piloted the questionnaire with 30 interviews with individuals from households with different demographic characteristics . The Gauteng City-Region Observatory Quality of Life Survey 2009 questionnaire collected data on demographic details of the enumerated population (population group, gender, age, language) and on housing (dwelling type, tenure, satisfaction with dwelling, perceived quality of housing and housing allocation) as well as household services (water, sanitation, refuse, energy sources). Questions included those on migration, health (including disability), education and employment (including employment sector). Questions on community services and amenities were included, and questions on transport, leisure activities and safety and crime. Financial data was collected (including on debts, income, and social grants) and data on household assets. Finally, data on public participation and governance was also collected, and data on the perceived personal wellbeing and quality of life of respondents.