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Abstract
This study investigated how far the post-apartheid government has progressed in creating a better life for all South Africans. Secondary data analysis was employed using the 2008 South African Social Attitudes Survey (SASAS) of 3321 randomly selected adult respondents. A series of general linear models examined the impact of race, gender, age, geographic location, education level, living standard measure (LSM), satisfaction with basic services, and fear of crime on quality of life (QOL) as measured respectively by subjective well-being indicators, namely happiness, life-satisfaction and optimism.Footnote1 The main findings were that: 1) Those respondents who feared crime less, had higher LSM levels, and were satisfied with basic services reported higher levels of happiness. 2) Those respondents who had a higher LSM, feared crime less, and were more satisfied with the basic services reported higher life-satisfaction. 3) Those respondents who were black African with lower levels of education, feared crime less, and were most satisfied with basic services displayed the most optimism about the future. We concluded that government interventions need to focus more on black Africans, the least educated, the low LSM group, those living in the urban informal areas, and those who fear crime to significantly improve South Africans' QOL.
Acknowledgements
The authors acknowledge the use of the South African Social Attitudes Survey (SASAS) data from 2003 to 2008. The insightful comments on the original drafts by Prof Demetre Labadarios and Dr Moses Mefika Sithole of the Human Science Research Council (HSRC) are greatly appreciated. The views expressed are those of the authors and do not necessarily reflect those of any other party.
Notes
The living standard measure (LSM) used in this study is based on the South African Advertising Research Foundation (SAARF) AMPS 2005 survey. The SAARF LSM has become the most widely used marketing research tool in Southern Africa. It divides the population into 10 LSM groups, 10 (highest) to 1 (lowest). For the present study we categorized groups 1 to 3 as the low LSM category, groups 4 to 7 as the medium LSM category and groups 8 to 10 as the high LSM category. The LSM is a unique means of segmenting the South African market. It cuts across race and other outmoded techniques of categorizing people, and instead groups people according to their living standards using criteria such as degree of urbanization and ownership of cars and major appliances. A total of 29 variables are used. Each variable carries a different weight, some positive, others negative, and the respondent's position on the SAARF LSM scale is arrived at by adding together the weights of the variables that she/he possesses. A constant is also added to the total score to remove negative total scores. For more information on LSMs, please visit: www.saarf.co.za
Refer to which presented the results of respondents' assessments about their level of happiness. These results were based on the 2008 SASAS Survey (see www.hsrc.ac.za).
These results were obtained through on-line analysis of the 2008 Afrobarometer survey using the Afrobarometer website (www.org.afrobarometer). The specific question analysed asked respondents ‘In general how would you describe your own present living conditions’. The response options ranged from 1= ‘very good’, 2 = ‘fairly good’, 3 = ‘neither good nor bad’, 4 = ‘fairly good’ and 5 = ‘very good’.
Respondents were asked ‘All things considered, how satisfied are you with your life as a whole these days?’ A scale from 0 to 10 was used, where 0 is dissatisfied and 10 satisfied (www.gallup.com)
The 2007 data from STATS SA's Community Survey stated that 14.4% of South African households are informal dwellings, 8.3%. have no toilet facilities, 7.1% of households received no refuse removal services, 74.4% of households had access to piped water within 200 meters, and 80.0%, 66.5%, and 58.8% of households used electricity for lighting, cooking and heating, respectively.
Figures reported at the end of October 2010 in the Statistical Report on Social Grants by the South African Social Security Agency (SASSA), Department Monitoring and Evaluation, Branch: Strategy and Business Development. See www.sassa.gov.za
South Africa National Treasury 2009 Budget Review. www.treasury.gov.za
The income Gini coefficient is a measure of income inequality that ranges between 0, indicating perfect equality, and 1, indicating complete inequality UNDP, Citation2010).
The 2005 South African Social Attitude Survey (SASAS) conducted by the Human Science Research Council (see www.hsrc.ac.za). Also note the 2005 SASAS survey use the same methodology as the other SASAS surveys (see Endnote 13 for more information on SASAS surveys).
The questions on race and gender were completed by the interviewers. The interviewer selected the race group from the following categories: Black African, coloured, Indian/Asian and white, other. Gender was recorded by the interviewer as either male or female. Geographic location or settlement type was based on the selected Census Enumerator Areas (EAs). In other words, respondents were grouped into urban formal, urban informal, traditional and rural formal areas based on the classification of the EAs. To obtain the age groups we used the age given by the respondents and grouped it into the categories used in the analysis. The level of education was obtained by grouping respondents into three categories: primary/some primary (i.e. all those with a sub A or grade 1 up to standard 5 or grade 7), secondary/some secondary (i.e. all those from standard 6 or grade 8 up to standard 10 or grade 12; this category also included those respondents with a National Training Certificate (NTC) I, NTC II and NTC III), and tertiary/some tertiary (i.e. all those respondents with some form of education higher than standard 10 or grade 12). Lastly, the question on fear of crime was asked in the following manner: ‘How safe or unsafe do you (or would you) feel personally on most days?’ The response option ranged from 1 = ‘very safe’, 2 = ‘fairly safe’, 3 = ‘a bit safe’, 4 = ‘very unsafe’ and 8 = ‘Do not know’.
Economic status was measured by LSM. Respondents were categorized according to low LSM, medium LSM and high LSM.
The results of the preliminary analysis were not reported in this study.
The SASAS surveys measure the South African public's attitudes, beliefs, behaviour patterns and values with regards to democracy and governance, social identity, service delivery, access to information and other important social issues such as perceptions of crime. The SASAS survey also contains questions to measure QOL. All SASAS surveys are designed to yield a representative sample of adults of 16 years of age and older, regardless of their nationality or citizenship. The HSRC's Master Sample was developed using the Census 2001 and with the Enumerator Area (EA) as the primary sampling unit (Pillay, Roberts and Rule, Citation2006). The value of using the HSRC Master Sample was that a nationally representative sample can be drawn and the results of the survey can be properly weighted to the 2001 census population figures. Explicit and implicit stratification is applied to ensure that the geographic profiles of the targeted population such as province, geographic location, age category, gender, race, education level, living standard measurement (LSM) and current employment status are represented in the sample. The 2001 census database contains descriptive statistics, such as total number of people and total number of households, for all EAs in South Africa. Detailed maps were also developed for each EA showing the boundaries and households within it. Households were selected from the master sampling frame and are geographically spread across the nine provinces. Once interviewers arrived at the households, they randomly selected the respondents from these households for interview. Direction maps to enable fieldworkers to reach the selected EA were also provided (Pillay, Roberts and Rule, Citation2006).
The univariate statistics in stage 1 were calculated using the weighted SASAS data. However, the GLM analysis was done without the weighted variable because the Statistical Package for the Social Sciences (SPSS) version 18 was unable to perform the function with the weighted data.
It is important to note that the SBSI was constructed through factor and reliability analysis using four questions—items that assessed satisfaction with the ‘provision of water’, ‘electricity’, ‘water-borne sewerage’ and ‘refuse removal’. The response options on the Likert scale ranged from 1 (very dissatisfied) to 5 (very satisfied), with the higher values indicating greater importance as to why people are satisfied with basic services. The results of the factor and reliability analysis showed that the SBSI, with an Eigenvalue of 2.648, explained 66.21% of the common variance. The index was reliable (Cronbach's = 0.821).
It should be noted that tests were conducted to determine whether there was any violation of the statistical assumptions underpinning the GLM analysis procedures. For example: 1) The histograms of the standardized residuals revealed normal distributions. 2) The multicollinearity tests showed that the levels of intercorrelation among the dependent variables were acceptable and that the effects of the independent variables could be separated. 3) The scatter plots and ANOVA were used to test for linearity.
It is important to note that we were unable to form a QOL composite index because the response options for life-satisfaction, happiness, and optimism were substantially different.
Note that in the reporting of the results of this study, ‘happy’ refers to both the ‘happy’ and ‘very happy’ responses and ‘satisfied’ to both the ‘very satisfied’ and ‘satisfied’ responses combined. Similarly, when we report on ‘not happy’ and ‘dissatisfied’ respondents, this includes both ‘not happy’ and ‘not at all happy’, and ‘dissatisfied'and ‘very dissatisfied’ respondents, respectively.
The response options for the question on happiness ranged from ‘very happy’, represented by 1, and ‘not at all happy’, represented by 5. In other words, higher values indicated a higher degree of unhappiness.
The response options for the question on life-satisfaction ranged from ‘very satisfied’, represented by 1, to ‘very unsatisfied’, represented by 5. This meant that higher values demonstrated a higher degree of dissatisfaction with life.
The response option for the question on optimism ranged from ‘improve’, scored as 1, to ‘get worse’, scored as 3. In other words, higher values showed less optimism about the future.
Model 1: Happiness (F (56, 2201) = 14.064, p <0.001), Model 2: Life-satisfaction - (F (56, 2187) = 12.902, p <0.001) and Model 3: Optimism - (F (56, 2207) = 5.751, p <0.001).
The traditional land dwellers refer to those people living mostly in the former apartheid homelands. TBVC refer to Transkei, Bophuthatswana, Venda and Ciskei. These are predominantly rural areas where traditional ethnic groups reside.
Of the three models it is clear that Model 3 accounts for the least variation. We therefore want to emphasize that the explanatory power of Model 3 is quite weak (as indicated by Adjusted R2) and should be interpreted with caution.
These results were obtained through on-line analysis of the 2008 Afrobarometer survey using the Afrobarometer website (www.org.afrobarometer). The 2008 Afrobarometer results were confirmed by the 2008 SASAS survey (www.hsrc.ac.za).
Note that we analysed the 2008 SASAS data to compare it with the Afrobarometer 2008 data. See the Afrobarometer network website: www.afrobarometer.org
It is important to note, however, that had housing been included in the perceptions of basic services asked of respondents, the results might have looked very different. Available statistics on housing show that 14.4% of South Africans live in informal dwellings (Statistics SA, 2008). The overwhelming majority of these South Africans are black Africans. Many of the public protests in the last few years have been to express frustration with lack of or inadequate housing.
The Harris study measured optimism by asking respondents ‘Would you say that the country is moving in the right or wrong direction?’
The 2007 study assessed QOL in informal settlements in South Africa through a set of indicators: subjective domains of life, belonging or sense of community, rating of residential areas, satisfaction with basic household services, and satisfaction with municipal facilities and amenities.
In section 2, which focused on defining QOL, we outlined the well-being model of Higgs. This covers a wide range of factors, ranging from external to very personal.
The present study acknowledged that more over-time analysis should have been employed when assessing QOL. However, this study is considered as an explorative study upon which future studies can build.