The relationship between subjective well-being and domain ... - CORE

The relationship between subjective well-being and domain satisfactions in South Africa Valerie Møller and Willem E. Saris Institute of Social and Economic Research, Rhodes University, P O Box 94, Grahamstown, 6140, South Africa Statistics and Methodology Department, Faculty: PSCW, University of Amsterdam, O.Z. Achterburgwal 237, 1012 DL Amsterdam, The Netherlands

Abstract This paper examines the relationship between subjective well-being and domain satisfactions. In the past different models have been specified. The most commonly applied model is the bottom-up model in which domain satisfactions affect subjective well-being. The more recent top-down model suggests a reversed relationship. Finally there is the supposition that the correlations between these variables can be spurious due to the effect of personality characteristics. Empirical research has shown that different models are found for different domains and in different countries. Focussing on the effects of the domain satisfactions of finances, housing and social contacts it has been found that subjective well-being is mainly affected by satisfaction with social contacts in Western developed countries and by satisfaction with finances in East European countries. The question we should like to answer in this study is whether a similar pattern obtains for the factors which influence subjective well-being among the different race groups in South Africa. Interestingly, coloured people and Asians did indeed show the expected effects but the groups with the most extreme living conditions did not. Evaluation of life circumstances by black and white South Africans was determined by expectations for the future rather than by current living conditions. This surprising result is discussed in the light of the political situation in South Africa.

Introduction One of the main issues in social indicator studies has been the explanation of subjective well-being (SW). In many older studies, SW was explained by a linear combination of domain specific satisfaction (DS) variables, such as satisfaction with income, housing, or social contacts (Andrews and Withey, 1976; Argyle, 1987; Campbell et al., 1976; Headey et al., 1985). Diener (1984) was the first to criticize this intuitive approach. He suggested that the effects could just as well be reversed, that is, go from SW to DS. He called his model the “top-down” model in contrast to the intuitive “bottom-up” model that was in use at the time. This suggestion was based on the idea that satisfaction might be determined more by personality characteristics than situational circumstances. It seems that some people have a disposition to be satisfied while others do not, so that satisfaction with life in general (SW) is the more fundamental variable which spills over onto domain satisfactions. A third possibility, suggested in the literature by Costa and McCrae (1980, 1984) is that there are personality traits which cause subjective well-being and domain satisfactions to be more or less in line with each other. In this case the relationships between the SW and the DS variables would be spurious and due to personality characteristics. Since the publication of Diener’s paper in 1984, several studies have been conducted to test the different models (Lance et al., 1989; Headey et al., 1991). Far from resolving the dispute, they have tended to complicate it as they found different effects for the different domains studied: Lance et al. found a significant top-down effect of SW on the domains of job satisfaction and satisfaction with social activities, but a bottom-up effect of marriage satisfaction on SW. Headey et al. also found a top-down effect for job satisfaction and satisfaction with leisure activities, but a reciprocal relationship for satisfaction with marriage. Furthermore they found no significant

effects in either direction for satisfaction with friendship and health. For the last two topics the relationships between SW and DS variables turned out to be spurious, due to personality characteristics such as neuroticism and extraversion. These studies make it clear that all three types of relationships have been found between these variables. It is also evident that the direction of the effects varies with the domain studied. Recently it has been found that in countries with a lower gross national product (GNP) the effect of income satisfaction might be bottom up from DS to SW while in countries with a higher GNP the effect might be reversed or non-existent. In the richer countries the effect of the domain satisfactions of social contacts or marriage on SW might be significant instead because income levels are sufficiently high so people are no longer unduly worried about this domain. In this case other issues such as social relationships become more salient as suggested in Saris, Veenhoven, Scherpenzeel and Bunting (1996) on the basis of the correlations between these variables. Moreover, in the Russian context, Saris and Stams’s study (2000) found a bottom-up effect only for satisfaction with one’s financial situation but for no other variable. These between-country differences in bottom-up/top-down effects are in line with the “post materialist theory” of Inglehart (1990), which states that people in Western developed countries are currently less concerned about income matters than by non-material issues such as the environment and the like. Similarly, Maslow’s (1970) classical theory proposes a hierarchy of values where the higher values only become salient after lower level values such as basic needs and security have been satisfied. In this paper we continue the research of this hypothesis by analysing data for five different socio-economic groups in South Africa: rural blacks, urban blacks, coloured people, Asians and whites. In the first section we will show that these groupings are indeed different entities for purposes of our analysis. We show the disparities in the living conditions and how differently members of these five groups react to their living conditions. Next we will formulate a combined top-down bottom-up model which will be applied to data for all five groups. We aim to see whether (a) the same model fits the data for each of the five groups which share the same national boundaries, or whether (b) satisfaction with one’s financial situation is the more important explanatory factor for SW among groups living in more depressed living conditions while satisfaction with social contacts or marriage is the more important factor among groups that are better off. We then discuss the data and methods used to test these hypotheses. The presentation of results follows. Finally, we discuss and interpret the results.

Inequality and difference in South African society A lot has been written about the legacy of apartheid, which has resulted in vastly unequal life chances and living circumstances for the people of South Africa. World historian Johnson (1996: p. 728) refers to South Africa as a microcosm in which we find the problems of global society within a national boundary. South Africa comprises first and third world circumstances in demography, health, livelihoods and technology in close juxtaposition. As such it is an ideal context in which to test our hypothesis concerning bottom-up/top-down effects in different settings. Differential expenditure on social welfare and allocation of geographical living space according to a racially defined population classification system under apartheid entrenched a socio-economic hierarchy that saw whites at the top, followed by Indians and people of Asian descent, then so-called “coloured people” of mixed race, and blacks at the bottom. Following a negotiated settlement and the peaceful introduction of democratic rule in 1994, the new government has stepped up social spending and passed equal opportunity legislation to achieve a more equitable society. However, the backlog in jobs, housing, education and infrastructure for the formerly disadvantaged under apartheid rule who make up the majority of the population is still apparent in South Africa’s social indicators (see Møller, 1998, 1999; May, 1998).

This study makes a distinction between race groups, which in contemporary South African society are by and large self-defined ethnic markers unlike under apartheid when race classification was used as a tool of oppression. In South Africa’s social accounts, social indicators are typically broken down by race to determine progress in redressing the injustices of the past and realising the ideal of an equal society as set out in South Africa’s constitution. In popular usage race is shorthand for a bundle of social definitions including current socioeconomic status, advantaged or disadvantaged status in the past, and expectations of future entitlement. Given South Africa’s history, racial categories are still a critical determinant of many aspects of life as we shall show in the next section. In this paper we select variables to describe living conditions which have been used in this study to estimate the top-down/ bottom-up model. The data for this study come from the ongoing South African Quality of Life Trends Study and were collected in September 1995 by the research organisation, MarkData, in a syndicated survey. South African residents 18 years and older were selected countrywide using a multistage cluster probability sample design. In total, 2163 respondents were interviewed personally in the language of choice: 1400 black, 233 coloured, 169 Asian and 361 white (Møller, 1998). The racial breakdown according to South Africa’s 1996 population census is 77% African/Black, 11% white, 9% coloured, and 3% Indian/Asian. In Table I we show differences in living conditions for the different race groups using select data from the study.

The variables shown in Table I are of course only a small selection of all possible variables characterizing the living conditions of the different groups in South African society. Nevertheless, even these few variables make it clear that there are still huge disparities between the groupings, not only regarding income but also with respect to housing conditions and even marital status. The ordering is in all three cases as follows: rural blacks have the lowest scores followed more or less closely by urban blacks and coloureds, then Asians follow at a distance and whites score higher than Asians on all variables. This ordering holds not only for these variables but also for education, employment and other housing variables not shown here (see Møller, 1998). This means that for purposes of the present analysis we have defined distinctly different groups. We are interested in seeing whether these groups also differ with regard to their satisfaction with aspects of life and overall subjective well-being. Table II shows the differences between the groups for satisfaction with select domains and life in general and happiness. In order of presentation in the table, the domains include: “Your ability to provide for your family”,

“your dwelling”, and “your family’s happiness”. Domain satisfactions were measured on a five-point satisfaction scale from “very satisfied” to “very dissatisfied” with a neutral mid-point. Respondents were only required to assess aspects that were salient for them. The two global indicators of subjective well-being read: “Taking all things together, how satisfied are you with your life as a whole these days?” and “Taking all things together in your life, how would you say things are these days? Are you very happy, . . . or very unhappy?” The two global indicators of life satisfaction and happiness were presented at the beginning and end of the questionnaire module on quality of life, respectively, and were also coded on a five-point scale with a neutral mid-point.

Table II summarises responses to the indicators described above. We have presented the percentages of people who replied positively to the questions; i.e. who stated they were “very satisfied” and “satisfied” or “very happy” and “fairly happy”. There are very satisfied and very dissatisfied people in all population groups but the distributions over the five groups in Table II are very different. The ordering of the groups for the subjective variables is the same as for the objective variables shown in Table I above. Generally, the progression from lowest to highest scores is from rural black to urban black, coloured, Asian, and finally to white. Asians score one or two percentage points higher than whites on life satisfaction and happiness. The reason that rural blacks score higher than urban blacks on housing may be due to the fact that about one fifth of South Africa’s black households live in shacks (SAIRR, 1999: p. 164) that is generally considered to be inferior housing. The one in four black South African households still living in traditional mud huts (SAIRR, 1999: p. 164) may for the most part consider their housing to be appropriate for a rural African lifestyle. In addition to the objective and subjective assessments of the different domains shown above we also used evaluations of the past and the future in our analyses. The item used to elicit a projection on the future read as follows: How do you think things will be for people like yourself in five year’s time? Taking all things together will things be better, worse or will they remain about the same as today?

The item calling for an evaluation of past satisfaction read: Thinking back, how would you have described your satisfaction with all aspects of your life five years ago? Generally speaking would you have said you were . . .

Responses to both items were coded on five-point scales (from “much better” to “much worse” and from “very satisfied” and “very dissatisfied”) with uncertain mid-points (“about the same”/ “neither, nor”). The responses to the projections of life satisfaction on the past and the future are shown in Table III.

The relevance of these projections is strikingly clear. Expectations for the future held by the different groups are more or less opposite to their evaluations of the past. We expect that these variables will have a strong effect on the current satisfaction and happiness of the groups that are most affected by future expectations, namely blacks and whites. Having confirmed that there are indeed considerable differences between the race groups in South Africa, it makes sense to see if the same model can be applied to all groups with respect to the relationship between domain satisfactions (DS) and subjective wellbeing (SW). For this purpose we will develop a general model in the next section.

A general model to study the relationship between SW and DS variables The data for this study come from a cross-sectional survey. Therefore the model used in this study to determine for which domains a bottom-up effect occurs and for which a top-down effect occurs has to be based on variables measured at the same time. In order to get an identified model the objective variables introduced above have been used as exogenous variables while relations between the domain satisfaction variables and SW reciprocal relations have been formulated, i.e. simultaneous bottom-up and top-down effects. The model specified in Figure 1 has been formulated on the basis of this starting point. This model is the same as one of the models tested by Scherpenzeel and Saris (1996) on data for The Netherlands. In this model all variables are directly observed variables except the variable SW. The manner in which the different variables have been measured has been indicated above. With respect to SW two observed variables were available: satisfaction with life in general and global happiness. These two variables are both treated as indicators of SW in this study. In this way it is possible to separate unexplained variables from measurement error for the SW variable. However this only works well if both variables are only indicators of the same variables SW and do not have unique components. When analysing the data we shall test this point and, if necessary, make corrections in the model. In Figure 1 each arrow specifies a causal relationship and indicates the direction of the effect. Two arrows in opposite directions between two variables indicate reciprocal causal relationships. Lines with several arrows between Marstat, House, Hhinc, SP and EF indicate the correlations that are assumed to exist between the exogenous variables. Although not indicated in the figure, it is expected that each dependent variable is not only

determined by causal variables but also by disturbance terms which represent unexplained variance due to omitted variables or random processes and random error (except for the variable SW). We have also not indicated that we assumed that the disturbance terms of the domain satisfactions were correlated. These correlations represent all omitted variables that cause spurious relationships between these domain satisfactions.

The simple idea underlying this model is that each domain specific objective variable affected the corresponding domain satisfaction variable with a few exceptions as indicated in Figure 1. Furthermore it was assumed that the general evaluations of the future and the past only affected SW, which is also a general evaluation, but not the domain satisfactions. Furthermore, reciprocal causal relationships were assumed between the SW and the domain satisfaction variables. The purpose of this aspect of the model is to specify the effects that have to be estimated and tested on significance in order to determine which effects are relevant for the different groups and which not. How this is done will be discussed in the next section.

Research methodology An example of a correlation matrix used in the analysis is presented in Appendix 1. The analyses have been done with Lisrel 8 assuming the above-specified relationships were linear and additive as is normally assumed. The ML estimation procedure has been used even though some variables are not normally distributed but the robustness studies of Anderson and Amemiya (1988) and Satorra (1990) have shown that this so-called “quasi maximum likelihood” estimator is quite robust under quite general conditions. The procedure used in this study was as follows: First the data pertaining to each race group were analysed with the model specified above. If the estimation of the effects was possible (convergence was obtained) the fit of the model was inspected looking at the Chi square test provided by the programme. If the model had an acceptable fit the effects were tested on significance on the basis of the estimated standard errors of the effects. It was assumed that an effect was significant if its size was twice the size of the estimated standard error that is equal to testing at the 5% level. If the model did not converge we started the analysis with the bottom-up model and added as many top-down effects as possible and then started with the top-down effects and added as many bottom-up effects as

possible. This process was continued until we achieved an acceptable solution that was the same independent of the starting point and fitted well to the data according to the Chi square test. If the obtained solution was not acceptable according to the Chi square test we looked for the necessary corrections of the model until the model fitted the data according to the Chi square test. After that we again applied the test on the significance of the different effects.

Results The results of these analyses have been summarized in Table IV. An asterisk (*) indicates that an effect is significantly different from zero. Note that occasionally coefficients seem rather large but are nevertheless not significantly different from zero because the standard errors are also rather large.

Table IV shows that results are very similar for rural and urban blacks and whites. Only the top-down effects are significant. These groups are also very similar regarding the effect of their expectations of the future. For all three groups the effect of the future evaluation is a relatively large one. This seems plausible because blacks are currently the ‘have-nots’ in South African society but expect to become ‘haves’ in future and this expectation affects how satisfied they are now. The opposite holds in the case of white South Africans. They are relatively well-off but they are very concerned about a possible decline in their living standards in future. If they have positive expectations for the future they are currently more satisfied than if they have negative expectations. The top-down effects are significant for all three groups suggesting that the domain satisfactions will be higher if the SW is higher. Coloured people are better off than blacks in terms of income but can see possibilities for improvement of their livelihood. So for them their financial situation determines their satisfaction. In the case of the Asians we get a result which is expected for a group in society which is better off. Asians have less reason to be concerned about their financial situation, which is relatively good, and thus can afford to be more concerned about their social relationships. Therefore satisfaction with family life is the variable in the set which has a bottom-up effect. The question is why do we not see this effect in the white community which has the highest level of income in South Africa? We think it is because whites are more preoccupied with their future than any aspect of their present situation. This is not so much the case for the Asians as the analysis also shows.

Discussion and conclusions This study has generated results that are partly (for the coloured people) in agreement with the study of Saris and Stams on Russians (2000) and partly (for the Asian people) in agreement with the results of Lance et al. (1989) and of those of Headey et al. (1991). The results for the coloured people and the Asian people seem to suggest

that indeed differences between countries and groups within countries do occur due to variations in living conditions as suggested by Saris and Stams (2000). Results for the coloured people of South Africa conform to those for less developed countries while results of Asians conform to those of more developed countries. We have found no reference comparisons for the results on the black and white groups. It seems that an explanation must be sought in the history of the study context. We do not think that the different results found for the distinctive groups identified in the survey are due to differences in the approach, measurement procedures or other technical aspects. In fact our analysis was approximately the same for all groups so that we do not expect that technical artifacts account for the differences. Our hypothesis is that the economic situation dominated perceived quality of life among the coloured population at the time of study, which would account for the bottom-up effect of the income domain. Among Asians the income domain was far less dominant because most people had sufficient income to cover their necessary household expenses. In such a situation other life domains, like marriage, the family and social contacts, became more important. This idea would be in line with Maslow’s hierarchy of values. As long as financial security is uncertain people continue to be concerned about this domain and this concern also determines their subjective wellbeing. Once a certain degree of financial security has been achieved other concerns become salient such as family life and marriage. Only in the case of the coloured group do our results generally confirm the idea espoused by Veenhoven that happiness is not ‘relative’ (1991) and living conditions (1995) are a major determinant of subjective well-being. In the case of the black and white groups raised expectations and anxiety for the future “crowd out” or overshadow domain concerns so they have no bottom-up effects on overall subjective well-being. One year after South Africa’s first democratic elections the models of subjective well-being are distinctly different for each of the racial groupings distinguished in this study. This might be expected given the history of social divides in South African society. In the case of blacks and whites in South Africa the dynamics underlying subjective well-being may be more closely aligned to political perspectives. Certainly, the black and white groups are numerically the largest and also represent the major political players. It is possibly also significant that these two groups represent the extreme poles on a continuum of living conditions that are comparable to First and Third World ones. What has been called a “crossover” effect in earlier South African quality of life studies (Møller, 1998) is a constellation where whites are predominantly satisfied with life but have negative expectations for the future while blacks are predominantly dissatisfied but have positive expectations for the future. However, in the extreme cases of provinces with the best and poorest living conditions subjective well-being and optimism coincide. For example, the people of the Western Cape, the province which boasts Cape Town as its capital city, are overwhelmingly happy, satisfied and optimistic while the people of the Eastern Cape, the province with the highest levels of unemployment and deep poverty, are predominantly unhappy, dissatisfied and pessimistic. For the black South Africans the end of apartheid signaled the prospect of a better life for blacks while white South Africans welcomed the change with some unease about their future. Thus, the blacks of the Eastern Cape are concerned with rising above the restrictions imposed on their life circumstances under apartheid while the whites of the Western Cape are mainly concerned with preserving their relative situation of privilege and the requisites for growing South Africa’s economy and sustaining development in future. In both cases, concern with the future dominates current perceptions of well-being: Optimism promotes feelings of wellbeing while pessimism fosters ill-being. Moreover, this study shows that future-dominated perceptions of well-being “colour” or spill over onto domain satisfactions in terms of the top-down effects of SW on DS. One of the major issues in post-apartheid South African society is how to accommodate the seemingly disparate perceptions of life quality to forge a unified nation and a strong economy. Reconciliation between the races was a major theme of the first five years after the first nonracial elections of 1994. A recent study by Dickow and Møller (2000) shows that blacks, and particularly whites, who were attuned to reconciliation tend to be more optimistic than others. Since the second general elections in 1999 the focus has shifted and the need to close the wealth and skills gap has become top priority. Although the whole of society expresses a common sense of urgency, black and white perceptions differ on how best to tackle the socio-economic problems besetting South Africa’s new democracy. Perhaps the finding from this study that both black and white South Africans pin their

hopes on the future rather than on the past and derive their sense of well-being from optimism augurs well for a country in transition.

APPENDIX 1. CORRELATION MATRIX FOR RURAL BLACKS Analysis of SA data for rural blacks CORRELATION MATRIX TO BE ANALYZED happy happy swb sf sh sm swbp swbf hhinc water marstat

1.00 0.59 0.44 0.31 0.42 0.20 0.37 -0.23 0.10 0.01

swb

1.00 0.41 0.32 0.43 0.20 0.29 -0.23 0.13 -0.06

sf

1.00 0.41 0.43 0.20 0.21 -0.20 0.11 0.04

sh

sm

1.00 0.42 0.10 0.13 -0.11 0.16 -0.03

1.00 0.18 0.23 -0.12 0.18 0.03

swbp

1.00 0.17 -0.08 0.08 0.00

CORRELATION MATRIX TO BE ANALYZED swbf swbf hhinc water marstat

1.00 -0.13 0.15 -0.07

hhinc

1.00 -0.09 0.00

water

marstat

1.00 0.02

1.00

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